A sun tracking method based on light spot profile detection

Abstract

This invention discloses a solar tracking method based on solar spot contour detection, belonging to the field of solar spot contour detection and tracking technology. This solar tracking method based on solar spot contour detection acquires and analyzes image data of the solar spot contour region in real time, obtaining the spot position deviation index, spot shape distortion index, spot brightness uniformity index, and spot contrast uniformity index of the solar spot contour region. These are then comprehensively analyzed to obtain the comprehensive deviation index of the solar spot contour region. The comprehensive deviation index of the solar spot contour region is compared with a preset deviation threshold range. When the comprehensive deviation index of the solar spot contour region is outside the preset deviation threshold range, the receiving solar equipment is adjusted. This invention, through multi-dimensional comprehensive analysis of the solar spot, can accurately assess the deviation of the solar spot, thereby ensuring that the tracking system can capture subtle deviations in real time and adjust the tracking direction and amplitude promptly.

Description

Technical Field

[0001] This invention relates to the field of light spot contour detection and tracking technology, specifically a solar tracking method based on light spot contour detection. Background Technology

[0002] Solar tracking technology adjusts the orientation of a solar-receiving device in real time to ensure it always faces the sun. Traditional solar tracking methods primarily rely on mechanical structures (such as single-axis or dual-axis tracking systems) for adjustment. These methods measure the sun's position using sensors or calculate it using pre-set algorithms. However, these methods are often limited by factors such as weather conditions, equipment accuracy, and sensor costs, and the complex mechanical structures can increase the difficulty of system maintenance.

[0003] In recent years, image processing technology has been widely used in the field of solar tracking. Image-based solar tracking methods use cameras or optical sensors to capture images of sunspots in the sky, and then estimate the sun's accurate position by analyzing image features (such as the shape, position, or brightness distribution of the sunspots). Compared with traditional sensor methods, image processing technology has higher flexibility and accuracy, and performs particularly well when dealing with obstructions or environmental changes.

[0004] Sun spot contour detection, as an image analysis-based technique, can estimate the actual position of the sun by capturing the shape and features of sun spots formed at different locations. The advantage of this method is that it does not rely on the measurement of external light intensity, but achieves efficient sun tracking through accurate spot contour capture and analysis. In addition, spot contour detection can avoid interference caused by reflected or scattered sunlight, providing a relatively stable and reliable tracking method.

[0005] The limitations of existing technologies include at least the following problems. First, in solar tracking systems, traditional methods often rely on models or simplified assumptions, and the detection and analysis of the solar spot profile are often not precise enough, making it difficult to fully reflect the multi-dimensional changes of the solar spot. This can easily lead to the tracking system being difficult to adjust accurately, resulting in the accumulation of tracking errors in actual operation. Second, when performing deviation analysis on solar spots, existing technologies often simplify multiple influencing factors into a single index, ignoring the relationships and importance between the various factors. This can easily lead to the system being unable to respond or adjust in a timely manner when the solar spot deviates slightly, thus delaying equipment adjustments, increasing energy waste, and reducing tracking accuracy. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a solar tracking method based on spot contour detection. This method solves the problem that traditional methods in solar tracking systems often rely on models or simplified assumptions, resulting in insufficient precision in spot contour detection and analysis. This makes it difficult to comprehensively reflect multi-dimensional changes in the spot, leading to inaccurate adjustment of the tracking system and the accumulation of tracking errors during actual operation. Furthermore, existing technologies often simplify multiple influencing factors into a single index when performing deviation analysis on the solar spot, ignoring the relationships and importance between these factors. This makes it difficult for the system to respond or adjust promptly when the solar spot deviates slightly, delaying equipment adjustments, increasing energy waste, and reducing tracking accuracy.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a solar tracking method based on solar spot contour detection, comprising the following steps: acquiring solar spot imaging surface data of a solar receiving device in real time and preprocessing it, wherein the solar spot imaging surface data includes the pixel value and position coordinates of each pixel in the aperture imaging surface; identifying, detecting, and analyzing the preprocessed solar spot imaging surface data to obtain solar spot contour region image data, wherein the solar spot region image data includes the pixel value and position coordinates of each solar spot pixel in the solar spot contour region image; and comprehensively analyzing the solar spot contour region image data to obtain a solar spot wheel. The solar spot position deviation index, solar spot shape distortion index, solar spot brightness uniformity index, and solar spot contrast uniformity index of the solar spot outline region are analyzed comprehensively to obtain the solar spot comprehensive deviation index of the solar spot outline region. The comprehensive deviation index of the solar spot outline region is compared with a preset deviation threshold range. When the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, the solar receiving equipment is adjusted until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range.

[0008] Further, the specific steps for preprocessing the solar spot imaging surface data of the receiving solar device are as follows: The solar spot imaging surface data of the receiving solar device is divided into several solar imaging surface regions, and each solar imaging surface region includes several pixels; a comprehensive analysis is performed on each solar imaging surface region to obtain the pixel variance value of each solar imaging surface region; the pixel variance value of each solar imaging surface region is compared with a preset variance threshold range; if the pixel variance value is within the preset variance threshold range, the corresponding solar imaging surface region is pixel-preserving, i.e., the pixel value is retained; if the pixel variance value is outside the preset variance threshold range and below the lower limit of the preset variance threshold range, the corresponding solar imaging surface region is subjected to Gaussian filtering; if the pixel variance value is outside the preset variance threshold range and above the upper limit of the preset variance threshold range, the corresponding solar imaging surface region is subjected to median filtering.

[0009] Further, the specific steps for identifying, detecting, and analyzing the preprocessed solar spot imaging surface data to obtain solar spot contour region image data are as follows: A comprehensive analysis is performed on the data of each solar imaging surface region to obtain the pixel mean of each region, and this pixel value is marked as a pixel division threshold for each region; based on the pixel division threshold, each solar imaging surface region data is binarized to obtain a binarized image of each region; edge detection and contour extraction are performed on the binarized image of each region to obtain solar spot contour region image data.

[0010] Furthermore, the specific formula for calculating the comprehensive deviation index of the solar spot outline region is as follows: Where ZpL is the comprehensive deviation index of the solar spot outline region, GpL is the position deviation index of the solar spot outline region, α1 is the deviation coefficient stored in the database, GsZ is the shape distortion index of the solar spot outline region, α2 is the distortion coefficient stored in the database, GjY is the brightness uniformity index of the solar spot outline region, α3 is the brightness uniformity coefficient stored in the database, GdB is the contrast uniformity index of the solar spot outline region, α4 ​​is the contrast uniformity coefficient stored in the database, α1+α2+α3+α4=1, and e is the natural constant.

