Unmanned aerial vehicle route layout method and device for complex terrain, equipment and medium

Abstract

The invention provides an unmanned aerial vehicle route layout method and device for complex terrains, equipment and a medium, and relates to the technical field of unmanned aerial vehicle aerial photogrammetry. The method comprises the following steps: carrying out preliminary region division on a terrain region to be surveyed, and determining a plurality of region units; clustering the area units according to topographic observation data to obtain survey sub-areas; determining a survey priority corresponding to each survey sub-region, and matching a basic flight route corresponding to the unmanned aerial vehicle according to the survey priority; and splicing the different types of basic flight routes corresponding to the survey sub-regions, and performing optimization adjustment on the spliced basic flight routes to obtain a flight route of the target unmanned aerial vehicle so as to perform topographic survey on the topographic region to be surveyed. According to the technical scheme, the route planning of the unmanned aerial vehicle can adapt to various types of landform features, and the safety and aerial survey efficiency of the unmanned aerial vehicle are ensured while the integrity and accuracy of aerial survey data are improved.

Description

Background Art

[0002] Areas with complex terrain and harsh climatic conditions (such as plateau mountains, canyon reservoir slopes, etc.) pose great challenges to engineering surveying. The traditional parallel route layout method is usually suitable for flat areas, but in areas with large elevation differences, steep mountains and deep valleys, this method is difficult to fully adapt to terrain changes, which can easily lead to insufficient aerial survey coverage and limited image data acquisition in some areas, affecting the overall surveying and mapping accuracy. In particular, it is difficult to obtain high-precision data through conventional image matching methods in weak texture areas (such as snow-covered areas or sparsely vegetated areas) on plateaus or canyon reservoir slopes, which affects the accuracy of subsequent data processing and three-dimensional reconstruction.

[0003] At present, the relevant UAV route planning technology is mainly aimed at conventional terrain environments, lacks adaptability design for the special terrain of plateau mountainous areas, and mainly relies on manual experience judgment and setting, resulting in low efficiency of aerial survey work in plateau mountainous areas, low coverage, accuracy and reliability of aerial survey data, and the quality of results cannot meet engineering needs. At the same time, the instability of airflow in plateau mountainous areas and the complex distribution of obstacles (such as mountains and cliffs) also put forward higher requirements for the safety of UAV flights. The relevant methods also have many deficiencies in obstacle avoidance and flight path optimization, which can easily lead to interruption of flight missions and even equipment loss.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a method for laying out a UAV route for complex terrain, a device for laying out a UAV route for complex terrain, an electronic device, and a computer-readable storage medium, so that the route planning of the UAV can adapt to various types of terrain features, improve the integrity and accuracy of the aerial survey data, and ensure the safety of the UAV and the aerial survey efficiency.

[0006] According to a first aspect of an embodiment of the present disclosure, a method for laying out a UAV route for complex terrain is provided, comprising: Acquire terrain observation data of the terrain area to be surveyed, wherein the terrain observation data includes digital elevation model, terrain orthophoto, geological disaster data and remote sensing multispectral data; Performing preliminary regional division on the terrain area to be surveyed to determine a plurality of regional units; Clustering each of the regional units according to the terrain observation data to obtain a survey sub-region; Determine a survey priority corresponding to each of the survey sub-areas, and match a basic flight route to be adopted by the UAV in the survey sub-area according to the survey priority; Different types of basic flight routes corresponding to each of the survey sub-areas are spliced ​​together, and the spliced ​​basic flight routes are optimized and adjusted to obtain a target UAV flight route, so as to conduct terrain survey on the terrain area to be surveyed through the target UAV flight route.

[0007] According to a second aspect of an embodiment of the present disclosure, there is provided a device for laying out a route for a drone in complex terrain, comprising: A terrain observation module is used to obtain terrain observation data of the terrain area to be surveyed, wherein the terrain observation data includes digital elevation model, terrain orthophoto, geological disaster data and remote sensing multispectral data; A region division module is used to perform preliminary region division on the terrain region to be surveyed and determine a plurality of region units; A regional clustering module, used for clustering each of the regional units according to the terrain observation data to obtain a survey sub-region; A flight route matching module, used to determine the survey priority corresponding to each of the survey sub-areas, and match the basic flight route to be adopted by the UAV in the survey sub-area according to the survey priority; The flight route splicing module is used to splice different types of basic flight routes corresponding to each of the survey sub-areas, and optimize and adjust the spliced ​​basic flight routes to obtain a target UAV flight route, so as to conduct terrain survey on the terrain area to be surveyed through the target UAV flight route.

[0008] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for laying out a UAV route for complex terrain in the first aspect is implemented.

[0009] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for laying out a UAV route for complex terrain in the first aspect is implemented.

[0010] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The method for laying out a UAV route for complex terrain in the example embodiment of the present disclosure can provide rich terrain information support for UAV route laying by acquiring multi-source terrain observation data of the terrain area to be surveyed, so that the route laying can be finely adjusted based on the terrain characteristics, avoiding the survey blind spots caused by a single data source, thereby improving the integrity and accuracy of the aerial survey data; preliminary regional division is performed on the terrain area to be surveyed, and each regional unit is further clustered based on the terrain observation data, so that the UAV route laying can implement differentiated planning for different terrain types, and reduce the problem of route duplication or omission caused by large differences in terrain characteristics, especially in weak texture areas (such as ice and snow covered areas or sparsely vegetated areas) on plateau mountainous areas or canyon reservoir slopes. By analyzing the characteristic vectors of regional units, accurate division of regional types can be achieved, so that route planning can adapt to different landform characteristics, thereby improving the reliability of surveying and mapping data and the accuracy of subsequent three-dimensional reconstruction; combined with the division results of the survey sub-areas, further The survey priority of each survey sub-area is determined in the first step, and the corresponding basic flight routes are matched. The flight resources of the UAV can be reasonably allocated according to the complexity of the terrain to avoid low-priority areas from occupying too much flight time, thereby improving the overall efficiency of the aerial survey work. At the same time, when facing the complex distribution of obstacles in plateau mountainous areas, the division of different survey priorities can provide a basis for obstacle avoidance strategies, enabling UAVs to reasonably avoid obstacles in complex environments and reduce the risks of mission interruption and equipment loss caused by unreasonable flight path planning; in the process of route splicing and optimization adjustment, by splicing different types of basic flight routes in each survey sub-area, and adaptively adjusting the regional characteristic parameters during the splicing process, the flight redundancy at the route splicing can be effectively reduced, the route coverage can be optimized, and the accuracy of the aerial survey data can be further improved. At the same time, through path smoothing and verification of heading overlap and lateral overlap, the route planning has better adaptability in complex terrain areas, ensuring the high accuracy and consistency of surveying and mapping results.

[0011] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0013] Figure 1 The figure schematically shows a flow chart of a method for laying out a UAV route for complex terrain according to some embodiments of the present disclosure.

[0014] Figure 2 The flowchart of dividing the survey sub-areas according to some embodiments of the present disclosure is schematically shown.

[0015] Figure 3 The flowchart of constructing a terrain recognition feature vector according to some embodiments of the present disclosure is schematically shown.

[0016] Figure 4 The figure schematically shows a flow chart of clustering to obtain survey sub-areas according to some embodiments of the present disclosure.

[0017] Figure 5 The flowchart of splicing basic flight routes to obtain a target UAV flight route according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 6 A schematic diagram of a UAV route layout device for complex terrain according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 7 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0020] Figure 8 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0021] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0022] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0023] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0024] In this example embodiment, a method for laying out UAV routes for complex terrain is first provided. This method for laying out UAV routes for complex terrain can be applied to terminal devices or to servers. This example embodiment does not make any special limitation on this, and the following description will take the execution of the method by a server as an example. Figure 1 The following is a schematic diagram of a process flow of a method for laying out a UAV route in complex terrain according to some embodiments of the present disclosure. Figure 1 As shown, the method for laying out a UAV route for complex terrain may include the following steps: Step S110, obtaining terrain observation data of the terrain area to be surveyed, wherein the terrain observation data includes a digital elevation model, terrain orthophoto, geological disaster data, and remote sensing multispectral data; Step S120, performing preliminary regional division on the terrain area to be surveyed to determine a plurality of regional units; Step S130, clustering the regional units according to the terrain observation data to obtain survey sub-regions; Step S140, determining the survey priority corresponding to each of the survey sub-areas, and matching the basic flight route to be adopted by the UAV in the survey sub-area according to the survey priority; Step S150, stitching together the different types of basic flight routes corresponding to the survey sub-areas, and optimizing and adjusting the stitched basic flight routes to obtain a target UAV flight route, so as to conduct terrain survey on the terrain area to be surveyed through the target UAV flight route.

