A topographic survey method, device, and electronic device based on drone collaboration

Abstract

The present disclosure provides a terrain survey method, device and electronic device based on drone collaboration, relating to the technical fields of engineering surveying and complex terrain mapping. The method includes: determining a first survey flight route of the drone in combination with the ground elevation data of the area to be surveyed, controlling the drone to collect initial area terrain data, identifying and classifying it to determine weak texture false terrain areas; then re-planning a second survey flight route of the drone according to the weak texture false terrain areas, turning on the millimeter-wave radar and scanning and collecting supplementary terrain survey data at different angles; fusing the supplementary terrain survey data at different angles to obtain local terrain survey data; splicing and fusing the initial area terrain data and the local terrain survey data to obtain real terrain survey data. This technical solution can improve the measurement accuracy and survey efficiency of terrain survey data in complex terrain areas, and improve the reliability and stability of real terrain survey data.

Description

Background Art

[0002] Complex terrains (such as cliffs, dense forests, plateau snow cover, etc.) pose severe challenges to traditional terrain surveying technologies. Complex terrain mapping is an important research direction in current engineering surveys and the construction of geographic information systems. However, when traditional terrain surveying technologies face environments such as high-altitude and high-drop areas, vegetation or snow cover, large surface undulations, and inconvenient transportation, it is often difficult to obtain efficient and accurate data. For example, the manual ground measurement method requires manual layout of points and point-by-point data collection, which is not only time-consuming and laborious but also risks missing key terrain features.

[0003] Currently, related technologies mainly rely on lidar (Light Detection And Ranging, LiDAR) and optical cameras carried by unmanned aerial vehicles (UAVs) for terrain data measurement. However, in high-altitude and high-drop areas, areas with large terrain undulations, dense vegetation and snow cover, or weak surface textures, there are often problems such as insufficient accuracy of terrain data measurement, low survey efficiency, missing key terrain features, or some key terrain feature data being false terrain feature data, resulting in poor reliability and stability of the measured terrain data. Summary of the Invention

[0004] The purpose of the embodiments of the present disclosure is to provide a terrain surveying method based on UAV collaboration, a terrain surveying device based on UAV collaboration, an electronic device, and a computer-readable storage medium, thereby improving the measurement accuracy and survey efficiency of terrain survey data in complex terrain areas and enhancing the reliability and stability of real terrain survey data.

[0005] Other features and advantages of the present disclosure will become apparent through the following detailed description, or be learned in part through the practice of the present disclosure.

[0006] According to the first aspect of the embodiments of the present disclosure, a terrain surveying method based on UAV collaboration is provided, including:

[0007] Obtain the ground elevation data of the area to be surveyed, and determine the first survey flight routes of at least two of the UAVs in combination with the ground elevation data;

[0008] Control the UAVs to collect initial area terrain data based on the first survey flight routes, and the initial area terrain data is obtained by real-time collection using the lidar and optical cameras carried by the UAVs;

[0009] Identify and classify the initial area terrain data to determine the weak texture false terrain areas in the initial area terrain data;

[0010] Re-plan at least two second survey flight routes corresponding to the respective drones according to the weak-texture false terrain area, where the second survey flight routes are used to control at least two drones to turn on millimeter-wave radars and scan and collect supplementary terrain survey data of the weak-texture false terrain area at different angles;

[0011] Fuse the supplementary terrain survey data at different angles to obtain local terrain survey data of the weak-texture false terrain area;

[0012] Stitch and fuse the initial area terrain data and the local terrain survey data to obtain the true terrain survey data of the area to be surveyed.

[0013] According to a second aspect of the embodiments of the present disclosure, there is provided a terrain survey device based on drone collaboration, including:

[0014] An initial flight survey module, configured to obtain ground elevation data of an area to be surveyed, and determine at least two first survey flight routes of the drones in combination with the ground elevation data;

[0015] A terrain data acquisition module, configured to control the drones to acquire initial area terrain data based on the first survey flight routes, where the initial area terrain data is obtained by real-time acquisition by lidars and optical cameras carried by the drones;

[0016] A false terrain area identification module, configured to identify and classify the initial area terrain data to determine weak-texture false terrain areas in the initial area terrain data;

[0017] A survey route update module, configured to re-plan at least two second survey flight routes corresponding to the respective drones according to the weak-texture false terrain area, where the second survey flight routes are used to control at least two drones to turn on millimeter-wave radars and scan and collect supplementary terrain survey data of the weak-texture false terrain area at different angles;

[0018] A supplementary survey data fusion module, configured to fuse the supplementary terrain survey data at different angles to obtain local terrain survey data of the weak-texture false terrain area;

[0019] A true terrain data output module, configured to stitch and fuse the initial area terrain data and the local terrain survey data to obtain the true terrain survey data of the area to be surveyed.

[0020] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a processor; and a memory, where computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, the terrain survey method based on drone collaboration as described in any one of the above is implemented.

[0021] According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it implements the method for terrain survey based on drone collaboration according to any one of the above.

[0022] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects:

[0023] In the method for terrain survey based on drone collaboration in the exemplary embodiments of the present disclosure, on the one hand, by dynamically planning the survey flight routes of multiple drones based on the ground elevation data of complex terrain areas, the problems of overlapping drone flight routes and insufficient blind area coverage can be effectively reduced, so that the collaborative operation of multiple drones can more efficiently and evenly cover complex terrain areas, improving the survey efficiency of the terrain features of the surveyed area; in addition, by using the lidar and optical cameras carried by the drones to collect the initial area terrain data in real time, more abundant and multi-dimensional terrain information can be obtained under different terrain conditions, enhancing the spatial perception ability of the target area and improving the accuracy and precision of the measured terrain feature data; on the other hand, by identifying and classifying the initial area terrain data, the weak-texture false terrain areas and real terrain areas can be effectively distinguished, thereby avoiding incorporating mis-identified false terrain features into the terrain modeling data, which helps to improve the authenticity and integrity of the terrain data. Further, based on the identified weak-texture false terrain areas, the flight routes of multiple drones are re-planned, and the drones are controlled to enable millimeter-wave radar to perform multi-angle scanning to collect supplementary data, so that the drones can break through the penetration limit of traditional lidar under complex terrain conditions, thereby making up for the data collection blind area, realizing the comprehensive perception of the surface terrain, avoiding the omission or mis-identification of key terrain feature data, and further improving the accuracy and precision of the measured terrain feature data and the reliability and stability of the real terrain survey data; on yet another hand, by fusing the supplementary terrain data collected from different angles and efficiently stitching and fusing it with the initial area terrain data, the data stitching error and model gap can be effectively reduced, optimizing the spatial continuity and precision of the terrain data; combined with the real-time data processing and multi-source data fusion strategy, the density and integrity of the point cloud data can be significantly improved, thereby effectively reducing the information loss and data redundancy problems in the data collection process.

[0024] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0026] Figure 1 A schematic flowchart showing the process of a terrain survey method based on drone collaboration according to some embodiments of the present disclosure.

[0027] Figure 2 A schematic flowchart showing the process of generating a first survey flight route according to some embodiments of the present disclosure.

[0028] Figure 3 A schematic flowchart showing the process of dividing survey sub - regions according to some embodiments of the present disclosure.

[0029] Figure 4 A schematic flowchart showing the process of determining weak - texture false terrain regions according to some embodiments of the present disclosure.

[0030] Figure 5 A schematic flowchart showing the process of determining point - cloud spatial features according to some embodiments of the present disclosure.

[0031] Figure 6 A schematic flowchart showing the process of generating a second survey flight route according to some embodiments of the present disclosure.

[0032] Figure 7 A schematic flowchart showing the process of generating local terrain survey data according to some embodiments of the present disclosure.

[0033] Figure 8 A schematic diagram showing the composition of a terrain survey device based on drone collaboration according to some embodiments of the present disclosure.

[0034] Figure 9 A schematic diagram showing the structure of a computer system of an electronic device according to some embodiments of the present disclosure.

[0035] Figure 10 A schematic diagram showing a computer - readable storage medium according to some embodiments of the present disclosure.