[0011] Further, the specific steps for obtaining the spot position deviation index of the solar spot outline region are as follows: Read the abscissa, ordinate, and pixel value of each spot pixel in the solar spot outline region image, and perform comprehensive analysis to obtain the geometric center coordinates of the spot, including the center abscissa and center ordinate values; obtain the reference center coordinates of the solar spot outline region, and perform comprehensive analysis with the geometric center coordinates of the spot to obtain the spot position deviation index of the solar spot outline region, wherein the reference center coordinates include the reference center abscissa and reference center ordinate values; the specific formula for calculating the center abscissa and center ordinate values ​​of the geometric center coordinates of the spot is as follows: Where x′ is the abscissa of the geometric center of the light spot, x i Let XsZ be the x-coordinate of the i-th pixel in the solar spot outline region image. i Let y' be the pixel value of the i-th spot pixel in the image of the sunspot outline region, and y' be the center ordinate value of the geometric center coordinates of the spot. i Let be the ordinate of the i-th spot pixel in the solar spot outline region image, i = 1, 2, 3, ..., i0, where i0 is the number of spot pixels in the solar spot outline region image.

[0012] Furthermore, the specific formula for calculating the deviation index of the sunspot position in the outline region is as follows: Wherein, GpL is the deviation index of the solar spot position in the outline region, x′ is the abscissa of the geometric center coordinate of the spot, x″ is the abscissa of the reference center coordinate of the spot, y′ is the ordinate of the geometric center coordinate of the spot, y″ is the ordinate of the reference center coordinate of the spot, and DjX is the diagonal length of the solar spot outline region.

[0013] Further, the specific steps for obtaining the shape distortion index of the solar spot contour region are as follows: Count the number of spot pixels in the solar spot contour region image and mark them as the area value of the solar spot contour region; count the number of boundary spot pixels in the solar spot contour region image and mark them as the perimeter value of the solar spot contour region; comprehensively analyze the area value and perimeter value of the solar spot contour region to obtain the shape distortion index of the solar spot contour region; the specific formula for calculating the shape distortion index of the solar spot contour region is as follows: Wherein, GsZ is the distortion index of the shape of the solar spot outline region, π is pi, MjZ is the area of ​​the solar spot outline region, and ZcZ is the perimeter of the solar spot outline region.

[0014] Further, the specific steps for obtaining the uniformity index of the solar spot brightness in the solar spot outline region are as follows: For each spot pixel in the solar spot outline region image, identify the number of neighboring spot pixels within a set neighborhood; combine the pixel value of each spot pixel in the solar spot outline region image with the pixel value of each neighboring spot pixel within the set neighborhood for comprehensive analysis to obtain the neighborhood mean and neighborhood standard deviation of the solar spot brightness for each spot pixel in the solar spot outline region image; comprehensively analyze the neighborhood mean and neighborhood standard deviation of the solar spot brightness for each spot pixel in the solar spot outline region image to obtain the uniformity index of the solar spot brightness in the solar spot outline region; the specific formulas for calculating the neighborhood mean, neighborhood standard deviation of the solar spot brightness for each spot pixel in the solar spot outline region image, and the uniformity index of the solar spot brightness in the solar spot outline region image are as follows: Among them, LjZ i Let XsZ be the neighborhood mean of the brightness of the i-th spot pixel in the solar spot contour region image. i Let XsZ be the pixel value of the i-th spot pixel in the solar spot contour region image. it LbC is the pixel value of the t-th neighboring pixel in the neighborhood of the i-th pixel in the solar spot outline region image. i Let LjY be the neighborhood standard deviation of the brightness of the i-th spot pixel in the solar spot outline region image, LjY be the uniformity index of the brightness of the solar spot outline region, i = 1, 2, 3, ..., i0, i0 is the number of spot pixels in the solar spot outline region image, and t = 1, 2, 3, ..., t0, t0 is the number of adjacent spot pixels in the defined neighborhood.

[0015] Further, the specific steps for obtaining the uniformity index of the contrast of the solar spot outline region are as follows: The pixel values ​​of each spot pixel in the solar spot outline image data are compared and analyzed to obtain the maximum and minimum spot pixel values ​​in the solar spot outline image; the maximum and minimum spot pixel values ​​in the solar spot outline image are comprehensively analyzed to obtain the comprehensive contrast index of the solar spot outline region; the solar spot outline image data is divided into several solar spot outline segmentation region image data, and each solar spot outline segmentation region image data includes several spot pixels; the pixel values ​​of each spot pixel in each solar spot outline segmentation region image data are compared and analyzed to obtain the uniformity index of the contrast of each spot pixel in the solar spot outline region image data. The maximum and minimum pixel values ​​of the sunspot contour regions in the image are used to determine the region's contrast index. A comprehensive analysis of these values ​​yields the region's contrast ratio for each sunspot contour region. Furthermore, a comprehensive analysis of the overall contrast ratio of the sunspot contour region and the contrast ratio of each individual sunspot contour region yields the uniformity index of the sunspot contour region's contrast. The specific formulas for calculating these indices are as follows: Where ZdB is the overall contrast index of the sunspot outline region, XsZ′ Max XsZ′ represents the maximum pixel value of the sunspot in the solar spot contour image. Min QdB represents the minimum pixel value of the sunspot in the sunspot contour image. u The contrast index of the region segmentation for the u-th solar spot outline is given. The maximum pixel value of the sunspot in the segmented region of the image for the u-th sunspot contour is given. Let denot be the minimum pixel value of the segmented region in the image of the u-th solar spot contour segmentation region, GdB be the uniformity index of the solar spot contrast in the solar spot contour region, u = 1, 2, 3, ..., u0, u0 be the number of solar spot contour segmentation region image data.

[0016] Further, when the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, the receiving solar equipment is adjusted and analyzed until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range. The steps are as follows: When the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, abnormal indices in the comprehensive deviation index of the solar spot outline region are identified, and corresponding adjustment measures are taken; the comprehensive deviation index of the adjusted solar spot outline region is analyzed and compared with the preset deviation threshold range; if the comprehensive deviation index of the adjusted solar spot outline region is within the preset deviation threshold range, the adjustment ends; if the comprehensive deviation index of the adjusted solar spot outline region is outside the preset deviation threshold range, the steps of taking adjustment measures, analyzing the comprehensive deviation index of the adjusted solar spot outline region, and making judgments are repeated until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range.

[0017] The present invention has the following beneficial effects:

[0018] (1) The solar tracking method based on spot contour detection comprehensively analyzes the solar spot based on the spot position deviation index, spot shape distortion index, spot brightness uniformity index and spot contrast uniformity index. It can accurately assess the deviation of the spot. This multi-dimensional analysis method not only considers the position error of the spot, but also the uniformity of shape distortion, brightness and contrast, thus ensuring that the tracking system can capture subtle deviations in real time and adjust the tracking direction and amplitude in a timely manner. Traditional solar tracking methods are often based only on position offset or simple brightness changes, while this comprehensive analysis method can provide more accurate deviation index and ensure high-precision solar tracking under dynamic lighting conditions.

[0019] (2) The solar tracking method based on spot contour detection first divides the solar spot imaging surface data into several regions and calculates the pixel variance value of each region after fine preprocessing. Then, different filtering methods are used according to the different variance values ​​to effectively suppress noise. Unlike the traditional fixed filtering method, this adaptive filtering based on data variance can automatically select a suitable filtering method according to the changes in image quality, which further improves the quality of the data and provides a more reliable data source for subsequent spot contour recognition and deviation analysis.

