Photovoltaic multi-source sensing intelligent cleaning and cooling method and system

Abstract

The invention discloses a photovoltaic multi-source sensing intelligent cleaning and cooling method and system. The method comprises the steps that S1, weather data, photovoltaic generating capacity data, photovoltaic equipment temperature, pollution information of a photovoltaic panel and pollution information of a cleaning sponge of a photovoltaic cleaner are obtained; s2, preprocessing the obtained data; s3, analyzing the weather data, the photovoltaic generating capacity data and the attachment condition of pollutants on the surface of the photovoltaic panel after preprocessing, and generating a cleaning instruction of a photovoltaic cleaner; s4, analyzing the temperature of the photovoltaic equipment in the step S1, and generating a photovoltaic cleaner cooling instruction; s5, the photovoltaic cleaner executes a cleaning or cooling instruction; and S6, according to the pollution degree of the cleaning sponge and the water consumption of the cleaning or cooling operation in the cleaning process, automatically replacing and cleaning the cleaning sponge of the photovoltaic cleaner and supplementing water to the water tank. Unmanned and intelligent deployment of the photovoltaic power station is realized, the power generation efficiency is improved, the operation and maintenance cost of the photovoltaic power station is reduced, and the economic benefit of the photovoltaic power station is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic pollution detection, cleaning and cooling, and particularly to a photovoltaic multi-source perception intelligent cleaning and cooling method and system. Background Art

[0002] In recent years, as an important development direction in the field of clean energy, photovoltaic power generation has gradually become the core pillar of the global energy structure transformation. With the continuous progress of photovoltaic technology and the continuous expansion of the photovoltaic installed capacity, its proportion in the renewable energy market has been steadily increasing. However, the photovoltaic power generation efficiency is affected by various factors, among which the accumulation of pollutants on the surface of photovoltaic panels and the over-high temperature of photovoltaic equipment are two key issues, seriously restricting the actual power generation performance and economic benefits of photovoltaic systems.

[0003] Since photovoltaic is exposed to the outdoors for a long time, its surface is extremely prone to accumulating dust, bird droppings, industrial pollutants (such as paint, chemical deposits, etc.). These pollutants will not only block the incident sunlight, weaken the light absorption ability of photovoltaic cells, resulting in a significant decline in the photoelectric conversion efficiency, but also may exacerbate heat accumulation, further reducing the power generation performance, and greatly affecting the economic benefits of photovoltaic power stations.

[0004] In addition, the accumulation of pollutants may also trigger the hot spot effect. When some photovoltaic panels cannot generate electricity normally due to pollutant occlusion, this area may become a local "load", resulting in abnormal temperature rise inside the battery. Long-term temperature accumulation will not only accelerate the deterioration of photovoltaic materials, but also may cause thermal damage, shorten the service life, and increase the operation and maintenance costs. Therefore, regularly cleaning the photovoltaic to keep its surface clean is a key measure to ensure the long-term stable operation of the photovoltaic system and maintain high power generation efficiency.

[0005] In addition to pollutant accumulation, photovoltaic also faces the problem of declining power generation performance in high-temperature environments. Since the working efficiency of photovoltaic cells is closely related to temperature, when the ambient temperature is too high, the operating temperature of photovoltaic may rise significantly, resulting in a decrease in its output voltage, thereby reducing the overall power generation efficiency. Therefore, in high-temperature environments, intelligent cooling technology has become an important means to improve the stability and power generation efficiency of photovoltaic systems.

[0006] Currently, the existing cleaning and equipment cooling solutions for photovoltaic pollutants still have problems such as high cost, low efficiency, and insufficient automation. For example, although the traditional manual method is simple to operate, it is costly in large-scale photovoltaic power stations and it is difficult to ensure the cleaning frequency and coverage; although using mechanical equipment can improve the cleaning and cooling efficiency, it often lacks intelligent scheduling and cannot accurately judge the cleaning and cooling timing according to the pollutant accumulation situation and ambient temperature, affecting the overall economy of photovoltaic power stations.

[0007] In view of the above problems, this patent proposes a photovoltaic adaptive cleaning and cooling method and system based on multi-source information perception and intelligent decision-making. By analyzing weather data, power generation data, photovoltaic device temperature, and pollutant monitoring results based on computer vision in real time, photovoltaic cleaning and cooling strategies are formulated to meet the conditional requirements of high-quality photovoltaic power generation, thereby improving the power generation efficiency and economic benefits of photovoltaic power stations. Summary of the Invention

[0008] The first object of the present invention is to overcome the deficiencies and drawbacks in the prior art and provide a photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making. This method formulates photovoltaic cleaning and cooling strategies by analyzing weather data, power generation data, photovoltaic device temperature, and pollutant monitoring results based on computer vision in real time, effectively solving the problems of high cost, low efficiency, and insufficient automation and intelligence in existing photovoltaic cleaning and cooling solutions, and playing a key role in subsequent intelligent energy management, intelligent cleaner scheduling, and photovoltaic system adaptive operation and maintenance of photovoltaic power stations.

[0009] The second object of the present invention is to provide a photovoltaic adaptive cleaning and cooling system based on multi-source information perception and intelligent decision-making.

[0010] The third object of the present invention is to provide a medium.

[0011] The fourth object of the present invention is to provide a computing device.

[0012] The first object of the present invention is achieved through the following technical solutions: A photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making, including the following steps: S1. Obtain monitoring data, namely weather data, photovoltaic power generation data, photovoltaic device temperature, pollution information of the photovoltaic panel, and pollution information of the cleaning sponge of the photovoltaic cleaner; S2. Data preprocessing, extract key data from the weather data and photovoltaic power generation data in S1 and optimize the pollution information of the photovoltaic panel and the pollution information of the cleaning sponge of the photovoltaic cleaner; S3. Jointly analyze the key data of the weather data and photovoltaic power generation data in S2 and the attachment situation of pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel photo to generate a cleaning execution instruction for the photovoltaic cleaner; S4. Analyze the photovoltaic device temperature in S1 to generate a cooling execution instruction for the photovoltaic cleaner; S5. According to the cleaning execution instruction of the photovoltaic cleaner in S3 and the cooling execution instruction of the photovoltaic cleaner in S4, the photovoltaic cleaner performs adaptive cleaning and cooling operations on the photovoltaic panel; S6. Automatically complete the replacement and cleaning of the cleaning sponge and the water replenishment operation of the water tank of the photovoltaic cleaner according to the water consumption of the photovoltaic cleaner and the pollution degree of the cleaning sponge in S5.

[0013] Further, in S1, the weather data is real-time weather data obtained by accessing the China Meteorological Data Network interface, which is a text data describing the weather conditions; the photovoltaic power generation data is the photovoltaic power generation recorded in the photovoltaic power station system; the photovoltaic device temperature is provided by the temperature sensor of the photovoltaic panel; the pollution information of the photovoltaic panel is the monitored video stream file of the photovoltaic panel; the pollution information of the cleaning sponge of the photovoltaic cleaner is the photo of the cleaning sponge taken by the camera under the photovoltaic panel. Among them, every half hour, the weather data update, the acquisition of photovoltaic power generation data, and the operation of intercepting photos from the photovoltaic panel video stream file are automatically synchronized. The cleaning sponge photos are taken every half minute when the photovoltaic cleaner executes the cleaning instruction.

[0014] Further, in S2, the specific situation of extracting the key data of the weather data and photovoltaic power generation data in S1 and optimizing the pollution information of the photovoltaic panel and the pollution information of the cleaning sponge of the photovoltaic cleaner is as follows: A. Extract the air quality index, temperature (°C), wind speed (m / s), precipitation (mm), air humidity (%), and solar irradiance (W / m 2 ) from the weather data; B. Extract the actual power generation of each photovoltaic panel from the photovoltaic power generation data, and obtain the theoretical power generation of each photovoltaic panel under ideal conditions from the equipment manual. C. The specific steps for optimizing the intercepted photovoltaic panel photos and the cleaning sponge photos of the photovoltaic cleaner are as follows: C.1. Introduce a new photovoltaic pollutant image enhancement method based on adaptive frequency domain fusion and super-resolution reconstruction to perform noise reduction, sharpening, and super-resolution optimization on multiple monitored photovoltaic panel photos and cleaning sponge photos of the photovoltaic cleaner. The specific steps are as follows: a. Decompose the image by wavelet transform WT, and decompose the image into a low-frequency part L and a high-frequency part H: ; Among them, the low-frequency L contains illumination and shadow information, and the high-frequency H contains pollutant edges and high-frequency noise information; b. Use the BayesShrink method to calculate the noise standard deviation : ; Among them, is the median function, and then obtain the adaptive threshold , Filter image noise: ; Among them, is the signal standard deviation, is the sign function, is the high-frequency component of the image after filtering noise; c. Combine the high-frequency component after noise filtering and the low-frequency part L, and perform inverse wavelet transform reconstruction to obtain the image after filtering noise: C.2. The specific steps for enhancing image sharpness based on structure tensor gradient are as follows: a. Calculate the Sobel direction gradient: ; Among them, represents the gradient along the horizontal direction, represents the gradient along the horizontal direction, and then calculate the structure tensor Identify the pollutant boundary: ; Among them, and represent the main direction change rate of the structure.

[0015] b. Calculate the sharpness weight of each pixel: ; c. Perform adaptive sharpening on the image: ; Among them, is the second-order Laplacian derivative for enhancing edges, is the adaptive sharpening coefficient; C.3. The specific steps for optimizing image sharpness using adversarial learning-based super-resolution are as follows: a. Use ESRGAN for super-resolution reconstruction to obtain the image after super-resolution reconstruction: ; Among them, is the ESRGAN generator, calculate the feature difference between the SR image and the original high-definition image: ; Among them, is the high-level feature of the VGG network extraction layer, is the actual real image, and adopt adversarial loss to naturalize the super-resolution image: ; wherein; is the discriminator network; b. The final training goal is to optimize the super-resolution image: ; wherein; is the L1 loss, and are the hyperparameter weights for controlling different losses.