[0036] In the accompanying drawings, the same or corresponding reference numerals represent the same or corresponding parts. Detailed embodiments

[0037] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this specification. On the contrary, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0038] In addition, the accompanying drawings are only schematic diagrams and are not necessarily drawn to scale. The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0039] In the present exemplary embodiment, first, a terrain survey method based on drone collaboration is provided. This terrain survey method based on drone collaboration can be applied to drone devices or the host devices of drone devices, and can also be applied to the servers of the terrain survey control center. The present exemplary embodiment does not make special limitations on this. Figure 1 A flowchart of a terrain survey method based on drone collaboration according to some embodiments of the present disclosure is schematically shown. Refer to Figure 1 As shown, this terrain survey method based on drone collaboration may include the following steps:

[0040] Step S110, obtain the ground elevation data of the area to be surveyed, and determine the first survey flight routes of at least two of the drones in combination with the ground elevation data;

[0041] Step S120, control the drones to collect initial area terrain data based on the first survey flight routes. The initial area terrain data is collected in real time by the lidar and optical cameras carried by the drones;

[0042] Step S130, identify and classify the initial area terrain data to determine the weak texture false terrain areas in the initial area terrain data;

[0043] Step S140, re-plan the second survey flight routes respectively corresponding to at least two of the drones according to the weak texture false terrain areas. The second survey flight routes are used to control at least two of the drones to turn on millimeter-wave radars and scan and collect supplementary terrain survey data of the weak texture false terrain areas at different angles;

[0044] Step S150, fuse the supplementary terrain survey data at different angles to obtain local terrain survey data of the weak texture false terrain areas;

[0045] In step S160, splice and fuse the initial area terrain data and the local terrain survey data to obtain the true terrain survey data of the area to be surveyed.

[0046] According to the terrain survey method based on UAV collaboration in this exemplary embodiment, on the one hand, by dynamically planning the survey flight routes of multiple UAVs based on the ground elevation data of complex terrain areas, the problems of UAV flight path overlap and insufficient blind area coverage can be effectively reduced, so that the collaborative operation of multiple UAVs can more efficiently and evenly cover complex terrain areas, improving the survey efficiency of the terrain features of the surveyed area; in addition, by using the lidar and optical cameras carried by UAVs to collect the initial area terrain data in real time, more abundant and multi-dimensional terrain information can be obtained under different terrain conditions, enhancing the spatial perception ability of the target area and improving the accuracy and precision of the measured terrain feature data; on the other hand, by identifying and classifying the initial area terrain data, the weak texture false terrain area and the true terrain area can be effectively distinguished, thus avoiding incorporating mis-identified false terrain features into the terrain modeling data, which helps to improve the authenticity and integrity of the terrain data. Further, based on the identified weak texture false terrain area, re-plan the flight routes of multiple UAVs, and control the UAVs to enable millimeter-wave radar to perform multi-angle scanning to collect supplementary data, so that the UAVs can break through the penetration limit of traditional lidar under complex terrain conditions, thereby making up for the data collection blind area, realizing the comprehensive perception of the surface terrain, avoiding the omission or mis-identification of key terrain feature data, and further improving the accuracy and precision of the measured terrain feature data and the reliability and stability of the true terrain survey data; on the further hand, by fusing the supplementary terrain data collected from different angles and efficiently splicing and fusing it with the initial area terrain data, the data splicing error and model gap can be effectively reduced, optimizing the spatial continuity and accuracy of the terrain data; combined with the real-time data processing and multi-source data fusion strategy, the density and integrity of the point cloud data can be significantly improved, thus effectively reducing the information loss and data redundancy problems in the data collection process.

[0047] Next, the terrain survey method based on UAV collaboration in this exemplary embodiment will be further described.

[0048] In step S110, obtain the ground elevation data of the area to be surveyed, and determine the first survey flight routes of at least two of the UAVs in combination with the ground elevation data.

[0049] In an exemplary embodiment of the present disclosure, the ground elevation data refers to the data used to characterize the terrain undulation characteristics of the target area. For example, the ground elevation data can be obtained through remote sensing images, Digital Elevation Model (DEM), and Digital Surface Model (DSM). Specifically, the ground elevation data can be obtained by analyzing satellite remote sensing images to obtain DEM or DSM data, or by historical survey data or existing Geographic Information System (GIS) data. Of course, the ground elevation data can also be obtained through preliminary aerial surveys. In this embodiment, there is no special limitation on the acquisition method of the ground elevation data.

[0050] The unmanned aerial vehicle (UAV) can be a multi-rotor UAV or a fixed-wing UAV equipped with multiple sensors. The sensors carried by the UAV can include one or a combination of lidar, optical camera, millimeter-wave radar, and infrared camera. It can be understood that the UAV can also be equipped with navigation devices such as Inertial Navigation System (INS) and Global Positioning System (GPS) for precise positioning and attitude control. In this embodiment, there is no special limitation on the type and configuration of the UAV.

[0051] When determining the first survey flight route of the UAV, the terrain of the area to be surveyed can be analyzed based on the ground elevation data, and terrain undulation parameters including elevation change rate, slope distribution, height difference gradient, and surface roughness can be extracted. Through the analysis of the terrain undulation parameters, a dynamic path planning algorithm (such as A* algorithm, Dijkstra algorithm, ant colony algorithm, or genetic algorithm, etc.) is used to perform dynamic grid division on the area to be surveyed. The grid division can set different division densities based on the terrain complexity, so as to adopt a higher density of grid division in high-undulation or complex terrain areas and a lower density of grid division in flat areas, thereby optimizing the flight route planning and improving the survey efficiency.

[0052] In step S120, based on the first survey flight route, the UAV is controlled to collect initial area terrain data, and the initial area terrain data is obtained by real-time collection using the lidar and optical camera carried by the UAV.

[0053] In an exemplary embodiment of the present disclosure, a lidar can be used to emit laser pulses and receive multi-echo signals to obtain multi-level terrain data including a vegetation layer and a ground surface layer. The lidar can adopt a multi-echo acquisition technique. Specifically, the laser pulses emitted by the lidar will generate multiple echo signals when encountering different objects (such as tree canopies, tree trunks, and the ground surface). Among them, the first echo signal can be used to extract upper-layer vegetation information, the middle echo signal can be used to extract middle-layer structure information, and the last echo signal can be used to extract bare ground surface information. To improve the data acquisition accuracy, the lidar can adopt a high-frequency emission and multi-angle scanning technique. Of course, the emission frequency and scanning angle of the lidar can also be dynamically adjusted according to the terrain complexity. This embodiment does not make special limitations on the specific configuration of the lidar.

[0054] An optical camera can be used to obtain high-resolution image data of the area to be surveyed. The image data can include, but is not limited to, red-green-blue (RGB) images, multi-spectral images, or panchromatic images. The optical camera can adopt functions such as automatic exposure, automatic focusing, and real-time image enhancement to stably obtain clear image data under different lighting conditions. To enhance the spatial synchronization of the data, the data acquisition processes of the optical camera and the lidar are carried out synchronously. The synchronization method can be based on timestamp alignment or an external trigger mechanism. This embodiment does not make special limitations on the synchronization method.

[0055] In step S130, the initial area terrain data is identified and classified to determine the weak-texture false terrain area in the initial area terrain data.

[0056] In an exemplary embodiment of the present disclosure, the initial area terrain data can include image data and lidar point cloud data. The initial area terrain data can be preprocessed. For example, the preprocessing can be operations such as noise removal, texture enhancement, data standardization, and point cloud density equalization to improve the data quality. Specifically, for the texture enhancement processing of the image data, texture feature extraction algorithms based on gray-level co-occurrence matrix (GLCM), edge detection algorithms, or local binary pattern (LBP) algorithms can be used; for the processing of lidar point cloud data, noise reduction and sparsification methods based on voxel downsampling and normal vector filtering can be used. For example, in one implementation, an adaptive filtering algorithm based on local point cloud density can be used to identify and remove noise points in the lidar point cloud data. This method assumes that noise points are usually isolated and there are not enough neighboring points around them. By calculating the local density of each point, low-density noise points can be effectively identified and removed, which can be specifically represented by the following relationship:

[0057] ;

[0058] where can represent point The local density can represent the set of neighborhoods of point and can represent the adjustment parameter for density calculation, which is used to control the size of the neighborhood. can represent the Euclidean distance between point and point When it is detected that the local density is less than a certain set threshold, this point can be considered a noise point and needs to be removed.

[0059] The weak texture false terrain area refers to the area in the terrain survey process where, due to the limitations of sensors (such as lidar, optical cameras, etc.) by environmental factors or their own technical characteristics, the texture features in the terrain data are weak, the terrain feature information is incomplete, or there are false terrain features. This area usually shows missing texture details, blurred terrain contours, or insufficient data density, and is easily misidentified as real terrain, affecting the accuracy and integrity of the terrain data. For example, the weak texture false terrain area can be a high vegetation coverage area, or a snow and glacier coverage area, or a complex terrain occlusion area such as a steep cliff, a precipice, or a canyon.