[0020] (3) The solar tracking method based on spot contour detection calculates the comprehensive deviation index of the solar spot contour region and compares it with the preset deviation threshold to determine whether equipment adjustment is needed. When the comprehensive deviation index exceeds the preset range, the system will identify the abnormal index and take corresponding adjustment measures until the deviation index returns to the normal range. This dynamic adjustment mechanism can optimize and adjust in real time according to different environmental conditions, avoiding misjudgment or delayed response that may be caused by static threshold judgment, and ensuring that the solar tracking equipment can always maintain the best working state.

[0021] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0022] Figure 1 This is a flowchart of a solar tracking method based on spot contour detection according to the present invention.

[0023] Figure 2 This is a flowchart illustrating the specific steps involved in obtaining the deviation index of the solar spot position in a solar tracking method based on spot contour detection, as described in this invention. Detailed Implementation

[0024] This application provides a solar tracking method based on spot contour detection. This addresses the problem that traditional methods in solar tracking systems often rely on models or simplified assumptions, resulting in insufficient precision in spot contour detection and analysis. This makes it difficult to comprehensively reflect multi-dimensional changes in the spot, leading to inaccurate adjustment of the tracking system and the accumulation of tracking errors during actual operation. Furthermore, existing technologies often simplify multiple influencing factors into a single index when performing deviation analysis on solar spots, neglecting the relationships and importance between factors. This makes it difficult for the system to respond or adjust promptly when the solar spot deviates slightly, delaying equipment adjustments, increasing energy waste, and reducing tracking accuracy.

[0025] The problem addressed in this application's embodiments can be summarized as follows:

[0026] First, the system acquires real-time data of the solar spot imaging surface, including the pixel value and position coordinates of each pixel. Then, this data is divided into multiple regions, and comprehensive analysis is performed to calculate the pixel variance value of each region. Based on the magnitude of the variance value, the system performs different types of processing on the pixels: if the variance is within a preset threshold range, the original pixel value is retained; if the variance is too high, median filtering is performed; if it is too low, Gaussian filtering is performed. Next, the preprocessed data is used to identify and detect the solar spot contour. The pixel value of each region is binarized according to a set threshold, and then the image data of the solar spot contour region is extracted through edge detection and contour extraction algorithms. Finally, by comprehensively analyzing multiple indicators such as the solar spot position deviation, shape distortion, brightness uniformity, and contrast uniformity of the solar spot contour region, a comprehensive deviation index is calculated. If the deviation index exceeds the preset threshold range, the system will adjust the equipment until the comprehensive deviation index returns to the predetermined range.

[0027] Please see Figure 1 This invention provides a technical solution: a solar tracking method based on solar spot contour detection, comprising the following steps: acquiring solar spot imaging surface data of a receiving solar device in real time and preprocessing it, wherein the solar spot imaging surface data includes the pixel value and position coordinates of each pixel in the aperture imaging surface; identifying, detecting, and analyzing the preprocessed solar spot imaging surface data to obtain solar spot contour region image data, wherein the solar spot region image data includes the pixel value and position coordinates of each solar spot pixel in the solar spot contour region image; and comprehensively analyzing the solar spot contour region image data to obtain the solar spot contour region... The solar spot position deviation index, solar spot shape distortion index, solar spot brightness uniformity index, and solar spot contrast uniformity index are analyzed comprehensively to obtain the solar spot comprehensive deviation index of the solar spot outline region. The comprehensive deviation index of the solar spot outline region is compared with a preset deviation threshold range. When the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, the solar receiving equipment is adjusted until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range.

[0028] Specifically, the preprocessing steps for the solar spot imaging surface data of the receiving solar device are as follows: The solar spot imaging surface data of the receiving solar device is divided into several solar imaging surface regions (e.g., 10x10 or 16x16 pixels), and each solar imaging surface region includes several pixels; a comprehensive analysis is performed on each solar imaging surface region to obtain the pixel variance value of each region; the pixel variance value of each region is compared with a preset variance threshold range; if the pixel variance value is within the preset variance threshold range, then the corresponding solar spot imaging surface data is processed. The sunlight imaging area is processed by pixel preservation, meaning that pixel values ​​are retained. If the pixel variance value is outside the preset variance threshold range but below the preset lower limit of the variance threshold range, the corresponding sunlight imaging area is processed by Gaussian filtering. The specific steps are as follows: assign a weight to each pixel in the sunlight imaging area, the weight of which is determined by a Gaussian function; then calculate the new value of each pixel, that is, multiply the values ​​of all pixels surrounding the current pixel by the corresponding Gaussian weight, then sum them up, and finally obtain the new pixel value. A specific implementation example is as follows: There is a 3x3 window, and the original pixel value matrix is ​​as follows: The Gaussian weight matrix (with a standard deviation of 1 in this implementation example) is as follows: Then, the original image matrix and the Gaussian weight matrix are multiplied one by one, and the products of each pixel are summed to obtain the new pixel. For example, for the center pixel position (2, 2), the pixel values ​​of the 3x3 window are multiplied one by one with the weight matrix, and then summed to obtain: New pixel value = (100*0.075)+(150*0.125)+(200*0.075)+(150*0.125)+(100*0.200)+(50*0.125)+(200*0.075)+(100*0.125)+(50*0.075)=7.5+18.75+15+18.75+20+6.25+15+12.5+3.75=118.5.

[0029] If the pixel variance value is outside the preset variance threshold range and higher than the upper limit of the preset variance threshold range, then median filtering will be applied to the corresponding solar imaging area. The specific steps are as follows: sort the pixels in the solar imaging area to obtain the median pixel value, and use it as the new pixel value for each pixel in the solar imaging area. A specific implementation example is as follows: There is a 3x3 window with the following original pixel values: Sort them to get: [50, 100, 100, 150, 200, 200, 255, 255]. The median pixel value is 150, so the new pixel value of each pixel in the sunlight imaging area is 150.

[0030] In this implementation, by analyzing data from the solar imaging surface area, the processing method is adaptively selected based on the pixel variance. For low-noise areas with small variance, the original pixel data is retained; while for high-noise areas with large variance, different filtering methods are used: Gaussian filtering is applied to low-noise areas to remove minor noise and maintain smoothness; median filtering is used to effectively remove salt-and-pepper noise and retain edge information in high-noise areas. This adaptive processing method can adjust the filtering method according to local information, avoiding the singleness and over-smoothing problems of traditional methods, thus improving image quality. Through local processing, pixels in each region are analyzed independently, ensuring reduced computation in low-noise areas and accurate denoising in noisy areas, thereby balancing processing accuracy and efficiency. The data preprocessing step, through denoising, ensures more accurate spot contour recognition and deviation index calculation, especially under complex lighting and environmental changes, improving the stability and robustness of the system.

[0031] Specifically, the steps for identifying, detecting, and analyzing the preprocessed solar spot imaging surface data to obtain the solar spot contour region image data are as follows: First, comprehensively analyze the data of each solar imaging surface region to obtain the pixel mean of each region, and mark it as the pixel division threshold for each region. Second, based on the pixel division threshold, binarize the data of each solar imaging surface region to obtain a binarized image of each region. If a pixel value in the image is ≥ the pixel division threshold, it is considered to belong to the solar spot region and marked as 1; otherwise, it is considered background and marked as 0. Third, perform edge detection and contour extraction processing on the binarized image of each region to obtain the solar spot contour region image data.

