Method and device for acquiring photographing data of unmanned aerial vehicle in complex mountainous area by considering gradient and slope direction

Abstract

The invention relates to the technical field of unmanned aerial vehicle aerial photogrammetry, and discloses a slope and slope orientation-considered complex mountain area unmanned aerial vehicle photography data acquisition method and device, and the method comprises the steps: S1, obtaining initial terrain point cloud data of a target measurement area, and generating digital earth surface model raster data through interpolation operation; s2, calculating a neighborhood pixel elevation difference based on digital earth surface model raster data to obtain topographic parameters such as a gradient and a slope direction, and calculating unmanned aerial vehicle parameters such as a flight height, a course angle, a camera main optical axis direction and a pan-tilt pitch angle in combination with preset reference parameters; and S3, generating an unmanned aerial vehicle route and a waypoint command based on the parameters. According to the method, the digital earth surface model is fused with gradient and direction analysis, adaptive matching of unmanned aerial vehicle parameters and the terrain is achieved, it is ensured that the principal optic axis of the camera is perpendicular to the slope, the problem of data vulnerabilities in complex mountainous areas is effectively solved, data redundancy is reduced, and the precision and efficiency of photography data collection are improved.

Description

Technical Field

[0001] This invention relates to the field of drone aerial photogrammetry technology, and in particular to a method and apparatus for acquiring drone photogrammetric data in complex mountainous areas that takes into account slope and aspect. Background Technology

[0002] In the field of UAV aerial photogrammetry, data acquisition in complex mountainous areas (including steep cliffs, overhanging structures, and other extreme terrain) has always been a technical challenge. Currently, the mainstream data acquisition methods are fixed-altitude flight or terrain-following flight based on digital elevation models (DEMs). Fixed-altitude flight struggles to adapt to terrain undulations, resulting in poor consistency between photographic resolution and point density. While DEM-based terrain-following flight improves these issues to some extent, reducing data redundancy and post-processing burden, DEMs cannot accurately represent the true elevation information of surface vegetation, power towers, and other features, making flight path design prone to errors. Especially in extreme terrain areas such as steep cliffs and overhanging structures, existing methods are still prone to data gaps. While increasing overlap or densifying flight paths can compensate for these gaps, it leads to a surge in data volume and a significant decrease in acquisition efficiency, making it difficult to balance data integrity and acquisition efficiency.

[0003] Existing patent CN117406778B discloses a method for UAV LiDAR terrain-following flight based on geospatial data, belonging to the technical field of UAV LiDAR terrain-following flight methods; it includes the following steps: acquiring high-definition image data of the scanning area; target detection and coordinate extraction based on deep learning; acquiring initial terrain data of the scanning area; constructing electronic fences for dangerous areas; and planning terrain-following flight routes taking into account geospatial data. This invention focuses on using deep learning to identify terrain features and construct electronic fences to avoid flight hazards. It relies on DEM data and does not integrate slope and aspect analysis, failing to solve the problem of data gaps and data redundancy coexisting in extreme terrains such as steep cliffs and overhanging bodies. It also fails to achieve adaptive matching of UAV flight altitude, heading angle, and gimbal angle with the terrain, making it difficult to balance the accuracy and efficiency of photographic data acquisition in complex mountainous areas. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies, such as reliance on DEM data for drone photography in complex mountainous areas, failure to consider slope and aspect leading to frequent data vulnerabilities, large data redundancy, and low acquisition efficiency. This invention provides a method and apparatus for acquiring drone photography data in complex mountainous areas that takes into account slope and aspect.

[0005] In a first aspect, the present invention provides a method for acquiring drone photography data in complex mountainous areas, taking into account slope and aspect. The method includes the following steps: S1. Obtain the initial terrain point cloud data of the target survey area, perform interpolation on the initial terrain point cloud data, and generate digital land surface model raster data. S2. Calculate the elevation difference between neighboring pixels based on the above digital land surface model raster data, and then obtain the terrain parameters of each raster; based on the above terrain parameters, calculate the UAV parameters corresponding to each terrain parameter in combination with the preset benchmark parameters. The terrain parameters mentioned above include slope and aspect; the UAV parameters mentioned above include flight altitude, heading angle, camera principal axis direction, and gimbal pitch angle. Based on the above flight altitude, target image resolution, forward overlap and lateral overlap, the waypoint spacing is calculated. S3. Based on the above terrain parameters, UAV parameters and waypoint spacing, generate UAV flight path and waypoint commands; based on the above UAV flight path and waypoint commands, the UAV performs flight operations and acquires aerial photography data.

