A Drone-Based Geographic Mapping Method and System

Abstract

The present invention relates to the technical field of geographic information processing, and specifically to a geographic mapping method and system based on an unmanned aerial vehicle, comprising the following steps: acquiring the point cloud data and the vectorized boundary data of aerial survey of the unmanned aerial vehicle, calculating the spatial distance to screen adjacent points, and constructing a topological adjacent point set. In the present invention, the construction of the topological adjacent point set is optimized through the spatial screening of the point cloud data and the vectorized boundary data, reducing data redundancy and improving the structural rationality of the mapping result. The projection difference analysis is combined with the point cloud density to screen out abnormal data, realizing the accurate identification of the conflict area. The shortest path calculation optimizes the connection between the conflict points and the boundary points, reducing path redundancy and improving the spatial consistency of the mapping data. The fitting error screening adjusts the boundary points, enhancing the stability of the mapping boundary. The closure calculation is combined with the topological weight optimization to improve the integrity of the regional mapping and the boundary connection quality, making the mapping result more standardized in terms of spatial distribution and connectivity.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information processing, and in particular, to a geographic mapping method and system based on an unmanned aerial vehicle (UAV). Background Art

[0002] The technical field of geographic information processing includes multiple aspects such as the acquisition, storage, management, analysis, and visualization of geospatial data. The core content of this technical field is to use computer technology to efficiently process spatial data to support applications such as surveying and mapping, urban planning, environmental monitoring, and traffic management. The development of geographic information processing technology involves key links such as remote sensing surveying and mapping, global navigation satellite systems, geographic information systems, and data fusion, mainly relying on spatial data acquisition equipment, high-precision positioning technology, geographic data modeling methods, and visualization technology to achieve a comprehensive analysis and application of geographic information.

[0003] Among them, the geographic mapping method based on an unmanned aerial vehicle refers to using an unmanned aerial vehicle equipped with a sensing device to collect data on a target area and generating geographic information data through computer processing. This patent theme covers technical matters such as data acquisition, data preprocessing, geographic coordinate conversion, and mapping data modeling. High-precision optical sensors or lidar are carried by the unmanned aerial vehicle to obtain surface information, and synchronous processing is carried out in combination with high-precision navigation and positioning data. In data preprocessing, the original surveying and mapping data is optimized through methods such as point cloud data denoising and image orthorectification, and the collected data is unified into a standard geographic coordinate system using coordinate conversion methods. In the mapping data modeling link, a three-dimensional terrain model is constructed based on stereo image matching methods or point cloud data fitting, and finally, mapping data results that can be used for geographic analysis and mapping are formed.

[0004] In the prior art, there is a lack of a method for optimizing topological adjacent points in the process of organizing mapping data, and there is redundancy in data connection, which affects the rationality of the boundary structure. The screening of conflict areas relies on fixed rules, resulting in limited abnormal data recognition ability and affecting the overall accuracy of mapping data. The path calculation adopts a static connection method, which is difficult to adapt to complex terrains and affects the coherence of mapping results. The boundary point adjustment method does not combine the dynamic changes of mapping data, the boundary stability is poor, and the integrity control ability of the mapping area is insufficient, affecting the quality of subsequent applications of geographic data. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a geographic mapping method and system based on an unmanned aerial vehicle.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A geographic mapping method based on an unmanned aerial vehicle, comprising the following steps:

[0007] S1: Obtain the UAV mapping point cloud data and aerial survey vectorized boundary data. Calculate the Euclidean distance based on the three-dimensional spatial coordinates between the point cloud points. Screen the point pairs with a distance less than the adjacency threshold, establish a topological adjacent point set, call the topological adjacent point set to screen the boundary line segments and calculate the boundary connection weight. Eliminate redundant connections for the data points with a boundary connection weight lower than the connection threshold to obtain the mapping topological adjacent point index;

[0008] S2: Based on the mapping topological adjacent point index, calculate the projection coordinate difference of the topological adjacent points, screen the data points with a projection offset exceeding the set threshold, classify topological conflicts according to the projection offset, including aerial survey redundant topological conflicts, mapping intersection topological conflicts and topological isolated point conflicts. Calculate the point cloud density of the data points for aerial survey redundant topological conflicts and identify redundant points. Calculate the spatial offset of the intersection points of the boundary line segments for mapping intersection topological conflicts and locate misconnected points. Calculate the adjacency distance of the unconnected points within the closed area for topological isolated point conflicts and determine independence. Establish the UAV mapping topological conflict area index and obtain the topological conflict area parameters;

[0009] S3: Call the topological conflict area parameters, calculate the shortest connection path between the topological conflict points and the mapping boundary points, screen the candidate paths with a path length lower than the topological consistency threshold, calculate the curvature change rate of the candidate paths, and screen the paths with a stable curvature change rate as the optimal paths. Perform local supplementary point interpolation for the topological conflict points without available paths to obtain the UAV mapping optimal topological connection path;

[0010] S4: Call the UAV mapping optimal topological connection path, calculate the fitting error between the adjusted boundary points and the original aerial survey boundary points, screen the boundary points with a fitting error value exceeding the fitting threshold, and readjust the positions of the boundary points according to the topological adjacent point set to obtain the mapping topological adjusted boundary points;

[0011] S5: Call the mapping topological adjusted boundary points, calculate the mapping area closure, screen the areas with a closure value lower than the set standard, recalculate the topological connection weight according to the topological adjacent point set, and allocate connection paths according to the topological connection weight to obtain the UAV mapping topological integrity index.

[0012] As a further solution of the present invention, the surveying and mapping topological adjacent point index includes spatial distance screening adjacent points, topological adjacent point set, and boundary connection weight. The topological conflict area parameters include projection difference comparison threshold, offset over-threshold data point cloud density, and topological conflict area index. The optimal topological connection path for UAV surveying and mapping is specifically the shortest path between conflict points and surveying boundary points, and candidate paths with path lengths lower than the threshold. The boundary points after topological adjustment of UAV surveying and mapping include adjusted boundary points, fitting errors of original boundary points, and boundary points with fitting error values exceeding the fitting threshold. The topological integrity index of UAV surveying and mapping includes surveying area closure, areas with closure lower than the standard, topological connection weight, and connection path.