[0025] According to the method for laying out UAV routes for complex terrain in this example embodiment, on the one hand, by acquiring multi-source terrain observation data of the terrain area to be surveyed, rich terrain information support can be provided for UAV route laying, so that route laying can be finely adjusted based on terrain characteristics, avoiding survey blind spots caused by a single data source, thereby improving the integrity and accuracy of aerial survey data; preliminary regional division is performed on the terrain area to be surveyed, and each regional unit is further clustered based on the terrain observation data, so that UAV route laying can implement differentiated planning for different terrain types, reducing route duplication or omission problems caused by large differences in terrain characteristics, especially in weak texture areas (such as ice and snow covered areas or sparsely vegetated areas) on plateau mountainous areas or canyon reservoir slopes. By analyzing the characteristic vectors of regional units, accurate division of regional types can be achieved, so that route planning can adapt to different landform characteristics, and the reliability of surveying and mapping data and the accuracy of subsequent three-dimensional reconstruction can be improved; on the other hand, combined with the division results of the survey sub-areas, Further determining the survey priority of each survey sub-area and matching the corresponding basic flight routes can reasonably allocate the flight resources of the UAV according to the complexity of the terrain, avoid low-priority areas from occupying too much flight time, and thus improve the overall efficiency of the aerial survey work. At the same time, when facing the complex distribution of obstacles in plateau mountainous areas, the division of different survey priorities can provide a basis for obstacle avoidance strategies, enabling UAVs to reasonably avoid obstacles in complex environments and reduce the risks of mission interruption and equipment loss caused by unreasonable flight path planning; on the other hand, in the process of route splicing and optimization adjustment, by splicing different types of basic flight routes in each survey sub-area, and adaptively adjusting the regional characteristic parameters during the splicing process, the flight redundancy at the route splicing can be effectively reduced, the route coverage can be optimized, and the accuracy of the aerial survey data can be further improved. At the same time, through path smoothing and verification of heading overlap and lateral overlap, the route planning can have better adaptability in complex terrain areas, ensuring the high accuracy and consistency of surveying and mapping results.

[0026] Next, the method for laying out a UAV route for complex terrain in this example embodiment will be further described.

[0027] In step S110, terrain observation data of the terrain area to be surveyed is obtained, wherein the terrain observation data includes a digital elevation model, a terrain orthophoto, geological disaster data, and remote sensing multispectral data.

[0028] In an example embodiment of the present disclosure, the terrain area to be surveyed refers to a target area that needs to be surveyed by drones. The area may be an area with complex terrain features. For example, the terrain area to be surveyed may be a plateau mountain area, a canyon reservoir slope, a hilly area, etc. The determination of the terrain area to be surveyed is usually based on surveying and mapping needs, engineering project planning, and environmental monitoring requirements. The boundaries of the terrain area to be surveyed can be set through geographic information system (GIS) data. For example, it can be based on administrative area boundaries, natural terrain boundaries, or existing surveying and mapping areas. Of course, the terrain area to be surveyed can also be determined by remote sensing image analysis or ground survey methods. This example embodiment does not specifically limit the method for determining the terrain area to be surveyed.

[0029] Terrain observation data refers to a data set used to characterize the terrain features of the terrain area to be surveyed. For example, terrain observation data may include but is not limited to digital elevation models (DEM), terrain orthophotos, geological disaster data, and remote sensing multispectral data. The acquisition method of terrain observation data can be based on remote sensing technology, ground measurement technology, and historical data integration. For example, digital elevation models can obtain high-precision point cloud data through the airborne laser radar (Light Detection And Ranging, LiDAR) scanning of drones, and terrain orthophotos can be obtained by geometric correction of aerial photography or satellite remote sensing images. Of course, terrain observation data can also obtain geological disaster data and remote sensing multispectral data from surveying and mapping departments or geographic information databases. This example embodiment does not specifically limit the data source of terrain observation data. In an optional implementation, terrain observation data can be managed in a hierarchical storage manner to improve data processing efficiency. For example, hierarchical storage can be performed according to region, time, or data type. Of course, cloud data storage and processing mode can also be used for remote access and sharing. This example embodiment does not specifically limit the storage method of terrain observation data.

[0030] The digital elevation model can be used to collect high-precision point clouds through airborne laser radar technology, and combined with the global positioning system (GPS) and inertial measurement unit (IMU) for spatial positioning. Of course, the digital elevation model can also be implemented using stereo image pair photogrammetry technology or synthetic aperture radar (SAR) mapping technology. This example embodiment does not specifically limit the construction method of the digital elevation model. Optionally, the data accuracy of the digital elevation model can be adjusted according to the survey requirements. For example, the data accuracy of the digital elevation model can be set to a high-precision mode with a resolution of 1 meter, which is suitable for fine mapping requirements. Of course, a medium-precision mode with a resolution of 10 meters can also be used, which is suitable for large-scale terrain surveys. This example embodiment does not specifically limit the setting method of the data accuracy.

[0031] A terrain orthophoto refers to an aerial image or satellite image that has been orthorectified to eliminate image distortion caused by terrain undulations, so that the image has a uniform scale and geographic coordinates. For example, a terrain orthophoto can be acquired by using a drone equipped with a high-resolution optical camera to collect multi-angle images at a preset flight altitude, and geometric correction can be performed in combination with ground control points (GCP). Of course, a terrain orthophoto can also be acquired through satellite remote sensing, such as using multispectral or panchromatic images provided by high-resolution commercial satellites, and orthophoto processing can be performed in combination with terrain data. This example embodiment does not specifically limit the acquisition method of the terrain orthophoto. In an optional implementation, the terrain orthophoto can be synthesized using a multi-band image synthesis method to enhance the recognition ability of terrain features. For example, visible light, near-infrared and short-wave infrared images can be combined for fusion to enhance the recognition ability of vegetation and geological structures. Of course, a single-band panchromatic image can also be used to improve the spatial resolution. This example embodiment does not specifically limit the image band combination method.

[0032] Geological hazard data refers to geological risk information related to the survey area, which may include historical records and predicted risks of disasters such as landslides, collapses, and debris flows. For example, geological hazard data can be spatially analyzed through historical disaster distribution maps provided by the geological department in combination with a geographic information system. Of course, geological hazard data can also be used to monitor surface deformation in real time through remote sensing technology, such as using synthetic aperture radar interferometry technology to monitor small surface displacements. This example embodiment does not specifically limit the method for obtaining geological hazard data. In an optional implementation, geological hazard data can be dynamically analyzed in combination with meteorological data to predict potential disaster risks. For example, the landslide risk level can be calculated based on historical rainfall, soil moisture, and slope. Of course, the probability of debris flow can also be predicted based on surface temperature and rainfall trends. This example embodiment does not specifically limit the disaster risk assessment method.

[0033] Remote sensing multispectral data refers to the surface reflectance information of different bands obtained by multispectral sensors, which is used to identify the composition and distribution of surface materials. For example, remote sensing multispectral data can be observed from multiple angles at different flight altitudes by using a multispectral camera mounted on an unmanned aerial vehicle, and feature extraction of multispectral images can be performed using image processing software. Of course, remote sensing multispectral data can also be obtained through hyperspectral imaging technology, such as using satellite remote sensing or airborne remote sensing equipment to collect large-scale multi-band images. This example embodiment does not specifically limit the method for obtaining remote sensing multispectral data.

[0034] In step S120, the terrain area to be surveyed is preliminarily divided into regions to determine a plurality of region units.

[0035] In an exemplary embodiment of the present disclosure, preliminary region division refers to the process of spatially partitioning the survey area according to terrain observation data to optimize the aerial survey planning. For example, the region division can be divided into units of equal size using a regular grid-based method. Of course, the region division can also be performed by dynamically adjusting the unit size according to the complexity of the terrain through an adaptive grid segmentation technology. This exemplary embodiment does not specifically limit the region division method.

