Urban rainfall flood simulation-oriented building modeling method based on point cloud data

Abstract

The invention discloses an urban rainfall flood simulation-oriented building modeling method based on point cloud data, and belongs to the technical field of urban hydrological modeling, and the method comprises the steps: carrying out the neighborhood search of the rasterized point cloud data, constructing a covariance matrix according to a neighborhood point set, and carrying out the eigenvalue decomposition to obtain a normal vector; plane feature points and slope feature points are determined by extracting vertical components of normal vectors, and then the number of the plane feature points and the number of the slope feature points are counted to determine the building type; projecting the point cloud data to an XOY plane to generate a regularized contour boundary; if the building is a flat roof building, combining the regularized contour boundary with a preset earth surface model to obtain a building model; and if the building is a pitched roof building, constructing a triangular grid through the point cloud data, and combining the triangular grid with a preset earth surface model by using the regularized contour boundary to obtain a building model. According to the method, the problems of low modeling efficiency and geometric distortion caused by incapability of constructing an adaptive model based on a building type in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to a building modeling method for urban rainstorm flood simulation based on point cloud data, belonging to the technical field of urban hydrological modeling. Background Art

[0002] In the field of urban rainstorm flood simulation, building modeling is the core link for constructing a high-precision surface elevation model. Traditional methods mainly adopt three types of technical paths: the fixed wall boundary method, which simplifies the flood process simulation by directly deducting the building area and assuming it is completely impermeable; the real terrain method, which integrates the building elevation into the digital elevation model (DEM) to reflect its water blocking effect; and the increased roughness method, which simulates the water flow obstruction by assigning a very high roughness coefficient to the building area. However, the above methods all focus on a single modeling strategy or the combined application of different methods, lacking functional classification modeling of buildings participating in the rainstorm flood process. For example, flat-roof buildings have significant differences in rainwater runoff characteristics from sloping-roof buildings due to their water storage structures and pipe network systems, but the existing technology fails to build an adapted model for such functional differences, resulting in limited simulation accuracy.

[0003] The core defect of the existing technology is the inability to build a differentiated model based on building types, which in turn leads to low modeling efficiency and geometric distortion problems. Specifically: traditional methods adopt a unified processing strategy, without considering the rainwater pipe network diversion characteristics of flat-roof buildings and the slope surface runoff characteristics of sloping-roof buildings, resulting in geometric distortion in the interaction process between buildings and water flow; relying on traditional 2.5D DEM data and hydrological models, it is difficult to capture the three-dimensional details of building roofs, walls, etc. Especially in complex urban scenarios, the simplified processing of key geometric features such as ridge lines and eaves by traditional models further exacerbates the simulation error; the potential of high-precision three-dimensional point cloud data is not fully utilized. The existing technology only represents the building form through two-dimensional projection or rough geometric abstraction, unable to meet the requirements of small-scale and high-precision urban rainstorm flood simulation. Such defects directly lead to the underestimation or misjudgment of the water blocking and diversion effects of buildings in the rainstorm flood process, ultimately affecting the prediction accuracy of key indicators such as surface runoff and inundation range by the hydrodynamic model. Summary of the Invention

[0004] The purpose of the present invention is to provide a building modeling method for urban rainstorm flood simulation based on point cloud data. By extracting the vertical component of the normal vector to determine the plane feature points and inclined plane feature points, and then judging the building type, a triangular grid is constructed according to the building type and integrated with the surface model, so as to solve the problems of low modeling efficiency and geometric distortion caused by the inability of the existing technology to build an adapted model based on building types.

[0005] To solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] The present invention provides a building modeling method for urban rainstorm simulation based on point cloud data, including:

[0007] Obtaining the point cloud data of the target building;

[0008] Performing eigenvalue enhancement normalization on the point cloud data and setting grids to tile on the normalized point cloud data to obtain rasterized point cloud data;

[0009] Using the adaptive neighborhood search algorithm to perform neighborhood search on all rasterized point cloud data to obtain the set of neighboring points of each rasterized point cloud data, constructing a covariance matrix based on the set of neighboring points, and obtaining the normal vector of each rasterized point cloud data through eigenvalue decomposition of the covariance matrix;

