Building surface data merging method and device, electronic equipment and storage medium

Abstract

The invention relates to a building surface data merging method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring data of a plurality of original building surfaces; constructing a global outer frame surface capable of covering the data of the plurality of original building surfaces by calculating a minimum outer enveloping rectangle of the data of the plurality of original building surfaces; the multiple pieces of original building surface data are erased through a global outer frame surface, void surface data are obtained, and the shape of the void surface data is closely related to distribution of the multiple pieces of original building surface data; and erasing void surface data by using a global outer frame surface, and according to a spatial analysis principle, obtaining surface data formed by merging a plurality of original building surface data. According to the method, the building surface data can be quickly and accurately combined, the data processing time can be greatly shortened, and the data processing complexity is greatly reduced.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, particularly to the fields of big data and artificial intelligence, and especially to methods, apparatus, electronic devices and storage media for merging building surface data. Background Technology

[0002] With the rapid iteration of information technology, especially cloud computing, the Internet of Things, and edge computing, digital twins have expanded from manufacturing to smart cities, energy, power, and public safety, becoming one of the core platforms supporting digital decision-making throughout the entire business lifecycle. When constructing city-level or park-level digital twin models, it is essential to integrate and convert multi-source, heterogeneous geospatial data into a unified 3D scene in real time. Building surface data is crucial for describing the morphology of surface entities and determining spatial occupancy relationships. This type of data is massive and scattered; insufficient processing efficiency during loading, merging, or spatial analysis will directly lead to decreased rendering frame rates, network simulation step-up delays, and distortion of disaster simulation results, thereby weakening the real-time feedback capability of the digital twin system.

[0003] However, the time complexity of the face-by-face Boolean merge algorithm widely used in related technologies is... O ( n 2 Data at scales exceeding tens of thousands is insufficient to meet the second-level rendering and collaboration requirements of digital twin systems. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for merging building surface data.

[0005] In a first aspect, embodiments of this disclosure provide a method for merging building floor data, including: Acquire multiple raw building surface data; By calculating the minimum outer envelope rectangle of the multiple original building surface data, a global outer frame that can cover the multiple original building surface data is constructed. The global outer frame is used to erase the multiple original building surface data to obtain void surface data. The shape of the void surface data is closely related to the distribution of the multiple original building surface data. The void surface data is erased using the global outer frame surface, and the resulting surface data is obtained by merging the multiple original building surface data according to the principle of spatial analysis.

[0006] Secondly, embodiments of this disclosure provide a building floor data merging apparatus, comprising: The acquisition module is used to acquire multiple raw building surface data; The construction module is used to construct a global outer frame that can cover the multiple original building surface data by calculating the minimum outer envelope rectangle of the multiple original building surface data; The first erasure module is used to erase the multiple original building surface data using the global outer frame to obtain void surface data. The shape of the void surface data is closely related to the distribution of the multiple original building surface data. The second erasure module is used to erase the hole surface data using the global outer frame surface. According to the principle of spatial analysis, the resulting surface data is the merged surface data after merging the multiple original building surface data.

[0007] Thirdly, embodiments of this disclosure provide an electronic device, including: One or more processors; The processor is used to invoke instructions to cause the electronic device to perform the method described in the first aspect above.

[0008] Fourthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect above.

[0009] Fifthly, embodiments of this disclosure provide a program product including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method described in the first aspect.

[0010] According to the technical solution disclosed herein, building surface data can be merged quickly and accurately, which can greatly shorten data processing time and significantly reduce data processing complexity.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0013] Figure 1 This is a flowchart illustrating a method for merging building surface data according to an exemplary embodiment.

[0014] Figure 2 This is a schematic diagram illustrating the effect of expanding the envelope diagonal to obtain the outer frame area according to an exemplary embodiment.

[0015] Figure 3 This is a flowchart illustrating a method for merging building surface data according to an exemplary embodiment.

[0016] Figure 4(a) is a schematic diagram of a set of building face vertices according to an exemplary embodiment.

[0017] Figure 4(b) is an example diagram illustrating scene distribution perception of the building surface data shown in Figure 4(a) according to an exemplary embodiment.

[0018] Figure 4(c) is a schematic diagram illustrating the clustering results obtained by density clustering of the building surface data shown in Figure 4(a) according to an exemplary embodiment.

[0019] Figure 4(d) is an example diagram illustrating the clustering direction determination of Figure 4(c) according to an exemplary embodiment.

[0020] Figure 4(e) is an example diagram illustrating the determination of the cluster circumscribed rectangle based on the clustering direction determination of Figure 4(d) according to an exemplary embodiment.

[0021] Figure 4(f) is an example diagram of a convex polygonal surface that can cover the building surface data shown in Figure 4(a) according to an exemplary embodiment.

[0022] Figure 5 This is a block diagram illustrating a building surface data merging apparatus according to an exemplary embodiment.

[0023] Figure 6(a) is an example diagram illustrating the comparison of resource utilization rates for 1000 surfaces according to an exemplary embodiment.

[0024] Figure 6(b) is an example diagram illustrating the comparison of resource utilization of 10,000 surfaces according to an exemplary embodiment.

[0025] Figure 6(c) is an example diagram illustrating the utilization of 20,000 surface resources according to an exemplary embodiment.

[0026] Figure 7 This is a block diagram of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0028] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0029] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0030] It should be noted that the acquisition, transmission, storage, use, and processing of data in this disclosed technical solution comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0031] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0032] It is worth noting that in the embodiments disclosed herein, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary, and their purpose is only to illustrate the feasibility of implementing the technical solutions disclosed herein. However, this does not mean that the applicant has used or necessarily used such solutions.

