Sunlight analysis and measurement method based on aerial survey of unmanned aerial vehicle

Abstract

The invention discloses a sunlight analysis and measurement method based on aerial survey of an unmanned aerial vehicle, which comprises the following steps of: adjusting flight parameters of the unmanned aerial vehicle in real time according to a solar altitude to realize dynamic illumination compensation; building image data are collected through multi-camera oblique photography; ground and facade image control points are arranged, and the density is dynamically adjusted; a live-action model is generated through three-dimensional reconstruction, and curvature-driven lightweight processing is carried out; dynamically selecting a solar declination angle calculation formula and calculating an elevation angle and an azimuth angle; and a sunlight conical surface algorithm is adopted to analyze the shielding condition and generate a visual report. The method effectively improves image quality consistency, three-dimensional model precision and sunlight analysis reliability, and is suitable for the field of urban and rural planning and building sunlight assessment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of engineering surveying, and particularly relates to a sunshine analysis surveying method based on unmanned aerial vehicle photogrammetry. BACKGROUND

[0002] Building sunshine analysis is an important link in urban and rural planning, building design and environmental impact assessment, and its core lies in accurately calculating and evaluating the sunshine blocking situation of a building itself and its surrounding environment at a specific period. Traditional sunshine analysis surveying methods usually rely on ground surveying means such as total station and GPS-RTK to obtain the spatial coordinates of limited feature points, and then construct a simplified three-dimensional model for analysis. However, such methods have large field work and low efficiency, and it is difficult to obtain complete geometric information of the top of the building and the complex facade, resulting in missing details and limited precision of the constructed model, which directly affects the accuracy and reliability of the final sunshine analysis results.

[0003] With the development of low-altitude remote sensing technology, unmanned aerial vehicle photogrammetry technology can efficiently obtain multi-view image data including buildings and generate high-precision real three-dimensional models due to its high operation efficiency and flexible data collection, providing a richer and more realistic data basis for sunshine analysis.

[0004] However, direct application of unmanned aerial vehicle photogrammetry technology to sunshine analysis still faces significant technical challenges. In particular, sunshine analysis relies on the change of the sun's position at different times of the day, and the continuous change of the sun's elevation angle and light intensity during the unmanned aerial vehicle photogrammetry operation will cause problems such as uneven brightness and abnormal exposure in the collected image sequence. The inconsistency of image quality caused by dynamic changes in lighting conditions will seriously affect the geometric integrity, texture authenticity and accuracy of the subsequent three-dimensional reconstruction model, and ultimately lead to deviations in the sunshine blocking calculation based on this model. The existing technology lacks an effective mechanism for real-time perception and compensation of dynamic lighting conditions during photogrammetry, and does not integrate the sun's trajectory factor into the photogrammetry path planning, making it difficult to ensure the uniformity and stability of the collected image data quality, thus failing to meet the stringent requirements of high-precision sunshine analysis on the basic data source. SUMMARY

[0005] The embodiment of the present application provides a sunshine analysis surveying method based on unmanned aerial vehicle photogrammetry, which aims to solve the problems of inconsistent image quality, insufficient three-dimensional model reconstruction accuracy and poor reliability of the final sunshine analysis results caused by the inability to effectively compensate for dynamic lighting conditions during photogrammetry and the failure to integrate the sun's trajectory factor into the photogrammetry planning in the prior art.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present application is to provide a sunshine analysis surveying method based on unmanned aerial vehicle photogrammetry, comprising the following steps:

[0007] S1, real-time adjusting unmanned aerial vehicle flight parameters according to the solar elevation angle, forming a dynamic light compensation closed loop, and planning an unmanned aerial vehicle flight path based on a fusion algorithm, optimizing a heading and a lateral overlap ratio;

[0008] S2, collecting building image data in a survey area by using a multi-camera combined oblique photography method, ensuring that the top and side elevations of the building are covered;

[0009] S3, laying out photo control points in the survey area, dynamically adjusting the density of the photo control points according to the terrain features, and ensuring the accuracy of the height control;

[0010] S4, processing aerial photography image data by using a three-dimensional reconstruction software, generating a building real scene three-dimensional model, and performing curvature-driven lightweight processing on the three-dimensional model;

[0011] S5, dynamically selecting a solar declination angle calculation formula according to the ordinal year, and calculating the solar elevation angle and the azimuth angle in combination with the time difference and the hour angle;

[0012] S6, importing the lightweight processed three-dimensional model into a sunshine analysis software, calculating the building sunshine shading impact, and performing visual processing on the analysis results to generate a sunshine time line graph and a shading impact analysis report.