[0010] Determining planar feature points and inclined surface feature points by extracting the vertical components of the normal vectors of each rasterized point cloud data, and then counting the number of planar feature points and inclined surface feature points to determine the building type;

[0011] Projecting the point cloud data of the target building onto the XOY plane, extracting the minimum X-axis / Y-axis boundaries of the point cloud data to generate a minimum circumscribed rectangle as the regularized contour boundary;

[0012] Modeling according to the type of the target building:

[0013] If it is a flat-roof building, merging the regularized contour boundary and the preset ground surface model to obtain a building model;

[0014] If it is a pitched-roof building, performing feature enhancement, resampling, and regularization operations on the point cloud data of the target building to obtain regularized roof points to construct a triangular grid, and using the regularized contour boundary to merge the triangular grid and the preset ground surface model to obtain a building model.

[0015] Further, after merging the regularized contour boundary and the preset ground surface model, it further includes dividing the preset ground surface model into different sub-regions and setting reflection boundaries for the regularized contour boundary.

[0016] Further, after merging the triangular grid and the preset ground surface model according to the regularized contour boundary, setting interaction boundaries for the regularized contour boundary.

[0017] Further, performing eigenvalue enhancement normalization on the point cloud data and setting grids to tile on the normalized point cloud data to obtain rasterized point cloud data includes:

[0018] Normalizing the point cloud data and setting grids to tile on the normalized point cloud data;

[0019] Enlarge the Z-axis coordinate value of the point cloud data with the largest Z-axis coordinate value in each grid according to a preset proportional multiple;

[0020] Take the center of the grid as the center coordinate of the point cloud data with the largest Z-axis coordinate value to obtain the rasterized point cloud data.

[0021] Furthermore, use the adaptive neighborhood search algorithm to perform neighborhood search on all rasterized point cloud data to obtain the set of neighboring points of each rasterized point cloud data, construct a covariance matrix based on the set of neighboring points, and obtain the normal vector of each rasterized point cloud data by performing eigenvalue decomposition on the covariance matrix, including: According to the density preset search radius of the rasterized point cloud data and the threshold of the number of neighboring points of each rasterized point cloud data , where ; Traverse all rasterized point cloud data in sequence: If the number of neighboring points is less than or equal to the threshold of the number of neighboring points of each rasterized point cloud data , then use the KNN search algorithm to obtain neighboring points to obtain the set of neighboring points; If the number of neighboring points is greater than the threshold of the number of neighboring points of each rasterized point cloud data , then use the search radius to search for neighboring points to obtain the set of neighboring points; Based on the set of neighboring points, calculate the centroid of all neighboring points of each rasterized point cloud data and construct a covariance matrix; Perform eigenvalue decomposition on the covariance matrix to obtain the normal vector of each rasterized point cloud data.

[0022] Furthermore, the method for extracting the vertical component of the normal vector of each rasterized point cloud data includes: Pre-define the global upward vector ; Take the dot product of the normal vector of each rasterized point cloud data and the searched global upward vector and take the absolute value as the vertical component of the normal vector of each rasterized point cloud data.

[0023] Furthermore, the method for determining planar feature points and inclined surface feature points includes: Preset the critical value of the normal vector of the sloping roof and the critical value of the normal vector of the flat roof , where ; If the vertical component of the normal vector is greater than or equal to the critical value of the normal vector of the flat roof and less than or equal to 1, it is determined as a planar feature point; If the vertical component of the normal vector is greater than the critical value of the normal vector of the sloping roof and less than the critical value of the normal vector of the flat roof , it is determined as an inclined plane feature point; If the vertical component of the normal vector is greater than or equal to 0 and less than or equal to the critical value of the normal vector of the sloping roof , it is determined as a wall surface or an abnormal point.

[0024] Furthermore, the number of plane feature points and the number of inclined plane feature points are counted to determine the building type, including:

[0025] Ignoring wall surfaces or abnormal points, count the number of plane feature points and the number of inclined plane feature points;

[0026] If the ratio of the number of plane feature points to the sum of the number of plane feature points and the number of inclined plane feature points is greater than the preset ratio threshold, it is determined as a flat roof building;

[0027] If the ratio of the number of plane feature points to the sum of the number of plane feature points and the number of inclined plane feature points is less than or equal to the preset ratio threshold, it is determined as a sloping roof building.