[0060] After the preprocessing is completed, the texture features of the image data and the spatial features of the point cloud data can be extracted respectively. The texture feature extraction methods can include gray difference analysis, gradient direction distribution analysis, and edge texture distribution analysis. The point cloud spatial features can include point cloud density, normal vector distribution, and curvature change rate. Based on the above features, the terrain data can be classified and recognized through clustering analysis or classification algorithms (such as the K-means algorithm, support vector machine SVM, or convolutional neural network CNN), and then the area with texture features lower than the preset threshold and abnormal spatial features can be recognized as the weak texture false terrain area.

[0061] In step S140, at least two second survey flight routes corresponding to the drones are re-planned according to the weak texture false terrain area. The second survey flight routes are used to control at least two drones to turn on the millimeter wave radar and scan and collect supplementary terrain survey data of the weak texture false terrain area at different angles.

[0062] In an exemplary embodiment of the present disclosure, the second survey flight route refers to the flight path re-planned in the terrain survey based on drone collaboration to more accurately and comprehensively supplement the data of the weak texture false terrain area identified in the initial survey process. This flight route is dynamically adjusted based on the spatial distribution characteristics, terrain complexity, and data collection requirements of the weak texture false terrain area on the basis of the first survey flight route to achieve refined supplementary survey of the target area.

[0063] Supplementary topographic survey data refers to data obtained through re - survey to make up for and improve topographic information for data blind spots, incomplete data, or error data caused by factors such as terrain complexity, environmental occlusion, and sensor performance limitations during the initial topographic survey. This data is mainly used to enhance the integrity, accuracy, and reliability of the topographic model, ensuring a comprehensive and accurate expression of the topographic features in weakly - textured false terrain areas.

[0064] For weakly - textured false terrain areas, at least two second survey flight routes corresponding to different unmanned aerial vehicles (UAVs) can be re - planned. For example, by analyzing the spatial distribution characteristics of the weakly - textured false terrain areas, the regional boundary range, terrain undulation characteristics, and spatial distribution density can be extracted, and an optimized second survey flight route can be generated using a dynamic path - planning algorithm based on a cost function. The path planning can comprehensively consider the remaining battery power of the UAV, the flight environment, and the working parameters of the millimeter - wave radar to ensure that the UAV comprehensively covers the target area at different angles.

[0065] Under the second survey flight route, control the UAV to enable the millimeter - wave radar to scan and collect supplementary topographic survey data of the weakly - textured false terrain area at different angles. The millimeter - wave radar has strong penetration and anti - interference capabilities and can penetrate vegetation and snow to obtain surface feature information. The transmission frequency, power, and scanning angle of the millimeter - wave radar can be dynamically adjusted according to the terrain complexity of the target area to supplement the measurement blind spots of lidar in complex terrains.

[0066] In step S150, fuse the supplementary topographic survey data at different angles to obtain local topographic survey data of the weakly - textured false terrain area.

[0067] In step S160, splice and fuse the initial regional topographic data and the local topographic survey data to obtain the true topographic survey data of the area to be surveyed.

[0068] In an exemplary embodiment of the present disclosure, the initial regional topographic data can be fused with the supplementary topographic survey data. For example, the splicing and fusion of the initial regional topographic data and the local topographic survey data can be achieved through the following relational expression:

[0069] ;

[0070] where can represent the global point cloud data corresponding to the true topographic survey data obtained by splicing and fusion, and can represent the rotation matrix, and can represent the translation vector, can represent the point cloud data corresponding to the initial regional topographic data, It can represent the point cloud data corresponding to the local topographic survey data. It can represent the Euclidean distance between point clouds. Through continuously adjusting the rotation and translation parameters in the optimization process, the error after stitching is minimized, thereby generating a seamlessly connected point cloud model, that is, obtaining the true topographic survey data corresponding to the area to be surveyed.

[0071] By fusing the initial regional topographic data with the supplementary topographic survey data, using the multi-view data registration algorithm to align the data at different angles, and implementing high-precision data stitching based on feature point matching and point cloud density optimization algorithm, a continuous and complete three-dimensional topographic model is finally generated, realizing the acquisition of the true topographic survey data of the area to be surveyed, thereby being able to improve the measurement accuracy and survey efficiency of the topographic survey data in complex terrain areas, and enhancing the reliability and stability of the true topographic survey data.

[0072] Next, the content in steps S110 to S160 will be described in detail.

[0073] In an exemplary embodiment of the present disclosure, step S110 of determining the first survey flight routes of at least two unmanned aerial vehicles by combining ground elevation data can be implemented through the steps in Figure 2 , as shown in reference Figure 2 , and specifically may include:

[0074] Step S210, determining the topographic undulation parameters of the area to be surveyed according to the ground elevation data, where the topographic undulation parameters include elevation change rate, slope distribution, height difference gradient, and surface roughness;

[0075] Step S220, dynamically dividing the area to be surveyed based on the topographic undulation parameters to determine the survey sub-areas corresponding to the area to be surveyed;

[0076] Step S230, allocating the unmanned aerial vehicles to each of the survey sub-areas to obtain the initial survey flight routes;

[0077] Step S240, analyzing the overlapping areas and blind spots of the initial survey flight routes, and adjusting the flight line spacing and scanning angle of the unmanned aerial vehicles based on the analysis results to generate the first survey flight routes.

[0078] Among them, the terrain undulation parameter refers to the terrain feature data extracted from the ground elevation data. For example, the terrain undulation parameter may include elevation change rate, slope distribution, height difference gradient, and surface roughness. Specifically, the elevation change rate can be used to represent the intensity of terrain height change, the slope distribution can be used to reflect the steepness of the terrain, the height difference gradient can be used to describe the spatial change trend of elevation difference, and the surface roughness can be used to measure the undulation and irregularity of the ground surface. By analyzing the terrain undulation parameter, the terrain complexity and surface characteristics of the area to be surveyed can be comprehensively understood.

[0079] Dynamic grid division refers to a processing method that flexibly adjusts the density and size of the grid according to the complexity of the regional terrain to achieve refined coverage of complex terrain areas. Specifically, an adaptive grid division algorithm can be used to divide complex terrain areas into high-density small grids and flat areas into low-density large grids. For example, the three-dimensional space of the regional terrain can be refined layer by layer based on the Quadtree or Octree algorithm, or a method based on slope threshold can be used to increase the grid density in areas with large slopes. By performing dynamic grid division on the area to be surveyed, the rationality of the UAV flight route and the integrity of the survey data can be effectively improved.

[0080] After the dynamic grid division is completed, UAVs can be assigned to each survey sub-area. In one implementation, different sub-areas can be assigned to each UAV according to the flight performance parameters of each UAV (such as flight speed, maximum flight height, payload capacity, endurance time) and the area and terrain complexity of each survey sub-area, using a task assignment algorithm (such as the partition method, load balancing algorithm). During the task assignment process, the task load can be dynamically adjusted in combination with the real-time flight environment (such as meteorological conditions, wind speed, air pressure) to ensure that multiple UAVs efficiently and coordinately execute the survey task.

[0081] The initial survey flight route refers to the flight route of the UAV formed by assigning survey sub-areas to be measured to the UAV. The initial survey flight route may include parameters such as flight height, flight route direction, scanning angle, and flight speed. For example, the flight height can be adjusted according to the terrain undulation to avoid collisions with obstacles; the flight route direction can be optimized according to the distribution of the survey sub-areas to ensure that there is no overlap in the flight route; the scanning angle and flight speed can be dynamically matched according to the sensor performance (such as the scanning frequency of lidar and the resolution of optical cameras). In some optional implementations, the flight route can be generated by a path planning algorithm. For example, the path planning algorithm can choose the A* algorithm, or it can also choose the Dijkstra algorithm, ant colony algorithm, etc. This embodiment is not limited thereto, aiming to minimize flight energy consumption, avoid blind spots and overlapping areas.

[0082] After completing the initial flight route planning, perform an overlap area and blind area analysis on the initial survey flight route. The overlap area and blind area analysis can adopt an evaluation algorithm based on route overlap degree and point cloud coverage density to detect the route overlap situation and area coverage situation between different UAVs. If route overlap or area blind spots are found, the route spacing and scanning angle can be dynamically adjusted. Specifically, the route spacing adjustment can be based on terrain complexity and sensor coverage range, and the scanning angle adjustment can be based on surface slope and terrain feature change trend to ensure the integrity and consistency of data collection.

[0083] Through terrain undulation parameter analysis, dynamic grid division, route optimization adjustment, and task load balancing, the UAV can flexibly and efficiently perform survey tasks in complex terrain environments, thereby ensuring the comprehensiveness, accuracy, and stability of terrain data measurement, not only improving the survey and collection efficiency of terrain data, but also enhancing the accurate perception ability of complex terrain features.

