Photovoltaic panel snow melting time prediction method and system based on machine vision

Abstract

The invention relates to a photovoltaic panel snow melting time prediction method and system based on machine vision, and the method comprises the steps: regularly collecting photovoltaic panel images, without any coverage, of a photovoltaic panel region in winter according to a preset flight path through an unmanned plane equipped with a high-definition camera; collecting an image of the photovoltaic panel covered with accumulated snow in the photovoltaic panel area in snowy weather in winter; performing data enhancement preprocessing on the photovoltaic panel image covered with the accumulated snow to serve as a training image data set, building a YOLO model to detect whether the accumulated snow exists on the photovoltaic panel or not, and if yes, calculating the accumulated snow coverage rate and the accumulated snow thickness; according to the actual accumulated snow thickness obtained through calculation, the accumulated snow water equivalent is obtained in combination with the accumulated snow density measured on site by an accumulated snow analyzer, then the actual snow melting amount of the accumulated snow area area is obtained in combination with the accumulated snow coverage rate, and the snow melting time is obtained based on the actual snow melting amount and a degree-day factor model. The snow melting time can be timely and accurately predicted, the normal operation of the photovoltaic power station is ensured, and the power generation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and in particular relates to a method and system for predicting snowmelt time of photovoltaic panels based on machine vision. Background Art

[0002] The existing technology has related technologies for snow detection and simulation modeling of the snow melting process, but lacks discussion on local snow detection and snow melting rate and time. The specific technical defects are as follows:

[0003] The snow state is affected by many factors, such as changes in lighting and different weather conditions. Faced with different environmental conditions, the naive Bayes classifier may need to be retrained or adjusted to adapt to these changes, resulting in poor environmental adaptability and affected real-time performance. At the same time, the performance of the naive Bayes classifier is largely overly dependent on the quality and quantity of training data, and lacks accuracy when processing complex image data.

[0004] Traditional image processing methods perform well on specific data sets, but their generalization ability is poor when it comes to unseen data or environmental changes, which affects detection accuracy. In the case of complex backgrounds or multiple targets, traditional methods may find it difficult to accurately segment and identify targets, especially when the target is similar to the background or is obscured. Traditional image processing methods do not have the ability to learn from data, and therefore have difficulty adapting to changing external environments.

[0005] Photovoltaic panels are relatively small in size, while remote sensing images usually cover a large area. Image acquisition may not be possible in real time, resulting in unclear details of the photovoltaic panels, limiting the ability to monitor dynamic changes in snow accumulation. At the same time, remote sensing images may find it difficult to accurately estimate the thickness of snow, and differences in thickness will significantly affect the melting rate of snow and the power generation efficiency of photovoltaic panels. Atmospheric conditions (such as fog, clouds, smoke, etc.) will also affect the quality of remote sensing images, thereby affecting the accuracy of snow detection. Frequent remote sensing image acquisition and processing will also involve high costs and resource consumption, especially when high-resolution and high-frequency monitoring are required.

[0006] Most existing technologies build snowmelt models based on remote sensing images, but lack discussion on the snowmelt rate and time in local areas. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for predicting the snowmelt time of photovoltaic panels based on machine vision, which realizes the detection of snow on photovoltaic panels through an image processing unit, and improves the detail detection capability of photovoltaic panels through a method for calculating the thickness of snow on photovoltaic panels, thereby reducing the difficulty and cost of implementation. By calculating the actual amount of snowmelt in the snow-covered area and the amount of snowmelt per unit time through a temperature-dominated degree-day factor model, the snowmelt scene in the local area is simulated, and the time required for snowmelt in the final local area is obtained.

[0008] The present invention provides a method for predicting snow melting time of photovoltaic panels based on machine vision, comprising the following steps:

[0009] Step 1: using a drone equipped with a high-definition camera to regularly collect images of uncovered photovoltaic panels in the photovoltaic panel area in winter according to a preset flight path, and collecting images of photovoltaic panels covered with snow in the photovoltaic panel area in snowy weather in winter, and making a one-to-one correspondence between the images of the uncovered photovoltaic panels and the images of the photovoltaic panels covered with snow;

[0010] Step 2: Perform data augmentation preprocessing on the snow-covered photovoltaic panel images and use them as training image datasets to build a YOLO model to detect whether there is snow on the photovoltaic panels. If so, calculate the snow coverage rate and snow thickness.

