Geographic surveying and mapping method and system based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of geographic information processing, in particular to a geographic surveying and mapping method and system based on an unmanned aerial vehicle, and the method comprises the following steps: obtaining surveying and mapping point cloud data and aerial survey vectorization boundary data of the unmanned aerial vehicle, calculating a spatial distance, screening adjacent points, and constructing a topological adjacent point set. According to the method, construction of a topological adjacent point set is optimized through spatial screening of surveying and mapping point cloud data and vectorized boundary data, data redundancy is reduced, the structural rationality of surveying and mapping results is improved, abnormal data is screened through combination of projection difference analysis and point cloud density, and accurate recognition of conflict areas is achieved. Shortest path calculation optimizes connection of conflict points and boundary points, reduces path redundancy, improves spatial consistency of surveying and mapping data, fits error screening to adjust the boundary points, enhances stability of surveying and mapping boundaries, and combines closure calculation with topological weight optimization to improve integrity of regional surveying and mapping and boundary connection quality. And the surveying and mapping result is more standard in the aspects of spatial distribution and connectivity.

Description

Technical Field

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

[0002] The field of geographic information processing technology includes the collection, storage, management, analysis and visualization of geographic spatial 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 mapping, global navigation satellite system, geographic information system, and data fusion. It mainly relies on spatial data collection equipment, high-precision positioning technology, geographic data modeling methods, and visualization technology to achieve comprehensive analysis and application of geographic information.

[0003] Among them, the geographic surveying and mapping method based on drones refers to the use of drones equipped with sensor equipment to collect data on the target area and generate geographic information data through computer processing. The subject of this patent covers technical matters such as data collection, data preprocessing, geographic coordinate conversion, and surveying and mapping data modeling. It uses drones equipped with high-precision optical sensors or lidar to obtain surface information, and combines it with high-precision navigation and positioning data for synchronous processing. In data preprocessing, the original surveying and mapping data is optimized through point cloud data denoising and image orthorectification, and the coordinate conversion method is used to unify the collected data into a standard geographic coordinate system. The surveying and mapping data modeling stage constructs a three-dimensional terrain model based on stereo image matching methods or point cloud data fitting, and ultimately forms surveying and mapping data results that can be used for geographic analysis and mapping.

[0004] The existing technology lacks methods for optimizing topological adjacent points in the process of surveying and mapping data organization, and there is redundancy in data connections, which affects the rationality of the boundary structure. The screening of conflicting areas relies on fixed rules, which limits the ability to identify abnormal data and affects the overall accuracy of surveying and mapping data. Path calculation uses a static connection method, which is difficult to adapt to complex terrain and affects the consistency of surveying and mapping results. The boundary point adjustment method does not combine the dynamic changes of surveying and mapping data, the boundary stability is poor, and the integrity control ability of the surveying and mapping area is insufficient, which affects the subsequent application quality of geographic data. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a geographic surveying and mapping method and system based on drones.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a geographic surveying and mapping method based on an unmanned aerial vehicle, comprising the following steps: S1: Obtain UAV surveying and mapping point cloud data and aerial surveying and vectorization boundary data, calculate the Euclidean distance based on the three-dimensional spatial coordinates between point cloud points, filter out point pairs less than the adjacency threshold, establish a topological adjacent point set, call the topological adjacent point set to filter boundary segments and calculate the boundary connectivity weight, remove redundant connections for data points with boundary connectivity weights lower than the connection threshold, and obtain the surveying and mapping topological adjacent point index; S2: Based on the surveying and mapping topological adjacent point index, calculate the projection coordinate difference of the topological adjacent points, filter out data points whose projection offset exceeds the set threshold, and classify topological conflicts according to the projection offset, including aerial survey redundant topological conflicts, surveying and mapping cross topological conflicts and topological isolated point conflicts. For aerial survey redundant topological conflicts, calculate the point cloud density of data points and identify redundant points. For surveying and mapping cross topological conflicts, calculate the spatial offset of the intersection of boundary segments and locate the misconnected points. For topological isolated point conflicts, calculate the adjacent distance of unconnected points in the closed area and determine the independence. Establish the drone surveying and mapping topological conflict area index and obtain the topological conflict area parameters. S3: calling the topology conflict area parameters, calculating the shortest connection path between the topology conflict point and the surveying and mapping boundary point, screening candidate paths whose path length is lower than the topology consistency threshold, calculating the curvature change rate of the candidate paths, screening the path with a stable curvature change rate as the optimal path, performing local supplementary point interpolation for the topology conflict points without available paths, and obtaining the optimal topology connection path for UAV surveying and mapping; S4: calling the optimal topological connection path of the UAV surveying and mapping, calculating the fitting error between the adjusted boundary points and the original aerial survey boundary points, screening the boundary points whose fitting error values ​​exceed the fitting threshold, re-adjusting the boundary point positions according to the topological adjacent point set, and obtaining the boundary points after the surveying and mapping topology adjustment; S5: Call the boundary points after the surveying and mapping topology adjustment, calculate the closure of the surveying and mapping area, filter out areas with closure values ​​lower than the set standard, recalculate the topological connection weights based on the topological adjacent point set, and allocate connection paths based on the topological connection weights to obtain the drone surveying and mapping topological completeness index.

