Method and system for determining cleaning frequency of photovoltaic panel

Abstract

The invention discloses a photovoltaic panel cleaning frequency determination method and system, and the method comprises the steps: obtaining the historical surface image data of a photovoltaic panel in a last period, and collecting a plurality of surface images from the historical surface image data; performing graying processing on the surface image, and extracting image features from the surface image; evaluating the dust deposition degree of the photovoltaic panel based on the image features to obtain a dust deposition degree value; constructing a dust deposition degree change broken line graph based on the dust deposition degree value of each surface image, and determining data change features from the dust deposition degree change broken line graph; analyzing and calculating the data change characteristics, determining the ash deposition degree change coefficient of the photovoltaic panel, and determining the cleaning frequency of the photovoltaic panel in the current period according to the ash deposition degree change coefficient; and when the next period is reached, the steps are repeated, and the cleaning frequency of the photovoltaic panel is updated. The photovoltaic panel cleaning frequency can be accurately and dynamically adjusted, it is ensured that the photovoltaic panel can be reasonably cleaned and maintained in different periods, and therefore the cleaning efficiency is improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a method and system for determining a cleaning frequency of a photovoltaic panel. Background Art

[0002] In photovoltaic power generation systems, the cleanliness of photovoltaic panels plays a vital role in the power generation efficiency and performance of the system. As time goes by, dust, dirt and other impurities will accumulate on the surface of photovoltaic panels, which will affect the absorption and conversion efficiency of light and reduce the power generation capacity of photovoltaic panels. Therefore, regular cleaning of photovoltaic panels is one of the important measures to maintain the efficient operation of the system.

[0003] However, traditional methods usually determine the cleaning frequency based on experience and conventional rules, and lack accurate analysis and evaluation of historical data and dust accumulation of photovoltaic panels, resulting in the cleaning frequency being set unscientific and inaccurate, causing the cleaning frequency to be too high or too low. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for determining the cleaning frequency of a photovoltaic panel, comprising: Acquire historical surface image data of the photovoltaic panel in the last cycle, and collect a number of surface images from the historical surface image data according to a preset collection interval; graying the surface image and extracting image features from the grayed surface image; The dust accumulation degree of the photovoltaic panel is evaluated based on the image features to obtain the dust accumulation degree value of the surface image; A dust accumulation degree change line graph is constructed based on the dust accumulation degree value of each surface image, and data change characteristics are determined from the dust accumulation degree change line graph; Analyze and calculate the data change characteristics, determine the dust accumulation degree change coefficient of the photovoltaic panel, and determine the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; When the next cycle is reached, the above steps are repeated to update the cleaning frequency of the photovoltaic panel.

[0005] Furthermore, the acquiring of historical surface image data of the photovoltaic panel in the previous cycle and collecting a plurality of surface images from the historical surface image data according to a preset collection interval includes: Obtain historical surface image data of the photovoltaic panel in the last cycle from the database; A preset acquisition interval set in advance is obtained, and a plurality of surface images are acquired from the historical surface image data according to the preset acquisition interval.

[0006] Furthermore, the grayscale processing of the surface image and extracting image features from the grayscale processed surface image includes: The surface image is gray-processed to obtain a surface gray-scale image, and color features, texture features and brightness features are extracted from the surface gray-scale image.

[0007] Furthermore, the evaluation of the dust accumulation degree of the photovoltaic panel based on the image features to obtain the dust accumulation degree value of the surface image includes: Acquire color features, texture features and brightness features in the surface grayscale image, and perform quantitative analysis on the color features, texture features and brightness features respectively to determine the corresponding color feature values, texture feature values ​​and brightness feature values; Obtaining the standard feature values ​​corresponding to the color feature, texture feature and brightness feature, and respectively calculating the differences between the color feature value, texture feature value and brightness feature value and the corresponding standard feature value, to obtain the color feature difference value, texture feature difference value and brightness feature difference value respectively; The color feature difference, the texture feature difference and the brightness feature difference are evaluated and valued respectively to obtain a color difference evaluation value, a texture difference evaluation value and a brightness difference evaluation value; The gray level value of the surface image is determined based on the preset weights corresponding to the color difference evaluation value, the texture difference evaluation value, and the brightness difference evaluation value and the color features, the texture features, and the brightness features. The calculation formula for the gray level value of the surface image is: S = C*α+W*β+L*γ; Among them, S is the dust accumulation value, C is the color difference evaluation value, α is the preset weight of the color feature, W is the texture difference evaluation value, β is the preset weight of the texture feature, L is the brightness difference evaluation value, and γ is the preset weight of the brightness feature.