[0011] Step 3: The snow water equivalent is obtained based on the actual snow thickness calculated and the snow density measured on site by the snow analyzer. The actual snowmelt in the snow-covered area is obtained based on the snow cover rate. The snowmelt time is obtained based on the actual snowmelt and degree-day factor model.

[0012] Furthermore, in step 2, the image of the photovoltaic panel covered with snow is subjected to data enhancement preprocessing and used as a training image dataset. The YOLO model is constructed to detect whether there is snow on the photovoltaic panel, including:

[0013] 1) Dataset creation

[0014] Collect the images needed for training, and use rotation, flipping, and contrast enhancement to perform data augmentation as training image datasets;

[0015] Labelme is used to annotate the photovoltaic panel area and the snow-covered area of ​​each image in the training set. The bounding box is used to determine their location and classify them into snow and photovoltaic panel categories. The images are preprocessed, the image size is adjusted to a uniform size and normalized, and the Gaussian filter is used to perform convolution operations and weight allocation to remove noise in the image. The convolution images are convolved with Sobel operators in the horizontal and vertical directions respectively.

[0016] Calculate the gradient amplitude of the convolution result, and use the non-maximum suppression method to detect the local maximum value of each pixel in the gradient image to remove redundancy;

[0017] 2) Object Detection

[0018] Image prediction is performed based on the YOLO model. If the drone transmits an image file and the image prediction result contains both snow and photovoltaic panel labels, the image and the corresponding uncovered photovoltaic panel image are transmitted to the snow parameter calculation module for processing. If the image prediction result contains only snow or photovoltaic panel labels, the operation is terminated. If the drone transmits a video file, the image in the video stream is extracted according to the set frame rate. If the image prediction result contains both snow and photovoltaic panel labels, the image and the corresponding uncovered photovoltaic panel image are transmitted to the snow parameter calculation module for processing. If the image prediction result contains only snow or photovoltaic panel labels, the processing of the next frame of image continues.

[0019] Furthermore, the images required for the collection of training include images of photovoltaic panels covered with snow in different weather conditions, different lighting conditions, and different environments.

[0020] Furthermore, the image prediction based on the YOLO model includes:

[0021] Based on the YOLO model, the labeled data set is divided into small batches for training and weight files are obtained. When processing the input image, the same preprocessing operation is first performed on the image transmitted by the drone, and then the YOLO model is used to divide the image into grids. For the photovoltaic panel target image whose center point falls within the grid, each grid predicts multiple bounding boxes of the target and the confidence of the bounding boxes. The bounding boxes have different shapes and proportions to adapt to the sizes of different targets. The confidence indicates the probability that the bounding box contains the target and the degree of match between the predicted box and the actual box. At the same time, the conditional probability that the target matches the snow class or the photovoltaic panel class is predicted. After the network predicts multiple bounding boxes, the overlapping bounding boxes are removed and only the best prediction results are retained. Finally, YOLO outputs the position, size, confidence and probability of matching the existing label category of each bounding box.

[0022] Furthermore, the calculation method of the snow cover rate includes:

[0023] The number of pixel blocks occupied by the snow-covered photovoltaic panel image and the number of pixel blocks occupied by the photovoltaic panel label frame are calculated respectively, and the ratio of the two is the coverage rate of snow on the photovoltaic panel.

[0024] Furthermore, the calculation method of the snow thickness includes:

[0025] Perform edge detection on the two sets of images transmitted, divide edge pixels into strong edges and weak edges through double threshold processing, compare the calculated image gradient amplitude with the threshold, and form a complete edge by connecting adjacent strong edge pixels;

[0026] The edge length and boundary point coordinates of the photovoltaic panel without snow in the image and the boundary point coordinates of the photovoltaic panel covered with snow in the image are recorded, and the proportionality coefficient is calculated using the ratio of the edge length to the actual length of the photovoltaic panel obtained by field measurement.

[0027] By comparing the coordinates of the boundary points before and after snow accumulation on the photovoltaic panel, the thickness of snow at each point on the upper and lower edges of the photovoltaic panel is obtained, and the actual thickness of snow on the photovoltaic panel is obtained based on the proportional coefficient.