[0007] 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 sets, and boundary connectivity weights. The topological conflict area parameters include projection difference comparison thresholds, offset over-threshold data point cloud density, and topological conflict area indexes. The optimal topological connection path for drone surveying and mapping is specifically the shortest path between the conflict point and the surveying and mapping boundary point, and the candidate path with a path length lower than the threshold. The surveying and mapping topology adjusted boundary points include adjusted boundary points, fitting errors of original boundary points, and boundary points whose fitting error values ​​exceed the fitting threshold. The drone surveying and mapping topological completeness index includes the closure of the surveying and mapping area, the closure area lower than the standard area, the topological connection weight, and the connection path.

[0008] As a further solution of the present invention, the step of obtaining the surveying and mapping topology adjacent point index is specifically as follows: S101: Obtain UAV surveying point cloud data and aerial survey vectorized boundary data, calculate the spatial distance between the point cloud data and the boundary data, filter adjacent points according to the spatial distance threshold, construct an initial adjacent point set, remove isolated points and correct abnormal adjacent relationships based on the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, and obtain an initial topological adjacent point index; S102: Based on the initial topological adjacent point index, the boundary line segments are screened, the boundary connectivity weight is calculated, and the boundary connectivity weight is adjusted according to the spatial direction of the boundary line segments, the adjacent point density and the connection angle, using the formula: ; The weight distribution of boundary connectivity is obtained by operation, low-weight connections are removed, and the index of adjacent points of surveying and mapping topology is obtained; in, represents the boundary connectivity weight, Representative The spatial distance between the adjacent points and the boundary segment, Representative The angle between the adjacent points and the boundary normal, Represents the total number of neighboring points.

[0009] As a further solution of the present invention, the step of obtaining the topology conflict area parameters is specifically as follows: S201: Based on the surveying and mapping topology adjacent point index, calling the aerial survey point cloud data, calculating the projection coordinate difference of multiple point cloud data, selecting the projection coordinate difference of all adjacent points and performing normalization processing, and obtaining the projection coordinate difference normalization matrix; S202: calling the projection coordinate difference normalization matrix, setting the projection difference comparison threshold, filtering the point cloud data exceeding the threshold, and calculating the point cloud density per unit area to obtain the point cloud density distribution exceeding the threshold; S203: calling the super-threshold point cloud density distribution, calculating the offset conflict area based on the point cloud density gradient, establishing a topological conflict area index, and calculating the associated parameters of the topological conflict area, using the formula: ; Obtain the conflict strength value of the topological conflict area through calculation, call the topological conflict area index, and obtain the topological conflict area parameters in combination with the conflict strength value; 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.

[0010] As a further solution of the present invention, the steps of obtaining the optimal topological connection path for drone mapping are specifically as follows: S301: calling the topological conflict area parameters, obtaining a coordinate set of the conflict point and the surveying and mapping boundary points, calculating the Euclidean distance from the conflict point to multiple surveying and mapping boundary points, establishing an initial path set of the conflict point and the surveying and mapping boundary points, and generating a conflict point path distance matrix; S302: Based on the conflict point path distance matrix, select candidate paths whose path lengths are lower than a set path length threshold, calculate the connectivity weights of all candidate paths, and sort the candidate path set to obtain a candidate path optimization sequence; S303: Call the candidate path optimization sequence, and calculate the optimal topological connection path score according to the path length, connectivity weight and survey area coverage, using the formula: ; Calculate and obtain the score values ​​of all paths, and select the path with the highest score to obtain the optimal topological connection path for drone 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.

[0011] As a further solution of the present invention, the step of obtaining the boundary points after the surveying and mapping topology adjustment is specifically as follows: S401: Based on the optimal topological connection path of the UAV surveying, 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; S402: Based on the boundary point fitting error value, the boundary points whose fitting error values ​​exceed the fitting threshold are screened, and the position offset of the screened boundary points is calculated according to the topological adjacent point set, using the formula: ; Calculate and obtain the adjustment offset of the topological adjacent points; in, Represents the coordinates of the adjusted boundary points, represents the original boundary point coordinates, Represents the first node in the set of topological adjacent nodes. The coordinates of neighboring points, Representatives and The topological weight of the neighboring points, Represents the total number of topological adjacent points; S403: adjusting the offset based on the topological adjacent points, updating the spatial coordinate data of the boundary points, calling the surveying and mapping data for coordinate correction, and obtaining the boundary points after the surveying and mapping topology adjustment.

[0012] As a further solution of the present invention, the steps for obtaining the topological integrity index of the drone surveying and mapping are specifically as follows: S501: calling the boundary points after the surveying and mapping topology adjustment, calculating the boundary closure of the surveying and mapping area, obtaining the topological connectivity state of each boundary point, and calculating the closure degree of the surveying and mapping area to obtain the closure degree value of the surveying and mapping area; S502: Based on the closure value of the surveyed area, filter out areas below the set standard, recalculate the connection weights of the multi-topology nodes, and adjust the connection path to optimize the topology structure, using the formula: ; Calculate the optimized topological connection weights, adjust the topological path of the surveying area, and obtain the optimized topological path distribution; in, represents the optimized topological connection weight, Represents a topological node With other topology nodes The original connection distance between Represents the topological node after topology optimization adjustment With other topology nodes The target connection distance between represents the topological connectivity adjustment coefficient, Represents the connection stability deviation, represents the rate of change of surveying and mapping coverage, Represents the total number of topological nodes, Represents a topological node With other topology nodes The original connection distance between Represents the topological node after topology optimization adjustment With other topology nodes The target connection distance between S503: Calculate the topological completeness of the UAV mapping according to the optimized topological path distribution, count the topological path coverage and the completeness index, and obtain the UAV mapping topological completeness index.