[0016] Furthermore, in S3, the key data of the weather data and the photovoltaic power generation data in S2 and the attachment situation of pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel photo are jointly analyzed, and the specific situation of generating the cleaning execution instruction of the photovoltaic cleaner is as follows: A. According to the influence of weather factors at different levels on the degree of pollutant attachment and the interaction between various weather factors, corresponding pollutant attachment weights are assigned to different weather factors. The pollutant attachment weight can indicate whether the weather factor is conducive to pollutant settlement. A positive weight indicates that the weather factor is conducive to pollutant settlement, a weight of zero indicates that the weather factor has no obvious influence on pollutant settlement, and a negative weight indicates that the weather factor is not conducive to pollutant settlement. By comprehensively analyzing the comprehensive influence of various weather factors on pollutant attachment, the pollutant attachment weights of the following six key weather factors are determined: First, the pollutant attachment weight corresponding to the air quality index: If the air quality index within the selected period is less than 50, it is considered that the pollutant accumulation is less, and the pollutant attachment weight corresponding to the air quality index is assigned as -0.2; if the air quality index within the selected period is greater than 50 and less than 150, it is considered that the pollutant accumulation speed is relatively fast, and the pollutant attachment weight corresponding to the air quality index is assigned as 0.1; if the air quality index within the selected period is greater than 150, it is considered that the pollutant accumulation speed is extremely fast, and the pollutant attachment weight corresponding to the air quality index is assigned as 0.3; Second, the pollutant attachment weight corresponding to the precipitation: If the precipitation within the selected time period is less than 2 mm, it cannot only clean the dust on the photovoltaic surface but may even adsorb the dust in the air. Therefore, the pollutant attachment weight corresponding to the precipitation is assigned as 0.2; if the precipitation within the selected period is greater than 2 mm and less than 10 mm, it may only clean some of the pollutants on the photovoltaic surface. Therefore, the pollutant attachment weight corresponding to the precipitation is assigned as -0.1; if the precipitation within the selected period is greater than 10 mm, it may clean most of the pollutants on the photovoltaic surface. Therefore, the pollutant attachment weight corresponding to the precipitation is assigned as -0.3; Third, the pollutant attachment weight corresponding to air humidity: If the air humidity within the selected time period is less than 30%, it indicates that the air is dry and electrostatic adsorption pollution is likely to form. Therefore, the pollutant attachment weight corresponding to air humidity is given as 0.2; if the air humidity within the selected time period is greater than 30% and less than 80%, there is no obvious impact on the attachment of pollutants. Therefore, the pollutant attachment weight corresponding to air humidity is given as 0.0; if the air humidity within the selected time period is greater than 80%, it may cause muddy dust pollution on the photovoltaic surface. Therefore, the pollutant attachment weight corresponding to air humidity is given as 0.15; Fourth, the pollutant attachment weight corresponding to solar irradiance: If the solar irradiance within the selected time period is less than 200 W / m 2 , the temperature of the photovoltaic surface is low and pollutants are not easily solidified. Therefore, the pollutant attachment weight corresponding to solar irradiance is given as -0.1; if the solar irradiance within the selected time period is greater than 200 W / m 2 and less than 600 W / m 2 , there is no obvious impact on the attachment of pollutants. Therefore, the pollutant attachment weight corresponding to solar irradiance is given as 0.0; if the solar irradiance within the selected time period is greater than 600 W / m 2 , it may cause the temperature of the photovoltaic surface to be high, resulting in the solidification of pollutants and difficult cleaning. Therefore, the pollutant attachment weight corresponding to solar irradiance is given as 0.2; Fifth, the pollutant attachment weight corresponding to wind speed: If the wind speed within the selected time period is less than 1 m / s, it indicates that the air is relatively static and pollutant sedimentation is serious. Therefore, the pollutant attachment weight corresponding to wind speed is given as 0.3; if the wind speed within the selected time period is greater than 1 m / s and less than 5 m / s, the wind speed is moderate and helps to reduce pollutant sedimentation. Therefore, the pollutant attachment weight corresponding to wind speed is given as -0.1; if the wind speed within the selected time period is greater than 5 m / s, it may bring new dust. Therefore, the pollutant attachment weight corresponding to wind speed is given as 0.1; Sixth, the pollutant attachment weight corresponding to temperature: If the temperature within the selected time period is less than 20 °C, the environment is in a low temperature and the pollutants on the photovoltaic surface are not easily solidified. Therefore, the pollutant attachment weight corresponding to temperature is given as -0.2; if the temperature within the selected time period is greater than 20 °C and less than 30 °C, the environmental temperature is moderate and the pollutants are moderately solidified. Then the pollutant attachment weight corresponding to temperature is given as -0.1; if the temperature within the selected time period is greater than 30 °C, the environment is in a high temperature and the pollutants on the photovoltaic surface are significantly solidified and difficult to clean. Therefore, the pollutant attachment weight corresponding to temperature is given as 0.3; Calculate the necessity index for photovoltaic cleaning based on the above different weather factors , and the calculation formula is: ; Among them, is the air quality index, is the precipitation, is the air humidity, is the solar irradiance, is the wind speed, is the temperature, is the pollutant attachment weight corresponding to the air quality index, is the pollutant attachment weight corresponding to the precipitation, is the pollutant attachment weight corresponding to the air humidity, is the pollutant attachment weight corresponding to the solar irradiance, is the pollutant attachment weight corresponding to the wind speed and is the pollutant attachment weight corresponding to the temperature; B. According to the influence of weather factors at different levels on the photovoltaic power generation amount and the interaction between various weather factors, corresponding weights of the influence of weather factors on the photovoltaic power generation amount are assigned. The weight of the influence of weather factors on the photovoltaic power generation amount can indicate whether the weather factors are favorable for photovoltaic power generation. The closer the influence weight of the weather factors is to 1, the more favorable the weather factor conditions are for photovoltaic power generation. By comprehensively analyzing the influence of various weather factors on the photovoltaic power generation amount, the weights of the influence of the following seven key weather factors on the photovoltaic power generation amount are determined: First, the weight of the influence of solar irradiance on the photovoltaic power generation amount: Calculate the ratio of the actual solar irradiance to the solar irradiance in the theoretical test scenario as the weight of the influence of solar irradiance on the power generation amount: ; Among them, is the actual solar irradiance, is the solar irradiance in the theoretical test scenario; Second, the weight of the influence of cloud cover on the photovoltaic power generation amount: Use the exponential decay model to calculate the weight of the influence of cloud cover on the photovoltaic power generation amount: ; Among them, is the cloud cover, is the empirical coefficient that can be adjusted according to the measured data, is the weight of the influence of cloud cover on the photovoltaic power generation amount; Third, the weight of the influence of temperature on the photovoltaic power generation amount: The temperature of the photovoltaic module and the ambient temperature around the photovoltaic comprehensively affect the efficiency of photovoltaic power generation: ; Among them, is the temperature of the photovoltaic device measured by the temperature sensor on the photovoltaic, is the ambient temperature, is the temperature coefficient, is the standard temperature of 25 °C, is the influence weight of photovoltaic power generation on temperature; Fourth, the influence weight of wind speed on photovoltaic power generation: Wind speed affects the heat dissipation of the photovoltaic system, thereby affecting the power generation efficiency: ; Among them, is the actual wind speed, is the standard reference wind speed; is the wind speed influence coefficient, which is the influence weight of wind speed on photovoltaic power generation; Fifth, the influence weight of air humidity on photovoltaic power generation: Different air humidities have different effects on the attachment of pollutants on the photovoltaic surface, further affecting the photovoltaic power generation efficiency. When the air humidity is greater than 80%, the influence weight of air humidity on photovoltaic power generation is assigned as 0.95. When the air humidity is greater than 30% and less than 80%, the influence weight of air humidity on photovoltaic power generation is assigned as 1. When the air humidity is less than 30%, the influence weight of air humidity on photovoltaic power generation is assigned as 0.98; Sixth, the influence weight of precipitation on photovoltaic power generation: Rainwater can refract sunlight in the air, affecting the direct reception of light sources by the photovoltaic system. There is a relationship between the precipitation of rainwater and the refraction of sunlight. When the precipitation is greater than 10 mm, the influence weight of precipitation on photovoltaic power generation is assigned as 1.2. When the precipitation is greater than 2 mm and less than 10 mm, the influence weight of precipitation on photovoltaic power generation is assigned as 1.05. When the precipitation is less than 2 mm, the influence weight of precipitation on photovoltaic power generation is assigned as 0.9; Seventh, the influence weight of air quality on photovoltaic power generation: Dust and other impurities in the air will reduce the intensity of sunlight radiation. When the air quality index is greater than 150, the influence weight of air quality on photovoltaic power generation is assigned as 0.8. When the air quality index is greater than 50 and less than 150, the influence weight of air quality on photovoltaic power generation is assigned as 0.9. When the air quality index is less than 50, the influence weight of air quality on photovoltaic power generation is assigned as 1; Calculate the theoretical power generation of the photovoltaic system according to the above different weather factors. The calculation formula is: ; Among them, is the power generation of the photovoltaic system under ideal environmental conditions, is the theoretical power generation of the photovoltaic in the actual environmental conditions. Then, compare the actual power generation of the photovoltaic with the actual theoretical power generation. If the actual power generation of the photovoltaic can reach 90% of the actual theoretical power generation, it indicates that there is no obvious abnormality in the actual power generation of the photovoltaic. Let the photovoltaic power generation abnormality index , otherwise, it is considered that the power generation of the photovoltaic is abnormal. Let the photovoltaic power generation abnormality index ; C. Introduce a new pollutant recognition method based on adaptive feature fusion to identify the specific situation of small-scale pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel monitoring photos in S2 as follows: C.1. Use YOLOv8 for pollutant target detection and output the pollutant bounding box: ; Among them, represents the leftmost position of the pollutant area, represents the bottommost position of the pollutant area, represents the rightmost position of the pollutant area, represents the topmost position of the pollutant area. Crop the target area based on the bounding box: ; C.2. Use Swin Transformer to extract pollutant area features through the sliding window attention mechanism: ; Calculate the spatial consistency of the pollutant area and merge pollutants with adjacent boundaries: ; C.3. Combine CNN and Swin Transformer to extract features: ; Calculate the final fusion features: ; Among them, and are adaptive weights; C.4. Introduce a multi-frame information fusion method to calculate the consistency of pollutants in multiple photos: ; Among them, when is greater than the retention threshold, retain ; C.5. Use U-Net to segment the retained pollutant area and generate a pollutant mask: ; D. Use wide - area pollutant identification based on spatial color analysis. The specific steps for identifying large - scale pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel monitoring photo in S2 are as follows: D.1. Calculate the color histogram difference between the clean photovoltaic panel and the panel in the actual image: ; Among them, is the color space, respectively represent the red, green, and blue channels in the RGB space, represents the color channel in the th color interval frequency, represents the color histogram of the clean photovoltaic panel, represents the color histogram of the actual image; b. Calculate the mean value of the color channels of the clean photovoltaic panel ; Among them, is the color value of the pixel point in the clean photovoltaic panel. Calculate the mean value of the color channels of the panel in the actual image: ; Among them, is the color mean value of the pixel point in the photovoltaic panel in the actual image. Calculate the difference in the mean value of the color channels between the clean photovoltaic panel and the photovoltaic panel in the actual image: ; c. Generate a mask for the pollutant area according to the comparison result of the color histogram difference in a and the color histogram difference threshold and the comparison result of the difference in the mean value of the color channels in b and the difference threshold of the mean value of the color channels : ; E. Calculate the small - scale pollutant area and the large - scale pollutant area by the number of pixels: ; Among them, the area of each pixel is 1 cm 2 , represents the number of pixels of the small - scale pollutant, that is, the pollutant area, represents the number of pixels of the large - scale pollutant, that is, the pollutant area, and pollution region represents the pollutant area; F. Comprehensively analyze the necessity index of photovoltaic cleaning in A , the photovoltaic power generation anomaly index in B and the pollutant area in C and The specific steps to determine whether the photovoltaic needs to be cleaned are as follows: F.1. According to the influence of the photovoltaic cleaning necessity index, the photovoltaic power generation anomaly index, and the pollutant area on the photovoltaic cleaning demand, different importance weights are assigned to them respectively: ; F.2. Calculate the comprehensive photovoltaic cleaning index for pollutants in different ranges according to the importance weights in F.1: ; When or the photovoltaic cleaner obtains the execution instruction to clean the pollutants, where is the comprehensive cleaning threshold interval for small-range pollutants, is the comprehensive cleaning threshold interval for large-range pollutants.