[0008] Further, the color features, texture features and brightness features are quantitatively analyzed respectively to determine the corresponding color feature values, texture feature values ​​and brightness feature values, including: Numerical extraction is performed on color features, texture features and brightness features respectively, to obtain a color feature binary sequence, a texture feature binary sequence and a brightness feature binary sequence respectively; Combining the color feature binary sequence, the texture feature binary sequence and the brightness feature binary sequence respectively to obtain a high-dimensional color feature vector, a high-dimensional texture feature vector and a high-dimensional brightness feature vector; The high-dimensional color feature vector, the high-dimensional texture feature vector and the high-dimensional brightness feature vector are subjected to dimensionality reduction processing to obtain color feature values, texture feature values ​​and brightness feature values.

[0009] Furthermore, constructing a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determining data change characteristics from the dust accumulation degree change line graph includes: Obtaining the dust accumulation value of each surface image, and constructing a time-sequential dust accumulation change line graph based on the dust accumulation value of each surface image; Determine the broken line segments included in the dust accumulation degree change broken line graph, and calculate the change amount of each broken line segment; The broken line segments with a variation smaller than a preset value are eliminated, and the slope of each remaining broken line segment is calculated, and the slope and variation of each remaining broken line segment are determined as data variation features.

[0010] Furthermore, the analysis and calculation of the data variation characteristics to determine the coefficient of variation of the dust accumulation degree of the photovoltaic panel includes: Obtain the slope and change of each remaining polyline segment, and calculate the average value of the slope and the average value of the change of each remaining polyline segment; The coefficient of variation of the degree of dust accumulation of the photovoltaic panel is determined based on the average value of the slope of each remaining broken line segment and the average value of the variation. The calculation formula of the coefficient of variation of the degree of dust accumulation of the photovoltaic panel is: , Among them, F is the coefficient of change of dust accumulation degree, a is the slope conversion coefficient, Ki is the slope of each remaining broken line segment, b is the change conversion coefficient, Pi is the change of each remaining broken line segment, and n is the number of remaining broken line segments.

[0011] Furthermore, the step of determining the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree variation coefficient includes: The corresponding relationship between cleaning frequency and dust accumulation degree variation coefficient interval is pre-set, and each dust accumulation degree variation coefficient interval is associated with a corresponding cleaning frequency; Obtain the dust accumulation degree variation coefficient, and based on the mapping relationship between the dust accumulation degree variation coefficient interval to which the dust accumulation degree variation coefficient belongs and the cleaning frequency-dust accumulation degree variation coefficient interval correspondence relationship, select the cleaning frequency corresponding to the dust accumulation degree variation coefficient interval as the corresponding cleaning frequency of the photovoltaic panel.

[0012] The present invention also provides a photovoltaic panel cleaning frequency determination system, comprising: An acquisition module, used to acquire historical surface image data of the photovoltaic panel in the last cycle, and to acquire a plurality of surface images from the historical surface image data according to a preset acquisition interval; An extraction module, used for graying the surface image and extracting image features from the grayed surface image; An evaluation module, used to evaluate the dust accumulation degree of the photovoltaic panel based on the image features, and obtain the dust accumulation degree value of the surface image; An analysis module is used to construct a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determine data change characteristics from the dust accumulation degree change line graph; A determination module is used to analyze and calculate the data change characteristics, determine the dust accumulation degree change coefficient of the photovoltaic panel, and determine the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; The updating module is used to repeat the above steps and update the cleaning frequency of the photovoltaic panel when the next cycle is reached.