[0006] By generating digital surface models from point cloud data, calculating terrain and UAV parameters, and generating flight routes and waypoint commands, adaptive acquisition of UAV photography data in complex mountainous areas can be achieved, effectively adapting to terrains such as steep cliffs and overhanging bodies, avoiding data vulnerabilities and reducing data redundancy.

[0007] Preferably, the initial terrain point cloud data is derived from the result of feature matching and three-dimensional reconstruction of aerial images of the target survey area; the feature matching adopts a scale-invariant feature transformation algorithm or an accelerated robust feature algorithm.

[0008] By using initial point cloud data obtained from aerial image feature matching and 3D reconstruction, the initial terrain point cloud data can be accurately acquired, providing a reliable data foundation for subsequent digital surface model generation and terrain parameter calculation.

[0009] Preferably, the method further includes: performing a first preprocessing on the aerial image of the target survey area, and performing the feature matching and three-dimensional reconstruction based on the aerial image of the target survey area after the first preprocessing; the first preprocessing includes image registration, color equalization, and denoising; the image registration is used to eliminate image shift caused by differences in shooting angle and exposure, the color equalization is used to unify image brightness and color, and the denoising is used to retain terrain features while removing high-frequency noise and salt-and-pepper noise; the color equalization uses an adaptive histogram, and the denoising uses a combination of Gaussian filtering and median filtering.

[0010] By performing a first preprocessing on the aerial imagery of the target survey area, followed by the aforementioned feature matching and 3D reconstruction, image quality is optimized, shooting deviations and noise interference are eliminated, and the accuracy of feature matching and 3D reconstruction is improved.

[0011] Preferably, S1 further includes: performing a second preprocessing on the initial terrain point cloud data, and performing the interpolation operation based on the initial terrain point cloud data after the second preprocessing; the second preprocessing includes using the elevation threshold method and the region growing method to remove non-ground points and retaining terrain point cloud data that can reflect the real terrain undulations.

[0012] By employing the elevation threshold method and the region growing method to perform a second preprocessing on the initial terrain point cloud data, non-ground points are effectively removed, real terrain points are retained, and the digital surface model raster data can reflect the actual terrain undulations.

[0013] Preferably, the interpolation operation in S1 above uses Kriging interpolation or inverse distance weighted interpolation.

[0014] By employing Kriging interpolation or inverse distance weighted interpolation, accurate interpolation of terrain point cloud data can be achieved, adapting to different terrain features and ensuring the continuity and accuracy of digital land surface model raster data.

[0015] Preferably, the timeliness of the above-mentioned digital surface model raster data is better than 1 meter.

[0016] By setting a timeliness threshold for digital land surface model raster data, accurate representation of terrain information can be achieved, ensuring accurate calculation of terrain parameters such as slope and aspect.

[0017] Preferably, the formula for calculating the above slope is as follows:

[0018] in, Slope; The elevation difference between the target grid point and its neighboring grid points; This represents the horizontal straight-line distance between the target grid point and its neighboring grid points. The formula for calculating the slope aspect is as follows:

[0019] in, Angle of slope; It is the ratio of the change in elevation along the horizontal x-axis to the change in horizontal distance. This represents the change in unit distance along the horizontal x-axis.

[0020] Preferably, the formula for calculating the above-mentioned flight altitude is as follows:

[0021] in, For flight altitude; Used as the reference flight altitude; For adaptive adjustment coefficients; The formula for calculating the heading angle is as follows:

[0022] in, For heading angle; This is the initial preset heading angle without considering slope adjustment; The formula for calculating the direction of the principal optical axis of the camera is as follows:

[0023] in, The direction of the camera's principal optical axis; This represents the horizontal projected distance of the UAV from its current waypoint location to the slope. The formula for the gimbal pitch angle is as follows:

[0024] in, The tilt angle of the gimbal; This is the horizontal projection distance from the center of the camera lens to the slope.