[0084] Optionally, the dynamic grid division of the area to be surveyed based on terrain undulation parameters in step S220 to determine the survey sub-areas corresponding to the area to be surveyed can be implemented through the steps in Figure 3 As shown in Figure 3 , it can specifically include:

[0085] Step S310, determine the complex terrain area and flat terrain area of the area to be surveyed according to the terrain undulation parameters and a preset terrain undulation threshold;

[0086] Step S320, divide the complex terrain area into grids with a first division density, and divide the flat terrain area into grids with a second division density to obtain the survey sub-areas corresponding to the area to be surveyed; where the first division density is greater than the second division density.

[0087] Among them, the preset terrain undulation threshold refers to a critical value preset based on terrain change characteristics and survey requirements for distinguishing the complexity of the terrain. Specifically, when the elevation change rate, slope distribution, or height difference gradient of a terrain area is greater than or equal to the preset terrain undulation threshold, the current terrain area can be determined as a complex terrain area; otherwise, it can be determined as a flat terrain area. The terrain undulation threshold can be set through historical survey data analysis or terrain feature standard experience. Of course, it can also be dynamically adjusted according to real-time survey data. This embodiment does not limit the threshold setting method.

[0088] After completing the division of complex terrain and flat terrain areas, different division densities can be used to perform grid division on the areas. Specifically, the complex terrain area is divided into grids with the first division density. Since the density value of the first division density is relatively high, the divided grid cells are relatively small, which can more finely cover the areas with large terrain undulations and complex landforms, ensuring the comprehensive acquisition of detailed terrain features. For the flat terrain area, the second division density is used for grid division. Since the density value of the second division density is relatively low, the divided grid cells are relatively large, effectively reducing unnecessary data redundancy and improving the UAV survey efficiency. The grid division method can use a quadtree or an adaptive division method based on a slope threshold to dynamically adjust the size and distribution of grid cells to better adapt to terrain changes.

[0089] Through differential grid division processing, multiple survey sub-areas covering the entire area to be surveyed are formed. The survey sub-areas have different division densities and sizes, enabling refined and efficient data collection for complex and simple terrain areas. The UAV survey tasks can be reasonably allocated according to the division results of these survey sub-areas to ensure the balance and efficiency of the survey tasks, thereby improving the integrity and accuracy of the terrain data of the area to be surveyed obtained by measurement.

[0090] In an exemplary embodiment of the present disclosure, the identification and classification of the initial area terrain data in step S130 to determine the weak texture false terrain area in the initial area terrain data can be implemented through the steps in Figure 4 , as shown in Figure 4 . Specifically, it may include:

[0091] Step S410, preprocessing the initial area terrain data, where the initial area terrain data includes image data and laser point cloud data, and the preprocessing at least includes noise removal, texture enhancement processing, data normalization, and point cloud density equalization;

[0092] Step S420, extracting the image texture features in the preprocessed image data, and extracting the point cloud spatial features of the preprocessed laser point cloud data;

[0093] Step S430, determining the terrain area where the image texture features are lower than the preset texture threshold and the point cloud spatial features belong to abnormal spatial features as the weak texture false terrain area.

[0094] Among them, the preprocessing of the initial area terrain data can include noise removal, texture enhancement processing, data normalization, and point cloud density equalization, and the initial area terrain data can include image data and laser point cloud data.

[0095] For image data and laser point cloud data, noise removal algorithms can be used to eliminate outliers. For example, isolated points and noise points can be removed by statistical filtering (SOR) or radius filtering (ROR). Statistical filtering can calculate the distance between each point in the image or point cloud data and the neighboring points, and remove points that deviate too much based on the standard deviation; radius filtering can remove points with insufficient number of neighboring points based on a set radius range.

[0096] After noise removal, the image data and laser point cloud data can be standardized. For example, the multi-source data collected by different devices can be unified into the same coordinate system through coordinate unification, scale normalization and spatial alignment. Coordinate unification can use geographic coordinate system (WGS-84) or UTM coordinate system. Scale normalization can normalize the data to a consistent scale through scaling. Spatial alignment can align the point cloud data through the iterative closest point (ICP) algorithm to ensure the consistency of the data in spatial position.

[0097] For laser point cloud data, point cloud density equalization can also be used to solve the problem of uneven distribution of point cloud data. Especially in areas with large terrain fluctuations or severe occlusion, point cloud data may be dense or sparse. Point cloud density equalization can be achieved through voxel grid downsampling (Voxel Grid Filter) and adaptive density compensation algorithm. For example, voxel grid downsampling can divide point cloud data into voxel grids of fixed size, retain a representative point in each voxel, reduce data redundancy and balance point cloud density. Adaptive density compensation algorithm dynamically adjusts the sampling rate according to the local point cloud density, downsampling in dense areas and interpolating points in sparse areas. Interpolation can use triangulated irregular network (TIN) or kriging interpolation to generate supplementary points by fitting neighborhood points.

[0098] For optical image data, the recognizability of terrain texture information can be improved through texture enhancement processing. For example, texture enhancement processing can enhance image contrast through histogram equalization, enhance edge details through Laplace sharpening, and smooth image noise through Gaussian filtering. In addition, edge features of optical image data can be extracted through edge detection algorithms (such as Canny operator and Sobel operator), which is helpful for the subsequent recognition of weak texture areas.

[0099] After the preprocessing is completed, feature extraction can be performed on the image data and the laser point cloud data respectively. For the image data, image texture features are mainly extracted. Image texture features refer to the spatial distribution law of pixel gray levels or color changes in the image. Algorithms such as Gray-level co-occurrence matrix (GLCM), Local Binary Patterns (LBP), or Histogram of Oriented Gradient (HOG) can be used for extraction. GLCM can quantify the contrast, uniformity, and correlation of image texture. LBP can be used to detect local texture patterns, and HOG can be used to capture edge information and shape features. For the laser point cloud data, point cloud spatial features can be extracted. Point cloud spatial features can include point cloud density, normal vector distribution, and curvature change rate, etc. The point cloud density can be obtained by calculating the number of points per unit volume. The normal vector distribution can be calculated through point cloud surface fitting. The curvature change rate can be used to identify the change trend of the surface morphology. By extracting multi-dimensional features, the spatial and texture characteristics of terrain data can be characterized from different dimensions.

[0100] After the feature extraction is completed, comprehensive analysis of the image texture features and the point cloud spatial features can be carried out based on the multi-modal data fusion algorithm. Multi-modal data fusion can adopt weighted fusion, feature-level fusion, or decision-level fusion methods. Weighted fusion can linearly combine the texture features and the spatial features by assigning different weights to different data sources. Feature-level fusion can map multi-modal data to a unified feature space for joint analysis. Decision-level fusion can classify each data source separately and finally synthesize the classification results. Through multi-modal fusion analysis, abnormal regions in the terrain data can be identified more accurately.

[0101] During the comprehensive analysis process, clustering algorithms or classification algorithms can be used to classify the feature data. Clustering algorithms (such as K-means, DBSCAN) can automatically cluster the data according to feature similarity and identify regions with significantly abnormal features. Classification algorithms (such as Support Vector Machine SVM, Convolutional Neural Network CNN) can be trained based on labeled samples to accurately distinguish between weak-texture false terrain regions and real terrain regions. For regions where the image texture features are lower than the preset threshold and the spatial features are abnormal, they are marked as weak-texture false terrain regions. The threshold can be set according to historical data experience or dynamically adjusted, and the classification model can also be continuously optimized through a real-time feedback mechanism.

[0102] By identifying weak-texture pseudo-topography regions and using the identification results as input data for subsequent flight path planning and as key target regions for subsequent supplementary surveys by drones, it is possible to ensure targeted supplementation of terrain data missing and error regions, further improving the integrity and accuracy of real terrain survey data.

[0103] Optionally, the point cloud spatial features of the preprocessed laser point cloud data in step S420 can be achieved through the steps in Figure 5 , as shown in Figure 5 , and specifically may include:

[0104] Step S510: Classify the laser signals in the laser point cloud data to obtain multi-echo signals, where the multi-echo signals include initial echo signals, intermediate echo signals, and end echo signals;

[0105] Step S520: Determine the echo layer point cloud features corresponding to the point cloud data of each echo layer in the multi-echo signals, where the echo layer point cloud features include point cloud density, normal vector distribution, and curvature change rate;

[0106] Step S530: Construct the point cloud spatial features of the laser point cloud data according to the echo layer point cloud features.