[0033] With the rapid iteration of information technology, especially cloud computing, the Internet of Things, and edge computing, digital twins have expanded from manufacturing to smart cities, energy, power, and public safety, becoming one of the core platforms supporting digital decision-making throughout the entire business lifecycle. When constructing city-level or park-level digital twin models, it is essential to integrate and convert multi-source, heterogeneous geospatial data into a unified 3D scene in real time. Building surface data is crucial for describing the morphology of surface entities and determining spatial occupancy relationships. This type of data is massive and scattered; insufficient processing efficiency during loading, merging, or spatial analysis will directly lead to decreased rendering frame rates, network simulation step-up delays, and distortion of disaster simulation results, thereby weakening the real-time feedback capability of the digital twin system.

[0034] However, the time complexity of the face-by-face Boolean merge algorithm widely used in related technologies is... O ( n 2 Data at scales exceeding tens of thousands is insufficient to meet the second-level rendering and collaboration requirements of digital twin systems.

[0035] Based on this, this disclosure provides a method for merging building surface data, which can reduce complexity to a minimum using a spatial analysis algorithm based on the "outer frame + two erasures" principle. O ( n It supports parallel processing, fundamentally solving the bottleneck of real-time processing of large-scale building surface data.

[0036] It should be noted that this disclosure provides a method for merging building surface data based on the spatial analysis function of GIS (Geographic Information System). Its core idea is to cleverly achieve efficient merging of multiple surface data by constructing an outer frame and utilizing an erasure operation. This method breaks through the traditional linear thinking pattern of iterative traversal, simplifying the complex merging process into more efficient geometric operations by utilizing the principles of spatial analysis. Specifically, firstly, an outer frame that completely contains all surface data is constructed by calculating the bounding rectangle of all surface data. This outer frame serves as the basis for subsequent operations, and its boundary values ​​precisely define the spatial range of all surface data. All original surface data is erased using this outer frame. This operation creates a void in the erased area, the shape of which is closely related to the distribution of all original surface data. Erasing this void using the outer frame, according to the principles of spatial analysis, yields the final result of merging all surface data. This method, through two erasure operations, avoids the tedious process of sequential merging in traditional methods, greatly improving merging efficiency.

[0037] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for merging building surface data according to embodiments of the present disclosure.

[0038] It should be noted that the execution subject of the building surface data merging method in this embodiment of the disclosure can be a building surface data merging device, which can be implemented by software and / or hardware. The device can be configured in an electronic device, which may include a terminal or a server.

[0039] Figure 1 This is a flowchart illustrating a method for merging building surface data according to an exemplary embodiment. Figure 1 As shown, the method for merging building surface data may include, but is not limited to, the following steps.

[0040] In step 101, multiple original building surface data are obtained.

[0041] In the embodiments of this disclosure, the multiple original building surface data can be geospatial data used to construct city-level or park-level digital twin models. Specifically, the building surface data can be key foundational data describing the morphology of surface entities and determining spatial occupancy relationships.

[0042] In step 102, a global outer frame that can cover multiple original building surface data is constructed by calculating the minimum outer envelope rectangle of multiple original building surface data.

[0043] Following the embodiments disclosed herein, a unified coordinate transformation, geometric normalization, and multi-level spatial indexing can be performed on multiple original building surface data (also called original building surface features). This enables the subsequent double-erasure algorithm to maintain linear performance on complex city-level data.

[0044] In some embodiments, each original building surface data can be described by a vertex set, for example, through a vertex set. Description. A unified coordinate transformation can be performed on multiple original building surface data to obtain the projected coordinates of each original building surface data in the projected coordinate system. Converting all data to the projected coordinate system ensures that distance and area can be directly measured. Based on the projected coordinates of each original building surface data, the minimum outer envelope rectangle and its diagonal that can cover multiple original building surface data are determined. The rectangle is then expanded outward along its diagonal to obtain a global outer frame that can cover multiple original building surface data.

[0045] For example, a minimum outer envelope rectangle that can cover multiple original building surface data is available. B min This indicates that the minimum outer envelope rectangle B min The boundary formula can be: The minimum outer envelope rectangle that completely covers the multiple original building surface data can be obtained using this boundary formula. The diagonal of this minimum outer envelope rectangle is then calculated based on its vertex coordinates. To address potential topological gaps caused by floating-point errors or overlap between the bounding box and feature boundaries, the minimum outer envelope rectangle can be expanded outwards along its diagonal. For example, such as... Figure 2 As shown, the minimum outer envelope rectangle can be expanded by one-thousandth of the diagonal, and the resulting expanded area can be used as a global outer frame that can cover multiple original building surface data.

[0046] In step 103, multiple original building surface data are erased using the global outer frame to obtain void surface data. The shape of the void surface data is closely related to the distribution of multiple original building surface data.

[0047] In the embodiments of this disclosure, when a global outline that can cover multiple original building surface data is obtained, a first erasure operation can be performed. Essentially, this "subtracts" the multiple original building surface data from the global outline, forming a closed region with holes. For example, this closed region with holes can be represented by the following mathematical formula: ,in, R 1 represents the closed area with the void (also called void surface data). R 0 represents a global bounding box that can cover multiple original building surface data; f i For the first i Original building surface data (also called original building surface features); n This represents the total number of original building surface data. In other words: R 1 represents the erased result, which is the region after removing all building faces from the global outline. This region is still a planar geometry, but it contains multiple spatial voids whose shapes and locations correspond precisely to the original set of building faces. The key to this step is not actually generating the geometric boundaries of these voids, but rather precisely subtracting the complex and varied set of building faces from a large closed shape.

[0048] It should be noted that, geometrically, this process requires precise handling of topological relationships such as the intersection, difference, and boundary continuity between the building surface and the global outer frame to ensure the generated... R 1. There are no breaks, overlaps, or topological errors. From a spatial analysis perspective, this step constructs the "negative shape" of the building surface set in space by reverse engineering, laying the foundation for restoring the overall building outline through anti-erasure, thereby avoiding the problems of repeated calculations and error accumulation caused by multiple merging in traditional algorithms.

[0049] In step 104, the global outer frame surface is used to erase the void surface data, and the surface data obtained by merging multiple original building surface data is obtained according to the principle of spatial analysis.