[0025] By listing the quantitative calculation forms of terrain parameters and UAV parameters, the UAV parameters are accurately matched with the terrain features, ensuring that the flight and shooting parameters meet the needs of complex terrain data collection.

[0026] Preferably, when the UAV is traveling along the aforementioned UAV route, the main optical axis of the UAV's camera is always vertically aligned with the slope.

[0027] The above slope is the set of slope normal vectors represented by the terrain parameters of each of the above grid cells. The above slope normal vectors are:

[0028] in, This is the slope normal vector; To ensure that the main optical axis of the camera of the aforementioned UAV is always vertically aligned with the slope, specifically by jointly adjusting the heading angle of the aforementioned UAV and the pitch angle of the gimbal, the spatial direction vector of the main optical axis of the aforementioned camera is parallel to the normal vector of the slope.

[0029] By setting a standard that the camera's main optical axis is always perpendicular to the slope when the drone is flying, image distortion-free and scale-consistent images can be achieved when shooting on slopes, thus improving the accuracy of data collection in complex mountainous areas.

[0030] In a second aspect, the present invention provides an apparatus for acquiring drone photography data in complex mountainous areas, taking into account slope and aspect. The apparatus includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform the aforementioned method for acquiring drone photography data in complex mountainous areas, taking into account slope and aspect.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method and apparatus for acquiring UAV photography data in complex mountainous areas, taking into account slope and aspect. It acquires initial terrain point cloud data to generate digital surface model raster data, calculates terrain parameters such as slope and aspect, derives UAV adaptation parameters, and generates flight and waypoint commands. This enables adaptive matching of UAV flight and shooting parameters to complex mountainous terrain, with flight paths accurately adapting to extreme terrains such as steep cliffs and overhanging bodies, directly ensuring the integrity of photography data coverage and image quality. Thus, it achieves flawless and low-redundancy acquisition of UAV photography data in complex mountainous areas, balancing acquisition accuracy and operational efficiency. Attached Figure Description

[0032] Figure 1 This is a flowchart of the method for acquiring UAV photography data in complex mountainous areas, taking into account slope and aspect, as described in Example 1.

[0033] Figure 2 This is a schematic diagram of the adaptive flight path and terrain adaptation of a UAV in complex mountainous areas in Example 1. Detailed Implementation

[0034] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0035] Unless otherwise specified, the terms "upper," "lower," "left," "right," "center," "inner," and "outer," etc., used in the description of specific embodiments of the present invention to indicate orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is usually placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, and for enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0036] Furthermore, the use of terms such as "horizontal," "vertical," "suspended," "parallel," and "coaxial" does not imply that the corresponding device / component / element must be absolutely horizontal, vertical, suspended, parallel, or coaxial. Slight tilt or deviation is permissible, as long as it does not affect the normal function of the relevant component. For example, "horizontal" simply means that its direction is more horizontal relative to "vertical," not that the structure must be perfectly horizontal; a slight tilt is acceptable. "Coaxial" means that two components are arranged as coaxially as possible, allowing them to move coaxially or approximately coaxially when their relative positions change. Alternatively, it can be simplified to mean that the corresponding device / component / element, when arranged in "horizontal," "vertical," "suspended," "parallel," or "coaxial" directions, can have an error / deviation of ±10% relative to the corresponding direction, more preferably within ±8%, more preferably within ±6%, more preferably within ±5%, and more preferably within ±4%. For example, the deviation in the "coaxial" direction is controlled within 0.2-1mm, preferably within 0.2-0.5mm. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its function in the solution of the present invention.

[0037] Furthermore, the use of terms such as "first," "second," and "third" in terminology is merely for distinguishing descriptions of identical or similar components and should not be interpreted as emphasizing or implying the relative importance of a particular component.

