Method and system for judging light spot quality of tower type mirror field

Abstract

The invention belongs to the technical field of heliostat control, and particularly relates to a tower type heliostat field light spot quality judgment method and system, and the method comprises the following steps: S1, image preprocessing; s2, detecting the edge of the light spot based on an improved edge detection algorithm; and S3, carrying out region communication on the light spot edge, and solving the light spot area. Filtering in a Canny operator is optimized, a self-adaptive filtering operator and a filter can evaluate discontinuity between gray values of pixels, the higher the discontinuity is, the smaller the weight of the filtering operator at the point is, otherwise, the smaller the discontinuity of the gray values is, the larger the weight in the filter is, and the accuracy of data is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of heliostat control, and specifically relates to a method and system for judging the quality of a light spot in a tower mirror field. Background Art

[0002] In the process of solar thermal power generation, the reflection and focusing of sunlight are key links. However, due to factors such as the angle and intensity of sunlight, as well as the shape and material of the reflective surface, light spots may appear on the reflective surface, that is, areas with abnormal local light intensity. These light spots not only affect the efficiency of solar energy collection and conversion, but may also damage the reflective surface. The existing technology is not flexible in determining the threshold of edge computing and has large errors. Summary of the invention

[0003] The present invention has improved the accuracy of light spot detection, proposed an adaptive threshold algorithm, and improved the accuracy of light spot recognition. Its technical solution is: A method for judging the quality of a light spot in a tower mirror field comprises the following steps: S1. Image preprocessing; S2. Detect the edge of the light spot based on the improved edge detection algorithm; S3. Connect the edges of the light spot to obtain the area of ​​the light spot.

[0004] Preferably, in step S1, image preprocessing includes filtering and edge enhancement; wherein: Filtering: Smooth the image by calculating the average of the pixels around the pixel, filtering by the mean; Edge enhancement: Convolve the image with a sharpening filter to enhance the edges of the image.

[0005] In step S2, an adaptive filtering operator is added. The specific steps are as follows: S21. Image grayscale, the formula is as follows: ; Represents the pixel value after grayscale conversion. , and They are the pixel values ​​on the red, green, and blue channels of the corresponding pixels of the original image respectively; S22. Perform Gaussian filtering on the preprocessed image. The formula is as follows: ; Calculate the weights of the image: ; ; Adaptive filtering operator, the formula is as follows: ; Where (x, y) is the relative coordinate of the pixel, indicating the distance between the point and the center point; f(x, y) is the Gaussian function; σ is the standard deviation of the Gaussian distribution; exp is the exponential function; G(x,y) represents the improved Gaussian function; h is the edge amplitude coefficient; N is the number of pixels; d (x,y) is the integrated gradient, w (x,y) is the calculated weight; S23. Sobel operator formula: ; ; G x and G y Represent the gradient values ​​in the horizontal and vertical directions respectively, and * represents the convolution symbol; S24. Calculate the gradient magnitude and direction: ; ; M(x,y) Indicates the gradient strength of the pixel; θ(x,y) Indicates the gradient direction of the pixel; S25. Use non-maximum suppression to find the local maximum, thereby eliminating most non-edge points. The non-maximum suppression formula is as follows: ; M L is the low threshold of gradient strength, and They are the offset values ​​of the gradient in the x and y directions respectively; S26. Double threshold algorithm detection, the formula is as follows: ; in M H is the high threshold of gradient strength, Strong, Weak, and Non-edge represent strong edge, weak edge, and non-edge, respectively.

[0006] Preferably, in step 26, the hysteresis threshold used by the CANNY algorithm includes a high threshold and a low threshold, and an adaptive threshold function is constructed to solve M L and M H , the calculation formula is as follows: ; ; T is the total number of feature points.

[0007] Preferably, in step S3, the intersection of edge lines is searched in the parameter space to achieve edge connection and detection, and the specific steps are as follows: The gradient strength calculated for the image M(x,y) and the gradient direction θ , for the edge points that have been obtained (x,y) , along the gradient direction θ Draw a line, the intersection of this line and the circle is the center of the circle (a,b) , for each center (a,b) , calculate the distance r from all edge points to the center of the circle, and use r as the radius for voting. The distance value with the highest frequency is the radius of the center of the circle, that is: ; A ring-shaped area is formed by connecting the center and the radius.

[0008] Preferably, in step S3, the area of ​​the annular region is solved, that is, the spot area. If the spot area exceeds a set threshold, the result is fed back to the mirror field control system, and the heliostat is defocused in the abnormal area.