[0107] Among them, the multi-echo signal refers to the multiple echo responses generated when the laser pulse emitted by the lidar encounters different object surfaces (such as for the terrain in a vegetated area, it can include the tree canopy, tree trunks, and the ground). The multi-echo signal usually can include an initial echo signal (the first echo), an intermediate echo signal, and an end echo signal (the last echo); where the initial echo signal can reflect the upper structure of the vegetation (such as the tree canopy), the intermediate echo signal can reflect the internal structure of the vegetation (such as the tree trunks and shrubs), and the end echo signal can reflect the surface information of the ground after penetrating the vegetation. By performing layered processing on the multi-echo signal, it is possible to achieve layer-by-layer decomposition of the terrain in the vegetated area and obtain more detailed ground object information.

[0108] For example, the intensity of the echo signal is related to factors such as the reflectivity and distance of the target object in the terrain area. The lidar analyzes the time delay and intensity of the echo signal to determine the relative positions between different ground objects. For example, the separation of the multi-echo signal can be achieved through the following relational expressions:

[0109] ;

[0110] Among them, can represent the signal intensity of the echo signal; can represent the received power; can represent the transmitted power; can represent the receiving area; can represent the reflectivity of the target object in the terrain area; can represent the relative distance between the lidar and the target object in the terrain area.

[0111] The point cloud features of the echo layer can include point cloud density, normal vector distribution, and curvature change rate. Point cloud density refers to the number of points per unit volume and is used to measure the density of data distribution; the normal vector distribution can be used to describe the spatial orientation change of the point cloud surface and reflect the geometric characteristics of the terrain surface; the curvature change rate can reflect the bending degree and undulation characteristics of the point cloud surface and help identify terrain boundaries and mutation features. The point cloud density can be calculated by setting a radius neighborhood to calculate the number of points in the neighborhood, the normal vector can be calculated by a local plane fitting algorithm (such as principal component analysis), and the curvature change rate can be calculated by a local surface fitting or curvature analysis method (such as Gaussian curvature or mean curvature).

[0112] When constructing the point cloud spatial features of lidar point cloud data, the point cloud features of each echo layer can be combined for comprehensive analysis to form a multi-dimensional spatial feature expression. Specifically, a multi-scale feature extraction algorithm can be used to extract the point cloud density, normal vector distribution, and curvature change rate at different scales to capture terrain change information at different levels. In addition, voxel grid downsampling can be used to perform structured processing on the point cloud data to optimize the balance and processing efficiency of data distribution; of course, in order to enhance the expression ability of spatial features, a deep learning model (such as PointNet++) can also be combined to perform feature learning on the point cloud data to automatically extract complex spatial features, and this embodiment is not limited thereto.

[0113] In the process of point cloud spatial feature extraction, in order to improve the integrity and accuracy of terrain data, a multi-echo signal weighted fusion strategy can be adopted to weight and integrate the data of different echo layers. Specifically, different weights can be assigned according to the intensity, echo order, and echo interval time of each echo signal, giving priority to the surface data of the end echo signal, and at the same time using the initial and intermediate echo signals to supplement the vegetation structure information to achieve multi-level and multi-scale data fusion; this weighted fusion strategy can effectively avoid information loss or error accumulation caused by single echo data and improve the reliability of terrain data.

[0114] When identifying complex terrain and vegetation-covered areas, in order to further enhance the spatial feature analysis of point cloud data, a terrain classification algorithm can be combined to classify the point cloud data. For example, the terrain classification algorithm can adopt a rule-based classification method (such as slope, curvature, and density threshold judgment) or a machine learning-based classification method (such as random forest, support vector machine, convolutional neural network) to automatically classify and identify the terrain data. Through the classification algorithm, the ground surface, vegetation, and other ground objects can be effectively distinguished, and the accuracy and stability of spatial feature extraction can be improved.

[0115] Finally, through the refined classification processing and spatial feature extraction of multi-echo laser point cloud data, the terrain surface structure and feature distribution can be comprehensively and accurately described, providing high-quality data support for subsequent terrain data analysis and model construction.

[0116] In an optional implementation, the determination of abnormal spatial features can be achieved through the following steps:

[0117] The terrain gradient distribution estimation data of the current terrain area can be determined by combining the echo layer point cloud features corresponding to the middle echo signal and the end echo signal; the echo layer point cloud features corresponding to the initial echo signal are compared with the terrain gradient distribution estimation data; if the terrain gradient corresponding to the echo layer point cloud features of the initial echo signal is inconsistent with the terrain gradient distribution estimation data, and the point cloud density of the initial echo signal is lower than the preset density value, the change rate of the normal vector distribution is greater than the preset change rate threshold, and the curvature change rate is in an abnormal state, it is determined that the point cloud spatial features of the current terrain area belong to abnormal spatial features.

[0118] Among them, the terrain gradient distribution estimation data refers to a set of spatial data calculated based on the analysis of the spatial features of the terrain surface, used to describe the degree and trend of terrain undulation changes. This data mainly reflects the undulation characteristics and spatial distribution characteristics of the terrain surface at different scales through the quantitative analysis of terrain elevation changes, i.e., terrain gradient, slope distribution, curvature characteristics, etc. In flat areas, the gradient of the point cloud data is small, and the terrain elevation changes gently; while in areas with terrain turns, boundaries, or large curvature changes, the gradient value of the point cloud data is large, and the terrain elevation changes violently. For example, the gradient of the point cloud data can be calculated through the following relational expression:

[0119] ;

[0120] Among them, can represent the gradient at point , , , can respectively represent the partial derivatives of point in the x, y, and z directions, characterizing the change rate of point in each direction.

[0121] The curvature of the point cloud data can be calculated through the following relational expression:

[0122] ;

[0123] Among them, can represent the curvature of the point cloud surface, can represent the determinant of the Hessian matrix, characterizing the degree of curvature of the quadratic surface, It can represent the square of the gradient, characterizing the rate of surface change.

[0124] Based on the extracted spatial feature data, a terrain gradient analysis algorithm can be used to construct terrain gradient distribution estimation data. For example, terrain gradient analysis can reflect the undulating characteristics of the terrain by calculating the elevation difference and slope change in a local area; specifically, the slope distribution of point cloud data can be calculated using a slope calculation algorithm, the trend of surface direction change can be analyzed using a gradient direction histogram, and the surface mutation boundary can be extracted by combining curvature analysis, so as to comprehensively describe the undulating form and spatial distribution characteristics of the surface.

[0125] After obtaining the terrain gradient distribution estimation data, the point cloud features of the echo layer corresponding to the initial echo signal can be compared with the terrain gradient distribution estimation data to effectively avoid abnormal data caused by the terrain gradient distribution. When it is determined that the terrain gradient corresponding to the point cloud features of the echo layer corresponding to the initial echo signal is inconsistent with the terrain gradient distribution estimation data, if there are abnormal problems in the point cloud features of the echo layer corresponding to the initial echo signal at this time, it can be determined as an abnormal spatial feature; specifically, the differences in the point cloud density, normal vector change rate of the initial echo signal and the terrain gradient data can be analyzed. If the point cloud density corresponding to the initial echo signal is lower than the preset density threshold, the normal vector distribution change rate is higher than the preset change rate threshold, and the curvature change rate is in the abnormal range, it can be judged that there are data anomalies or misjudgments of terrain features in this area. It can be understood that this comparison process can use a difference detection algorithm based on threshold judgment or a machine learning classification algorithm (such as support vector machine or random forest) to automatically identify abnormal data.

[0126] Optionally, a multi-scale comparison strategy can be introduced. By calculating the point cloud density, normal vector distribution and curvature change rate at different scales, the local features and global features can be analyzed respectively. Multi-scale comparison can effectively avoid the errors caused by single-scale analysis and enhance the accuracy of terrain data anomaly recognition. For example, the Pyramid Analysis Method can be used to extract features and make comprehensive judgments at the coarse scale and the fine scale respectively; during the comparison process, if obvious differences are detected between the point cloud spatial features corresponding to the initial echo signal and the terrain gradient distribution estimation data, this area can be marked as an abnormal spatial feature area. This abnormal area may be due to terrain information misjudgment caused by vegetation occlusion, insufficient lidar penetration ability or data noise. For the marked abnormal area, the system will automatically feedback it to the path planning module to re-plan the supplementary survey flight route of the UAV to ensure the integrity and accuracy of the data.