[0038] Furthermore, in the description of the embodiments of the present invention, "several", "more than", and "a number of" represent at least two. The number can be any number, such as two, three, four, five, six, seven, eight, or nine, and can even exceed nine.

[0039] Furthermore, in the description of the technical solution of this invention, unless otherwise explicitly specified / limited / restricted, the terms "set up," "install," "connect," "link," "provided with," "laid out," and "arranged" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to connection methods commonly used in the art, such as welding, riveting, bolting, and threaded connections. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components.

[0040] Example 1 This embodiment is applied to the aerial photography data acquisition of drones in rugged mountainous areas (including steep cliffs and overhanging terrain). The method flow in this embodiment is as follows: Figure 1As shown in the figure, DSM corresponds to the DSM raster data generated in this embodiment. Flight altitude and waypoint spacing correspond to the flight altitude and waypoint spacing determined by image resolution in this embodiment, respectively. Waypoint coordinates (BLH) correspond to the "latitude and longitude + flight altitude" coordinates in the waypoint command in this embodiment. Image resolution and flight altitude (photographic distance constraint) correspond to the constraint logic of "reference flight altitude is determined based on camera focal length and target image resolution" in this embodiment. Forward overlap and lateral overlap (overlap constraint) correspond to the constraint logic of "waypoint spacing is set based on forward / lateral overlap" in this embodiment. The flight path file (waypoints, forwards and gimbal angles) corresponds to the "waypoint command containing extended parameters" generated in this embodiment.

[0041] The specific steps of this method are as follows: S1. Generate digital land surface model raster data: The initial terrain point cloud data of the target survey area is obtained. This data can be obtained from the results of feature matching and 3D reconstruction of low-resolution aerial images collected by UAVs in the survey area. The feature matching adopts the scale-invariant feature transform (SIFT) algorithm.

[0042] Before feature matching, the aerial imagery undergoes a first preprocessing step: image registration eliminates image shifts caused by differences in shooting angle and exposure, adaptive histogram equalization is used for color equalization to unify image brightness and color, and a combination of Gaussian filtering and median filtering is used for noise reduction, preserving terrain features while removing high-frequency noise and salt-and-pepper noise.

[0043] The initial terrain point cloud data is preprocessed a second time. The elevation threshold method and the region growing method are used to remove non-ground points such as vegetation and power towers, and retain the real terrain points. The Kriging interpolation method is used to interpolate the preprocessed terrain point cloud data to generate digital surface model (DSM) raster data with a timeliness better than 1 meter.

[0044] The aforementioned initial terrain point cloud data can also be generated through: using a drone equipped with lidar to perform a full-coverage scan of the survey area, simultaneously recording lidar echo data and POS attitude data, and generating the data after denoising and coordinate correction; or fusing existing satellite remote sensing images and low-altitude drone images of the survey area, and generating initial point cloud data that balances large-scale coverage and local details through multi-source data registration and joint 3D reconstruction algorithms; or using total stations and GNSS static measurements to obtain high-precision control point coordinates for key terrain areas of the survey area, and combining them with low-altitude photographic images for constrained reconstruction to supplement and generate initial point cloud data.

[0045] S2. Calculate terrain parameters and UAV parameters: (1) Determine the baseline constraint parameters First, based on project requirements, determine the target image resolution (e.g., better than 5cm / pixel) and photography distance constraints (the photography distance from the camera to the slope should not be less than 80% of the lidar range). Combined with the focal length and pixel size of the camera on the UAV, calculate the reference flight altitude using a formula. At the same time, determine the forward overlap (set to 75%) and lateral overlap (set to 65%) according to aerial photography specifications, as constraints for setting the waypoint spacing.