[0050] In the embodiments of this disclosure, the void surface data obtained after the first erasure (i.e., the aforementioned closed area with voids) R 1) Still based on the global outline R 0 represents a solid with an outer shell and several internal voids; the outlines of these voids correspond exactly to the union of all building surfaces. The second erasure method involves "removing the voids entirely using the original global outline," transforming the voids themselves into solids. Its mathematical expression is as follows: ,Will R Substituting 1, we get Therefore, it can be seen that R2 is the complete union of all building surfaces. Geometrically, this is equivalent to first hollowing out the interior of the global outer frame, and then "flipping" this set of holes back in one go: the outer shell is preserved, and the hollow parts become solids, thus obtaining a continuous, seamless, topologically closed single surface. This process only involves the difference operation between two surfaces, without traversing them one by one. n This approach reduces computational load and avoids error accumulation caused by multiple Boolean merges; it also directly guarantees area conservation and boundary consistency.

[0051] It should be noted that, logically, the merged data of multiple original building surfaces is a single entity, but geometrically, the merged data of multiple original building surfaces is still a series of independent, closed building areas.

[0052] Optionally, in some embodiments, when multiple original building surface data are merged into surface data, the merged surface data can be saved to a specified data source. For example, a new dataset can be created to store the merging results, and the merged surface data can be added to that dataset.

[0053] Optionally, in some embodiments, before saving the merged surface data to a specified data source, the merged surface data may be subjected to integrity verification (including area conservation verification and geometric consistency verification, etc.). For example, based on multiple original building surface data, area conservation verification and geometric consistency verification may be performed on the merged surface data.

[0054] For example, a dedicated dataset (which can be understood as the "outcome layer") can be created in the target data source. The record fields may include, but are not limited to: ID (identifier), generation timestamp, number n of original building surface data, and total area. and topological state, etc. The principle of area conservation verification can be as follows: for each closed fault... (The interior represents vertices). Each closed loop can be understood as a closed loop corresponding to each original building surface element. The area of ​​each closed loop can be calculated using the Gauss-Green formula, which can be expressed as follows: .

[0055] The area is negative for clockwise rotation and positive for counterclockwise rotation; the "outer ring minus hole" can be obtained by directly summing the algebraic sums over all rings. R 2. Total area, expressed by the formula as follows:

[0056] in, O j This is the j-th outer ring; Hjp Indicates it belongs to the outer ring. O j The p-th hole; s represents the number of outer rings; h j Indicates outer ring O j The number of holes it contains. Among them, It is the area of ​​the outer ring. Since it is counterclockwise, the Gauss-Green formula result is positive. This is the area of ​​the hole; since it's clockwise, the Gauss-Green formula gives a negative result.

[0057] calculate R The relative error between the total area and the total area of ​​the original building surface data is calculated using the following formula: When the relative error is less than or equal to the error threshold, for example... Under these circumstances, the area conservation check can be confirmed as passed.

[0058] It should be noted that the main double-erasure process shares the same traversal during a single scan to accumulate the original total area, incurring no additional overhead, and its time complexity is O(n log n). O (M), calculating the area of ​​the combined ring requires m multiplications and m additions; by summing all the rings together, the total number of vertices is V. tot Then its complexity is T= θ (V tot Therefore, the time complexity is linear with the total number of vertices and will not significantly increase the time cost.

[0059] To ensure the merger results R 2. It is accurate and can also perform geometric consistency checks (topological closure checks). For example, SuperMap's built-in geometric checking method can be used, or Euler characteristic numbers can be used for rapid screening. Let a single piece (referring to each topologically independent closed polygon segment formed after topological merging of all building surface elements) contain the number of vertices V, the number of edges E, and the number of rings (faces) F. Theoretically, if this piece is composed of only one outer ring plus several inner rings, then it should satisfy: First, a linear traversal of the cycle vertices is performed to establish a vertex-edge adjacency list. Then, the number of vertices V, edges E, and cycles (faces) F of a single piece are counted before calculating... ,like If ≠1 or isolated or overlapping edges are found, a topology error is determined, and the merge is rolled back and performed again.

[0060] In the above embodiments, an outer frame that completely contains all face data is constructed by calculating the outer envelope rectangle of all face data. This outer frame serves as the basis for subsequent operations, and its boundary values ​​precisely define the spatial range of all face data. Erasing all original face data using this outer frame creates a void in the erased area; the shape of this void is closely related to the distribution of all original face data. Erasing this void using the outer frame, based on the principles of spatial analysis, ultimately yields the face after merging all face data. This disclosure breaks through the traditional linear thinking mode of loop traversal, utilizing the principles of spatial analysis to simplify the complex merging process into more efficient geometric operations. Specifically, by constructing an outer frame and utilizing the erasure operation, efficient merging of multiple face data can be achieved, enabling rapid and accurate merging of building face data, significantly shortening data processing time, and greatly reducing data processing complexity.

[0061] It is worth noting that in the existing "fixed outer frame + double erase" basic scheme, even if the building occupies only a very small corner of the rectangular envelope, the algorithm still needs to complete tile loading, fast intersection determination, and scan line splitting for the entire blank area, causing the memory table and I / O scheduling to expand by an area scale. To reduce this constant overhead, which is independent of the number of features but increases with the expansion of the outer frame, this disclosure further proposes an adaptive outer frame morphology technology: first, perform main direction analysis and density clustering on the building distribution, and dynamically shrink the rectangular outer frame to the minimum convex hull while maintaining 100% coverage, while automatically degenerating to a rectangular envelope for near-rectangular dense scenes, ensuring that the worst-case performance is no lower than the original scheme. This adaptive outer frame mechanism is seamlessly integrated with the double erase main process, and can significantly optimize the constant term without changing the linear complexity, realizing a new efficient multi-morphological building surface merging scheme of "scene perception - on-demand shrinkage - local double erase". Specifically, Figure 3 This is a flowchart illustrating a method for merging building surface data according to an exemplary embodiment. Figure 3 As shown, the method for merging building surface data may include, but is not limited to, the following steps.