[0013] Preferably, in S3, the photo control point layout includes:

[0014] a) ground photo control points;

[0015] b) building facade photo control points;

[0016] The building facade photo control points are uniformly distributed at different height positions to improve the height measurement accuracy.

[0017] Preferably, in S1, the dynamic light compensation calculates a brightness coefficient according to the solar elevation angle, removes abnormal values by low-pass filtering, and adjusts the exposure time and the ISO value of the unmanned aerial vehicle camera.

[0018] Preferably, in S4, the curvature-driven lightweight processing includes:

[0019] a) calculating a model vertex curvature value;

[0020] b) dynamically adjusting a simplification ratio according to a curvature threshold value;

[0021] The curvature value retains the details of the area greater than the preset threshold value.

[0022] Preferably, in S5, the dynamic sun trajectory algorithm selection includes:

[0023] a) comparing the applicable year ranges and the calculation errors of different formulas;

[0024] b) selecting the formula with the least error for the calculation of the declination angle.

[0025] Preferably, in S6, the building sunshine sheltering influence is calculated by a sunshine cone algorithm, which generates a sunshine cone with the moving track of the sun in a day as a bottom surface and a measuring point as a vertex, to calculate the collision interval of the building model and the sunshine cone.

[0026] Preferably, after S4, a step of marking the three-dimensional model with building attribute parameters is further included, and the three-dimensional model marking adopts a CGA parameterized language to automatically mark key building attribute parameters such as window sill height and floor height.

[0027] Preferably, in S6, the three-dimensional model is loaded according to the level of detail (LOD) technology, and models of different levels of detail are selected according to the analysis accuracy requirement for sunshine analysis and calculation.

[0028] Preferably, in S4, the volume of the three-dimensional model after light-weight processing is 3.7% to 15% of the volume of the original model, and the geometric deformation error of the key building features is not greater than 0.02 mm.

[0029] Preferably, in S6, the sheltering influence analysis report includes a general plan, a sunshine isochrone diagram, a window position sunshine time comparison table, and a sunshine analysis conclusion.

[0030] As can be seen from the above technical solutions,

[0031] 1. The present application combines real-time sun elevation angle sensing with dynamic adjustment of unmanned aerial vehicle surveying parameters to construct a closed-loop feedback system. Through illumination coefficient calculation based on astronomical algorithms and low-pass filter optimization, it can effectively suppress the image exposure abnormality problem caused by dynamic changes in sunlight conditions, ensuring the brightness consistency and texture authenticity of the image sequence from the source of data acquisition, and overcoming the model reconstruction precision deficiency caused by the lack of effective light compensation in the prior art.

[0032] 2. The fusion path planning algorithm (Bi-RRT combined with DWA) is adopted, the sun trajectory parameters are included in the surveying path decision variable, and the dynamic optimization of the heading and lateral overlap rate is realized. The limitation of traditional surveying path planning considering only terrain obstacles is solved, and the optimal image coverage efficiency can still be maintained in complex lighting environments, providing a complete data basis for high-precision three-dimensional reconstruction.

[0033] 3. Through the image control point system of ground and building facade coordinated control, the control point distribution is dynamically encrypted according to the terrain slope and building height difference, a three-dimensional control network is established, the problem of weak control of building facade elevation in the prior art is solved, and the spatial geometric accuracy of the three-dimensional model is improved.

[0034] 4、Existing three-dimensional modeling techniques often result in the loss of key architectural features (such as windowsills, eaves) due to oversimplification, affecting the accuracy of sunshine calculation. The present application proposes a lightweight method of dynamic simplification of curvature threshold, which retains the detailed features of high curvature areas, maintains the controllability of geometric deformation error while reducing model data volume, effectively reduces storage and computing resource consumption while ensuring model accuracy.