[0028] Furthermore, project the point cloud data of the target building onto the XOY plane, and extract the minimum X-axis / Y-axis boundaries of the point cloud data to generate a minimum bounding rectangle as the regularized contour boundary, including:

[0029] Set a grid according to the density of the point cloud data of the target building;

[0030] Use the grid to perform deduplication and downsampling operations on the point cloud data of the target building to obtain the gridded X-axis coordinates and Y-axis coordinates of the point cloud data of the target building;

[0031] Traverse all the gridded X-axis coordinates and Y-axis coordinates, and respectively record the maximum and minimum values of the X-axis coordinates and the maximum and minimum values of the Y-axis coordinates to obtain the bounding rectangle boundary range of the point cloud data of the target building;

[0032] Construct four sets of corner point coordinates according to the bounding rectangle boundary range of the point cloud data of the target building to generate a minimum bounding rectangle as the regularized contour boundary;

[0033] The four sets of corner point coordinates include the lower left corner coordinates, the upper left corner coordinates, the lower right corner coordinates, and the upper right corner coordinates.

[0034] Furthermore, perform feature enhancement, resampling, and regularization operations on the point cloud data of the target building to obtain regularized roof points, including:

[0035] Extract the Z-axis coordinate values of the point cloud data of the target building using a grid, magnify the Z-axis coordinate values according to a preset proportional multiple, take the center of the grid as the center coordinate of the point cloud data with the largest Z-axis coordinate value, and obtain the grid-based point cloud data;

[0036] Use the principal component analysis method to identify the ridge line direction based on the grid-based point cloud data;

[0037] Identify the connection line of the grid center points parallel to the ridge line direction according to the ridge line direction;

[0038] Construct an index sequence that expands from the ridge line to both sides of the connection line;

[0039] Traverse the Z-axis coordinate values of the grid center points in the order of the index sequence:

[0040] Reset the Z-axis coordinate values in the same index sequence to the average value of the Z-axis coordinates of the grid center points in the index sequence, as the regularized roof points.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention:

[0042] 1. The present invention effectively improves the data quality and processing efficiency by performing eigenvalue enhancement normalization and rasterization processing on the point cloud data of the target building; accurately calculates the normal vector by using the adaptive neighborhood search algorithm, and accurately distinguishes the plane feature points and inclined plane feature points by combining the vertical component analysis, thereby determining the building type; the present invention also models by respectively using the regularized contour boundary to merge the surface model and constructing a triangular grid according to the building type, significantly improving the accuracy and efficiency of building modeling, providing a more refined and reliable building model for urban rainstorm simulation, and helping to improve the scientificity and accuracy of urban rainstorm management.

[0043] 2. The present invention realizes the optimization of water flow interaction between the surface and the building model by setting a reflection boundary for the flat roof and an interaction boundary for the sloping roof. Among them, the reflection boundary can accurately simulate the reflection flow path of rainwater at the building edge, avoiding the water flow fault caused by the lack of boundary in traditional modeling. The interaction boundary realizes the real-time coupling calculation of water flow parameters between the triangular grid roof model and the surface model through the dynamic data exchange mechanism, significantly improving the authenticity of runoff simulation under complex urban terrain, especially reducing the water flow path error in the building-intensive area.

[0044] 3. The present invention automatically switches between the KNN search and radius search modes according to the point cloud density by presetting the search radius and the threshold of the number of neighboring points, improving the recognition accuracy of plane feature points and inclined plane feature points, and at the same time reducing the calculation time consumption, which is applicable to the processing of large-scale urban-level point cloud data and provides a feasible technical support for real-time rainstorm simulation.