[0062] In step 301, multiple original building surface data are obtained.

[0063] Optionally, step 301 can be implemented using any of the implementation methods in the various embodiments of this disclosure. This disclosure does not limit this implementation and will not elaborate further.

[0064] In step 302, a unified coordinate transformation is performed on multiple original building surface data to obtain the projected coordinates of each original building surface data in the projected coordinate system.

[0065] In some embodiments, each original building surface data can be described by a vertex set, for example, through a vertex set. Description. This function can perform a unified coordinate transformation on multiple original building surface data points to obtain the projected coordinates of each original building surface data point in the projected coordinate system. Converting all data points to the projected coordinate system ensures that distance and area can be directly measured.

[0066] In step 303, based on the principal direction analysis, the principal axis aligned bounding boxes of the convex hulls of multiple original building surface data are calculated, and the aspect ratio of the overall scene distribution is obtained based on the principal axis aligned bounding boxes.

[0067] In the embodiments of this disclosure, principal component analysis (PCA) can be used to perform principal direction analysis on multiple original building surface data, calculate the principal axis aligned bounding box of the convex hull of the multiple original building surface data, and calculate the aspect ratio of the overall scene distribution based on the principal axis aligned bounding box. For example, the PCA principal component analysis formula is expressed as follows: ,in, F This represents a set of vertices representing multiple original building roof data, where (x, y) represents the coordinates of a vertex in this set. , These represent the average values ​​of the x-coordinate and y-coordinate of the vertex in the combined equation; v i It is an eigenvector, representing the principal direction; λ i v Eigenvalues ​​represent the degree of dispersion in the corresponding direction. The first eigenvalue is calculated by solving for the eigenvectors of the covariance matrix and then applying the characteristic equation. λ 1 and second eigenvalues λ 2. Let λ 1 is the main axis. λ 2 is the secondary axis, calculated λ 1 and λ 2. Calculate the aspect ratio of the overall scene distribution. As an example, the aspect ratio of the overall scene distribution... ρ The calculation formula is as follows: .

[0068] For example, the vertex set distribution of multiple original building surface features is shown in Figure 4(a). Using the PCA formula, principal direction analysis is performed on the multiple original building surface data to calculate the principal axis aligned bounding box of the convex hull of these multiple original building surface features. Based on this principal axis aligned bounding box, the aspect ratio of the overall scene distribution is calculated. ρ This yields the scene distribution perception result shown in Figure 4(b), which is obtained through the aspect ratio of the overall scene distribution. ρ This allows you to determine the scene distribution.

[0069] It should be noted that, in the embodiments of this disclosure, the aspect ratio of the overall scene distribution can be considered. ρ The size of the global outline that can cover the multiple original building surface data is determined by selecting the appropriate method. The aspect ratio of the overall scene distribution is also considered. ρ The larger the value, the narrower / strip-shaped the overall scene appears, and the more dispersed the building surface point cloud may be. Density clustering can be performed on these multiple original building surface data points first, and the rectangular outline can be dynamically shrunk to the minimum convex hull while maintaining 100% coverage. This indicates the overall aspect ratio of the scene. ρ The smaller the value, the more square the overall shape. For densely packed near-square scenes, it automatically degenerates into a rectangular envelope, meaning the diagonal rectangle can be directly used as the global bounding surface. For example, the aspect ratio of the entire scene can be... ρ A comparison is made with the first ratio. If the aspect ratio of the overall scene distribution is less than or equal to the first ratio, then steps 304 and 305 are executed, and the diagonal rectangle can be directly used as the global outer frame. If the aspect ratio of the overall scene distribution is greater than the first ratio, then step 306 is executed, which involves density clustering of these multiple original building surface data, and dynamically shrinking the rectangular outer frame to the minimum convex hull while maintaining 100% coverage.

[0070] In step 304, when the aspect ratio of the overall scene distribution is less than or equal to the first ratio, the minimum outer envelope rectangle and the diagonal of the minimum outer envelope rectangle that can cover multiple original building surface data are determined based on the projection coordinates of each original building surface data.

[0071] For example, the first ratio can be a pre-set ratio. As an example, the first ratio can be 1.5, which is the aspect ratio of the overall scene distribution. ρ≤ In step 1.5, the global shape can be considered nearly square. In this case, a diagonal rectangle can be directly used as the global outer frame. Based on the projected coordinates of each original building surface data, the minimum outer envelope rectangle and its diagonal can be determined to cover multiple original building surface data. For example, the minimum outer envelope rectangle covering multiple original building surface data can be... B min This indicates that the minimum outer envelope rectangle B min The boundary formula can be: The minimum outer envelope rectangle that completely covers the multiple original building surface data can be obtained using this boundary formula. The diagonal of this minimum outer envelope rectangle is then calculated based on its vertex coordinates. .

[0072] In step 305, the outer edge is expanded along the diagonal of the minimum outer envelope moment to obtain a global outer frame that can cover multiple original building surface data.

[0073] To address potential topological gaps caused by floating-point errors or overlap between the bounding box and feature boundaries, the minimum outer envelope rectangle can be expanded outwards along its diagonal. For example, such as... Figure 2 As shown, the minimum outer envelope rectangle can be expanded by one-thousandth of the diagonal, and the resulting expanded area can be used as a global outer frame that can cover multiple original building surface data.

[0074] In step 306, when the aspect ratio of the overall scene distribution is greater than the first ratio, the multiple original building surface data are spatially clustered to obtain at least one cluster. A global convex hull is constructed based on the corner points of the outer rectangle of each cluster to form a global outer frame that can cover the multiple original building surface data.

[0075] As an example, this first ratio could be 1.5, when the overall aspect ratio of the scene is... ρ When the density is greater than 1.5, the global shape can be considered narrow / strip-shaped. In this case, density clustering can be performed on these multiple original building surface data. In some embodiments, high-density cluster identification and spatial connectivity merging can be performed on multiple original building surface data to obtain at least one cluster.

