Unmanned aerial vehicle variable-speed aerial photography method and device for multi-class ground feature elevation and medium

Abstract

The invention discloses an unmanned aerial vehicle variable-speed aerial photography method and device for multi-class ground feature elevation and a medium, and relates to the technical field of photogrammetry, and the method mainly comprises the steps: taking the texture of each direction of a static target as an information collection demand, and generating a cross route with mutually vertical horizontal planes as initial route data; controlling the unmanned aerial vehicle to fly according to the initial route data, and shooting a sequence image at a forward-looking preset angle; integrating the digital earth surface model data and the sequence image, and extracting an elevation data set of each static target in the sequence image; according to the elevation data set, analyzing the elevation distribution proportion change of the static targets in the adjacent sequence images, and carrying out speed adjustment along with the proportion change; and generating final route data according to the speed regulation result, and controlling the unmanned aerial vehicle to perform variable-speed aerial photography according to the final route data. The flight speed of the unmanned aerial vehicle is dynamically adjusted, dense collection of high-rise building images is guaranteed, and redundant data in low areas are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of photogrammetry, and particularly to a method, device and medium for variable-speed aerial photography of a drone for multi-category ground object elevations. Background Art

[0002] As an important tool for modern surveying and mapping and 3D modeling, the drone oblique photography technology has been widely used in urban planning, disaster emergency response and real-scene 3D reconstruction. Traditional aerial photography methods usually design based on fixed flight altitude, uniform flight speed and a single flight line, combined with vertical or fixed-angle shooting, which are applicable to flat terrains or areas with low-density buildings. However, when facing scenes with high-rise building clusters, complex ground object distributions or significant terrain undulations, the existing technologies mostly adopt a constant flight speed and lack a dynamic adaptation mechanism to the height distribution of ground objects. In high-rise building areas, it is necessary to reduce the flight speed to extend the exposure time and increase the image acquisition density to ensure the integrity of complex facade textures; while in low or open areas, maintaining a high flight speed can improve the operation efficiency. However, traditional methods cannot adjust the flight speed according to real-time elevation data, resulting in blurred images or matching failures in high-rise areas, and redundant data in low areas increases the storage and processing costs. Summary of the Invention

[0003] To solve the above problems, the present invention proposes a method for variable-speed aerial photography of a drone for multi-category ground object elevations, including the steps of: S1: According to the parameters of the aerial survey instrument, the requirements of image overlap degree and the terrain overview, taking the textures of each azimuth of the static target as the information acquisition requirements, generating cross flight lines perpendicular to each other in the horizontal plane as the initial flight line data; S2: Controlling the drone to fly according to the initial flight line data and taking a sequence of images at a preset forward-looking angle; S3: Integrating the digital surface model data and the sequence of images, and extracting the elevation data set of each static target in the sequence of images; S4: Analyzing the change in the elevation distribution ratio of static targets in adjacent sequence of images according to the elevation data set, and performing speed adjustment according to the change in the ratio; S5: Generating the final flight line data according to the speed adjustment result, and controlling the drone to perform variable-speed aerial photography according to the final flight line data.

[0004] Further, in the step S1, the parameters of the aerial survey instrument include pixel size , focal length , flight altitude , equivalent image width and equivalent image height , and the image overlap degree includes the forward overlap degree p and the side overlap degree q.

[0005] Further, the equivalent image width and the equivalent image height It is obtained through the following formula: In the formula, W is the number of horizontal pixels of the sensor of the aerial survey instrument, and H is the number of vertical pixels of the sensor of the aerial survey instrument.

[0006] Further, in the step S3, the elevation data set is extracted in the following manner: Perform sparse point cloud reconstruction on the initial three-dimensional point cloud data of the sequence images through the multi-view algorithm, and after rasterizing the initial three-dimensional point cloud data, perform raster overlay with the digital surface model data. Based on the overlay result, extract the elevation values of the corresponding geographical coordinates pixel by pixel to form an elevation data set with the sequence images as units.