[0013] As a further solution of the present invention, the steps for obtaining the surveying and mapping topological adjacent point index are specifically as follows:

[0014] S101: Obtain the point cloud data of UAV surveying and mapping and the vectorized boundary data of aerial surveying, calculate the spatial distance between the point cloud data and the boundary data, screen adjacent points according to the spatial distance threshold, construct an initial adjacent point set, and based on the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, eliminate isolated points and correct abnormal adjacent relationships to obtain the initial topological adjacent point index;

[0015] S102: Based on the initial topological adjacent point index, screen boundary line segments, calculate the boundary connection weight, and adjust the boundary connection weight according to the spatial direction, adjacent point density, and connection angle of the boundary line segment. Use the formula:

[0016] ;

[0017] Perform operations to obtain the boundary connection weight distribution, eliminate low-weight connections, and obtain the surveying and mapping topological adjacent point index;

[0018] Among them, represents the boundary connection weight, represents the th spatial distance between the adjacent point and the boundary line segment, represents the th angle between the adjacent point and the boundary normal, represents the total number of adjacent points.

[0019] As a further solution of the present invention, the steps for obtaining the topological conflict area parameters are specifically as follows:

[0020] S201: Based on the surveying and mapping topological adjacent point index, call the aerial survey point cloud data, calculate the projection coordinate differences of multi-point cloud data, select the projection coordinate differences of all adjacent points and perform normalization processing to obtain the projection coordinate difference normalization matrix;

[0021] S202: Call the projection coordinate difference normalization matrix, set the projection difference comparison threshold, screen the point cloud data exceeding the threshold, calculate the point cloud density per unit area, and obtain the distribution of the point cloud density exceeding the threshold;

[0022] S203: Call the distribution of the point cloud density exceeding the threshold, and at the same time calculate the offset conflict area based on the point cloud density gradient, establish the topological conflict area index, calculate the correlation parameters of the topological conflict area, and use the formula:

[0023] ;

[0024] Perform operations to obtain the conflict intensity value of the topological conflict area, call the topological conflict area index, and combine the conflict intensity value to obtain the topological conflict area parameters;

[0025] Among them, represents the conflict intensity value of the topological conflict area, represents the point cloud density exceeding the threshold of the th point, represents the projection difference comparison threshold, represents the area of the point cloud distribution within the topological conflict area, represents the total amount of point cloud data within the topological conflict area.

[0026] As a further solution of the present invention, the steps for obtaining the optimal topological connection path of the UAV mapping are specifically as follows:

[0027] S301: Call the topological conflict area parameters, obtain the coordinate sets of the conflict points and the mapping boundary points, calculate the Euclidean distances from the conflict points to multiple mapping boundary points, establish the initial path set of the conflict points and the mapping boundary points, and generate the conflict point path distance matrix;

[0028] S302: Based on the conflict point path distance matrix, screen the candidate paths with path lengths lower than the set path length threshold, calculate the connectivity weights of all candidate paths, and sort the candidate path set to obtain the candidate path preference sequence;

[0029] S303: Call the candidate path preference sequence, calculate the optimal topological connection path score according to the path length, connectivity weight, and mapping area coverage rate, and use the formula:

[0030] ;

[0031] Perform operations to obtain the score values of all paths, and screen the path with the highest score value to obtain the optimal topological connection path of the UAV mapping;

[0032] Among them, represents the optimal topological connection path score, represents the path length threshold, represents the length of the th candidate path, represents the connectivity weight of the th candidate path, represents the mapping area coverage rate of the th candidate path,

[0033] As a further solution of the present invention, the step of obtaining the adjusted boundary points of the mapping topology is specifically as follows:

[0034] S401: Based on the optimal topological connection path of the UAV mapping, calculate the fitting error between the adjusted boundary points and the original boundary points. At the same time, according to the set of spatial coordinates of the adjusted boundary points and the original boundary points, calculate the Euclidean distance of each boundary point to obtain the boundary point fitting error value;

[0035] S402: Based on the boundary point fitting error value, screen the boundary points whose fitting error value exceeds the fitting threshold, and calculate the position offset of the screened boundary points according to the topological adjacent point set, using the formula:

[0036] ;

[0037] Calculate the adjusted offset of the topological adjacent points;

[0038] Among them, represents the coordinates of the adjusted boundary points, represents the coordinates of the original boundary points, represents the th adjacent point coordinates in the topological adjacent point set, represents the th topological weight with the th adjacent point,

[0039] S403: Based on the adjusted offset of the topological adjacent points, update the spatial coordinate data of the boundary points, call the mapping data for coordinate correction, and obtain the adjusted boundary points of the mapping topology.

[0040] As a further solution of the present invention, the step of obtaining the integrity index of the UAV mapping topology is specifically as follows:

[0041] S501: Call the adjusted boundary points of the mapping topology, calculate the boundary closing situation of the mapping area, obtain the topological connectivity state of each boundary point, and calculate the closing degree of the mapping area to obtain the mapping area closing degree value;

[0042] S502: Based on the closure value of the surveyed area, filter out the areas below the set standard, recalculate the connection weights of multiple topological nodes, and adjust the connection paths to optimize the topological structure, using the formula:

[0043] ;

[0044] Calculate the optimized topological connection weights, adjust the topological paths of the surveyed area, and obtain the distribution of the optimized topological paths;

[0045] Among them, represents the optimized topological connection weight, represents the topological node and the original connection distance between other topological nodes ; represents the topological node after topological optimization adjustment and the target connection distance between other topological nodes ; represents the topological connectivity adjustment coefficient, represents the connection stability deviation, represents the survey coverage change rate, represents the total number of topological nodes, represents the topological node and the original connection distance between other topological nodes ; represents the topological node after topological optimization adjustment and the target connection distance between other topological nodes ;

[0046] S503: According to the distribution of the optimized topological paths, calculate the topological integrity of the UAV surveying and mapping, count the topological path coverage range and integrity index, and obtain the UAV surveying and mapping topological integrity index.