[0035] 5、Traditional sunshine analysis relies on single grid method or shadow tracing method, which is difficult to balance the efficiency and accuracy of large-scale building group. The present application generates dynamic cone surface by taking solar trajectory as the bottom surface and measuring point as the vertex, combined with LOD hierarchical loading technology, calling different accuracy models as needed. Through the combination of algorithm and data structure, the efficiency and adaptability of sunshine shading calculation are improved, solving the contradiction between model complexity and computing performance in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The flowchart of the sunshine analysis measurement method based on unmanned aerial vehicle photogrammetry provided by the embodiment of the present application is provided.

[0037] Figure 2 The dynamic light compensation timing chart of the sunshine analysis measurement method based on unmanned aerial vehicle photogrammetry provided by the embodiment of the present application is provided.

[0038] Figure 3 The three-dimensional model lightweight processing flowchart of the sunshine analysis measurement method based on unmanned aerial vehicle photogrammetry provided by the embodiment of the present application is provided.

[0039] Figure 4 The solar trajectory calculation flowchart of the sunshine analysis measurement method based on unmanned aerial vehicle photogrammetry provided by the embodiment of the present application is provided.

[0040] Figure 5 The report generation system workflow chart of the sunshine analysis measurement method based on unmanned aerial vehicle photogrammetry provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0041] In order to make the technical problems, technical solutions and beneficial effects of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0042] The present application discloses a sunshine analysis measurement method based on unmanned aerial vehicle photogrammetry, referring to Figure 1 , comprising the following steps:

[0043] S1, adjust the flight parameters of the unmanned aerial vehicle according to the solar elevation angle in real time, form a dynamic light compensation closed loop, and plan the flight path of the unmanned aerial vehicle based on a fusion algorithm, optimize the heading and lateral overlap ratio;

[0044] The dynamic light compensation calculates the brightness coefficient according to the solar elevation angle, removes abnormal values through low-pass filtering, and adjusts the exposure time and ISO value of the unmanned aerial vehicle camera.

[0045] In implementation, referring to Figure 2 During the unmanned aerial vehicle aerial survey, the solar elevation angle changes over time, resulting in unstable light intensity. This embodiment adjusts the unmanned aerial vehicle camera parameters in real time through dynamic light compensation to ensure stable image quality.

[0046] First, the unmanned aerial vehicle position information is obtained through GPS, and the solar elevation angle and azimuth angle are calculated by astronomical algorithm. The solar declination angle The calculation formula is:

[0047]

[0048] In the formula: is the corrected obliquity of the ecliptic (needs to be corrected in combination with astronomical calendar);

[0049] is defined as an intermediate variable:

[0050]

[0051] N is the Nth day of the year (N ).

[0052] Secondly, the brightness coefficient is calculated according to the solar elevation angle

[0053]

[0054] In the formula: is the pixel value at the coordinate in the image, is the average brightness value of the image.

[0055] Then, the abnormal values in the brightness coefficient are removed through a 7×7 low-pass filter:

[0056]

[0057] In the formula: : The offset of the filter window (range: −3 to 3).

[0058] Finally, the camera parameters are dynamically adjusted according to the filtered brightness coefficient:

[0059] Exposure time adjustment:

[0060]

[0061] ISO value adjustment:

[0062]

[0063] In the formula: , Reference exposure time and reference ISO value;

[0064] Meanwhile, the Bi-RRT and DWA fusion algorithm is used to plan the UAV flight path:

[0065] The Bi-RRT algorithm improves global path search efficiency by setting heuristic functions, dynamic step sizes, and safety distances.

[0066] Specifically, random trees are constructed from the start and end points respectively, and the algorithm is improved by using heuristic functions, dynamic step size and safety distance to speed up convergence and improve sampling efficiency;

[0067] The DWA algorithm improves the accuracy of local trajectory scoring by modifying the obstacle distance evaluation function and introducing the target point distance evaluation function.

[0068] Specifically, the evaluation function in the DWA algorithm is improved by incorporating the global yaw angle and global path distance into the evaluation function, thereby improving the accuracy of trajectory direction evaluation.

[0069] When combined, the two technologies can dynamically optimize the heading and lateral overlap rates based on the solar altitude angle, thereby improving image coverage efficiency.

[0070] In this embodiment, the Bi-RRT and DWA fusion algorithm is implemented as follows:

[0071] A globally optimal reference path that takes into account the solar azimuth angle is generated by the Bi-RRT algorithm, and then the DWA algorithm is used for local real-time tracking. The flight attitude and speed are dynamically fine-tuned according to the real-time solar altitude angle to achieve the optimal overlap rate.