[0013] A geographical surveying and mapping system based on a drone, wherein the geographical surveying and mapping system based on a drone is used to execute the geographical surveying and mapping method based on a drone, and the system comprises: The surveying and mapping data acquisition module acquires the UAV surveying and mapping point cloud data and the aerial surveying and mapping 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 surveying and mapping point cloud data and the boundary data, and selects the adjacent points that meet the distance conditions to obtain the surveying and mapping adjacent point set; The topological adjacent point construction module calculates the spatial topological relationship between adjacent points based on the surveying and mapping adjacent point set, screens the connection between the boundary line segments and the adjacent points, calculates the topological connectivity weight of the connection boundary, removes redundant connections with connection weights lower than a threshold, and obtains the surveying and mapping topological adjacent point index; The topological conflict area identification module calls the surveying and mapping 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 point cloud density changes in the adjacent area, calculates the redundant conflict degree of the aerial survey boundary, screens 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 surveying and mapping boundary point, screens the candidate paths whose path length is lower than the set threshold, screens the topological connectivity index of the optimal path, calculates the topological adjustment path of the surveying and mapping boundary, and obtains the optimal topological connection path for UAV surveying and mapping; The surveying and mapping topology integrity assessment module calls the optimal topology connection path of the UAV surveying and mapping, calculates the fitting error between the adjusted boundary points and the original boundary points, filters the boundary points whose fitting error exceeds the threshold, adjusts the position of the boundary points, calculates the closure of the surveying and mapping area, filters the areas with closure below the standard, recalculates the topology connection weights, assigns new connection paths, and obtains the UAV surveying and mapping topology integrity index.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the construction of the topological adjacent point set is optimized through spatial screening of surveying and mapping point cloud data and vectorized boundary data, thereby reducing data redundancy and improving the structural rationality of the surveying and mapping results. Projection difference analysis is combined with point cloud density to screen abnormal data and achieve accurate identification of conflicting areas. The shortest path calculation optimizes the connection between conflicting points and boundary points, reduces path redundancy, and improves the spatial consistency of surveying and mapping data. Fitting error screening adjusts boundary points to enhance the stability of surveying and mapping boundaries. Closure calculation is combined with topological weight optimization to improve the integrity of regional surveying and mapping and the quality of boundary connection, making the surveying and mapping results more standardized in terms of spatial distribution and connectivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flowchart of the steps for obtaining the topological adjacent point index of the present invention; Figure 3 A flowchart of the steps for obtaining topology conflict area parameters of the present invention; Figure 4 A flowchart of the steps for obtaining the optimal topological connection path for mapping by the unmanned aerial vehicle of the present invention; Figure 5 This is a flow chart of the steps for obtaining the boundary points after the topology adjustment of the present invention; Figure 6 This is a flow chart of the steps for obtaining the topological integrity index of the unmanned aerial vehicle surveying and mapping of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0018] Example 1: Please refer to Figure 1 The present invention provides a technical solution: a geographic surveying and mapping method based on an unmanned aerial vehicle, comprising the following steps: S1: Obtain UAV surveying and mapping point cloud data and aerial surveying and vectorization boundary data, calculate the Euclidean distance based on the three-dimensional spatial coordinates between point cloud points, filter out point pairs less than the adjacency threshold, establish a topological adjacent point set, call the topological adjacent point set to filter boundary segments and calculate the boundary connectivity weight, remove redundant connections for data points with boundary connectivity weights lower than the connection threshold, and obtain the surveying and mapping topological adjacent point index; S2: Based on the surveying and mapping topological adjacent point index, calculate the projection coordinate difference of the topological adjacent points, filter out data points whose projection offset exceeds the set threshold, and classify topological conflicts according to the projection offset, including aerial survey redundant topological conflicts, surveying and mapping cross topological conflicts and topological isolated point conflicts. For aerial survey redundant topological conflicts, calculate the point cloud density of data points and identify redundant points. For surveying and mapping cross topological conflicts, calculate the spatial offset of the intersection of boundary segments and locate the misconnected points. For topological isolated point conflicts, calculate the adjacent distance of unconnected points in the closed area and determine the independence. Establish the drone surveying and mapping topological conflict area index and obtain the topological conflict area parameters. S3: Call the topology conflict area parameters, calculate the shortest connection path between the topology conflict point and the mapping boundary point, screen the candidate paths whose path length is lower than the topology consistency threshold, calculate the curvature change rate of the candidate paths, screen the path with a stable curvature change rate as the optimal path, perform local supplementary point interpolation for the topology conflict points without available paths, and obtain the optimal topology connection path for drone mapping; S4: Call the optimal topological connection path of the UAV surveying and mapping, calculate the fitting error between the adjusted boundary points and the original aerial survey boundary points, select the boundary points whose fitting error values ​​exceed the fitting threshold, readjust the boundary point positions according to the topological adjacent point set, and obtain the boundary points after the surveying and mapping topology adjustment; S5: Call the boundary points after the surveying and mapping topology adjustment, calculate the closure of the surveying and mapping area, filter out the areas with closure values ​​lower than the set standard, recalculate the topological connection weights based on the topological adjacent point set, and allocate connection paths based on the topological connection weights to obtain the drone surveying and mapping topological completeness index.

[0019] The surveying and mapping topological adjacent point index includes spatial distance screening adjacent points, topological adjacent point set, and boundary connectivity 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 drone surveying and mapping is specifically the shortest path between the conflict point and the surveying and mapping boundary point, and the candidate path with a path length lower than the threshold. The surveying and mapping topology adjusted boundary points include adjusted boundary points, fitting errors of the original boundary points, and boundary points whose fitting error values ​​exceed the fitting threshold. The drone surveying and mapping topological completeness index includes the closure of the surveying and mapping area, the closure area lower than the standard area, the topological connection weight, and the connection path.

