Mountain terrain analysis and photovoltaic optimization layout method based on UAV images

Abstract

The present invention relates to the field of terrain analysis technology, and specifically to a mountain terrain analysis and photovoltaic optimization layout method based on drone images, including using a drone to take aerial photos of a target mountain, obtaining terrain image data, importing the image data into a three-dimensional modeling software, generating a three-dimensional terrain model, performing slope, aspect and shadow analysis based on the three-dimensional terrain model, determining the best layout area for photovoltaic components, combining satellite image data, identifying and eliminating areas that are not suitable for development, designing a layout plan for photovoltaic components, and simulating and verifying them in the three-dimensional model. In the project acceptance stage, the drone is used again for data collection to generate a three-dimensional point cloud model of the actual construction situation, and based on the actual three-dimensional point cloud model, the inclination, azimuth and shadow obstruction of the photovoltaic components are analyzed to evaluate the power generation loss. The present invention optimizes the layout plan of the photovoltaic system, improves the power generation efficiency, and provides an efficient quality acceptance means.

Description

Technical Field

[0001] The present invention relates to the technical field of terrain analysis, and in particular to a method for analyzing mountain terrain and optimizing photovoltaic layout based on unmanned aerial vehicle images. Background Art

[0002] With the continuous growth of global energy demand and the enhancement of environmental protection awareness, photovoltaic power generation systems have been widely used around the world. Especially in mountainous areas, the construction of photovoltaic power generation systems using abundant sunlight resources can not only effectively utilize land resources, but also increase the proportion of renewable energy. However, the mountainous terrain is complex and changeable, with steep slopes, changing slope directions, and various terrain obstacles such as gullies, seasonal rivers, and surface vegetation. These factors have brought great challenges to the layout design and construction of photovoltaic modules. In order to achieve efficient photovoltaic power generation, it is necessary to accurately analyze the terrain in the early stage of design, and conduct detailed evaluations in the construction and acceptance stages to ensure the accuracy of the design and the feasibility of implementation.

[0003] Existing technologies mainly rely on on-site surveys and traditional topographic map data when arranging photovoltaic modules in complex mountainous terrain. These methods lack a complete understanding of the actual terrain on site and centimeter-level accuracy, resulting in a mismatch between the design plan and the actual situation. A large number of adjustments and changes are required during construction, affecting the economy and efficiency of the project. In addition, existing photovoltaic design software usually only considers slope and aspect, ignoring seasonal terrain and unusable land in micro-topography, and cannot accurately identify areas such as gullies and seasonal rivers that are not suitable for development. During the project acceptance stage, the method of relying on manpower for quality assessment is inefficient and it is difficult to quickly and accurately evaluate the three-dimensional information of the photovoltaic field, such as the inclination, azimuth and shadow obstruction of the module. Summary of the invention

[0004] Based on the above objectives, the present invention provides a mountain terrain analysis and photovoltaic optimization layout method based on drone images.

[0005] The mountain terrain analysis and photovoltaic optimization layout method based on UAV images includes the following steps:

[0006] S1: Use a drone to take aerial photos of the target mountain and obtain high-resolution terrain image data;

[0007] S2: Import the acquired image data into 3D modeling software to generate a 3D terrain model of the mountain;

[0008] S3: Slope, aspect and shadow analysis based on the 3D terrain model to determine the best layout area for PV panels;

[0009] S4: Combine satellite imagery data to identify and remove areas that are not suitable for development, including gullies and seasonal rivers;

[0010] S5: Design the layout of photovoltaic modules and simulate and verify them in a three-dimensional model;

[0011] S6: During the project acceptance stage, drones are used again to collect data and generate a 3D point cloud model of the actual construction situation;

[0012] S7: Based on the actual 3D point cloud model, analyze the inclination, azimuth and shadowing of the photovoltaic modules and evaluate the power generation loss.

[0013] Optionally, in step S1, the drone is equipped with a high-resolution aerial camera to perform multi-angle and multi-altitude aerial photography to obtain high-resolution terrain image data, specifically including:

[0014] S21: Preset the drone’s aerial photography path based on the range and terrain characteristics of the target mountain to ensure coverage of the target area;

[0015] S22: The drone takes multiple shots at different heights and angles to obtain terrain images from multiple perspectives;

[0016] S23: Use image stitching software to stitch multiple images into a complete high-resolution terrain image.

[0017] Optionally, the S2 further comprises:

[0018] S21: Import the terrain image data collected by the drone into Pix4D 3D modeling software;

[0019] S22: Using the multi-view stereo matching algorithm of the 3D modeling software, the image data is stitched into a complete 3D terrain model;

[0020] S23: perform precision calibration on the three-dimensional terrain model, and use known ground control points to calibrate the generated three-dimensional model to ensure the geographic accuracy of the model.

