Method for rapidly detecting and calculating dip angle of photovoltaic support based on aerial photography

Abstract

The invention relates to a photovoltaic support inclination angle rapid detection and calculation method based on aerial photography. The method is suitable for the technical field of construction site quality management. According to the technical scheme, the method comprises the steps of image acquisition: performing aerial photography by using an unmanned aerial vehicle to obtain construction area bracket images in batches; image pre-processing: pre-processing the image, including de-noising the image, enhancing the contrast ratio and the like; feature extraction: extracting feature points in the image; bracket positioning calculation: positioning the bracket according to the coordinates of the feature points, and calculating the geometrical shape and size of the bracket; calculating an inclination angle: calculating the inclination angle of the bracket according to the geometrical shape and the size of the bracket and the known bracket height, the shooting angle and the shooting height; bracket flatness detection: detecting the bracket flatness according to the shot picture and the laser radar scanning data, and calculating and comparing whether the mounting inclination angles of the brackets are in the same plane or not; and error analysis and correction: carrying out error analysis and correction on the calculation result to improve the calculation precision.

Description

Technical Field

[0001] The present invention relates to the field of construction site quality management, and in particular to a method for quickly detecting and calculating the inclination angle of a photovoltaic support based on aerial photography. Background Art

[0002] Photovoltaic power generation is an emerging renewable energy power generation technology with broad application prospects. In photovoltaic power generation systems, the inclination of photovoltaic brackets is one of the important factors affecting power generation efficiency. Traditional photovoltaic bracket inclination measurement methods mainly rely on manual measurement or the use of professional equipment, such as theodolites and total stations. These methods require a lot of manpower, material resources and time, and there are problems such as large measurement errors and complex operations.

[0003] The development of drone technology provides a new way to solve the above problems. Drones can be equipped with cameras, radars and other equipment to take high-precision photos of photovoltaic brackets, thereby obtaining information on the inclination angle of the brackets. However, the current drone aerial photography technology still has some shortcomings, such as complex image processing algorithms, low calculation accuracy, and being greatly affected by factors such as weather.

[0004] Therefore, it is necessary to propose a rapid detection and calculation method for the inclination of photovoltaic brackets based on aerial photography to improve the accuracy and efficiency of inclination calculation. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography. The algorithm is based on images taken by drones and radar scanning data. Through automation technology and image processing methods, it can quickly and accurately calculate the inclination angle of the photovoltaic plant installation bracket and detect its flatness, which is convenient for the project engineering department to detect the construction quality.

[0006] The objective of the present invention is achieved through the following technical solutions. The method for rapid detection and calculation of the inclination angle of a photovoltaic support based on aerial photography of the present invention comprises the following steps:

[0007] Step 1: Image acquisition: Use drones to take aerial photos to batch acquire images of supports in the construction area;

[0008] Step 2: Image preprocessing: Preprocess the image, including denoising, contrast enhancement, etc.

[0009] Step 3: Feature extraction: extract feature points in the image, such as the end points and inflection points of the stent;

[0010] Step 4: Bracket positioning calculation: Position the bracket according to the coordinates of the feature points, and calculate the geometric shape and size of the bracket;

[0011] Step 5: Inclination angle calculation: Calculate the inclination angle of the bracket according to the geometric shape and size of the bracket, as well as the known bracket height, shooting angle and shooting height;

[0012] Step 6: Bracket flatness detection: Detect the bracket flatness based on the photos and laser radar scanning data, and calculate and compare whether the installation inclination angles of the brackets are in the same plane;

[0013] Step 7: Error analysis and correction: Perform error analysis and correction on the calculation results to improve the calculation accuracy.

[0014] Furthermore, in step 1, aerial photography using a drone equipped with a high-precision camera can achieve comprehensive coverage of a large area. Before aerial photography, it is necessary to determine the altitude and flight attitude of the drone according to the size and characteristics of the photovoltaic support, and select a suitable shooting angle to ensure that the monitoring point can cover the area to be monitored.

[0015] In addition, according to the limitations of the construction terrain, the best monitoring points and navigation routes are formulated to maximize the efficiency and accuracy of image data collection. UAVs can quickly collect and transmit images, and obtain photos of photovoltaic brackets collected by drones. Compared with traditional manual or ground surveys, a large amount of image data can be obtained more quickly.