[0045] 4. Through grid extraction and Z-axis coordinate value amplification processing, and combining with principal component analysis to identify the ridge line trend, the present invention effectively captures the geometric features of the sloping roof and eliminates point cloud noise. Further, by constructing an index sequence and resetting the Z-axis coordinate value to the grid center average value, the regularization of the roof points is realized, significantly improving the geometric accuracy and topological consistency of the sloping roof triangular grid model, providing a high-precision roof drainage surface model for urban rain flood simulation, being able to more accurately predict the rainwater runoff path and roof water collection efficiency, and providing key data support for urban flood control and drainage planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic flow chart of a building modeling method for urban rain flood simulation based on point cloud data provided by an embodiment of the present invention;

[0047] Figure 2 is a schematic diagram of the point cloud data of a sloping roof building provided by an embodiment of the present invention;

[0048] Figure 3 is a schematic diagram of the point cloud data of a flat roof building provided by an embodiment of the present invention;

[0049] Figure 4 is a schematic diagram of the principle of an adaptive neighborhood search algorithm provided by an embodiment of the present invention;

[0050] Figure 5 is a schematic diagram of the point cloud data after rasterization of a sloping roof building provided by an embodiment of the present invention;

[0051] Figure 6 is a schematic diagram of the point cloud data after rasterization of a flat roof building provided by an embodiment of the present invention;

[0052] Figure 7 is a schematic diagram of the process of solving the normal vector provided by an embodiment of the present invention;

[0053] Figure 8 is a schematic diagram of the solution result of the normal vector of a sloping roof building provided by an embodiment of the present invention;

[0054] Figure 9 is a schematic diagram of the solution result of the normal vector of a flat roof building provided by an embodiment of the present invention;

[0055] Figure 10 is a schematic diagram of the result of projecting a sloping roof building onto the XY plane and grid simplification provided by an embodiment of the present invention;

[0056] Figure 11 is a schematic diagram of the result of projecting a flat roof building onto the XY plane and grid simplification provided by an embodiment of the present invention;

[0057] Figure 12 It is a schematic diagram of the regularized contour boundary of a sloping roof building provided by an embodiment of the present invention;

[0058] Figure 13 It is a schematic diagram of the regularized contour boundary of a flat roof building provided by an embodiment of the present invention;

[0059] Figure 14 It is a schematic diagram of the result of feature enhancement of a sloping roof building provided by an embodiment of the present invention;

[0060] Figure 15 It is a schematic diagram of the triangular grid of a sloping roof building when simulating that the roof surface is too rough provided by an embodiment of the present invention;

[0061] Figure 16 It is a schematic diagram of the triangular grid of a sloping roof building when simulating the "wall hanging" phenomenon on the roof surface provided by an embodiment of the present invention;

[0062] Figure 17 It is a schematic diagram of the regularized roof points provided by an embodiment of the present invention;

[0063] Figure 18 It is a schematic diagram of the triangular grid of a sloping roof building constructed using the regularized roof points provided by an embodiment of the present invention;

[0064] Figure 19 It is a schematic diagram of the flat roof building model merged with a preset surface model provided by an embodiment of the present invention;

[0065] Figure 20 It is a schematic diagram of the sloping roof building model after merging the triangular grid with a preset surface model provided by an embodiment of the present invention;

[0066] Figure 21 It is a schematic diagram of the rainstorm flood simulation results of sloping roof and flat roof building models under uniform rainfall intensity provided by an embodiment of the present invention. Detailed implementation manners

[0067] The technical solution of the present invention will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0068] Embodiment 1

[0069] As Figure 1 shown, this embodiment introduces a building modeling method for urban rainstorm flood simulation based on point cloud data, including:

[0070] Step 1: Obtain the point cloud data of the target building.

[0071] In the present invention, the three-dimensional coordinate information on the building surface is captured by lidar or photogrammetry technology to obtain the point cloud data of the target building, and the point cloud data directly reflects the geometric shape and spatial distribution characteristics of the building.

[0072] Step 2: Perform eigenvalue enhancement normalization on the point cloud data and set grids to tile on the normalized point cloud data to obtain the rasterized point cloud data.

[0073] In the present invention, by performing eigenvalue enhancement normalization on the coordinate values of the point cloud data, the differential features of the target building can be strengthened, and the normalized point cloud is projected onto a two-dimensional grid. Rasterization can reduce data complexity and support fast neighborhood operations.

[0074] Step 3: Use the adaptive neighborhood search algorithm to perform neighborhood search on all rasterized point cloud data to obtain the set of neighboring points for each rasterized point cloud data, construct a covariance matrix based on the set of neighboring points, and obtain the normal vector of each rasterized point cloud data by performing eigenvalue decomposition on the covariance matrix.