[0072] Integration mechanism:

[0073] Global path planning and local path planning are integrated in a hierarchical manner. The Bi-RRT algorithm first plans an initial global path in 3D space from the starting point to the ending point, avoiding static obstacles. The generation of this path not only takes geographical obstacles into account, but also the solar azimuth angle. As a decision variable, it is used to optimize the lighting effect along the flight direction. Subsequently, along the drone's... During flight, the DWA algorithm is activated at a certain frequency, sampling a large number of feasible local trajectories in its dynamic window, and selecting an optimal local path from them. . The evaluation criteria not only include traditional obstacle avoidance and speed smoothness, but more importantly, they also include... The fit and the achievement of the heading overlap rate .

[0074] Heuristic function setting:

[0075] In the construction of the random tree, a heuristic cost function based on the sun azimuth is introduced to guide the growth direction of the tree, so that the generated global path is more conducive to obtaining high-quality images.

[0076]

[0077] In the formula:

[0078] is the Euclidean distance from the current node to the target point.

[0079] is the obstacle density cost near the current node.

[0080] is the planned heading angle of the UAV at the current node.

[0081] is the sun azimuth at the current time.

[0082] , , is the weight coefficient, which is determined by experiment (for example , , ).

[0083] This function aims to make the UAV heading as much as possible to match the sun azimuth, reduce the backlit flight, and optimize the lighting conditions.

[0084] Dynamic coupling with the sun elevation angle:

[0085] The evaluation function of the DWA algorithm is expanded, and a "overlap rate achievement term" is added , which is dynamically coupled with the sun elevation angle .

[0086]

[0087] In the formula: , , , is the normalized weight, is the smoothing function.

[0088] The calculation method is as follows:

[0089]

[0090]

[0091] is the predicted overlap ratio at the next time instant according to the current sampling trajectory's speed and angular speed The DWA algorithm finally selects the local trajectory that maximizes the function, which not only avoids obstacles but also dynamically adjusts the speed so that the actual overlap ratio infinitely approaches the target overlap ratio determined by the solar elevation angle , thereby realizing closed-loop dynamic coupling of flight parameters and lighting conditions.

[0092] Dynamic adjustment of overlap ratio: dynamically adjust the heading and lateral overlap ratios according to the solar elevation angle. When the solar elevation angle > 60°, set the heading overlap ratio to 80% and the lateral overlap ratio to 70%. When the solar elevation angle < 60°, set the heading overlap ratio to 85% and the lateral overlap ratio to 75%, to ensure high-quality image data under different lighting conditions.

[0093] S2, adopt a multi-camera combined oblique photography method to collect building image data in the survey area, ensuring that the top and side elevations of the building are covered.

[0094] The UAV carries a multi-camera combination system including at least one front-view camera and four side-view cameras. The front-view camera is vertically downward, and the four side-view cameras are respectively directed towards the southeast, southwest, and northwest, forming a 5-angle image acquisition system.

[0095] Flight parameters are set as follows:

[0096]

[0097] During flight, the front-view camera is responsible for collecting building top and front images, and the side-view camera is responsible for collecting building side and back images, ensuring that the top and side elevations of the building are covered. After image data collection, high-precision three-dimensional reconstruction data is generated through aerial triangulation and multi-view matching technology.

[0098] At the same time, according to the terrain features and building height differences in the survey area, different flight altitudes and ground resolutions (GSD) are set:

[0099] Flat area: flight altitude 150 m, GSD = 0.05 m, flight speed 6 m / s;

[0100] Mountainous or high-rise building area: flight altitude 250 m, GSD = 0.08 m, flight speed 5 m / s;

[0101] The heading overlap rate is 80%-85%, the lateral overlap rate is 70%-75%, and the data quality is ensured.

[0102] During the acquisition process, the image quality is monitored in real time. When the signal-to-noise ratio (SNR) is less than 30 dB or the dynamic range (DR) is less than 1000:1, the light compensation mechanism is automatically triggered to adjust the exposure time and ISO value.