[0076] In some embodiments, for each cluster, the covariance matrix of all building vertices in the cluster can be calculated, and the principal axis tilt angle of the cluster can be calculated based on the largest eigenvector of the covariance matrix. If the principal axis tilt angle is greater than or equal to a first angle and less than or equal to a second angle, each vertex in the cluster is rotated based on the principal axis tilt angle, and the minimum bounding matrix of the cluster is obtained based on the coordinates obtained after all rotations. The vertices of the minimum bounding matrix are then rotated based on the principal axis tilt angle to obtain candidate points of the outer bounding rectangle of the cluster. Alternatively, if the principal axis tilt angle is less than the first angle or greater than the second angle, the four corner points of the axis-aligned minimum rectangle are used as candidate points of the outer bounding rectangle of the cluster. A global convex hull is constructed based on the candidate points of the outer bounding rectangle of each cluster to form a global outer frame that can cover multiple original building surface data.

[0077] For example, a spatial index (R-Tree) can be generated to identify high-density clusters and merge spatial connectivity data from multiple original building surface datasets F. This spatial index (R-Tree) can be constructed by inserting the minimum bounding rectangles (MBRs) of multiple original building surface datasets one by one. For instance, for a set of building surface features F, the MBR of each feature is first calculated, and then insertion nodes are selected according to the minimum area increment principle of the R-Tree. If the nodes are overloaded, they are split using a linear or quadratic splitting strategy. The MBRs of each layer of nodes are updated from bottom to top until the root node is stable. This results in a hierarchical spatial index structure.

[0078] It should be noted that the purpose of generating the spatial index (R-Tree), identifying high-density clusters and merging spatial connectivity among multiple original building surface data F, and providing the smallest computational unit for subsequent adaptive outline construction, is to eliminate the need for feature-by-feature traversal throughout the entire process, instead completing the determination directly at the index leaf node level. As an example, the implementation method for leaf node extraction and threshold setting is as follows: Let RT be the established R-Tree, and let... That is, the set of all leaf nodes obtained by batch loading. p Number of leaf nodes. Preset building quantity threshold: ,Pick ,in The leaf node capacity set when constructing the R-Tree; that is, the upper limit of building surfaces that can be stored in each leaf node. For example, this disclosure may take... .

[0079] For any ,like remember L j For high-density nodes, set a marker. d ( L j )=1, otherwise d ( L j )=0; Define a two-leaf node L j , L k The minimum bounding rectangles are as follows: , If satisfied If they are horizontally adjacent, then they are considered horizontally adjacent; if This is considered a vertical adjacency. A breadth-first search method can then be used to initialize the cluster number counter. m =0, for each high-density node L j If not yet assigned to a cluster: Place Create a new cluster C m ; by L j Starting with a queue Q Perform a breadth-first search of the four-neighborhood and add all high-density nodes connected to it. C m Output cluster set: Cluster-MBR refers to a technology that can cover a cluster in one go. C k The smallest axisymmetric rectangle encompassing the entire space of all leaf nodes is denoted as . This refers to the smallest encapsulated region in a planar coordinate system; for each leaf node in the cluster, extract its saved building surface (feature) primary key ID and perform a union operation: ,in FID ( L The `<feature ID>` is an array of feature IDs attached to the leaf node during R-Tree construction, recording the unique identifier of each building face contained within that leaf node in the original dataset. For example, based on the aspect ratio of the overall scene distribution as shown in Figure 4(b). ρ When the value is greater than 1.5, density clustering can be performed on these multiple original building surface data to obtain the clustering results shown in Figure 4(c).

[0080] After obtaining the clustering results, for each cluster, the covariance matrix of all building vertices in the cluster can be calculated. The principal axis tilt angle of the cluster is then calculated based on the largest eigenvector of the covariance matrix. Subsequently, the cluster-level outline shape is generated based on the principal axis tilt angle, obtaining candidate outer rectangles for each cluster. A global convex hull is then constructed based on these candidate outer rectangles to form a global outline that can cover multiple original building surface data. For example, as shown in Figure 4(d), orientation can be determined for each cluster. For instance, for clusters... C k Calculate the covariance matrix for all building vertices (x, y): Find the largest eigenvector of the covariance matrix. ,in θ k For clusters C k The tilt angle of the main axis. If the first angle (e.g., 15°) ≤ θ k If the second angle is less than or equal to 75°, then the cluster is considered. C k Significantly tilted; otherwise, it is considered "paraxially aligned".

[0081] For paraxially aligned clusters, the smallest axis-aligned rectangle can be used directly.B k The four corners, and the vertices of the four corners are taken as the... C k Candidate points for the outer network rectangle. For significantly tilted clusters, directly taking the axis-aligned rectangle will introduce a large number of blanks. Therefore, the cluster can be straightened first and then the smallest rectangle can be taken, that is: take each vertex in the cluster ( x , y )according to Rotation, that is ,in For all rotated coordinates By taking the minimum and maximum values ​​respectively, we obtain the minimum bounding rectangle: ,Will The four vertices are arranged according to Spin: The four vertices obtained are denoted as: Figure 4(e) shows a schematic diagram of the bounding rectangle for each cluster. The global set of candidate points for the bounding rectangle can be aggregated. By calculating the global convex hull of the points in the candidate point set once, a unique and compact global outline can be obtained in a single operation. The specific method is as follows: First, sort the point set in ascending order to the left of X (or Y if they are the same). P The process involves scanning the sorted results sequentially and adding points to stack S. If the top three points of the stack form a non-left turn (e.g., an interior angle greater than or equal to 180 degrees), the middle point is popped. After scanning, the stack contains the next chain. The same sorted results are then scanned again in reverse order, and the above logic is repeated to obtain the next chain. The next and last chains are then concatenated to obtain the complete convex hull vertex sequence. This G can be used as the minimum global bounding surface that can cover the multiple original building surface data, and then brought into the "double erase" main process. For example, as shown in Figure 4(f), the convex polygon bounding surface of the multiple original building roof features shown in Figure 4(a) can be used as the minimum global bounding surface that can cover the multiple original building surface data.