[0017] Furthermore, in S4, analyze the temperature of the photovoltaic device in S1 to generate a cooling execution instruction for the photovoltaic cleaner. When the temperature of the photovoltaic device is greater than the high-temperature resistance threshold for normal operation of the photovoltaic, the photovoltaic cleaner obtains the cooling execution instruction; Furthermore, in S5, according to the cleaning execution instruction of the photovoltaic cleaner in S3 and the cooling execution instruction of the photovoltaic cleaner in S4, the specific situation of the photovoltaic cleaner performing adaptive cleaning and cooling operations on the photovoltaic panel is as follows: A. The specific steps for the photovoltaic cleaner to execute the cleaning pollutant instruction are as follows: A.1. According to the information of the pollutants in S3, locate the number of the photovoltaic panel where the pollutants are located; A.2. According to the number of the photovoltaic panel in A.1, the specific steps for the corresponding photovoltaic cleaner to execute the cleaning pollutant instruction are as follows: a. Use YOLOv8 to perform real-time target recognition of the photovoltaic cleaner on the monitored video stream. Taking the coordinate system of the monitoring screen as a reference, the photovoltaic cleaner starts running to the right from the starting point while the photovoltaic cleaner lifts up, the cleaning sponge in the photovoltaic cleaner does not contact the photovoltaic panel, and the water tank of the photovoltaic cleaner does not release water; b. When the right coordinate of the photovoltaic cleaner is 10 cm away from the leftmost position of the pollutant, the photovoltaic cleaner lowers while the right water pipe of the water tank of the photovoltaic cleaner releases water, so that the cleaning sponge in the photovoltaic cleaner contacts the photovoltaic panel to clean the pollutants. When the left coordinate of the photovoltaic cleaner is 10 cm away from the rightmost position of the pollutant, the photovoltaic cleaner lifts up while the right water pipe of the water tank of the photovoltaic cleaner stops releasing water, and the first cleaning of the target pollutant ends. Record the water release time of this process, and repeat this operation for the pollutants that have not been cleaned for the first time; c. After the first cleaning of all pollutants is completed, the photovoltaic cleaner starts to run back. During this process, the pollutants cleaned in step b are cleaned again. When the left coordinate of the photovoltaic cleaner is 10 cm away from the rightmost position of the pollutants, the photovoltaic cleaner descends and the left water pipe of the water tank of the photovoltaic cleaner discharges water, so that the cleaning sponge in the photovoltaic cleaner contacts the photovoltaic panel to clean the pollutants. When the right coordinate of the photovoltaic cleaner is 10 cm away from the leftmost position of the pollutants, the photovoltaic cleaner lifts up and the left water pipe of the water tank of the photovoltaic cleaner stops discharging water. The second cleaning of the target pollutants is completed, and the water discharge time of this process is recorded. Repeat this operation for the pollutants that have not been cleaned for the second time; d. The photovoltaic cleaner returns to the starting point, and the total water discharge time of the water tank for the round-trip of the photovoltaic cleaner is statistically calculated; B. The specific steps for the photovoltaic cleaner to execute the cooling instruction are as follows: a. The photovoltaic cleaner runs back and forth two times from the starting point to the rightmost end of the photovoltaic panel. During the process, the photovoltaic cleaner lifts up, and the water pipes on both the left and right sides of the water tank discharge water simultaneously; b. The photovoltaic cleaner returns to the starting point, the water pipes on both the left and right sides of the water tank stop discharging water, and the water discharge time for the round-trip of the photovoltaic cleaner is statistically calculated; Furthermore, in S6, according to the pollution degree of the cleaning sponge during the cleaning process in S5 and the water consumption of the photovoltaic cleaner when performing cleaning or cooling tasks, the specific situations of automatically completing the replacement, cleaning of the cleaning sponge of the photovoltaic cleaner, and water replenishment of the water tank are as follows: a. There are the following two operation methods for the automatic cleaning of the cleaning sponge of the photovoltaic cleaner: The first one is to replace the cleaning sponge: Use the wide-area pollutant recognition method based on spatial color analysis in S3 to identify the photo of the cleaning sponge of the optimized photovoltaic cleaner in S2, and judge the pollution situation of the cleaning sponge. During the operation of the photovoltaic cleaner, when the pollution degree of each cleaning sponge is greater than the pollution threshold of the cleaning sponge for the first time, the photovoltaic cleaner controls the cleaning sponge to turn over. When the pollution degree of each cleaning sponge is greater than the pollution threshold of the cleaning sponge for the second time, the photovoltaic cleaner controls to replace a new cleaning sponge; The second one is to clean the cleaning sponge: When the photovoltaic cleaner returns to the starting point after completing the cleaning task, the flushing nozzle at the starting point is automatically turned on to flush the used cleaning sponge in the photovoltaic cleaner, and at the same time, the photovoltaic cleaner controls the cleaning sponge to rotate.

[0018] b. After the photovoltaic cleaner completes the cleaning or cooling task and returns to the starting point, the battery on the photovoltaic cleaner and the power supply of the photovoltaic water replenishing device are turned on. The water filling port of the photovoltaic water replenishing device and the water replenishing port of the water tank of the photovoltaic cleaner are magnetically docked. At the same time, the water filling pipe valve is opened to replenish water to the water tank. The water replenishing volume is calculated according to the total water discharge time of 300 Ml / s. After the water replenishing is completed, the battery on the photovoltaic cleaner and the power supply of the photovoltaic water replenishing device are turned off and the water filling pipe valve is closed at the same time; The second object of the present invention is achieved through the following technical solutions: A photovoltaic adaptive cleaning and cooling system based on multi-source information perception and intelligent decision-making is used to implement the above-mentioned photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making, and it includes: A data acquisition module for acquiring weather data, photovoltaic power generation data, photovoltaic device temperature, photovoltaic panel monitoring video, and cleaning sponge photos of the photovoltaic cleaner.

[0019] A data preprocessing module for extracting key weather data, power generation data of each photovoltaic panel, photovoltaic device temperature, and optimizing the intercepted photovoltaic panel photos and cleaning sponge photos of the photovoltaic cleaner; A photovoltaic pollution condition analysis module for analyzing the cleaning requirements of the photovoltaic panel based on the weather conditions, photovoltaic power generation conditions, and actual attachment conditions of pollutants on the photovoltaic panel, and giving a cleaning execution instruction to the photovoltaic cleaner; A photovoltaic device temperature analysis module for analyzing the cooling requirements of the photovoltaic device based on the photovoltaic device temperature and giving a cooling execution instruction to the photovoltaic cleaner; A photovoltaic cleaner scheduling module for executing photovoltaic cleaning and cooling instructions according to the cleaning and cooling requirements of the photovoltaic panel; A photovoltaic cleaner water replenishing module for automatically replenishing water according to the water consumption of the photovoltaic cleaner during cleaning and cooling operations; A photovoltaic cleaner cleaning module for replacing or cleaning the cleaning sponge in the photovoltaic cleaner according to the pollution condition of the cleaning sponge;

[0020] The third object of the present invention is achieved through the following technical solutions. A storage medium stores a program, and when the program is executed by a processor, the above-mentioned photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making is implemented.

[0021] The fourth object of the present invention is achieved through the following technical solutions: A computing device includes a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making is implemented.

[0022] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. For the first time, the present invention calculates the pollutant attachment weight and the cleaning necessity index based on weather data, photovoltaic power generation data, and image recognition technology, makes an intelligent decision on when to clean, avoids unnecessary cleaning, optimizes the cleaning schedule, and improves the cleaning efficiency.

[0023] 2. For the first time, the present invention adopts an image enhancement method based on adaptive frequency-domain fusion and super-resolution reconstruction, uses wavelet transform for noise reduction, structure tensor gradient for edge enhancement and sharpening, and combines ESRGAN for super-resolution optimization to make pollutant recognition more accurate.

[0024] 3. For the first time, the present invention constructs a photovoltaic power generation influence weight model, combines factors such as solar irradiance, cloud cover, temperature, wind speed, and humidity to calculate the theoretical power generation, and compares it with the actual power generation to accurately determine whether the power generation efficiency has decreased due to pollutants.

[0025] 4. For the first time, the present invention combines the efficient object detection ability of YOLOv8 and the global feature extraction ability of Swin Transformer to improve the ability to detect pollutants and enhance the accuracy of pollutant recognition.

[0026] 5. For the first time, the present invention intelligently analyzes data, automatically provides execution instructions for photovoltaic cleaning robots, realizes autonomous scheduling and adaptive cleaning, reduces manual intervention, and improves the degree of automation.

[0027] 6. For the first time, the present invention intelligently sets the trigger conditions for photovoltaic cleaning based on the influence of pollutant areas in different ranges on the power generation performance of photovoltaic devices, thereby effectively reducing the occurrence probability of hot spot effects, while reducing unnecessary cleaning times and lowering the maintenance cost.

[0028] 7. The present invention has a wide range of application space in the identification of photovoltaic pollution conditions and the improvement of cleaning technology, and has broad prospects in improving the power generation efficiency and economic benefits of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic logical flow diagram of the method of the present invention.

[0030] Figure 2 It is an architecture diagram of the system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] The present invention will be further described below in conjunction with specific embodiments.