[0036] The regional unit refers to the basic measurement unit obtained after the preliminary regional division of the terrain area to be surveyed, and its division principle is usually based on the similarity of terrain features, survey accuracy requirements and work efficiency considerations. The regional unit can be a regular grid method that divides the terrain area to be surveyed into sub-areas of equal size according to a preset resolution, for example, divided by a fixed geographic coordinate grid. Of course, the regional unit can also automatically adjust the unit size according to the change of terrain slope to adapt to the complex terrain environment. This example embodiment does not specifically limit the division method of the regional unit. In an optional implementation, the division of regional units can be combined with the automatic segmentation algorithm of remote sensing images to improve the accuracy and rationality of the division. For example, based on the cluster analysis algorithm, areas with similar terrain features are divided into the same unit. Of course, the unit boundaries can also be manually adjusted to meet special survey needs. This example embodiment does not specifically limit the adjustment method of the regional unit.

[0037] In step S130, the regional units are clustered according to the terrain observation data to obtain survey sub-regions.

[0038] In an example embodiment of the present disclosure, regional clustering refers to the process of merging regional units with similar features based on the similarity of terrain features in order to perform differentiated aerial survey planning. The principle is to classify and group terrain features through machine learning algorithms to form a number of survey sub-regions with different terrain features. For example, clustering can use the K-means algorithm to calculate the similarity of regional units based on terrain feature vectors. Of course, clustering can also use the density clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) to automatically identify cluster boundaries through spatial density. This example embodiment does not specifically limit the clustering method.

[0039] A survey sub-region refers to a set of regions with similar terrain features obtained by clustering regional units by similarity. The formation of a survey sub-region is based on the terrain identification feature vector extracted from terrain observation data, which may include terrain slope characteristics, terrain undulation characteristics, surface cover occlusion characteristics, and geological disaster distribution characteristics.

[0040] In step S140, the survey priority corresponding to each of the survey sub-areas is determined, and the basic flight route to be adopted by the UAV in the survey sub-area is matched according to the survey priority.

[0041] In an example embodiment of the present disclosure, survey priority refers to the priority data for allocating different aerial survey resources to different regions based on factors such as terrain complexity, risk level, and data requirements. The principle is to sort the importance of the regions based on terrain features, and to reasonably allocate flight missions in combination with the aerial survey capabilities of drones. For example, survey priority can be set according to parameters such as terrain steepness and geological disaster risk index. Of course, survey priority can also be adjusted based on aerial survey accuracy requirements and data coverage requirements. This example embodiment does not specifically limit the priority setting method. For example, survey priority can be divided into highest, high, relatively high, medium, and low to meet different survey needs. Of course, the division of survey priority can be customized according to actual conditions, and this embodiment does not specifically limit this.

[0042] The basic flight route refers to the initial UAV flight path pre-established according to different terrain features in the survey sub-areas of different terrain types. The design of the basic flight route takes into account parameters such as heading, lateral overlap, flight altitude, and shooting angle. Different basic flight routes are used in survey sub-areas of different terrain types or different survey priorities. The design of the basic flight route can be based on the complexity of the terrain and the requirements of survey accuracy. For example, a parallel sparse route layout is adopted in flat areas (low survey priority), while a winding dense route layout along the contour lines is adopted in steep mountainous areas (high survey priority). Of course, the basic flight route can also be adjusted according to meteorological conditions to ensure flight safety. This example embodiment does not specifically limit the layout of the basic flight route.

[0043] In step S150, different types of basic flight routes corresponding to each of the survey sub-areas are spliced ​​together, and the spliced ​​basic flight routes are optimized and adjusted to obtain a target UAV flight route, so as to perform terrain survey on the terrain area to be surveyed through the target UAV flight route.

[0044] In an example embodiment of the present disclosure, route splicing refers to the process of merging basic flight routes of different survey sub-areas into a complete flight path. Route splicing can connect route nodes in sequence based on regional adjacency and priority order. For example, route splicing can use the shortest path algorithm to ensure the minimization of the flight path. Route splicing can also optimize route turning points through a route smoothing algorithm (such as Bezier curve). Of course, the splicing process can also be combined with an obstacle avoidance algorithm to ensure the safety and stability of the drone flight. This example embodiment does not specifically limit the splicing method.

[0045] The target UAV flight route refers to the final flight path after splicing and optimizing the basic flight routes of each survey sub-area. This path ensures that the UAV covers all areas to be surveyed when performing the survey mission and meets the data collection accuracy requirements. The generation of the target UAV flight route can be based on the route splicing algorithm to seamlessly connect the flight paths of adjacent areas. For example, the shortest path algorithm can be used to optimize the route connection sequence. Of course, the target UAV flight route can also be smoothed to reduce the track deviation and improve the flight stability. This example embodiment does not specifically limit the optimization method of the target UAV flight route. It can be understood that the target UAV flight route can be dynamically adjusted according to real-time flight data to cope with complex terrain changes. For example, the flight altitude is automatically adjusted in high wind speed areas to maintain stability. Of course, the route can also be monitored in real time by a ground station and manually intervened. This example embodiment does not specifically limit the adjustment method of the flight route.

[0046] By surveying the survey priorities of the sub-areas and matching the corresponding basic flight routes, and then splicing the target UAV flight routes according to the basic flight routes, the layout efficiency of the target UAV flight routes can be effectively improved. At the same time, the flight resources of the UAV can be reasonably allocated according to the complexity of the terrain to avoid low-priority areas from occupying too much flight time, thereby improving the overall efficiency of the aerial survey work. At the same time, when facing the complex distribution of obstacles in plateau and mountainous areas, the division of different survey priorities can provide a basis for obstacle avoidance strategies, enabling UAVs to reasonably avoid obstacles in complex environments, reduce the risk of mission interruption and equipment loss due to unreasonable flight path planning, and improve the precision, accuracy and reliability of aerial survey results.

[0047] The contents of step S110 to step S150 are described in detail below.

[0048] In an exemplary embodiment of the present disclosure, Figure 2 The steps in step S130 are implemented by clustering each regional unit according to the terrain observation data to obtain the survey sub-region, referring to Figure 2 As shown, it may specifically include: Step S210, determining a terrain identification feature vector corresponding to each of the regional units according to the terrain observation data; Step S220: performing similarity clustering on the area units based on the terrain recognition feature vector to obtain survey sub-areas with different terrain types.

[0049] Among them, the terrain identification feature vector is a feature expression of the regional unit in different dimensions of terrain observation data, which is used for subsequent regional unit similarity clustering. The terrain identification feature vector can be obtained by extracting the core information in the terrain observation data through a multidimensional data analysis method and quantifying it into a unified feature representation, so that the similarity between regional units can be evaluated mathematically in the subsequent processing process. The construction of the terrain identification feature vector depends on the key parameters in the terrain observation data, such as terrain slope characteristics, terrain relief characteristics, surface cover occlusion characteristics and geological disaster distribution characteristics, and data normalization is performed based on these characteristics to ensure that data from different sources are calculated at the same scale. For example, the terrain identification feature vector can extract the terrain slope characteristics and terrain relief characteristics corresponding to each regional unit from the digital elevation model and the terrain orthophoto, calculate the regional slope change trend through the boundary extraction algorithm, and use the filtering method to reduce data noise. Of course, the terrain slope characteristics can also be analyzed by Fourier transform terrain spectrum characteristics to reflect the detailed characteristics of the surface relief. This example embodiment does not specifically limit the feature extraction method.

[0050] The terrain identification feature vector can also use principal component analysis (PCA) or factor analysis (FA) methods to reduce feature dimensionality to reduce redundant data and improve computing efficiency. For example, in mountainous areas, the emphasis can be placed on slope and elevation differences, while in flat areas, the surface cover features can be highlighted. Of course, deep learning feature extraction methods can also be used to automatically identify the most distinctive terrain features. This example embodiment does not specifically limit the feature dimensionality reduction method.

[0051] Similarity clustering refers to the classification of regional units with similar terrain features into the same survey sub-regions through a specific clustering algorithm based on the terrain identification feature vector of the regional unit, so as to carry out targeted data collection and route planning. Similarity clustering can form survey sub-regions with high cohesion and low intervals by calculating the feature vector distance or similarity index between the regional units. For example, the similarity between each of the regional units can be calculated using Euclidean distance or cosine similarity in a multidimensional feature space, and the similarity matrix can be processed using a clustering algorithm to form a survey sub-region; taking the K-means clustering algorithm as an example, K cluster centers can be initialized, and then the regional units can be classified to the nearest cluster center according to the minimum distance principle. Of course, the position of the cluster center can be updated iteratively to minimize the intra-cluster variance. This example embodiment does not specifically limit the selection of the clustering algorithm.