[0082] It is worth noting that this disclosure first performs spatial clustering on the building surface data, and then constructs a global convex hull based on the corner points of the bounding rectangles of each cluster, forming a more compact erased outer frame. Although this process introduces additional steps such as cluster determination and convex hull calculation, its computational complexity is extremely low compared to the traversal, sorting, and memory scheduling overhead that is linearly related to the outer frame area in the two Boolean difference operations. In most non-square or multi-clustered scenarios, the convex hull outer frame can reduce the area involved in Boolean operations by more than 40%, thereby significantly reducing the overall processing time and resource consumption.

[0083] In step 307, multiple original building surface data are erased using the global outer frame to obtain void surface data. The shape of the void surface data is closely related to the distribution of multiple original building surface data.

[0084] Optionally, step 307 can be implemented using any of the implementation methods in the various embodiments of this disclosure. This disclosure does not limit this implementation and will not elaborate further.

[0085] In step 308, the global outer frame surface is used to erase the void surface data, and the surface data obtained by merging multiple original building surface data is obtained according to the principle of spatial analysis.

[0086] Optionally, step 308 can be implemented using any of the implementation methods in the various embodiments of this disclosure. This disclosure does not limit this implementation and will not elaborate further.

[0087] In the above embodiments, the building distribution is first analyzed by main direction and density clustering. While maintaining 100% coverage, the rectangular outline is dynamically shrunk to the minimum convex hull. For near-rectangular dense scenes, it automatically degenerates to a rectangular envelope, ensuring that the worst-case performance is no worse than the original solution. Through this adaptive outline mechanism and seamless integration with the dual-erasure main process, the constant term can be significantly optimized without changing the linear complexity, thereby further improving processing efficiency.

[0088] Figure 5 This is a block diagram illustrating a building surface data merging apparatus according to an exemplary embodiment. Figure 5 As shown, the building surface data merging device may include: an acquisition module 501, a construction module 502, a first erasure module 503, and a second erasure module 504.

[0089] The acquisition module 501 is used to acquire multiple original building surface data.

[0090] Module 502 is used to construct a global outer frame that can cover multiple original building surface data by calculating the minimum outer envelope rectangle of multiple original building surface data.

[0091] The first erasure module 503 is used to erase multiple original building surface data using the global outer frame to obtain void surface data. The shape of the void surface data is closely related to the distribution of multiple original building surface data.

[0092] The second erasure module 504 is used to erase the hole surface data using the global outer frame surface. According to the principle of spatial analysis, the resulting surface data is the merged surface data after merging multiple original building surface data.

[0093] In some embodiments, the construction module 502 is used to: perform a unified coordinate transformation on multiple original building surface data to obtain the projected coordinates of each original building surface data in the projected coordinate system; determine the minimum outer envelope rectangle and the diagonal of the minimum outer envelope rectangle that can cover multiple original building surface data based on the projected coordinates of each original building surface data; and expand outward on the diagonal of the minimum outer envelope rectangle to obtain a global outer frame that can cover multiple original building surface data.

[0094] In some embodiments, the construction module 502 is further configured to: calculate the principal axis aligned bounding boxes of the convex hulls of multiple original building surface data based on principal direction analysis, and obtain the aspect ratio of the overall scene distribution based on the principal axis aligned bounding boxes. Wherein, if the aspect ratio of the overall scene distribution is less than or equal to a first ratio, the construction module 502 performs the step of determining the minimum outer envelope rectangle and the diagonal of the minimum outer envelope rectangle that can cover the multiple original building surface data based on the projection coordinates of each original building surface data; or, if the aspect ratio of the overall scene distribution is greater than the first ratio, the construction module 502 is further configured to: perform spatial clustering on the multiple original building surface data to obtain at least one cluster, and construct a global convex hull based on the corner points of the bounding rectangle of each cluster to form a global outer frame that can cover the multiple original building surface data.

[0095] In some embodiments, the construction module 502 is used to: perform high-density cluster identification and spatial connectivity merging on multiple original building surface data to obtain at least one cluster.

[0096] In some embodiments, the construction module 502 is configured to: for each cluster, calculate the covariance matrix for all building vertices in the cluster, and calculate the principal axis tilt angle of the cluster based on the largest eigenvector of the covariance matrix; if the principal axis tilt angle is greater than or equal to a first angle and less than or equal to a second angle, rotate each vertex in the cluster based on the principal axis tilt angle, obtain the minimum bounding matrix of the cluster based on the coordinates obtained after all rotations, and rotate the vertices of the minimum bounding matrix based on the principal axis tilt angle to obtain candidate points of the outer bounding rectangle of the cluster; or, if the principal axis tilt angle is less than the first angle or greater than the second angle, use the four corner points of the axis-aligned minimum rectangle as candidate points of the outer bounding rectangle of the cluster; and construct a global convex hull based on the candidate points of the outer bounding rectangle of each cluster to form a global outer frame that can cover multiple original building surface data.

[0097] In some embodiments, the building surface data merging device may further include a verification module. The verification module is used to: perform area conservation verification on the surface data merged from multiple original building surface data; and perform geometric consistency verification on the surface data merged from multiple original building surface data.

[0098] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0099] The following experiments will verify the differences between the building surface data merging method disclosed herein and the traditional loop traversal merging method. The practical application effects can be verified in the following configuration environment: Table 1 Configuration Environment

[0100] Test dataset construction: To comprehensively evaluate the performance of the two facet data merging methods under different data scales, a representative test dataset can be constructed, covering facet data of varying quantities and sizes, as follows: (1) 1000-facet data: represents small-scale scenarios, such as community-level building planning or local plot modeling, which is suitable for verifying the response speed and resource consumption of the algorithm under lightweight data; (2) 10,000 facets: This corresponds to medium-sized scenarios, such as towns or small urban sub-regions, which facilitates observation of the algorithm's scalability as the amount of data increases; (3) 20,000 facets: This belongs to the category of large-scale data, which is close to the typical load of urban core areas or dense building clusters. It is suitable for testing the stability and performance bottlenecks of algorithms. (4) 100,000-facet data: Simulates ultra-large-scale facet data, used only to test the extreme processing capability of the new algorithm under high load. Traditional methods cannot complete the processing within a reasonable time at this level, and therefore are not included in the comparison.

