Heterogeneous terrain map reconstruction method and device based on spatial index and multi-dimensional ray detection, equipment and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for heterogeneous topographic map reconstruction based on spatial indexing and multidimensional ray detection, relating to the field of geographic information system processing technology. The method includes: emitting scanning rays within the geographic range corresponding to the target mapping area and obtaining their intersections with vector boundaries; classifying the target mapping area based on the intersections and scanning rays to identify boundary depressions and cavity edges; extracting background elevation points from a preset background topographic map library based on the identified boundary depressions and cavity edges; constructing a constrained Delaunay triangulation network and then smoothing it to obtain the target topographic file. This solves the problem of not being able to automatically identify irregular boundaries and cavities during stitching, which leads to elevation faults. It achieves automatic identification of the mapping area boundary morphology and accurate positioning of the elevation points to be filled without manual intervention.

Description

Technical Field

[0001] This application relates to the field of geographic information system processing technology, and in particular to a method, apparatus, device and storage medium for heterogeneous topographic map reconstruction based on spatial indexing and multidimensional ray detection. Background Technology

[0002] As wind power projects expand into complex mountainous areas and coastal intertidal zones, the accuracy requirements for topographic maps in wind farm micro-site selection are constantly increasing. At the same time, it is necessary to integrate high-precision local mapping data with low-resolution global background topographic data to support the accurate execution of large-scale computational fluid dynamics simulations.

[0003] In existing technologies, high-precision surveying areas typically present complex boundary shapes that are non-convex, have holes, or are multiple unconnected blocks, while the background topographic map is a regular rectangular grid. Conventional methods use rectangular clipping or simple layer overlay for stitching, which cannot automatically identify the irregular boundaries and internal voids of the surveying area, resulting in elevation faults at the stitching boundaries. Furthermore, in complex topographic structures, a large amount of manual intervention is required for boundary trimming.

[0004] Therefore, in the process of stitching together high-precision local survey data with low-resolution global background terrain data, the inability to automatically identify irregular boundaries and voids leads to elevation faults in the reconstructed terrain model, which is a technical problem that urgently needs to be solved.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a method, apparatus, device and storage medium for reconstructing heterogeneous topographic maps based on spatial indexing and multidimensional ray detection, which aims to solve the technical problem that the reconstructed topographic model has elevation faults due to the inability to automatically identify irregular boundaries and voids.

[0007] To achieve the above objectives, this application proposes a heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection. The method includes: A scanning ray is emitted within the geographical area corresponding to the target mapping area, and the intersection point of the scanning ray and the vector boundary of the target mapping area is obtained; The target mapping area is classified according to the intersection points and the scanning rays to obtain the classification results; Based on the classification results, identify the boundary depressions and the edges of the void areas of the target mapping area; Based on the boundary depression points and the edge of the cavity area, multiple background elevation points are extracted from a preset background topographic map library; Based on the background elevation points and the target mapping area, a constrained Delaunay triangulation network is constructed. The target terrain file is obtained by smoothing the constrained Delaunay triangulation.

[0008] In one embodiment, the step of emitting a scanning ray within the geographical area corresponding to the target mapping area and obtaining the intersection point of the scanning ray and the vector boundary of the target mapping area includes: A spatial index structure is established based on the vector boundary of the target mapping area; A regular grid array is established within the geographical area corresponding to the target mapping area, and scanning rays are emitted based on the regular grid array; Based on the spatial index structure, the intersection point of the scanning ray and the vector boundary is obtained.

[0009] In one embodiment, classifying the target mapping area based on the intersection point and the scanning ray to obtain a classification result includes: Determine the boundary closure loop based on the vector boundary of the target mapping area; Multiple scanning rays that intersect with the boundary closed loop are used as target scanning rays; The nesting relationship of the boundary closed loop is obtained based on the jump direction and jump number of the intersection points located on the same target scanning ray; The target mapping area is classified according to the nesting relationship to obtain the classification result.

[0010] In one embodiment, identifying the boundary depressions and cavity edges of the target mapping area based on the classification results includes: When the classification result is a multi-connected region containing an inner hole, the inner hole boundary is obtained based on the vector boundary of the target mapping region; The edge of the cavity region is obtained based on the inner hole boundary; When the classification result is a simply connected non-convex region or a discrete region that is not connected to each other, the boundary depression point is obtained according to the vector boundary of the target mapping region.

[0011] In one embodiment, the step of extracting multiple background elevation points from a preset background topographic map library based on the boundary depression point and the edge of the cavity area includes: Obtain the geographic coordinate indexes of the boundary depression point and the edge of the cavity area; The extraction density is obtained based on the resolution of the preset background topographic map library and the resolution of the target mapping area; Based on the extraction density and the geographic coordinate index, multiple background elevation points are extracted from the preset background topographic map library.

[0012] In one embodiment, constructing a constrained Delaunay triangulation network based on each of the background elevation points and the target mapping area includes: Based on the aforementioned background elevation points, a background elevation point cloud is obtained; Based on the target mapping area, a point cloud of the mapping area is obtained; Identify the overlapping boundary between the point cloud of the surveyed area and the background elevation point cloud; Based on the overlapping boundaries, the topological joint area is obtained; A constrained Delaunay triangulation network is constructed within the topological seam region.