[0103] S3, laying image control points in the survey area, dynamically adjusting the image control point density according to the terrain features, and ensuring the elevation control accuracy;

[0104] The image control point layout includes:

[0105] a) ground image control points;

[0106] b) building facade image control points;

[0107] The building facade image control points are uniformly distributed at different height positions to improve the elevation measurement accuracy.

[0108] When laying image control points in the survey area, the embodiment adopts a layout strategy combining ground image control points and building facade image control points.

[0109] The ground image control points are laid in flat areas with a density of 1-2 image control points per 100 m²; the building facade image control points are uniformly distributed at different height positions with a density of 1-2 image control points per 5 m height.

[0110] The image control point density is dynamically adjusted according to the terrain features:

[0111] For areas with a slope greater than 15°, the image control point density is increased to 1-2 per 50 m²;

[0112] For areas with a building height difference greater than 20 m, the facade image control point density is increased to 1-2 per 3 m height.

[0113] The image control point layout follows the following principles:

[0114] Corner control: image control points are laid at the boundaries of the survey area and the corners of buildings;

[0115] Middle encryption: increase the image control point density in the center of the survey area and in areas with high building density;

[0116] Uniform distribution: ensure that the image control points are uniformly distributed in the survey area to avoid insufficient control in local areas.

[0117] The image control point marker uses a high-contrast black-and-white target with a size of 30 cm x 30 cm and a center point coordinate accuracy of ±1 cm. After laying the image control points, a field inspection is required to ensure that the image control points are clearly visible on the image without obstruction or damage.

[0118] After the control point data is collected, precise coordinates are obtained through GPS-RTK technology to provide high-precision control points for three-dimensional reconstruction.

[0119] S4, process aerial image data by using three-dimensional reconstruction software to generate a three-dimensional model of the building, and perform curvature-driven lightweight processing on the three-dimensional model.

[0120] Reference Figure 3 In this embodiment, ContextCapture or Smart3D three-dimensional reconstruction software is used to process aerial image data to generate a three-dimensional model of the building.

[0121] The three-dimensional model reconstruction process is as follows:

[0122] Image preprocessing: remove blurred, overexposed or underexposed images;

[0123] Aerial triangulation: determine the relative position and attitude of the image;

[0124] Dense point cloud generation: generate high-density point clouds through multi-view matching;

[0125] Three-dimensional surface reconstruction: generate continuous three-dimensional surfaces based on point clouds;

[0126] Texture mapping: map the original image texture to the three-dimensional surface;

[0127] The volume of the reconstructed three-dimensional model is usually large, in order to improve the efficiency of subsequent analysis, this embodiment adopts curvature-driven lightweight processing.

[0128] In this embodiment, the curvature-driven lightweight processing includes:

[0129] a) Calculate the curvature value of the model vertex, for each vertex, use the local surface representation method based on Euclidean distance to obtain the local surface of the point cloud:

[0130] The curvature calculation formula is:

[0131]

[0132] In the formula: are three eigenvalues of the local neighborhood covariance matrix of the point cloud, arranged in descending order ( );

[0133] is the smallest eigenvalue, used to represent the minimum principal curvature direction of the local surface.

[0134] b) dynamically adjust the simplification ratio according to the curvature threshold;

[0135] The simplification ratio is dynamically adjusted according to the curvature threshold. The simplification ratio formula is:

[0136]

[0137] wherein: is the vertex curvature value, is the preset curvature threshold (usually 0.1-0.3).

[0138] Finally, the three-dimensional model is dynamically simplified to retain the details of areas with curvature values greater than the preset threshold, such as window sills, eaves, and other key architectural features. The lightweight processed three-dimensional model data volume is 3.7% to 15% of the original model volume, and the geometric deformation error of key architectural features is not greater than 0.02mm.

[0139] It also includes the step of labeling the three-dimensional model with architectural attribute parameters, using CGA parameterized language to automatically label key architectural attribute parameters such as window sill height and floor height.

[0140] First, import the two-dimensional architectural plan into the CityEngine platform and use CGA rules to automatically generate a three-dimensional model.