[0127] Optionally, an adaptive threshold adjustment mechanism can be introduced during the comparison process; the adaptive threshold mechanism can dynamically adjust the point cloud density threshold, the normal vector change rate threshold, and the curvature change rate threshold by analyzing the data distribution in real time, avoiding false positives or missed detections caused by overly strict or loose fixed threshold settings. This mechanism can be implemented through a fuzzy logic algorithm or a Bayesian updating algorithm to enhance the flexibility and accuracy of anomaly detection.

[0128] Through the above steps, the abnormal spatial features in the terrain data can be accurately identified, further optimizing the integrity and accuracy of the terrain data, and providing a reliable data basis for subsequent data fusion and 3D terrain modeling.

[0129] In an exemplary embodiment of the present disclosure, it is possible to Figure 6 Implement the re-planning of the second survey flight routes respectively corresponding to at least two unmanned aerial vehicles according to the weak texture false terrain areas in step S140, referring to Figure 6 As shown, it may specifically include:

[0130] Step S610, perform a spatial distribution analysis on the point cloud data corresponding to the weak texture false terrain area to determine the regional spatial features corresponding to the weak texture false terrain area, where the regional spatial features include the regional boundary range, the regional area, and the terrain undulation change characteristics;

[0131] Step S620, determine the survey priorities corresponding to each of the weak texture false terrain areas based on the regional spatial features;

[0132] Step S630, obtain the device parameters and flight attitude data corresponding to at least two of the unmanned aerial vehicles, and perform a local flight attitude update on the first survey flight routes corresponding to the unmanned aerial vehicles in combination with the survey priorities, the regional spatial features, the device parameters, and the flight attitude data to obtain the second survey flight routes.

[0133] Among them, spatial distribution analysis refers to the detailed analysis of the spatial scope, shape, and density characteristics of the identified weak-texture pseudo-terrain areas, so as to provide a basis for the subsequent optimization of the UAV flight path. For example, the content of spatial distribution analysis can be the regional boundary range, regional area, and terrain undulation change characteristics. Specifically, the regional boundary range can be accurately determined by the boundary extraction algorithm of point cloud data (such as the α-Shape algorithm or the Convex Hull algorithm); the regional area can be obtained through spatial projection calculation; the terrain undulation change characteristics can be calculated through slope analysis, curvature change rate, and elevation change rate. These analysis results can comprehensively reflect the distribution characteristics and terrain complexity of the weak-texture pseudo-terrain areas, providing key parameters for path planning.

[0134] The survey priority refers to the priority of measurement obtained by comprehensively evaluating the importance, complexity, and urgency of the area. The evaluation of the survey priority can use a multi-factor decision-making model (such as the analytic hierarchy process or the fuzzy comprehensive evaluation method) to perform weighted calculations on factors such as the terrain complexity, area size, and data missing degree of each area. For example, the areas with greater terrain undulation and sparser point cloud density have higher survey priorities and need to be supplemented with survey tasks first. The results of this priority ranking will directly affect the task allocation and flight path planning of the UAVs, and the areas with higher priorities will be arranged for supplementary surveys first.

[0135] The device parameters can include the flight speed, endurance, payload capacity, flight altitude limit, sensor working status (lidar, millimeter-wave radar, etc.) of the UAV, and the flight attitude data can include the real-time position, heading angle, pitch angle, and roll angle of the UAV. These parameters can be obtained in real time through the flight control system of the UAV, or updated in real time through the inertial navigation system and global positioning system carried by the UAV. The purpose of obtaining these data is to comprehensively understand the flight status and operation capabilities of each UAV, providing real-time reference for path planning.

[0136] The local flight attitude of the first survey flight route corresponding to the UAV can be updated by combining the survey priority, regional spatial characteristics, device parameters, and flight attitude data. The update process can be based on real-time path optimization algorithms (such as the A* algorithm, genetic algorithm, or ant colony algorithm) to locally adjust the initial flight route to meet the needs of supplementary surveys. Specifically, according to the spatial distribution characteristics of the weak-texture pseudo-terrain areas, the flight altitude, flight route direction, and flight speed of the UAV can be dynamically adjusted, while optimizing the scanning angle and sensor working mode; the UAV will reduce the flight altitude and slow down the flight speed in complex terrain areas to improve the data acquisition accuracy; in areas with relatively simple terrain, the flight speed will be appropriately increased and the scanning angle will be increased to improve the survey efficiency.

[0137] Optionally, the flight attitude update can also involve optimizing the obstacle avoidance strategy and dynamic obstacle avoidance function of the UAV. Based on the real-time obtained environmental information (such as wind speed, temperature, obstacle position), the flight attitude is adjusted through obstacle avoidance algorithms such as the Dynamic Window Approach (DWA) to ensure that the UAV can perform tasks safely and stably in complex environments. The obstacle avoidance strategy can combine lidar obstacle avoidance and visual obstacle avoidance to detect obstacles in real time and dynamically adjust the flight path.

[0138] The second survey flight route generated through the above steps can cover the weak texture pseudo-terrain area more accurately, achieving efficient supplementary scanning of the initial survey blind area and data missing area; combined with the dynamic update of the real-time state and environmental information of the UAV, the optimized flight route ensures the stability of multi-UAV collaborative operations and the integrity of data collection, further improving the accuracy and efficiency of terrain survey.

[0139] In an exemplary embodiment of the present disclosure, the steps in Figure 7 can be used to fuse the supplementary terrain survey data from different angles to obtain the local terrain survey data of the weak texture pseudo-terrain area. As shown in Figure 7 , it specifically may include:

[0140] Step S710: Preprocess the supplementary terrain survey data from different angles and determine the key feature points in the preprocessed supplementary terrain survey data. The key feature points include elevation mutation points, boundary points, and curvature feature points;

[0141] Step S720: Perform multi-view data registration on the preprocessed supplementary terrain survey data according to the key feature points to obtain the spatially aligned supplementary terrain survey data;

[0142] Step S730: Based on the pre-determined allocation weights, perform weighted fusion of the spatially aligned supplementary terrain survey data and the initial area terrain data corresponding to the weak texture pseudo-terrain area to obtain the fused terrain survey data;

[0143] Step S740: Perform density compensation and data smoothing on the fused terrain survey data to obtain the local terrain survey data of the weak texture pseudo-terrain area.

[0144] Among them, the supplementary topographic survey data can include point cloud data obtained by millimeter-wave radar and lidar, as well as image data obtained by optical cameras. The supplementary topographic survey data can be preprocessed. For example, the preprocessing can include format standardization, time synchronization, and spatial coordinate unification, etc., to ensure the consistency and fusibility of the supplementary topographic survey data. Format standardization can unify data in different formats (such as LAS format, PLY format, or TIFF format) to a standard format through a data conversion tool; time synchronization can be based on a global timestamp (GPS timestamp or internal timestamp) to ensure the time consistency of data collected by multiple drones; spatial coordinate unification uses inertial navigation system and global positioning system data to perform spatial registration on point cloud and image data to ensure that the data is aligned under a unified geospatial reference system.

[0145] After completing the preprocessing of the supplementary topographic survey data, key feature points in the preprocessed supplementary topographic survey data can be extracted. For example, the key feature points can include elevation mutation points, boundary points, and curvature feature points; elevation mutation points can be used to describe the sharp changes in terrain height and are usually located in areas such as cliffs, ridges, or gullies; boundary points can be used to represent the junction of different ground objects (such as vegetation and bare ground surface); curvature feature points can be used to detect the concave and convex changes of the ground surface and can effectively reflect the terrain undulation. The elevation mutation points can be extracted by using a slope analysis algorithm (such as the gradient-based Sobel operator), the boundary points can be extracted by the α-Shape algorithm or the Concave Hull algorithm, and the curvature feature points can be obtained by calculating the Gaussian curvature and the mean curvature.

[0146] Multi-view data registration refers to adjusting the spatial relationship between data sets by comparing key feature points to make them seamlessly docked under the same coordinate system. Feature point-based registration methods can be used, such as the Iterative Closest Point (ICP) algorithm and the global feature matching algorithm. For example, the ICP algorithm can align data by minimizing the point-to-point distance and is suitable for local alignment of dense point cloud data; the global feature matching algorithm can be applied to data alignment of sparse point clouds or data with large rotational deviations. In addition, a deep learning registration model (such as the PointNetLK model) can also be used for automated multi-view data registration to further improve the registration accuracy.