[0032] As Figure 1As shown in the figure, this embodiment discloses a photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making. First, weather data, photovoltaic power generation data, pollution information of the photovoltaic panel, sensor temperature for analyzing the temperature of the photovoltaic equipment, and pollution information of the cleaning sponge of the photovoltaic cleaner for analyzing the pollution situation of the cleaning sponge are obtained. Then, the acquired data is preprocessed, key information is extracted, and the intercepted photovoltaic panel photos and the taken cleaning sponge photos are optimized. After that, based on the preprocessed weather data, photovoltaic power generation data, photovoltaic panel photos, and photovoltaic equipment temperature, an analysis of the cleaning or cooling requirements of the photovoltaic panel is carried out to generate an execution instruction for the photovoltaic cleaner. Then, the photovoltaic cleaner performs panel cleaning or cooling operations according to the execution instruction. During this period, the photovoltaic cleaner intelligently replaces the cleaning sponge according to the pollution situation of the cleaning sponge. Finally, the used cleaning sponge during the cleaning process is washed and water is replenished according to the water consumption of the cleaning or cooling operation. It includes the following steps: S1. Obtain monitoring data, namely weather data, photovoltaic power generation data, photovoltaic equipment temperature, pollution information of the photovoltaic panel, and pollution information of the cleaning sponge of the photovoltaic cleaner; the weather data is real-time weather data obtained by accessing the China Meteorological Data Network interface, which is a text data describing the weather conditions; the photovoltaic power generation data is the photovoltaic power generation recorded in the photovoltaic power station system; the photovoltaic equipment temperature is provided by the temperature sensor of the photovoltaic panel; the pollution information of the photovoltaic panel is the monitored photovoltaic panel video stream file; the pollution information of the cleaning sponge of the photovoltaic cleaner is the cleaning sponge photo taken by the camera under the photovoltaic panel; Among them, every half hour, the weather data update, photovoltaic power generation data acquisition, and photo intercepting operation from the photovoltaic panel video stream file are automatically synchronized. The cleaning sponge photo is taken every half minute when the photovoltaic cleaner executes the cleaning instruction.

[0033] By adopting the above steps, the original weather data and the original photovoltaic power generation data , photovoltaic equipment temperature , photovoltaic panel video , the intercepted photovoltaic panel photos and the original cleaning sponge photos are obtained. Among them, represents the rd photovoltaic panel photo intercepted from the photovoltaic panel video.

[0034] S2. The specific situations of extracting the key data of the weather data and photovoltaic power generation data in S1 and optimizing the pollution information of the photovoltaic panel and the pollution information of the cleaning sponge of the photovoltaic cleaner are as follows: A. Extract the air quality index, temperature (°C), wind speed (m / s), precipitation (mm), air humidity (%), and solar irradiance (W / m 2 ) from the weather data; B. Extract the actual power generation of each photovoltaic panel from the photovoltaic power generation data, and obtain the theoretical power generation of each photovoltaic panel under ideal conditions from the equipment manual; C. The specific steps for optimizing the intercepted photovoltaic panel photos and the cleaning sponge photos of the photovoltaic cleaner are as follows: C.1. The specific steps for denoising, sharpening, and super-resolution optimization of multiple monitored photovoltaic panel photos and cleaning sponge photos of the photovoltaic cleaner using a new photovoltaic pollutant image enhancement method based on adaptive frequency domain fusion and super-resolution reconstruction are as follows: a. Decompose the image by wavelet transform WT, and decompose the image into a low-frequency part L and a high-frequency part H: ; Among them, the low-frequency L contains light and shadow information, and the high-frequency H contains pollutant edge and high-frequency noise information; b. Calculate the noise standard deviation using the BayesShrink method : ; Among them, is the median function, and then obtain the adaptive threshold , and filter the image noise: ; Among them, is the signal standard deviation, is the sign function, is the high-frequency component of the image after filtering the noise; c. After combining the high-frequency component after noise filtering and the low-frequency part L, perform wavelet inverse transform reconstruction to obtain the image after filtering the noise: C.2. The specific steps for image sharpening based on structure tensor gradient enhancement are as follows: a. Calculate the Sobel direction gradient: ; Among them, represents the gradient along the horizontal direction of , represents the gradient along the horizontal direction of , and then calculate the structure tensor to identify the pollutant boundary: ; Among them, and represent the main direction change rate of the structure.

[0035] b. Calculate the sharpening weight of each pixel: ; c. Perform adaptive sharpening on the image: ; Among them, is the second-order Laplacian derivative for enhancing edges, is the adaptive sharpening coefficient; C.3. The specific steps to optimize the image sharpness using adversarial learning-based super-resolution are as follows: a. Use ESRGAN for super-resolution reconstruction to obtain the super-resolution reconstructed image: ; Among them, is the ESRGAN generator, calculating the feature difference between the SR image and the original high-definition image: ; Among them, is the high-level feature of the VGG network extraction layer, is the actual real image, and the adversarial loss is used to naturalize the super-resolution image: ; Among them; is the discriminator network; b. Train the final target to optimize the super-resolution image: ; Among them; is the L1 loss, and are the hyperparameter weights for controlling different losses.

[0036] Using the above steps, the air quality index , precipitation , air humidity , solar irradiance , wind speed , and temperature are extracted from the original weather data, and the power generation of each photovoltaic panel is extracted from the original photovoltaic power generation data, , where , among them, is the power generation of the th photovoltaic panel, and the intercepted photovoltaic panel photos are optimized And the photo of the original cleaning sponge Obtain high-definition photos of the photovoltaic panels And the photo of the cleaning sponge , where Represents the photo after the th optimized intercepted photo of the photovoltaic panel