[0020] See also Figure 2 , the specific steps for obtaining the mapping topology adjacent point index are: S101: Obtain UAV surveying point cloud data and aerial survey vectorized boundary data, calculate the spatial distance between the point cloud data and the boundary data, filter adjacent points according to the spatial distance threshold, construct an initial adjacent point set, remove isolated points and correct abnormal adjacent relationships based on the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, and obtain an initial topological adjacent point index; First, the laser radar sensor or photogrammetry equipment carried by the drone is called to record the high-precision three-dimensional coordinate data of the target area and generate point cloud data. The point cloud data should contain the three-dimensional coordinates (X, Y, Z), reflection intensity, classification label and other information of the point. At the same time, the acquisition of aerial survey vectorized boundary data needs to be based on digital aerial photography. The boundary line of the target area is extracted by manual or automatic vectorization method and stored in vector data format. Next, the spatial distance between the point cloud data and the boundary data is calculated. For each point in the point cloud data, the three-dimensional Euclidean distance formula is used. Calculate point cloud points To the border point The distance, where the boundary points are obtained by interpolating the line segment coordinates of the vectorized boundary data, and the calculation results of all point cloud points form a distance array , set the spatial distance threshold , filter to meet Point cloud points, construct the initial adjacent point set, assuming If the value is 0.5m, then all the selected point cloud points are within 0.5m of the boundary line segment. Next, based on the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, isolated points are removed. The method for removing isolated points is to count the number of neighboring points of each point. ,like (If set ), the point is determined to be an isolated point and removed. Finally, by correcting the abnormal adjacency relationship, the point cloud points that maintain connectivity are screened to obtain the initial topological adjacent point index.

[0021] S102: Based on the initial topological adjacent point index, the boundary line segments are screened, the boundary connectivity weight is calculated, and the boundary connectivity weight is adjusted according to the spatial direction of the boundary line segments, the adjacent point density and the connection angle, using the formula: ; The weight distribution of boundary connectivity is obtained by operation, low-weight connections are removed, and the index of adjacent points of surveying and mapping topology is obtained; in, represents the boundary connectivity weight, Representative The spatial distance between the adjacent points and the boundary segment, Representative The angle between the adjacent points and the boundary normal, represents the total number of neighboring points; First, filter the boundary segments. Each boundary segment corresponds to multiple adjacent points. Calculate the boundary connectivity weight of each boundary segment. , for each adjacent point , record the spatial distance between it and the boundary segment and the angle with the boundary normal , using the formula: ; Calculate the weight distribution, where Through the above calculation, we can get The calculation method is ,in is the boundary normal vector, assuming that the adjacent point data of a boundary segment is as follows: Table 1 Adjacent point parameters

[0022] Then we calculate: ; ; ; For all boundary segments, calculate , set the weight threshold ,filter If the boundary line segment If it is set to 0.5, boundary segments that meet the conditions are screened out, low-weight connections are removed, and finally the mapping topology adjacent point index is obtained.

[0023] See also Figure 3 , the specific steps for obtaining the topology conflict area parameters are: S201: Based on the surveying and mapping topology adjacent point index, calling the aerial survey point cloud data, calculating the projection coordinate difference of the multi-point cloud data, selecting the projection coordinate difference of all adjacent points and performing normalization processing, and obtaining the projection coordinate difference normalization matrix; First, call the aerial survey point cloud data, retrieve the 3D coordinate information of each topological adjacent point, extract its projection coordinates (X, Y) respectively, and calculate the projection coordinate difference between adjacent point pairs, that is, for each point and its adjacent points , calculate the difference in projection coordinates and , forming a coordinate difference set And store it in a matrix, and then normalize the matrix. The normalization formula is as follows: ; ; After normalizing the projection coordinate difference set, a projection coordinate difference normalization matrix is ​​constructed. Each element of the matrix represents the standardized projection difference of adjacent point pairs, which is convenient for subsequent calculations. Assume that the input point cloud data is shown in Table 2 below: Table 2 Projection coordinate difference calculation example table

[0024] As shown in Table 2, the calculated normalized projection coordinate difference can be used for subsequent projection difference comparison analysis, and finally a projection coordinate difference normalized matrix is ​​obtained.

[0025] S202: calling the projection coordinate difference normalization matrix, setting the projection difference comparison threshold, filtering the point cloud data exceeding the threshold, and calculating the point cloud density per unit area to obtain the point cloud density distribution exceeding the threshold; Take the mean of all elements of the normalized matrix plus the standard deviation, that is: ; in, is the mean of the normalized matrix, is the standard deviation. Then, filter out all or The point cloud data set is obtained by calculating the point cloud density per unit area of ​​these points. The calculation method is as follows: ; in, is the number of point clouds exceeding the threshold, is the area of ​​the selected area. The point cloud density per unit area can be used to reflect the point cloud density of the local area. Assume that the screened data is shown in Table 3 below: Table 3 Super-threshold point cloud density distribution table

[0026] See Table 3. The calculated super-threshold point cloud density distribution is used to calculate the topological conflict area parameters in the next step.

[0027] S203: Call the super-threshold point cloud density distribution, calculate the offset conflict area based on the point cloud density gradient, establish the topological conflict area index, calculate the associated parameters of the topological conflict area, and use the formula: ; Obtain the conflict strength value of the topological conflict area through calculation, call the topological conflict area index, and obtain the topological conflict area parameters in combination with the conflict strength value; 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.