[0047] A UAV-based geographic surveying and mapping system, the UAV-based geographic surveying and mapping system is used to execute the above UAV-based geographic surveying and mapping method, and the system includes:

[0048] The surveying and mapping data acquisition module acquires the UAV surveying and mapping point cloud data and the aerial survey vectorized boundary data, extracts the spatial coordinates, intensity information of the point cloud data and the line segment coordinates of the vectorized boundary, calculates the spatial distance between the surveying and mapping point cloud data and the boundary data, filters out the adjacent points that meet the distance conditions, and obtains the surveying and mapping adjacent point set;

[0049] The topological adjacent point construction module, based on the surveying and mapping adjacent point set, calculates the spatial topological relationship between adjacent points, filters out the connection situation between the boundary line segments and adjacent points, calculates the topological connectivity weight of the connection boundary, and eliminates the redundant connections with connection weights below the threshold to obtain the surveying and mapping topological adjacent point index;

[0050] The topological conflict area recognition module calls the surveyed mapping topological adjacent point index, calculates the projection difference comparison threshold, filters out the offset over-threshold data points of the boundary point cloud data, counts the change in the point cloud density of the adjacent area, calculates the redundancy conflict degree of the aerial survey boundary, filters out the topological connection conflict area, calculates the point cloud distribution density, the projection difference of the boundary points, and the connection redundancy in the conflict area, establishes a topological conflict area index, and obtains the topological conflict area parameters;

[0051] The optimal topological path calculation module calls the topological conflict area parameters, calculates the shortest path between the conflict points and the surveyed mapping boundary points, filters out the candidate paths with a path length lower than the set threshold, filters out the topological connectivity index of the optimal path, calculates the topological adjustment path of the surveyed mapping boundary, and obtains the optimal topological connection path for unmanned aerial vehicle (UAV) surveying and mapping;

[0052] The surveyed mapping topological integrity evaluation module calls the optimal topological connection path for UAV surveying and mapping, calculates the fitting error between the adjusted boundary points and the original boundary points, filters out the boundary points with a fitting error exceeding the threshold, adjusts the positions of the boundary points, calculates the closure degree of the surveyed mapping area, filters out the areas with a closure degree lower than the standard, recalculates the topological connection weight, and assigns new connection paths to obtain the topological integrity index for UAV surveying and mapping.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0054] In the present invention, by spatially screening the surveyed mapping point cloud data and the vectorized boundary data to optimize the construction of the topological adjacent point set, data redundancy is reduced, and the structural rationality of the surveying and mapping results is improved. The projection difference analysis combined with the point cloud density is used to screen out abnormal data, realizing the accurate identification of the conflict area. The shortest path calculation optimizes the connection between the conflict points and the boundary points, reduces path redundancy, and improves the spatial consistency of the surveying and mapping data. The fitting error is used to screen and adjust the boundary points, enhancing the stability of the surveyed mapping boundary. The closure degree calculation combined with the optimization of the topological weight improves the integrity of the regional surveying and mapping and the boundary connection quality, making the surveying and mapping results more standardized in terms of spatial distribution and connectivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the working process of the present invention;

[0056] Figure 2 It is a flow chart of the steps for obtaining the surveyed mapping topological adjacent point index of the present invention;

[0057] Figure 3 It is a flow chart of the steps for obtaining the topological conflict area parameters of the present invention;

[0058] Figure 4 It is a flow chart of the steps for obtaining the optimal topological connection path for UAV surveying and mapping of the present invention;

[0059] Figure 5 This is the flowchart of the steps for obtaining the boundary points after the mapping topology adjustment of the present invention;

[0060] Figure 6 This is the flowchart of the steps for obtaining the mapping topology integrity index of the unmanned aerial vehicle of the present invention. Specific embodiments

[0061] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0063] Embodiment 1: Please refer to Figure 1 , the present invention provides a technical solution: a geographic mapping method based on an unmanned aerial vehicle, including the following steps:

[0064] S1: Obtain the mapping point cloud data and aerial survey vectorized boundary data of the unmanned aerial vehicle, calculate the Euclidean distance based on the three-dimensional spatial coordinates between the point cloud points, screen the point pairs with a distance less than the adjacency threshold, establish a topological adjacent point set, call the topological adjacent point set to screen the boundary line segments and calculate the boundary connection weight, and eliminate redundant connections for the data points with a boundary connection weight lower than the connection threshold to obtain the mapping topological adjacent point index;

[0065] S2: Based on the mapping topological adjacent point index, calculate the projection coordinate difference of the topological adjacent points, screen the data points with a projection offset exceeding the set threshold, classify the topological conflicts according to the projection offset, including aerial survey redundant topological conflicts, mapping cross-topological conflicts and topological isolated point conflicts, calculate the point cloud density of the data points for the aerial survey redundant topological conflicts and identify the redundant points, calculate the spatial offset of the intersection points of the boundary line segments for the mapping cross-topological conflicts and locate the misconnected points, calculate the adjacency distance of the unconnected points within the closed area for the topological isolated point conflicts and determine the independence, establish the mapping topological conflict area index of the unmanned aerial vehicle, and obtain the topological conflict area parameters;

[0066] S3: Call the topological conflict area parameters, calculate the shortest connection path between the topological conflict points and the mapping boundary points, filter the candidate paths with path lengths lower than the topological consistency threshold, calculate the curvature change rate of the candidate paths, filter the paths with stable curvature change rate as the optimal paths, and perform local supplementary point interpolation for the topological conflict points without available paths to obtain the optimal topological connection path for UAV mapping;

[0067] S4: Call the optimal topological connection path for UAV mapping, calculate the fitting error between the adjusted boundary points and the original aerial survey boundary points, filter the boundary points with fitting error values exceeding the fitting threshold, and re-adjust the positions of the boundary points according to the topological adjacent point set to obtain the boundary points after mapping topology adjustment;

[0068] S5: Call the boundary points after mapping topology adjustment, calculate the closure degree of the mapping area, filter the areas with closure degree values lower than the set standard, re-calculate the topological connection weights according to the topological adjacent point set, and allocate connection paths according to the topological connection weights to obtain the topological integrity index for UAV mapping.

[0069] The topological adjacent point index for mapping includes adjacent point screening by spatial distance, topological adjacent point set, and boundary connection weight. The topological conflict area parameters include projection difference comparison threshold, point cloud density of offset over-threshold data points, and topological conflict area index. The optimal topological connection path for UAV mapping is specifically the shortest path between conflict points and mapping boundary points, and candidate paths with path lengths lower than the threshold. The boundary points after mapping topology adjustment include adjusted boundary points, fitting error between the original boundary points, and boundary points with fitting error values exceeding the fitting threshold. The topological integrity index for UAV mapping includes the closure degree of the mapping area, areas with closure degree lower than the standard, topological connection weights, and connection paths.