[0052] Optionally, similarity clustering of regional units can also be achieved through a density clustering algorithm. Specifically, a survey sub-region can be formed in the feature space based on a density threshold. This method is more flexible in identifying terrain boundaries and is suitable for mountainous terrain with irregular boundaries. Of course, a hierarchical clustering method can also be used to refine the regional division layer by layer at different scales to facilitate refined surveying. This example embodiment does not impose any special limitations on the use of the clustering method.

[0053] By constructing terrain recognition feature vectors, the terrain attributes of regional units can be accurately expressed, providing a scientific basis for the subsequent clustering process and improving the accuracy and consistency of survey area division; through similarity clustering, areas with similar terrain features can be automatically identified, the targeted layout of UAV routes can be improved, duplicate coverage can be reduced, and the effectiveness and accuracy of data collection can be improved.

[0054] Optionally, you can Figure 3 The steps in step S210 are used to determine the terrain identification feature vector corresponding to each regional unit according to the terrain observation data, referring to Figure 3 As shown, it may specifically include: Step S310, extracting terrain slope characteristics and terrain relief characteristics corresponding to each of the regional units from the digital elevation model and the terrain orthophoto; Step S320, extracting the surface cover shielding features corresponding to each of the regional units from the terrain orthophoto and the remote sensing multispectral data; Step S330, determining the geological disaster distribution characteristics corresponding to the regional unit according to the geological disaster data; Step S340, constructing a terrain recognition feature vector based on the terrain slope feature, the terrain undulation feature, the ground cover shielding feature and the geological disaster distribution feature.

[0055] Among them, the terrain slope feature refers to the change of the surface slope in the regional unit, which is used to reflect the steepness of the terrain. Based on the elevation data in the digital elevation model and combined with the image slope feature points in the terrain orthophoto, the elevation change rate or slope feature of each point in the regional unit can be calculated by differential calculation method, and the continuous slope distribution can be obtained by spatial interpolation algorithm. For example, the terrain slope matrix, i.e., the terrain slope feature, can be calculated by using the gradient calculation method, the raster data based on the digital elevation model, the image slope feature points in the terrain orthophoto, the third-order finite difference method or the Sobel operator. Of course, the terrain slope feature can also be combined with the digital elevation model and the terrain orthophoto through the local plane fitting method to estimate the average slope in the regional unit to smooth the local terrain changes. This example embodiment does not specifically limit the slope calculation method. In some optional implementations, the extraction of terrain slope features can be combined with a terrain classification algorithm to further identify steep slopes, gentle slopes, and flat areas. For example, regional units can be divided into different slope levels based on a threshold classification method. Of course, a machine learning model can also be used to automatically extract slope features to improve the accuracy of feature recognition. This example embodiment does not specifically limit the classification method of slope features.

[0056] The terrain relief feature refers to the degree of fluctuation of the terrain elevation within the regional unit, reflecting the complexity of the terrain relief. Based on the digital elevation model and the terrain orthophoto, the elevation mean, the maximum and minimum elevation difference and the elevation variation coefficient of the regional unit can be calculated to quantify the relief of the terrain. For example, based on the moving window calculation method, a 3×3 or 5×5 window can be used to perform statistical analysis on the elevation data or image texture within the regional unit to calculate the elevation standard deviation and the relief index. Of course, the terrain relief feature can also extract terrain spectrum information through Fourier transform to distinguish between gentle and violent relief terrain. This example embodiment does not specifically limit the calculation method of the terrain relief feature. The terrain relief feature can also be combined with the slope feature for comprehensive analysis to more accurately describe the complexity of the terrain. For example, the terrain roughness index (Terrain Ruggedness Index, TRI) can be used to measure the complexity of the terrain. Of course, the terrain relief index (Terrain Surface Curvature, TSC) can also be used for multi-scale analysis. This example embodiment does not specifically limit the calculation method of the relief feature.

[0057] The surface coverage occlusion feature refers to the degree of occlusion of the aerial survey line of sight by the surface objects within the regional unit, which affects the effectiveness of drone image acquisition. The distribution, type and height of the surface objects can be analyzed based on the terrain orthophoto and remote sensing multispectral data to identify the occlusion factors that may affect the aerial survey. For example, the surface features can be classified based on the image classification algorithm, such as using the Support Vector Machine (SVM) or Random Forest (RF) classification model, and the type and distribution of the occlusion objects can be extracted. Of course, the surface coverage occlusion feature can also calculate the vegetation density through the Normalized Difference Vegetation Index (NDVI) to identify the high occlusion area. This example embodiment does not specifically limit the identification method of the occlusion feature. In some optional implementations, the surface coverage occlusion feature can be combined with the laser radar point cloud data to obtain the height information of the ground object to more accurately assess the occlusion risk. For example, a point-by-point classification algorithm is used to distinguish between ground and non-ground points. Of course, the occlusion area can also be automatically detected based on the deep learning model, and this embodiment is not limited to this.

[0058] The distribution characteristics of geological disasters refer to the types of geological disasters that may exist in a regional unit and their spatial distribution. They are used to assess the safety of aerial survey operations. Based on geological disaster data, the frequency, distribution range and risk level of geological disasters in a regional unit can be analyzed to provide a safety reference for drone aerial survey planning. For example, a geological disaster database can be used to analyze the occurrence trend of geological disasters in combination with remote sensing images, and a regional risk map can be constructed using a geological model. Of course, the distribution characteristics of geological disasters can also be comprehensively evaluated in combination with environmental factors such as rainfall and soil moisture to improve the accuracy of the prediction. This example embodiment does not specifically limit the geological disaster assessment method. In some embodiments, the distribution characteristics of geological disasters can be dynamically predicted by a machine learning model. For example, a random forest model can be used in combination with historical disaster data for risk assessment, and a spatial interpolation method can also be used to make a refined prediction of the distribution of geological disasters.

[0059] A terrain recognition feature vector can be constructed based on terrain slope characteristics, terrain undulation characteristics, surface cover occlusion characteristics and geological disaster distribution characteristics. The terrain recognition feature vector represents various terrain features in a unified manner for subsequent similarity clustering processing.

[0060] The terrain slope characteristics can be used to accurately characterize the slope changes in the area, provide reliable slope information for UAV route layout, and avoid uneven aerial survey coverage caused by excessive slope; by extracting terrain undulation characteristics, the UAV flight altitude strategy can be optimized to ensure the uniformity of route coverage and image quality; by accurately identifying surface occlusion features, the UAV flight altitude and route layout can be adjusted to ensure the integrity of data collection; by determining the distribution characteristics of geological disasters, high-risk areas can be avoided during UAV aerial surveys to ensure flight safety; by constructing terrain recognition feature vectors, the accuracy of regional unit classification can be improved to ensure the rationality of survey sub-area division.

[0061] Optionally, you can Figure 4 The steps in step S220 implement similarity clustering of the regional units based on the terrain recognition feature vector to obtain survey sub-regions with different terrain types, referring to Figure 4 As shown, it may specifically include: Step S410, constructing a multidimensional feature space based on the terrain slope feature, terrain relief feature, surface cover shielding feature and geological disaster distribution feature contained in the terrain recognition feature vector; Step S420, obtaining a predetermined density radius parameter and a minimum number of neighborhood points; Step S430, calculating similarity data between terrain recognition feature vectors corresponding to each of the area units in the multi-dimensional feature space; Step S440, marking the area unit whose similarity data is less than the density radius parameter and whose number of neighborhood points is greater than or equal to the minimum number of neighborhood points as a core area; Step S450: clustering the adjacent core areas and the boundary areas associated with the core areas based on the terrain recognition feature vectors to obtain survey sub-areas with different terrain types, wherein the terrain types are determined by the terrain recognition feature vectors of the core areas.

[0062] Among them, the multidimensional feature space refers to mapping the various components of the terrain identification feature vector into a high-dimensional space in order to perform similarity analysis on the regional units. The construction principle of the multidimensional feature space is to map multiple feature variables into a unified coordinate system in mathematical form, so that each regional unit can be compared and classified in the same feature space. Specifically, the various features in the terrain identification feature vector can be standardized to eliminate the scale differences between different features, and the principal component analysis (Principal Component Analysis, PCA) can be used to reduce the dimension of the terrain identification feature vector to reduce data redundancy. Of course, the multidimensional feature space can also be used through the weighted feature fusion method to assign different weights according to the importance of each feature to form a feature space that is more in line with the complexity of the terrain. This example embodiment does not specifically limit the setting method of feature weights.