[0013] In one embodiment, constructing a constrained Delaunay triangulation within the topological seam region includes: The background elevation point cloud and the survey area point cloud within the topological joint area are merged to obtain a point cloud set; Based on the point cloud set, construct the Delaunay triangulation; Obtain the length values ​​of the edges of all triangles in the Delaunay triangulation; The edges of triangles whose length values ​​satisfy the preset length constraints are taken as the edges of the target triangles. Based on the edge of the target triangle, samples are taken from the preset background topographic map to obtain secondary sampling elevation points; Insert the secondary sampling elevation point into the edge of the target triangle to obtain multiple sub-edges, and use the sub-edges as the triangle edges in the Delaunay triangulation. Then return to the step of obtaining the length values ​​of all triangle edges in the Delaunay triangulation until the length values ​​of all triangle edges satisfy the preset length constraint.

[0014] Furthermore, to achieve the above objectives, this application also proposes a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection, wherein the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection includes: The input module is used to emit scanning rays within the geographical area corresponding to the target mapping area and obtain the intersection points of the scanning rays and the vector boundary of the target mapping area; The classification module is used to classify the target mapping area according to the intersection point and the scanning ray, and obtain the classification result; The identification module is used to identify the boundary depressions and the edges of the void areas of the target mapping area based on the classification results. The extraction module is used to extract multiple background elevation points from a preset background topographic map library based on the boundary depression points and the edge of the cavity area; The construction module is used to construct a constrained Delaunay triangulation based on the background elevation points and the target mapping area. The output module is used to perform smoothing based on the constrained Delaunay triangulation to obtain the target terrain file.

[0015] Furthermore, to achieve the above objectives, this application also proposes a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection as described above.

[0016] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection as described above.

[0017] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection as described above.

[0018] This application emits scanning rays within the geographical area corresponding to the target mapping area and obtains the intersection points of the scanning rays and the vector boundary of the target mapping area; classifies the target mapping area based on the intersection points and the scanning rays to obtain classification results; identifies boundary depression points and cavity edges of the target mapping area based on the classification results; extracts multiple background elevation points from a preset background topographic map library based on the boundary depression points and cavity edges; constructs a constrained Delaunay triangulation network based on each background elevation point and the target mapping area; and performs smoothing processing based on the constrained Delaunay triangulation network to obtain the target topographic file. By employing a technique based on ray scanning to automatically identify irregular boundaries and cavity locations, this application solves the problem of elevation faults caused by the inability to automatically identify irregular boundaries and cavities during splicing. It achieves automatic identification of the boundary morphology of the mapping area and accurate positioning of the elevation points to be filled without manual intervention, thereby eliminating elevation faults at the splicing boundary. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in this application. Figure 2 This is a schematic diagram of terrain region topology classification provided in Embodiment 1 of the heterogeneous terrain map reconstruction method based on spatial indexing and multidimensional ray detection in this application. Figure 3 This is a boundary elevation smoothing map provided in Embodiment 1 of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in this application; Figure 4 This is a flowchart illustrating Embodiment 2 of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in this application. Figure 5 This is a schematic diagram of the module structure of the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection according to an embodiment of this application. Figure 6 This is a schematic diagram of the hardware operating environment involved in the heterogeneous terrain map reconstruction method based on spatial indexing and multidimensional ray detection in the embodiments of this application.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is as follows: A scanning ray is emitted within the geographical area corresponding to the target mapping area, and the intersection point of the scanning ray and the vector boundary of the target mapping area is obtained; the target mapping area is classified according to the intersection point and the scanning ray to obtain a classification result; the boundary depression points and cavity edges of the target mapping area are identified according to the classification result; multiple background elevation points are extracted from a preset background topographic map library according to the boundary depression points and cavity edges; a constrained Delaunay triangulation is constructed according to each background elevation point and the target mapping area; and a smoothing process is performed based on the constrained Delaunay triangulation to obtain the target terrain file.

[0026] In this embodiment, for ease of description, the following description uses a computer as the execution subject.

[0027] Because in existing technologies, high-precision surveying areas usually present complex boundary shapes that are non-convex, have holes, or are multiple unconnected blocks, while the background topographic map is a regular rectangular grid; conventional methods use rectangular clipping or simple layer overlay for splicing, which cannot automatically identify the irregular boundaries and internal voids of the surveying area, resulting in elevation faults at the splicing boundaries, and requiring a lot of manual intervention to trim the boundaries under complex topological structures.

[0028] This application provides a solution that involves emitting scanning rays within the geographical area corresponding to the target mapping area and obtaining the intersection points of the scanning rays with the vector boundary of the target mapping area; classifying the target mapping area based on the intersection points and the scanning rays to obtain classification results; identifying boundary depression points and cavity edges of the target mapping area based on the classification results; extracting multiple background elevation points from a preset background topographic map library based on the boundary depression points and cavity edges; constructing a constrained Delaunay triangulation network based on each background elevation point and the target mapping area; and smoothing the constrained Delaunay triangulation network to obtain the target topographic file. By employing a technique based on ray scanning to automatically identify irregular boundaries and cavity locations, this solution solves the problem of elevation faults caused by the inability to automatically identify irregular boundaries and cavities during splicing. It achieves automatic identification of the boundary morphology of the mapping area and accurate positioning of the elevation points to be filled without manual intervention, thereby eliminating elevation faults at the splicing boundary.

[0029] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or computer capable of performing the above functions. The following description uses a computer as an example to illustrate this embodiment and the subsequent embodiments.

[0030] Based on this, embodiments of this application provide a heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in this application.

[0031] In this embodiment, the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection includes steps S10 to S60: Step S10: Emit a scanning ray within the geographical area corresponding to the target mapping area, and obtain the intersection point of the scanning ray and the vector boundary of the target mapping area; It should be noted that the target mapping area refers to the geographical area covered by the high-precision topographic data uploaded by the user. The boundary of this area is defined by vector boundaries and presents a complex shape that is non-convex, has holes, or consists of multiple unconnected blocks. The geographical range refers to the entire two-dimensional geographical plane space including the target mapping area and its surrounding transition zone. The scanning ray is a virtual ray that extends outward from a discrete position within the geographical range in a vertical or horizontal direction. The vector boundary refers to the set of line segments that constitute the boundary of the target mapping area. The intersection point refers to the spatial position where the scanning ray intersects with the line segment of the vector boundary during its extension.