[0007] Further, before the step S4, there is also a step: S40: Filter out the elevation data of static targets lower than the preset ratio according to the ratio relationship between the elevation data of each static target in the sequence images and the flight height.

[0008] Further, in the step S4, the specific analysis of the change in the elevation distribution ratio of static targets in adjacent sequence images according to the elevation data set is as follows: Take the maximum elevation data in the elevation data set of the current sequence image as the first extreme value, take the preset ratio threshold of the maximum elevation data as the second extreme value, and form a judgment interval with the first extreme value and the second extreme value. Analyze the change in the elevation distribution ratio of static targets in adjacent sequence images according to the ratio of the elevation data in the judgment interval in the elevation data set of the current sequence image.

[0009] Further, in the step S4, the adjustment with the change of the ratio is specifically as follows: When the ratio of the elevation data of the previous sequence image is in the first ratio interval: If the ratio of the elevation data of the current sequence image is in the first ratio interval, adjust to the first flight speed; if the ratio of the elevation data of the current sequence image is in the second ratio interval, adjust to the second flight speed; if the ratio of the elevation data of the current sequence image is in the third ratio interval, adjust to the third flight speed; When the ratio of the elevation data of the previous sequence image is in the second ratio interval: If the ratio of the elevation data of the current sequence image is in the first ratio interval, adjust to the first flight speed; if the ratio of the elevation data of the current sequence image is in the second ratio interval, adjust to the second flight speed; if the ratio of the elevation data of the current sequence image is in the third ratio interval, adjust to the third flight speed; When the proportion of elevation data in the previous sequence of images is in the third proportion interval: If the proportion of elevation data in the current sequence of images is in the first proportion interval, adjust to the first flight speed; if the proportion of elevation data in the current sequence of images is in the second proportion interval, adjust to the second flight speed; if the proportion of elevation data in the current sequence of images is in the third proportion interval, adjust to the third flight speed; The first flight speed is greater than the second flight speed, and the second flight speed is greater than the third flight speed; the numerical intervals of the first proportion interval, the second proportion interval, and the third proportion interval are adjacent to each other in sequence and do not overlap.

[0010] Furthermore, the speed adjustment according to the proportion change is expressed by the following formula: In the formula, is the adjusted speed, is the first flight speed, is the second flight speed, is the third flight speed, is the proportion of elevation data in the previous sequence of images, is the proportion of elevation data in the current sequence of images, is the first proportion interval, is the second proportion interval, is the third proportion interval.

[0011] The present invention further includes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method for variable-speed aerial photography of an unmanned aerial vehicle for multi-category ground object elevations are implemented.

[0012] It further includes a device for processing data, including: a memory, on which a computer program is stored; a processor, configured to execute the computer program in the memory to implement the steps of the method for variable-speed aerial photography of an unmanned aerial vehicle for multi-category ground object elevations.

[0013] Compared with the prior art, the present invention has at least the following beneficial effects: (1) The method for variable-speed aerial photography of an unmanned aerial vehicle for multi-category ground object elevations proposed by the present invention, through the design of cross flight lines perpendicular to each other combined with forward-looking oblique photography, systematically covers the four elevations of the building in the east, west, south, and north, and solves the problem of texture loss caused by building occlusion in traditional aerial photography; (2) Based on the pixel-level integration of the digital surface model and the image, dynamically extract the local elevation distribution characteristics, and achieve the precise matching of the flight speed and the ground object height; (3)Based on the real-time analysis of the elevation distribution ratio, dynamically adjust the flight speed of the UAV, which not only ensures the intensive acquisition of high-rise building images but also avoids redundant data in low-lying areas, significantly improving the aerial photography efficiency and the success rate of image matching. Description of the Drawings

[0014] Figure 1 It is a step diagram of a UAV variable-speed aerial photography method for multi-category ground object elevations. Detailed Implementation Manner

[0015] The following are specific embodiments of the present invention and in combination with the accompanying drawings, the technical solutions of the present invention are further described, but the present invention is not limited to these embodiments.