[0028] Furthermore, the step 3 comprises:

[0029] The actual snowmelt amount is calculated by the following formula:

[0030] M=SWE*F=H*ρ*F

[0031] Among them, M is the actual snowmelt, H is the snow thickness, ρ is the snow density, and F is the snow cover rate;

[0032] At the same time, a simple linear regression model was constructed. The long-term series temperature and the corresponding snowmelt data were linearly fitted by the least squares method, and a degree-day factor model dominated by temperature was built. The temperature was used as the independent variable and the snowmelt was used as the dependent variable to obtain the following snowmelt model:

[0033]

[0034] Where SM is the amount of snowmelt, T a is the outside temperature, T r is the critical temperature for snow melting;

[0035] The snowmelt volume per unit time is calculated based on the real-time temperature of the photovoltaic array at a certain moment. Combined with the actual snowmelt volume in the snow-covered area, the actual snowmelt time t of the snow-covered area is obtained. The formula is as follows:

[0036]

[0037] The present invention also provides a photovoltaic panel snowmelt time prediction system based on machine vision, comprising a snowmelt time prediction module, and the snowmelt time prediction module executes the photovoltaic panel snowmelt time prediction method based on machine vision.

[0038] The present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method for predicting the snowmelt time of photovoltaic panels based on machine vision is implemented.

[0039] The present invention also provides an electronic device, comprising:

[0040] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting snowmelt time of photovoltaic panels based on machine vision by executing the computer instructions.

[0041] Through the above scheme, the photovoltaic panel snow melting time prediction method and system based on machine vision have the following technical effects:

[0042] 1) In winter or in low-temperature areas, snow will cover photovoltaic panels, seriously affecting their photoelectric conversion efficiency and service life. The present invention can timely and accurately predict the snow melting time by building a local snow melting model, ensuring the normal operation of the photovoltaic power station and improving the power generation efficiency.

[0043] 2) Based on machine vision technology, the snow accumulation and snow melting time of photovoltaic panels are accurately measured and predicted. That is, high-resolution image acquisition equipment is combined with deep learning algorithms to monitor the snow accumulation on photovoltaic panels in real time, and the snow melting time is predicted by analyzing image data. Compared with traditional manual observation or simple temperature sensors, this method can provide more accurate and real-time data, thereby improving the accuracy of prediction.

[0044] 3) Combining the machine vision system with automated snow removal equipment can also achieve intelligent photovoltaic panel maintenance.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a method for predicting snow melting time of photovoltaic panels based on machine vision according to the present invention;

[0047] Figure 2 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0048] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0049] Ginseng Figure 1 As shown, this embodiment provides a method for predicting snow melting time of photovoltaic panels based on machine vision, which is characterized by comprising the following steps:

[0050] Step 1: using a drone equipped with a high-definition camera to regularly collect images of uncovered photovoltaic panels in the photovoltaic panel area in winter according to a preset flight path, and collecting images of photovoltaic panels covered with snow in the photovoltaic panel area in snowy weather in winter, and making a one-to-one correspondence between the images of the uncovered photovoltaic panels and the images of the photovoltaic panels covered with snow;

[0051] Step 2: Perform data augmentation preprocessing on the snow-covered photovoltaic panel images and use them as training image datasets to build a YOLO model to detect whether there is snow on the photovoltaic panels. If so, calculate the snow coverage rate and snow thickness.

[0052] Step 3: The snow water equivalent is obtained based on the actual snow thickness calculated and the snow density measured on site by the snow analyzer. The actual snowmelt in the snow-covered area is obtained based on the snow cover rate. The snowmelt time is obtained based on the actual snowmelt and degree-day factor model.

[0053] The present invention is described in further detail below:

[0054] 1. Data Collection

[0055] A high-definition camera is installed on the drone, and a flight path is preset for the drone. Images of the photovoltaic panel area are collected regularly in winter. Images of the photovoltaic panels without any coverage are obtained and stored in the computer as reference images. Images of the photovoltaic panels covered with snow are obtained in snowy weather. The front and back images correspond to each other one by one. The images and videos taken by the drone are transmitted to the ground station via wireless communication and processed by the image processing unit.