[0063] In an optional implementation, the multidimensional feature space can be represented by Euclidean space or cosine space. For example, the Euclidean distance is used to measure the similarity of regional units in the Euclidean space. Of course, cosine similarity can also be used in the cosine space to perform comparative analysis of feature vectors. This example embodiment does not specifically limit the measurement method of the multidimensional feature space. By constructing a multidimensional feature space, the terrain characteristics of the regional units can be more accurately described, ensuring the accuracy of the similarity analysis and improving the rationality of the division of the survey sub-areas.

[0064] The density radius parameter refers to the neighborhood range for determining the similarity of regional units in the multidimensional feature space. This parameter directly affects the compactness of clustering; the minimum number of neighborhood points refers to the minimum number of data points that must be included in the set density radius to ensure the reliability of clustering. The density radius parameter and the minimum number of neighborhood points can be set as clustering parameters suitable for different terrain types by statistical analysis of historical data, combined with factors such as terrain complexity and data distribution density. Specifically, a preliminary scan of the multidimensional feature space can be performed, and the K-distance Graph can be used to analyze the inflection point position to determine the appropriate density radius parameter, and a threshold for the minimum number of neighborhood points can be set based on experimental statistics. The density radius parameter can also be dynamically adjusted through an adaptive optimization algorithm to adapt to the terrain characteristics of different regions. This example embodiment does not specifically limit the parameter adjustment method. Of course, in some optional implementations, the density radius parameter can also be dynamically set according to the range of changes in the terrain slope characteristics and the terrain undulation characteristics. For example, in areas with large terrain undulations, the density radius can be increased to cover more similar units. Of course, in areas with large changes in surface coverage, the density radius can be reduced to improve clustering accuracy. By reasonably setting the density radius and the minimum number of neighborhood points, the accuracy of clustering can be improved and the reasonable division of the survey sub-areas can be ensured.

[0065] Similarity data refers to the result of quantifying the similarity of regional units in a multidimensional feature space through mathematical measurement. The principle of similarity calculation is to evaluate the similarity between regional units through a specific mathematical model, thereby providing a basis for the subsequent clustering process. For example, the Euclidean distance can be used to represent the similarity data between the terrain identification feature vectors corresponding to each regional unit, which can be calculated using the following relationship: ; in, It can represent the similarity data between the terrain recognition feature vectors corresponding to each regional unit. and can represent the terrain recognition feature vector The feature values ​​on the feature dimensions are The total number of feature dimensions that can represent the terrain recognition feature vector.

[0066] In some optional implementations, similarity calculation can be combined with a weighted similarity index to assign different weights to different terrain features. For example, in areas with higher geological disaster risks, the weight of geological disaster features can be increased. Of course, the weight of surface cover features can also be increased in areas with dense vegetation. This embodiment does not specifically limit this. By accurately calculating similarity data, the reliability of clustering results can be improved to ensure the reasonable grouping of regional units.

[0067] The core area refers to a regional unit with sufficiently dense neighborhood points in the multidimensional feature space, indicating that the area has a high terrain consistency. The core area marking can be based on the basic principle of density clustering, by detecting the local density of the regional unit to identify the core part of the survey sub-area. Specifically, the similarity data can be sorted, and the number of neighborhood points of the regional unit whose similarity data is less than the density radius parameter can be calculated in turn, and compared with the minimum number of neighborhood points, and the regional unit that meets the conditions can be marked as the core area; the marking of the core area can also be further optimized by the boundary detection algorithm to remove isolated points. This example embodiment does not specifically limit the identification method of the core area. Of course, the marking of the core area can be adjusted in combination with the regional morphological characteristics. For example, in an area with a drastic slope change, the density radius parameter of the core area can be increased. Of course, the density radius parameter can also be reduced in a flat area to ensure accurate coverage of the key area. This example embodiment does not specifically limit the adjustment method of the core area marking. By accurately marking the core area, the stability and scientificity of the subsequent clustering results are ensured, and the division accuracy of the sub-area is improved.

[0068] Optionally, the density radius parameter and the minimum number of neighborhood points may be determined by the following steps, which may specifically include: The density radius parameter can be determined according to the variance variation range of the terrain slope characteristics and terrain relief characteristics in all terrain identification feature vectors; the minimum number of neighborhood points can be determined according to the density of the surface cover occlusion characteristics and the geological disaster distribution characteristics in all terrain identification feature vectors.

[0069] The variance range refers to the distribution of terrain undulation reflected by statistical analysis of terrain slope characteristics and terrain undulation characteristics in each regional unit, which is used to provide a quantitative reference for terrain complexity to assist in the reasonable setting of density radius parameters. The variance range can evaluate the discreteness of terrain data by calculating the mean and deviation of slope and undulation characteristic data, thereby providing data support for subsequent parameter settings. Variance calculation can be based on sliding window analysis, and the local and overall change trends of terrain slope characteristics and terrain undulation characteristics can be evaluated by setting windows of different sizes. For example, a smaller window is used in steep mountainous areas to finely evaluate local slope changes. Of course, a larger window can be used in flat areas to obtain global terrain trends. Through the analysis of the variance range, the density radius parameter can be dynamically adjusted to make the division of regional units more consistent with terrain characteristics and improve the clustering accuracy of survey sub-areas.

[0070] Density refers to the spatial distribution density of surface cover (such as vegetation, buildings) and geological disaster risk points in the area to be surveyed. It can be used to measure the distribution of obstacles in the survey area to optimize the setting of the number of neighborhood points in the clustering process. Spatial statistical analysis can be performed on surface cover data and geological disaster data to evaluate the density of feature points per unit area, thereby providing a basis for setting the minimum number of neighborhood points. Specifically, the surface cover occlusion features can be classified, and remote sensing image classification algorithms (such as support vector machines and random forests) can be used to classify and label vegetation, water bodies, buildings and other covers, and calculate the spatial distribution density of each category. Of course, the density of surface cover occlusion features can also be calculated based on the lidar point cloud data. The height distribution of objects can be calculated to more accurately describe the density of obstacles. The density of geological hazard distribution features can be estimated by spatial interpolation methods. For example, the Kriging interpolation method can be used to interpolate existing geological hazard data to generate a disaster density distribution map. Of course, it can also be combined with machine learning models to predict potential disaster areas in the future to dynamically adjust the minimum number of neighborhood points. Through density analysis, the setting of the minimum number of neighborhood points can accurately reflect the spatial distribution characteristics of surface coverage and geological hazards, thereby improving the scientificity and adaptability of clustering results.

[0071] Combined with the variance variation range of terrain slope characteristics, terrain undulation characteristics, surface cover occlusion characteristics, and the density of geological disaster distribution characteristics, the parameter settings in the clustering process are optimized so that the setting of density radius parameters and the minimum number of neighborhood points can take into account the complexity of terrain characteristics and the distribution of surface characteristics at the same time, and comprehensively consider the mutual influence of various terrain and surface elements to ensure the rationality and scientificity of clustering parameters. Optionally, a multi-factor weight analysis model can also be constructed to calculate the influence weights of various features through a weighted comprehensive analysis method, which is used as the basis for setting the density radius parameter and the minimum number of neighborhood points. Of course, parameter optimization can also be performed through intelligent optimization technologies such as genetic algorithms and particle swarm optimization algorithms to achieve the optimal parameter combination. This example embodiment does not specifically limit the method of parameter optimization. In an optional implementation, a regional parameter setting strategy can be adopted, that is, different density radius parameters and minimum number of neighborhood points are set according to the differences in regional terrain and geological characteristics. For example, a smaller density radius and a higher number of neighborhood points are used for high and steep slope areas to ensure survey accuracy. Of course, the density radius can be appropriately increased in flat areas to reduce calculation complexity. This example embodiment does not specifically limit the regional parameter setting method. By combining multi-factor analysis, optimizing parameter settings, and improving the accuracy of the clustering process, the division of survey sub-areas can be made more scientific and efficient.

[0072] By combining terrain recognition feature vectors to achieve similarity clustering of regional units, the terrain feature analysis can be further refined, making the classification of terrain data more accurate and ensuring that factors such as slope, undulation, surface cover occlusion characteristics and geological disaster distribution characteristics are fully considered when dividing regions; by constructing a multidimensional feature space, the terrain features are quantified from multiple dimensions, so that the similarity between terrain units can be scientifically evaluated in a high-dimensional space; through the predetermined density radius parameter and the minimum number of neighborhood points, it is ensured that the boundary range of each regional unit can be reasonably defined during the density clustering process, thereby improving the division accuracy of the survey sub-regions; using similarity data to perform density clustering of regional units can automatically identify terrain change areas, reduce overlapping operations in aerial surveys, and optimize flight path planning; the identification of core areas helps to determine the main survey areas and improve the efficiency and integrity of data acquisition.