[0037] S3. The specific situation of generating the cleaning execution instruction of the photovoltaic cleaner by jointly analyzing the key data of the weather data and the photovoltaic power generation data in S2 and the attachment situation of pollutants on the surface of the photovoltaic panel in the optimized photo of the photovoltaic panel is as follows: A. According to the influence of weather factors at different levels on the degree of pollutant attachment and the interaction between various weather factors, corresponding pollutant attachment weights are assigned to different weather factors. The pollutant attachment weight can indicate whether the weather factor is conducive to pollutant sedimentation. A positive weight indicates that the weather factor is conducive to pollutant sedimentation, a weight of zero indicates that the weather factor has no obvious influence on pollutant sedimentation, and a negative weight indicates that the weather factor is not conducive to pollutant sedimentation. By comprehensively analyzing the comprehensive influence of various weather factors on pollutant attachment, the pollutant attachment weights of the following six key weather factors are determined: First, the pollutant attachment weight corresponding to the air quality index: If the air quality index within the selected period is less than 50, it is considered that the pollutant accumulation is less, and the pollutant attachment weight corresponding to the air quality index is assigned as -0.2; if the air quality index within the selected period is greater than 50 and less than 150, it is considered that the pollutant accumulation speed is relatively fast, and the pollutant attachment weight corresponding to the air quality index is assigned as 0.1; if the air quality index within the selected period is greater than 150, it is considered that the pollutant accumulation speed is extremely fast, and the pollutant attachment weight corresponding to the air quality index is assigned as 0.3; Second, the pollutant attachment weight corresponding to the precipitation: If the precipitation within the selected time period is less than 2 mm, it cannot even clean the dust on the photovoltaic surface and may even adsorb the dust in the air. Therefore, the pollutant attachment weight corresponding to the precipitation is assigned as 0.2; if the precipitation within the selected period is greater than 2 mm and less than 10 mm, it may only clean some of the pollutants on the photovoltaic surface. Therefore, the pollutant attachment weight corresponding to the precipitation is assigned as -0.1; if the precipitation within the selected period is greater than 10 mm, it may clean most of the pollutants on the photovoltaic surface. Therefore, the pollutant attachment weight corresponding to the precipitation is assigned as -0.3; Third, the pollutant attachment weight corresponding to air humidity: If the air humidity within the selected time period is less than 30%, it indicates that the air is dry and static electricity adsorption pollution is likely to form. Therefore, the pollutant attachment weight corresponding to air humidity is given as 0.2. If the air humidity within the selected time period is greater than 30% and less than 80%, it has no obvious impact on pollutant attachment. Therefore, the pollutant attachment weight corresponding to air humidity is given as 0.0. If the air humidity within the selected time period is greater than 80%, it may cause muddy dust pollution on the photovoltaic surface. Therefore, the pollutant attachment weight corresponding to air humidity is given as 0.15; Fourth, the pollutant attachment weight corresponding to solar irradiance: If the solar irradiance within the selected time period is less than 200 W / m 2 , the temperature of the photovoltaic surface is low and pollutants are not easily solidified. Therefore, the pollutant attachment weight corresponding to solar irradiance is given as -0.1. If the solar irradiance within the selected time period is greater than 200 W / m 2 and less than 600 W / m 2 , it has no obvious impact on pollutant attachment. Therefore, the pollutant attachment weight corresponding to solar irradiance is given as 0.0. If the solar irradiance within the selected time period is greater than 600 W / m 2 , it may cause high temperature on the photovoltaic surface, resulting in pollutant solidification and difficult cleaning. Therefore, the pollutant attachment weight corresponding to solar irradiance is given as 0.2; Fifth, the pollutant attachment weight corresponding to wind speed: If the wind speed within the selected time period is less than 1 m / s, it indicates that the air is relatively static and pollutant sedimentation is serious. Therefore, the pollutant attachment weight corresponding to wind speed is given as 0.3. If the wind speed within the selected time period is greater than 1 m / s and less than 5 m / s, the wind speed is moderate, which helps to reduce pollutant sedimentation. Therefore, the pollutant attachment weight corresponding to wind speed is given as -0.1. If the wind speed within the selected time period is greater than 5 m / s, it may bring new dust. Therefore, the pollutant attachment weight corresponding to wind speed is given as 0.1; Sixth, the pollutant attachment weight corresponding to temperature: If the temperature within the selected time period is less than 20 °C, the environment is at low temperature and pollutants on the photovoltaic surface are not easily solidified. Therefore, the pollutant attachment weight corresponding to temperature is given as -0.2. If the temperature within the selected time period is greater than 20 °C and less than 30 °C, the environmental temperature is moderate and pollutants are moderately solidified. Then the pollutant attachment weight corresponding to temperature is given as -0.1. If the temperature within the selected time period is greater than 30 °C, the environment is at high temperature and pollutants on the photovoltaic surface are significantly solidified and difficult to clean. Therefore, the pollutant attachment weight corresponding to temperature is given as 0.3; Calculate the necessity index for photovoltaic cleaning based on the above different weather factors , and the calculation formula is: ; where, is the air quality index, is the precipitation, is the air humidity, is the solar irradiance, is the wind speed, is the temperature, is the pollutant attachment weight corresponding to the air quality index, is the pollutant attachment weight corresponding to the precipitation, is the pollutant attachment weight corresponding to the air humidity, is the pollutant attachment weight corresponding to the solar irradiance, is the pollutant attachment weight corresponding to the wind speed and is the pollutant attachment weight corresponding to the temperature; B. According to the influence of weather factors at different levels on the photovoltaic power generation, and the interaction between various weather factors, corresponding weights of the influence on photovoltaic power generation are assigned to different weather factors. The weight of the influence on photovoltaic power generation can indicate whether the weather factor is favorable for photovoltaic power generation. The closer the influence weight of the weather factor is to 1, the more favorable the weather factor condition is for photovoltaic power generation. By comprehensively analyzing the comprehensive influence of various weather factors on photovoltaic power generation, the weights of the influence on photovoltaic power generation of the following seven key weather factors are determined: First, the weight of the influence on photovoltaic power generation of solar irradiance: Calculate the ratio of the actual solar irradiance to the solar irradiance in the theoretical test scenario as the weight of the influence on the power generation of solar irradiance: ; Among them, is the actual solar irradiance, is the solar irradiance in the theoretical test scenario; Second, the weight of the influence on photovoltaic power generation of cloud cover: Use the exponential decay model to calculate the weight of the influence on photovoltaic power generation of cloud cover: ; Among them, is the cloud cover, is the empirical coefficient that can be adjusted according to the measured data, is the weight of the influence on photovoltaic power generation of cloud cover; Third, the weight of the influence on photovoltaic power generation of temperature: The temperature of the photovoltaic module and the ambient temperature around the photovoltaic comprehensively affect the efficiency of photovoltaic power generation: ; Among them, is the temperature of the photovoltaic device measured by the temperature sensor on the photovoltaic, is the ambient temperature, is the temperature coefficient, is the standard temperature of 25 °C, Namely, the influence weight of photovoltaic power generation by temperature; Fourthly, the influence weight of photovoltaic power generation by wind speed: Wind speed affects the heat dissipation of the photovoltaic system and thus affects the power generation efficiency: ; Among them, is the actual wind speed, is the standard reference wind speed; is the wind speed influence coefficient, Namely, the influence weight of photovoltaic power generation by wind speed; Fifthly, the influence weight of photovoltaic power generation by air humidity: Different air humidities have different effects on the attachment of pollutants on the photovoltaic surface, further affecting the photovoltaic power generation efficiency. When the air humidity is greater than 80%, the influence weight of photovoltaic power generation by air humidity is assigned as 0.95. When the air humidity is greater than 30% and less than 80%, the influence weight of photovoltaic power generation by air humidity is assigned as 1. When the air humidity is less than 30%, the influence weight of photovoltaic power generation by air humidity is assigned as 0.98; Sixthly, the influence weight of photovoltaic power generation by precipitation: Rainwater can refract sunlight in the air, affecting the direct reception of light sources by the photovoltaic system. There is a relationship between the precipitation of rainwater and the refraction of sunlight. When the precipitation is greater than 10 mm, the influence weight of photovoltaic power generation by precipitation is assigned as 1.2. When the precipitation is greater than 2 mm and less than 10 mm, the influence weight of photovoltaic power generation by precipitation is assigned as 1.05. When the precipitation is less than 2 mm, the influence weight of photovoltaic power generation by precipitation is assigned as 0.9; Seventhly, the influence weight of photovoltaic power generation by air quality: Dust and other impurities in the air will reduce the intensity of sunlight radiation. When the air quality index is greater than 150, the influence weight of photovoltaic power generation by air quality is assigned as 0.8. When the air quality index is greater than 50 and less than 150, the influence weight of photovoltaic power generation by air quality is assigned as 0.9. When the air quality index is less than 50, the influence weight of photovoltaic power generation by air quality is assigned as 1; Calculate the theoretical power generation of the photovoltaic system according to the above different weather factors. The calculation formula is: ; Among them, is the power generation of the photovoltaic system under ideal environmental conditions, is the theoretical power generation of the photovoltaic in the actual environmental conditions. Then, compare the actual power generation of the photovoltaic with the actual theoretical power generation. If the actual power generation of the photovoltaic can reach 90% of the actual theoretical power generation, it indicates that there is no obvious abnormality in the actual power generation of the photovoltaic. Set the photovoltaic power generation anomaly index , otherwise, it is considered that the power generation of the photovoltaic is abnormal. Set the photovoltaic power generation anomaly index ; C. Introduce a new pollutant recognition method based on adaptive feature fusion to identify the specific situation of small-scale pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel monitoring photos in S2 as follows: C.1. Use YOLOv8 for pollutant target detection and output the pollutant bounding box: ; Among them, represents the leftmost position of the pollutant area, represents the bottommost position of the pollutant area, represents the rightmost position of the pollutant area, represents the uppermost position of the pollutant area. Crop the target area based on the bounding box: ; C.2. Use Swin Transformer to extract the pollutant area features through the sliding window attention mechanism: ; Calculate the spatial consistency of the pollutant area and merge the pollutants with adjacent boundaries: ; C.3. Combine CNN and Swin Transformer to extract features: ; Calculate the final fused features: ; Among them, and are adaptive weights; C.4. Introduce a multi-frame information fusion method to calculate the consistency of pollutants in multiple photos: ; Among them, when is greater than the retention threshold, retain ; C.5. Use U-Net to segment the retained pollutant area and generate a pollutant mask: ; D. Using wide - area pollutant identification based on spatial color analysis. The specific steps for identifying large - scale pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel monitoring photo in S2 are as follows: D.1. Calculate the difference in color histograms between the clean photovoltaic panel and the panel in the actual image: ; Among them, is the color space, respectively represent the red, green, and blue channels in the RGB space, represents the color channel in the th color interval frequency, represents the color histogram of the clean photovoltaic panel, represents the color histogram of the actual image; b. Calculate the mean value of the color channels of the clean photovoltaic panel ; Among them, is the color value of the pixel point in the clean photovoltaic panel. Calculate the mean value of the color channels of the panel in the actual image: ; Among them, is the color mean value of the pixel point in the photovoltaic panel in the actual image. Calculate the difference in the mean values of the color channels between the clean photovoltaic panel and the photovoltaic panel in the actual image: ; c. Generate a mask for the pollutant area based on the comparison result of the difference in the color histogram in a and the color histogram difference threshold and the comparison result of the difference in the mean value of the color channels in b and the difference threshold of the mean value of the color channels : ; E. Calculate the area of small - scale pollutants and large - scale pollutants through the number of pixels: ; Among them, the area of each pixel is 1 cm 2 , represents the number of pixels of small - scale pollutants, that is, the pollutant area, represents the number of pixels of large - scale pollutants, that is, the pollutant area, and pollution region represents the pollutant area; F. Comprehensively analyze the necessity index of photovoltaic cleaning in A , the abnormal index of photovoltaic power generation in B and the pollutant area in C and The specific steps for determining whether a photovoltaic panel needs to be cleaned are as follows: F.1. According to the influence of the photovoltaic cleaning necessity index, the photovoltaic power generation anomaly index, and the pollutant area on the photovoltaic cleaning demand, different importance weights are assigned to them respectively: ; F.2. Calculate the photovoltaic comprehensive cleaning index for different ranges of pollutants according to the importance weights in F.1: ; When or the photovoltaic cleaner obtains the execution instruction to clean the pollutants, where is the comprehensive cleaning threshold interval for small-range pollutants, is the comprehensive cleaning threshold interval for large-range pollutants; Adopting the above steps, according to the key weather data extracted in S2: the air quality index precipitation , air humidity H, solar irradiance , wind speed and temperature and their corresponding weights, calculate the photovoltaic cleaning necessity index , then according to the solar irradiance , cloud cover , temperature , wind speed , air humidity , precipitation and the air quality index calculate the theoretical power generation of a single photovoltaic panel in the actual weather environment , compare it with the actual power generation of each photovoltaic panel to generate the photovoltaic power generation anomaly index , and then use the pollutant recognition method based on adaptive feature fusion and the wide-area pollutant recognition method based on spatial color analysis to recognize the small-range pollutants and large-range pollutant areas in the optimized photovoltaic panel photo in S2, generate a pollutant area mask, and calculate the small-range pollutant area and the large-range pollutant area by calculating the number of pixels respectively, and then calculate the photovoltaic comprehensive cleaning index corresponding to the small-range pollutants and the photovoltaic comprehensive cleaning index corresponding to the large-range pollutants according to the photovoltaic cleaning necessity index, the photovoltaic power generation anomaly index, the pollutant area, and the assigned influence weights, and then compare them with the small-range pollutant comprehensive cleaning threshold interval and the large-range pollutant comprehensive cleaning threshold interval respectively to generate the execution instruction for the photovoltaic cleaner to clean the pollutants.

[0038] S4. Analyze the temperature of the photovoltaic device in S1, generate a cooling execution instruction for the photovoltaic cleaner. When the temperature of the photovoltaic device is greater than the high-temperature resistance threshold for normal operation of the photovoltaic device, the photovoltaic cleaner obtains the cooling execution instruction; By using the above steps, compare the actual temperature of the photovoltaic device with the high-temperature resistance threshold for normal operation of the photovoltaic device, and generate a cooling execution instruction for the photovoltaic cleaner.