[0070] Please refer to Figure 2 , and the specific steps for obtaining the topological adjacent point index for mapping are as follows:

[0071] S101: Obtain the UAV mapping point cloud data and aerial survey vectorized boundary data, calculate the spatial distance between the point cloud data and the boundary data, screen adjacent points according to the spatial distance threshold, construct an initial adjacent point set, and eliminate isolated points and correct abnormal adjacent relationships according to the spatial distribution of the point cloud data and the geometric characteristics of the boundary data to obtain the initial topological adjacent point index;

[0072] First, call the lidar sensor or photogrammetry equipment carried by the drone to record the high-precision three-dimensional coordinate data of the target area and generate point cloud data. The point cloud data should contain information such as the three-dimensional coordinates (X, Y, Z), reflection intensity, and classification labels of the points. At the same time, the acquisition of the aerial survey vectorized boundary data is based on digital aerial images, and the boundary line of the target area is extracted through manual or automatic vectorization methods and stored in the vector data format. Next, calculate the spatial distance between the point cloud data and the boundary data. For each point in the point cloud data, use the three-dimensional Euclidean distance formula Calculate the point cloud point to the boundary point The distance, where the boundary point is obtained by interpolating the line segment coordinates of the vectorized boundary data. The calculation results of all point cloud points form a distance array , set the spatial distance threshold , filter the point cloud points that meet , and construct an initial adjacent point set. Assume takes a value of 0.5m, then the filtered point cloud points are all within 0.5m of the boundary line segment. Next, according to the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, remove the isolated points. The method for removing isolated points is to count the number of adjacent points of each point , if (such as setting ), then determine that the point is an isolated point and remove it. Finally, by correcting the abnormal adjacent relationship, filter the point cloud points that maintain connectivity and obtain the initial topological adjacent point index.

[0073] S102: Based on the initial topological adjacent point index, filter the boundary line segments, calculate the boundary connectivity weight, and adjust the boundary connectivity weight according to the spatial direction, adjacent point density, and connection angle of the boundary line segment. Use the formula:

[0074] ;

[0075] Calculate the boundary connectivity weight distribution through the operation, remove the low-weight connections, and obtain the mapping topological adjacent point index;

[0076] Among them, represents the boundary connectivity weight, represents the spatial distance between the th adjacent point and the boundary line segment, represents the angle between the th adjacent point and the boundary normal, represents the total number of adjacent points;

[0077] First, filter the boundary line segments. Each boundary line segment corresponds to multiple adjacent points, and calculate the boundary connectivity weight of each boundary line segment , for each adjacent point , record its spatial distance from the boundary line segment and the included angle with the boundary normal , using the formula:

[0078] ;

[0079] Calculate the weight distribution, where is obtained through the aforementioned calculation, The calculation method is , where is the boundary normal vector. Assume the adjacent point data of a certain boundary line segment is as follows:

[0080] Table 1 Adjacent Point Parameter Table

[0081]

[0082] Then the calculation results are:

[0083] ;

[0084] ;

[0085] ;

[0086] Calculate for all boundary line segments, set the weight threshold , and filter of the boundary line segments. If is set to 0.5, then filter out the boundary line segments that meet the conditions, eliminate the low-weight connections, and finally obtain the mapping topological adjacent point index.

[0087] Please refer to Figure 3 , the specific steps for obtaining the topological conflict area parameters are as follows:

[0088] S201: Based on the mapping topological adjacent point index, call the aerial survey point cloud data, calculate the projection coordinate differences of the multi-point cloud data, select the projection coordinate differences of all adjacent points and perform normalization processing to obtain the normalized matrix of the projection coordinate differences;

[0089] First, call the aerial survey point cloud data, retrieve the three-dimensional coordinate information of each topological adjacent point, extract its projection coordinates (X, Y) respectively, calculate the projection coordinate differences between adjacent point pairs, that is, for each point and its adjacent point , calculate its projection coordinate differences and , form a set of coordinate differences and store it in a matrix. Subsequently, perform normalization processing on this matrix. The normalization formula is as follows:

[0090] ;

[0091] ;

[0092] After normalizing the set of projection coordinate differences, a normalized matrix of projection coordinate differences is constructed. Each element of this matrix represents the standardized projection difference of adjacent point pairs, which is convenient for subsequent calculations. Assume the input point cloud data is as shown in Table 2 below:

[0093] Table 2 Example Table for Calculating Projection Coordinate Differences

[0094]

[0095] As shown in Table 2, the calculated normalized projection coordinate differences can be used for subsequent projection difference comparison and analysis, and finally a normalized matrix of projection coordinate differences is obtained.

[0096] S202: Call the normalized matrix of projection coordinate differences, set the projection difference comparison threshold, filter the point cloud data exceeding the threshold, and calculate the point cloud density per unit area to obtain the distribution of point cloud density exceeding the threshold;

[0097] Take the mean value plus the standard deviation of all elements of the normalized matrix, that is:

[0098] ;

[0099] Among them, is the mean value of the normalized matrix, is the standard deviation. Then, filter out all the point cloud data sets that satisfy or , and calculate the point cloud density per unit area of these points. The calculation method is as follows:

[0100] ;

[0101] Among them, is the number of point clouds exceeding the threshold, is the area of the selected region. The point cloud density per unit area can be used to reflect the density of the point cloud in the local region. Assume the filtered data is as shown in Table 3 below:

[0102] Table 3 Distribution Table of Point Cloud Density Exceeding the Threshold

[0103]

[0104] Referring to Table 3, the calculated distribution of point cloud density exceeding the threshold is used for the next step of calculating the parameters of the topological conflict region.

[0105] S203: Call the density distribution of point clouds above the threshold. At the same time, calculate the offset conflict area based on the point cloud density gradient, establish an index for the topological conflict area, calculate the correlation parameters of the topological conflict area, and use the formula:

[0106] ;

[0107] Obtain the conflict intensity value of the topological conflict area through operations, call the topological conflict area index, and combine the conflict intensity value to obtain the topological conflict area parameters;

[0108] Among them, represents the conflict intensity value of the topological conflict area, represents the density of point clouds above the threshold of the th point, represents the projection difference comparison threshold, represents the area of the point cloud distribution within the topological conflict area, represents the total amount of point cloud data within the topological conflict area.

[0109] Traverse the point cloud density distribution and calculate the change rate of the point cloud density in adjacent areas :

[0110] ;

[0111] Among them, is the Euclidean distance between the center points of regions and . Then, calculate the gradient values for all adjacent areas, set the gradient threshold to filter out the regions with sudden changes in point cloud density, and then establish an index for the topological conflict area and calculate the conflict intensity value of the topological conflict area:

[0112] ;

[0113] Among them, represents the sum of all points in the conflict area, calculate the difference between each point cloud density and the threshold, which is used to normalize the area effect.

[0114] Assume the input parameters are as follows:

[0115] ;

[0116] Region R1: ;

[0117] Region R2: ;

[0118] Region R3: ;

[0119] Substitute into the formula for calculation:

[0120] ;

[0121] ;

[0122] ;

[0123] Finally, obtain the parameters of the topological conflict area . This result indicates that there is a certain degree of topological conflict in this area, and subsequent optimization processing can be carried out in combination with the conflict intensity value.