[0021] Optionally, the S3 further includes:

[0022] S31: importing the generated three-dimensional terrain model into ArCGIS (geographic information system) software;

[0023] S32: Use the Slope tool in ArcGIS software to calculate the slope of the terrain and output a slope map showing the slope values ​​of each point on the terrain. The slope refers to the inclination of the terrain surface, expressed in degrees or percentage;

[0024] S33: Use the Aspect tool in ArcGIS software to calculate the slope aspect of the terrain and output an aspect map showing the slope value of each point on the terrain. The slope aspect refers to the direction in which the terrain surface faces, expressed as an azimuth.

[0025] S34: In ArcGIS software, set the sun path parameters, including the sun's altitude angle and azimuth. These parameters are determined according to the geographic location and time, and can also be obtained using the sun path calculation tool. Use the Hillshade tool in ArcGIS software to simulate the shadow distribution at different time periods. The tool calculates the shadow distribution of the terrain surface at a certain moment according to the sun path parameters and generates multiple shadow maps, which respectively represent the shadow distribution at different time periods of the day.

[0026] S35: Combine shadow maps of multiple time periods to analyze the shadow distribution throughout the year or in a specific season, mark areas with long shadow coverage and large shadow impact, and avoid arranging PV modules in these areas;

[0027] S36: Combine the slope map and the aspect map to identify areas suitable for PV panel layout;

[0028] S37: The slope map, aspect map and shadow map are superimposed together to comprehensively analyze the suitability of each area, and the areas with moderate slope, facing the sun and less shadow are marked as the best layout areas for photovoltaic modules.

[0029] Optionally, the S4 further includes:

[0030] S41: Download satellite image data of the target area from satellite image data providers or public data platforms to ensure that the images cover all seasons of the year and obtain comprehensive terrain change information;

[0031] S42: Perform radiation correction, atmospheric correction and geometric correction on the acquired satellite image data;

[0032] S43: Compare satellite images from different periods and identify seasonally changing terrain, including gullies and seasonal rivers, using a change detection algorithm that uses the Normalized Difference Vegetation Index method;

[0033] S44: Combine digital elevation model data to conduct water flow path analysis. Import the digital elevation model data into ArcGIS software and use the Flow Direction tool in ArcGIS software to generate a water flow path map. The digital elevation model data can be obtained through the geographic information public service platform or commercial data providers;

[0034] S45: Use the Flow Accumulation tool in ArcGIS software to input the water flow path map and generate a flow accumulation map;

[0035] S46: Combine slope map, aspect map and flow accumulation map to identify and eliminate areas that are not suitable for development.

[0036] Optionally, the arrangement scheme of photovoltaic modules is designed in S5, specifically including:

[0037] S51: Select the optimal photovoltaic module arrangement method according to the results of slope and aspect analysis;

[0038] S52: Set the inclination and azimuth of the photovoltaic module: According to the local solar radiation conditions, set the optimal inclination and azimuth of the photovoltaic module. The inclination should be as close to the local latitude as possible, and the azimuth should be towards due south (in the northern hemisphere) or due north (in the southern hemisphere);

[0039] S53: Determine the distance between the front and rear rows of photovoltaic modules to ensure that there is no shading between the modules;

[0040] S54: Taking into account terrain, shading and construction feasibility, further optimize the layout of photovoltaic modules to maximize the power generation efficiency of the photovoltaic system.

[0041] Optionally, the simulation and verification in the three-dimensional model in S5 specifically includes:

[0042] S55: importing the generated three-dimensional terrain model into Helioscope photovoltaic design software;

[0043] S56: Arrange the photovoltaic modules in the three-dimensional terrain model according to the designed layout plan, ensuring that the inclination, azimuth and spacing of each module meet the design requirements;

[0044] S57: Use the simulation function of the photovoltaic design software to simulate the power generation performance of the photovoltaic system, taking into account factors such as solar radiation, shadow effects, component efficiency, etc., and generate sunshine analysis diagrams, shadow analysis diagrams and power generation prediction diagrams;

[0045] S58: Analyze the simulation results, verify the feasibility and optimization degree of the design solution, and adjust the layout of photovoltaic modules according to the simulation results to ensure that the power generation efficiency reaches the expected level.