[0016] In the process of building a smart city, it is necessary to combine UAV oblique photography, computers, and high-precision positioning technologies to produce remote sensing big data with high structural accuracy, high geographical accuracy, and strong currency. Under the conditions of relatively single ground object categories, scattered building distributions, and normal elevation ranges in the aerial photography area, the image matching degree is well controlled, and the UAV aerial photography results basically meet the requirements of subsequent technical production and result utilization. When the aerial photography area involves a building-intensive area and complex high-rise building structures, further requirements for the UAV aerial photography flight matching degree and the number of images are needed. For this reason, as Figure 1 shown, the present invention proposes a UAV variable-speed aerial photography method for multi-category ground object elevations, including the steps of: S1: According to the parameters of the aerial survey instrument, the requirements for image overlap, and the terrain overview, taking the textures of each azimuth of the static target as the information acquisition requirements, generate cross flight lines perpendicular to each other in the horizontal plane as the initial flight line data; S2: Control the UAV to fly according to the initial flight line data and take a sequence of images at a preset forward viewing angle; S3: Integrate the digital surface model data and the sequence of images, and extract the elevation data sets of each static target in the sequence of images; S4: Analyze the change in the elevation distribution ratio of the static targets in adjacent sequence images according to the elevation data sets, and perform speed adjustment according to the change in the ratio; S5: Generate the final flight line data according to the speed adjustment result, and control the UAV to perform variable-speed aerial photography according to the final flight line data.

[0017] In the process of generating the initial flight line data, it is first necessary to perform precise calculations by comprehensively considering the core parameters of the aerial survey instrument, including the pixel size , focal length , flight altitude , equivalent image width and equivalent image height . Among them, the equivalent image width and height are determined by the ratio of the number of horizontal pixels W of the sensor to the number of vertical pixels H of the flight altitude, and the calculation formula is: to ensure that the ground sampling distance (GSD) of the image meets the preset accuracy requirements . The image overlap is dynamically adjusted according to the forward overlap p (set to 80%) and the side overlap q (set to 80%), and the photographic baseline is calculated through the formula , and the flight line interval is determined, where to ensure the continuity and coverage density of the images.

[0018] On this basis, combined with the terrain trend and mapping requirements, a cross flight line network perpendicular to each other is designed. Specifically, when implementing, a rectangular coordinate system OXY is constructed, with the center of the survey area as the origin, and the X-axis and Y-axis correspond to the east-west and north-south directions respectively. By controlling the UAV to fly along the cross flight lines and taking pictures with a preset forward tilt angle (such as 45°), all-round texture acquisition of the four elevations of the east, west, south, and north of the target in the survey area is realized. For example, in the east-west flight line, the forward tilt angle of the camera covers the west building elevation, and when flying in the reverse direction, it covers the east elevation; the north-south flight line covers the south and north directions in the same way, ensuring that the occlusion blind area in the high-rise building dense area is minimized. At the same time, the flight line laying needs to consider the terrain undulation, and in the steep slope area, the overlap is guaranteed by adjusting the flight altitude or adding frame flight lines (perpendicular to the main flight line) to avoid image matching failure. The finally generated initial flight line data not only meets the requirements for multi-angle texture acquisition, but also lays a foundation for subsequent elevation data extraction and dynamic variable-speed aerial photography.

[0019] During the process of the UAV flying according to the initial flight line data, the flight attitude and shooting parameters need to be precisely controlled to ensure the image acquisition quality. First, based on the cross flight line network generated by S1, the UAV realizes centimeter-level accurate flight line tracking through the RTK positioning system and the inertial navigation unit (IMU). The flight altitude is maintained within the preset value range, and the flight speed runs stably according to the initial design value. The camera controls the forward tilt angle through a three-axis gimbal, and combines the dynamic adjustment of the focal length and the exposure time ISO to ensure the clarity and contrast of the images under different lighting conditions. During the flight, the UAV triggers the shutter at a fixed time interval or distance interval to generate a sequence of images that continuously cover the survey area. Each image is embedded with GPS coordinates, flight altitude, attitude angles (pitch angle, roll angle, yaw angle) and time stamp information to form a complete metadata record.