[0032] The edge detection process specifically uses Canny edge detection, which involves calculating the gradient value of each pixel using the Sobe operator (or other gradient operators). The gradient value represents the magnitude of the brightness change (including gradient magnitude and gradient direction), and areas with large gradient values ​​are the edges of the image.

[0033] In the gradient map, if the gradient value of a pixel is not a local maximum of the edge, the pixel is suppressed (i.e. set to 0), and the edge point is preserved. This process is used to reduce the number of detected edges, making the edges more accurate.

[0034] For each pixel, if the gradient value of that pixel is not a local maximum in its gradient direction, then set it to 0.

[0035] Two thresholds (high threshold and low threshold) are used to determine whether a pixel is an edge. Pixels above the high threshold are strong edges, and pixels below the low threshold are weak edges. Pixels in between are determined based on their connection to strong edges. Strong edge pixels are directly marked as edges, and weak edge pixels are also marked as edges if they are connected to strong edges, otherwise they are removed.

[0036] After Canny edge detection, a new binary image is obtained, in which the edge part is 1 (white) and the other part is 0 (black). This image represents the edge of the light spot.

[0037] The specific steps of contour extraction are as follows: After edge detection, contour extraction is performed, that is, using depth-first search (DFS) or breadth-first search (BFS) algorithms, starting from an edge pixel and propagating to the surrounding pixels until a complete connected region is found. Each time a connected region is found, a unique identifier is assigned to this region.

[0038] Each pixel in the image is checked to determine if it has been marked as part of the light spot. If it has not been marked, the region is expanded until all light spot regions are marked.

[0039] Once connected regions are marked, contours can be extracted from these marked regions. Contours typically refer to the boundaries of regions or sets of pixels adjacent to the background.

[0040] In this implementation scheme, by calculating the pixel mean of the solar imaging surface area and performing binarization, the spot area can be effectively separated from the background. The binarization is based on a preset pixel threshold, and the part greater than or equal to the threshold is marked as the spot area (1), and the rest is the background (0). This method is particularly suitable for situations where the spot intensity changes greatly, and can clearly separate the spot from the background, providing an accurate region definition for subsequent edge detection and contour extraction. The Canny edge detection method is used, which can accurately identify the edge of the spot. Potential edges are determined by calculating gradient values, and irrelevant edges are removed by local maximum suppression. High and low thresholds are set to distinguish strong and weak edges, ensuring the accuracy of edge extraction. This process can effectively eliminate noise, ensure clear spot edges, and improve the stability of subsequent contour extraction. After edge detection, DFS or BFS algorithms are used to identify connected regions. The complete spot contour is extracted by scanning pixel by pixel, and a unique identifier is assigned to each region. This can accurately obtain the real contour data of the spot, providing a basis for deviation analysis and solar tracking error correction, thereby improving the system's response speed and accuracy in dynamic spot changes.

[0041] Specifically, the formula for calculating the comprehensive deviation index of the solar spot outline region is as follows: Wherein, ZpL is the comprehensive deviation index of the solar spot outline region, GpL is the position deviation index of the solar spot outline region, α1 is the deviation coefficient stored in the database, GsZ is the shape distortion index of the solar spot outline region, α2 is the distortion coefficient stored in the database, GjY is the brightness uniformity index of the solar spot outline region, α3 is the brightness uniformity coefficient stored in the database, GdB is the contrast uniformity index of the solar spot outline region, α4 ​​is the contrast uniformity coefficient stored in the database, α1+α2+α3+α4=1, and e is the natural constant, which is taken as 2.71 in this embodiment.

[0042] It should be explained that in calculating the comprehensive deviation index of the solar spot outline region, the spot position deviation index, spot shape distortion index, spot brightness uniformity index, and spot contrast uniformity index of the solar spot outline region have all been processed by removing units, so they can be calculated directly.

[0043] The specific calculation process for α1, α2, α3, and α4 stored in the database is as follows: Obtain the reference spot position deviation index, reference spot shape distortion index, reference spot brightness uniformity index, and reference spot contrast uniformity index for the reference solar spot outline region (i.e., the spot outline region is not deviated from), and perform standardization processing. Then, sum and analyze the standardized reference solar spot outline region (i.e., the spot outline region is not deviated from), obtaining the index sum value. Finally, perform a ratio analysis between the standardized reference solar spot outline region (i.e., the spot outline region is not deviated from), and the index sum value, obtaining the corresponding coefficients.

[0044] In this implementation scheme, by calculating the spot position deviation index, shape distortion index, brightness uniformity index, and contrast uniformity index, this method can comprehensively evaluate the spot quality from multiple dimensions. Each index reflects the spatial position, shape, brightness distribution, and contrast distribution characteristics of the spot, accurately capturing changes in the spot and providing a basis for solar tracking adjustments. This comprehensive evaluation avoids misjudgments that may arise from a single index, improving the tracking accuracy and adaptability of the method. To enhance the flexibility of the method, coefficients stored in a database are used in the deviation index calculation, allowing for dynamic adjustment of the weights of each index according to different environmental conditions and needs. This ensures that the evaluation process is personalized based on the actual situation of the spot. The weighted summation method balances the influence of various deviation factors to obtain the final comprehensive deviation index of the light spot. This weighted summation makes the deviation assessment more in line with the needs of practical applications. In addition, the method introduces the natural constant (approximately 2.71) into the formula, which helps to enhance the stability of the calculation, making the calculation results unaffected by changes in units and scale, and enhancing the universality and consistency of the formula. All deviation indices are normalized during the calculation process, simplifying the calculation process, improving the calculation efficiency, and avoiding complex unit conversion and processing steps. This design improves the accuracy, flexibility and stability of the system, ensuring that the light spot assessment process is more efficient and reliable, and adaptable to different light spot changes and environmental conditions.

[0045] Specifically, such as Figure 2 As shown, the specific steps for obtaining the spot position deviation index of the solar spot contour region are as follows: Read the horizontal coordinate value, vertical coordinate value, and pixel value of each spot pixel in the solar spot contour region image, and perform comprehensive analysis to obtain the geometric center coordinates of the spot, including the center horizontal coordinate value and the center vertical coordinate value; obtain the spot reference center coordinates of the solar spot contour region, and perform comprehensive analysis with the spot geometric center coordinates to obtain the spot position deviation index of the solar spot contour region, wherein the spot reference center coordinates include the reference center horizontal coordinate value and the reference center vertical coordinate value.

[0046] The specific formulas for calculating the abscissa and ordinate values ​​of the geometric center of the light spot are as follows: Where X′ is the abscissa of the geometric center of the light spot, x i Let XsZ be the x-coordinate of the i-th pixel in the solar spot outline region image. i Let y' be the pixel value of the i-th spot pixel in the image of the sunspot outline region, and y' be the center ordinate value of the geometric center coordinates of the spot. i Let be the ordinate of the i-th spot pixel in the solar spot outline region image, i = 1, 2, 3, ..., i0, where i0 is the number of spot pixels in the solar spot outline region image.