[0075] In the present invention, a search radius or a threshold value of the number of neighboring points is preset according to the density of the point cloud data of the target building, balancing the computational efficiency and feature integrity. The present invention also calculates the covariance matrix based on the set of neighboring points, and obtains the eigenvector corresponding to the minimum eigenvalue through eigenvalue decomposition as the normal vector, accurately depicting the local surface direction of each grid. The normal vector is the core basis for judging plane / slope features, and the adaptive search mechanism can ensure the robustness to point clouds with different densities.

[0076] Step 4: Determine the planar feature points and slope feature points by extracting the vertical components of the normal vectors of each rasterized point cloud data, and then count the number of planar feature points and slope feature points to determine the building type.

[0077] In the present invention, the dot product of the normal vector and the global upward vector is calculated to quantify the vertical degree of the surface orientation. By presetting the critical values of the normal vectors of the flat roof and the sloping roof, and counting the ratio of the planar feature points to the slope feature points, the automatic classification of the target building type is realized, providing a decision basis for subsequent differential modeling and avoiding manual intervention.

[0078] Step 5: Project the point cloud data of the target building onto the XOY plane, and extract the minimum X-axis / Y-axis boundaries of the point cloud data to generate the minimum bounding rectangle as the regularized contour boundary.

[0079] The present invention projects the point cloud data of the target building onto the XOY plane, and generates a minimum circumscribed rectangle by extracting the minimum X-axis / Y-axis boundaries of the point cloud data to generate a standardized contour boundary of the building, which serves as the base for flat roof modeling and also provides spatial constraints for the triangular grid of the pitched roof.

[0080] Step Six: Model according to the type of the target building:

[0081] If it is a flat roof building, merge the standardized contour boundary and the preset ground surface model to obtain the building model.

[0082] The present invention directly merges the standardized contour of the flat roof building and the ground surface model, simplifies the calculation process, and is applicable to buildings with simple roof structures.

[0083] If it is a pitched roof building, perform feature enhancement, resampling, and regularization operations on the point cloud data of the target building to obtain the regularized roof points to construct a triangular grid, and use the standardized contour boundary to merge the triangular grid and the preset ground surface model to obtain the building model.

[0084] The present invention highlights the ridge line by magnifying the Z-axis coordinate value of the point cloud data of the target building, eliminates noise by grid center repositioning, also identifies the ridge orientation through principal component analysis, reconstructs the roof topology through the index sequence, generates continuous triangular patches based on the regularized roof points, simulates the slope of the inclined surface drainage, and accurately depicts the geometric details of the pitched roof, improving the prediction accuracy of the roof runoff path in the rainstorm flood simulation.

[0085] Embodiment 2

[0086] Based on the same inventive concept as Embodiment 1, this embodiment introduces the implementation steps of a building modeling method for urban rainstorm flood simulation based on point cloud data, including:

[0087] Step 1: Obtain the point cloud data of the target building.

[0088] Buildings, as complex artificial surface elements, can be classified into two categories: pitched roof buildings and flat roof buildings according to the morphological characteristics of the roof surface. Since pitched roof buildings lack a water storage structure, the rainfall within their coverage will directly flow along the roof surface to the ground, generating continuous hydraulic interaction with the external environment. Therefore, in the rainstorm flood simulation, they need to participate in the hydrological process simulation as special terrain elements throughout the process. In contrast, since flat roof buildings have a water storage area with a pool-like structure at the top and usually are equipped with an internal pipe network system to directly drain accumulated water, the roof runoff of flat roof buildings will not participate in the rainstorm flood evolution process outside the building. Therefore, in this embodiment, spatial hollowing treatment is adopted during modeling to represent this hydrological characteristic.

[0089] Through the in-depth application of measured point cloud data, the automatic extraction and classification of roof features can be achieved: First, based on normal vector analysis, the inclined plane and flat plane feature regions are accurately identified, and then the building type determination is completed; Subsequently, different geometric models are constructed for different types - for sloping roof buildings, the roof topology structure needs to be completely retained to simulate surface runoff, while for flat roof buildings, the internal drainage characteristics are reflected through three-dimensional space hollowing treatment. This embodiment establishes a complete mapping relationship from the original point cloud to a high-precision hydrological model, providing key spatial decision-making support for urban rainstorm flood simulation.