[0073] In an exemplary embodiment of the present disclosure, the following steps may be used to determine the survey priority corresponding to each survey sub-area, which may specifically include: The mapping relationship between the preset terrain type and the survey priority can be obtained. The survey priority is set based on the survey difficulty of different terrain types. The survey priority is directly proportional to the survey difficulty, and the survey priority corresponding to the survey sub-area is matched from the mapping relationship according to the terrain type corresponding to the survey sub-area.

[0074] The mapping relationship between terrain type and survey priority refers to associating different terrain types with corresponding survey priorities, so as to allocate reasonable UAV survey resources according to the complexity of the terrain during route planning. The classification of terrain types can be based on factors such as the geomorphic characteristics of the terrain, the surface coverage and the risk level of geological disasters. For example, the terrain can be divided into categories such as flat areas, hilly areas, steep slopes and high-risk geological disaster areas.

[0075] The principle of determining the survey priority is based on the positive correlation between the complexity of the terrain and the difficulty of the survey, that is, the more complex the terrain and the greater the difficulty of surveying and mapping, the higher the survey priority. Specifically, a terrain type classification standard can be constructed, based on the weighted scores of the slope, undulation, surface cover occlusion characteristics and geological disaster distribution characteristics in the terrain identification feature vector, to form a terrain type classification model, and use expert knowledge or historical surveying and mapping data to optimize the classification standard; the mapping relationship between terrain types can also be automatically extracted through big data analysis methods. The survey priority pattern of historical aerial survey data is used to generate a mapping relationship table; of course, the mapping relationship between terrain type and survey priority can also be set using a multi-level scoring system. For example, using the terrain complexity index as an evaluation indicator, different terrains are divided into five or more priority levels. Of course, fuzzy logic methods can also be used to prioritize according to the uncertainty of the survey area characteristics to improve adaptability to complex terrain. This example embodiment does not specifically limit the classification method of the mapping relationship. For example, the mapping relationship between survey priority and terrain type can refer to Table 1: Table 1 Mapping relationship between survey priorities and terrain types It can be understood that Table 1 only shows the mapping relationship between survey priority and terrain type based on a single combination of characteristics such as slope, undulation, surface cover, and geological disaster risk. In actual application, more types of mapping combination relationships between survey priority and terrain type can be combined according to the above characteristics, and can be customized according to actual application conditions. The mapping relationship in Table 1 should not constitute a special limitation on this embodiment.

[0076] The matching process refers to automatically or semi-automatically assigning appropriate survey priorities to each survey sub-area based on the mapping relationship between the constructed terrain type and the survey priority to ensure the reasonable allocation of drone aerial survey resources. Priority matching can adopt a hierarchical processing strategy, that is, manual review and correction are performed after the initial matching to ensure accurate survey of key areas. For example, the priority can be manually increased for geological disaster risk areas. Of course, an automated adjustment mechanism can also be used to dynamically correct the priority allocation scheme based on real-time surveying and mapping feedback data. This example embodiment does not specifically limit the matching method.

[0077] In the process of determining the survey priority corresponding to each survey sub-area, the survey priority can be reasonably divided according to the corresponding relationship between terrain type and survey difficulty, ensuring priority survey of areas with high geological disaster risks and drastic terrain changes, avoiding the loss of surveying and mapping data or the reduction of accuracy due to improper route layout; the division of survey priorities is conducive to the reasonable allocation of UAV aerial survey resources, ensuring that high-priority areas can complete high-precision aerial surveys within limited time and flight resources, and improving the scientific nature of the overall aerial survey mission; by obtaining the mapping relationship between terrain type and survey priority, a reasonable aerial survey mode can be automatically matched according to the complexity of the terrain, avoiding resource waste and survey omissions caused by human intervention.

[0078] In an example embodiment of the present disclosure, a basic flight route may be obtained by adopting different route layout strategies according to the terrain features of different terrain types. For example, the route layout strategy of the basic flight route may be a parallel sparse route layout in a flat area (low survey priority), while a winding and dense route layout along the contour lines may be adopted in a steep mountainous area (high survey priority). Specifically, different route layout strategies may be customized according to the terrain features of different terrain types, but the present embodiment is not limited thereto.

[0079] Can be Figure 5 The steps in the above are used to stitch together different types of basic flight routes corresponding to each survey sub-area, and then optimize and adjust the stitched basic flight routes to obtain the target UAV flight route. Figure 5 As shown, it may specifically include: Step S510, determining the area parameters corresponding to the survey sub-areas, and adaptively adjusting the basic flight routes according to the area parameters to obtain the adapted basic flight routes of the survey sub-areas; Step S520, according to the survey priority and spatial position relationship of each survey sub-area, the starting points and end points of different adapted basic flight routes are sequentially spliced ​​head to tail to obtain a spliced ​​basic flight route; Step S530, using a Bezier curve to perform path smoothing on the stitched basic flight routes, and verifying the heading overlap and lateral overlap of the smoothed basic flight routes to obtain the target UAV flight route.

[0080] Among them, the regional parameters refer to the key indicators used to describe the geometric characteristics and spatial attributes of the survey sub-region. For example, the regional parameters may include the area size, shape, size, etc. of the region to ensure that the basic flight route can cover the entire survey area and adapt to its specific spatial layout. The regional parameters can be analyzed by geographic information system (GIS) or remote sensing data processing to extract the geometric features of the survey sub-region, and determine the optimal aerial survey layout in combination with the flight mission requirements. Specifically, the boundary of the survey sub-region can be processed by vectorization, the area size of the region can be calculated, and the length and width of the region can be determined by the minimum enclosing rectangle method. At the same time, the complexity of the region can be analyzed by shape factors, for example, the aspect ratio, the perimeter area ratio, etc. can be calculated to evaluate the regularity of the region. Of course, the determination of the regional parameters can also be manually corrected by manual input of the parameters of the key area. This example embodiment does not specifically limit the determination method of the regional parameters. In some embodiments, the regional parameters can be optimized in combination with the flight capability of the drone. For example, in areas with larger areas, the route spacing can be appropriately increased to improve the efficiency of aerial survey, and in areas with complex shapes, the route direction can be adjusted adaptively. The regional parameters can also be dynamically adjusted in combination with real-time environmental data to adapt to changes in actual survey conditions. Through precise regional parameter setting, the route layout can be made more reasonable, aerial survey blind spots can be avoided, and the UAV operation efficiency can be optimized.

[0081] Route splicing refers to sequentially connecting the starting and ending points of the basic flight routes of each survey sub-area based on the survey priority and spatial position relationship of the survey sub-area according to the principle of optimal path to ensure the continuity and efficiency of the aerial survey operation. When splicing, a greedy algorithm can be used to select the route splicing order in order from high to low priority, and a path optimization algorithm (such as A* or Dijkstra algorithm) is used to calculate the shortest connection path. Of course, route splicing can also be manually adjusted in key areas through manual intervention to ensure that surveying and mapping of important areas is completed first.

[0082] It can be understood that the route stitching can also be adjusted in combination with real-time flight data. For example, if it is found during the flight that aerial survey of a certain area cannot be completed due to environmental changes, the stitching order can be automatically re-planned. Of course, the stitching method can also be manually adjusted in combination with remote instructions from the ground station to deal with emergencies. This example embodiment does not specifically limit the adjustment method of route stitching.

[0083] Path smoothing refers to the curve optimization of the spliced ​​routes to reduce route inflection points and improve the stability and energy efficiency of UAV flight. The Bezier curve can adjust the route parameters of the spliced ​​basic flight route through the control points so that the basic flight route can form a smooth curve in space, reduce sharp turns during UAV flight, reduce energy consumption, and improve aerial survey endurance. Specifically, control points can be inserted at the route inflection points, and smoothing calculations can be performed using third-order or fifth-order Bezier curves to ensure the continuity of the route. Of course, path smoothing can also be combined with B-spline curves to further optimize the smoothness of the route. This example embodiment does not specifically limit the path smoothing algorithm.