[0124] Please refer to Figure 4 , and the specific steps for obtaining the optimal topological connection path of UAV mapping are as follows:

[0125] S301: Invoke the parameters of the topological conflict area, obtain the coordinate sets of the conflict points and the mapping boundary points, calculate the Euclidean distances from the conflict points to multiple mapping boundary points, establish the initial path set between the conflict points and the mapping boundary points, and generate the conflict point path distance matrix;

[0126] First, obtain the coordinate sets of the conflict points and the mapping boundary points. The conflict points can be determined by the path intersection points, signal interference areas, or terrain obstacles identified during the UAV flight path planning process. The mapping boundary points can be extracted through the set mapping area boundary or the coordinates of key monitoring points. Next, calculate the Euclidean distances from the conflict points to multiple mapping boundary points. The calculation formula is:

[0127] ;

[0128] Where represents the coordinates of the conflict point, represents the coordinates of the mapping boundary point. Taking a certain conflict point (10, 20) as an example, if its adjacent mapping boundary points are (30, 40), (25, 15), and (5, 10) respectively, the calculated distances are 28.28, 18.03, and 11.18 in sequence. After the calculation, establish the initial path set between the conflict points and the mapping boundary points. Each path is composed of the connection line between the conflict point and the mapping boundary point, and store its Euclidean distance as the path length. At the same time, generate the conflict point path distance matrix. The rows of the matrix represent the conflict points, and the columns represent the mapping boundary points. Each element of the matrix stores the Euclidean distance of the corresponding path. For example, for 3 conflict points and 3 mapping boundary points, the matrix can be expressed as:

[0129] ;

[0130] The results show that the establishment of the path matrix can clearly reflect the straight-line distance between the conflict points and the mapping boundary points, providing basic data for subsequent path screening and optimization. This matrix will be used in the following steps for path length screening and topological optimization to ensure the optimal UAV mapping path.

[0131] S302: Based on the conflict point path distance matrix, screen the candidate paths whose path lengths are lower than the set path length threshold, calculate the connectivity weights of all candidate paths, and sort the candidate path set to obtain the preferred sequence of candidate paths;

[0132] Based on the above conflict point path distance matrix, screen the candidate paths whose path lengths are lower than the set path length threshold. The path length threshold can be set according to the UAV endurance and mapping task requirements. For example, if the threshold is set to 25, the paths in the path matrix less than 25 will be used as candidate paths. The candidate path set obtained after screening is {(10,20)-(25,15),(10,20)-(5,10),(15,30)-(25,15),(15,30)-(5,10),(20,25)-(25,15),(20,25)-(5,10)}. For all candidate paths, calculate their connectivity weights. The connectivity weight can be set based on factors such as the number of network nodes passed by the path and the path redundancy. For example, assuming that the fewer nodes a path passes through, the higher the connectivity, the weight can be calculated as follows:

[0133] ;

[0134] where represents the number of intermediate nodes passed by the path. If a path only contains the starting point and the ending point, then ; if it contains 1 intermediate node, then . After calculation, sort all candidate paths according to their connectivity weights. The larger the weight, the higher the ranking, as shown in Table 4.

[0135] Table 4 Candidate path connectivity weight table

[0136]

[0137] The results show that the P1 and P2 paths have the highest connectivity, which means these paths are more reliable and pass through the fewest nodes. They will occupy a greater weight in the next path score calculation, thus increasing their possibility of being the optimal path.

[0138] S303: Call the preferred sequence of candidate paths, and calculate the score of the optimal topological connection path according to the path length, connectivity weight, and mapping area coverage rate, using the formula:

[0139] ;

[0140] Calculate the scoring values of all paths through operations, filter the path with the highest scoring value, and obtain the optimal topological connection path for UAV mapping;

[0141] Among them, represents the score of the optimal topological connection path, represents the path length threshold, represents the th length of the candidate path, represents the connectivity weight of the th candidate path, represents the th mapping area coverage rate of the candidate path, represents the total number of candidate paths.

[0142] Formula:

[0143] ;

[0144] Among them, the path length threshold , the lengths of each candidate path have been calculated in the previous steps, the connectivity weight is shown in Table 4, the mapping area coverage rate represents the importance degree of the mapping area connected by this path, which can be set according to historical mission data or regional mapping density. For example, assume the mapping area coverage rate is set as follows:

[0145] ;

[0146] Substitute the parameters into the formula. Taking the P1 path as an example, , , , calculate:

[0147] ;

[0148] Similarly, calculate the scores of other paths:

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] Finally, the path with the highest score was selected as the optimal topological connection path. The P1 path had the highest score, so P1, i.e. (10,20)-(5,10), was finally selected as the optimal topological connection path for UAV mapping. The result shows that the P1 path has the highest comprehensive score while taking into account the path length, connectivity and coverage of the mapping area, which means that this path can reduce the mapping blind area during the UAV mapping process while ensuring the connectivity of the path, thereby improving the mapping efficiency and stability.

[0154] See also Figure 5 ,The specific steps for obtaining boundary points after surveying and mapping topology adjustment are:

[0155] S401: Based on the optimal topological connection path of the UAV mapping, the fitting error between the adjusted boundary points and the original boundary points is calculated, and at the same time, the Euclidean distance of each boundary point is calculated according to the spatial coordinate set of the adjusted boundary points and the original boundary points to obtain the boundary point fitting error value;

[0156] It is necessary to calculate the fitting error between the adjusted boundary points and the original boundary points. The fitting error can be calculated using the root mean square error (RMSE), that is, the square sum of the deviations of all boundary points in the X, Y, and Z coordinate directions is calculated, and the root mean square value is calculated. Assume that the coordinates of the original boundary points are , the coordinates of the boundary points after adjustment are , the fitting error is calculated as follows:

[0157] ;

[0158] in, Indicates the total number of boundary points.

[0159] Next, the Euclidean distance of each boundary point needs to be calculated to evaluate the spatial offset between the adjusted boundary point and the original boundary point. The calculation uses the standard Euclidean distance formula:

[0160] ;

[0161] Specifically, in practical applications, such as drone mapping of the boundary points of a mining area, if a boundary point After adjustment, it becomes , then calculate the Euclidean distance:

[0162] ;

[0163] The result shows that the boundary point has undergone a spatial displacement of 0.54 meters after adjustment. If the error value is greater than the set threshold, the point needs to be further optimized, otherwise it can be considered that its position adjustment has achieved the expected accuracy.