[0101] Performance indicator system construction: To comprehensively and objectively evaluate the performance of the two surface data merging methods, a performance index system was established from two key dimensions: time efficiency and resource consumption. Time efficiency, as a key dimension for evaluating the performance of data processing methods, directly reflects the rate at which the method processes data. In this experiment, we evaluated the time efficiency of two methods by precisely measuring the time required to process surface data of different sizes.

[0102] Traditional method: When processing 1000 faces, it took 2.044 seconds. This is because the traditional loop-based merging method needs to iterate through the existing merge results and the faces to be merged in each merge operation. As the data size continues to increase, this repeated traversal operation consumes a significant amount of time resources. When the data volume increases to 10000 faces, the processing becomes extremely slow due to its time complexity increasing to O(n²), failing to complete the processing within a reasonable timeframe. This fully demonstrates the limitations of traditional methods when dealing with large-scale data.

[0103] The building surface data merging method disclosed herein (hereinafter referred to as the novel method) takes only 0.982 seconds to process 1000 surfaces, a significant speed improvement compared to traditional methods. When the data volume increases to 10000 surfaces, the time taken is 3.609 seconds; and when processing 20000 surfaces, the time taken is 7.823 seconds. The novel method simplifies the complex successive merging process into a more efficient spatial analysis operation by constructing the outer frame and performing two erasure operations, significantly reducing the time cost of data processing. Its time complexity is optimized to O(n), demonstrating a significant advantage when processing massive amounts of data.

[0104] Table 2 Test Time Comparison Table

[0105] Resource consumption is also a key factor in evaluating the performance of data processing methods, including CPU (Central Processing Unit) utilization and memory consumption.

[0106] 1. CPU Utilization: Throughout the experiment, CPU utilization was tracked in real time using system monitoring tools for both methods. For example, ... Figure 6(a) , 6(b) As shown in Figure 6(c), the experimental results indicate that the CPU utilization of both methods remains at the same level. This is because both the traditional and novel methods primarily rely on the CPU's computing power to perform geometric operations and logical judgments during data processing. Although the traditional method is slower, its relatively simple algorithmic logic results in a relatively stable computational load on the CPU. While the novel method is faster, it does not place an excessive load on the CPU during spatial analysis operations. This demonstrates that both methods exhibit a certain degree of stability in CPU resource utilization.

[0107] 2. Memory consumption: such as Figure 6(a) , 6(b)As shown in Figure 6(c), experimental data shows that the peak memory consumption of the novel method is not significantly lower than that of the traditional method. Traditional methods require frequent memory operations when processing data; for example, temporary memory space needs to be created to store intermediate results during each merge operation. As the amount of data increases, the frequency and scale of these memory operations also increase, leading to slightly higher memory consumption. The novel method, through clever algorithm design, reduces unnecessary memory operations. During the erase operation, spatial indexing technology is used to quickly locate the surface data that needs to be processed, avoiding blind traversal of the entire dataset, thereby reducing memory usage and improving the efficiency of memory resource utilization.

[0108] Test result verification: To ensure the accuracy and reliability of the results generated by the two surface data merging methods, rigorous result verification can be performed from two aspects: geometric correctness and topological consistency. 1. Geometric Correctness: Using spatial analysis tools, the area of ​​the merged face data generated by the two methods was calculated and compared with the theoretical sum of all face data. Experimental results show that the area error of the faces generated by both methods is less than 0.01%. This is because both the traditional loop traversal merging method and the new two-stage erasure merging method strictly follow the geometric algorithm provided by SuperMapiObject.Net10i during geometric calculations, ensuring the accuracy of the geometry during the merging process, thus guaranteeing that the area of ​​the generated face is highly close to the theoretical value.

[0109] 2. Topological Consistency: Using SuperMap's geometric inspection tool, a topological check was performed on the generated merged surface data, including checking whether the surface boundaries were correct and whether there were any overlaps or holes. The results showed that the merged surface data generated by both methods passed the verification of the geometric inspection tool, and the topological relationships were correct. This indicates that both methods can accurately handle the topological relationships between surfaces when processing surface data, ensuring the consistency of the merged results in spatial topology.

[0110] Optionally, by implementing the embodiments of this disclosure, in the field of digital twins, building surface data can be merged quickly and accurately, providing strong support for constructing high-precision urban digital twin models. In 3D GIS analysis, the efficiency of the novel method enables real-time analysis of large-scale geographic data, providing timely and accurate data support for applications such as resource exploration and environmental monitoring, and improving the scientific rigor and timeliness of decision-making. In smart city construction, facing massive amounts of urban spatial data, the novel method can significantly shorten data processing time, improve the level of intelligent urban management, and promote sustainable urban development.

[0111] In emergency response scenarios, the rapid and accurate integration of multi-source heterogeneous data is crucial for developing effective rescue plans. For example, after natural disasters such as earthquakes and floods, it is necessary to quickly merge and analyze building surface data from different sources, including Geographic Information Systems (GIS), remote sensing imagery, and on-site monitoring equipment, to assess disaster damage and determine rescue priorities. The novel two-stage erasure method (i.e., the method disclosed herein) not only efficiently processes large-scale data but also possesses good compatibility, supporting the rapid integration of multi-source heterogeneous data. In practical applications, it can complete data processing in a short time, providing comprehensive and accurate information to the emergency command center.