[0046] Optionally, the S6 further includes:

[0047] S 61: Plan the flight route of the drone according to the size and shape of the project area, ensure that the entire PV site is covered, and the flight route has sufficient overlap (usually 60-80%) to ensure the integrity and accuracy of the generated 3D point cloud model, set the flight altitude, speed and shooting frequency of the aerial camera. The flight altitude should be moderate to ensure the resolution and coverage of the image. The flight speed and shooting frequency should be adjusted according to the performance of the drone and camera. Perform the flight mission to collect high-resolution images of the project area. Ensure that the drone flies stably during data collection to avoid data loss or blurring;

[0048] S62: Import the images collected by the drone into Pix4D 3D modeling software;

[0049] S63: Use the software's automatic matching and alignment functions to stitch multiple images together, identify feature points in the images through a multi-view stereo matching algorithm, and align the feature points to generate a sparse point cloud;

[0050] S64: Pix4D will use the dense point cloud generation function based on the sparse point cloud to calculate the 3D coordinates of each pixel in the image and generate a 3D point cloud model;

[0051] S65: Use known ground control points for precision calibration, import the location data of the ground control points into the software, and mark these points in the image to improve the geographic accuracy of the model;

[0052] S66: Use the Point Cloud Editor tool in Pix4D to remove noise and erroneous points and smooth and optimize the point cloud data.

[0053] Optionally, the S7 further includes:

[0054] S71: Import the 3D point cloud model generated in S6 into the 3D point cloud processing software CloudCompare, and use the Segment tool to manually select and extract the point cloud data of the photovoltaic module. The area can be extracted by the rectangle selection or polygon selection tool;

[0055] S72: Select the extracted photovoltaic module point cloud, generate a best fit plane through CloudCompare, and provide normal vector information (A, B, C);

[0056] S73: Calculating the inclination angle of the photovoltaic module according to the normal vector of the fitting plane;

[0057] S74: Calculate the azimuth angle of the photovoltaic module according to the normal vector of the fitting plane;

[0058] S75: In CloudCompare, the point cloud data is converted into a digital surface model to accurately represent the height information of the terrain and the photovoltaic module. The digital surface model is imported into the ArcGIS software, and the sun path model (including the sun altitude angle and azimuth) is set. The shadow distribution is simulated at different time periods of the day, and the shadow situation in each time period is calculated using the ray casting method. A shadow occlusion map is generated, and the shadow occlusion situation of each photovoltaic module in different time periods is marked. The cumulative time and area of ​​the shadow occlusion are calculated;

[0059] S76: Calculate the shading coefficient of each photovoltaic module according to the shadow shading map, where the shading coefficient represents the proportion of the module being shaded, ranging from 0 to 1;

[0060] S77: Calculate the power generation loss of photovoltaic modules using the shading coefficient;

[0061] S78: Summarize the power generation losses of all PV modules and evaluate the total power generation losses of the entire PV system.

[0062] Beneficial effects of the present invention:

[0063] The present invention obtains high-resolution terrain image data through drone aerial photography, and uses 3D modeling software to generate an accurate 3D terrain model. On this basis, combined with slope, aspect and shadow analysis, the area suitable for the layout of photovoltaic modules can be accurately identified. With the assistance of satellite image data, areas that are not suitable for development, such as gullies and seasonal rivers, are further eliminated to ensure that the design scheme is highly matched with the actual terrain. Compared with the traditional method that relies on on-site investigation and topographic map data, this method significantly improves the accuracy and feasibility of photovoltaic module layout design, reduces adjustments and changes during construction, and improves the economic benefits and implementation efficiency of the overall project.

[0064] The present invention can optimize the arrangement of photovoltaic modules, set the optimal inclination and azimuth, and calculate the distance between the front and rear rows to ensure the maximum power generation efficiency of the photovoltaic system by performing detailed analysis and simulation of the three-dimensional terrain model. During the project acceptance stage, drones are used again for data collection to generate a three-dimensional point cloud model of the actual construction situation. Based on the model, the inclination, azimuth and shadow occlusion of the photovoltaic modules are analyzed to evaluate the power generation loss. Through accurate shadow simulation and occlusion analysis, problems in actual construction can be identified and solved, the layout of the photovoltaic system can be further optimized, and the power generation efficiency and reliability of the overall system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 A schematic diagram of the method steps of an embodiment of the present invention;

[0067] Figure 2 Schematic diagram of step S3 of an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0069] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0070] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0071] like Figure 1-Figure 2 As shown, the mountain terrain analysis and photovoltaic optimization layout method based on drone images includes the following steps:

[0072] S1: Use a drone to take aerial photos of the target mountain and obtain high-resolution terrain image data;

[0073] S2: Import the acquired image data into 3D modeling software to generate a 3D terrain model of the mountain;

[0074] S3: Slope, aspect and shadow analysis based on the 3D terrain model to determine the best layout area for PV panels;

[0075] S4: Combine satellite imagery data to identify and remove areas that are not suitable for development, including gullies and seasonal rivers;

[0076] S5: Design the layout of photovoltaic modules and simulate and verify them in a three-dimensional model;

[0077] S6: During the project acceptance phase, drones are used again to collect data and generate a 3D point cloud model of the actual construction situation;

[0078] S7: Based on the actual 3D point cloud model, analyze the inclination, azimuth and shadowing of the photovoltaic modules and evaluate the power generation loss.