[0141] The basic rules are as follows:

[0142] attr height=0

[0143] attr floorHeight=3.5

[0144] attr windowHeight=0.9

[0145] Lot --> extrude(height) building

[0146] building--> split(y) {floor: floorHeight | window: windowHeight}

[0147] Second, extract key architectural attribute parameters from the three-dimensional model through a Python script:

[0148] import mxcpp

[0149] def extract_params(model):

[0150] floors = []

[0151] windows=[]

[0152] for entity in model.get_entities():

[0153] if entity.get_class() == 'Floor':

[0154] floors.append(entity.get_height())

[0155] elif entity.get_class() == 'Window':

[0156] windows.append(entity.get_position())

[0157] return {'floors': floors, 'windows': windows}

[0158] Finally, the extracted parameters are automatically annotated to the three-dimensional model, forming an annotated model containing key architectural attribute parameters such as balcony height, floor height, etc. The annotated information can be directly used for subsequent sunshine analysis and calculation, improving analysis efficiency and accuracy.

[0159] After the curvature-driven lightweight processing, the obtained three-dimensional model retains high-precision key geometric features, but its data structure is still a triangular mesh, lacking semantic information. The parametric model generated by the CGA rule has rich architectural attributes, but its geometric precision depends on the rule definition and may deviate from the actual scene.

[0160] To balance geometric precision and attribute semantics, the present embodiment adopts a "geometry-driven parameterization" method to integrate the two. The specific process is as follows:

[0161] Model matching and alignment: The parametric model generated by CGA is matched and aligned with the lightweight real scene model in three-dimensional space to ensure that their spatial positions and basic outlines are consistent.

[0162] Attribute mapping: The architectural attributes defined in the parametric model (such as balcony height, floor height, room function) are mapped to the corresponding geometric components of the lightweight real scene model. For example, the "balcony height" attribute of the parametric window is assigned to the corresponding window mesh retained by the curvature calculation in the lightweight real scene model.

[0163] Generating a semantic real scene model: Finally, a semantic three-dimensional model is generated that combines the high-precision geometry of the lightweight real scene model and the rich attributes of the parametric model. This model not only has a small file size, but each key component (such as windows, eaves) has an accurate architectural attribute label.

[0164] S5, dynamically select the solar declination angle calculation formula according to the ordinal number of the year, and calculate the solar altitude angle and azimuth angle in combination with the time difference and the hour angle;

[0165] See Figure 4 , the dynamic solar trajectory algorithm selection includes:

[0166] a) compare the applicable year range and calculation error of different formulas;

[0167] b) select the formula with the smallest error for declination angle calculation.

[0168] The embodiment adopts a dynamic solar trajectory algorithm selection technology, dynamically selects a solar declination angle calculation formula according to the ordinal number of the year, and improves the accuracy of solar trajectory calculation.

[0169] First, determine the ordinal number of the year For a given date, calculate the Julian day:

[0170]

[0171] In the formula: is the accumulated day coefficient

[0172] Second, select the solar declination angle calculation formula according to the value of n. By comparing the errors of different declination angle calculation formulas, select the formula with the smallest error:

[0173] When , the formula is:

[0174]

[0175] When , the formula is:

[0176]

[0177] When , the formula is:

[0178]

[0179] When 3, the formula is:

[0180]

[0181] In the formula: β is the accumulated day factor, , , is the coefficient corresponding to different years.

[0182] Solar declination angle calculation formula coefficient table:

[0183] The application adopts a high-precision solar declination angle calculation model from Jean Meeus' Astronomical Algorithms. Wherein is the annual sequence (day of the year, 1≤ ≤365).

[0184] The values of the coefficients , , are shown in the following table:

[0185]

[0186] Finally, the solar elevation angle h and the azimuth angle A are calculated in combination with the time difference and the hour angle .

[0187]

[0188]

[0189] Wherein: φ is the local latitude, and τ is the solar hour angle.

[0190] S6, the three-dimensional model after light weight processing is imported into the sunshine analysis software, the building sunshine sheltering influence is calculated, and the analysis result is visualized, the sunshine hour line graph and the sheltering influence analysis report are generated.

[0191] Referring to Figure 5 , the building sunshine sheltering influence is calculated by the sunshine cone algorithm, the sunshine cone algorithm takes the moving track of the sun in a day as a bottom surface, takes a measuring point as a vertex, generates a sunshine cone, and calculates the collision interval of the building model and the sunshine cone.

[0192] Meanwhile, the LOD technology is adopted to load the three-dimensional model according to the detail level, and the model of different detail levels is selected according to the analysis accuracy requirement to carry out the sunshine analysis calculation, and the analysis efficiency is improved.