[0084] In some optional implementations, path smoothing processing can also be optimized in combination with the dynamic model of the UAV. For example, the curve parameters can be adjusted considering the minimum turning radius of the UAV. Of course, the smoothness can also be dynamically adjusted according to the flight speed to reduce data distortion caused by excessive curves at high speeds. This example embodiment does not specifically limit the optimization method of the smoothing parameters.

[0085] Heading overlap refers to the degree of overlap between adjacent images in the direction of the drone's flight, usually expressed as a percentage. Its purpose is to ensure that when the drone continuously shoots on the same route, there is enough overlap between the front and rear images (such as 70%) to facilitate image stitching, 3D modeling, and later image matching processing. Heading overlap can determine the image overlap ratio in the heading direction based on the drone's flight speed, camera shooting interval, ground resolution, and image coverage to ensure that each target area is covered by at least two or more shots. The focal length, ground resolution, shooting frequency, and flight speed of the drone's camera can be used to calculate the ground projection length of a single image, and the heading overlap can be calculated using the following formula: Heading overlap = [1-(drone flight distance × shooting interval) / affected ground projection length] × 100% Among them, the image ground projection length refers to the coverage of a single image taken by the drone camera on the ground; the calculation of heading overlap can also be combined with the terrain undulations, and a variable interval shooting strategy can be adopted to adapt to the changes in different terrains. Of course, the heading overlap can also be dynamically adjusted in combination with real-time flight data. For example, in areas with high wind speeds, the shooting interval can be shortened to increase the overlap rate and ensure image quality. The overlap can also be appropriately reduced in flat areas to reduce data redundancy and improve work efficiency. This example embodiment does not specifically limit the adjustment method of heading overlap.

[0086] Lateral overlap refers to the degree of image overlap between adjacent routes in the lateral direction, usually expressed as a percentage, with the purpose of ensuring complete coverage of the survey area and reducing the loss of surveying and mapping data caused by terrain undulations, drone attitude changes or environmental factors. Lateral overlap can be determined by calculating the ground coverage width between adjacent routes and comparing it with parameters such as the camera field of view and flight altitude to determine the overlap ratio of aerial survey images in the lateral direction.

[0087] The focal length, resolution and flight altitude of the drone aerial camera can be used to calculate the ground projection width of a single image, and based on the preset lateral overlap requirements (such as 60%-80%), the optimal spacing between adjacent routes is calculated to ensure that the edge areas of the image have sufficient overlap; the calculation of lateral overlap can also be combined with terrain height difference data, and a segmented adjustment method is adopted to adapt to terrain changes. This example embodiment does not specifically limit the calculation method of lateral overlap. In some implementations, the lateral overlap can also be combined with an automatic optimization algorithm to dynamically adjust the route spacing based on regional terrain characteristics before the drone aerial survey mission is performed. For example, the overlap is reduced in flat terrain areas to improve work efficiency, and the overlap is increased in areas with large terrain fluctuations to ensure the quality of image stitching. Of course, real-time flight adjustment technology can also be used to adjust the lateral overlap according to the real-time posture of the drone. This example embodiment does not specifically limit the adjustment method of lateral overlap.

[0088] In this embodiment, the layout strategy of the basic flight route enables the UAV to adopt targeted route layouts in different survey sub-areas, thereby improving the surveying and mapping accuracy and data coverage integrity; the basic flight route is adaptively adjusted according to the regional parameters corresponding to the survey sub-areas, so that the route parameters can be flexibly adjusted under different terrain features to ensure the accuracy of aerial surveys. The determination of regional parameters enables the aerial survey tasks to be optimally adapted to various regional characteristics such as area size, shape and size, thereby improving the efficiency of aerial surveys and data integrity. Through route splicing and optimization adjustment, the range of the UAV is effectively reduced, energy consumption is reduced, and the efficiency of surveying and mapping operations is improved.

[0089] By using Bezier curves to smooth the path of the stitched basic flight route, the turning points of the route can be reduced, the stability of the UAV flight can be improved, image acquisition distortion or data loss caused by sudden changes in the route can be avoided, and the UAV's endurance can be improved; by checking the heading overlap and lateral overlap, the image data can be fully overlapped to meet the requirements of three-dimensional reconstruction and optimize the integrity and consistency of data acquisition. The adjustment of lateral overlap helps to ensure the lateral stitching accuracy of the aerial survey images, while the optimization of heading overlap can ensure that the time interval of data collection is reasonable, reduce data loss, and improve surveying and mapping accuracy; the target UAV flight route finally generated can adapt to complex terrain features to the greatest extent, meet the comprehensiveness and high-precision requirements of aerial survey data, and ensure the efficient implementation of survey tasks.

[0090] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0091] In addition, in this exemplary embodiment, a device for laying out a route for a drone in complex terrain is also provided. Figure 6 As shown, the UAV route layout device 600 for complex terrain includes: a terrain observation module 610, a region division module 620, a region clustering module 630, a flight route matching module 640 and a flight route splicing module 650. Among them: A terrain observation module 610 is used to obtain terrain observation data of the terrain area to be surveyed, wherein the terrain observation data includes a digital elevation model, terrain orthophoto, geological disaster data and remote sensing multispectral data; The region division module 620 is used to perform preliminary region division on the terrain region to be surveyed and determine a plurality of region units; A regional clustering module 630, configured to cluster the regional units according to the terrain observation data to obtain survey sub-regions; The flight route matching module 640 is used to determine the survey priority corresponding to each of the survey sub-areas, and match the basic flight route to be adopted by the UAV in the survey sub-area according to the survey priority; The flight route splicing module 650 is used to splice different types of basic flight routes corresponding to each of the survey sub-areas, and optimize and adjust the spliced ​​basic flight routes to obtain a target UAV flight route, so as to perform terrain survey on the terrain area to be surveyed through the target UAV flight route.

[0092] In some example embodiments of the present disclosure, based on the aforementioned scheme, the regional clustering module 630 is configured to: determine the terrain identification feature vector corresponding to each of the regional units according to the terrain observation data; perform similarity clustering on the regional units based on the terrain identification feature vector to obtain survey sub-regions with different terrain types.

[0093] In some example embodiments of the present disclosure, based on the aforementioned scheme, the regional clustering module 630 is configured to: extract the terrain slope characteristics and terrain relief characteristics corresponding to each of the regional units from the digital elevation model and the terrain orthophoto; extract the surface cover occlusion characteristics corresponding to each of the regional units from the terrain orthophoto and the remote sensing multispectral data; determine the geological disaster distribution characteristics corresponding to the regional units according to the geological disaster data; and construct a terrain identification feature vector based on the terrain slope characteristics, the terrain relief characteristics, the surface cover occlusion characteristics and the geological disaster distribution characteristics.

[0094] In some example embodiments of the present disclosure, based on the aforementioned scheme, the regional clustering module 630 is configured to: construct a multidimensional feature space based on the terrain slope characteristics, terrain undulation characteristics, surface cover occlusion characteristics and geological disaster distribution characteristics contained in the terrain identification feature vector; obtain a predetermined density radius parameter and a minimum number of neighborhood points; in the multidimensional feature space, calculate the similarity data between the terrain identification feature vectors corresponding to each of the regional units; mark the regional units whose similarity data is less than the density radius parameter and whose number of neighborhood points is greater than or equal to the minimum number of neighborhood points as core areas; cluster the adjacent core areas and the boundary areas associated with the core areas based on the terrain identification feature vector to obtain survey sub-areas with different terrain types, wherein the terrain type is determined by the terrain identification feature vector of the core area.

[0095] In some example embodiments of the present disclosure, based on the aforementioned scheme, the UAV route layout device 600 for complex terrain also includes a clustering parameter determination unit, which is configured to: determine the density radius parameter according to the variance variation range of the terrain slope characteristics and the terrain undulation characteristics in all the terrain identification feature vectors; determine the minimum number of neighborhood points according to the density of the surface cover occlusion characteristics and the geological disaster distribution characteristics in all the terrain identification feature vectors.

[0096] In some example embodiments of the present disclosure, based on the aforementioned scheme, the flight route matching module 640 is configured to: obtain a mapping relationship between a pre-set terrain type and a survey priority, wherein the survey priority is set based on the survey difficulty of different terrain types, and the survey priority is directly proportional to the survey difficulty; and match the survey priority corresponding to the survey sub-area from the mapping relationship according to the terrain type corresponding to the survey sub-area.