[0079] In step S1, the drone is equipped with a high-resolution aerial camera to take aerial photos from multiple angles and altitudes to obtain high-resolution terrain image data, including:

[0080] S21: Preset the drone’s aerial photography path based on the range and terrain characteristics of the target mountain to ensure coverage of the target area;

[0081] S22: The drone takes multiple shots at different heights and angles to obtain terrain images from multiple perspectives;

[0082] S23: Use image stitching software to stitch multiple images into a complete high-resolution terrain image.

[0083] S2 further includes:

[0084] S21: Import the terrain image data collected by the drone into Pix4D 3D modeling software;

[0085] S22: Using the multi-view stereo matching algorithm of the 3D modeling software, the image data is stitched into a complete 3D terrain model;

[0086] S23: Precision calibrate the 3D terrain model. Use known ground control points to calibrate the generated 3D model to ensure the geographic accuracy of the model. Import the data of the ground control points into the 3D modeling software and mark the corresponding positions in the image to adjust the model to ensure the accuracy and detail integrity of the model.

[0087] S3 further includes:

[0088] S31: importing the generated three-dimensional terrain model into ArcGIS (Geographic Information System) software;

[0089] S32: Use the Slope tool in ArcGIS software to calculate the slope of the terrain and output a slope map showing the slope values ​​of each point on the terrain. The slope refers to the inclination of the terrain surface, expressed in degrees or percentage;

[0090] S33: Use the Aspect tool in ArCGIS software to calculate the slope aspect of the terrain and output an aspect map showing the slope value of each point on the terrain. The slope aspect refers to the direction in which the terrain surface faces, expressed as an azimuth.

[0091] S34: In ArcGIS software, set the sun path parameters, including the sun's altitude angle and azimuth. These parameters are determined according to the geographic location and time, and can also be obtained using the sun path calculation tool. Use the Hillshade tool in ArcGIS software to simulate the shadow distribution at different time periods. The tool calculates the shadow distribution of the terrain surface at a certain moment according to the sun path parameters and generates multiple shadow maps, which respectively represent the shadow distribution at different time periods of the day.

[0092] S35: Combine shadow maps of multiple time periods to analyze the shadow distribution throughout the year or in a specific season, mark areas with long shadow coverage and large shadow impact, and avoid arranging PV modules in these areas;

[0093] S36: Combine the slope map and the aspect map to identify areas suitable for the layout of photovoltaic modules. The slope of the area suitable for the layout of photovoltaic modules should not be too large (e.g., no more than 30 degrees). A larger slope will increase the difficulty and cost of construction and may affect the stability and power generation efficiency of the photovoltaic modules. The slope should be oriented towards the main direction of the sun as much as possible. In the northern hemisphere, the best slope direction for photovoltaic modules is south (180 degrees), and in the southern hemisphere it is north (0 degrees). Other suitable slope directions should be as close as possible to these directions to maximize the reception of solar radiation.

[0094] S37: The slope map, aspect map and shadow map are superimposed together to comprehensively analyze the suitability of each area, and the areas with moderate slope, facing the sun and less shadow are marked as the best layout areas for photovoltaic modules.

[0095] S4 further includes:

[0096] S41: Download satellite image data of the target area from satellite image data providers or public data platforms to ensure that the images cover all seasons of the year and obtain comprehensive terrain change information;

[0097] S42: Perform radiation correction, atmospheric correction and geometric correction on the acquired satellite image data to ensure the accuracy and consistency of the image data. If the target area is large, it is necessary to stitch multiple satellite images to generate a complete regional coverage image for subsequent analysis;

[0098] Radiation correction mainly involves converting digital values ​​(DN values) into physical radiation measurement values, such as radiation brightness or reflectivity;

[0099] The radiation brightness is calculated as: L = G rescale ×Q cal +B rescale ;

[0100] Where L is the radiant brightness (W / (m 2 ·sr·μm)), Grescale is the radiation calibration factor (Gain), usually provided by the data provider, Q cal is the DN value of the image, B rescale is the radiation bias coefficient (Bias), usually provided by the data provider;

[0101] The reflectivity is calculated as:

[0102] Where ρ is the reflectivity (unit is dimensionless), L is the radiant brightness (W / (m 2 ·sr·μm)), d is the average distance between the earth and the sun (astronomical unit), E sun is the solar radiation (W / m 2 ), θ s is the solar zenith angle;