[0028] Traverse the point cloud density distribution and calculate the rate of change of point cloud density in adjacent areas : ; in, For Region and Then, the gradient values ​​are calculated for all adjacent regions and the gradient threshold is set. To filter out the point cloud density mutation area, then establish the topological conflict area index, and calculate the conflict intensity value of the topological conflict area: ; in, It means to sum all conflict area points. Calculate the difference between the density of each point cloud and the threshold, Used to normalize area effects.

[0029] Assume the following input parameters: ; Region R1: ; Region R2: ; Region R3: ; Enter the formula to calculate: ; ; ; Finally, the topological conflict area parameters are obtained The result indicates that there is a certain degree of topological conflict in the area, and the conflict intensity value can be combined for subsequent optimization.

[0030] See also Figure 4 ,The specific steps for obtaining the optimal topological connection path for UAV mapping are: S301: calling topological conflict area parameters, obtaining a coordinate set of conflict points and surveying boundary points, calculating the Euclidean distance from the conflict point to multiple surveying boundary points, establishing an initial path set of conflict points and surveying boundary points, and generating a conflict point path distance matrix; First, the coordinate sets of conflict points and mapping boundary points are obtained. The conflict points can be determined by the path intersections, signal interference areas or terrain obstacles identified during the UAV trajectory planning process. The mapping boundary points can be extracted by the set mapping area boundaries or key monitoring point coordinates. Next, the Euclidean distance from the conflict point to multiple mapping boundary points is calculated. The calculation formula is: ; in Represents the coordinates of the conflict point, Represents the coordinates of the surveying and mapping boundary points. Take a conflict point (10,20) as an example. If its neighboring surveying and mapping boundary points are (30,40), (25,15), and (5,10), the calculated distances are 28.28, 18.03, and 11.18, respectively. After the calculation is completed, an initial set of paths between the conflict point and the surveying and mapping boundary points is established. Each path is composed of the line connecting the conflict point and the surveying and mapping boundary point, and its Euclidean distance is stored as the path length. At the same time, a conflict point path distance matrix is ​​generated. The rows of the matrix represent conflict points, and the columns represent surveying and mapping boundary points. Each element of the matrix stores the Euclidean distance of the corresponding path. For example, for 3 conflict points and 3 surveying and mapping boundary points, the matrix can be expressed as: ; The results show that the establishment of the path matrix can clearly reflect the straight-line distance between the conflict point and the mapping boundary point, providing basic data for subsequent path screening and optimization. The matrix will be used for path length screening and topology optimization in subsequent steps to ensure the optimal UAV mapping path.

[0031] S302: Based on the conflict point path distance matrix, candidate paths whose path lengths are lower than a set path length threshold are screened, connectivity weights of all candidate paths are calculated, and the candidate path set is sorted to obtain a candidate path optimization sequence; Based on the above conflict point path distance matrix, candidate paths with path lengths lower than the set path length threshold are screened. It can be set according to the endurance of the UAV and the requirements of the mapping task. For example, if the threshold is set to 25, the paths less than 25 in the path matrix will be used as candidate paths. The set of candidate paths 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, their connectivity weights are calculated. It can be set based on factors such as the number of network nodes the path passes through, the degree of path redundancy, etc. For example, assuming that the fewer nodes the path passes through, the higher the connectivity, the weight can be as follows: ; in Indicates the number of intermediate nodes on the path. If a path only contains the starting point and the end point, then , if it contains 1 intermediate node, then ,After the calculation, all candidate paths are sorted by the connectivity weight, and the larger weight is, the higher ranking is,as shown in Table 4.

[0032] Table 4 Candidate path connectivity weight table

[0033] This result shows that the P1 and P2 paths have the highest connectivity, which means that these paths have higher reliability and pass through the least nodes, and will occupy a greater weight in the next step of path score calculation, thereby increasing the possibility of them being the optimal path.

[0034] S303: Call the candidate path optimization sequence, and calculate the optimal topological connection path score based on the path length, connectivity weight and survey area coverage, using the formula: ; Calculate and obtain the score values ​​of all paths, and select the path with the highest score to obtain the optimal topological connection path for drone 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.

[0035] formula: ; Among them, the path length threshold , the length of each candidate path The connectivity weights have been calculated in the previous steps. See Table 4, Surveying and Mapping Area Coverage Indicates the importance of the survey area connected by the path. It can be set based on historical task data or regional survey density. For example, assuming the survey area coverage rate The settings are as follows: ; Substitute the parameters into the formula, taking P1 path as an example, , , ,calculate: ; The other path scores are calculated similarly: ; ; ; ; 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.

[0036] See also Figure 5 ,The specific steps for obtaining boundary points after surveying and mapping topology adjustment are: 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; 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: ; in, Indicates the total number of boundary points.

[0037] 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: ; 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: ; 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.

[0038] S402: Based on the boundary point fitting error value, the boundary points whose fitting error value exceeds the fitting threshold are screened, and the position offset of the screened boundary points is calculated according to the topological adjacent point set, using the formula: ; Calculate and obtain the adjustment offset of the topological adjacent points; in, Represents the coordinates of the adjusted boundary points, represents the original boundary point coordinates, Represents the first node in the set of topological adjacent nodes. The coordinates of neighboring points, Representatives and The topological weight of the neighboring points, Represents the total number of topological adjacent points; It is necessary to filter out the boundary points whose errors exceed the fitting threshold. Assuming that the fitting threshold is set to 0.5 meters, when the error value of a boundary point When , the point is considered to have too large an error and needs further adjustment. After screening out these boundary points, the position offset is calculated based on the topological adjacent point set. The formula is as follows: ; in, is the coordinate of the boundary point after adjustment, is the coordinate of the original boundary point, is the first node in the topological adjacent point set The coordinates of neighboring points, For the The topological weight of the neighboring points, is the total number of topological adjacent points.