[0016] In step 2, the collected photovoltaic support image is preprocessed. The present invention uses fast bilateral filtering to denoise the image. When filtering, it can not only maintain edge information, but also has high processing efficiency and fast processing speed, meeting the requirements of real-time processing. The calculation formula of fast bilateral filtering is as follows:

[0017]

[0018] Among them, I p is the grayscale of point p = (x, y) in the original image I; W p is the normalization coefficient; I q is the gray value of point q = (u, v); q belongs to the neighborhood set S of the center pixel p; σ S and σ r are the standard deviations of the Gaussian functions, respectively; is the airspace weight; is the filter weight; BF p is the grayscale of point p in the filtering result image; σ r Parameters that adjust the filter function processing details; σ s The spatial range of the bilateral filter function is determined.

[0019] By adjusting image parameters such as brightness, contrast, and color saturation, the visual effects and details of the image can be improved and the image contrast can be enhanced.

[0020] And the edge detection algorithm is used to extract the boundary information in the image, which helps to segment the objects and features in the image for subsequent processing and analysis.

[0021] In step 3, Harris corner detection, a feature point detection algorithm in computer vision technology, is used on the data set processed in step 2 to further extract feature points, such as the end points and inflection points of the bracket. Based on the feature extraction algorithm, the exact position of each feature point in the image is determined, and its coordinate information is recorded.

[0022] In step 4, the geometric shape and size of the bracket are calculated according to the coordinates of the feature points:

[0023] First, the feature points are sorted and sorted in a certain order to determine the continuous boundary of the bracket and extract the boundary, that is, the line segment or curve connecting the feature points. Then the geometric feature calculation is performed. The length, width, angle, etc. of the bracket can be calculated based on the boundary. At the same time, the curvature calculation is performed. For the curved part of the photovoltaic bracket, the curvature can be calculated based on the curvature. According to the shape of the boundary, the shape characteristics of the bracket can be further analyzed.

[0024] In step 5, the bracket inclination angle is calculated. First, based on the known bracket height, shooting angle, shooting height, etc., trigonometric functions can be used to calculate and obtain the horizontal distance between the camera and the first support rod.

[0025] Then the horizontal distance between the two support rods is calculated, and the height difference between the two support rods is calculated based on the known support height and geometry, which can be determined based on the geometric model and dimensions of the support, such as the length, width, angle, etc. of the support.

[0026] Finally, the inclination angle of the bracket is calculated according to the height difference of the support rod and the horizontal distance between the two support brackets. The inverse trigonometric function is used, and the inclination angle = arctan (bracket height difference / horizontal distance). It should be noted that this method assumes that the projection of the bracket on the camera plane is a right triangle.

[0027] In step 6, calculations are performed based on the photos taken by the drone and the bracket angle data scanned by the lidar to calculate whether the installation inclination angle of each bracket is within the specified angle range. A scoring calculation can be performed based on the calculated data to make further adjustments based on the situation.

[0028] In step 7, the possible error sources in the inclination angle calculation are first analyzed and identified, including shooting scanning angle error, laser foot point positioning error, airborne platform vibration error, camera distortion, etc.

[0029] Model optimization: If the calculation results are inaccurate or affected by external factors, such as camera distortion, image distortion, etc., a more complex geometric model or more parameters can be used to calculate the bracket inclination.

[0030] Correction algorithm: Based on the results of error analysis, a correction algorithm suitable for error correction is developed. The correction algorithm can correct the calculation results according to the actual error situation to improve the accuracy and obtain more accurate inclination calculation results.

[0031] The beneficial effects of the present invention are:

[0032] 1. Efficient and accurate: The image data is obtained through drone aerial photography, and the bracket inclination angle is automatically calculated using image processing and analysis technology, which avoids the tediousness and errors of manual measurement and improves calculation efficiency and accuracy.

[0033] 2. All-weather operation: The drone can shoot in any weather conditions without waiting for good weather conditions, thus ensuring the continuity and reliability of inclination calculation.

[0034] 3. Large-scale application: This algorithm can be applied to large-scale photovoltaic power generation systems. Through drone aerial photography, the inclination angle of the entire system can be monitored and adjusted, which greatly improves the system's operational efficiency and economic benefits.

[0035] 4. Low cost: Compared with traditional manual measurement and monitoring methods, drone aerial photography and image processing technology have low costs, which greatly reduces the cost of bracket inclination calculation and monitoring.

[0036] 5. Intelligent adjustment: The algorithm can intelligently adjust the bracket inclination angle according to the calculation results, thereby improving the photovoltaic power generation efficiency and system stability, and further reducing the operating costs of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments are briefly introduced below.