[0020] After shooting, multi-source data registration of the sequential images and high-precision digital surface model (DSM) data is performed through a cross-platform geographic planning software (such as Omap). Specifically, the multi-view geometry algorithm (such as Structure from Motion, SFM) is used to reconstruct the sparse point cloud of the sequential images, generating the initial three-dimensional point cloud data, and performing spatial coordinate matching with the DSM data. During the matching process, calibration is performed based on control points (such as ground markers or known elevation points). Subsequently, the point cloud data is overlaid with the DSM raster through rasterization processing, and the elevation values corresponding to the geographic coordinates are extracted pixel by pixel to form a rasterized elevation dataset with each photo as a unit, and sorted according to the sequential points of the initial flight line data.

[0021] Furthermore, the elevation data of each sequential image is refined. For the dynamic interferences (such as moving vehicles, temporary facilities) existing in the sequential images, the time series analysis method combined with morphological filtering (such as opening operation) is used to remove the noise data. For example, variance analysis is performed on the elevation values of multiple consecutive photos in the same geographic area. If the elevation value of a certain pixel fluctuates beyond a certain threshold, it is determined as a dynamic target and marked as invalid data. At the same time, for the abnormal elevation values (such as the height of trees misjudged as buildings) caused by vegetation or shadows in the DSM data, the edge detection algorithm combined with texture analysis is used to distinguish the land cover types, and only the elevation information of static targets such as buildings and roads is retained.

[0022] Finally, the processed elevation data is arranged according to the flight order of the initial flight line to construct an elevation database that strictly corresponds to the image sequence. The elevation dataset of each sequential image contains the raster height values of the target area, land cover classification labels, and confidence scores, providing a quantitative basis for subsequent dynamic speed change strategies.

[0023] Dynamic speed change rules are generated by analyzing the constructed elevation database, thereby providing a basis for speed adjustment for subsequent aerial photography. First, certain preprocessing needs to be performed on the extracted elevation data: set the flight height ratio threshold (such as 20% of the flight height), and filter out static targets (such as low vegetation, temporary facilities) below this threshold. For example, when the flight height is 120 meters, only the building data with elevation values ≥ 24 meters is retained to reduce noise interference and focus on key features. This process is achieved through a spatial filtering algorithm to ensure that the dataset only contains elevation information that has a significant impact on the airspeed decision.

[0024] Subsequently, extreme value analysis and proportion statistics are performed. Specifically, the maximum elevation value within the current sequential image is used as the first extreme value (such as ), and the second extreme value is set as (such as , then the second extreme value is 80 meters). The proportion of the number of buildings with elevation values in the range of 80 to 100 meters within the current photo is statistically analyzed and the proportion of the adjacent photos is recorded , resulting in a change in proportion.

[0025] Here, in this embodiment, the proportion range is divided into three intervals, such as 0 - 30% (the first proportion interval), 30% - 60% (the second proportion interval), and 60% - 100% (the third proportion interval), and a speed adjustment mapping relationship is established: When the proportion of the elevation data of the previous sequence of images is in the first proportion interval: If the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first flight speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second flight speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third flight speed; When the proportion of the elevation data of the previous sequence of images is in the second proportion interval: If the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first flight speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second flight speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third flight speed; When the proportion of the elevation data of the previous sequence of images is in the third proportion interval: If the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first flight speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second flight speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third flight speed; Among them, the first flight speed is greater than the second flight speed, and the second flight speed is greater than the third flight speed; the numerical intervals of the first proportion interval, the second proportion interval, and the third proportion interval are adjacent in sequence and do not overlap with each other.

[0026] The formula is expressed as follows: Among them, is the adjusted speed, is the first flight speed, is the second flight speed, is the third flight speed, is the first proportion interval, is the second proportion interval, is the third proportion interval.