[0013] Compared with the prior art, the photovoltaic panel cleaning frequency determination method and system according to the embodiment of the present invention have the following beneficial effects: The present invention can achieve accurate assessment of the dust accumulation degree of solar photovoltaic panels and provide objective data support by processing and analyzing historical surface image data; The present invention can realize real-time analysis of the dust accumulation of photovoltaic panels by periodically collecting and analyzing historical image data, and dynamically adjust the cleaning frequency in each cycle; The present invention determines the cleaning frequency according to the dust accumulation degree variation coefficient, which can avoid over-cleaning or under-cleaning, thereby improving the performance and economic benefits of the photovoltaic system; The present invention can continuously optimize the cleaning frequency of photovoltaic equipment by repeating the process in each cycle, and make adjustments according to actual conditions to ensure that the equipment is always in the best condition; The present invention can reduce unnecessary cleaning times, lower maintenance costs, and extend the service life of photovoltaic equipment through intelligent cleaning strategies; In general, the present invention can help photovoltaic system managers better understand the dust accumulation of photovoltaic panels, formulate more reasonable cleaning plans, improve the power generation efficiency of the system, reduce operating costs, and ensure long-term stable operation of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a schematic diagram of the flow structure of a method for determining a photovoltaic panel cleaning frequency in an embodiment of the present invention; Figure 2 Schematic diagram of the composition of a photovoltaic panel cleaning frequency determination system in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0016] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0017] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0018] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0019] like Figure 1 As shown, in an embodiment of the present application, a method for determining the cleaning frequency of a photovoltaic panel is provided, including: S100: obtaining historical surface image data of the photovoltaic panel in the previous cycle, and collecting a number of surface images from the historical surface image data according to a preset collection interval; S200: graying the surface image, and extracting image features from the grayed surface image; S300: evaluating the dust accumulation degree of the photovoltaic panel based on the image features to obtain the dust accumulation degree value of the surface image; S400: constructing a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determining data change characteristics from the dust accumulation degree change line graph; S500: analyzing and calculating the data change characteristics to determine the dust accumulation degree change coefficient of the photovoltaic panel, and determining the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; S600: when the next cycle is reached, repeating the above steps to update the cleaning frequency of the photovoltaic panel.

[0020] Furthermore, the present invention can realize accurate assessment of the dust accumulation degree of solar photovoltaic panels and provide objective data support by processing and analyzing historical surface image data; the present invention can realize real-time analysis of the dust accumulation situation of photovoltaic panels and dynamically adjust the cleaning frequency in each cycle by periodically collecting and analyzing historical image data; the present invention determines the cleaning frequency according to the coefficient of variation of the dust accumulation degree, thereby avoiding excessive cleaning or insufficient cleaning, thereby improving the performance and economic benefits of the photovoltaic system; the present invention can continuously optimize the cleaning frequency of photovoltaic equipment by repeating the process in each cycle, and make adjustments according to actual conditions to ensure that the equipment is always in the best condition; the invention can reduce unnecessary cleaning times, reduce maintenance costs, and extend the service life of photovoltaic equipment through intelligent cleaning strategies; in general, the present invention can help photovoltaic system managers better understand the dust accumulation situation of photovoltaic panels, formulate more reasonable cleaning plans, improve the power generation efficiency of the system, reduce operating costs, and ensure long-term stable operation of the equipment.

[0021] In an embodiment of the present application, a method for determining the cleaning frequency of a photovoltaic panel is provided, wherein historical surface image data of the photovoltaic panel in the previous cycle is obtained, and a plurality of surface images are collected from the historical surface image data according to a preset collection interval, including: obtaining the historical surface image data of the photovoltaic panel in the previous cycle from a database; obtaining a preset collection interval set in advance, and collecting a plurality of surface images from the historical surface image data according to the preset collection interval.

[0022] Specifically, the historical surface image data of the photovoltaic panel in the last cycle is retrieved from the database. These data include images of the photovoltaic panel at different time points, recording the surface condition of the panel; in the setting stage, the preset collection interval is determined, that is, the interval at which the surface image data is collected is specified; according to the preset collection interval, a number of surface images are extracted from the historical surface image data according to the interval rule, and these images represent the surface state of the photovoltaic panel at different time points. This step realizes the data collection and integration of the surface condition of the photovoltaic panel by extracting the historical image data from the database, providing a basis for subsequent analysis and decision-making; surface images are collected at preset intervals to generate a series of time series images, which can be used to analyze the changes in the surface condition of the photovoltaic panel over time; by regularly collecting surface image data, real-time monitoring of the surface condition of the photovoltaic panel can be achieved, which helps to find problems and make adjustments in time. Through this process, the collection and analysis of the surface condition data of the photovoltaic panel can be realized, providing important support for subsequent maintenance and management, thereby helping to improve the performance and reliability of the photovoltaic system.

[0023] In an embodiment of the present application, a method for determining the cleaning frequency of a photovoltaic panel is provided, wherein the surface image is grayscaled and image features are extracted from the grayscaled surface image, including: grayscale processing the surface image to obtain a surface grayscale image, and extracting color features, texture features and brightness features from the surface grayscale image.