[0038] Figure 1 This is a flow chart of the method for calculating the inclination angle of a photovoltaic support based on drone aerial photography of the present invention;

[0039] Figure 2 A schematic diagram of the drone of the present invention;

[0040] Figure 3This is the relationship between the coordinate error and the UAV flight height H.

[0041] Explanation of the accompanying drawings: 1-inclination angle; 2-horizontal distance x between two support poles; α-UAV shooting angle; β-UAV shooting angle; γ-inclination co-position angle; H-UAV shooting height; d1-horizontal distance between the camera and the first support pole; d2-horizontal distance between the camera and the second support pole; h-height difference of support poles; h1-vertical height of the first support pole; h2-vertical height of the first support pole; h3-bracket height. DETAILED DESCRIPTION

[0042] The following describes the implementation of the present invention through specific examples and in conjunction with the accompanying drawings. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific examples, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the viewpoint of the present invention.

[0043] like Figure 1 As shown, the present invention provides a method for quickly detecting and calculating the inclination angle of a photovoltaic support based on aerial photography, which can effectively improve the accuracy and efficiency of inclination angle calculation.

[0044] The method comprises the following steps:

[0045] Step 1: Use drone aerial photography to batch obtain construction area support images, including the following:

[0046] Using drones equipped with high-precision cameras for aerial photography can achieve comprehensive coverage of large areas. Before taking aerial photos, it is necessary to determine the altitude and flight attitude of the drone based on the size and characteristics of the photovoltaic bracket, and select a suitable shooting angle to ensure that the monitoring points can cover the area to be monitored.

[0047] In addition, according to the limitations of the construction terrain, the best monitoring points and navigation routes are formulated to maximize the efficiency and accuracy of image data collection. UAVs can quickly collect and transmit images, and obtain photos of photovoltaic brackets collected by drones. Compared with traditional manual or ground surveys, a large amount of image data can be obtained more quickly.

[0048] In one example, based on the building height, site size, and support density, the drone's flight altitude is calculated to be 20 meters, the camera angle is 30 degrees, and a picture is taken every 25 meters.

[0049] Step 2: Preprocess the collected photovoltaic support image. The present invention uses fast bilateral filtering to denoise the image. When filtering, it can not only maintain edge information, but also has high processing efficiency and fast processing speed, meeting the requirements of real-time processing. The calculation formula of fast bilateral filtering is as follows:

[0050]

[0051] Among them, I p is the grayscale of point p = (x, y) in the original image I; W p is the normalization coefficient; I q is the gray value of point q = (u, v); q belongs to the neighborhood set S of the center pixel p; σ S and σ r are the standard deviations of the Gaussian functions, respectively; is the airspace weight; is the filter weight; BF p is the grayscale of point p in the filtering result image; σ r Parameters that adjust the filter function processing details; σ s The spatial range of the bilateral filter function is determined.

[0052] By adjusting image parameters such as brightness, contrast, and color saturation, the visual effects and details of the image can be improved and the image contrast can be enhanced.

[0053] Through edge detection algorithms, boundary information in images is extracted, which helps to segment objects and features in the image for subsequent processing and analysis. Edge detection algorithms can detect edges and contours in images, which is very important for tasks such as image recognition, object tracking, and image segmentation.

[0054] Step 3: Based on the data set processed in step 2, Harris corner detection, a feature point detection algorithm in computer vision technology, can be used to further extract feature points in the image, such as the end points and inflection points of the bracket. These feature points are the key features of the photovoltaic bracket and can reflect the shape and position information of the bracket in the image.

[0055] The core of Harris corner detection is to define the corner response value R, and determine whether a pixel is a corner by determining the size of R. R is defined as:

[0056] R = det(M) - k(trace(M))2

[0057] Where, det(M) = λ1λ2, trace(M) = λ1+λ2. M is the covariance gradient of the gradient, λ1λ2 is the eigenvalue of M, and k is an empirical constant, usually ranging from 0.04 to 0.06.

[0058] Based on the feature extraction algorithm, the exact position of each feature point in the image can be determined and its coordinate information can be recorded. The coordinates of these feature points can be used for subsequent image processing and analysis. At the same time, by extracting the feature points in the image, the morphology and position information of the photovoltaic bracket in the image can be better represented, providing a more accurate and reliable data basis for subsequent image processing and analysis.