[0032] It is understandable that, due to the complex shape of the vector boundary of the target mapping area and the possible presence of voids inside, conventional rectangular clipping methods cannot identify the specific shape of the boundary and the location of voids. This makes it impossible to determine the extraction location when extracting elevation points from the background topographic map. Therefore, step S10 is performed to establish the spatial correspondence between the boundary and the ray by emitting scanning rays and obtaining the intersection points. This avoids blindly extracting elevation points due to the unknown shape of the boundary and improves the accuracy of the location of subsequent data completion.

[0033] In one feasible implementation, step S10 may include: establishing a spatial index structure based on the vector boundary of the target mapping area; establishing a regular grid array within the geographical range corresponding to the target mapping area, and emitting a scanning ray based on the regular grid array; and obtaining the intersection point of the scanning ray and the vector boundary according to the spatial index structure.

[0034] It should be noted that the spatial index structure refers to a data organization method based on a quadtree for quickly locating the position of vector boundary line segments; the regular grid array refers to a set of grid cells arranged at uniform intervals within a geographical area, with the center point of each grid cell serving as the starting point for the scanning ray.

[0035] Specifically, the process involves obtaining the vector boundary file of the target mapping area uploaded by the user. This file contains several boundary line segments connected end to end. Each boundary line segment is discretized into an independent geometric object, and inserted into a quadtree structure according to the geographic coordinate range of each line segment, thereby establishing a spatial index for the line segments. Then, the geographic range is determined based on the bounding rectangle range of the target mapping area. Within this range, uniformly arranged grid points are generated along the horizontal and vertical directions at preset intervals. Each grid point serves as a discrete starting point, and all discrete starting points together form a regular grid array. A ray is generated vertically from each discrete starting point, and this ray is input into the quadtree index as a scanning ray. The quadtree quickly locates the leaf nodes that may intersect with the ray based on the spatial position through which the ray passes, and extracts the stored set of boundary line segments from the leaf nodes. The intersection points of the ray with each boundary line segment are calculated sequentially. Invalid intersection points located on the backward extension line of the ray are filtered out, and valid intersection points in the forward extension direction of the ray are retained, resulting in the set of intersection points between the scanning ray and the vector boundary.

[0036] In this embodiment, by establishing a quadtree spatial index structure, the search range of boundary line segments is reduced from the global to the local grid cell, which solves the problem of large computational cost of ray and boundary intersection under large-scale point cloud data.

[0037] The above are merely feasible implementations of step S10 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S10.

[0038] Step S20: Classify the target mapping area according to the intersection point and the scanning ray to obtain the classification result; It should be noted that the classification result refers to the topological type of the target mapping area, including three categories: simply connected non-convex regions, multi-connected regions containing internal holes, and discrete regions that are not connected to each other.

[0039] Specifically, such as Figure 2As shown in the figure, three different topology types are illustrated: simply connected & non-convex regions, non-simply connected & non-convex regions (i.e., multi-connected regions containing internal holes), and non-simply connected & convex regions (i.e., multiple discrete regions that are not interconnected). After a scanning ray is emitted from a discrete starting point, it sequentially crosses the vector boundary along its extension direction, generating multiple intersection points. The system records the total number of intersection points on each scanning ray and the order of these intersection points. Based on the parity of the total number of intersection points, it determines whether the discrete starting point is located inside or outside the target mapping area. It identifies the nesting relationship between boundary closure loops based on the number of jumps between intersection points on the same scanning ray from inside to outside or from outside to inside. When a scanning ray crosses the boundary twice, with the two crossing directions being from outside to inside and from inside to outside respectively, and there is no nesting, it is classified as a simply connected non-convex region. When a scanning ray crosses the boundary more than four times, with the presence of an inner loop boundary causing multiple inside-outside state switches, it is classified as a multi-connected region containing internal holes. When multiple non-adjacent vector boundary sets exist within the same geographical area, and the scanning ray jumps between different sets at its intersection points, it is classified as a discrete region that is not interconnected.

[0040] It is understandable that different data completion strategies (concave point completion or hole edge completion) are required for target mapping areas with different topological types. Therefore, step S20 can automatically identify the topological category of the target mapping area, thereby avoiding data redundancy or missing data caused by using a uniform completion method for all areas and improving the targeting of subsequent completion operations.

[0041] Step S30: Identify the boundary depression points and cavity edges of the target mapping area based on the classification results; It should be noted that the boundary depression point refers to the vertex position of the inward depression on the boundary of a simply connected non-convex region. This position is a key point in the transition zone where the elevation points need to be supplemented from the background topographic map; the edge of the cavity region refers to the boundary line of the internal cavity in a multi-connected region containing an internal hole.

[0042] It is understandable that the data around the depression needs to be filled in for the concave area, and the data inside the hole needs to be filled in for the pore area. Since the locations to be filled in are different for different types of areas, step S30 can accurately locate the boundary positions to be filled in based on the classification results, thereby avoiding unnecessary calculations caused by uniform processing of all boundary positions and improving the efficiency of data filling.

[0043] In one feasible implementation, step S30 may include: when the classification result is a multi-connected region containing an inner hole, obtaining the inner hole boundary based on the vector boundary of the target mapping region; obtaining the edge of the cavity region based on the inner hole boundary; when the classification result is a single-connected non-convex region or a discrete region that is not interconnected, obtaining the boundary depression point based on the vector boundary of the target mapping region.