[0164] S402: Based on the fitting error values of boundary points, screen out the boundary points whose fitting error values exceed the fitting threshold. Calculate the position offset for the screened boundary points according to the topological adjacent point set, using the formula:

[0165] ;

[0166] Calculate the adjusted offset of the topological adjacent points;

[0167] Among them, represents the coordinates of the adjusted boundary point, represents the coordinates of the original boundary point, represents the th adjacent point coordinate in the topological adjacent point set, represents the th topological weight of the adjacent point, represents the total number of topological adjacent points;

[0168] It is necessary to screen out the boundary points with errors exceeding the fitting threshold. Assume that the fitting threshold is set to 0.5 meters. Then when the error value of a certain boundary point, this point is considered to have too large an error and needs to be further adjusted. After screening out these boundary points, calculate the position offset based on the topological adjacent point set. The formula is as follows:

[0169] ;

[0170] Among them, is the coordinates of the adjusted boundary point, is the coordinates of the original boundary point, is the th adjacent point coordinate in the topological adjacent point set, is the th topological weight of the adjacent point, is the total number of topological adjacent points.

[0171] In practical applications, for example, the original coordinates of a certain boundary point have two adjacent points:

[0172] ;

[0173] The corresponding topological weights are and , respectively. Then calculate the adjusted coordinates:

[0174] ;

[0175] Calculate the adjustment amounts in the X, Y, and Z directions respectively:

[0176] ;

[0177] ;

[0178] ;

[0179] The coordinates of the boundary points after final adjustment are .

[0180] The coordinates of the boundary points after final adjustment are .

[0181] S403: Adjust the offset based on the topological adjacent points, update the spatial coordinate data of the boundary points, call the surveying and mapping data for coordinate correction, and obtain the boundary points after topological adjustment of the surveying and mapping. This result shows that the adjustment of this point fully considers the influence of the topological adjacent points, making the position of the boundary point after adjustment closer to the true boundary, and at the same time reducing the error between the origin point and the adjacent points.

[0182] During the process of surveying and mapping data correction, multiple surveying and mapping sample points can be used for mean offset correction. For example, based on the actual surveying and mapping samples, the final coordinates of a certain point are calculated as:

[0183] ;

[0184] If the data of the actual surveying and mapping sample points are as follows:

[0185] ;

[0186] Then the calculation of the final boundary point coordinates is as follows:

[0187] ;

[0188] ;

[0189] ;

[0190] Finally, obtain the boundary points after topological adjustment of the surveying and mapping , this result shows that after the adjustment of the topological adjacent points and the correction of the surveying and mapping data, the boundary point coordinates have been stabilized within the error threshold range, meeting the requirements of surveying and mapping accuracy, and can be used for subsequent surveying and mapping analysis and the optimization of the UAV path planning.

[0191] Please refer to Figure 6 , the specific steps for obtaining the UAV surveying and mapping topological integrity index are as follows:

[0192] S501: Call the boundary points after topological adjustment of the surveying and mapping, calculate the boundary closure situation of the surveying and mapping area, obtain the topological connectivity status of each boundary point, and calculate the closure degree of the surveying and mapping area to obtain the closure degree value of the surveying and mapping area;

[0193] First, all boundary points in the surveyed area need to be parsed to extract the coordinate information of each boundary point, and a complete sequence of boundary points is constructed based on the topological relationship between adjacent points. By calculating the Euclidean distance between adjacent boundary points to determine the connectivity between points. If the distances from a certain boundary point to two adjacent points are both lower than the set maximum allowable boundary distance threshold , then this point is determined to be connected. If the connectivity number of a point is lower than the set minimum connectivity number threshold , then this point is identified as an isolated point and marked as invalid. On this basis, the number of all connected boundary points is counted and the closure degree of the surveyed area is calculated. The calculation method of the closure degree is as follows:

[0194] ;

[0195] where is the number of connected boundary points, is the total number of boundary points. For example, in a certain area, there are a total of 150 boundary points, and 130 of them are identified as connected points. Then the closure degree is calculated as:

[0196] ;

[0197] This result indicates that the boundary closure degree of the surveyed area has reached 86.67%. Since this value is lower than the set minimum closure degree standard (such as 90%), it indicates that there is a problem of incomplete boundaries in the topological structure of the current surveyed area, and the topological structure needs to be further optimized to improve the overall survey integrity.

[0198] S502: Based on the closure degree value of the surveyed area, select the areas below the set standard, recalculate the connection weights of multiple topological nodes, and adjust the connection paths to optimize the topological structure. Use the formula:

[0199] ;

[0200] Calculate the optimized topological connection weight, adjust the topological path of the surveyed area, and obtain the optimized topological path distribution;

[0201] where, represents the optimized topological connection weight, represents the topological node and the original connection distance between other topological nodes , represents the topological node after topological optimization adjustment and the target connection distance between other topological nodes , represents the topological connectivity adjustment coefficient, represents the connection stability deviation amount, represents the survey coverage change rate, represents the total number of topological nodes, represents a topological node and other topological nodes the original connection distance between them, represents the topological node after topological optimization adjustment and other topological nodes the target connection distance between them;

[0202] Set the minimum closure standard value , such as 0.9, and compare it with the calculated closure value , if the closure of the area is lower than 0.9, it is considered that the topological integrity of the area is insufficient and topological optimization adjustment is required. After screening out the low-closure areas, first construct a connection weight matrix for the topological nodes in each area , where the initial connection weight calculation method is:

[0203] ;

[0204] where is the topological node and the initial connection distance between them, is the topological connectivity adjustment coefficient, is the connection stability deviation, is the mapping coverage change rate. Calculate the connection weights of each topological node based on this formula. For example, for a certain area, assume the original connection distance matrix is:

[0205] Table 5 Initial connection distance matrix (unit: m)

[0206]

[0207] Assume the target connection distance matrix after adjustment is:

[0208] Table 6 Target connection distance matrix after topological optimization (unit: m)

[0209]

[0210] Based on the above data, calculate the optimized connection weight of node :

[0211] ;

[0212] Assume , , , then the calculation is as follows:

[0213] ;

[0214] ;

[0215] ;

[0216] ;

[0217] The result shows that the node After topological optimization, its connection weight value is adjusted to 0.52, which is significantly optimized compared with the initial value, meaning that the stability of the connection path is enhanced, and the topological integrity of the mapping area is further improved.

[0218] S503: Calculate the topological integrity of the UAV mapping according to the optimized topological path distribution, count the coverage range and integrity index of the topological path, and obtain the UAV mapping topological integrity index.