[0027] The above rules are quantitatively expressed by this mathematical formula, and combined with the acceleration threshold (such as ≤ 2.5 m / s²) to constrain the speed change rate, ensuring that the generated flight speed data is smooth and continuous.

[0028] The variable-speed route data generated based on this rule includes waypoint coordinates, target airspeeds, and acceleration parameters. It can be foreseen that, designed based on this rule, the system can prospectively predict the terrain elevation changes and generate an adapted variable-speed strategy. For example, it decelerates in advance before entering a high-rise dense area to ensure the image acquisition density, and cruises efficiently in low-lying areas to reduce the ineffective flight time. This process is completely data-driven and does not require manual intervention, significantly improving the aerial photography efficiency and model accuracy in complex scenarios.

[0029] After the UAV flight control system analyzes the final route data, it dynamically adjusts the flight speed in combination with real-time positioning (RTK / PPK) and attitude sensor data. Specifically, before entering a high-rise dense area, the system predicts the elevation distribution ratio of the current flight segment (such as ), starts the deceleration procedure in advance, smoothly reduces the airspeed from the initial airspeed to , and at the same time controls the acceleration not to exceed (2.5 m / s 2 ) to avoid image blurring or positioning drift caused by sudden deceleration. In low-lying areas, if is detected, it gradually resumes to the initial airspeed , and automatically optimizes the flight path to bypass redundant areas to improve efficiency.

[0030] During the aerial photography execution process, the system monitors the image overlap and airspeed matching status in real time. Through the on-board computing unit (such as a GPU embedded module), it performs real-time quality analysis on the captured images. If it detects that the overlap in the key area is lower than the preset threshold (such as the heading overlap p < 75%), it immediately triggers dynamic route correction: inserting supplementary waypoints or making local detours to ensure the integrity of image coverage. At the same time, the flight control system continuously feeds back the execution data (such as actual airspeed, acceleration, overlap) to the ground station to form a closed-loop control.

[0031] Finally, all aerial photography data (images, metadata, and flight logs) are archived according to the task number, and the execution effects of the variable-speed strategy are marked (such as the number of speed adjustments, the passing rate of overlap), providing data support for subsequent task optimization. This process not only realizes the refined acquisition in high-rise areas and the efficient coverage in low-lying areas, but also significantly improves the aerial photography robustness and model reconstruction accuracy in complex scenarios through the closed-loop feedback mechanism.

[0032] The present invention also includes a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it realizes the steps of a method for variable-speed aerial photography of UAVs for multi-category terrain elevations.

[0033] It also includes a device for processing data, including: a memory, on which a computer program is stored; A processor for executing a computer program in the memory to implement the steps of an unmanned aerial vehicle variable-speed aerial photography method for multi-category ground object elevations.

[0034] In summary, an unmanned aerial vehicle variable-speed aerial photography method for multi-category ground object elevations proposed by the present invention systematically covers the four elevations of the building, namely, east, south, west, and north, through the design of mutually perpendicular cross flight lines combined with forward-looking oblique photography, and solves the problem of texture loss caused by building occlusion in traditional aerial photography.

[0035] Based on the pixel-level integration of the digital surface model and the image, the local elevation distribution characteristics are dynamically extracted to achieve precise matching of the flight speed and the ground object height. At the same time, based on the real-time analysis of the elevation distribution ratio, the flight speed of the unmanned aerial vehicle is dynamically adjusted, which not only ensures the intensive acquisition of high-rise building images but also avoids redundant data in low-lying areas, significantly improving the aerial photography efficiency and the image matching success rate.

[0036] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the attached drawings). If the specific posture changes, the directional indication will also change accordingly.