[0044] It should be noted that a multi-connected region containing an inner hole refers to a topological form in which one or more unmapped blank areas (such as lakes or forests) exist within the target mapping area. The vector boundary of this region is formed by the outer boundary and the inner hole boundary. The inner hole boundary refers to the closed boundary line of the blank area. A single-connected non-convex region refers to a target mapping area whose boundary forms a single closed loop with an inwardly concave portion. Discrete regions that are not connected to each other refer to multiple separate mapping sub-regions within the same geographical area.

[0045] For example, such as Figure 2 As shown, for the simply connected non-convex region displayed on the left, the system traverses all adjacent line segments on the vector boundary, calculates the turning angle between adjacent line segments, and outputs vertices with a turning angle greater than 180 degrees (i.e., inward concavity) as boundary concavity points. For the non-simply connected non-convex region displayed in the middle (i.e., a multi-connected region containing internal holes), the system extracts all internal hole boundaries (i.e., closed loops nested inside the outer boundary) from the vector boundary data, and outputs the coordinates of all vertices on each internal hole boundary as the edge of the void region. For the non-simply connected convex region displayed on the right (i.e., discrete regions that are not connected to each other), the system extracts the vector boundary of each sub-region and performs the same concavity point identification operation as for the simply connected non-convex region on each sub-region.

[0046] In this embodiment, by selecting different identification strategies based on the classification results, the problem of inconsistent identification and completion of points for different types of areas is solved.

[0047] The above are merely feasible implementations of step S30 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S30.

[0048] Step S40: Extract multiple background elevation points from a preset background topographic map library based on the boundary depression points and the edge of the cavity area.

[0049] It should be noted that the preset background topographic map library refers to a built-in or online low-resolution global topographic map data source; the background elevation points refer to the elevation value points extracted from the background topographic map library used to fill in the surrounding area and cavity locations of the target mapping area.

[0050] It is understandable that since the locations of the boundary depressions and the edges of the void areas are precisely the areas where high-precision mapping data is missing, it is necessary to obtain elevation information from low-resolution background data to fill them in. Therefore, step S40 can achieve precise filling of the missing data, thereby avoiding data redundancy caused by full-coverage extraction of the entire geographical area and improving the efficiency of data filling.

[0051] In one feasible implementation, step S40 may include: obtaining the geographic coordinate indexes of the boundary depression point and the edge of the cavity area; obtaining the extraction density based on the resolution of the preset background topographic map library and the resolution of the target mapping area; and extracting multiple background elevation points from the preset background topographic map library based on the extraction density and the geographic coordinate index.

[0052] It should be noted that the geographic coordinate index refers to the latitude and longitude or projected coordinate values ​​of each point on the edge of the boundary depression point and the cavity area; the extraction density refers to the distance between adjacent background elevation points when sampling the transition zone between the high-precision survey area and the background topographic map library. This density is dynamically determined according to the gradient ratio between the high-precision area resolution and the background map resolution.

[0053] Specifically, the geographic coordinates of all vertices on the edge of the boundary depression points and the cavity area are obtained to form a coordinate index list. The high-resolution values ​​of the target mapping area and the low-resolution values ​​of a preset background topographic map library are obtained, and the gradient ratio between the two is calculated. Based on this ratio, the extraction density of background elevation points within the transition zone is determined. Using the geographic coordinate index as the center point, local sampling is performed in the preset background topographic map library according to the extraction density to extract the elevation values ​​at the corresponding locations, generating multiple background elevation points.

[0054] In this embodiment, the extraction density is dynamically determined according to the resolution gradient ratio, which solves the problem of elevation jump at the boundary caused by the large resolution difference between high-precision data and low-precision data.

[0055] The above are merely feasible implementations of step S40 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S40.

[0056] Step S50: Construct a constrained Delaunay triangulation network based on the background elevation points and the target mapping area.

[0057] It should be noted that the constrained Delaunay triangulation refers to a triangular mesh structure generated by merging the background elevation points with the high-precision point cloud of the target mapping area, using the boundary line as a constraint condition. This mesh is used to eliminate elevation faults at the splicing boundary between the two types of data.

[0058] It is understandable that since the background elevation points and the high-precision survey point cloud have different densities and resolutions in spatial distribution, direct splicing will produce a step effect and numerical non-convergence. Therefore, step S50 can be performed to merge the two types of point clouds into a unified topological structure by constructing a constrained Delaunay triangulation network, thereby avoiding geometric discontinuities caused by data heterogeneity and improving the numerical stability of the terrain model.

[0059] In one feasible implementation, step S50 may include: obtaining a background elevation point cloud based on each of the background elevation points; obtaining a mapping area point cloud based on the target mapping area; identifying the overlapping boundary between the mapping area point cloud and the background elevation point cloud; obtaining a topological seam region based on the overlapping boundary; and constructing a constrained Delaunay triangulation network within the topological seam region.

[0060] It should be noted that the background elevation point cloud refers to the set of all extracted background elevation points; the mapping area point cloud refers to the set of original high-precision mapping data points within the target mapping area; the overlapping boundary refers to the area where the boundary of the mapping area point cloud and the boundary of the background elevation point cloud are spatially adjacent or overlapping; and the topological seam area refers to the strip-shaped area near the overlapping boundary that needs to be fused.