[0112] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0113] like Figure 7 The diagram shown is a block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0114] like Figure 7 As shown, the electronic device includes one or more processors 701, a memory 702, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take the 701 processor as an example.

[0115] The memory 702 is the non-transitory computer-readable storage medium provided in this disclosure. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the building surface data merging method provided in this disclosure. The non-transitory computer-readable storage medium of this disclosure stores computer instructions for causing a computer to perform the building surface data merging method provided in this disclosure.

[0116] Memory 702, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the building surface data merging method in the embodiments of this disclosure (e.g., appendix). Figure 5 The acquisition module 501, construction module 502, first erasure module 503, and second erasure module 504 are shown. The processor 701 executes various functional applications and data processing of the server by running non-transient software programs, instructions, and modules stored in the memory 702, thereby implementing the building surface data merging method in the above method embodiment.

[0117] The memory 702 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 702 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 702 may optionally include memory remotely located relative to the processor 701, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0118] The electronic device may also include an input device 703 and an output device 704. The processor 701, memory 702, input device 703, and output device 704 can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0119] Input device 703 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as touch screens, keypads, mice, trackpads, touchpads, joysticks, one or more mouse buttons, trackballs, joysticks, etc. Output device 704 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The display device may include, but is not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

[0120] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] These computational programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0123] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0124] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0125] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0126] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A building face data merging method characterized by, The method comprises the following steps: obtaining a plurality of original building surface data; constructing a global outer frame surface covering the plurality of original building surface data by calculating the minimum outer envelope rectangle of the plurality of original building surface data; erasing the plurality of original building surface data using the global outer frame surface to obtain hollow surface data, the shape of the hollow surface data being closely related to the distribution of the plurality of original building surface data; erasing the hollow surface data using the global outer frame surface to obtain the surface data after merging the plurality of original building surface data according to the principle of spatial analysis.

2. The method of claim 1, wherein, The step of constructing a global outer frame surface covering the plurality of original building surface data by calculating the minimum outer envelope rectangle of the plurality of original building surface data comprises the following steps: performing uniform coordinate conversion on the plurality of original building surface data to obtain the projection coordinates of each original building surface data in the projection coordinate system; determining the minimum outer envelope rectangle covering the plurality of original building surface data and the diagonal line of the minimum outer envelope rectangle according to the projection coordinates of each original building surface data; extending the diagonal line of the minimum outer envelope rectangle to obtain the global outer frame surface covering the plurality of original building surface data.

3. The method of claim 2, wherein, The method further comprises the following steps: calculating the principal axis alignment bounding box of the convex hull of the plurality of original building surface data based on the principal direction analysis, and obtaining the aspect ratio of the overall distribution of the scene according to the principal axis alignment bounding box; wherein, in the case that the aspect ratio of the overall distribution of the scene is less than or equal to a first ratio, the step of determining the minimum outer envelope rectangle covering the plurality of original building surface data and the diagonal line of the minimum outer envelope rectangle according to the projection coordinates of each original building surface data is performed; or in the case that the aspect ratio of the overall distribution of the scene is greater than the first ratio, performing spatial clustering division on the plurality of original building surface data to obtain at least one cluster, and constructing a global convex hull based on the corner points of the circumscribed rectangle of each cluster to form a global outer frame surface covering the plurality of original building surface data.

4. The method of claim 3, wherein, The step of performing spatial clustering division on the plurality of original building surface data to obtain at least one cluster comprises the following steps: performing high-density cluster identification and spatial connectivity merging on the plurality of original building surface data to obtain at least one cluster.

5. The method according to claim 3 or 4, characterized in that, The step of constructing a global convex hull based on the corner points of the circumscribed rectangle of each cluster to form a global outer frame surface covering the plurality of original building surface data comprises the following steps: for each cluster, calculating the covariance matrix of all building vertices in the cluster, and calculating the principal axis inclination angle of the cluster according to the maximum eigenvector of the covariance matrix; In a case where the main shaft inclination angle is greater than or equal to the first angle and less than or equal to the second angle, each vertex in the cluster is rotated based on the main shaft inclination angle, a minimum circumscribed matrix of the cluster is obtained based on the coordinates obtained after the rotation, and a vertex of the minimum circumscribed matrix is rotated based on the main shaft inclination angle to obtain an outer circumscribed rectangular candidate point of the cluster; or in a case where the main shaft inclination angle is less than the first angle or greater than the second angle, four corner points of an axis-aligned minimum rectangle are used as the outer circumscribed rectangular candidate point of the cluster; A global convex hull is constructed based on the outer circumscribed rectangular candidate point of each cluster to form a global outer frame surface that can cover the plurality of original building surface data.

6. The method of claim 1, wherein, The method further comprises: Based on the plurality of original building surface data, the area-conserved verification is performed on the surface data after the plurality of original building surface data are merged; The geometric consistency verification is performed on the surface data after the plurality of original building surface data are merged.

7. A building face data merging apparatus characterized by comprising: It comprises: An acquisition module is configured to acquire a plurality of original building surface data; A construction module is configured to construct a global outer frame surface that can cover the plurality of original building surface data by calculating a minimum outer envelope rectangle of the plurality of original building surface data; A first erasing module is configured to erase the plurality of original building surface data using the global outer frame surface to obtain hollow surface data, and the shape of the hollow surface data is closely related to the distribution of the plurality of original building surface data; A second erasing module is configured to erase the hollow surface data using the global outer frame surface, and according to the principle of spatial analysis, the obtained result surface data is the merged surface data after the plurality of original building surface data are merged.

8. An electronic device, comprising: It comprises: One or more processors; The processor is configured to call instructions to enable the electronic device to perform the method in any one of claims 1-6.

9. A storage medium, the storage medium storing instructions, wherein, When the instructions run on the electronic device, the electronic device is enabled to perform the method in any one of claims 1-6.

10. A program product comprising at least one of a program, instructions, characterized in that The steps of the method in any one of claims 1-6 are implemented when at least one of the program and the instructions is executed by the electronic device.

Images