[0039] In practical applications, for example, the original coordinates of a boundary point There are two adjacent points: ; The corresponding topological weights are and , then calculate the adjusted coordinates: ; Calculate the adjustment amounts in the X, Y, and Z directions respectively: ; ; ; The final adjusted boundary point coordinates are .

[0040] The final adjusted boundary point coordinates are .

[0041] 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 the surveying and mapping topology adjustment. The result shows that the adjustment of the points fully considers the influence of the topological adjacent points, so that the adjusted position of the boundary points is closer to the real boundary, while reducing the error between the origin and the adjacent points.

[0042] In the process of surveying and mapping data correction, multiple surveying and mapping sample points can be used for mean shift correction. For example, based on the actual surveying and mapping samples, the final coordinates of a point are calculated as: ; If the actual surveying sample point data is as follows: ; The final boundary point coordinates are calculated as follows: ; ; ; Finally, the boundary points after the surveying and mapping topology adjustment are obtained ,The result shows that after the topological adjacent points adjustment and ,surveying data correction, the coordinates of the boundary points have ,been stabilized within the error threshold range, meeting the ,surveying accuracy requirements, and can be used for subsequent surveying ,analysis and optimization of UAV path planning.

[0043] See also Figure 6 ,The specific steps for obtaining the topological completeness index of UAV mapping are: S501: Calling the boundary points after the surveying and mapping topology adjustment, calculating the boundary closure of the surveying and mapping area, obtaining the topological connectivity state of each boundary point, and calculating the closure degree of the surveying and mapping area to obtain the closure degree value of the surveying and mapping area; First, it is necessary to parse all the boundary points in the survey area, extract the coordinate information of each boundary point, and construct a complete boundary point sequence based on the topological relationship between adjacent points. Determine the connectivity between points. If the distance between a boundary point and two adjacent points is lower than the set maximum allowable boundary distance threshold , then the point is determined to be connected. If the number of connections of a point is lower than the set minimum connection number threshold , then the 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 surveying area is calculated. The calculation method of closure degree is: ; in is the number of connected boundary points, is the total number of boundary points. For example, there are 150 boundary points in a certain area, of which 130 are identified as connected points. The closure degree is calculated as: ; The result shows that the boundary closure of the surveying area has reached 86.67%. Since this value is lower than the set minimum closure standard (for example, 90%), it indicates that the topological structure of the current surveying area has an incomplete boundary problem, and the topological structure needs to be further optimized to improve the overall surveying integrity.

[0044] S502: Based on the closure value of the surveyed area, screen the areas below the set standard, recalculate the connection weights of the multi-topology nodes, and adjust the connection path to optimize the topology structure, using the formula: ; Calculate the optimized topological connection weights, adjust the topological path of the surveying area, and obtain the optimized topological path distribution; in, represents the optimized topological connection weight, Represents a topological node With other topology nodes The original connection distance between Represents the topological node after topology optimization adjustment With other topology nodes The target connection distance between represents the topological connectivity adjustment coefficient, Represents the connection stability deviation, represents the rate of change of survey coverage, Represents the total number of topological nodes, Represents a topological node With other topology nodes The original connection distance between Represents the topological node after topology optimization adjustment With other topology nodes The target connection distance between Set the minimum closure standard value , such as 0.9, compared with the calculated closure value If the closure degree of the region is lower than 0.9, the topological integrity of the region is considered insufficient and topological optimization adjustment is required. After screening out the low closure regions, the connection weight matrix is ​​first constructed for the topological nodes of each region. , where the initial connection weight is calculated as: ; in For topological nodes and The initial connection distance between is the topological connectivity adjustment coefficient, is the connection stability deviation, To map the coverage change rate, the connection weight of each topological node is calculated based on this formula. For example, for a certain area, assuming that the original connection distance matrix is: Table 5 Initial connection distance matrix (unit: m)

[0045] Assume that the adjusted target connection distance matrix is: Table 6 Target connection distance matrix after topology optimization (unit: m)

[0046] Based on the above data, the calculation node The optimized connection weights are: ; Assumptions , , , then the calculation is as follows: ; ; ; ; This result shows that the node After topology optimization, its connection weight value was adjusted to 0.52, which is significantly optimized compared to the initial value, which means that the stability of the connection path has been enhanced, and the topological integrity of the surveying area has been further improved.

[0047] S503: Calculate the topological completeness of the UAV mapping according to the optimized topological path distribution, count the topological path coverage and the completeness index, and obtain the UAV mapping topological completeness index.

[0048] First, the coverage of all topological paths is counted, that is, whether the optimized topological paths can completely cover the surveying area, and the topological completeness index is defined. for: ; in Indicates the number of valid mapping nodes covered by the topological path, Represents the number of all nodes in the surveying area. Assuming that there are 200 nodes in a surveying area and the optimized topological path covers 180 nodes, the calculation is: ; The results show that after topology optimization, the topological completeness index of the surveyed area reached 0.9, which is significantly improved compared with the low closure area before optimization. This shows that the overall topological connectivity of the UAV survey has been improved, the coverage is more complete, and the path optimization strategy has effectively improved the completeness of the survey, thereby ensuring the effectiveness and accuracy of the final survey data.