[0090] Among them, Figure 2 is a schematic diagram of the point cloud data of the sloping roof building provided by this embodiment, Figure 3 is a schematic diagram of the point cloud data of the flat roof building provided by this embodiment.

[0091] Step 2: Perform eigenvalue enhancement normalization on the point cloud data and set grids to tile on the normalized point cloud data to obtain the rasterized point cloud data.

[0092] Among them, Figure 5 is a schematic diagram of the rasterized point cloud data of the sloping roof building provided by this embodiment, Figure 6 is a schematic diagram of the rasterized point cloud data of the flat roof building provided by this embodiment,

[0093] In this embodiment, performing eigenvalue enhancement normalization on the point cloud data and setting grids to tile on the normalized point cloud data to obtain the rasterized point cloud data includes:

[0094] Step 2.1: Normalize the point cloud data and set grids to tile on the normalized point cloud data.

[0095] Step 2.2: Enlarge the Z-axis coordinate value of the point cloud data with the largest Z-axis coordinate value in each grid according to a preset proportional multiple.

[0096] Step 2.3: Take the grid center as the center coordinate of the point cloud data with the largest Z-axis coordinate value to obtain the rasterized point cloud data.

[0097] Step 3: Use the adaptive neighborhood search algorithm to perform neighborhood search on all rasterized point cloud data to obtain the neighboring point set of each rasterized point cloud data, construct a covariance matrix based on the neighboring point set, and obtain the normal vector of each rasterized point cloud data through eigenvalue decomposition of the covariance matrix.

[0098] In this embodiment, using as Figure 4The adaptive neighborhood search algorithm shown performs neighborhood search on all rasterized point cloud data to obtain the set of neighboring points for each rasterized point cloud data, constructs a covariance matrix based on the set of neighboring points, and obtains the normal vector of each rasterized point cloud data through eigenvalue decomposition of the covariance matrix, including:

[0099] Step 3.1: Preset the search radius and the threshold of the number of neighboring points for each rasterized point cloud data according to the density of the rasterized point cloud data , where ;

[0100] Step 3.2: Sequentially traverse all rasterized point cloud data: If the number of neighboring points is less than or equal to the preset threshold of the number of neighboring points for each rasterized point cloud data , then use the KNN search algorithm to obtain neighboring points to obtain the set of neighboring points; If the number of neighboring points is greater than the preset threshold of the number of neighboring points for each rasterized point cloud data , then use the search radius to search for neighboring points to obtain the set of neighboring points

[0101] Step 3.3: Based on the set of neighboring points, calculate the centroid of all neighboring points of each rasterized point cloud data and construct a covariance matrix;

[0102] Step 3.4: Perform eigenvalue decomposition on the covariance matrix to obtain the normal vector of each rasterized point cloud data. The process of solving the normal vector is as Figure 7 shown.

[0103] Among them, Figure 8 is the schematic diagram of the solution result of the normal vector of the sloping roof building provided in this embodiment, Figure 9 is the schematic diagram of the solution result of the normal vector of the flat roof building provided in this embodiment

[0104] Step 4: Determine the plane feature points and inclined plane feature points by extracting the vertical components of the normal vectors of each rasterized point cloud data, and then count the number of plane feature points and inclined plane feature points to determine the building type.

[0105] Step 4.1: Extract the vertical components of the normal vectors of each rasterized point cloud data, including:

[0106] Step 4.1.1: Pre-define the global upward vector ;

[0107] Step 4.1.2: Take the dot product of the normal vector of each rasterized point cloud data and the search global upward vector and take the absolute value as the vertical component of the normal vector of each rasterized point cloud data.

[0108] Step 4.2: Determine the planar feature points and the inclined-plane feature points, including:

[0109] Step 4.2.1: Preset the critical value of the normal vector of the pitched roof and the critical value of the normal vector of the flat roof , where ;

[0110] Step 4.2.2: Judge the planar feature points and the inclined-plane feature points:

[0111] If the vertical component of the normal vector is greater than or equal to the critical value of the normal vector of the flat roof and less than or equal to 1, it is determined as a planar feature point;

[0112] If the vertical component of the normal vector is greater than the critical value of the normal vector of the pitched roof and less than the critical value of the normal vector of the flat roof , it is determined as an inclined-plane feature point;

[0113] If the vertical component of the normal vector is greater than or equal to 0 and less than or equal to the critical value of the normal vector of the pitched roof , it is determined as a wall surface or an abnormal point.