[0024] Specifically, grayscale processing is the process of converting a color image into a grayscale image, that is, converting the RGB value of each pixel into a single grayscale value. The grayscale image retains the structure and information of the image; extracting color features from a grayscale image can involve aspects such as color distribution, hue, and saturation. By analyzing the brightness distribution of pixels in a grayscale image, the color features of the image can be indirectly inferred; texture features describe the spatial arrangement and distribution of pixels in an image, and reflect the surface features of the image. By applying a texture analysis algorithm to a grayscale image, the texture features of the image can be extracted; brightness features reflect the brightness level of pixels in an image, which can help identify brightness changes and regions in an image. By analyzing the brightness distribution of pixels in a grayscale image. This step can fully describe the image content from different angles by extracting color, texture, and brightness features from a grayscale image, providing more information for subsequent analysis and processing; the extracted color, texture, and brightness features can help analyze the features and changes on the surface of a photovoltaic panel; the feature extraction process can be automated, using computer vision and image processing technology to achieve rapid analysis and processing of a large number of images. By extracting color, texture and brightness features from the surface grayscale image, the surface condition of the photovoltaic panel can be comprehensively analyzed, providing important support for the maintenance and management of the equipment, thereby improving the efficiency and reliability of the system.

[0025] In an embodiment of the present application, a method for determining a cleaning frequency of a photovoltaic panel is provided, wherein the dust accumulation degree of the photovoltaic panel is evaluated based on image features to obtain a dust accumulation degree value of the surface image, including: obtaining color features, texture features, and brightness features in the surface grayscale image, and performing quantitative analysis on the color features, texture features, and brightness features respectively to determine corresponding color feature values, texture feature values, and brightness feature values; obtaining standard feature values ​​corresponding to the color features, texture features, and brightness features, and calculating the differences between the color feature values, texture feature values, and brightness feature values ​​and the corresponding standard feature values ​​respectively to obtain color feature difference values, texture feature difference values, and brightness feature difference values ​​respectively; evaluating and taking values ​​of the color feature difference values, texture feature difference values, and brightness feature difference values ​​respectively to obtain color difference evaluation values, texture difference evaluation values, and brightness difference evaluation values; determining the dust accumulation degree value of the surface image based on preset weights corresponding to the color difference evaluation values, texture difference evaluation values, and brightness difference evaluation values ​​and the color features, texture features, and brightness features, wherein the calculation formula for the dust accumulation degree value of the surface image is: S = C*α+W*β+L*γ; Among them, S is the dust accumulation value, C is the color difference evaluation value, α is the preset weight of the color feature, W is the texture difference evaluation value, β is the preset weight of the texture feature, L is the brightness difference evaluation value, and γ is the preset weight of the brightness feature.

[0026] Specifically, color features, texture features and brightness features are extracted from the surface grayscale image; the extracted features are quantitatively analyzed and converted into numerical representations for subsequent calculations and comparisons, which helps to convert image features into operable data forms; standard feature values ​​of color features, texture features and brightness features are determined as reference benchmarks for evaluating the differences between the features of the surface image and the expected features; the difference between the extracted feature values ​​and the corresponding standard feature values ​​is calculated to obtain color feature difference values, texture feature difference values ​​and brightness feature difference values, which helps to quantify the differences between the surface image features and the standard features; evaluation is performed based on the color feature difference values, texture feature difference values ​​and brightness feature difference values ​​to obtain color difference evaluation values, texture difference evaluation values ​​and brightness difference evaluation values, which reflect the degree of difference between the image features and the standard features; based on the color difference evaluation values, texture difference evaluation values ​​and brightness difference evaluation values, combined with preset weight values, the dust accumulation value of the surface image is determined, which can help determine the cleaning frequency and maintenance plan of the photovoltaic equipment. This step can more accurately evaluate the dust accumulation on the surface of the photovoltaic panel by analyzing and quantifying the image features, avoiding errors caused by subjective judgment; based on the feature difference evaluation value obtained by quantitative analysis, data-driven decision-making can be achieved to help optimize cleaning strategies and maintenance plans; through the analysis and evaluation of image features, intelligent management of the surface condition of photovoltaic equipment can be achieved, improving management efficiency and equipment performance; regular feature analysis and evaluation can continuously optimize the cleaning strategy of photovoltaic equipment, ensure that the equipment is in the best condition, and extend the life of the equipment. This process combines image analysis and data processing technology, provides a scientific basis for photovoltaic equipment management, and helps improve the efficiency and reliability of equipment.