[0009] A tower mirror field spot quality judgment system, comprising a data acquisition module, a data processing module, a mirror field control module, and an output module; The data acquisition module uses a camera and a sensor to acquire data information; The data processing module detects the edge of the light spot using an edge detection algorithm, connects the light spot edge to a region, and calculates the area of ​​the light spot; The mirror field control module is used to defocus the heliostats in abnormal areas; The output module is used to output the results visually.

[0010] Compared with the prior art, the present invention has the following beneficial effects: Using an adaptive filtering operator, the filter will evaluate the discontinuity between the grayscale values ​​of each pixel. The stronger the discontinuity, the smaller the weight of the filter operator at that point. Conversely, the smaller the discontinuity of the grayscale value, the greater the weight in the filter.

[0011] CANNY The hysteresis threshold used in the algorithm includes a high threshold and a low threshold. Both thresholds need to be set based on experience, which reduces the accuracy. This application improves the threshold acquisition and uses the new threshold to detect the entire image to obtain an accurate spot shape.

[0012] The annular area method is used to determine whether the light spot is abnormal. The data is more objective than the traditional estimation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is the flow chart of this application.

[0014] Figure 2 Schematic diagram of the spot area (the left side and bottom are length in M, and the right side is energy flux density in W / m^2). DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of the present application.

[0016] A method for judging the quality of a light spot in a tower mirror field comprises the following steps: S1. Image preprocessing; Image preprocessing includes filtering and edge enhancement; among them: Filtering: Smooth the image by calculating the average of the pixels around the pixel, filtering by the mean; Edge enhancement: Convolve the image with a sharpening filter to enhance the edges of the image.

[0017] S2. Detect the edge of the light spot based on the improved edge detection algorithm; In step S2, an adaptive filtering operator is added. The specific steps are as follows: S21. Image grayscale, the formula is as follows: ; Represents the pixel value after grayscale conversion. , and They are the pixel values ​​on the red, green, and blue channels of the corresponding pixels in the original image, respectively.

[0018] S22. Perform Gaussian filtering on the preprocessed image. The formula is as follows: ; Calculate the weights of the image: ; ; Adaptive filtering operator, the formula is as follows: ; In the formula, (x,y) is the relative coordinate of the pixel, indicating the distance between the point and the center point; f(x,y) is a Gaussian function; σ is the standard deviation of the Gaussian distribution; exp is an exponential function; G(x,y) represents the improved Gaussian function; h is the edge amplitude coefficient; N is the number of pixels; d (x,y) is the comprehensive gradient, including the gradient in the x direction and the gradient in the y direction. w (x,y) is the calculated weight.

[0019] S23.Sobel operator formula: ; ; Gx and Gy represent the gradient values ​​in the horizontal and vertical directions respectively, and * represents the convolution symbol.

[0020] S24. Calculate the gradient magnitude and direction: ; ; M(x,y) represents the gradient strength of the pixel; θ(x,y) represents the gradient direction of the pixel.

[0021] S25. In the obtained amplitude image, there may be multiple large amplitudes close to each other, but there is only one real edge point. By using non-maximum suppression, the local maximum value can be found, so that most non-edge points can be eliminated. The non-maximum suppression formula is as follows: ; M L is the low threshold of gradient strength, M H A high threshold for gradient strength, and They are the offset values ​​of the gradient in the x and y directions respectively.

[0022] S26. Double threshold algorithm detection, the formula is as follows: ; Among them, Strong, Weak and Non-edge represent strong edge, weak edge and non-edge respectively.

[0023] The hysteresis threshold used by the CANNY algorithm includes a high threshold and a low threshold. The image is processed using the Shi-Tomasl feature detection algorithm to obtain the feature point set K of the image. i, because the feature points are often located at the edge of the light spot, and these parts are exactly what need to be processed. Therefore, the gradient strength M(x,y) of these points is calculated first. For all the feature point gradient strengths M(x,y) that have been suppressed by non-maximum values, they are arranged from large to small, and then solved according to the constructed adaptive threshold function. M L and M H , the calculation formula is as follows: ; ; T is the total number of feature points, i and j are the labels of the convolution kernel grid. For example, if the convolution kernel size is 3, then i is from -1 to 1.

[0024] S3. Connect the edges of the light spot to obtain the area of ​​the light spot.

[0025] Find the intersection of edge lines in the parameter space to achieve edge connection and detection; the specific steps are as follows: The gradient strength calculated for the image M(x,y) and the gradient direction θ , for the edge points that have been obtained (x,y) , draw a line along the gradient direction θ, and the intersection of this line and the circle is the center of the circle (a,b) , for each center (a,b) , calculate the distance r from all edge points to the center of the circle, and use r as the radius for voting. The distance value with the highest frequency is the radius of the center of the circle, that is: ; A ring-shaped area is formed by connecting the center and the radius.