[0061] Specifically, all background elevation points are merged into a background elevation point cloud, and the original high-precision mapping data points within the target mapping area are merged into a mapping area point cloud. The boundary ranges of the two types of point clouds are calculated separately, and the spatially adjacent areas are identified as overlapping boundaries. Using the overlapping boundary as the center line, a preset distance (e.g., 5-10 meters) is extended to both sides to form a topological seam zone. Within the topological seam zone, the two types of point clouds are merged, and a constrained Delaunay triangulation is constructed using the overlapping boundary as a constraint.

[0062] Further, the step of constructing a constrained Delaunay triangulation within the topological seam area includes: merging the background elevation point cloud and the mapping area point cloud within the topological seam area to obtain a point cloud set; constructing a Delaunay triangulation based on the point cloud set; obtaining the length values ​​of all triangle edges in the Delaunay triangulation; using the triangle edges whose length values ​​satisfy a preset length constraint as target triangle edges; sampling from the preset background topographic map based on the target triangle edges to obtain secondary sampling elevation points; inserting the secondary sampling elevation points into the target triangle edges to obtain multiple sub-edges, and using the sub-edges as triangle edges in the Delaunay triangulation, then returning to the step of obtaining the length values ​​of all triangle edges in the Delaunay triangulation until the length values ​​of all triangle edges satisfy the preset length constraint.

[0063] It should be noted that the point cloud set refers to the total point set after merging the background elevation point cloud and the survey area point cloud; the Delaunay triangulation refers to the triangular mesh generated based on the point cloud set according to the empty circle criterion; the triangle edge refers to the line connecting any two adjacent points in the Delaunay triangulation; the length value refers to the spatial straight-line distance of the triangle edge; the preset length constraint refers to a threshold of 1.5 times the background map resolution; the target triangle edge refers to the triangle edge whose length value exceeds the preset length constraint; the secondary sampled elevation point refers to the elevation value point obtained by resampling from the preset background topographic map library; and the sub-edge refers to the two new edges obtained by dividing the target triangle edge from its midpoint.

[0064] Specifically, the background elevation point cloud and the survey area point cloud within the topological seam area are merged into a single point cloud set. Based on this point cloud set, an initial triangulation is generated according to the Delaunay empty circle criterion. All triangle edges in the triangulation are traversed, and the length value of each edge is calculated. Edges with length values ​​exceeding 1.5 times the background map resolution are marked as target triangle edges. For each target triangle edge, the geographic coordinates of its midpoint are calculated, and the elevation value at that midpoint is sampled from a preset background topographic map library to form a secondary sampling elevation point. This secondary sampling elevation point is inserted into the triangulation, dividing the target triangle edge into two sub-edges. The above edge length checking and subdivision operations are repeated until the length values ​​of all triangle edges are less than or equal to the preset length constraint.

[0065] In this embodiment, the edge of the ultra-long triangle is recursively subdivided and secondary sampling elevation points are inserted through a length constraint mechanism. This solves the problem of large aspect ratio triangles caused by uneven point density in the transition zone of heterogeneous resolution point clouds, and ensures that the geometric quality of the triangulation meets the simulation convergence requirements.

[0066] The above are merely feasible implementations of step S50 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S50.

[0067] Step S60: Smoothing is performed based on the constrained Delaunay triangulation to obtain the target terrain file.

[0068] It should be noted that smoothing refers to applying the Laplacian smoothing operator to the vertices of the triangulation edge, moving the vertices to the average position of the adjacent vertices to eliminate elevation faults; the target terrain file refers to the terrain model file in a unified format output after stitching, reconstruction and smoothing.

[0069] Specifically, a Laplacian smoothing operator is applied to each vertex of the edge of the constrained Delaunay triangulation, shifting each vertex towards the average position of its neighboring vertices. This smoothing operation is repeated for a preset number of iterations (e.g., 5-10 times) to gradually smooth the elevation values ​​of the triangulation edges. Finally, the smoothed triangulation is converted into a uniform resolution raster terrain file output through a resampling operation, such as... Figure 3 As shown in the figure, the smoothness of the reconstructed profile in this application is illustrated.

[0070] It is understandable that, due to the resolution difference between the high-precision survey point cloud and the background elevation point cloud at the splicing boundary, the directly generated triangular mesh will have abrupt elevation changes at the boundary, causing numerical non-convergence in subsequent fluid simulation calculations. Therefore, step S60 can eliminate elevation discontinuities at the boundary through Laplace smoothing, thereby avoiding simulation divergence caused by abrupt elevation changes and improving the accuracy of wind resource assessment.

[0071] This embodiment provides a heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection. Scanning rays are emitted within the geographical area corresponding to the target mapping area, and the intersection points of the scanning rays and the vector boundary of the target mapping area are obtained. The target mapping area is classified according to the intersection points and the scanning rays to obtain classification results. Boundary depressions and cavity edges of the target mapping area are identified based on the classification results. Multiple background elevation points are extracted from a preset background topographic map library based on the boundary depressions and cavity edges. A constrained Delaunay triangulation is constructed based on each background elevation point and the target mapping area. The constrained Delaunay triangulation is then smoothed to obtain the target topographic file. By employing a ray-based scanning technique to automatically identify irregular boundaries and cavity locations, the method solves the problem of elevation faults caused by the inability to automatically identify irregular boundaries and cavities during splicing. This achieves automatic identification of the mapping area boundary morphology and accurate positioning of the elevation points to be filled without manual intervention, thereby eliminating elevation faults at the splicing boundary.

[0072] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 The heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection further includes steps S21 to S24 in step S20: Step S21: Determine the boundary closure loop based on the vector boundary of the target mapping area; It should be noted that a boundary closure loop refers to a closed loop formed by line segments connected end to end in a vector boundary; the vector boundary of a target mapping area may contain one or more boundary closure loops, among which the closure loop with the largest area is the outer boundary loop, and the nested closure loops are the inner hole boundary loops.