[0027] In an embodiment of the present application, a method for determining a cleaning frequency of a photovoltaic panel is provided, wherein the color features, texture features, and brightness features are quantitatively analyzed respectively to determine corresponding color feature values, texture feature values, and brightness feature values, including: numerically extracting the color features, texture features, and brightness features respectively to obtain a color feature binary sequence, a texture feature binary sequence, and a brightness feature binary sequence respectively; combining the color feature binary sequence, the texture feature binary sequence, and the brightness feature binary sequence respectively to obtain a high-dimensional color feature vector, a high-dimensional texture feature vector, and a high-dimensional brightness feature vector; and performing dimensionality reduction processing on the high-dimensional color feature vector, the high-dimensional texture feature vector, and the high-dimensional brightness feature vector to obtain color feature values, texture feature values, and brightness feature values.

[0028] Specifically, color features, texture features and brightness features are extracted into the form of binary sequences, which represent the specific numerical values ​​of color, texture and brightness features in the image; the color feature binary sequence, texture feature binary sequence and brightness feature binary sequence are respectively combined into high-dimensional vectors to form high-dimensional color feature vectors, high-dimensional texture feature vectors and high-dimensional brightness feature vectors, which contain information of multiple feature dimensions in the image; dimensionality reduction is performed on the high-dimensional color feature vectors, high-dimensional texture feature vectors and high-dimensional brightness feature vectors to convert them into vectors of lower dimensionality to obtain color feature values, texture feature values ​​and brightness feature values. This step can simplify high-dimensional feature information into a low-dimensional representation that is easier to process and understand by converting color features, texture features, and brightness features into vector form and performing dimensionality reduction processing, thereby reducing computational complexity; by combining binary sequences of different features into high-dimensional vectors, it helps to comprehensively consider information from multiple feature dimensions and improve the ability to comprehensively analyze image features; by dimensionality reduction processing, color feature values, texture feature values, and brightness feature values ​​are obtained, and image features are converted into numerical form to facilitate subsequent data analysis and processing; dimensionality reduction processing of high-dimensional vectors helps to optimize the data processing process, improve the efficiency and accuracy of the algorithm, and provide a better foundation for subsequent data mining and pattern recognition. This process realizes the effective extraction and processing of image features by converting image features into numerical vectors and performing dimensionality reduction processing on them, providing strong support for further data analysis and decision-making.

[0029] In an embodiment of the present application, a method for determining the cleaning frequency of a photovoltaic panel is provided, wherein a dust accumulation level change line graph is constructed based on the dust accumulation level value of each surface image, and data change characteristics are determined from the dust accumulation level change line graph, including: obtaining the dust accumulation level value of each surface image, and constructing a time-sequential dust accumulation level change line graph based on the dust accumulation level value of each surface image; determining the line segments contained in the dust accumulation level change line graph, and calculating the change amount of each line segment; eliminating the line segments whose change amount is less than a preset value, and calculating the slope of each remaining line segment, and determining the slope and change amount of each remaining line segment as the data change characteristics.

[0030] Specifically, for each surface image, the dust accumulation value is calculated by the method described above, and these values ​​reflect the degree of dust in the image; according to the dust accumulation value of each surface image, a dust accumulation change line graph is constructed in chronological order, wherein the horizontal axis represents time and the vertical axis represents the dust accumulation value; in the dust accumulation change line graph, different line segments are determined, and each line segment represents the dust accumulation trend over a period of time; for each line segment, its change amount is calculated, that is, the difference between the dust accumulation values ​​at both ends of the line segment, reflecting the dust accumulation change situation over the time period; the line segments with a change amount less than the preset value are eliminated, and the slopes of the remaining line segments are calculated, and the slopes reflect the rate of change of the dust accumulation, so as to conduct a more detailed analysis of the dust accumulation situation. This step can dynamically monitor the dust accumulation on the surface of the photovoltaic panel and detect the change trend in time by constructing a line graph of the dust accumulation level. By calculating the change amount and slope of the line segment, the change trend of the dust accumulation level can be analyzed more deeply to help predict future development trends. Eliminating the line segment with a change amount less than the preset value can help identify abnormal situations and improve the accuracy of the change in the dust accumulation level. Using the slope and change amount as data change features can help quantify the change in the dust accumulation level and provide a basis for subsequent data analysis and decision-making. This process combines time series data analysis and change trend identification technology to provide a more comprehensive solution for monitoring and analyzing the dust accumulation of photovoltaic equipment.