[0114] Step 4.3: Count the number of planar feature points and the number of inclined-plane feature points to determine the building type, including:

[0115] Step 4.3.1: Ignore the wall surfaces or abnormal points and count the number of planar feature points and the number of inclined-plane feature points;

[0116] Step 4.3.2: Judge the building type:

[0117] If the ratio of the number of planar feature points to the sum of the number of planar feature points and the number of inclined-plane feature points is greater than the preset ratio threshold, it is determined as a flat-roof building;

[0118] If the ratio of the number of planar feature points to the sum of the number of planar feature points and the number of inclined-plane feature points is less than or equal to the preset ratio threshold, it is determined as a pitched-roof building.

[0119] Step 5: Project the point cloud data of the target building onto the XOY plane, extract the minimum X-axis / Y-axis boundaries of the point cloud data to generate a minimum bounding rectangle, and use it as the regularized contour boundary.

[0120] Among them, Figure 10 is the schematic diagram of the result of projecting the pitched-roof building provided in this embodiment onto the XY plane and performing mesh simplification, Figure 11 is the schematic diagram of the result of projecting the flat-roof building provided in this embodiment onto the XY plane and performing mesh simplification.

[0121] In this embodiment, the point cloud data of the target building is projected onto the XOY plane, and the minimum X-axis / Y-axis boundaries of the point cloud data are extracted to generate a minimum bounding rectangle as the regularized contour boundary, including:

[0122] Step 5.1: Set a grid according to the density of the point cloud data of the target building.

[0123] Step 5.2: Use the grid to perform duplicate removal and downsampling operations on the point cloud data of the target building to obtain the gridded X-axis coordinates and Y-axis coordinates of the point cloud data of the target building.

[0124] Step 5.3: Traverse all the gridded X-axis coordinates and Y-axis coordinates, and respectively record the maximum and minimum values of the X-axis coordinates and the maximum and minimum values of the Y-axis coordinates to obtain the bounding rectangle boundary range of the point cloud data of the target building.

[0125] Step 5.4: Construct four sets of corner point coordinates according to the bounding rectangle boundary range of the point cloud data of the target building to generate a minimum bounding rectangle as the regularized contour boundary.

[0126] In this embodiment, the four sets of corner point coordinates include the lower left corner coordinate, the upper left corner coordinate, the lower right corner coordinate, and the upper right corner coordinate.

[0127] Among them, Figure 12 is a schematic diagram of the regularized contour boundary of the pitched roof building provided in this embodiment, Figure 13 is a schematic diagram of the regularized contour boundary of the flat roof building provided in this embodiment.

[0128] Step 6: Model according to the type of the target building:

[0129] If it is a flat roof building, merge the regularized contour boundary and the preset ground surface model to obtain a building model.

[0130] Among them, Figure 19 is a schematic diagram of the flat roof building model merged with the preset ground surface model provided in this embodiment.

[0131] After merging the regularized contour boundary and the preset ground surface model in this embodiment, it further includes dividing the preset ground surface model into different sub-regions and setting reflection boundaries for the regularized contour boundary.

[0132] If it is a pitched roof building, perform feature enhancement, resampling, and regularization operations on the point cloud data of the target building to obtain regularized roof points to construct a triangular grid, and use the regularized contour boundary to merge the triangular grid and the preset ground surface model to obtain a building model.

[0133] Among them, Figure 15It is a schematic diagram of the triangular grid of a pitched - roof building when the simulated roof surface is overly rough provided by this embodiment. Figure 16 It is a schematic diagram of the triangular grid of a pitched - roof building when the "wall - hanging" phenomenon appears on the simulated roof surface provided by this embodiment.

[0134] In this embodiment, after merging the triangular grid and the preset ground surface model according to the regularized contour boundary, an interactive boundary is set for the regularized contour boundary.

[0135] Among them, Figure 20 It is a schematic diagram of the pitched - roof building model after the triangular grid and the preset ground surface model are merged provided by this embodiment. Figure 21 It is a schematic diagram of the rain - flood simulation results of pitched - roof and flat - roof building models under uniform rainfall intensity provided by the embodiments of the present invention.