[0193] Firstly, the sunshine cone is generated based on the moving track of the sun in a day. The equation of the sunshine cone is as follows:

[0194]

[0195] Wherein, L is the length of the conic generatrix, φ is the geographical latitude, δ is the declination angle, and t is the solar hour angle.

[0196] Secondly, the sunshine cone is generated with the measuring point as the vertex, and the collision interval of the building model and the sunshine cone is calculated. The ray tracing algorithm is adopted for the collision detection, and the intersection of the triangular mesh of the building model and the sunshine cone is judged.

[0197] The specific steps are as follows:

[0198] Discretization of the sun trajectory and the cone generation:

[0199] Discretize the time of a day (such as from sunrise to sunset) into ( =1,2,...,M), which can be 1 minute or 5 minutes. For each time point , calculate its corresponding sun elevation angle and azimuth angle , and generate a ray with the analysis point (such as the center of the window) as the vertex and as the direction. . The rays together form a discretized "sun cone".

[0200] Collision detection (occlusion judgment):

[0201] For each ray , perform ray intersection calculation with the three-dimensional building model loaded using the LOD technology:

[0202] Ray equation: where is the direction vector determined by the azimuth angle and the elevation angle , and is the length of the ray.

[0203] Intersection calculation: use an efficient ray-triangle intersection algorithm (such as the Möller–Trumbore algorithm) to quickly determine whether the ray intersects any triangle face of the building model.

[0204] If there is no intersection point: it indicates that at , the analysis point is not occluded and can receive sunlight.

[0205] If there is an intersection point: record the intersection point as , and calculate the intersection distance . This intersection point is the occlusion point.

[0206] Sunlight time calculation and visualization:

[0207] After traversing all rays, a Boolean sequence of length is obtained, where each value in the sequence indicates whether the analysis point is occluded at the corresponding time.

[0208] The sequence is compared with the analysis duration, and the total daily sunshine time of the analysis point is accurately calculated .

[0209] Generate sunshine isochrone map: densely arrange analysis point grid on the building facade, repeat the above process for each point to get the sunshine time of each point. According to these time data, an isochrone map with smooth color gradient can be drawn by using interpolation algorithm.

[0210] Generate window position sunshine time table: take each window center point as the analysis point , calculate its sunshine time , and organize it into a table.

[0211] It is worth noting that in order to deal with large-scale building models, a bounding volume hierarchy (BVH) is constructed for the triangular patches of the model to accelerate the calculation. Before ray casting, it is first determined whether the ray intersects with the model bounding box, so as to quickly skip a large number of triangular patches that are not likely to intersect, greatly improving the calculation efficiency.

[0212] At the same time, the detection between different rays and the calculation between different analysis points are independent of each other, and parallel computing (such as multi-threaded CPU or GPU acceleration) can be used to improve the analysis speed.

[0213] Then, use LOD technology to load the model in layers.

[0214] In implementation, the three-dimensional model is divided into LOD0, LOD1, and LOD2 layers according to the level of detail

[0215] LOD0: complete detail model, containing all architectural features, with about 1 million patches;

[0216] LOD1: medium detail model, simplifying the details of non-critical areas, with about 100,000 patches;

[0217] LOD2: simplified model, only retaining the building outline, with about 10,000 patches.

[0218] Secondly, according to the analysis accuracy requirement, select the appropriate model level:

[0219] For detailed analysis of window sill height, select the LOD0 level model;

[0220] For building group shading analysis, select the LOD1 level model;

[0221] For regional sunshine analysis, LOD2 level model is selected. Finally, the sunshine analysis results are visualized to generate sunshine time line graph and shading impact analysis report. The report includes general plan, sunshine isochrone graph, window position sunshine time comparison table and sunshine analysis conclusion, which intuitively displays the sunshine shading situation of the building.

[0222] Specifically, the general plan is set to a scale of 1:500, showing the distribution of buildings and the results of sunshine analysis.

[0223] The isochrone interval of the sunshine isochrone graph is set to 15 minutes, and the color gradient is set to blue→green→yellow→red, indicating the sunshine time from short to long.

[0224] The window position sunshine time comparison table contains parameters such as sunshine time, sunshine angle and sunshine intensity of each window position.