[0031] In an embodiment of the present application, a method for determining the cleaning frequency of a photovoltaic panel is provided, wherein the data change characteristics are analyzed and calculated to determine the dust accumulation degree variation coefficient of the photovoltaic panel, including: obtaining the slope and variation of each remaining broken line segment, and calculating the average value of the slope of each remaining broken line segment and the average value of the variation; determining the dust accumulation degree variation coefficient of the photovoltaic panel based on the average value of the slope of each remaining broken line segment and the average value of the variation, and the calculation formula of the dust accumulation degree variation coefficient of the photovoltaic panel is: , Among them, F is the coefficient of change of dust accumulation degree, a is the slope conversion coefficient, Ki is the slope of each remaining broken line segment, b is the change conversion coefficient, Pi is the change of each remaining broken line segment, and n is the number of remaining broken line segments.

[0032] Specifically, the slope and variation of each remaining broken line segment are calculated to obtain the average value of the slope and the average value of the variation, which reflect the overall trend of the dust accumulation degree; based on the average value of the slope and the average value of the variation, the dust accumulation degree variation coefficient of the photovoltaic panel is determined. This step provides a comprehensive evaluation of the change in the dust accumulation degree by calculating the average value of the slope and the variation. The dust accumulation degree variation coefficient comprehensively considers the change rate and overall change of the dust accumulation degree of the photovoltaic panel, and provides a comprehensive evaluation of the change in the dust accumulation degree; the variation coefficient can help analyze the trend of the dust accumulation of the photovoltaic panel, understand whether the dust accumulation degree is gradually increasing or decreasing, and provide an important reference for maintenance and management; the average value of the slope and the variation is converted into a variation coefficient, and the change and rate of the dust accumulation degree are quantified, which is convenient for comparing and evaluating the dust accumulation in different time periods. By determining the dust accumulation degree variation coefficient of the photovoltaic panel, we can have a more comprehensive understanding of the dust accumulation of the photovoltaic panel, and make corresponding management and maintenance decisions based on these data, thereby improving the efficiency and performance of the photovoltaic system.

[0033] In an embodiment of the present application, a method for determining the cleaning frequency of a photovoltaic panel is provided, wherein the cleaning frequency of the photovoltaic panel in the current cycle is determined based on the dust accumulation degree variation coefficient, comprising: presetting a cleaning frequency-dust accumulation degree variation coefficient interval correspondence relationship, and associating a corresponding cleaning frequency with each dust accumulation degree variation coefficient interval; obtaining the dust accumulation degree variation coefficient, and based on a mapping relationship between the dust accumulation degree variation coefficient interval to which the dust accumulation degree variation coefficient belongs and the corresponding relationship between the cleaning frequency-dust accumulation degree variation coefficient interval, selecting the cleaning frequency corresponding to the dust accumulation degree variation coefficient interval as the corresponding cleaning frequency of the photovoltaic panel.

[0034] Specifically, for different intervals of the coefficient of variation of the degree of dust accumulation, the relationship between the cleaning frequency and the cleaning frequency is pre-set. For example, a lower coefficient of variation of the degree of dust accumulation can be set to correspond to a lower cleaning frequency, while a higher coefficient of variation of the degree of dust accumulation can be set to correspond to a higher cleaning frequency. According to the coefficient of variation of the degree of dust accumulation calculated previously, the actual dust accumulation of the current photovoltaic panel is determined. According to the interval to which the actual coefficient of variation of the degree of dust accumulation belongs, the corresponding cleaning frequency is found in the corresponding relationship between the cleaning frequency and the interval of the coefficient of variation of the degree of dust accumulation. According to the mapping relationship, the cleaning frequency corresponding to the interval of the actual coefficient of variation of the degree of dust accumulation is selected as the cleaning frequency of the photovoltaic panel, so that the most suitable cleaning frequency can be determined according to the actual degree of dust accumulation. This step can formulate a personalized cleaning frequency for each photovoltaic panel according to the actual coefficient of variation of the degree of dust accumulation, avoiding unnecessary cleaning or delayed cleaning that leads to performance degradation. By dynamically adjusting the cleaning frequency, resources can be effectively utilized, cleaning costs can be reduced, and efficient operation of the photovoltaic system can be ensured. Establishing the corresponding relationship between the cleaning frequency and the coefficient of variation of the degree of dust accumulation can realize automated management, improve operation and maintenance efficiency, and reduce the need for manual intervention. Regular cleaning can maintain the cleanliness of the photovoltaic panel, improve the power generation efficiency of the photovoltaic system, and extend the life of the equipment, thereby achieving more stable and continuous power generation. By combining the cleaning frequency with the coefficient of variation of dust accumulation, the cleaning management strategy of the photovoltaic system can be effectively optimized to improve the performance and reliability of the system.