[0073] Specifically, the system obtains the vector boundary file of the target survey area uploaded by the user. This file contains several line segments, each with coordinate information for its start and end points. It then traverses all line segments, tracking them according to the endpoint connections. Starting from the start of any unvisited line segment, it sequentially searches for the next line segment connected to its end point, repeating this process until it returns to the starting point, forming a closed loop. The system continues to track the remaining unvisited line segments, obtaining all boundary loops. For each closed loop, the system calculates its enclosed area, marking the loop with the largest area as the outer boundary loop and the remaining loops with smaller areas as inner boundary loops.

[0074] It is understandable that since the vector boundary of the target mapping area may contain multiple closed loops (such as outer boundary and inner hole boundary), and the spatial relationship between different closed loops determines the strategy for subsequent data completion, step S21 can organize discrete line segments into complete closed loops, thereby avoiding the inability to identify the relationship between inner and outer boundaries due to the dispersion of line segments and improving the accuracy of topology analysis.

[0075] Step S22: Select multiple scanning rays that intersect with the boundary closed loop as target scanning rays; It should be noted that the target scanning rays refer to multiple scanning rays that intersect with the boundary closed loop.

[0076] Specifically, from step S10, all emitted scanning rays and their intersection points with the vector boundary are obtained; the intersection points on each scanning ray are traversed to determine which boundary closure loop each intersection point belongs to; scanning rays that have intersection points with at least one boundary closure loop are selected, and the information of these scanning rays and their corresponding intersection points is extracted to form a set of scanning rays that have intersection points with the boundary closure loop; for each selected scanning ray, the system sorts them in ascending order according to the distance between the intersection point and the starting point of the ray, to obtain an ordered sequence of intersection points.

[0077] It is understandable that, since not all scanning rays will pass through the boundary closed loop (some rays may be completely outside or inside the survey area), and different scanning rays carry different intersection information, step S22 can filter out scanning rays that contain valid boundary crossing information, thereby avoiding computational redundancy caused by processing rays without intersections and improving the efficiency of subsequent nested relationship analysis.

[0078] Step S23: Based on the jumping direction and number of jumping points at the intersection points located on the same target scanning ray, the nesting relationship of the boundary closed loop is obtained.

[0079] It should be noted that the jump direction refers to the type of crossing when the scanning ray passes through the boundary closed loop, including crossing from the outside to the inside and crossing from the inside to the outside; the number of jumps refers to the number of times the crossing direction changes when the scanning ray passes through the boundary closed loop sequentially along the extension direction on the same scanning ray; the nesting relationship refers to the hierarchical relationship between multiple boundary closed loops, used to describe which outer boundary loop the inner hole boundary loop is located inside.

[0080] Specifically, a scanning ray and its ordered intersection sequence obtained in step S22 are selected; the initial values ​​of the current internal and external states are set according to whether the discrete starting point of the scanning ray is located inside or outside the target mapping area; each intersection is processed sequentially according to the order of the intersection sequence, and the crossing direction of the scanning ray at the intersection point is determined according to the angle between the normal direction of the closed loop of the boundary to which the intersection point belongs and the direction of the scanning ray; whenever the crossing direction is consistent with the movement direction indicated by the current internal and external states, the current internal and external states are updated and a jump is recorded, and the jump is also recorded. The direction of the change is taken as the jump direction; after traversing all intersections, the total number of jumps is counted as the jump count; based on the jump count and the jump direction of each jump, the inclusion relationship between boundary closed loops is determined: when the jump count is two and the two jump directions are from the outside to the inside and from the inside to the outside respectively, it means that the scanning ray has passed through a single boundary closed loop and there is no nesting; when the jump count is greater than two and the jump directions are alternating, it means that the scanning ray has passed through multiple nested boundary closed loops, and the hierarchical relationship of the inner loop being contained by the outer loop is determined according to the alternating order of the jump directions.

[0081] It is understandable that since there may be nesting relationships between boundary closed loops (such as an outer loop containing an inner loop), and the number of intersection points alone cannot distinguish between nesting and parallel structures, step S23 is performed. By analyzing the jump direction and number of jumps at the intersection points on the same scanning ray, the containment level between closed loops can be accurately identified, thereby avoiding misjudging nested structures as parallel structures and improving the accuracy of topology classification.

[0082] Step S24: Classify the target mapping area according to the nesting relationship to obtain the classification result.

[0083] For example, based on the nesting relationship between boundary closure loops obtained in step S23, and combined with the number of boundary closure loops, a comprehensive judgment is made: when the vector boundary of the entire target mapping area contains only one boundary closure loop and the number of transitions of that closure loop is two, the classification result is a simply connected non-convex region; when the vector boundary contains an outer boundary closure loop and at least one inner hole boundary closure loop, and the number of transitions when the scanning ray passes through the region is greater than two, the classification result is a multi-connected region containing an inner hole; when there are multiple non-adjacent vector boundary sets within the same geographical area, each set contains one or more boundary closure loops, and the scanning rays between different sets do not have intersection points, the classification result is a discrete region that is not connected; the system outputs the classification result as the basis for identifying boundary depression points or the edges of void regions in subsequent steps.

[0084] It is understandable that different data completion strategies are required for different types of surveying areas (concave points need to be completed for non-convex areas, and the interior of holes needs to be completed for areas with holes). Therefore, step S24 can automatically output the topology classification results based on the nesting relationship, thereby avoiding subjective errors caused by manual judgment of topology type and improving the automation of subsequent completion strategy selection.