[0039] S5. According to the cleaning execution instruction of the photovoltaic cleaner in S3 and the cooling execution instruction of the photovoltaic cleaner in S4, the specific situation of the photovoltaic cleaner performing adaptive cleaning and cooling operations on the photovoltaic panel is as follows: A. The specific steps for the photovoltaic cleaner to execute the cleaning pollutant instruction are as follows: A.1. According to the information of the pollutants in S3, locate the number of the photovoltaic panel where the pollutants are located; A.2. According to the number of the photovoltaic panel in A.1, the specific steps for the photovoltaic cleaner corresponding to the number to execute the cleaning pollutant instruction are as follows: a. Use YOLOv8 to perform real-time target recognition of the photovoltaic cleaner on the monitored video stream. Taking the coordinate system of the monitoring screen as a reference, the photovoltaic cleaner starts running to the right from the starting point while the photovoltaic cleaner lifts up, the cleaning sponge in the photovoltaic cleaner does not contact the photovoltaic panel, and the water tank of the photovoltaic cleaner does not release water; b. When the right coordinate of the photovoltaic cleaner is 10 cm away from the leftmost position of the pollutant, the photovoltaic cleaner lowers while the right water pipe of the water tank of the photovoltaic cleaner releases water, so that the cleaning sponge in the photovoltaic cleaner contacts the photovoltaic panel to clean the pollutants. When the left coordinate of the photovoltaic cleaner is 10 cm away from the rightmost position of the pollutant, the photovoltaic cleaner lifts up while the right water pipe of the water tank of the photovoltaic cleaner stops releasing water, and the first cleaning of the target pollutant ends. Record the water release time of this process, and repeat this operation for the pollutants that have not been cleaned for the first time; c. After the first cleaning of all pollutants ends, the photovoltaic cleaner starts to return. During this process, clean the pollutants that have been cleaned in step b again. When the left coordinate of the photovoltaic cleaner is 10 cm away from the rightmost position of the pollutant, the photovoltaic cleaner lowers while the left water pipe of the water tank of the photovoltaic cleaner releases water, so that the cleaning sponge in the photovoltaic cleaner contacts the photovoltaic panel to clean the pollutants. When the right coordinate of the photovoltaic cleaner is 10 cm away from the leftmost position of the pollutant, the photovoltaic cleaner lifts up while the left water pipe of the water tank of the photovoltaic cleaner stops releasing water, and the second cleaning of the target pollutant ends. Record the water release time of this process, and repeat this operation for the pollutants that have not been cleaned for the second time; d. The photovoltaic cleaner returns to the starting point, and statistically calculates the total water release time of the water tank for the round-trip of the photovoltaic cleaner for two trips; B. The specific steps for the photovoltaic cleaner to execute the cooling instruction are as follows: a. The photovoltaic cleaner runs back and forth from the starting point to the rightmost end of the photovoltaic panel twice. During the process, the photovoltaic cleaner is lifted, and the water pipes on both sides of the water tank release water simultaneously. b. The photovoltaic cleaner returns to the starting point, the water pipes on both sides of the water tank stop releasing water, and the water release time for the photovoltaic cleaner to run back and forth twice is statistically calculated. Using the above steps, according to the cleaning execution instruction of the photovoltaic cleaner generated in S3 and the cooling execution instruction of the photovoltaic cleaner generated in S4, clean the specified pollutant area and sprinkle water to cool the photovoltaic panel.

[0040] S6. According to the pollution degree of the cleaning sponge during the cleaning process in S5 and the water consumption of the photovoltaic cleaner when performing cleaning or cooling tasks, the specific situations of automatically replacing the cleaning sponge of the photovoltaic cleaner, cleaning, and replenishing water in the water tank are as follows: a. There are the following two operation methods for the automatic cleaning of the cleaning sponge of the photovoltaic cleaner: The first one is to replace the cleaning sponge: Use the wide - area pollutant identification method based on spatial color analysis in S3 to identify the photo of the cleaning sponge of the optimized photovoltaic cleaner in S2, judge the pollution situation of the cleaning sponge. During the operation of the photovoltaic cleaner, when the pollution degree of each cleaning sponge is greater than the pollution threshold of the cleaning sponge for the first time, the photovoltaic cleaner controls the cleaning sponge to turn over and change sides. When the pollution degree of each cleaning sponge is greater than the pollution threshold of the cleaning sponge for the second time, the photovoltaic cleaner controls to replace a new cleaning sponge. The second one is to wash the cleaning sponge: When the photovoltaic cleaner returns to the starting point after completing the cleaning task, the flushing nozzle at the starting point is automatically turned on to wash the used cleaning sponge in the photovoltaic cleaner, and at the same time, the photovoltaic cleaner controls the cleaning sponge to rotate.

[0041] b. After the photovoltaic cleaner completes the cleaning or cooling task and returns to the starting point, the battery on the photovoltaic cleaner and the power supply of the photovoltaic water replenishing device are turned on. The water filling port of the photovoltaic water replenishing device and the water replenishing port of the water tank of the photovoltaic cleaner are magnetically docked, and at the same time, the water replenishing water pipe valve is opened to replenish water to the water tank. The water replenishing volume is calculated according to the total water release time of 300Ml / s. After the water replenishing is completed, the battery on the photovoltaic cleaner and the power supply of the photovoltaic water replenishing device are turned off and the water replenishing water pipe valve is closed simultaneously. Using the above steps, during the cleaning process, it is automatically replaced according to the pollution degree of the cleaning sponge. After completing the cleaning task and the water - sprinkling cooling task, the water tank of the photovoltaic cleaner is automatically replenished with water, and the cleaning sponge is automatically sprayed and cleaned.

[0042] Embodiment 2 This embodiment discloses a photovoltaic adaptive cleaning and cooling system based on multi-source information perception and intelligent decision-making, which is used to implement the photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making described in Embodiment 1, as Figure 2 shown. The system includes the following functional modules: A data acquisition module, which is used to acquire weather data, photovoltaic power generation data, photovoltaic device temperature, photovoltaic panel monitoring videos, and cleaning sponge photos of the photovoltaic cleaner.

[0043] A data preprocessing module, which is used to extract key weather data, power generation data of each photovoltaic panel, photovoltaic device temperature, and optimize the intercepted photovoltaic panel photos and cleaning sponge photos of the photovoltaic cleaner; A photovoltaic pollution condition analysis module, which is used to analyze the cleaning requirements of the photovoltaic panel through weather conditions, photovoltaic power generation conditions, and the actual attachment of pollutants on the photovoltaic panel, and give the cleaning execution instruction to the photovoltaic cleaner; A photovoltaic device temperature analysis module, which is used to analyze the cooling requirements of the photovoltaic device according to the photovoltaic device temperature, and give the cooling execution instruction to the photovoltaic cleaner; A photovoltaic cleaner scheduling module, which is used to execute the photovoltaic cleaning and cooling instructions according to the cleaning and cooling requirements of the photovoltaic panel; A photovoltaic cleaner water replenishment module, which is used to automatically replenish water according to the water consumption of the photovoltaic cleaner during cleaning and cooling operations; A photovoltaic cleaner cleaning module, which is used to replace or clean the cleaning sponge in the photovoltaic cleaner according to its pollution condition.

[0044] Embodiment 3 This embodiment discloses a storage medium storing a program, which when executed by a processor, implements the photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making described in Embodiment 1.

[0045] The storage medium in this embodiment can be a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, RandomAccess Memory), a USB flash drive, a mobile hard disk, and other media.

[0046] Embodiment 4 This embodiment discloses a computing device, including a processor and a memory for storing the executable program of the processor. When the processor executes the program stored in the memory, it implements the photovoltaic adaptive cleaning and cooling method based on multi-source information perception and intelligent decision-making described in Embodiment 1.

[0047] The computing device described in this embodiment may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with processor functions.

[0048] In summary, the present invention proposes a photovoltaic adaptive cleaning and cooling method and system based on multi-source information perception and intelligent decision-making. By using weather information, photovoltaic power generation data, and computer vision-based pollutant identification technology, it accurately judges the cleaning time of different types of pollutants and intelligently formulates cleaning strategies. At the same time, according to the device temperature, it dynamically schedules the photovoltaic cleaner to perform sprinkler cooling operations to ensure the efficient operation of the photovoltaic panel. In addition, during the cleaning process, the system can monitor the pollution degree of the cleaning sponge in real time, realize intelligent replacement and cleaning, and ensure the continuous and stable cleaning effect. Finally, combined with the water consumption of the cleaning and cooling tasks, it realizes automatic water replenishment management. The whole process realizes unmanned and intelligent autonomous operation, not only effectively improves the photovoltaic power generation efficiency, but also greatly reduces the operation and maintenance costs of the photovoltaic power station, has practical promotion value, and is worthy of promotion.

[0049] The above-described embodiments are only the preferred embodiments of the present invention, and do not limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A photovoltaic multi-source sensing intelligent cleaning and cooling method, characterized in that: The following steps are involved: S1, obtaining monitoring data, namely weather data, photovoltaic power generation data, photovoltaic equipment temperature, pollution information of photovoltaic panels and pollution information of cleaning sponges of photovoltaic cleaners; S2, data preprocessing, extracting key data of weather data and photovoltaic power generation data in S1 and optimizing the pollution information of photovoltaic panels and the pollution information of the cleaning sponge of photovoltaic cleaner; S3, jointly analyzing the key data of weather data and photovoltaic power generation data in S2 and the adhesion of pollutants on the photovoltaic panel surface in the optimized photovoltaic panel photo, and generating a cleaning execution instruction for the photovoltaic cleaner; S4, analyzing the temperature of the photovoltaic equipment in S1 and generating a cooling execution instruction for the photovoltaic cleaner; S5, according to the cleaning execution instruction of the photovoltaic cleaner in S3 and the cooling execution instruction of the photovoltaic cleaner in S4, the photovoltaic cleaner performs adaptive cleaning and cooling operations on the photovoltaic panel; S6, according to the water consumption of the photovoltaic cleaner and the pollution degree of the cleaning sponge in S5, automatically complete the replacement and cleaning of the cleaning sponge and the water tank filling operation of the photovoltaic cleaner.

2. The photovoltaic multi-source sensing intelligent cleaning and cooling method according to claim 1 is characterized in that: In S1, the weather data is real-time weather data obtained by accessing the China Meteorological Data Network interface, and is text data describing weather conditions; the photovoltaic power generation data is the photovoltaic power generation recorded from the photovoltaic power station system; the photovoltaic equipment temperature is provided by the temperature sensor detection of the photovoltaic panel; the pollution information of the photovoltaic panel is the photovoltaic panel video stream file captured by monitoring; the pollution information of the cleaning sponge of the photovoltaic cleaner is the cleaning sponge photo taken by the camera under the photovoltaic panel; Among them, weather data updates, photovoltaic power generation data acquisition and photo capture from photovoltaic panel video stream files are automatically and synchronously performed every half hour, and cleaning sponge photos are taken every half minute when the photovoltaic cleaner executes cleaning instructions.

3. The photovoltaic multi-source sensing intelligent cleaning and cooling method according to claim 1 is characterized in that: In S2, the key data of weather data and photovoltaic power generation data in S1 are extracted and the pollution information of photovoltaic panels and the pollution information of the cleaning sponge of the photovoltaic cleaner are optimized as follows: A. Extract air quality index, temperature, wind speed, precipitation, air humidity and solar irradiance from weather data; B. Extract the actual power generation of each photovoltaic panel from the photovoltaic power generation data, and obtain the theoretical power generation of each photovoltaic panel under ideal conditions from the equipment manual; C. The specific steps for optimizing the intercepted photos of the photovoltaic panel and the cleaning sponge of the photovoltaic cleaner are as follows: C.