[0059] Step 4: Based on the coordinates of the feature points, the geometric shape and size of the bracket are calculated, including the following steps:

[0060] First, the feature points are sorted in a certain order to determine the continuous boundary of the bracket, and the boundary is extracted, that is, the line segment or curve connecting the feature points.

[0061] Then the geometric features are calculated. According to the boundary, the length, width, angle, etc. of the bracket can be calculated. Among them, pay attention to the bracket width (W):

[0062] For line segment boundaries,

[0063] W=max(d(i,i+1))

[0064] Among them, d(i,i+1) is the distance between adjacent feature points;

[0065] For curved boundaries,

[0066] W=max(d(p,C))

[0067] Among them, d(p,C) is the shortest distance from feature point p to curve C.

[0068] At the same time, the curvature calculation is performed. For the curved part of the photovoltaic bracket, the curvature degree can be calculated according to the curvature;

[0069] Finally, shape analysis is performed. According to the shape of the boundary, the shape characteristics of the stent can be further analyzed. Through the steps of feature point sorting, boundary extraction, geometric feature calculation, curvature calculation and shape analysis, it can provide the benefits of accurate, automated, consistent, quantitative analysis and curvature evaluation;

[0070] Step 5: Figure 2 As shown, first, based on the known bracket height h3, drone shooting angle β, the vertical height of the first support pole h1, and the drone shooting height H, the horizontal distance between the camera and the first support pole can be calculated using trigonometric functions:

[0071] d1=(H-h1-h3)×tanβ

[0072] Then, according to the shooting angle α of the drone, the shooting height H of the drone, the height h3 of the bracket and the vertical height h2 of the first support rod, the horizontal distance d2 between the camera and the second support rod is calculated, and then the horizontal distance x between the two support rods is calculated = the horizontal distance d2-d1 between the camera and the second support rod. Then, according to the known height and geometry of the bracket, the height difference h between the two support rods is calculated, which can be determined according to the geometric model and size of the bracket, such as the length, width, angle, etc. of the bracket.

[0073] Finally, according to the height difference h of the support rods and the horizontal distance x between the two support frames (shown by label 2), the inclination angle 1 of the support is calculated, and the inverse trigonometric function is used.

[0074] Inclination angle = arctan(h / x)

[0075] It should be noted that this method assumes that the projection of the bracket on the camera plane is a right triangle;

[0076] Step 6: The flatness test of the bracket includes the following steps:

[0077] The surface of the bracket is scanned by an airborne laser radar to obtain the three-dimensional coordinate information of each point of the bracket. Process the collected data: filter the point cloud data collected by the laser radar to remove noise and abnormal points to ensure data quality. The point cloud data is converted into a height model of the bracket surface, and interpolation and other methods can be used to obtain a smooth surface on the bracket surface. Then the height difference is calculated, the height model of the bracket surface is analyzed, and the height difference between the highest and lowest points of the bracket, as well as the height difference between other key points, is calculated. The collected images are processed and analyzed, and the bracket surface image data is obtained through the same processing method as above, and the corner detection algorithm is used to extract the bracket surface features. The optimal bracket inclination flatness score is set to 100 points, and the optimal bracket inclination is scored based on the data obtained from the laser radar and image acquisition.

[0078] After comprehensively evaluating the data obtained above, the flatness score of the bracket can be calculated using the following formula:

[0079] Flatness score = height difference score + image analysis score

[0080] Among them, the height difference score and image analysis score are evaluations based on the data obtained by different technologies. The flatness score obtained in this way can be used as an indicator to evaluate the flatness of the bracket. The higher the score, the better the flatness of the bracket, and the lower the score, the existence of flatness problems.

[0081] Step 7: Error analysis and correction of the calculation results include the following steps:

[0082] Firstly, the possible error sources in the inclination angle calculation are analyzed and identified, including shooting scanning angle error, laser foot point positioning error, airborne platform vibration error, camera distortion, etc.

[0083] Due to the design and installation of the scanning rotation axis inside the laser radar, there is a certain position deviation from the ideal state, so the actual starting angle of the shooting scan is not zero, and the angle data recorded by the system will have a certain error. It is the angle between the laser beam and the Z axis of the laser radar coordinate system. The deviation between the zero angle of the instantaneous scanning angle and the Z axis is called the index difference, which is represented by ε. Due to the existence of the index difference ε, the actual scanning angle is formed. Scanning angle error yes and The difference between the two, let n be the number of laser foot points on a scanning line, the ideal scanning angle of the i-th point Actual scanning angle and scanning angle error It can be expressed by the following formula:

[0084]

[0085] Where: is the actual scanning angle; is the ideal scanning angle; is the scanning angle error; ε is the index difference; n is the number of laser foot points on a scanning line; i refers to the laser foot point serial number.