[0103] Atmospheric correction is to eliminate the influence of atmosphere on the image, using the DOS method (Dark Object Subtraction);

[0104] The DOS method identifies the dark target pixels in the image (pixels with a theoretical reflectivity of 0), subtracts their radiation brightness values, and performs correction, which is calculated as:

[0105] L corrected =L measure -L dark ;

[0106] Among them, L corrected is the corrected radiance, L measured is the measured radiance, L dark is the radiance of the dark target;

[0107] The purpose of geometric correction is to eliminate the geometric distortion of the image and make the image have geographic reference. The geometric correction method is used and the calculation is:

[0108] X=a0+a1·x+a2·y;

[0109] Y = b0 + b1 · x + b2 · y;

[0110] Among them, X, Y are the corrected geographic coordinates, x, y are the image coordinates, a0, a1, a2, b0, b1, b2 are the geometric correction coefficients, which are obtained by fitting the ground control points;

[0111] S43: Comparing satellite images from different periods, identifying seasonally changing terrain, including gullies and seasonal rivers, using a change detection algorithm that uses the Normalized Difference Vegetation Index method;

[0112] The Normalized Difference Vegetation Index (NDVI) is calculated as:

[0113] Among them, NIR is the reflectivity of the near-infrared band, and RED is the reflectivity of the red band;

[0114] Example:

[0115] Assume that the satellite images of the target area at two time points are downloaded, namely Image t1 and Image t2 ;

[0116] (1) Calculate the NDVI at each time point:

[0117] Image t1 , calculate its NDVI value:

[0118] Image t2 , calculate its NDVI value:

[0119] (2) Calculate the NDVI difference: ΔNDVI = NDVI t2 -NDVI t1 ;

[0120] (3) Analyze the NDVI difference map:

[0121] Set a change threshold δ, for example δ = 0.2, to identify areas with significant changes;

[0122]

[0123] S44: Combine digital elevation model data to conduct water flow path analysis. Import the digital elevation model data into ArcGIS software and use the Flow Direction tool in ArcGIS software to generate a water flow path map. The digital elevation model data can be obtained through the geographic information public service platform or commercial data providers;

[0124] S45: Use the Flow Accumulation tool in ArcGIS software to input the water flow path map and generate a flow accumulation map;

[0125] S46: Combine slope map, aspect map and flow accumulation map to identify and eliminate areas that are not suitable for development;

[0126] Through the above steps, the NDVI difference method is used for change detection, and the DEM data is used for water flow path analysis. In combination with the slope map, aspect map and flow accumulation map, gullies, seasonal rivers and other areas that are not suitable for development are identified. This ensures that the layout area of ​​PV modules avoids these terrain features with significant changes, improves the accuracy and feasibility of the design, and ensures that the layout area of ​​PV modules is stable and suitable for development.

[0127] The layout of photovoltaic modules is designed in S5, including:

[0128] S51: According to the results of slope and aspect analysis, select the optimal photovoltaic module layout. Common layouts include:

[0129] Parallel arrangement: PV panels are arranged parallel to contour lines, which is suitable for areas with small slopes;

[0130] Vertical arrangement: PV panels are arranged perpendicular to the contour lines, which is suitable for areas with large slopes;

[0131] Mixed arrangement: combining parallel and vertical arrangements, optimized according to terrain characteristics;

[0132] S52: Set the inclination and azimuth of the photovoltaic module: According to the local solar radiation conditions, set the optimal inclination and azimuth of the photovoltaic module. The inclination should be as close to the local latitude as possible, and the azimuth should be towards due south (in the northern hemisphere) or due north (in the southern hemisphere);

[0133] S53: Determine the distance between the front and rear rows of photovoltaic modules to ensure that there is no shading between the modules. The calculation formula for the distance between the front and rear rows is as follows:

[0134] D = H × tan (θ), where D is the distance between the front and rear rows, H is the height of the photovoltaic module, and θ is the lowest altitude angle of the sun;

[0135] S54: Taking into account terrain, shading and construction feasibility, further optimize the layout of photovoltaic modules to maximize the power generation efficiency of the photovoltaic system.

[0136] S5 performs simulation and verification in a 3D model, including:

[0137] S55: importing the generated three-dimensional terrain model into Helioscope photovoltaic design software;

[0138] S56: Arrange the photovoltaic modules in the three-dimensional terrain model according to the designed layout plan, ensuring that the inclination, azimuth and spacing of each module meet the design requirements;

[0139] S57: Use the simulation function of the photovoltaic design software to simulate the power generation performance of the photovoltaic system, taking into account factors such as solar radiation, shadow effects, component efficiency, etc., and generate sunshine analysis diagrams, shadow analysis diagrams and power generation prediction diagrams;

[0140] S58: Analyze the simulation results, verify the feasibility and optimization degree of the design solution, and adjust the layout of photovoltaic modules according to the simulation results to ensure that the power generation efficiency reaches the expected level.