[0147] After completing multi-view data registration, the spatially aligned supplementary topographic survey data and the initial regional topographic data of the weak-texture pseudo-topographic area can be weighted and fused based on preset allocation weights. Weighted fusion refers to assigning different weight coefficients according to the accuracy, reliability, and redundancy of different data sources and integrating multi-source data. Millimeter-wave radar data has strong penetration ability and can be given a higher weight in vegetated areas; lidar data has high accuracy and can be given a higher weight in bare ground areas; optical image data can be used for texture enhancement and can be given a higher weight in areas with rich texture. Weighted fusion can be implemented using Bayesian fusion algorithms, Kalman filtering algorithms, or deep learning fusion algorithms (such as multi-modal neural networks) to improve the accuracy and stability of the fused data. This exemplary embodiment does not make special limitations on the method of weighted fusion.

[0148] Density compensation and data smoothing processing can be performed on the supplementary topographic survey data after weighted fusion. Among them, density compensation is aimed at the problem of uneven point cloud data density, and the data distribution of the supplementary topographic survey data is balanced through voxel grid downsampling and adaptive density compensation algorithms; interpolation can be used to supplement sparse areas, and downsampling optimization can be performed in dense areas. Data smoothing processing can eliminate data noise and boundary mutations through the Moving Least Square (MLS) surface reconstruction algorithm or Gaussian smoothing filtering to ensure the continuity and smoothness of the data.

[0149] Through multi-view data registration, weighted fusion, density compensation, and data smoothing processing, local topographic survey data of the weak-texture pseudo-topographic area that is continuous, complete, and refined is finally generated, which can accurately and comprehensively reflect the true topographic information of the weak-texture pseudo-topographic area and provide a reliable data basis for subsequent global topographic data stitching and three-dimensional topographic model construction.

[0150] It should be noted that although the steps of the methods in this disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution, etc.

[0151] In addition, in this exemplary embodiment, a topographic survey device based on drone collaboration is also provided. Referring to Figure 8 As shown, the topographic survey device 800 based on drone collaboration includes: an initial flight survey module 810, a topographic data acquisition module 820, a pseudo-topographic area identification module 830, a survey route update module 840, a supplementary survey data fusion module 850, and a true topographic data output module 860. Among them:

[0152] An initial flight survey module 810, configured to obtain ground elevation data of an area to be surveyed, and determine first survey flight routes of at least two of the unmanned aerial vehicles in combination with the ground elevation data;

[0153] A terrain data acquisition module 820, configured to control the unmanned aerial vehicles to acquire initial area terrain data based on the first survey flight routes, where the initial area terrain data is acquired in real time by a lidar and an optical camera carried by the unmanned aerial vehicles;

[0154] A false terrain area identification module 830, configured to identify and classify the initial area terrain data, and determine weak texture false terrain areas in the initial area terrain data;

[0155] A survey route update module 840, configured to re-plan second survey flight routes corresponding to at least two of the unmanned aerial vehicles according to the weak texture false terrain areas, where the second survey flight routes are used to control at least two of the unmanned aerial vehicles to turn on millimeter wave radars and scan and acquire supplementary terrain survey data of the weak texture false terrain areas at different angles;

[0156] A supplementary survey data fusion module 850, configured to fuse the supplementary terrain survey data at different angles to obtain local terrain survey data of the weak texture false terrain areas;

[0157] A true terrain data output module 860, configured to splice and fuse the initial area terrain data and the local terrain survey data to obtain true terrain survey data of the area to be surveyed.

[0158] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the initial flight survey module 810 is configured to: determine terrain undulation parameters of the area to be surveyed according to the ground elevation data, where the terrain undulation parameters include elevation change rate, slope distribution, height difference gradient, and surface roughness; perform dynamic grid division on the area to be surveyed based on the terrain undulation parameters to determine survey sub-areas corresponding to the area to be surveyed; allocate the unmanned aerial vehicles to each of the survey sub-areas to obtain initial survey flight routes; perform overlapping area and blind area analysis on the initial survey flight routes, and adjust the flight path spacing and scanning angle of the unmanned aerial vehicles based on the analysis results to generate first survey flight routes.

[0159] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the initial flight survey module 810 is configured to: determine the complex terrain area and the flat terrain area of the area to be surveyed according to the terrain undulation parameter and a preset terrain undulation threshold; perform grid division on the complex terrain area with a first division density, and perform grid division on the flat terrain area with a second division density to obtain the survey sub-areas corresponding to the area to be surveyed; wherein the first division density is greater than the second division density.

[0160] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the false terrain area identification module 830 is configured to: preprocess the initial area terrain data, where the initial area terrain data includes image data and laser point cloud data, and the preprocessing at least includes noise removal, texture enhancement processing, data standardization, and point cloud density equalization; extract the image texture features in the preprocessed image data, and extract the point cloud spatial features of the preprocessed laser point cloud data; determine the weak texture false terrain area for the terrain area where the image texture features are lower than a preset texture threshold and the point cloud spatial features belong to abnormal spatial features.

[0161] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the false terrain area identification module 830 is configured to: perform multi-echo classification on the laser signals in the laser point cloud data to obtain multi-echo signals, where the multi-echo signals include initial echo signals, intermediate echo signals, and end echo signals; determine the echo layer point cloud features corresponding to the point cloud data of each echo layer in the multi-echo signals, where the echo layer point cloud features include point cloud density, normal vector distribution, and curvature change rate; construct the point cloud spatial features of the laser point cloud data according to the echo layer point cloud features.

[0162] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the false terrain area identification module 830 is configured to: combine the echo layer point cloud features corresponding to the intermediate echo signal and the end echo signal to determine the terrain gradient distribution estimation data of the current terrain area; compare the echo layer point cloud features corresponding to the initial echo signal with the terrain gradient distribution estimation data; if the terrain gradient corresponding to the echo layer point cloud features of the initial echo signal is inconsistent with the terrain gradient distribution estimation data, and the point cloud density of the initial echo signal is lower than a preset density value, the change rate of the normal vector distribution is greater than a preset change rate threshold, and the curvature change rate is in an abnormal state, then determine that the point cloud spatial features of the current terrain area belong to abnormal spatial features.

[0163] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the survey route update module 840 is configured to: perform a spatial distribution analysis on the point cloud data corresponding to the weak texture false terrain area to determine the regional spatial features corresponding to the weak texture false terrain area, where the regional spatial features include the regional boundary range, the regional area, and the terrain undulation change features; determine the survey priorities corresponding to each of the weak texture false terrain areas based on the regional spatial features; obtain the device parameters and flight attitude data corresponding to at least two of the drones, and perform a local flight attitude update on the first survey flight route corresponding to the drones in combination with the survey priorities, the regional spatial features, the device parameters, and the flight attitude data to obtain a second survey flight route.

[0164] In an exemplary embodiment of the present disclosure, based on the foregoing solution, the supplementary survey data fusion module 850 is configured to: preprocess the supplementary terrain survey data at different angles and determine the key feature points in the preprocessed supplementary terrain survey data, where the key feature points include elevation mutation points, boundary points, and curvature feature points; perform multi-view data registration on the preprocessed supplementary terrain survey data according to the key feature points to obtain the spatially aligned supplementary terrain survey data; perform weighted fusion of the spatially aligned supplementary terrain survey data with the initial regional terrain data corresponding to the weak texture false terrain area based on pre-determined allocation weights to obtain fused terrain survey data; perform density compensation and data smoothing on the fused terrain survey data to obtain the local terrain survey data of the weak texture false terrain area.

[0165] The specific details of each module of the above-described terrain survey device based on drone collaboration have been described in detail in the corresponding terrain survey method based on drone collaboration, and thus will not be elaborated here.

[0166] It should be noted that although several modules or units of the terrain survey device based on drone collaboration are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0167] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-described terrain survey method based on drone collaboration is also provided.

[0168] Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, a method, or a program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0169] Reference will be made below Figure 9 to describe the electronic device 900 according to such an embodiment of the present disclosure. Figure 9 The illustrated electronic device 900 is merely an example and should not impose any limitation on the functions and the scope of use of the embodiments of the present disclosure.

[0170] As Figure 9 shown, the electronic device 900 is presented in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one of the above-mentioned processing units 910, at least one of the above-mentioned storage units 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.

[0171] Among them, the storage unit stores program codes, and the program codes can be executed by the processing unit 910, so that the processing unit 910 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 910 can execute steps such as Figure 1 shown in, step S110, obtaining the ground elevation data of the area to be surveyed, and determining the first survey flight routes of at least two of the drones in combination with the ground elevation data; step S120, controlling the drones to collect the initial area terrain data based on the first survey flight routes, and the initial area terrain data is obtained by real-time collection by the lidar and the optical camera carried by the drones; step S130, identifying and classifying the initial area terrain data to determine the weak texture false terrain area in the initial area terrain data; step S140, re-planning the second survey flight routes respectively corresponding to at least two of the drones according to the weak texture false terrain area, and the second survey flight routes are used to control at least two of the drones to turn on the millimeter-wave radar and scan and collect the supplementary terrain survey data of the weak texture false terrain area at different angles; step S150, fusing the supplementary terrain survey data at different angles to obtain the local terrain survey data of the weak texture false terrain area; step S160, splicing and fusing the initial area terrain data and the local terrain survey data to obtain the real terrain survey data of the area to be surveyed.