[0086] The main factors affecting the laser foot point positioning error include the flight altitude of the drone and the vibration of the drone. When the flight altitude changes, the tilt distance from the sensor to the laser point will also change accordingly. Since the attitude angle error and the scanning angle error are multiplied by the tilt distance, the positioning error increases, so the coordinate error will increase with the increase of the flight altitude. The relationship between the coordinate error and the flight altitude of the drone is as follows: Figure 3 As shown, the results show that the error m along the scanning direction X Affected by the change of the UAV's flight altitude, the error m along the height direction Z It is less affected by the change of the flying altitude of the drone. In the actual flight test, due to air disturbance, the aircraft will deviate from the designed route with the change of flight speed and the vibration of attitude. We define the difference between the data obtained in the actual situation and the data obtained in the ideal situation as the motion error. In a certain time interval t, the plane moves forward, resulting in the motion error coefficient m r , given by the following formula:

[0087]

[0088] Where: Δx, Δy, Δz are the motion error components along the three coordinate axes.

[0089] Vibration is classified into periodic vibration, non-periodic vibration and random vibration according to the motion law of system vibration. If the time expression of the periodic vibration quantity x(t) is a sine or cosine function, then this type of vibration is called simple harmonic vibration. The typical motion equation of simple harmonic vibration can be expressed as:

[0090]

[0091] In the formula, A represents the amplitude, which indicates the maximum displacement during vibration; ω represents the circular frequency or angular frequency, ω=2πf; Indicates the initial phase. Simple harmonic vibration is the simplest periodic vibration. A periodic vibration can be expanded into a superposition of multiple simple harmonic vibrations using the harmonic analysis method. The common characteristics of periodic vibration and non-periodic vibration are: the system characteristics are certain, and the vibration amount is certain at any moment of vibration, so they both belong to deterministic vibration. However, there is no definite functional relationship between the vibration amount and time of random vibration, so it is unpredictable. After analyzing and identifying the error, correction is performed in the following way.

[0092] Model optimization: If the calculation results are inaccurate or affected by external factors, such as camera distortion, image distortion, etc., a more complex geometric model or more parameters can be used to calculate the bracket inclination angle;

[0093] Correction algorithm: Based on the results of error analysis, develop a correction algorithm suitable for error correction. The correction algorithm can correct the calculation results according to the actual error situation to improve the accuracy and obtain more accurate inclination calculation results. For example, use zero offset correction to correct the zero offset error in the measurement system, or use model parameter calibration to calibrate and optimize the parameters of the model to better fit the actual situation, thereby improving the prediction accuracy and reliability of the model.

[0094] The invention discloses a method for rapidly detecting and calculating the inclination of a photovoltaic support based on aerial photography. The method can rapidly and accurately calculate the inclination of the photovoltaic plant installation support based on the image taken by the drone and the radar scanning data, and detect its flatness through automation technology and image processing means, so as to facilitate the project engineering department to detect the construction quality. The method for rapidly detecting and calculating the inclination of a photovoltaic support based on aerial photography has the advantages of high automation, global perspective, high-precision measurement, time and cost saving, and improved safety. Drone aerial photography can quickly obtain large-area photovoltaic support image data, and automatically calculate the inclination with the help of image processing and computer vision technology. This method can provide a global perspective and a comprehensive understanding of the inclination distribution, which is more comprehensive than the traditional local measurement method. In addition, the drone is equipped with a high-precision camera to capture subtle features and achieve precise measurement; compared with traditional manual measurement, drone aerial photography saves time and cost, improves operation safety, and avoids the risk of high-altitude operation. In summary, the method for calculating the inclination of a photovoltaic support based on drone aerial photography can quickly, accurately and comprehensively provide inclination information, providing an important reference for the installation, adjustment and maintenance of the support.

[0095] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Any person skilled in the art may modify and alter the above embodiments without violating the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be as set forth in the claims.