[0047] The specific formula for calculating the deviation index of the sunspot position in the outline region is as follows: Wherein, GpL is the deviation index of the solar spot position in the outline region, x′ is the abscissa of the geometric center coordinate of the spot, x″ is the abscissa of the reference center coordinate of the spot, y′ is the ordinate of the geometric center coordinate of the spot, y″ is the ordinate of the reference center coordinate of the spot, and DjX is the diagonal length of the solar spot outline region.

[0048] It needs to be explained that the specific calculation process for the diagonal length of the sunspot outline area is as follows:

[0049] The leftmost, smallest x-coordinate value of the sunspot outline area is recorded as the smallest x-coordinate value.

[0050] The rightmost and largest x-coordinate value of the sunspot outline area is recorded as the maximum x-coordinate value.

[0051] The smallest ordinate value at the top of the sunspot outline area is denoted as the smallest ordinate value.

[0052] The largest ordinate value at the bottom edge of the sunspot outline area is recorded as the maximum ordinate value.

[0053] Difference analysis was performed on the minimum abscissa, maximum abscissa, minimum ordinate, and maximum ordinate values ​​of the solar spot outline region to obtain the width and height values ​​of the solar spot outline region.

[0054] The width and height values ​​of the solar spot outline region are calculated together, i.e., the diagonal length of the solar spot outline region = (width value) 2 +height value 2 ) 1 / 2 .

[0055] In this implementation scheme, the geometric center position of the light spot is a key parameter for light spot offset analysis. By calculating the horizontal and vertical coordinates of the geometric center of the light spot and comparing them with the center of a reference light spot, this method can accurately evaluate the position information of the light spot. The geometric center is the weighted average position of all light spot pixels, taking into account the actual brightness distribution of the light spot, and therefore can more accurately reflect the positional changes of the light spot. Unlike simple geometric center calculation, this pixel brightness-weighted calculation method can better capture the actual changes of the light spot and avoid errors caused by irregularities in the light spot or changes in illumination. The calculation of the light spot position deviation index is based on the comparison with the center of the reference light spot. In this method, the reference center coordinates represent the center position of the light spot under ideal conditions. By calculating the deviation between the actual geometric center of the light spot and the reference center, the degree of light spot offset can be directly reflected. Using the reference center coordinates as a standard can effectively reduce the interference of external factors (such as changes in ambient light, equipment errors, etc.) on the offset calculation, thereby improving the accuracy of light spot offset analysis. When calculating the light spot position deviation index, this method introduces spatial size information by calculating the diagonal length of the light spot contour region. This approach not only considers the positional difference of the light spot but also the spatial size of the light spot region, avoiding the influence of light spot offset on the light spot size. Even a small displacement of a large spot may not significantly affect its quality due to its large size; however, even a small shift in a small spot can have a significant impact on tracking performance. Introducing diagonal length calculation ensures a more comprehensive calculation of the deviation exponent, adapting to spots of different sizes. When using a weighted average method to calculate the geometric center of the spot, the influence of the spot's brightness distribution on the center position is fully considered. Spot brightness is usually non-uniform; weighted averaging ensures that the calculation of the geometric center is not only based on the spot's geometric position but also reflects the actual brightness distribution, thereby improving calculation accuracy. This method avoids... The oversensitivity of weaker areas more accurately describes the actual positional deviation of the light spot. By combining the spot positional deviation index with the diagonal length, the calculation process is not limited to positional differences but also considers changes in the spot shape. The diagonal length, as a spatial metric, intuitively reflects the size and shape of the light spot, and can adapt to light spots of different sizes and shapes, enhancing the flexibility and robustness of the algorithm. In practical applications, the shape and size of the light spot may be affected by various factors such as equipment and lighting. The calculation based on the diagonal length allows this method to adapt to these changes more flexibly, thereby reducing errors and improving the stability of detection and analysis.

[0056] Specifically, the steps to obtain the shape distortion index of the solar spot contour region are as follows: count the number of spot pixels in the solar spot contour region image and mark them as the area value of the solar spot contour region; count the number of boundary spot pixels in the solar spot contour region image (the boundary is obtained from the edge detection result) and mark them as the perimeter value of the solar spot contour region; perform a comprehensive analysis on the area value and perimeter value of the solar spot contour region to obtain the shape distortion index of the solar spot contour region.

[0057] The specific formula for calculating the shape distortion index of the solar spot outline region is as follows: Wherein, GsZ is the distortion index of the shape of the solar spot outline region, π is pi, MjZ is the area of ​​the solar spot outline region, and ZcZ is the perimeter of the solar spot outline region.

[0058] In this implementation scheme, the shape of the solar spot is a crucial parameter in the solar tracking system. Especially when the spot deviates, deforms, or becomes distorted, it can lead to tracking errors. By statistically analyzing the area and perimeter of the solar spot's outline region, changes in the spot's shape can be quantitatively assessed, reflecting the degree of distortion. The ratio of area to perimeter is highly representative and effectively describes the spot's morphology. When the spot is circular or nearly circular, the ratio is close to the ideal circular value. If the spot is deformed (e.g., elliptical or irregular), this ratio will change. Through this calculation, the method can promptly detect and respond to distortions in the spot's shape, ensuring the system adjusts when deformation occurs. Introducing pi (π) as a calculation constant standardizes the spot shape distortion index, ensuring its physical meaning and consistency. Using pi helps compare the spot shape distortion index with a standard geometric shape (circle), making the calculation results more consistent with practical applications, particularly in real-world applications where the solar spot... Ideally, the shape should be nearly circular. Therefore, by standardizing the value of pi, this method can more accurately reflect the degree of deviation in shape. No matter how complex the spot shape is, the distortion index can be evaluated under a unified standard. Area and perimeter are two basic geometric parameters of spot morphology, which comprehensively reflect the spatial shape of the spot. The area value represents the total coverage area of ​​the spot, while the perimeter describes the complexity of the spot boundary. By comprehensively analyzing these two parameters, this method can more accurately determine whether the spot is distorted, avoiding errors that may be caused by a single parameter. The combined calculation of area and perimeter not only reflects the changes in the size of the spot, but also accurately captures the subtle changes in the spot boundary. When the spot shape is deformed, the extension or contraction of the boundary has a significant impact on the spot distortion, and the perimeter can effectively capture these changes. Therefore, by comprehensively analyzing the ratio of area to perimeter, a more comprehensive and accurate assessment of spot shape distortion can be provided, ensuring that the system can efficiently and accurately track and adjust under different spot shapes.

[0059] Specifically, the steps for obtaining the uniformity index of the brightness of the solar spot outline region are as follows: For each spot pixel in the solar spot outline region image, identify the number of neighboring spot pixels in a set neighborhood; combine the pixel value of each spot pixel in the solar spot outline region image with the pixel value of each neighboring spot pixel in the set neighborhood for comprehensive analysis to obtain the neighborhood mean and neighborhood standard deviation of the brightness of each spot pixel in the solar spot outline region image; and perform comprehensive analysis on the neighborhood mean and neighborhood standard deviation of the brightness of each spot pixel in the solar spot outline region image to obtain the uniformity index of the brightness of the solar spot outline region.