[0037] In addition, in the present invention, descriptions such as "first", "second", and "one" are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined as "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0038] In the present invention, unless otherwise clearly specified and limited, terms such as "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] In addition, the technical solutions between various embodiments of the present invention can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

Claims

1. A method for variable speed aerial photography of multi-category terrain features by using an unmanned aerial vehicle, characterized in that: Includes steps: S1: Based on the parameters of the aerial survey instrument, image overlap requirements and terrain profile, the texture of each orientation of the static target is used as the information collection requirement to generate cross routes that are perpendicular to each other in the horizontal plane as the initial route data; S2: Control the drone to fly according to the initial route data and take a sequence of images at a preset forward angle; S3: Integrate digital surface model data and sequence images, and extract elevation datasets of each static target in the sequence images; S4: Analyze the change in the proportion of the elevation distribution of static targets in adjacent sequence images according to the elevation data set, and adjust the speed according to the change in proportion; S5: Generate final route data according to the speed adjustment result, and control the UAV to perform variable speed aerial photography according to the final route data.

2. The method for variable speed aerial photography of multiple types of terrain features by using an unmanned aerial vehicle according to claim 1, characterized in that: In the step S1, the aerial survey instrument parameters include pixel size ,focal length , Hanggao , equivalent image width And the equivalent image height The image overlap includes the heading overlap p and the lateral overlap q.

3. The method for variable speed aerial photography of multiple types of terrain features by using an unmanned aerial vehicle as claimed in claim 2, characterized in that: The equivalent image width and equivalent image height Obtained through the following formula: Where W is the number of horizontal pixels of the aerial survey instrument's sensor, and H is the number of vertical pixels of the aerial survey instrument's sensor.

4. The method for variable speed aerial photography of multiple types of terrain features by unmanned aerial vehicle according to claim 1, characterized in that: In the step S3, the elevation dataset is extracted in the following manner: The initial three-dimensional point cloud data is reconstructed from the sparse point cloud of the sequence images through a multi-view algorithm. After the initial three-dimensional point cloud data is rasterized, it is rasterized and superimposed with the digital surface model data. Based on the superposition result, the elevation value of the corresponding geographic coordinate is extracted pixel by pixel to form an elevation dataset with sequence images as units.

5. The method for variable speed aerial photography of multiple types of terrain features by unmanned aerial vehicle according to claim 1, characterized in that: Before the S4 step, the following steps are also included: S40: According to the ratio between the elevation data of each static target in the sequence image and the flight altitude, the elevation data of the static target that is lower than a preset ratio is filtered out.

6. The method for variable speed aerial photography of multiple types of terrain features by unmanned aerial vehicle according to claim 1, characterized in that: In the step S4, the change in the elevation distribution ratio of static objects in adjacent sequence images is analyzed according to the elevation data set as follows: The maximum elevation data in the current sequence image elevation data set is taken as the first extreme value, the preset ratio threshold of the maximum elevation data is taken as the second extreme value, the first extreme value and the second extreme value form a judgment interval, and the changes in the elevation distribution ratio of static targets in adjacent sequence images are analyzed according to the proportion of the judgment interval at the elevation data in the current sequence image elevation data set.

7. The method for variable speed aerial photography of multiple types of terrain features by unmanned aerial vehicle according to claim 6, characterized in that: In the step S4, the speed adjustment according to the change of the proportion is specifically: When the proportion of the elevation data of the previous sequence of images is in the first proportion interval: if the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third speed; When the proportion of the elevation data of the previous sequence of images is in the second proportion interval: if the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third speed; When the proportion of the elevation data of the previous sequence of images is in the third proportion interval: if the proportion of the elevation data of the current sequence of images is in the first proportion interval, adjust to the first speed; if the proportion of the elevation data of the current sequence of images is in the second proportion interval, adjust to the second speed; if the proportion of the elevation data of the current sequence of images is in the third proportion interval, adjust to the third speed; The first speed is greater than the second speed, and the second speed is greater than the third speed; the numerical ranges of the first proportion range, the second proportion range and the third proportion range are adjacent to each other and do not overlap.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a method for variable-speed aerial photography of multi-category terrain features by a drone as described in any one of claims 1 to 7 are implemented.

9. A device for processing data, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of a method for variable-speed aerial photography of multi-category terrain features by a drone as described in any one of claims 1 to 7.

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