[0085] This embodiment provides a heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection. Based on the vector boundary of the target mapping area, boundary closure loops are determined; multiple scanning rays intersecting with the boundary closure loops are taken as target scanning rays; the nesting relationship of the boundary closure loops is obtained based on the jump direction and jump number of intersection points on the same target scanning ray; the target mapping area is classified according to the nesting relationship to obtain the classification result. Because it employs a technique based on the jump direction and jump number when scanning rays cross the boundary to identify the nesting relationship of closure loops, it avoids the problem of failing to distinguish between nested and parallel structures by simply counting the number of intersection points. This solves the technical problem of how to automatically identify complex topological shapes, thus achieving the effect of accurate topological classification of mapping areas with arbitrarily complex shapes without manual intervention.

[0086] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in this application. Any simple transformations based on this technical concept are within the protection scope of this application.

[0087] This application also provides a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection. Please refer to [reference needed]. Figure 5 The heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection includes: Input module 10 is used to emit scanning rays within the geographical area corresponding to the target mapping area and obtain the intersection point of the scanning rays and the vector boundary of the target mapping area; The classification module 20 is used to classify the target mapping area according to the intersection point and the scanning ray, and obtain the classification result; The identification module 30 is used to identify the boundary depression points and the edges of the cavity areas of the target mapping area based on the classification results. Extraction module 40 is used to extract multiple background elevation points from a preset background topographic map library based on the boundary depression points and the edge of the cavity area; Construction module 50 is used to construct a constrained Delaunay triangulation network based on each of the background elevation points and the target mapping area; Output module 60 is used to perform smoothing processing based on the constrained Delaunay triangulation to obtain the target terrain file.

[0088] The heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection provided in this application, employing the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in the above embodiments, can solve the technical problem of elevation faults in the reconstructed topographic model caused by the inability to automatically identify irregular boundaries and voids. Compared with the prior art, the beneficial effects of the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection provided in this application are the same as those of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection provided in the above embodiments, and other technical features in the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0089] The input module 10 is further configured to establish a spatial index structure based on the vector boundary of the target mapping area; establish a regular grid array within the geographical range corresponding to the target mapping area, and emit scanning rays based on the regular grid array; and obtain the intersection point of the scanning rays and the vector boundary according to the spatial index structure.

[0090] The classification module 20 is further configured to: determine a boundary closure loop based on the vector boundary of the target mapping area; take multiple scanning rays that intersect with the boundary closure loop as target scanning rays; obtain the nesting relationship of the boundary closure loop based on the jump direction and jump number of the intersection points located on the same target scanning ray; classify the target mapping area according to the nesting relationship to obtain a classification result.

[0091] The identification module 30 is further configured to, when the classification result is a multi-connected region containing an inner hole, obtain the inner hole boundary based on the vector boundary of the target mapping region; obtain the edge of the cavity region based on the inner hole boundary; and, when the classification result is a single-connected non-convex region or a discrete region that is not interconnected, obtain the boundary depression point based on the vector boundary of the target mapping region.

[0092] The extraction module 40 is further configured to obtain the geographic coordinate indexes of the boundary depression points and the edge of the cavity area; obtain the extraction density according to the resolution of the preset background topographic map library and the resolution of the target mapping area; and extract multiple background elevation points from the preset background topographic map library based on the extraction density and the geographic coordinate indexes.

[0093] The construction module 50 is also used to obtain a background elevation point cloud based on each of the background elevation points; Based on the target mapping area, a point cloud of the mapping area is obtained; the overlapping boundary between the mapping area point cloud and the background elevation point cloud is identified; based on the overlapping boundary, a topological seam area is obtained; and a constrained Delaunay triangulation network is constructed within the topological seam area.

[0094] The construction module 50 is further configured to merge the background elevation point cloud and the survey area point cloud within the topological joint area to obtain a point cloud set; construct a Delaunay triangulation network based on the point cloud set; obtain the length values ​​of all triangle edges in the Delaunay triangulation network; use the triangle edges whose length values ​​satisfy a preset length constraint as target triangle edges; sample from the preset background topographic map based on the target triangle edges to obtain secondary sampling elevation points; insert the secondary sampling elevation points into the target triangle edges to obtain multiple sub-edges, and use the sub-edges as triangle edges in the Delaunay triangulation network, then return to execute the step of obtaining the length values ​​of all triangle edges in the Delaunay triangulation network until the length values ​​of all triangle edges satisfy the preset length constraint.

[0095] This application provides a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection. The heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in the above embodiment 1.

[0096] The following is for reference. Figure 6This document illustrates a structural schematic diagram of a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection, suitable for implementing embodiments of this application. The heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0097] like Figure 6 As shown, the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the heterogeneous topographic map reconstruction equipment based on spatial indexing and multidimensional ray detection to wirelessly or wiredly communicate with other devices to exchange data. Although the figure shows a heterogeneous topographic map reconstruction equipment based on spatial indexing and multidimensional ray detection with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0098] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0099] The heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection provided in this application, employing the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in the above embodiments, can solve the technical problem of elevation faults in the reconstructed topographic model caused by the inability to automatically identify irregular boundaries and voids. Compared with the prior art, the beneficial effects of the heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection provided in this application are the same as those of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection provided in the above embodiments, and other technical features in this heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0100] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0102] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection in the above embodiments.

[0103] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0104] The aforementioned computer-readable storage medium may be included in a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection; or it may exist independently and not be assembled into a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection.