[0225] The three-dimensional sunshine analysis model supports functions such as rotation, scaling and sectioning, which facilitates intuitive display of sunshine conditions.

[0226] The visualization interface is set to include functions such as time slider, sunshine angle adjustment and sunshine intensity adjustment, which facilitates interactive analysis.

[0227] The sunshine analysis conclusion is based on the judgment of whether it meets the sunshine requirements according to the national standard.

[0228] The report generation adopts an automatic process without human intervention, improving work efficiency and accuracy. The report format can be customized to meet the needs of different application scenarios.

[0229] Specifically, the specific implementation parameters of report generation are as follows:

[0230] Set the report template, which includes the following contents:

[0231] Project basic information;

[0232] Survey area overview;

[0233] UAV aerial survey parameters;

[0234] Three-dimensional model reconstruction parameters;

[0235] Sunshine analysis parameters;

[0236] Analysis results;

[0237] Conclusion

[0238] Set the report output format, supporting PDF, Excel and HTML formats to meet the needs of different users.

[0239] Set the report automatic generation function to automatically fill in the report content according to the analysis results, improving work efficiency.

[0240] Set the report verification function, automatically verify the analysis result, ensure the accuracy of the report content.

[0241] Set the report signature function, support electronic signature, facilitate the formal release of the report.

[0242] The above only describes the preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A solar radiation analysis and measurement method based on UAV aerial surveying, characterized in that, Includes the following steps: S1. Adjust the UAV flight parameters in real time according to the solar altitude angle to form a dynamic illumination compensation closed loop, and plan the UAV flight path based on the fusion algorithm to optimize the heading and lateral overlap rate; S2. Use a multi-camera combination of oblique photography to collect image data of buildings in the survey area, ensuring that the top and side facades of the buildings are covered; S3. Set up image control points in the survey area and dynamically adjust the density of image control points according to the terrain features to ensure the accuracy of elevation control; S4. Use 3D reconstruction software to process aerial photography data, generate a real-world 3D model of the building, and perform curvature-driven lightweight processing on the 3D model. S5. Dynamically select the solar declination angle calculation formula based on the year ordinal number, and calculate the solar altitude angle and azimuth angle by combining the time difference and hour angle; S6. Import the lightweight 3D model into the solar radiation analysis software, calculate the impact of building solar shading, and visualize the analysis results to generate a solar radiation time-line diagram and a shading impact analysis report.

2. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that, In S3, the deployment of image control points includes: a) Ground image control points; b) Control points on the building facade; The control points on the building facade are evenly distributed at different heights to improve the accuracy of elevation measurement.

3. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that: In S1, the dynamic illumination compensation calculates the brightness coefficient based on the solar altitude angle, removes outliers through low-pass filtering, and adjusts the exposure time and ISO value of the drone camera.

4. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that, In S4, the curvature-driven lightweighting process includes: a) Calculate the curvature values ​​at the model vertices; b) Dynamically adjust the simplification ratio based on the curvature threshold; Among them, the curvature value retains the details of areas with a curvature value greater than a preset threshold.

5. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that, In S5, the selection of the dynamic solar trajectory algorithm includes: a) Compare the applicable year range and calculation errors of different formulas; b) Select the formula with the smallest error for calculating the declination angle.

6. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that, In S6, the impact of building shading is calculated using the solar cone algorithm. The solar cone algorithm uses the sun's daily movement trajectory as the base and the measuring point as the vertex to generate a solar cone and calculate the collision range between the building model and the solar cone.

7. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that: S4 is followed by a step of annotating the architectural attribute parameters of the 3D model. The annotation of the 3D model uses the CGA parametric language to automatically annotate key architectural attribute parameters such as window sill height and floor height.

8. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that: In S6, LOD technology is used to load 3D models in hierarchical order of detail, and different levels of detail are selected for solar radiation analysis calculations based on the required analysis accuracy.

9. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that: In S4, the volume of the lightweight 3D model data is 3.7% to 15% of the original model volume, and the geometric deformation error of key building features is no greater than 0.02mm.

10. The solar radiation analysis and measurement method based on UAV aerial survey as described in claim 1, characterized in that: In S6, the shading impact analysis report includes a site plan, a solar radiation isochrone diagram, a window position solar radiation time comparison table, and solar radiation analysis conclusions.

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