[0035] like Figure 2 As shown, in an embodiment of the present application, a photovoltaic panel cleaning frequency determination system is provided, including: an acquisition module, used to acquire historical surface image data of the photovoltaic panel in the previous cycle, and collect a number of surface images from the historical surface image data according to a preset acquisition interval; an extraction module, used to grayscale the surface image, and extract image features from the grayscaled surface image; an evaluation module, used to evaluate the dust accumulation degree of the photovoltaic panel based on the image features, and obtain the dust accumulation degree value of the surface image; an analysis module, used to construct a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determine the data change characteristics from the dust accumulation degree change line graph; a determination module, used to analyze and calculate the data change characteristics, determine the dust accumulation degree change coefficient of the photovoltaic panel, and determine the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; an update module, used to repeat the above steps when the next cycle is reached, and update the cleaning frequency of the photovoltaic panel.

[0036] In summary, the embodiment of the present invention provides a method and system for determining the cleaning frequency of a photovoltaic panel, which includes: obtaining the historical surface image data of the photovoltaic panel in the last cycle, and collecting a number of surface images therefrom; graying the surface image, and extracting image features therefrom; evaluating the dust accumulation degree of the photovoltaic panel based on the image features to obtain a dust accumulation degree value; constructing a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determining the data change characteristics therefrom; analyzing and calculating the data change characteristics, determining the dust accumulation degree change coefficient of the photovoltaic panel, and determining the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; when the next cycle is reached, repeating the above steps to update the cleaning frequency of the photovoltaic panel. The present invention can realize accurate dynamic adjustment of the cleaning frequency of the photovoltaic panel, ensuring that it can be reasonably cleaned and maintained in different cycles, thereby improving the cleaning efficiency and reducing the operating cost.

[0037] Finally, it should be noted that: Obviously, a person skilled in the art may 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 present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0038] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0039] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0040] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for determining the cleaning frequency of a photovoltaic panel, characterized in that: include: Acquire historical surface image data of the photovoltaic panel in the last cycle, and collect a number of surface images from the historical surface image data according to a preset collection interval; graying the surface image and extracting image features from the grayed surface image; The dust accumulation degree of the photovoltaic panel is evaluated based on the image features to obtain the dust accumulation degree value of the surface image; A dust accumulation degree change line graph is constructed based on the dust accumulation degree value of each surface image, and data change characteristics are determined from the dust accumulation degree change line graph; Analyze and calculate the data change characteristics, determine the dust accumulation degree change coefficient of the photovoltaic panel, and determine the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; When the next cycle is reached, the above steps are repeated to update the cleaning frequency of the photovoltaic panel.

2. A method for determining the frequency of cleaning a photovoltaic panel according to claim 1, characterized in that: The acquiring of historical surface image data of the photovoltaic panel in the previous cycle and collecting a plurality of surface images from the historical surface image data according to a preset collection interval includes: Obtain historical surface image data of the photovoltaic panel in the last cycle from the database; A preset acquisition interval set in advance is obtained, and a plurality of surface images are acquired from the historical surface image data according to the preset acquisition interval.

3. A method for determining the frequency of cleaning a photovoltaic panel according to claim 1, characterized in that: The grayscale processing of the surface image and extracting image features from the grayscale processed surface image includes: The surface image is gray-processed to obtain a surface gray-scale image, and color features, texture features and brightness features are extracted from the surface gray-scale image.

4. A method for determining the frequency of cleaning a photovoltaic panel according to claim 3, characterized in that: The step of evaluating the dust accumulation degree of the photovoltaic panel based on the image features to obtain the dust accumulation degree value of the surface image includes: Acquire color features, texture features and brightness features in the surface grayscale image, and perform quantitative analysis on the color features, texture features and brightness features respectively to determine the corresponding color feature values, texture feature values ​​and brightness feature values; Obtaining the standard feature values ​​corresponding to the color feature, texture feature and brightness feature, and respectively calculating the differences between the color feature value, texture feature value and brightness feature value and the corresponding standard feature value, to obtain the color feature difference value, texture feature difference value and brightness feature difference value respectively; The color feature difference, the texture feature difference and the brightness feature difference are evaluated and valued respectively to obtain a color difference evaluation value, a texture difference evaluation value and a brightness difference evaluation value; The gray level value of the surface image is determined based on the preset weights corresponding to the color difference evaluation value, the texture difference evaluation value, and the brightness difference evaluation value and the color features, the texture features, and the brightness features. The calculation formula for the gray level value of the surface image is: S = C*α+W*β+L*γ; Among them, S is the dust accumulation value, C is the color difference evaluation value, α is the preset weight of the color feature, W is the texture difference evaluation value, β is the preset weight of the texture feature, L is the brightness difference evaluation value, and γ is the preset weight of the brightness feature.