[0097] In some example embodiments of the present disclosure, based on the aforementioned scheme, the basic flight route is obtained by adopting different route layout strategies according to the terrain characteristics of different terrain types; the flight route splicing module 650 is configured to: determine the area parameters corresponding to the survey sub-area, and adaptively adjust the basic flight route according to the area parameters to obtain the adapted basic flight route of each survey sub-area; according to the survey priority and spatial position relationship of each survey sub-area, the starting points and end points of different adapted basic flight routes are spliced ​​head to tail in sequence to obtain the spliced ​​basic flight route; the path of the spliced ​​basic flight route is smoothed by using a Bezier curve, and the heading overlap and lateral overlap of the smoothed basic flight route are checked to obtain the target UAV flight route.

[0098] The specific details of each module of the above-mentioned UAV route layout device for complex terrain have been described in detail in the corresponding UAV route layout method for complex terrain, so they will not be repeated here.

[0099] It should be noted that although several modules or units of the drone route layout device for complex terrain are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0100] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned method for laying out a UAV route for complex terrain is also provided.

[0101] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0102] Refer to the following Figure 7hereinafter describes an electronic device 700 according to such an embodiment of the present disclosure. Figure 7 The electronic device 700 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0103] like Figure 7 As shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 may include but are not limited to: the at least one processing unit 710, the at least one storage unit 720, a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710), and a display unit 740.

[0104] The storage unit stores a program code, which can be executed by the processing unit 710, so that the processing unit 710 performs 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 710 can perform the following steps: Figure 1 In step S110, terrain observation data of the terrain area to be surveyed is obtained, and the terrain observation data includes a digital elevation model, a terrain orthophoto, geological disaster data and remote sensing multispectral data; in step S120, the terrain area to be surveyed is preliminarily divided into regions to determine a plurality of regional units; in step S130, each of the regional units is clustered according to the terrain observation data to obtain a survey sub-region; in step S140, a survey priority corresponding to each of the survey sub-regions is determined, and a basic flight route to be adopted by the UAV in the survey sub-region is matched according to the survey priority; in step S150, different types of basic flight routes corresponding to each of the survey sub-regions are spliced, and the spliced ​​basic flight routes are optimized and adjusted to obtain a target UAV flight route, so as to perform terrain survey on the terrain area to be surveyed through the target UAV flight route.

[0105] The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 721 and / or a cache memory unit 722 , and may further include a read-only memory unit (ROM) 723 .

[0106] The storage unit 720 may also include a program / utility 724 having a set (at least one) of program modules 725, such program modules 725 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0107] Bus 730 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0108] The electronic device 700 may also communicate with one or more external devices 770 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or communicate with any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 750. In addition, the electronic device 700 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 760. As shown, the network adapter 760 communicates with other modules of the electronic device 700 through a bus 730. 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 700, 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.

[0109] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment 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, and includes 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 embodiment of the present disclosure.

[0110] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, 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 a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0111] refer to Figure 8As shown, a program product 800 for implementing the above-mentioned method for laying out a route for a drone in complex terrain according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus, or a device.

[0112] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0115] 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 conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0116] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0117] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment 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, and includes 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 embodiment of the present disclosure.

[0118] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0119] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for laying out a UAV route in complex terrain, characterized in that: include: Acquire terrain observation data of the terrain area to be surveyed, wherein the terrain observation data includes digital elevation model, terrain orthophoto, geological disaster data and remote sensing multispectral data; Performing preliminary regional division on the terrain area to be surveyed to determine a plurality of regional units; Clustering each of the regional units according to the terrain observation data to obtain a survey sub-region; Determine a survey priority corresponding to each of the survey sub-areas, and match a basic flight route to be adopted by the UAV in the survey sub-area according to the survey priority; Different types of basic flight routes corresponding to each of the survey sub-areas are spliced ​​together, and the spliced ​​basic flight routes are optimized and adjusted to obtain a target UAV flight route, so as to conduct terrain survey on the terrain area to be surveyed through the target UAV flight route.

2. The method for laying out a UAV route according to claim 1, characterized in that: The step of clustering the regional units according to the terrain observation data to obtain survey sub-regions includes: Determine the terrain recognition feature vector corresponding to each of the regional units according to the terrain observation data; Similarity clustering is performed on the area units based on the terrain recognition feature vector to obtain survey sub-areas with different terrain types.

3. The method for laying out a UAV route according to claim 2, characterized in that: The determining, according to the terrain observation data, a terrain identification feature vector corresponding to each of the regional units comprises: Extracting terrain slope characteristics and terrain relief characteristics corresponding to each of the regional units from the digital elevation model and the terrain orthophoto; Extracting the surface cover shielding features corresponding to each of the regional units from the terrain orthophoto and the remote sensing multispectral data; Determine the geological disaster distribution characteristics corresponding to the regional unit according to the geological disaster data; A terrain recognition feature vector is constructed based on the terrain slope feature, the terrain undulation feature, the surface cover shielding feature and the geological disaster distribution feature.

4. The method for laying out a UAV route according to claim 3, characterized in that: Based on the terrain recognition feature vector, the regional units are clustered by similarity to obtain survey sub-regions with different terrain types, including: Constructing a multidimensional feature space based on the terrain slope feature, terrain undulation feature, surface cover shielding feature and geological disaster distribution feature contained in the terrain recognition feature vector; Obtain the predetermined density radius parameter and the minimum number of neighborhood points; In the multidimensional feature space, calculating similarity data between the terrain recognition feature vectors corresponding to the area units; Marking the area unit whose similarity data is less than the density radius parameter and whose number of neighborhood points is greater than or equal to the minimum number of neighborhood points as a core area; The adjacent core areas and boundary areas associated with the core areas are clustered based on the terrain recognition feature vectors to obtain survey sub-areas with different terrain types, where the terrain types are determined by the terrain recognition feature vectors of the core areas.

5. The method for laying out a UAV route according to claim 4, characterized in that: The method further comprises: Determining the density radius parameter according to the variance variation range of the terrain slope characteristics and the terrain relief characteristics in all the terrain recognition feature vectors; The minimum number of neighborhood points is determined based on the density of the surface cover shielding features and the geological disaster distribution features in all the terrain recognition feature vectors.

6. The method for laying out a UAV route according to claim 1, characterized in that: The determining of the survey priority corresponding to each of the survey sub-areas includes: Acquire a mapping relationship between a preset terrain type and a survey priority, wherein the survey priority is set based on the survey difficulty of different terrain types, and the survey priority is in direct proportion to the survey difficulty; The survey priority corresponding to the survey sub-area is matched from the mapping relationship according to the terrain type corresponding to the survey sub-area.

7. The method for laying out a UAV route according to claim 1, characterized in that: The basic flight routes are obtained by adopting different route layout strategies according to the terrain characteristics of different terrain types; The different types of basic flight routes corresponding to the survey sub-areas are spliced ​​together, and the spliced ​​basic flight routes are optimized and adjusted to obtain the target UAV flight route, including: Determine the regional parameters corresponding to the survey sub-areas, and adaptively adjust the basic flight routes according to the regional parameters to obtain the adapted basic flight routes of the survey sub-areas; According to the survey priority and spatial position relationship of each survey sub-area, the starting points and end points of different adapted basic flight routes are sequentially spliced ​​head to tail to obtain a spliced ​​basic flight route; The Bezier curve is used to perform path smoothing on the spliced ​​basic flight routes, and the heading overlap and lateral overlap of the smoothed basic flight routes are checked to obtain the target UAV flight route.

8. A device for laying out a route for a drone in complex terrain, characterized in that: include: A terrain observation module is used to obtain terrain observation data of the terrain area to be surveyed, wherein the terrain observation data includes digital elevation model, terrain orthophoto, geological disaster data and remote sensing multispectral data; A region division module is used to perform preliminary region division on the terrain region to be surveyed and determine a plurality of region units; A regional clustering module, used for clustering each of the regional units according to the terrain observation data to obtain a survey sub-region; A flight route matching module, used to determine the survey priority corresponding to each of the survey sub-areas, and match the basic flight route to be adopted by the UAV in the survey sub-area according to the survey priority; The flight route splicing module is used to splice different types of basic flight routes corresponding to each of the survey sub-areas, and optimize and adjust the spliced ​​basic flight routes to obtain a target UAV flight route, so as to conduct terrain survey on the terrain area to be surveyed through the target UAV flight route.

9. An electronic device, characterized in that: include: processor; as well as A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method for laying out a UAV route for complex terrain as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method for laying out a UAV route for complex terrain as described in any one of claims 1 to 7 is implemented.

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