[0105] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection, the device performs the following actions: emits scanning rays within the geographic area corresponding to the target mapping area and obtains the intersection points of the scanning rays and the vector boundaries of the target mapping area; classifies the target mapping area based on the intersection points and the scanning rays to obtain classification results; identifies boundary depression points and cavity edges of the target mapping area based on the classification results; extracts multiple background elevation points from a preset background topographic map library based on the boundary depression points and cavity edges; constructs a constrained Delaunay triangulation network based on each background elevation point and the target mapping area; and performs smoothing processing based on the constrained Delaunay triangulation network to obtain the target topographic file.

[0106] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0109] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection. This solves the technical problem of elevation faults in the reconstructed terrain model due to the inability to automatically identify irregular boundaries and voids. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection provided in the above embodiments, and will not be elaborated upon here.

[0110] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection as described above.

[0111] The computer program product provided in this application can solve the technical problem of heterogeneous topographic map reconstruction based on spatial indexing and multidimensional ray detection. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection provided in the above embodiments, and will not be repeated here.

[0112] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection, characterized in that, The method includes: A scanning ray is emitted within the geographical area corresponding to the target mapping area, and the intersection point of the scanning ray and the vector boundary of the target mapping area is obtained; The target mapping area is classified according to the intersection points and the scanning rays to obtain the classification results; Based on the classification results, identify the boundary depressions and the edges of the void areas of the target mapping area; Based on the boundary depression points and the edge of the cavity area, multiple background elevation points are extracted from a preset background topographic map library; Based on the background elevation points and the target mapping area, a constrained Delaunay triangulation network is constructed. The target terrain file is obtained by smoothing the constrained Delaunay triangulation.

2. The method as described in claim 1, characterized in that, The step of emitting a scanning ray within the geographical area corresponding to the target mapping area and obtaining the intersection point of the scanning ray and the vector boundary of the target mapping area includes: A spatial index structure is established based on the vector boundary of the target mapping area; A regular grid array is established within the geographical area corresponding to the target mapping area, and scanning rays are emitted based on the regular grid array; Based on the spatial index structure, the intersection point of the scanning ray and the vector boundary is obtained.

3. The method as described in claim 1, characterized in that, The classification of the target mapping area based on the intersection point and the scanning ray to obtain the classification result includes: Determine the boundary closure loop based on the vector boundary of the target mapping area; Multiple scanning rays that intersect with the boundary closed loop are used as target scanning rays; The nesting relationship of the boundary closed loop is obtained based on the jump direction and jump number of the intersection points located on the same target scanning ray; The target mapping area is classified according to the nesting relationship to obtain the classification result.

4. The method as described in claim 1, characterized in that, The step of identifying the boundary depressions and cavity edges of the target mapping area based on the classification results includes: When the classification result is a multi-connected region containing an inner hole, the inner hole boundary is obtained based on the vector boundary of the target mapping region; The edge of the cavity region is obtained based on the inner hole boundary; When the classification result is a simply connected non-convex region or a discrete region that is not connected to each other, the boundary depression point is obtained according to the vector boundary of the target mapping region.

5. The method as described in claim 1, characterized in that, The step of extracting multiple background elevation points from a preset background topographic map library based on the boundary depression points and the edge of the cavity area includes: Obtain the geographic coordinate indexes of the boundary depression point and the edge of the cavity area; The extraction density is obtained based on the resolution of the preset background topographic map library and the resolution of the target mapping area; Based on the extraction density and the geographic coordinate index, multiple background elevation points are extracted from the preset background topographic map library.

6. The method as described in claim 1, characterized in that, The construction of a constrained Delaunay triangulation network based on the background elevation points and the target mapping area includes: Based on the aforementioned background elevation points, a background elevation point cloud is obtained; Based on the target mapping area, a point cloud of the mapping area is obtained; Identify the overlapping boundary between the point cloud of the surveyed area and the background elevation point cloud; Based on the overlapping boundaries, the topological joint area is obtained; A constrained Delaunay triangulation network is constructed within the topological seam region.

7. The method as described in claim 6, characterized in that, The construction of a constrained Delaunay triangulation within the topological seam region includes: The background elevation point cloud and the survey area point cloud within the topological joint area are merged to obtain a point cloud set; Based on the point cloud set, construct the Delaunay triangulation; Obtain the length values ​​of the edges of all triangles in the Delaunay triangulation; The edges of triangles whose length values ​​satisfy the preset length constraints are taken as the edges of the target triangles. Based on the edge of the target triangle, sampling is performed from the preset background topographic map to obtain secondary sampling elevation points; Insert the secondary sampling elevation point into the edge of the target triangle to obtain multiple sub-edges, and use the sub-edges as the triangle edges in the Delaunay triangulation. Then return to the step of obtaining the length values ​​of all triangle edges in the Delaunay triangulation until the length values ​​of all triangle edges satisfy the preset length constraint.

8. A heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection, characterized in that, The device includes: The input module is used to emit scanning rays within the geographical area corresponding to the target mapping area and obtain the intersection points of the scanning rays and the vector boundary of the target mapping area; The classification module is used to classify the target mapping area according to the intersection point and the scanning ray, and obtain the classification result; The identification module is used to identify the boundary depressions and the edges of the void areas of the target mapping area based on the classification results. The extraction module is used to extract multiple background elevation points from a preset background topographic map library based on the boundary depression points and the edge of the cavity area; The construction module is used to construct a constrained Delaunay triangulation based on the background elevation points and the target mapping area. The output module is used to perform smoothing based on the constrained Delaunay triangulation to obtain the target terrain file.

9. A heterogeneous topographic map reconstruction device based on spatial indexing and multidimensional ray detection, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the heterogeneous topographic map reconstruction method based on spatial indexing and multidimensional ray detection as described in any one of claims 1 to 7.

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