[0049] A geographical surveying and mapping system based on a drone, the geographical surveying and mapping system based on a drone is used to execute the geographical surveying and mapping method based on a drone, and the system comprises: The surveying and mapping data acquisition module acquires the UAV surveying and mapping point cloud data and the aerial surveying and mapping 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 surveying and mapping point cloud data and the boundary data, and selects the adjacent points that meet the distance conditions to obtain the surveying and mapping adjacent point set; The topological adjacent point construction module calculates the spatial topological relationship between adjacent points based on the surveying and mapping adjacent point set, screens the connection between the boundary line segments and the adjacent points, calculates the topological connectivity weight of the connection boundary, removes redundant connections with connection weights lower than the threshold, and obtains the surveying and mapping topological adjacent point index; The topological conflict area identification module calls the surveying and mapping 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 point cloud density changes in the adjacent area, calculates the redundant conflict degree of the aerial survey boundary, screens 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 surveying and mapping boundary point, screens the candidate paths whose path length is lower than the set threshold, screens the topological connectivity index of the optimal path, calculates the topological adjustment path of the surveying and mapping boundary, and obtains the optimal topological connection path for UAV surveying and mapping; The surveying and mapping topology integrity assessment module calls the optimal topology connection path for drone mapping, calculates the fitting error between the adjusted boundary points and the original boundary points, filters out boundary points whose fitting error exceeds the threshold, adjusts the position of the boundary points, calculates the closure of the surveying and mapping area, filters out areas with closure below the standard, recalculates the topology connection weights, assigns new connection paths, and obtains the drone mapping topology integrity index.

[0050] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A geographic surveying and mapping method based on an unmanned aerial vehicle, characterized in that: The following steps are involved: S1: Obtain UAV surveying point cloud data and aerial survey vectorized boundary data, calculate spatial distance to filter adjacent points, construct a topological adjacent point set, filter boundary segments and calculate boundary connectivity weights, remove redundant connections, and obtain the surveying topology adjacent point index; S2: Based on the surveying and mapping topological adjacent point index, calculate the projection difference comparison threshold, screen the density of the offset over-threshold data point cloud to determine the aerial survey redundant conflict, establish the topological conflict area index, and obtain the topological conflict area parameters; S3: calling the topology conflict area parameters, calculating the shortest path between the conflict point and the surveying and mapping boundary point, screening the candidate paths with path lengths below the threshold, and outputting the optimal topology connection path for UAV surveying and mapping; S4: calling the optimal topological connection path of the UAV surveying and mapping, calculating the fitting error between the adjusted boundary points and the original boundary points, screening the boundary points whose fitting error values ​​exceed the fitting threshold, adjusting the positions according to the topological adjacent point set, and outputting the boundary points after the surveying and mapping topology adjustment; S5: Call the boundary points after the surveying and mapping topology adjustment, calculate the closure of the surveying and mapping area, filter out areas with closure below the standard, recalculate the topological connection weights, assign connection paths, and output the drone surveying and mapping topological integrity index.

2. The method for geographical surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The surveying and mapping topological adjacent point index includes spatial distance screening adjacent points, topological adjacent point sets, and boundary connectivity weights. The topological conflict area parameters include projection difference comparison thresholds, offset over-threshold data point cloud density, and topological conflict area indexes. The optimal topological connection path for drone surveying and mapping is specifically the shortest path between the conflict point and the surveying and mapping boundary point, and the candidate path with a path length lower than the threshold. The surveying and mapping topology adjusted boundary points include adjusted boundary points, fitting errors of original boundary points, and boundary points whose fitting error values ​​exceed the fitting threshold. The drone surveying and mapping topological completeness index includes the closure of the surveying and mapping area, the closure area lower than the standard area, the topological connection weight, and the connection path.

3. The method for geographical surveying and mapping based on an unmanned aerial vehicle according to claim 2, characterized in that: The steps for obtaining the surveying and mapping topology adjacent point index are specifically as follows: S101: Obtain UAV surveying point cloud data and aerial survey vectorized boundary data, calculate the spatial distance between the point cloud data and the boundary data, filter adjacent points according to the spatial distance threshold, construct an initial adjacent point set, remove isolated points and correct abnormal adjacent relationships based on the spatial distribution of the point cloud data and the geometric characteristics of the boundary data, and obtain an initial topological adjacent point index; S102: Based on the initial topological adjacent point index, the boundary line segments are screened, the boundary connectivity weight is calculated, and the boundary connectivity weight is adjusted according to the spatial direction of the boundary line segments, the adjacent point density and the connection angle, using the formula: ； The weight distribution of boundary connectivity is obtained by operation, low-weight connections are removed, and the index of adjacent points of surveying and mapping topology is obtained; in, represents the boundary connectivity weight, Representative The spatial distance between the adjacent points and the boundary segment, Representative The angle between the adjacent points and the boundary normal, Represents the total number of neighboring points.