[0172] The storage unit 920 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 921 and / or a cache storage unit 922, and may further include a read-only storage unit (ROM) 923.

[0173] The storage unit 920 may also include a program / utilities 924 having a set (at least one) of program modules 925. Such program modules 925 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0174] The bus 930 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0175] The electronic device 900 may also communicate with one or more external devices 970 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 900, and / or may communicate with any device that enables the electronic device 900 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be through an input / output (I / O) interface 950. Moreover, the electronic device 900 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 960. As shown in the figure, the network adapter 960 communicates with other modules of the electronic device 900 through the bus 930. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0176] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0177] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0178] Reference Figure 10 As shown, a program product 1000 for implementing the above method for terrain survey based on drone collaboration according to an embodiment of the present disclosure is described. It may be in the form of a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0179] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0180] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0181] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0182] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or alternatively, may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0183] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0184] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0185] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0186] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A terrain survey method based on drone collaboration, characterized in that, Including: Obtain the ground elevation data of the area to be surveyed, and determine the terrain undulation parameters of the area to be surveyed according to the ground elevation data. The terrain undulation parameters include elevation change rate, slope distribution, height difference gradient, and surface roughness; Dynamically divide the area to be surveyed based on the terrain undulation parameters to determine the survey sub-areas corresponding to the area to be surveyed; Allocate the drones to each of the survey sub-areas to obtain an initial survey flight route; Analyze the overlapping areas and blind spots of the initial survey flight route, and adjust the flight line spacing and scanning angle of the drones based on the analysis results to generate a first survey flight route. Control the drones to collect initial area terrain data based on the first survey flight route. The initial area terrain data is collected in real time by the lidar and optical cameras carried by the drones. Identify and classify the initial area terrain data to determine the weak texture false terrain areas in the initial area terrain data. Re-plan the second survey flight routes corresponding to at least two of the drones according to the weak texture false terrain areas. The second survey flight routes are used to control at least two of the drones to turn on the millimeter wave radar and scan and collect the supplementary terrain survey data of the weak texture false terrain areas at different angles. Fuse the supplementary terrain survey data at different angles to obtain the local terrain survey data of the weak texture false terrain areas. Stitch and fuse the initial area terrain data and the local terrain survey data to obtain the true terrain survey data of the area to be surveyed.

2. The terrain survey method based on drone collaboration according to claim 1, wherein, The dynamically dividing the area to be surveyed based on the terrain undulation parameters to determine the survey sub-areas corresponding to the area to be surveyed includes: Determine the complex terrain areas and flat terrain areas of the area to be surveyed according to the terrain undulation parameters and a preset terrain undulation threshold. Divide the complex terrain areas with a first division density and divide the flat terrain areas with a second division density to obtain the survey sub-areas corresponding to the area to be surveyed. Wherein, the first division density is greater than the second division density.

3. The terrain survey method based on UAV collaboration according to claim 1, characterized in that The identifying and classifying the initial area terrain data to determine the weak texture false terrain areas in the initial area terrain data includes: Preprocess the initial area terrain data. The initial area terrain data includes image data and laser point cloud data. The preprocessing at least includes noise removal, texture enhancement processing, data standardization, and point cloud density equalization. Extract the image texture features in the preprocessed image data and extract the point cloud spatial features of the preprocessed laser point cloud data. Determine the terrain areas with image texture features lower than a preset texture threshold and point cloud spatial features belonging to abnormal spatial features as weak texture false terrain areas.

4. The terrain survey method based on drone collaboration according to claim 3, characterized in that, The extracting the point cloud spatial features of the preprocessed laser point cloud data includes: Perform multi-echo classification on the laser signals in the laser point cloud data to obtain multi-echo signals. The multi-echo signals include initial echo signals, intermediate echo signals, and end echo signals. Determine the echo layer point cloud features corresponding to the point cloud data of each echo layer in the multi-echo signal, where the echo layer point cloud features include point cloud density, normal vector distribution, and curvature change rate; Construct the point cloud spatial features of the lidar point cloud data according to the echo layer point cloud features.

5. The terrain survey method based on drone collaboration according to claim 4, characterized in that, The method further includes: Combining the echo layer point cloud features corresponding to the intermediate echo signal and the end echo signal to determine the terrain gradient distribution estimation data of the current terrain area; Compare the echo layer point cloud features corresponding to the initial echo signal with the terrain gradient distribution estimation data; If the terrain gradient corresponding to the echo layer point cloud features of the initial echo signal is inconsistent with the terrain gradient distribution estimation data, and the point cloud density of the initial echo signal is lower than the preset density value, the change rate of the normal vector distribution is greater than the preset change rate threshold, and the curvature change rate is in an abnormal state, then determine that the point cloud spatial features of the current terrain area belong to abnormal spatial features.

6. The terrain survey method based on drone collaboration according to claim 1, characterized in that, The re-planning of at least two second survey flight routes corresponding to the UAVs according to the weak texture false terrain area includes: Perform spatial distribution analysis on the point cloud data corresponding to the weak texture false terrain area to determine the regional spatial features corresponding to the weak texture false terrain area, where the regional spatial features include regional boundary range, regional area, and terrain undulation change features; Determine the survey priority corresponding to each weak texture false terrain area based on the regional spatial features; Obtain the device parameters and flight attitude data corresponding to at least two UAVs, and perform local flight attitude update on the first survey flight route corresponding to the UAVs in combination with the survey priority, the regional spatial features, the device parameters, and the flight attitude data to obtain the second survey flight route.

7. The terrain survey method based on drone collaboration according to claim 1, wherein, The fusion of the supplementary terrain survey data at different angles to obtain the local terrain survey data of the weak texture false terrain area includes: Preprocess the supplementary terrain survey data at different angles and determine the key feature points in the preprocessed supplementary terrain survey data, where the key feature points include elevation mutation points, boundary points, and curvature feature points; Perform multi-view data registration on the preprocessed supplementary terrain survey data according to the key feature points to obtain the spatially aligned supplementary terrain survey data; Based on the pre-determined allocation weights, perform weighted fusion of the spatially aligned supplementary terrain survey data and the initial regional terrain data corresponding to the weak texture false terrain area to obtain the fused terrain survey data; Perform density compensation and data smoothing on the fused terrain survey data to obtain the local terrain survey data of the weak texture false terrain area.

8. A terrain survey device based on drone collaboration, characterized in that, Include: An initial flight survey module, configured to obtain ground elevation data of an area to be surveyed, and determine terrain undulation parameters of the area to be surveyed according to the ground elevation data, where the terrain undulation parameters include elevation change rate, slope distribution, height difference gradient, and surface roughness; perform dynamic grid division on the area to be surveyed based on the terrain undulation parameters, determine survey sub-areas corresponding to the area to be surveyed; allocate the unmanned aerial vehicles to each of the survey sub-areas to obtain an initial survey flight route; analyze overlapping areas and blind spots of the initial survey flight route, and adjust the flight line spacing and scanning angle of the unmanned aerial vehicles based on the analysis results to generate a first survey flight route; A terrain data acquisition module, configured to control the unmanned aerial vehicles to acquire initial area terrain data based on the first survey flight route, where the initial area terrain data is acquired in real time by a lidar and an optical camera carried by the unmanned aerial vehicles; A false terrain area identification module, configured to identify and classify the initial area terrain data to determine weak texture false terrain areas in the initial area terrain data; A survey route update module, configured to re-plan second survey flight routes respectively corresponding to at least two of the unmanned aerial vehicles according to the weak texture false terrain areas, where the second survey flight routes are used to control at least two of the unmanned aerial vehicles to turn on millimeter wave radars and scan and acquire supplementary terrain survey data of the weak texture false terrain areas at different angles; A supplementary survey data fusion module, configured to fuse the supplementary terrain survey data at different angles to obtain local terrain survey data of the weak texture false terrain areas; A real terrain data output module, configured to splice and fuse the initial area terrain data and the local terrain survey data to obtain real terrain survey data of the area to be surveyed.

9. An electronic device, characterized in that, Comprising: A processor; And A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the terrain survey method based on unmanned aerial vehicle collaboration as described in any one of claims 1 to 7 is implemented.

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