[0046] (2) Calculate terrain parameters and UAV adaptation parameters Based on the above digital land surface model raster data, the elevation difference between the target raster and neighboring raster is calculated using a 3×3 raster window. Terrain parameters and UAV adaptation parameters are then calculated sequentially using the following formulas: The formula for calculating slope is as follows:

[0047] The formula for calculating slope aspect is as follows:

[0048] The formula for calculating flight altitude is as follows:

[0049] The formula for calculating the heading angle is as follows:

[0050] The formula for calculating the direction of the camera's principal optical axis is as follows:

[0051] The formula for the gimbal's pitch angle is as follows:

[0052] (3) Determine the waypoint spacing Based on the adjusted flight altitude, target image resolution, forward overlap, and lateral overlap, the waypoint spacing is calculated to ensure that the overlap of the aerial images meets the project specifications.

[0053] S3. Generate flight path and waypoint commands; the drone performs flight operations and acquires aerial photography data. Based on the aforementioned terrain and UAV parameters, an adaptive flight path is generated to suit complex terrain, ensuring that the camera's principal optical axis remains perpendicularly aligned with the slope during UAV flight. Figure 2As shown; at the same time, it generates waypoint commands containing basic parameters (waypoint coordinates in absolute elevation mode, yaw angle, automatic turn mode, gimbal pitch angle, 5.0m / s flight speed, custom heading mode, 0.2m turning radius, and straight-line stopping waypoint type) and extended configuration parameters (disable fixed elevation of flight path, enable fixed speed of flight path, disable fixed heading mode of flight path, and disable fixed point type of flight path) to complete the collection of UAV photography data in complex mountainous areas.

[0054] In this application, the slope is not an infinite plane in a geometric sense, but a local tangent plane approximately represented by a DSM raster in a local neighborhood. Its definition is entirely derived from the calculated terrain parameters: DSM raster center point: P(x0,y0,z0). Calculated from the elevation difference of the 3×3 neighborhood For slope, Given the slope aspect, the slope surface at this grid point can be represented as a unit normal vector:

[0055] Based on the slope and aspect parameters calculated by the DSM grid, a normal vector of the corresponding slope is constructed at each waypoint. By jointly adjusting the UAV heading angle and the gimbal pitch angle, the direction vector of the camera's principal optical axis is made consistent with the direction of this normal vector. That is, the spatial direction vector of the camera's principal optical axis is parallel to the slope normal vector, thus ensuring that the camera's principal optical axis is always perpendicular to the corresponding slope in space.

[0056] Example 2 The XML-format waypoint command code provided in this embodiment corresponds to the "UAV Flight Path Planning Method Based on Digital Surface Model (DSM) Fusion with Slope and Aspect Analysis" of this invention. It is used to define the dynamic configuration parameters of a single waypoint for a UAV in complex terrain (such as steep slopes and cliffs). The code achieves adaptive matching of flight altitude, flight deviation angle, gimbal pitch angle, and slope and aspect by adding a terrain adaptation extension parameter module. This ensures that the camera's principal optical axis is perpendicular to the slope, supporting high-precision, flawless data acquisition. Those skilled in the art can adjust the variable values ​​in the code to adapt to different survey area terrains based on the formulas and parameter logic described in the specification.