4. The method for geographical surveying and mapping based on an unmanned aerial vehicle according to claim 3, characterized in that: The steps for obtaining the topology conflict area parameters are specifically as follows: S201: Based on the surveying and mapping topology adjacent point index, calling the aerial survey point cloud data, calculating the projection coordinate difference of the multi-point cloud data, selecting the projection coordinate difference of all adjacent points and performing normalization processing, and obtaining the projection coordinate difference normalization matrix; S202: calling the projection coordinate difference normalization matrix, setting the projection difference comparison threshold, filtering the point cloud data exceeding the threshold, and calculating the point cloud density per unit area to obtain the point cloud density distribution exceeding the threshold; S203: calling the super-threshold point cloud density distribution, calculating the offset conflict area based on the point cloud density gradient, establishing a topological conflict area index, and calculating the associated parameters of the topological conflict area using the formula: ； Obtain the conflict strength value of the topological conflict area through calculation, call the topological conflict area index, and obtain the topological conflict area parameters in combination with the conflict strength value; 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 geographic surveying and mapping method based on an unmanned aerial vehicle according to claim 4, characterized in that: The specific steps for obtaining the optimal topological connection path for drone mapping are as follows: S301: calling the topological conflict area parameters, obtaining a coordinate set of the conflict point and the surveying and mapping boundary points, calculating the Euclidean distance from the conflict point to multiple surveying and mapping boundary points, establishing an initial path set of the conflict point and the surveying and mapping boundary points, and generating a conflict point path distance matrix; S302: Based on the conflict point path distance matrix, select candidate paths whose path lengths are lower than a set path length threshold, calculate the connectivity weights of all candidate paths, and sort the candidate path set to obtain a candidate path optimization sequence; S303: Call the candidate path optimization sequence, and calculate the optimal topological connection path score according to the path length, connectivity weight and survey area coverage, using the formula: ； Calculate and obtain the score values ​​of all paths, and select the path with the highest score to obtain the optimal topological connection path for drone 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. The method for geographical surveying and mapping based on an unmanned aerial vehicle according to claim 5, characterized in that: The steps for obtaining the boundary points after the surveying and mapping topology adjustment are specifically as follows: S401: Based on the optimal topological connection path of the UAV surveying, 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; S402: Based on the boundary point fitting error value, the boundary points whose fitting error values ​​exceed the fitting threshold are screened, and the position offset of the screened boundary points is calculated according to the topological adjacent point set, using the formula: ； Calculate and obtain the adjustment offset of the topological adjacent points; in, Represents the coordinates of the adjusted boundary points, represents the coordinates of the original boundary points, Represents the first node in the set of topological adjacent nodes. The coordinates of neighboring points, Representatives and The topological weight of the neighboring points, Represents the total number of topological adjacent points; S403: adjusting the offset based on the topological adjacent points, updating the spatial coordinate data of the boundary points, calling the surveying and mapping data for coordinate correction, and obtaining the boundary points after the surveying and mapping topology adjustment.

7. The geographic surveying and mapping method based on an unmanned aerial vehicle according to claim 6, characterized in that: The steps for obtaining the topological integrity index of the UAV surveying and mapping are specifically as follows: S501: calling the boundary points after the surveying and mapping topology adjustment, calculating the boundary closure of the surveying and mapping area, obtaining the topological connectivity state of each boundary point, and calculating the closure degree of the surveying and mapping area to obtain the closure degree value of the surveying and mapping area; S502: Based on the closure value of the surveyed area, filter out areas below the set standard, recalculate the connection weights of the multi-topology nodes, and adjust the connection path to optimize the topology structure, using the formula: ； Calculate the optimized topological connection weights, adjust the topological paths in the surveying and mapping area, and obtain the optimized topological path distribution; in, represents the optimized topological connection weight, Represents a topological node With other topology nodes The original connection distance between Represents the topological node after topology optimization adjustment With other topology nodes The target connection distance between represents the topological connectivity adjustment coefficient, Represents the connection stability deviation, represents the rate of change of survey coverage, Represents the total number of topological nodes, Represents a topological node With other topology nodes The original connection distance between Represents the topological node after topology optimization adjustment With other topology nodes The target connection distance between S503: Calculate the topological completeness of the UAV mapping according to the optimized topological path distribution, count the topological path coverage and the completeness index, and obtain the UAV mapping topological completeness index.

8. A geographic surveying and mapping system based on drones, characterized in that: According to any one of claims 1 to 7, the geographic surveying and mapping method based on an unmanned aerial vehicle comprises: The surveying and mapping data acquisition module acquires the UAV surveying and mapping point cloud data and the aerial surveying and mapping 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 surveying and mapping point cloud data and the boundary data, and selects the adjacent points that meet the distance conditions to obtain the surveying and mapping adjacent point set; The topological adjacent point construction module calculates the spatial topological relationship between adjacent points based on the surveying and mapping adjacent point set, screens the connection between the boundary line segments and the adjacent points, calculates the topological connectivity weight of the connection boundary, removes redundant connections with connection weights lower than a threshold, and obtains the surveying and mapping topological adjacent point index; The topological conflict area identification module calls the surveying and mapping 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 point cloud density changes in the adjacent area, calculates the redundant conflict degree of the aerial survey boundary, screens 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 surveying and mapping boundary point, screens the candidate paths whose path length is lower than the set threshold, screens the topological connectivity index of the optimal path, calculates the topological adjustment path of the surveying and mapping boundary, and obtains the optimal topological connection path for UAV surveying and mapping; The surveying and mapping topology integrity assessment module calls the optimal topology connection path of the UAV surveying and mapping, calculates the fitting error between the adjusted boundary points and the original boundary points, filters the boundary points whose fitting error exceeds the threshold, adjusts the position of the boundary points, calculates the closure of the surveying and mapping area, filters the areas with closure below the standard, recalculates the topology connection weights, assigns new connection paths, and obtains the UAV surveying and mapping topology integrity index.

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