[0056] 2. Image processing

[0057] The image processing unit is divided into a target detection module and a snow parameter calculation module. The target detection module is divided into two steps: data set preparation and target detection. Data set preparation requires the collection of images required for training, which should include images of photovoltaic panels covered with snow in different weather, different lighting conditions, and different environments. Data enhancement is performed by rotation, flipping, contrast enhancement, etc. as training image data sets. Labelme is used to annotate the photovoltaic panel area and the photovoltaic panel snow area of ​​each image in the training set, and their positions are determined (through bounding boxes). They are specifically divided into two categories: "snow" and "panel", where "snow" refers to the snow on the photovoltaic panel and "panel" refers to the photovoltaic panel. At the same time, the image is preprocessed, the image size is adjusted to a uniform size and normalized, and the Gaussian filter is used for convolution operation and weight allocation to remove noise in the image. For the convolved image, the Sobel operator is used in the horizontal and vertical directions for convolution operation, and then the gradient amplitude of the convolution result is calculated. Finally, the non-maximum suppression method is used to detect the local maximum value of each pixel point in the gradient image to remove redundancy.

[0058] Target detection requires building a YOLO model, dividing the labeled data set into small batches for training and obtaining weight files. When processing the input image, first perform the same preprocessing operation on the image transmitted by the drone, and then use the YOLO model to divide the image into grids. For the photovoltaic panel target image whose center point falls within the grid, each grid predicts multiple bounding boxes of the target and the confidence of these bounding boxes. These bounding boxes have different shapes and proportions to adapt to the size of different targets. The confidence indicates the probability that the bounding box contains the target and the degree of match between the predicted box and the actual box. At the same time, the conditional probability that the target matches "snow" or "panel" is predicted. After the network predicts multiple bounding boxes, the overlapping bounding boxes are removed and only the best prediction results are retained. Finally, YOLO outputs the position, size, confidence and probability of matching the existing label category of each bounding box.

[0059] If the drone transmits an image file and the image prediction result contains both the "snow" and "panel" tags, the image and the corresponding uncovered photovoltaic panel image are transmitted to the snow parameter calculation module for processing. If the image prediction result contains only the "snow" or "panel" tag, the operation is terminated. If the drone transmits a video file, the image in the video stream is extracted according to the set frame rate. If the image prediction result contains both the "snow" and "panel" tags, the image and the corresponding uncovered photovoltaic panel image are transmitted to the snow parameter calculation module for processing. If the image prediction result contains only the "snow" or "panel" tag, the processing of the next frame of the image continues.

[0060] The snow parameter calculation module is divided into snow coverage calculation and snow thickness calculation. The snow coverage calculation requires calculating the number of pixel blocks occupied by the "snow" label box and the number of pixel blocks occupied by the "panel" label box in the snow-covered photovoltaic panel image, and then calculating the ratio of the two to obtain the snow coverage on the photovoltaic panel.

[0061] Snow thickness calculation requires edge detection of the two sets of images transmitted. The edge pixels are divided into strong edges and weak edges through double threshold processing. The calculated image gradient amplitude is compared with the threshold. The adjacent strong edge pixels are connected to form a complete edge. First, the edge length and boundary point coordinates of the snow-free photovoltaic panel in the image and the boundary point coordinates of the snow-covered photovoltaic panel image are recorded. The ratio of the edge length to the actual length of the photovoltaic panel obtained by field measurement is used to calculate the proportionality coefficient; then, by comparing the boundary point coordinates before and after the snow on the photovoltaic panel, the snow thickness at each point on the upper and lower edges of the photovoltaic panel (the upper and lower edges here are relative to the collected two-dimensional image) can be obtained. Finally, the actual thickness of the snow on the photovoltaic panel is obtained according to the proportionality coefficient. The snow image and the calculated snow coverage rate and actual snow thickness are transmitted to the time prediction unit for processing.

[0062] 3. Snowmelt time prediction

[0063] Based on the calculated actual snow thickness and the snow density measured by the snow analyzer, the snow water equivalent (SWE) can be obtained, which represents the depth or volume of water formed by the melting of snow per unit area. Combined with the snow cover rate, the actual amount of snow melted in the snowy area can be obtained. The calculation formula can be expressed as:

[0064] M=SWE*F=H*ρ*F

[0065] Where M is the actual snowmelt, H is the snow thickness, ρ is the snow density, and F is the snow coverage. At the same time, a simple linear regression model was constructed, and the least squares method was used to linearly fit the long-term temperature series and the corresponding snowmelt data based on the "Research on Simple Snowmelt Model Based on Temperature Changes" to build a degree-day factor model dominated by temperature, in which the temperature is the independent variable and the snowmelt is the dependent variable to obtain the following snowmelt model:

[0066]

[0067] Where SM is the amount of snowmelt, T a is the outside temperature, T r is the critical temperature for snowmelt. According to the formula, the amount of snowmelt per unit time can be calculated based on the real-time temperature of the photovoltaic array at a certain moment. Combined with the actual amount of snowmelt in the snow-covered area, the actual snowmelt time in the snow-covered area can be obtained. The formula is as follows:

[0068]

[0069] This embodiment also provides a photovoltaic panel snow-melting time prediction system based on machine vision, including a snow-melting time prediction module, and the snow-melting time prediction module executes the photovoltaic panel snow-melting time prediction method based on machine vision.

[0070] This embodiment also provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method for predicting the snow melting time of photovoltaic panels based on machine vision is implemented.

[0071] Ginseng Figure 2 As shown, this embodiment also provides an electronic device, including:

[0072] A memory 201 and a processor 202, wherein the memory 201 and the processor 202 are communicatively connected to each other, the memory 201 stores computer instructions, and the processor 202 executes the machine vision-based photovoltaic panel snowmelt time prediction method by executing the computer instructions.

[0073] The photovoltaic panel snow melting time prediction method and system based on machine vision have the following technical effects:

[0074] 1) In winter or in low-temperature areas, snow will cover photovoltaic panels, seriously affecting their photoelectric conversion efficiency and service life. The present invention can timely and accurately predict the snow melting time by building a local snow melting model, ensuring the normal operation of the photovoltaic power station and improving the power generation efficiency.

[0075] 2) Based on machine vision technology, the snow accumulation and snow melting time of photovoltaic panels are accurately measured and predicted. That is, high-resolution image acquisition equipment is combined with deep learning algorithms to monitor the snow accumulation on photovoltaic panels in real time, and the snow melting time is predicted by analyzing image data. Compared with traditional manual observation or simple temperature sensors, this method can provide more accurate and real-time data, thereby improving the accuracy of prediction.

[0076] 3) Combining the machine vision system with automated snow removal equipment can also achieve intelligent photovoltaic panel maintenance.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A photovoltaic panel snowmelt time prediction method based on machine vision, characterized in that: The steps include: Step 1: using a drone equipped with a high-definition camera to regularly collect images of uncovered photovoltaic panels in the photovoltaic panel area in winter according to a preset flight path, and collecting images of photovoltaic panels covered with snow in the photovoltaic panel area in snowy weather in winter, and making a one-to-one correspondence between the images of the uncovered photovoltaic panels and the images of the photovoltaic panels covered with snow; Step 2: Perform data augmentation preprocessing on the snow-covered photovoltaic panel images and use them as training image datasets to build a YOLO model to detect whether there is snow on the photovoltaic panels. If so, calculate the snow coverage rate and snow thickness. Step 3: The snow water equivalent is obtained based on the actual snow thickness calculated and the snow density measured on site by the snow analyzer. The actual snowmelt in the snow-covered area is obtained based on the snow cover rate. The snowmelt time is obtained based on the actual snowmelt and degree-day factor model.

2. The photovoltaic panel snow melting time prediction method based on machine vision according to claim 1 is characterized in that: In step 2, data enhancement preprocessing is performed on the snow-covered photovoltaic panel image, which is used as a training image dataset. The YOLO model is built to detect whether there is snow on the photovoltaic panel, including: 1) Dataset creation Collect the images needed for training, and use rotation, flipping, and contrast enhancement to perform data augmentation as training image datasets; Labelme is used to annotate the photovoltaic panel area and the snow-covered area of ​​each image in the training set. The bounding box is used to determine their location and classify them into snow and photovoltaic panel categories. The images are preprocessed, the image size is adjusted to a uniform size and normalized, and the Gaussian filter is used to perform convolution operations and weight allocation to remove noise in the image. The convolution images are convolved with Sobel operators in the horizontal and vertical directions respectively. Calculate the gradient amplitude of the convolution result, and use the non-maximum suppression method to detect the local maximum value of each pixel in the gradient image to remove redundancy; 2) Object Detection Image prediction is performed based on the YOLO model. If the drone transmits an image file and the image prediction result contains both snow and photovoltaic panel labels, the image and the corresponding uncovered photovoltaic panel image are transmitted to the snow parameter calculation module for processing. If the image prediction result contains only snow or photovoltaic panel labels, the operation is terminated. If the drone transmits a video file, the image in the video stream is extracted according to the set frame rate. If the image prediction result contains both snow and photovoltaic panel labels, the image and the corresponding uncovered photovoltaic panel image are transmitted to the snow parameter calculation module for processing. If the image prediction result contains only snow or photovoltaic panel labels, the processing of the next frame of image continues.