[0057] Universal waypoint codes for terrain-following flight: <placemark> <!--Base identification of waypoint: Waypoint number 3, visibility enabled, type is standard waypoint--> <name> 3< / name> <visibility> 1< / visibility> <description> Waypoint< / description> <styleurl> #waypointStyle< / styleurl> <point> <!--Elevation mode: absolute elevation (fixed elevation based on the geodetic coordinate system)--> <altitudemode> absolute< / altitudemode> <!--Waypoint coordinates: longitude and latitude (98.95791856816770, 30.73627976396220), fixed flight altitude 3185.0 m (without considering slope and aspect adjustment)--> <coordinates> 98.95791856816770,30.73627976396220,3185.0< / coordinates> < / point> < / placemark> Waypoint command codes of this invention (including slope and aspect adaptation parameters): <placemark> <!--Waypoint basic identification: Consistent with the general solution to ensure route compatibility--> <name> 3< / name> <visibility> 1< / visibility> <description> Waypoint< / description> <styleurl> #waypointStyle< / styleurl> <!--New terrain adaptation extension parameter module: The carrier for realizing the core innovation point, associated with the calculation results of slope and aspect--> <extendeddata xmlns:mis="www.dji.com"> <!--Disable fixed flight altitude for route: Turn off the fixed flight altitude mode in the general plan and enable the adjusted flight altitude calculated by formula. <mis:usewaylinealtitude> false< / mis:usewaylinealtitude> <!--Flight deviation angle: Corresponding to the adjusted heading angle, the value here is -15°, which adapts to the slope aspect angle of -15° to ensure that the flight direction is consistent with the slope aspect. <mis:heading> -15< / mis:heading> <!--Turning mode: Automatic turning, which adapts to the continuous terrain change of the segmented route and avoids flight attitude oscillation. <mis:turnmode> Auto< / mis:turnmode> <!--Pan-tilt pitch angle: Derived from the formula, the value here is -45°, which ensures that the main optical axis of the camera is perpendicular to the slope surface (corresponding to the calculation result of the main optical axis direction in the formula). <mis:gimbalpitch> -45< / mis:gimbalpitch> <!--Enable custom flight speed: Turn on the independent speed configuration to adapt to the data acquisition accuracy requirements in the steep slope area. <mis:usewaylinespeed> true< / mis:usewaylinespeed> <!--Flight speed: 5.0 m / s, which is the optimal speed value considering both acquisition efficiency and data accuracy in the steep slope area. <mis:speed> 5.0< / mis:speed> <!--Disable fixed heading mode: Turn off the fixed heading in the general plan and enable the custom heading mode adapted to the slope aspect. <mis:usewaylineheadingmode> false< / mis:usewaylineheadingmode> <!--Disable fixed waypoint type: Turn off the fixed waypoint type in the general plan and adapt to the dynamic terrain adjustment requirements. <mis:usewaylinepointtype> false< / mis:usewaylinepointtype> <!--Waypoint type: Straight-line stay type, which ensures that the UAV completes a complete shooting of the slope surface at this waypoint before entering the next flight segment. <mis:pointtype> LineStop< / mis:pointtype> <!--Heading mode: Adopt waypoint custom settings to ensure that the heading angle of each waypoint independently adapts to the slope aspect of the corresponding area. <mis:headingmode> UsePointSetting< / mis:headingmode> <!--Turning radius: 0.2 m, which reduces the deviation during route turning in the steep slope area and ensures route accuracy. <mis:cornerradius> 0.2< / mis:cornerradius> < / extendeddata> <point> <!--Elevation mode: Consistent with the general solution, maintaining the unity of the elevation reference. --> <altitudemode> absolute< / altitudemode> <!--Waypoint coordinates: The latitude and longitude are consistent with the general solution, and the flight altitude is calculated as 3178.7 m according to the above formula. --> <coordinates> 98.95791856816770,30.73627976396220,3178.7< / coordinates> < / point> < / placemark> Compared to general terrain-following flight waypoint codes, this invention adds... <extendeddata>The extended module enables the linkage between slope and aspect and flight / shooting parameters through 7 custom parameters, solving the problem that the fixed parameters of the general solution cannot adapt to extreme terrain.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.< / extendeddata>