[0026] The area of ​​the annular region is calculated as the spot area. If the spot area exceeds the set threshold, the result is fed back to the mirror field control system to defocus the heliostat in the abnormal area.

[0027] A tower mirror field spot quality judgment system, comprising a data acquisition module, a data processing module, a mirror field control module, and an output module; The data acquisition module uses a camera and a sensor to acquire data information; The data processing module detects the edge of the light spot using an edge detection algorithm, connects the light spot edge to a region, and calculates the area of ​​the light spot; The mirror field control module is used to defocus the heliostats in abnormal areas; The output module is used to output the results visually.

[0028] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for judging the quality of a light spot in a tower mirror field, characterized in that: The following steps are involved: S1. Image preprocessing; S2. Detect the edge of the light spot based on the improved edge detection algorithm; S3. Connect the edges of the light spot to obtain the area of ​​the light spot.

2. The method for judging the quality of the light spot of the tower mirror field according to claim 1, characterized in that: In step S1, image preprocessing includes filtering and edge enhancement; wherein: Filtering: Smooth the image by calculating the average of the pixels around the pixel, filtering by the mean; Edge enhancement: Convolve the image with a sharpening filter to enhance the edges of the image.

3. The method for judging the quality of the light spot of the tower mirror field according to claim 1, characterized in that: In step S2, an adaptive filtering operator is added. The specific steps are as follows: S21. Image grayscale, the formula is as follows: ； Represents the pixel value after grayscale conversion. , and They are the pixel values ​​on the red, green, and blue channels of the corresponding pixels of the original image respectively; S22. Perform Gaussian filtering on the preprocessed image. The formula is as follows: ； Calculate the weights of the image: ； ； Adaptive filtering operator, the formula is as follows: ； Where (x, y) is the relative coordinate of the pixel, indicating the distance between the point and the center point; f(x, y) is the Gaussian function; σ is the standard deviation of the Gaussian distribution; exp is the exponential function; G(x,y) represents the improved Gaussian function; h is the edge amplitude coefficient; N is the number of pixels; d (x,y) is the integrated gradient, w (x,y) is the calculated weight; S23. Sobel operator formula: ； ； G x and G y Represent the gradient values ​​in the horizontal and vertical directions respectively, and * represents the convolution symbol; S24. Calculate the gradient magnitude and direction: ； ； M(x,y) Indicates the gradient strength of the pixel; θ(x,y) Indicates the gradient direction of the pixel; S25. Use non-maximum suppression to find the local maximum, thereby eliminating most non-edge points. The non-maximum suppression formula is as follows: ； M L is the low threshold of gradient strength, and They are the offset values ​​of the gradient in the x and y directions respectively; S26. Double threshold algorithm detection, the formula is as follows: ； in M H is the high threshold of gradient strength, Strong, Weak, and Non-edge represent strong edge, weak edge, and non-edge, respectively.

4. The method for judging the quality of the light spot of the tower mirror field according to claim 3 is characterized in that: In step 26, the hysteresis threshold used by the CANNY algorithm, including the high threshold and the low threshold, constructs an adaptive threshold function and solves M L and M H , the calculation formula is as follows: ； ； T is the total number of feature points.

5. The method for judging the quality of the light spot of the tower mirror field according to claim 1, characterized in that: In step S3, the intersection of edge lines is found in the parameter space to achieve edge connection and detection. The specific steps are as follows: The gradient strength calculated for the image M(x,y) and the gradient direction θ , for the edge points that have been obtained (x,y) , along the gradient direction θ Draw a line, the intersection of this line and the circle is the center of the circle (a,b) , for each center (a, b) , calculate the distance r from all edge points to the center of the circle, and use r as the radius for voting. The distance value with the highest frequency is the radius of the center of the circle, that is: ； A ring-shaped area is formed by connecting the center and the radius.

6. The method for judging the quality of the light spot of the tower mirror field according to claim 1, characterized in that: In step S3, the area of ​​the annular region is solved, that is, the spot area. If the spot area exceeds a set threshold, the result is fed back to the mirror field control system, and the heliostat is defocused in the abnormal area.

7. A tower mirror field spot quality judgment system, using the tower mirror field spot quality judgment method according to any one of claims 1 to 6, characterized in that: It includes data acquisition module, data processing module, mirror field control module and output module; The data acquisition module uses a camera and a sensor to acquire data information; The data processing module detects the edge of the light spot using an edge detection algorithm, connects the light spot edge to a region, and calculates the area of ​​the light spot; The mirror field control module is used to defocus the heliostats in abnormal areas; The output module is used to output the results visually.

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