[0219] First, count the coverage range of all topological paths, that is, count whether the optimized topological path can completely cover the mapping area, and define the topological integrity index as:

[0220] ;

[0221] where represents the number of effective mapping nodes covered by the topological path, represents the total number of nodes in the mapping area. Suppose there are 200 nodes in a certain mapping area, and the optimized topological path covers 180 nodes, then calculate:

[0222] ;

[0223] The result shows that after topological optimization, the topological integrity index of the mapping area reaches 0.9, which is significantly improved compared with the low-closure area before optimization, indicating that the overall topological connectivity of the UAV mapping has been improved, the coverage range is more complete, and the path optimization strategy effectively improves the integrity of the mapping, thus ensuring the effectiveness and accuracy of the final mapping data.

[0224] A UAV-based geographic mapping system, the UAV-based geographic mapping system is used to execute the above UAV-based geographic mapping method, and the system includes:

[0225] The mapping data acquisition module acquires the UAV mapping point cloud data and the aerial survey vectorized boundary data, extracts the spatial coordinates, intensity information of the point cloud data and the line segment coordinates of the vectorized boundary, calculates the spatial distance between the mapping point cloud data and the boundary data, filters the adjacent points that meet the distance condition, and obtains the mapping adjacent point set;

[0226] Based on the surveyed adjacent point set, the topological adjacent point construction module calculates the spatial topological relationship between adjacent points, screens the connection situation between the boundary line segments and adjacent points, calculates the topological connectivity weight of the connected boundary, eliminates redundant connections with connection weights lower than the threshold, and obtains the surveyed topological adjacent point index;

[0227] The topological conflict area identification module calls the surveyed topological adjacent point index, calculates the projection difference comparison threshold, screens the offset over-threshold data points of the boundary point cloud data, counts the change in point cloud density in the adjacent area, calculates the redundant conflict degree of the aerial survey boundary, screens the topological connection conflict areas, calculates the point cloud distribution density, boundary point projection difference and connection redundancy in the conflict areas, establishes a topological conflict area index, and obtains the topological conflict area parameters;

[0228] The optimal topological path calculation module calls the topological conflict area parameters, calculates the shortest path between the conflict points and the surveyed boundary points, screens the candidate paths with path lengths lower than the set threshold, screens the topological connectivity index of the optimal path, calculates the topological adjustment path of the surveyed boundary, and obtains the optimal topological connection path for unmanned aerial vehicle surveying and mapping;

[0229] The surveyed topological integrity evaluation module calls the optimal topological connection path for unmanned aerial vehicle surveying and mapping, calculates the fitting error between the adjusted boundary points and the original boundary points, screens the boundary points with fitting errors exceeding the threshold, adjusts the positions of the boundary points, calculates the closure degree of the surveyed area, screens the areas with closure degrees lower than the standard, recalculates the topological connection weight, allocates new connection paths, and obtains the surveyed topological integrity index for unmanned aerial vehicle surveying and mapping.

[0230] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for geographical mapping based on an unmanned aerial vehicle, characterized in that, It includes the following steps: S1: Obtain the UAV mapping point cloud data and aerial survey vectorized boundary data, calculate the spatial distance to screen adjacent points, construct a topological adjacent point set, screen the boundary line segments and calculate the boundary connection weight, eliminate redundant connections, and obtain the mapping topological adjacent point index; S2: Based on the mapping topological adjacent point index, calculate the projection difference comparison threshold, screen the offset over-threshold data point cloud density to determine the aerial survey redundant conflict, establish a topological conflict area index, and obtain the topological conflict area parameters; S3: Call the topological conflict area parameters, calculate the shortest path between the conflict point and the mapping boundary point, screen the candidate paths with a path length lower than the threshold, and output the optimal topological connection path for UAV mapping; S4: Call the optimal topological connection path for UAV mapping, calculate the fitting error between the adjusted boundary point and the original boundary point, screen the boundary points with a fitting error value exceeding the fitting threshold, adjust the position according to the topological adjacent point set, and output the mapping topological adjusted boundary points; The specific steps for obtaining the mapping topological adjusted boundary points are as follows: S401: Based on the optimal topological connection path for UAV mapping, calculate the fitting error between the adjusted boundary point and the original boundary point. At the same time, according to the spatial coordinate sets of the adjusted boundary point and the original boundary point, calculate the Euclidean distance of each boundary point to obtain the boundary point fitting error value; S402: Based on the boundary point fitting error value, screen the boundary points with a fitting error value exceeding the fitting threshold. For the screened boundary points, calculate the position offset according to the topological adjacent point set, and use the formula: ； Calculate the topological adjacent point adjustment offset; Among them, represents the adjusted boundary point coordinates, represents the original boundary point coordinates, represents the th adjacent point coordinates in the topological adjacent point set, represents the th topological weight of the adjacent point, represents the total number of topological adjacent points; S403: Based on the topological adjacent point adjustment offset, update the spatial coordinate data of the boundary points, call the mapping data for coordinate correction, and obtain the mapping topological adjusted boundary points; S5: Call the mapping topological adjusted boundary points, calculate the closure degree of the mapping area, screen the areas with a closure degree lower than the standard, recalculate the topological connection weight, allocate the connection path, and output the UAV mapping topological integrity index; The specific steps for obtaining the UAV mapping topological integrity index are as follows: S501: Call the mapping topological adjusted boundary points, calculate the boundary closure situation of the mapping area, obtain the topological connectivity status of each boundary point, and calculate the closure degree of the mapping area to obtain the mapping area closure degree value; The specific formula for calculating the closure degree of the mapping area is: ； Calculate the boundary closure degree of the mapping area; Among them, is the boundary closure degree of the survey area, is the number of connected boundary points, is the total number of boundary points; S502: Based on the mapping area closure degree value, screen the areas lower than the set standard, recalculate the connection weight of multiple topological nodes, and adjust the connection path to optimize the topological structure, using the formula: ； Calculate the optimized topological connection weight, adjust the topological path of the mapping area, and obtain the optimized topological path distribution; Among them, represents the optimized topological connection weight, represents the topological node and the original connection distance between other topological nodes ; represents the topological node after topological optimization adjustment and the target connection distance between other topological nodes ; represents the topological connectivity adjustment coefficient, represents the connection stability deviation, represents the mapping coverage change rate, represents the total number of topological nodes, represents the topological node and the original connection distance between other topological nodes ; represents the topological node after topological optimization adjustment and the target connection distance between other topological nodes ; S503: According to the optimized topological path distribution, calculate the topological integrity of UAV mapping, count the topological path coverage range and integrity index, and obtain the UAV mapping topological integrity index; The specific formula for calculating the topological integrity of UAV mapping is: ； Calculate the topological integrity index; Among them, is the topological integrity index, represents the number of effective surveying and mapping nodes covered by the topological path, represents the total number of all nodes in the surveying and mapping area.