[0141] S6 further includes:

[0142] S 61: Plan the flight route of the drone according to the size and shape of the project area, ensure that the entire PV site is covered, and the flight route has sufficient overlap (usually 60-80%) to ensure the integrity and accuracy of the generated 3D point cloud model, set the flight altitude, speed and shooting frequency of the aerial camera. The flight altitude should be moderate to ensure the resolution and coverage of the image. The flight speed and shooting frequency should be adjusted according to the performance of the drone and camera. Perform the flight mission to collect high-resolution images of the project area. Ensure that the drone flies stably during data collection to avoid data loss or blurring;

[0143] S62: Import the images collected by the drone into Pix4D 3D modeling software;

[0144] S63: Use the software's automatic matching and alignment functions to stitch multiple images together, identify feature points in the images through a multi-view stereo matching algorithm, and align the feature points to generate a sparse point cloud;

[0145] The multi-view stereo matching algorithm generates a sparse point cloud by analyzing the corresponding feature points in multiple images and calculating their three-dimensional coordinates. The steps of the algorithm include feature point extraction, feature point matching, triangulation, and sparse point cloud generation.

[0146] The basic formula for feature point extraction is:

[0147] F = detect(I); where F is the extracted feature point set, I is the input image, and detect() is the feature point detection function;

[0148] The basic formula for feature point matching is:

[0149] M = match (F1, F2);

[0150] Among them, M is the set of matched feature point pairs, F1 and F2 are the feature point sets in the two images, and match() is the feature point matching function;

[0151] Triangulation is to calculate the coordinates of the matched feature point pairs in three-dimensional space. If there are two perspective images, triangulation is performed using the intrinsic and extrinsic matrix.

[0152] The basic formula for triangulation is: X = triangulate (P1, P2, x1, x2);

[0153] Among them, X is the coordinate of the 3D point, P1 and P2 are the camera projection matrices of the two perspectives, x1 and x2 are the coordinates of the matching feature points in the two perspectives, and triangulate() is the triangulation function;

[0154] The projection matrix P can be expressed as a combination of the camera intrinsic parameter matrix K and the extrinsic parameter matrix [R|t]: P = K[R|t];

[0155] By extracting, matching and triangulating feature points from multiple perspectives, a sparse point cloud can be generated. The basic formula for generating a sparse point cloud is: i =triangulate(P i , P j , x i , x j );

[0156] Among them, X i is the coordinate of the i-th 3D point, P i and P j is the camera projection matrix corresponding to the viewing angle, x i and x j are the coordinates of the matching feature points in the corresponding view;

[0157] S64: Pix4D will use the dense point cloud generation function based on the sparse point cloud to calculate the 3D coordinates of each pixel in the image and generate a 3D point cloud model;

[0158] The dense point cloud is generated by further processing the sparse point cloud, including depth map calculation and dense matching;

[0159] (1) Depth map calculation: Generate a depth map for each image, indicating the depth value of each pixel, expressed as: DepthMap(I i )=compute_depth(I i , {P j});

[0160] Among them, DepthMap(I i ) is the depth map of the i-th image, {P j} is a collection of camera projection matrices, compute_depth() is a depth map calculation function;

[0161] (2) Depth map fusion: The depth maps of multiple images are fused to generate a dense point cloud, which is expressed as:

[0162] X dense = fuse_depthmaps({DepthMap(I i )});

[0163] Among them, X dense It is high-density point cloud data, {DepthMap(I i )} is the depth map set of all images, fuse_depth_maps() is the depth map fusion function;

[0164] S65: Use known ground control points for precision calibration, import the location data of the ground control points into the software, and mark these points in the image to improve the geographic accuracy of the model;

[0165] S66: Use the Point Cloud Editor tool in Pix4D to remove noise and erroneous points and smooth and optimize the point cloud data.