[0060] The specific formulas for calculating the neighborhood mean of the brightness of each spot pixel in the solar spot contour region image, the neighborhood standard deviation of the brightness of the spot, and the uniformity index of the brightness of the solar spot contour region are as follows: Among them, LjZ i Let XsZ be the neighborhood mean of the brightness of the i-th spot pixel in the solar spot contour region image. i Let XsZ be the pixel value of the i-th spot pixel in the solar spot contour region image. it LbC is the pixel value of the t-th neighboring pixel in the neighborhood of the i-th pixel in the solar spot outline region image. i Let LjY be the neighborhood standard deviation of the brightness of the i-th spot pixel in the solar spot outline region image, LjY be the uniformity index of the brightness of the solar spot outline region, i = 1, 2, 3, ..., i0, i0 is the number of spot pixels in the solar spot outline region image, and t = 1, 2, 3, ..., t0, t0 is the number of adjacent spot pixels in the defined neighborhood.

[0061] In this implementation scheme, the uniformity of light spot brightness is a key indicator of light spot quality, especially in applications such as solar tracking and energy focusing, where brightness uniformity directly affects system performance. By calculating the mean and standard deviation of brightness in each pixel and its neighborhood, the fluctuation of brightness within the light spot area can be effectively measured, thus providing a detailed brightness distribution analysis. If the brightness in the light spot area is relatively uniform, this method can maintain optimal energy utilization and high tracking accuracy; if the brightness distribution is uneven, the method will detect and adjust in a timely manner to optimize light spot quality and tracking performance. By comprehensively analyzing the brightness information of each pixel and its surrounding neighborhood, this method can accurately detect local brightness deviations and avoid undetected small-scale brightness changes. The neighborhood mean and standard deviation can describe the light spot brightness in detail. This method considers not only the brightness of a single pixel but also neighborhood information to improve the accuracy of spot brightness uniformity assessment. Even minute brightness changes can be captured in a timely manner. By calculating the neighborhood mean and standard deviation of brightness, this method can identify and handle outliers with excessive brightness fluctuations in the spot. These outliers may originate from factors such as sensor noise and atmospheric interference. The standard deviation, as an effective tool for measuring brightness changes, can reflect the stability and uniformity of spot brightness, helping the system eliminate errors caused by the environment, equipment, or noise, and ensuring the stability of the spot brightness uniformity index. For solar tracking systems, brightness uniformity optimization can reduce deviations caused by errors, ensuring high accuracy and stability of the system.

[0062] Specifically, the steps for obtaining the uniformity index of the contrast of the solar spot outline region are as follows: Compare and analyze the pixel values ​​of each spot pixel in the solar spot outline image data to obtain the maximum and minimum spot pixel values ​​in the solar spot outline image; perform a comprehensive analysis of the maximum and minimum spot pixel values ​​in the solar spot outline image to obtain the comprehensive contrast index of the solar spot outline region; divide the solar spot outline image data into several solar spot outline region image data, and each solar spot outline region image data includes several spot pixels; for each solar spot ring... The pixel values ​​of each spot pixel in the image data of the outline region are compared and analyzed to obtain the maximum and minimum spot pixel values ​​in the outline region of each solar spot. The maximum and minimum spot pixel values ​​in the outline region of each solar spot are then analyzed to obtain the contrast index of each outline region. The overall contrast index of the solar spot outline region and the contrast index of each outline region are analyzed to obtain the uniformity index of the solar spot contrast region.

[0063] The specific formulas for calculating the comprehensive contrast index of the solar spot outline region, the contrast index of each solar spot outline region, and the uniformity index of the solar spot contrast in the solar spot outline region are as follows: Where ZdB is the overall contrast index of the sunspot outline region, XsZ′ Max XsZ′ represents the maximum pixel value of the sunspot in the solar spot contour image. Min QdB represents the minimum pixel value of the sunspot in the sunspot contour image. u The contrast index of the region segmentation for the u-th solar spot outline is given. The maximum pixel value of the sunspot in the segmented region of the image for the u-th sunspot contour is given. Let denot be the minimum pixel value of the segmented region in the image of the u-th solar spot contour segmentation region, GdB be the uniformity index of the solar spot contrast in the solar spot contour region, u = 1, 2, 3, ..., u0, u0 be the number of solar spot contour segmentation region image data.

[0064] In this implementation, the contrast uniformity of the light spot directly affects the accuracy and efficiency of the solar tracking system. Especially during detailed light spot tracking, uneven contrast can lead to or increase in spot positioning errors. By analyzing the maximum and minimum pixel values ​​of the light spot region and its subdivided areas, the contrast distribution of the light spot can be accurately determined, thereby assessing its uniformity. This method can identify whether there are excessively bright or dark areas in the light spot, avoiding the impact of uneven contrast on tracking accuracy. Contrast analysis allows the system to adjust the tracking process in real time, optimizing lens focal length or sensor gain to avoid local overexposure or shadowing of the light spot. Through the calculation of the contrast uniformity index, this method can dynamically optimize the tracking algorithm, ensuring that the contrast of the light spot remains consistent within the imaging area, avoiding interference with the tracking process. Excessive or insufficient contrast can lead to errors or blurring. Too much contrast may cause overexposure, while too little contrast may result in loss of light spot details, affecting accuracy. This method optimizes light spot quality by accurately analyzing the contrast index of each region, ensuring that details are preserved without being too strong. This method can effectively identify and optimize areas with abnormal contrast in the light spot, ensuring a reasonable contrast distribution and avoiding the impact of uneven light spot contrast on subsequent tracking and positioning. The contrast uniformity index provides a quantitative basis for light spot quality analysis. Combined with a set threshold, this method can monitor the contrast changes of the light spot in real time and make rapid adjustments. By quantifying contrast differences, this method can accurately identify light spot quality problems, reduce human error, and ensure high-precision light spot quality assessment and tracking performance.

[0065] Specifically, when the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, the solar receiving device is adjusted and analyzed until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range. The steps are as follows: When the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, abnormal indices in the comprehensive deviation index of the solar spot outline region are identified, and corresponding adjustment measures are taken; the comprehensive deviation index of the adjusted solar spot outline region is analyzed and compared with the preset deviation threshold range; if the comprehensive deviation index of the adjusted solar spot outline region is within the preset deviation threshold range, the adjustment ends; if the comprehensive deviation index of the adjusted solar spot outline region is outside the preset deviation threshold range, the steps of taking adjustment measures, analyzing the comprehensive deviation index of the adjusted solar spot outline region, and making judgments are repeated until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range.

[0066] The following is a specific example of implementing corresponding adjustment measures for the abnormal index in the comprehensive deviation index of the solar spot outline region:

[0067] If the abnormal index in the comprehensive deviation index of the solar spot outline area is the spot position deviation index, then the spot can be made closer to the target position by adjusting the azimuth, pitch, or other relevant angles of the equipment.

[0068] If the abnormal index in the comprehensive deviation index of the solar spot outline area is the spot shape distortion index, then the shape distortion can be reduced by adjusting the focus of the equipment or adjusting the optical components.

[0069] If the abnormal index in the comprehensive deviation index of the solar spot outline area is the spot brightness uniformity index, then it can be corrected by adjusting the lens, light source, or other equipment, or by making appropriate optical adjustments.