1. Introducing a new photovoltaic pollutant image enhancement method based on adaptive frequency domain fusion and super-resolution reconstruction. The specific steps for noise reduction, sharpening and super-resolution optimization of multiple photovoltaic panel monitoring photos and photovoltaic cleaning sponge photos are as follows: a. Decompose the image by wavelet transform WT, and decompose the image into a low-frequency part L and a high-frequency part H: ； The low frequency L contains the illumination and shadow information, and the high frequency H contains the pollutant edge and high frequency noise information; b. Calculate the noise standard deviation using the BayesShrink method : ； in, To obtain the median function, and then obtain the adaptive threshold , filter image noise: ； in, is the signal standard deviation, is a sign function, It is the high-frequency component of the image after the noise is filtered; c. The high-frequency components after noise filtering After combining with the low-frequency part L, the inverse wavelet transform is performed to reconstruct the image after the noise is filtered: C.

2. The specific steps of image sharpening based on structure tensor gradient enhancement are as follows: a. Calculate the Sobel directional gradient: ； in, Indicates along Directional horizontal gradient, Indicates along The gradient of the direction level, and then calculate the structure tensor Identify contaminant boundaries: ； in, and Represents the rate of change of the main directions of the structure; b. Calculate the sharpening weight of each pixel: ； c. Adaptively sharpen the image: ； in, is the second-order derivative of Laplacian used to enhance the edge, is the adaptive sharpening coefficient; C.

3. The specific steps for optimizing image clarity using super-resolution based on adversarial learning are as follows: a. Use ESRGAN for super-resolution reconstruction to obtain the super-resolution reconstructed image: ； in, It is the ESRGAN generator, which calculates the feature difference between the SR image and the original HD image: ； in, It is the high-level feature of the VGG network extraction layer. It is an actual real image, and the adversarial loss is used to make the super-resolution image natural: ； in; is the discriminator network; b. The final goal of training is to optimize the super-resolution image: ； in; is the L1 loss, and are the hyperparameter weights that control different losses.

4. The photovoltaic multi-source sensing intelligent cleaning and cooling method according to claim 1 is characterized in that: In S3, the key data of weather data and photovoltaic power generation data in S2 and the adhesion of pollutants on the photovoltaic panel surface in the optimized photovoltaic panel photo are jointly analyzed to generate the cleaning execution instructions of the photovoltaic cleaner as follows: A. According to the influence of weather factors of different levels on the degree of pollutant attachment and the interaction between weather factors, different weather factors are assigned corresponding pollutant attachment weights. The pollutant attachment weights can indicate whether the weather factors are conducive to pollutant deposition. A positive weight indicates that the weather factors are conducive to pollutant deposition. A weight of zero indicates that the weather factors have no obvious effect on pollutant deposition. A negative weight indicates that the weather factors are not conducive to pollutant deposition. By comprehensively analyzing the comprehensive influence of various weather factors on pollutant attachment, the pollutant attachment weights of the following six key weather factors are determined: The first type is the pollutant attachment weight corresponding to the air quality index: if the air quality index in the selected period is less than 50, it is considered that the pollutant accumulation is small, and the pollutant attachment weight corresponding to the air quality index is assigned to be -0.2; If the air quality index in the selected time period is greater than 50 and less than 150, it is considered that the pollutant accumulation speed is fast, and the pollutant attachment weight corresponding to the air quality index is 0.1; if the air quality index in the selected time period is greater than 150, it is considered that the pollutant accumulation speed is extremely fast, and the pollutant attachment weight corresponding to the air quality index is 0.3; The second type is the pollutant attachment weight corresponding to the precipitation: if the precipitation in the selected time period is less than 2 mm, it will not only fail to clean the dust on the photovoltaic surface, but may even adsorb dust in the air. Therefore, the pollutant attachment weight corresponding to the precipitation is 0.2; if the precipitation in the selected time period is greater than 2 mm and less than 10 mm, it may only clean some pollutants on the photovoltaic surface, so the pollutant attachment weight corresponding to the precipitation is -0.1; if the precipitation in the selected time period is greater than 10 mm, it may clean most of the pollutants on the photovoltaic surface, so the pollutant attachment weight corresponding to the precipitation is -0.3; The third type is the pollutant attachment weight corresponding to air humidity: if the air humidity in the selected time period is less than 30%, it indicates that the air is dry and prone to electrostatic adsorption pollution, so the pollutant attachment weight corresponding to the air humidity is 0.2; if the air humidity in the selected time period is greater than 30% and less than 80%, it has no obvious effect on the attachment of pollutants, so the pollutant attachment weight corresponding to the air humidity is 0.0; if the air humidity in the selected time period is greater than 80%, it may cause muddy dust pollution on the photovoltaic surface, so the pollutant attachment weight corresponding to the air humidity is 0.15; The fourth type is the pollutant attachment weight corresponding to solar irradiance: the solar irradiance in the selected time period is less than 200 W / m 2 The photovoltaic surface is low temperature and pollutants are not easy to solidify, so the pollutant attachment weight corresponding to the solar irradiance is -0.1; the solar irradiance in the selected time period is greater than 200 W / m 2 And less than 600 W / m 2 , there is no obvious effect on the attachment of pollutants, so the pollutant attachment weight corresponding to the solar irradiance is 0.0; the solar irradiance in the selected time period is greater than 600 W / m 2 , which may cause high temperature on the photovoltaic surface, resulting in solidification of pollutants and difficulty in cleaning. Therefore, the pollutant attachment weight corresponding to the solar irradiance is given as 0.2; The fifth type is the pollutant attachment weight corresponding to wind speed: if the wind speed in the selected time period is less than 1 m / s, it indicates that the air is relatively still and pollutants are seriously deposited, so the pollutant attachment weight corresponding to the wind speed is 0.3; if the wind speed in the selected time period is greater than 1 m / s and less than 5 m / s, the wind speed is moderate, which helps to reduce pollutant deposition, so the pollutant attachment weight corresponding to the wind speed is -0.1; if the wind speed in the selected time period is greater than 5 m / s, it may bring new dust, so the pollutant attachment weight corresponding to the wind speed is 0.1; The sixth type is the pollutant attachment weight corresponding to the temperature: if the temperature in the selected time period is less than 20 °C, the environment is at a low temperature, and the pollutants on the photovoltaic surface are not easy to solidify, the pollutant attachment weight corresponding to the temperature is -0.2; if the temperature in the selected time period is greater than 20 °C and less than 30 °C, the ambient temperature is moderate, and the pollutants are moderately solidified, the pollutant attachment weight corresponding to the temperature is -0.1; if the temperature in the selected time period is greater than 30 °C, the environment is at a high temperature, the pollutants on the photovoltaic surface are obviously solidified, and cleaning is difficult, so the pollutant attachment weight corresponding to the temperature is 0.3; Calculate the necessity index of photovoltaic cleaning based on the above different weather factors , the calculation formula is: ； in, The air quality index, is the amount of precipitation, is the air humidity, is the solar irradiance, is the wind speed, is the temperature, is the pollutant attachment weight corresponding to the air quality index, is the pollutant attachment weight corresponding to the precipitation, is the pollutant attachment weight corresponding to air humidity, is the pollutant attachment weight corresponding to the solar irradiance, is the pollutant attachment weight corresponding to wind speed and is the pollutant attachment weight corresponding to temperature; B. According to the impact of weather factors of different levels on photovoltaic power generation and the interaction between weather factors, different weather factors are assigned corresponding photovoltaic power generation impact weights. The photovoltaic power generation impact weight can indicate whether the weather factor is conducive to photovoltaic power generation. The closer the weather factor impact weight is to 1, the more conducive the weather factor is to photovoltaic power generation. By comprehensively analyzing the comprehensive impact of various weather factors on photovoltaic power generation, the photovoltaic power generation impact weights of the following seven key weather factors are determined: The first is the weight of solar irradiance affecting photovoltaic power generation: the ratio of actual solar irradiance to solar irradiance in the theoretical test scenario is calculated as the weight of solar irradiance affecting power generation: ； in, is the actual solar irradiance, is the solar irradiance in the theoretical test scenario; The second method is to use the cloud cover to calculate the weight of photovoltaic power generation: the exponential decay model is used to calculate the weight of photovoltaic power generation: ； in, It's cloud cover. is an empirical coefficient that can be adjusted according to the measured data. That is, the weight of cloud cover affecting photovoltaic power generation; The third type is the weight of the impact of temperature on photovoltaic power generation: the temperature of the photovoltaic module and the ambient temperature around the photovoltaic module have a comprehensive impact on the efficiency of photovoltaic power generation: ； in, is the temperature of the photovoltaic device measured by the temperature sensor on the photovoltaic device, is the ambient temperature, is the temperature coefficient, The standard temperature is 25°C. That is, the weight of the photovoltaic power generation affected by temperature; Fourth, the weight of wind speed affecting photovoltaic power generation: wind speed affects the heat dissipation of photovoltaics and thus affects the power generation efficiency: ； in, is the actual wind speed, is the standard reference wind speed; is the wind speed influence coefficient, That is, the impact weight of wind speed on photovoltaic power generation; Fifth, the weight of the impact of air humidity on photovoltaic power generation: Different air humidity has different effects on the attachment of pollutants on the photovoltaic surface, further affecting the efficiency of photovoltaic power generation. When the air humidity is greater than 80%, the weight of the impact of air humidity on photovoltaic power generation is Assigned as 0.95, air humidity is greater than 30% and less than 80%, the weight of air humidity affecting photovoltaic power generation Assigned as 1, air humidity is less than 30%, the air humidity affects the photovoltaic power generation weight The value is 0.98; The sixth type is the influence weight of photovoltaic power generation due to precipitation: Rainwater in the air can refract sunlight, affecting the direct reception of photovoltaic light sources. There is a relationship between the amount of rainwater and the refraction of sunlight. When the precipitation is greater than 10mm, the photovoltaic power generation due to precipitation affects the weight. Assigned as 1.2, precipitation is greater than 2mm and less than 10mm, the photovoltaic power generation of precipitation affects the weight Assigned as 1.05, precipitation is less than 2mm, the photovoltaic power generation of precipitation affects the weight Assign 0.9; Seventh, the weight of photovoltaic power generation affected by air quality: dust and other impurities in the air will reduce the intensity of solar radiation. When the air quality index is greater than 150, the weight of photovoltaic power generation affected by air quality is Assigned as 0.8, the air quality index is greater than 50 and less than 150, the weight of photovoltaic power generation affected by air quality Assigned as 0.9, the air quality index is less than 50, and the air quality affects the weight of photovoltaic power generation Assign 1; The theoretical power generation of photovoltaic power is calculated based on the above different weather factors. The calculation formula is: ； in, is the amount of electricity generated by photovoltaics under ideal environmental conditions. It is the theoretical power generation of photovoltaic under actual environmental conditions. Then compare the actual power generation of photovoltaic with the actual theoretical power generation. If the actual power generation of photovoltaic can reach 90% of the actual theoretical power generation, it means that there is no obvious abnormality in the actual power generation of photovoltaic. Set the photovoltaic power generation abnormality index Otherwise, it is considered that the photovoltaic power generation is abnormal. The photovoltaic power generation abnormality index is set ; C. Introducing a new pollutant identification method based on adaptive feature fusion The specific situation of the small-scale pollutants on the surface of the photovoltaic panel in the optimized photovoltaic panel monitoring photo in step 2) is as follows: C.