[0166] S7 further includes:

[0167] S71: Import the 3D point cloud model generated in S6 into the 3D point cloud processing software CloudCompare, and use the Segment tool to manually select and extract the point cloud data of the photovoltaic module. The area can be extracted by the rectangle selection or polygon selection tool;

[0168] S72: Select the extracted photovoltaic module point cloud, generate a best fit plane through CloudCompare, and provide normal vector information (A, B, C);

[0169] S73: Calculating the inclination angle of the photovoltaic module according to the normal vector of the fitting plane;

[0170] The inclination angle of the photovoltaic module is calculated as:

[0171] Where C is the component of the normal vector on the z-axis;

[0172] S74: Calculate the azimuth angle of the photovoltaic module according to the normal vector of the fitting plane;

[0173]

[0174] Where A and B are the components of the normal vector on the x-axis and y-axis;

[0175] S75: In CloudCompare, the point cloud data is converted into a digital surface model to accurately represent the height information of the terrain and the photovoltaic module. The digital surface model is imported into the ArcGIS software, and the sun path model (including the sun altitude angle and azimuth) is set. The shadow distribution is simulated at different time periods of the day, and the shadow situation in each time period is calculated using the ray casting method. A shadow occlusion map is generated, and the shadow occlusion situation of each photovoltaic module in different time periods is marked. The cumulative time and area of ​​the shadow occlusion are calculated;

[0176] The ray casting method is used to simulate the shadow situation when the sun shines. The specific steps and formulas are as follows:

[0177] (1) Calculation of sun position:

[0178] The sun's position is determined by the sun's altitude angle and the sun's azimuth angle. The sun's position is calculated based on the geographic location and time:

[0179] Altitude Angle=arcsin(sin(L)×sin(δ)+cos(L)×cos(δ)×cos(H));

[0180]

[0181] Among them, L is the latitude of the observation point, δ is the declination angle of the sun, and H is the hour angle;

[0182] (2) Ray casting: For each point on a photovoltaic module, calculate its shadow in the direction of the sun’s rays;

[0183] Suppose the point on the surface of the photovoltaic module is (x i ,y i , z i );

[0184] Assume that the direction of the sun's rays is represented by the vector d = (d x , d y , d z ) indicates that:

[0185] d x =cos(AltitudeAngle)×sin(Azimuth Angle);

[0186] d y =cos(AltitudeAngle)×Cos(Azimuth Angle);

[0187] d z = sin(AltitudeAngle);

[0188] Check from point (x i ,y i , z i ) Whether it intersects with other objects along direction d, if so, the point is blocked;

[0189] S76: Calculate the shading coefficient of each photovoltaic module according to the shadow shading map, where the shading coefficient represents the proportion of the module being shaded, ranging from 0 to 1;

[0190] The occlusion coefficient α is expressed as

[0191] S77: Calculate the power generation loss of photovoltaic modules using the shading coefficient;

[0192] Assuming that the power generation of the component without shading is P0 and the shading coefficient is α, the actual power generation P is:

[0193] P = P0 × (1-α);

[0194] S78: Summarize the power generation losses of all PV modules and evaluate the total power generation losses of the entire PV system.

[0195] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0196] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Mountain terrain analysis and photovoltaic optimization layout method based on drone images, characterized in that: The following steps are involved: S1: Use a drone to take aerial photos of the target mountain and obtain terrain image data; S2: Import the acquired image data into 3D modeling software to generate a 3D terrain model of the mountain; S3: Slope, aspect and shadow analysis based on the 3D terrain model to determine the best layout area for PV panels; S4: Combine satellite imagery data to identify and remove areas that are not suitable for development, including gullies and seasonal rivers, including: S41: Download satellite image data of the target area from a satellite image data provider or a public data platform; S42: Perform radiation correction, atmospheric correction and geometric correction on the acquired satellite image data; S43: Compare satellite images from different periods and identify seasonally changing terrain, including gullies and seasonal rivers, using a change detection algorithm that uses the Normalized Difference Vegetation Index method; S44: Combine the digital elevation model data to perform water flow path analysis, import the digital elevation model data into ArcGIS software, and use the Flow Direction tool in ArcGIS software to generate a water flow path map; S45: Use the Flow Accumulation tool in ArcGIS software to input the water flow path map and generate a flow accumulation map; S46: Combine slope map, aspect map and flow accumulation map to identify and eliminate areas that are not suitable for development; S5: Design the layout of photovoltaic modules and simulate and verify them in a three-dimensional model; S6: During the project acceptance phase, drones are used again to collect data and generate a 3D point cloud model of the actual construction situation, including: S61: Plan the flight route of the drone according to the size and shape of the project area to ensure coverage of the entire PV site, set the flight altitude, speed and shooting frequency of the aerial camera, execute the flight mission, and collect images of the project area; S62: Import the images collected by the drone into Pix4D 3D modeling software; S63: Use the software's automatic matching and alignment functions to stitch multiple images together, identify feature points in the images through a multi-view stereo matching algorithm, and align the feature points to generate a sparse point cloud; S64: Pix4D will use the dense point cloud generation function based on the sparse point cloud to calculate the 3D coordinates of each pixel in the image and generate a 3D point cloud model; S65: Accuracy calibration using known ground control points; S66: Use the Point Cloud Editor tool in Pix4D to remove noise and erroneous points, and smooth and optimize the point cloud data; S7: Based on the actual 3D point cloud model, analyze the inclination, azimuth and shadow of the photovoltaic modules and evaluate the power generation loss, including: S71: Import the 3D point cloud model generated in S6 into the 3D point cloud processing software CloudCompare, and use the Segment tool to manually select and extract the point cloud data of the photovoltaic module; S72: Select the extracted photovoltaic module point cloud, generate a best fit plane through CloudCompare, and provide normal vector information; S73: Calculating the inclination angle of the photovoltaic module according to the normal vector of the fitting plane; S74: Calculate the azimuth angle of the photovoltaic module according to the normal vector of the fitting plane; S75: In CloudCompare, the point cloud data is converted into a digital surface model, the digital surface model is imported into ArcGIS software, the sun path model is set, the shadow distribution is simulated at different time periods of the day, the shadow situation in each time period is calculated using the ray casting method, a shadow shading map is generated, the shadow shading situation of each PV module in different time periods is marked, and the cumulative time and area of ​​the shadow shading are calculated; S76: Calculate the shading coefficient of each photovoltaic module according to the shadow shading map; S77: Calculate the power generation loss of photovoltaic modules using the shading coefficient; S78: Summarize the power generation losses of all PV modules and evaluate the total power generation losses of the entire PV system.