Claims

1. A method for acquiring unmanned aerial vehicle (UAV) photography data in complex mountainous areas considering slope and aspect, characterized in that, The method comprises the following steps: S1, obtaining initial terrain point cloud data of a target survey area, performing interpolation operation on the initial terrain point cloud data, and generating digital terrain model grid data; S2, calculating the height difference between adjacent pixels based on the digital terrain model grid data, and then obtaining the terrain parameters of each grid; based on the terrain parameters, combining the preset reference parameters to calculate the unmanned aerial vehicle parameters corresponding to each terrain parameter; The terrain parameters include slope and slope direction; the unmanned aerial vehicle parameters include flight height, heading angle, camera main optical axis direction and gimbal pitch angle; Based on the flight height, target image resolution, heading overlap and lateral overlap, the flight point spacing is calculated; S3, based on the terrain parameters, unmanned aerial vehicle parameters and flight point spacing, generating unmanned aerial vehicle flight path and flight point command; based on the unmanned aerial vehicle flight path and flight point command, the unmanned aerial vehicle performs flight operation to obtain aerial photography data. 2.The complex mountainous area UAV photogrammetry data acquisition method considering slope gradient and aspect according to claim 1, wherein, The initial terrain point cloud data comes from the result of feature matching and three-dimensional reconstruction of the target survey area aerial image; the feature matching adopts scale invariant feature transform algorithm or speeded up robust features algorithm. 3.The complex mountainous area UAV photogrammetry data acquisition method considering slope and aspect according to claim 2, characterized in that, The method further comprises: performing first preprocessing on the target survey area aerial image, and performing feature matching and three-dimensional reconstruction based on the first preprocessed target survey area aerial image; the first preprocessing includes image registration, color equalization and denoising processing; the image registration is used to eliminate the image deviation caused by shooting angle and exposure difference, the color equalization is used to unify image brightness and color, and the denoising processing is used to retain terrain features while removing high frequency noise and salt and pepper noise; the color equalization adopts adaptive histogram, and the denoising processing adopts the combination of Gaussian filter and median filter. 4.The complex mountainous area UAV photogrammetry data acquisition method considering slope and aspect according to claim 1, wherein, In S1, the initial terrain point cloud data is further preprocessed, and the interpolation operation is performed based on the second preprocessed initial terrain point cloud data; the second preprocessing includes using elevation threshold method and region growing method to remove non-ground points, and retaining terrain point cloud data that can reflect the true terrain undulation. 5.The complex mountainous area UAV photogrammetry data acquisition method considering slope and aspect according to claim 1, wherein, The interpolation operation in S1 adopts Kriging interpolation method or inverse distance weighted interpolation method. 6.The complex mountainous area UAV photogrammetry data acquisition method with slope and aspect taken into account according to claim 1, characterized in that, The present situation of the digital terrain model grid data is better than 1 meter. 7.The complex mountainous area UAV photogrammetry data acquisition method with slope and aspect taken into account according to claim 1, characterized in that, The calculation formula of the slope is as follows: wherein, is the slope; is the elevation difference between the target grid point and the neighboring grid point; is the horizontal straight-line distance between the target grid point and the neighboring grid point; The calculation formula of the slope direction is as follows: wherein, is the slope aspect angle; is the ratio of the change in elevation to the change in horizontal distance along the horizontal x-axis; is the change in distance per unit distance along the horizontal x-axis.

8. The complex mountainous area UAV photogrammetry data acquisition method considering slope aspect according to claim 7, characterized in that, The calculation formula of the flight height is as follows: wherein, is the altitude of the aircraft; is the reference flight altitude; is the adaptive adjustment coefficient; The calculation formula of the heading angle is as follows: wherein, is the heading angle; is the initial preset heading angle without considering the slope adjustment; The calculation formula of the camera main optical axis direction is as follows: wherein, is the direction of the main optical axis of the camera; is the horizontal projection distance of the UAV from the current waypoint position to the slope surface; The formula of the gimbal pitch angle is as follows: wherein, is the pan angle of the gimbal; is the horizontal projection distance from the center of the camera lens to the slope surface. 9.The complex mountainous area UAV photogrammetry data acquisition method with slope and aspect taken into account according to claim 7, characterized in that, When the unmanned aerial vehicle navigates along the unmanned aerial vehicle flight path, the camera main optical axis of the unmanned aerial vehicle is always vertically aligned with the slope surface; The slope surface is a set of slope surface normal vectors represented by the terrain parameters of each grid, and the slope surface normal vector is: wherein is the slope normal vector; Making the camera main optical axis of the unmanned aerial vehicle always vertically aligned with the slope surface specifically includes: adjusting the unmanned aerial vehicle heading angle and gimbal pitch angle jointly, so that the spatial direction vector of the camera main optical axis is parallel to the slope surface normal vector.

10. The complex mountainous area unmanned aerial vehicle photography data acquisition device considering slope aspect, characterized in that, The device comprises at least one processor and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for obtaining complex mountainous area unmanned aerial vehicle photography data considering slope gradient and aspect as claimed in any one of claims 1 to 9.

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