2. The method for geodetic surveying based on an unmanned aerial vehicle according to claim 1, characterized in that The mapping topological adjacent point index includes spatial distance screening of adjacent points, topological adjacent point set, and boundary connection weight. The topological conflict area parameters include projection difference comparison threshold, offset over-threshold data point cloud density, and topological conflict area index. The optimal topological connection path for UAV mapping is specifically the shortest path between conflict points and mapping boundary points, and candidate paths with path lengths lower than the threshold. The adjusted boundary points after mapping topology include adjusted boundary points, fitting error of original boundary points, and boundary points with fitting error values exceeding the fitting threshold. The topological integrity index for UAV mapping includes mapping area closure, areas with closure lower than the standard, topological connection weight, and connection path.

3. The method for geodetic surveying based on an unmanned aerial vehicle according to claim 2, characterized in that, The specific steps for obtaining the mapping topological adjacent point index are as follows: S101: Obtain the UAV mapping point cloud data and aerial survey vectorized boundary data, calculate the spatial distance between the point cloud data and the boundary data, screen adjacent points according to the spatial distance threshold, construct an initial adjacent point set, and based on the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, remove isolated points and correct abnormal adjacent relationships to obtain the initial topological adjacent point index; S102: Based on the initial topological adjacent point index, screen boundary line segments, calculate the boundary connection weight, and adjust the boundary connection weight according to the spatial direction, adjacent point density, and connection angle of the boundary line segments. Use the formula: ； Perform operations to obtain the boundary connection weight distribution, remove low-weight connections, and obtain the mapping topological adjacent point index; Among them, represents the boundary connection weight, represents the spatial distance between the -th adjacent node and the boundary segment, represents the angle between the -th adjacent node and the boundary normal, represents the total number of topological adjacent nodes.

4. The method for geodetic surveying based on an unmanned aerial vehicle according to claim 3, characterized in that, The specific steps for obtaining the topological conflict area parameters are as follows: S201: Based on the mapping topological adjacent point index, call the aerial survey point cloud data, calculate the projection coordinate differences of multiple point cloud data, select the projection coordinate differences of all adjacent points and perform normalization processing to obtain the normalized matrix of projection coordinate differences; S202: Call the normalized matrix of projection coordinate differences, set the projection difference comparison threshold, screen the point cloud data exceeding the threshold, and calculate the point cloud density per unit area to obtain the distribution of point cloud density exceeding the threshold; S203: Call the distribution of point cloud density exceeding the threshold, and at the same time calculate the offset conflict area based on the point cloud density gradient, establish a topological conflict area index, calculate the correlation parameters of the topological conflict area, and use the formula: ； Perform operations to obtain the conflict intensity value of the topological conflict area, call the topological conflict area index, and combine the conflict intensity value to obtain the topological conflict area parameters; in, Represents the conflict intensity value of the topological conflict area, Representative The super-threshold point cloud density of points, represents the projection difference comparison threshold, Represents the point cloud distribution area in the topological conflict area, Represents the total amount of point cloud data within the topological conflict area.

5. The method for geodetic surveying based on an unmanned aerial vehicle according to claim 4, wherein The specific steps for obtaining the optimal topological connection path for UAV mapping are as follows: S301: Call the topological conflict area parameters, obtain the coordinate sets of conflict points and mapping boundary points, calculate the Euclidean distances from conflict points to multiple mapping boundary points, establish an initial path set between conflict points and mapping boundary points, and generate a conflict point path distance matrix; S302: Based on the conflict point path distance matrix, screen candidate paths with path lengths lower than the set path length threshold, calculate the connectivity weights of all candidate paths, and sort the candidate path set to obtain a preferred sequence of candidate paths; S303: Call the preferred sequence of candidate paths, and calculate the score of the optimal topological connection path according to the path length, connectivity weight, and mapping area coverage rate. Use the formula: ； The operation obtains the scoring values of all paths, filters the path with the highest scoring value, and obtains the optimal topological connection path for UAV mapping; in, represents the optimal topological connection path score, represents the path length threshold, Representative The length of the candidate paths, Representative The connectivity weights of the candidate paths, Representative The coverage of the survey area of the candidate paths, Represents the total number of candidate paths.

6. A drone-based geographic mapping system, characterized in that, According to the UAV-based geographical mapping method described in any one of claims 1-5, the system includes: The mapping data acquisition module acquires the UAV mapping point cloud data and the aerial survey vectorized boundary data, extracts the spatial coordinates and intensity information of the point cloud data and the line segment coordinates of the vectorized boundary, calculates the spatial distance between the mapping point cloud data and the boundary data, filters the adjacent points that meet the distance condition, and obtains the mapping adjacent point set; The topological adjacent point construction module calculates the spatial topological relationship between adjacent points based on the mapping adjacent point set, filters the connection situation between the boundary line segment and the adjacent points, calculates the topological connection weight of the connection boundary, and eliminates the redundant connections with connection weights lower than the threshold to obtain the mapping topological adjacent point index; The topological conflict area identification module calls the mapping topological adjacent point index, calculates the projection difference comparison threshold, filters the offset data points of the boundary point cloud data that exceed the threshold, calculates the point cloud density per unit area, calculates the redundant conflict degree of the aerial survey boundary, filters the topological connection conflict area, calculates the point cloud distribution density, boundary point projection difference and connection redundancy in the conflict area, establishes the topological conflict area index, and obtains the topological conflict area parameters; The optimal topological path calculation module calls the topological conflict area parameters, calculates the shortest path between the conflict point and the mapping boundary point, filters the candidate paths with path lengths lower than the set threshold, filters the topological connectivity index of the optimal path, calculates the topological adjustment path of the mapping boundary, and obtains the optimal topological connection path for UAV mapping; The mapping topological integrity evaluation module calls the optimal topological connection path for UAV mapping, calculates the fitting error between the adjusted boundary point and the original boundary point, filters the boundary points with fitting errors exceeding the threshold, adjusts the positions of the boundary points, calculates the closure degree of the mapping area, filters the areas with closure degrees lower than the standard, recalculates the topological connection weight, and assigns new connection paths to obtain the UAV mapping topological integrity index.

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