2. The method for mountain terrain analysis and photovoltaic optimization layout based on drone images according to claim 1 is characterized in that: In step S1, the drone is equipped with an aerial camera to perform aerial photography at multiple angles and heights to obtain terrain image data, specifically including: S21: Preset the drone’s aerial photography path based on the range and terrain characteristics of the target mountain to ensure coverage of the target area; S22: The drone takes multiple shots at different heights and angles to obtain terrain images from multiple perspectives; S23: Use image stitching software to stitch multiple images into a complete terrain image.

3. The method for mountain terrain analysis and photovoltaic optimization layout based on drone images according to claim 2 is characterized in that: The S2 further comprises: S21: Import the terrain image data collected by the drone into Pix4D 3D modeling software; S22: Using the multi-view stereo matching algorithm of the 3D modeling software, the image data is stitched into a complete 3D terrain model; S23: Perform accuracy calibration on the three-dimensional terrain model.

4. The method for mountain terrain analysis and photovoltaic optimization layout based on drone images according to claim 3 is characterized in that: The S3 further comprises: S31: importing the generated three-dimensional terrain model into ArcGIS software; S32: Use the Slope tool in ArcGIS software to calculate the slope of the terrain and output a slope map showing the slope values ​​of each point on the terrain; S33: Use the Aspect tool in ArcGIS software to calculate the slope of the terrain and output an aspect map showing the slope value of each point on the terrain; S34: In ArcGIS software, set the sun path parameters, including the sun's altitude angle and azimuth, use the Hillshade tool in ArcGIS software to simulate the shadow distribution in different time periods, and generate multiple shadow maps, which respectively represent the shadow distribution in different time periods of a day; S35: Combine shadow maps from multiple time periods to analyze the shadow distribution throughout the year or season; S36: Combine the slope map and the aspect map to identify areas suitable for PV panel layout; S37: Overlay the slope map, aspect map and shadow map to comprehensively analyze the suitability of each area.

5. The method for mountain terrain analysis and photovoltaic optimization layout based on drone images according to claim 4 is characterized in that: The arrangement scheme of photovoltaic components is designed in S5, specifically including: S51: Select the optimal photovoltaic module arrangement method according to the results of slope and aspect analysis; S52: Setting the inclination and azimuth of the photovoltaic module: setting the optimal inclination and azimuth of the photovoltaic module according to the local solar radiation conditions; S53: Determine the distance between the front and rear rows of photovoltaic modules to ensure that there is no shading between the modules; S54: Taking into account terrain, shading and construction feasibility, further optimize the layout of photovoltaic modules to maximize the power generation efficiency of the photovoltaic system.

6. The method for mountain terrain analysis and photovoltaic optimization layout based on drone images according to claim 5 is characterized in that: The simulation and verification in the three-dimensional model in S5 specifically includes: S55: importing the generated three-dimensional terrain model into Helioscope photovoltaic design software; S56: Arrange photovoltaic modules in the three-dimensional terrain model according to the designed arrangement plan; S57: Use the simulation function of the photovoltaic design software to simulate the power generation performance of the photovoltaic system and generate sunshine analysis diagrams, shadow analysis diagrams and power generation prediction diagrams; S58: Analyze the simulation results, verify the feasibility and optimization degree of the design solution, and adjust the layout of photovoltaic modules according to the simulation results to ensure that the power generation efficiency reaches the expected level.

Images