[0070] If the abnormal index in the comprehensive deviation index of the solar spot outline area is the spot contrast uniformity index, then the intensity of the light source can be recalibrated or the lens focal length can be adjusted.

[0071] And based on the determined adjustment direction and magnitude, the various parameters of the solar receiving equipment are adjusted during actual operation, for example:

[0072] Adjust the electric tracking system of the equipment to make angle adjustments.

[0073] Adjust the optical components of the equipment, such as condenser lenses, lenses, or apertures.

[0074] The light source intensity, exposure time, and other parameters can be adjusted using software.

[0075] In this implementation scheme, when the comprehensive deviation index of the solar spot outline exceeds a preset threshold, the method can automatically detect and take targeted adjustment measures according to the specific anomaly type of the spot (such as positional shift, shape distortion, etc.). By monitoring the spot deviation in real time, the method can quickly and automatically adjust the equipment to ensure that solar tracking is always in optimal condition, avoiding the impact of environmental changes or equipment errors on tracking accuracy. If the spot position deviates, the azimuth or elevation angle of the equipment can be adjusted to accurately align the spot; if the spot shape is distorted, the optical equipment can be adjusted to reduce distortion. This method ensures that the spot quality is optimized without the need for global recalibration. The method can automatically identify different types of deviation and take corresponding adjustment measures according to the specific type of deviation. For example, if the spot brightness is uneven, the lens can be adjusted to equalize the brightness; if the spot shape is distorted, the optical equipment can be adjusted to reduce distortion. This method ensures that the spot quality is optimized without the need for global recalibration. The method can automatically identify different types of deviation and take corresponding adjustment measures according to the specific type of deviation. For example, if the spot brightness is uneven, the lens can be adjusted to equalize the brightness; if the spot shape is distorted, the optical equipment can be adjusted to reduce distortion. If the shape of the light spot is distorted, the distortion is reduced by optimizing the focus. Through these fine adjustments, the quality of the light spot can be improved, ensuring that all indicators of the light spot (such as position, shape, brightness, and contrast) are always in optimal condition. This dynamic adjustment method reduces human intervention, improves the autonomy and efficiency of the system, and ensures that the solar tracking system can respond quickly and maintain high precision under different lighting conditions. Whenever the comprehensive deviation index of the light spot exceeds the preset threshold, the system will automatically execute adjustment steps until the light spot returns to normal. This not only saves time and resources but also reduces human error and improves the automation level and self-adjustment capability of the system. Through precise control of the light spot, this method can improve the accuracy of sunlight capture, ensuring that the tracker can more accurately capture the position and trajectory of the sun, thereby improving the reliability and accuracy of the entire solar tracking system.

[0076] In summary, this application has at least the following effects:

[0077] By comprehensively analyzing solar spots based on spot position deviation index, spot shape distortion index, spot brightness uniformity index, and spot contrast uniformity index, the deviation of the spot can be accurately assessed. This multi-dimensional analysis method not only considers the position error of the spot, but also the uniformity of shape distortion, brightness, and contrast, thus ensuring that the tracking system can capture subtle deviations in real time and adjust the tracking direction and amplitude in a timely manner. Traditional solar tracking methods are often based only on position offset or simple brightness changes, while this comprehensive analysis method can provide more accurate deviation indices, ensuring high-precision solar tracking even under dynamic lighting conditions.

[0078] By meticulously preprocessing the solar spot imaging surface data, the data is first divided into several regions, and the pixel variance value of each region is calculated. Then, different filtering methods are used according to the different variance values ​​to effectively suppress noise. Unlike traditional fixed filtering methods, this adaptive filtering based on data variance can automatically select a suitable filtering method according to changes in image quality, further improving the data quality and providing a more reliable data source for subsequent spot contour recognition and deviation analysis.

[0079] By calculating the comprehensive deviation index of the solar spot contour area and comparing it with a preset deviation threshold, it can be determined whether equipment adjustment is needed. When the comprehensive deviation index exceeds the preset range, the system will identify the abnormal index and take corresponding adjustment measures until the deviation index returns to the normal range. This dynamic adjustment mechanism can optimize and adjust in real time according to different environmental conditions (such as changes in light intensity, equipment aging, etc.), avoiding misjudgments or delayed responses that may be caused by static threshold judgment, and ensuring that the solar tracking equipment can always maintain the best working condition.

[0080] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0081] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A solar tracking method based on spot contour detection, characterized in that, Includes the following steps: Real-time acquisition and preprocessing of solar spot imaging surface data from the receiving solar device; the solar spot imaging surface data includes the pixel value and position coordinates of each pixel in the aperture imaging surface. The preprocessed solar spot imaging surface data is identified, detected and analyzed to obtain solar spot contour region image data, which includes the pixel value and position coordinates of each solar spot pixel in the solar spot contour region image; By comprehensively analyzing the image data of the solar spot outline region, the following indices are obtained: spot position deviation index, spot shape distortion index, spot brightness uniformity index, and spot contrast uniformity index. A comprehensive analysis of the spot position deviation index, spot shape distortion index, spot brightness uniformity index, and spot contrast uniformity index in the solar spot outline region is performed to obtain the comprehensive spot deviation index in the solar spot outline region. The comprehensive deviation index of the solar spot outline region is compared with the preset deviation threshold range for analysis; When the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, the solar receiving device is adjusted and analyzed until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range.

2. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific steps for preprocessing the solar spot imaging surface data of the receiving solar light equipment are as follows: The solar spot imaging surface data of the receiving solar light device is divided into several solar imaging surface region data, and each solar imaging surface region data includes several pixels. The data for each solar imaging surface region are analyzed separately to obtain the pixel variance value for each solar imaging surface region. The pixel variance value of each solar imaging area is compared with a preset variance threshold range for analysis. If the pixel variance value is within the preset variance threshold range, then the corresponding solar imaging surface area will be processed by pixel retention, that is, the pixel value will be retained. If the pixel variance value is outside the preset variance threshold range and below the preset lower limit of the variance threshold range, then the corresponding solar imaging surface area will be subjected to Gaussian filtering. If the pixel variance value is outside the preset variance threshold range and higher than the upper limit of the preset variance threshold range, then the corresponding solar imaging area will be subjected to median filtering.

3. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific steps for identifying and analyzing the preprocessed solar spot imaging surface data to obtain the solar spot contour region image data are as follows: The data of each solar imaging surface region are comprehensively analyzed to obtain the pixel mean of each solar imaging surface region, and the pixel division threshold of each solar imaging surface region is marked. Binarization is performed on the data of each solar imaging surface region based on the pixel division threshold to obtain a binary image of each solar imaging surface region; Edge detection and contour extraction are performed on the binarized image of each solar imaging surface region to obtain the solar spot contour region image data.

4. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific formula for calculating the comprehensive deviation index of the solar spot outline region is as follows: Where ZpL is the comprehensive deviation index of the solar spot outline region, GpL is the position deviation index of the solar spot outline region, α1 is the deviation coefficient stored in the database, GsZ is the shape distortion index of the solar spot outline region, α2 is the distortion coefficient stored in the database, GjY is the brightness uniformity index of the solar spot outline region, α3 is the brightness uniformity coefficient stored in the database, GdB is the contrast uniformity index of the solar spot outline region, α4 ​​is the contrast uniformity coefficient stored in the database, α1+α2+α3+α4=1, and e is the natural constant.

5. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific steps for obtaining the deviation index of the solar spot position in the outline region are as follows: Read the x-coordinate, y-coordinate, and pixel value of each spot pixel in the solar spot outline region image, and perform comprehensive analysis to obtain the geometric center coordinates of the spot, including the center x-coordinate and center y-coordinate. The reference center coordinates of the solar spot outline region are obtained and combined with the geometric center coordinates of the solar spot to obtain the solar spot position deviation index of the solar spot outline region. The reference center coordinates of the solar spot include the reference center horizontal coordinate value and the reference center vertical coordinate value. The specific formulas for calculating the abscissa and ordinate values ​​of the geometric center of the light spot are as follows: Where x′ is the abscissa of the geometric center of the light spot, x i Let XsZ be the x-coordinate of the i-th pixel in the solar spot outline region image. i Let y' be the pixel value of the i-th spot pixel in the image of the sunspot outline region, and y' be the center ordinate value of the geometric center coordinates of the spot. i Let be the ordinate of the i-th spot pixel in the solar spot outline region image, i = 1, 2, 3, ..., i0, where i0 is the number of spot pixels in the solar spot outline region image.

6. The solar tracking method based on spot contour detection according to claim 5, characterized in that, The specific formula for calculating the deviation index of the sunspot position in the outline region is as follows: Wherein, GpL is the deviation index of the solar spot position in the outline region, x′ is the abscissa of the geometric center coordinate of the spot, x″ is the abscissa of the reference center coordinate of the spot, y′ is the ordinate of the geometric center coordinate of the spot, y″ is the ordinate of the reference center coordinate of the spot, and DjX is the diagonal length of the solar spot outline region.

7. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific steps for obtaining the shape distortion index of the solar spot outline region are as follows: Count the number of sunspot pixels in the sunspot outline region image and mark them as the area value of the sunspot outline region; The number of boundary spot pixels in the solar spot contour region image is counted and marked as the perimeter value of the solar spot contour region. By comprehensively analyzing the area and perimeter of the solar spot outline region, the shape distortion index of the solar spot outline region is obtained. The specific formula for calculating the shape distortion index of the solar spot outline region is as follows: Wherein, GsZ is the distortion index of the shape of the solar spot outline region, π is pi, MjZ is the area of ​​the solar spot outline region, and ZcZ is the perimeter of the solar spot outline region.

8. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific steps for obtaining the uniformity index of solar spot brightness in the outline region are as follows: For each spot pixel in the solar spot contour region image, identify the number of adjacent spot pixels in a set neighborhood; By combining the pixel value of each spot pixel in the solar spot contour region image with the pixel value of each adjacent spot pixel in the set neighborhood, the neighborhood mean value and neighborhood standard deviation value of the spot brightness of each spot pixel in the solar spot contour region image are obtained. By comprehensively analyzing the neighborhood mean and neighborhood standard deviation of the brightness of each spot pixel in the solar spot outline region image, the uniformity index of the brightness of the solar spot outline region is obtained. The specific formulas for calculating the neighborhood mean of the brightness of each spot pixel in the solar spot contour region image, the neighborhood standard deviation of the brightness of the spot, and the uniformity index of the brightness of the solar spot contour region are as follows: Among them, LjZ i Let XsZ be the neighborhood mean of the brightness of the i-th spot pixel in the solar spot contour region image. i Let XsZ be the pixel value of the i-th spot pixel in the solar spot contour region image. it LbC is the pixel value of the t-th neighboring pixel in the neighborhood of the i-th pixel in the solar spot outline region image. i Let LjY be the neighborhood standard deviation of the brightness of the i-th spot pixel in the solar spot outline region image, LjY be the uniformity index of the brightness of the solar spot outline region, i = 1, 2, 3, ..., i0, i0 is the number of spot pixels in the solar spot outline region image, and t = 1, 2, 3, ..., t0, t0 is the number of adjacent spot pixels in the defined neighborhood.

9. The solar tracking method based on spot contour detection according to claim 1, characterized in that, The specific steps for obtaining the uniformity index of the solar spot contrast in the outline region are as follows: By comparing and analyzing the pixel values ​​of each spot pixel in the solar spot contour image data, the maximum and minimum spot pixel values ​​in the solar spot contour image are obtained. By comprehensively analyzing the maximum and minimum spot pixel values ​​in the solar spot contour image, a comprehensive contrast index of the solar spot contour region is obtained. The solar spot outline image data is divided into several solar spot outline region image data, and each solar spot outline region image data includes several spot pixels. By comparing and analyzing the pixel values ​​of each spot pixel in the image data of each solar spot contour region, the maximum spot pixel value and the minimum spot pixel value in the region of each solar spot contour region are obtained. By comprehensively analyzing the maximum and minimum spot pixel values ​​in the segmented regions of each solar spot contour image, the contrast index of each segmented region is obtained. By comprehensively analyzing the overall contrast index of the solar spot outline region and the contrast index of each segmented region of the solar spot outline, the uniformity index of the solar spot contrast in the solar spot outline region is obtained: The specific formulas for calculating the comprehensive contrast index of the solar spot outline region, the contrast index of each solar spot outline region, and the uniformity index of the solar spot contrast in the solar spot outline region are as follows: Where ZdB is the overall contrast index of the sunspot outline region, XsZ′ Max XsZ′ represents the maximum pixel value of the sunspot in the solar spot contour image. Min QdB represents the minimum pixel value of the sunspot in the sunspot contour image. u The contrast index of the region segmentation for the u-th solar spot outline is given. The maximum pixel value of the sunspot in the segmented region of the image for the u-th sunspot contour is given. Let denot be the minimum pixel value of the segmented region in the image of the u-th solar spot contour segmentation region, GdB be the uniformity index of the solar spot contrast in the solar spot contour region, u = 1.2, 3, ..., u0, u0 be the number of solar spot contour segmentation region image data.

10. The solar tracking method based on spot contour detection according to claim 1, characterized in that, When the comprehensive deviation index of the solar spot outline region is outside the preset deviation threshold range, the solar receiving equipment is adjusted and analyzed until the comprehensive deviation index of the solar spot outline region is within the preset deviation threshold range. The steps are as follows: When the overall deviation index of the solar spot outline region is outside the preset deviation threshold range, identify the abnormal index in the overall deviation index of the solar spot outline region and take corresponding adjustment measures. The comprehensive deviation index of the adjusted solar spot contour region is analyzed and compared with the preset deviation threshold range. If the overall deviation index of the adjusted solar spot contour area is within the preset deviation threshold range, the adjustment ends. If the overall deviation index of the adjusted solar spot outline region is outside the preset deviation threshold range, the adjustment measures, the analysis of the overall deviation index of the adjusted solar spot outline region, and the judgment and analysis steps are repeated until the overall deviation index of the solar spot outline region is within the preset deviation threshold range.

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