1. Use YOLOv8 to detect pollutant targets and output pollutant bounding boxes: ； in, Indicates the leftmost position of the pollutant area, Indicates the lowest position of the pollutant area, Indicates the rightmost position of the pollutant area, Indicates the top position of the pollutant area and crops the target area based on the bounding box: ； C.

2. Use Swin Transformer to extract pollutant area features through sliding window attention mechanism: ； Compute the spatial consistency of pollutant regions and merge pollutants that have adjacent boundaries: ； C.

3. Combining CNN and Swin Transformer to extract features: ； Calculate the final fusion features: ； in, and is the adaptive weight; C.

4. Introduce multi-frame information fusion method to calculate the consistency of pollutants in multiple photos: ； Among them, when When it is greater than the retention threshold, ; C.

5. Use U-Net to segment the retained pollutant area and generate a pollutant mask: ； D. The specific steps of using wide-area pollutant identification based on spatial color analysis to identify large-area pollutants on the photovoltaic panel surface in the optimized photovoltaic panel monitoring photos in step 2) are as follows: D.

1. Calculate the color histogram difference between the clean photovoltaic panel and the panel in the actual image: ； in, is the color space, Respectively represent the red, green and blue channels in the RGB space. Indicates color channels The The frequency of the color interval, A color histogram representing a clean photovoltaic panel, Represents the color histogram of the actual image; b. Calculate the color channel mean of the clean photovoltaic panel ； in, Cleaning pixels in photovoltaic panels Calculate the color channel mean of the panel in the actual image: ； in, is the pixel point in the photovoltaic panel in the actual image Calculate the color channel mean difference between the clean photovoltaic panel and the photovoltaic panel in the actual image: ； c. Based on the difference between the color histogram in a and the color histogram difference threshold The difference between the comparison result of and the color channel mean in b and the difference threshold of the color channel mean The comparison results in a mask of polluted areas: ； E. Calculate the small-scale pollutant area and large-scale pollutant area by the number of pixels: ； The area of ​​each pixel is 1cm 2 , The number of pixels representing small-scale pollutants is the pollutant area. The number of pixels representing large-scale pollutants is the pollutant area, and the pollution region represents the pollutant area; F. Comprehensive analysis of the necessity index of photovoltaic cleaning in A , Photovoltaic power generation abnormal index in B and the pollutant area in C and The specific steps to determine whether the photovoltaic needs to be cleaned are as follows: F.

1. According to the impact of the PV cleaning necessity index, PV power generation abnormality index and pollutant area on the PV cleaning demand, different importance weights are assigned to them respectively: ; F.

2. Calculate the photovoltaic comprehensive cleanliness index of pollutants in different ranges according to the importance weights in F.1: ； when or When the photovoltaic cleaner obtains the execution instruction of cleaning pollutants, It is the comprehensive cleaning threshold range of small-scale pollutants. It is the comprehensive cleaning threshold range for a wide range of pollutants.

5. The photovoltaic multi-source sensing intelligent cleaning and cooling method according to claim 1 is characterized in that: In S4, the temperature of the photovoltaic device in S1 is analyzed to generate a cooling execution instruction for the photovoltaic cleaner. When the temperature of the photovoltaic device is greater than the high temperature resistance threshold for normal photovoltaic operation, the photovoltaic cleaner obtains the cooling execution instruction.

6. The photovoltaic multi-source sensing intelligent cleaning and cooling method according to claim 1 is characterized in that: In S5, according to the cleaning execution instruction of the photovoltaic cleaner in S3 and the cooling execution instruction of the photovoltaic cleaner in S4, the photovoltaic cleaner performs adaptive cleaning and cooling operations on the photovoltaic panel as follows: A. The specific steps for the photovoltaic cleaner to execute the cleaning pollutant instruction are as follows: A.

1. According to the information of pollutants in S3, locate the number of the photovoltaic panel where the pollutants are located; A.

2. According to the number of the photovoltaic panel in A.1, the specific steps for the photovoltaic cleaner with the corresponding number to execute the cleaning pollutant instruction are as follows: a. Use YOLOv8 to perform real-time target recognition of the photovoltaic cleaner on the monitored video stream. With the coordinate system of the monitoring screen as a reference, the photovoltaic cleaner starts to run to the right from the starting point while the photovoltaic cleaner is lifted. The cleaning sponge in the photovoltaic cleaner does not contact the photovoltaic panel, and the photovoltaic cleaner water tank does not drain water. b. When the right coordinate of the photovoltaic cleaner is 10 cm away from the leftmost position of the pollutant, the photovoltaic cleaner is lowered and the water pipe on the right side of the photovoltaic cleaner water tank is drained, so that the cleaning sponge in the photovoltaic cleaner contacts the photovoltaic panel to clean the pollutant. When the left coordinate of the photovoltaic cleaner is 10 cm away from the rightmost position of the pollutant, the photovoltaic cleaner is lifted and the water pipe on the right side of the photovoltaic cleaner water tank stops draining water. The first cleaning of the target pollutant is completed, and the draining time of this process is recorded. Repeat this operation for pollutants that have not been cleaned for the first time. c. After the first cleaning of all pollutants is completed, the photovoltaic cleaner starts the return operation. In this process, the pollutants cleaned in step b are cleaned again. When the left coordinate of the photovoltaic cleaner is 10 cm away from the rightmost position of the pollutant, the photovoltaic cleaner is lowered and the water pipe on the left side of the photovoltaic cleaner water tank is drained, so that the cleaning sponge in the photovoltaic cleaner contacts the photovoltaic panel to clean the pollutants. When the right coordinate of the photovoltaic cleaner is 10 cm away from the leftmost position of the pollutant, the photovoltaic cleaner is lifted and the water pipe on the left side of the photovoltaic cleaner water tank stops draining water. The second cleaning of the target pollutants is completed, and the draining time of this process is recorded. Repeat this operation for pollutants that have not been cleaned for the second time. d. The photovoltaic cleaner returns to the starting point, and the total time of draining the water tank for the photovoltaic cleaner's two round trips is calculated; B. The specific steps for the photovoltaic cleaner to execute the cooling command are as follows: a. The photovoltaic cleaner runs two round trips from the starting point to the rightmost end of the photovoltaic panel. During the process, the photovoltaic cleaner is lifted and the water pipes on the left and right sides of the water tank are drained at the same time; b. The photovoltaic cleaner returns to the starting point, and the water pipes on the left and right sides of the water tank stop draining water. The drainage time of the photovoltaic cleaner's two round trips is calculated.

7. The photovoltaic multi-source sensing intelligent cleaning and cooling method according to claim 1 is characterized in that: In S6, according to the degree of contamination of the cleaning sponge during the cleaning process in S5 and the water consumption of the photovoltaic cleaner in performing the cleaning or cooling task, the specific conditions of automatically completing the replacement and cleaning of the cleaning sponge of the photovoltaic cleaner and the water tank refilling operation are as follows: a. There are two operating modes for automatic cleaning of photovoltaic cleaning sponge: The first method is to replace the cleaning sponge: use the wide-area pollutant identification method based on spatial color analysis in S3 to identify the cleaning sponge photo of the photovoltaic cleaner optimized in S2, and judge the pollution status of the cleaning sponge. During the operation of the photovoltaic cleaner, when the pollution level of each cleaning sponge exceeds the pollution threshold of the cleaning sponge for the first time, the photovoltaic cleaner controls the cleaning sponge to turn over. When the pollution level of each cleaning sponge exceeds the pollution threshold of the cleaning sponge for the second time, the photovoltaic cleaner controls the replacement of a new cleaning sponge. The second method is to clean the cleaning sponge: when the photovoltaic cleaner returns to the starting point after completing the cleaning task, the flushing nozzle at the starting point is automatically turned on to flush the used cleaning sponge in the photovoltaic cleaner, and the photovoltaic cleaner controls the cleaning sponge to rotate; b. After the photovoltaic cleaner completes the cleaning or cooling task, it returns to the starting point, the battery on the photovoltaic cleaner and the power supply of the photovoltaic water replenishment device are turned on, the water filling port of the photovoltaic water replenishment device and the water filling port of the photovoltaic cleaner water tank are connected by magnetic attraction, and at the same time, the water replenishment pipe valve is opened to replenish the water tank. The water replenishment amount is calculated according to the total water discharge time of 300Ml / s. After the water replenishment is completed, the battery on the photovoltaic cleaner and the power supply of the photovoltaic water replenishment device are turned off and the water replenishment pipe valve is closed.

8. A photovoltaic multi-source sensing intelligent cleaning and cooling system, characterized in that: The photovoltaic multi-source sensing intelligent cleaning and cooling method for realizing any one of claims 1 to 7 comprises: A data acquisition module, used to acquire weather data, photovoltaic power generation data, photovoltaic equipment temperature, pollution information of photovoltaic panels and pollution information of cleaning sponges of photovoltaic cleaners; Data preprocessing module, used to extract key weather data, power generation data of each photovoltaic panel, photovoltaic equipment temperature, and optimize the intercepted photos of photovoltaic panels and cleaning sponges of photovoltaic cleaners; The photovoltaic pollution status analysis module is used to analyze the cleaning needs of photovoltaic panels based on weather conditions, photovoltaic power generation and the actual adhesion of pollutants on photovoltaic panels, and give cleaning execution instructions to the photovoltaic cleaner; The photovoltaic equipment temperature analysis module is used to analyze the cooling demand of the photovoltaic equipment according to the temperature of the photovoltaic equipment and give the photovoltaic cleaner a cooling execution instruction; A photovoltaic cleaner scheduling module is used to execute photovoltaic cleaning and cooling instructions according to the cleaning and cooling requirements of photovoltaic panels; A photovoltaic cleaner water replenishment module, used for automatically replenishing water according to the water consumption of the photovoltaic cleaner in performing cleaning and cooling operations; The photovoltaic cleaner cleaning module is used to replace or clean the cleaning sponge in the photovoltaic cleaner according to the contamination condition of the cleaning sponge.

9. A storage medium storing a program, characterized in that: When the program is executed by a processor, the photovoltaic multi-source sensing intelligent cleaning and cooling method according to any one of claims 1 to 7 is implemented.

10. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the photovoltaic multi-source sensing intelligent cleaning and cooling method according to any one of claims 1 to 7 is implemented.

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