3. The photovoltaic panel snow melting time prediction method based on machine vision according to claim 2 is characterized in that: The images required for the training include images of photovoltaic panels covered with snow in different weather conditions, different lighting conditions, and different environments.

4. The photovoltaic panel snow melting time prediction method based on machine vision according to claim 3 is characterized in that: The image prediction based on the YOLO model includes: Based on the YOLO model, the labeled data set is divided into small batches for training and weight files are obtained. When processing the input image, the same preprocessing operation is first performed on the image transmitted by the drone, and then the YOLO model is used to divide the image into grids. For the photovoltaic panel target image whose center point falls within the grid, each grid predicts multiple bounding boxes of the target and the confidence of the bounding boxes. The bounding boxes have different shapes and proportions to adapt to the sizes of different targets. The confidence indicates the probability that the bounding box contains the target and the degree of match between the predicted box and the actual box. At the same time, the conditional probability that the target matches the snow class or the photovoltaic panel class is predicted. After the network predicts multiple bounding boxes, the overlapping bounding boxes are removed and only the best prediction results are retained. Finally, YOLO outputs the position, size, confidence and probability of matching the existing label category of each bounding box.

5. The photovoltaic panel snow melting time prediction method based on machine vision according to claim 4 is characterized in that: The calculation method of the snow cover rate includes: The number of pixel blocks occupied by the snow-covered photovoltaic panel image and the number of pixel blocks occupied by the photovoltaic panel label frame are calculated respectively, and the ratio of the two is the coverage rate of snow on the photovoltaic panel.

6. The photovoltaic panel snow melting time prediction method based on machine vision according to claim 5 is characterized in that: The calculation method of the snow thickness includes: Perform edge detection on the two sets of images transmitted, divide edge pixels into strong edges and weak edges through double threshold processing, compare the calculated image gradient amplitude with the threshold, and form a complete edge by connecting adjacent strong edge pixels; The edge length and boundary point coordinates of the photovoltaic panel without snow in the image and the boundary point coordinates of the photovoltaic panel covered with snow in the image are recorded, and the proportionality coefficient is calculated using the ratio of the edge length to the actual length of the photovoltaic panel obtained by field measurement. By comparing the coordinates of the boundary points before and after snow accumulation on the photovoltaic panel, the thickness of snow at each point on the upper and lower edges of the photovoltaic panel is obtained, and the actual thickness of snow on the photovoltaic panel is obtained based on the proportional coefficient.

7. The photovoltaic panel snow melting time prediction method based on machine vision according to claim 6 is characterized in that: The step 3 comprises: The actual snowmelt amount is calculated by the following formula: M=SWE*F=H*ρ*F Among them, M is the actual snowmelt, H is the snow thickness, ρ is the snow density, and F is the snow cover rate; At the same time, a simple linear regression model was constructed. The long-term series temperature and the corresponding snowmelt data were linearly fitted by the least squares method, and a degree-day factor model dominated by temperature was built. The temperature was used as the independent variable and the snowmelt was used as the dependent variable to obtain the following snowmelt model: Where SM is the amount of snowmelt, T a is the outside temperature, T r is the critical temperature for snow melting; The snowmelt volume per unit time is calculated based on the real-time temperature of the photovoltaic array at a certain moment. Combined with the actual snowmelt volume in the snow-covered area, the actual snowmelt time t of the snow-covered area is obtained. The formula is as follows:

8. A photovoltaic panel snowmelt time prediction system based on machine vision, characterized in that: It comprises a snowmelt time prediction module, and the snowmelt time prediction module executes the photovoltaic panel snowmelt time prediction method based on machine vision as described in any one of claims 1-7.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which, when executed by a processor, implement a photovoltaic panel snowmelt time prediction method based on machine vision as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting snowmelt time of photovoltaic panels based on machine vision as described in any one of claims 1 to 7 by executing the computer instructions.

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