5. A method for determining the frequency of cleaning a photovoltaic panel according to claim 4, characterized in that: The quantitative analysis of the color feature, the texture feature and the brightness feature is respectively performed to determine the corresponding color feature value, texture feature value and brightness feature value, including: Numerical extraction is performed on color features, texture features and brightness features respectively, to obtain a color feature binary sequence, a texture feature binary sequence and a brightness feature binary sequence respectively; Combining the color feature binary sequence, the texture feature binary sequence and the brightness feature binary sequence respectively to obtain a high-dimensional color feature vector, a high-dimensional texture feature vector and a high-dimensional brightness feature vector; The high-dimensional color feature vector, the high-dimensional texture feature vector and the high-dimensional brightness feature vector are subjected to dimensionality reduction processing to obtain color feature values, texture feature values ​​and brightness feature values.

6. A method for determining the frequency of cleaning a photovoltaic panel according to claim 4, characterized in that: The step of constructing a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determining data change characteristics from the dust accumulation degree change line graph includes: Obtaining the dust accumulation value of each surface image, and constructing a time-sequential dust accumulation change line graph based on the dust accumulation value of each surface image; Determine the broken line segments included in the dust accumulation degree change broken line graph, and calculate the change amount of each broken line segment; The broken line segments with a variation less than a preset value are eliminated, and the slope of each remaining broken line segment is calculated, and the slope and variation of each remaining broken line segment are determined as data variation features.

7. A method for determining the frequency of cleaning a photovoltaic panel according to claim 6, characterized in that: The analysis and calculation of the data variation characteristics to determine the coefficient of variation of the dust accumulation degree of the photovoltaic panel includes: Obtain the slope and change of each remaining polyline segment, and calculate the average value of the slope and the average value of the change of each remaining polyline segment; The coefficient of variation of the degree of dust accumulation of the photovoltaic panel is determined based on the average value of the slope of each remaining broken line segment and the average value of the variation. The calculation formula of the coefficient of variation of the degree of dust accumulation of the photovoltaic panel is: ， Among them, F is the coefficient of change of dust accumulation degree, a is the slope conversion coefficient, Ki is the slope of each remaining broken line segment, b is the change conversion coefficient, Pi is the change of each remaining broken line segment, and n is the number of remaining broken line segments.

8. A method for determining the frequency of cleaning a photovoltaic panel according to claim 7, characterized in that: Determining the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree variation coefficient includes: The corresponding relationship between cleaning frequency and dust accumulation degree variation coefficient interval is pre-set, and each dust accumulation degree variation coefficient interval is associated with a corresponding cleaning frequency; Obtain the dust accumulation degree variation coefficient, and based on the mapping relationship between the dust accumulation degree variation coefficient interval to which the dust accumulation degree variation coefficient belongs and the corresponding relationship between the cleaning frequency and the dust accumulation degree variation coefficient interval, select the cleaning frequency corresponding to the dust accumulation degree variation coefficient interval as the corresponding cleaning frequency of the photovoltaic panel.

9. A photovoltaic panel cleaning frequency determination system, characterized in that: include: An acquisition module, used to acquire historical surface image data of the photovoltaic panel in the last cycle, and to acquire a plurality of surface images from the historical surface image data according to a preset acquisition interval; An extraction module, used for graying the surface image and extracting image features from the grayed surface image; An evaluation module, used to evaluate the dust accumulation degree of the photovoltaic panel based on the image features, and obtain the dust accumulation degree value of the surface image; An analysis module is used to construct a dust accumulation degree change line graph based on the dust accumulation degree value of each surface image, and determine data change characteristics from the dust accumulation degree change line graph; A determination module is used to analyze and calculate the data change characteristics, determine the dust accumulation degree change coefficient of the photovoltaic panel, and determine the cleaning frequency of the photovoltaic panel in the current cycle according to the dust accumulation degree change coefficient; The updating module is used to repeat the above steps and update the cleaning frequency of the photovoltaic panel when the next cycle is reached.

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