Claims

1. A method for rapid detection and calculation of photovoltaic support inclination angle based on aerial photography. Features: The following steps are involved: Step 1: Image acquisition: Use drones to take aerial photos to batch acquire images of supports in the construction area; Step 2: Image preprocessing: Preprocess the image, including denoising and contrast enhancement. Step 3: Feature extraction: extract feature points from the image; Step 4: Bracket positioning calculation: Position the bracket according to the coordinates of the feature points, and calculate the geometric shape and size of the bracket; Step 5: Inclination angle calculation: Calculate the inclination angle of the bracket according to the geometric shape and size of the bracket, as well as the known bracket height, shooting angle and shooting height; Step 6: Bracket flatness detection: Detect the bracket flatness based on the photos and laser radar scanning data, and calculate and compare whether the installation inclination angles of the brackets are in the same plane; Step 7: Error analysis and correction: Perform error analysis and correction on the calculation results to improve the calculation accuracy.

2. According to the method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography according to claim 1, Features: In step 1, aerial photography is performed using a drone equipped with a high-precision camera to achieve comprehensive coverage of a large area. Before aerial photography, it is necessary to determine the altitude and flight attitude of the drone based on the size and characteristics of the photovoltaic bracket, and select a suitable shooting angle to ensure that the monitoring points can cover the area to be monitored.

3. According to the method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography according to claim 2, Features: In step 1, the best monitoring points and navigation routes are determined according to the restrictions of the construction terrain.

4. According to the method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography according to claim 1, Features: In step 2, the collected stent image is preprocessed, and the image is denoised using fast bilateral filtering. The fast bilateral filtering calculation formula is as follows: Among them, I p is the grayscale of point p = (x, y) in the original image I; W p is the normalization coefficient; I q is the gray value of point q = (u, v); q belongs to the neighborhood set s of the center pixel p; σ S and σ r are the standard deviations of the Gaussian functions, respectively; is the airspace weight; is the filter weight; BF p is the grayscale of point p in the filtering result image; σ r Parameters that adjust the filter function processing details; σ s The spatial range of the bilateral filter function is determined.

5. According to the method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography according to claim 4, Features: In the preprocessing of the stent image, the image brightness, contrast and color saturation related parameters are adjusted to improve the visual effect and details of the image and enhance the image contrast; and the edge detection algorithm is used to extract the boundary information in the image, which helps to segment the objects and features in the image.

6. According to the method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography according to claim 1, Features: In step 3, feature points are further extracted on the data set processed in step 2 using a feature point detection algorithm in computer vision technology; Based on the feature extraction algorithm, the exact position of each feature point in the image is determined and its coordinate information is recorded.

7. According to claim 1, the method for rapid detection and calculation of the inclination angle of a photovoltaic support based on aerial photography, Features: In step 4, the geometric shape and size of the bracket are calculated according to the coordinates of the feature points: First, the feature points are sorted in a certain order to determine the continuous boundary of the bracket, and the boundary is extracted, that is, the line segment or curve connecting the feature points; then the geometric features are calculated, and the length, width, and angle of the bracket are calculated according to the boundary. At the same time, the curvature is calculated. For the curved part of the photovoltaic bracket, the degree of curvature is calculated according to the curvature; according to the shape of the boundary, the shape characteristics of the bracket are further analyzed.

8. According to the method for rapid detection and calculation of the inclination angle of photovoltaic brackets based on aerial photography according to claim 1, Features: The calculation of the bracket inclination angle in step 5 includes the following steps: First, according to the known bracket height, shooting angle and shooting height, trigonometric functions are used to calculate the horizontal distance between the camera and the first support rod; Then find the horizontal distance between the two support rods, and calculate the height difference between the two support rods based on the known support height and geometry; Finally, the inclination angle of the bracket is calculated based on the height difference of the support rods and the horizontal distance between the two support frames.

9. The method for rapid detection and calculation of the inclination angle of a photovoltaic support based on aerial photography according to claim 1, Features: In step 6, calculations are performed based on the photos taken by the drone and the bracket angle data scanned by the lidar to compare and calculate whether the installation inclination angle of each bracket is within the specified angle range, and scoring is performed based on the calculated data to make further adjustments based on the situation.

10. The method for rapid detection and calculation of the inclination angle of a photovoltaic support based on aerial photography according to claim 1, Features: The step 7 specifically includes: Firstly, the possible error sources in the inclination angle calculation are analyzed and identified, including the shooting scanning angle error, the laser foot point positioning error, the airborne platform vibration error, and the camera distortion; Model optimization: If the calculation results are inaccurate or affected by external factors, a more complex geometric model or more parameters are used to calculate the bracket inclination angle; Correction algorithm: Based on the results of error analysis, the correction algorithm modifies the calculation results according to the actual error situation to obtain more accurate inclination calculation results.

Images