[0136] In this embodiment, the regularized roof points are obtained through feature enhancement, resampling, and regularization operations on the point - cloud data of the target building, including:

[0137] Step 6.1: Extract the Z - axis coordinate values of the point - cloud data of the target building using a grid, magnify the Z - axis coordinate values according to a preset proportional multiple, and take the center of the grid as the center coordinate of the point - cloud data with the largest Z - axis coordinate value to obtain the gridded point - cloud data.

[0138] Among them, Figure 14 It is a schematic diagram of the result of feature enhancement of the pitched - roof building provided by this embodiment.

[0139] Step 6.2: Use the principal - component analysis method to identify the ridge - line direction according to the gridded point - cloud data.

[0140] Step 6.3: Identify the connection line of the grid center points parallel to the ridge - line direction according to the ridge - line direction.

[0141] Step 6.4: Construct an index sequence that expands from the ridge - line to both sides of the connection line.

[0142] Step 6.5: Traverse the Z - axis coordinate values of the grid center points in the order of the index sequence.

[0143] Step 6.6: Reset the Z - axis coordinate values in the same index sequence to the average value of the Z - axis coordinates of the grid center points in the index sequence as the regularized roof points, as Figure 17 shown.

[0144] Among them, Figure 18 It is a schematic diagram of the triangular grid of a pitched - roof building constructed using the regularized roof points provided by the embodiments of the present invention.

[0145] Embodiment 3

[0146] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium with computer instructions stored thereon. When the computer instructions are executed by a processor, the steps of the method in Embodiment 1 or 2 above are implemented.

[0147] Embodiment 4

[0148] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product including computer instructions. When the computer instructions are executed by a processor, the steps of the method in Embodiment 1 or 2 above are implemented.

[0149] In summary of the above embodiments, the present invention effectively improves the data quality and processing efficiency by performing eigenvalue enhancement normalization and rasterization processing on the point cloud data of the target building; accurately calculates the normal vector by using the adaptive neighborhood search algorithm, and accurately distinguishes the plane feature points and slope feature points by combining the vertical component analysis, thereby determining the building type; the present invention also models by respectively using the regularized contour boundary to merge the ground surface model and constructing a triangular grid for different building types, significantly improving the accuracy and efficiency of building modeling, providing a more refined and reliable building model for urban rainstorm flood simulation, and helping to improve the scientificity and accuracy of urban rainstorm flood management.

[0150] The present invention realizes the optimization of water flow interaction between the ground surface and the building model by setting a reflection boundary for the flat roof and an interaction boundary for the sloping roof. Among them, the reflection boundary can accurately simulate the reflection flow path of rainwater at the building edge, avoiding the water flow fault caused by the lack of boundary in traditional modeling. The interaction boundary enables real-time coupling calculation of water flow parameters between the triangular grid roof model and the ground surface model through a dynamic data exchange mechanism, significantly improving the authenticity of runoff simulation under complex urban terrains, especially reducing the water flow path error in the building-intensive area.

[0151] The present invention automatically switches between the KNN search and radius search modes according to the point cloud density by presetting the search radius and the threshold of the number of neighboring points, improving the recognition accuracy of plane feature points and slope feature points, and at the same time reducing the calculation time consumption, which is applicable to the processing of large-scale urban-level point cloud data and provides a feasible technical support for real-time rainstorm flood simulation.

[0152] The present invention effectively captures the geometric features of the sloping roof and eliminates point cloud noise by grid extraction and Z-axis coordinate value amplification processing, combined with principal component analysis to identify the ridge line direction. Further, by constructing an index sequence and resetting the Z-axis coordinate value to the average value of the grid center, the regularization of roof points is realized, significantly improving the geometric accuracy and topological consistency of the sloping roof triangular grid model, providing a high-precision roof drainage surface model for urban rainstorm flood simulation, being able to more accurately predict the rainwater runoff path and roof water collection efficiency, and providing key data support for urban flood control and drainage planning.

[0153] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0154] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0155] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0157] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present invention and without departing from the spirit and scope protected by the present invention's claims, can also make many forms, and all of these fall within the protection scope of the present invention.

Claims

1. A building modeling method for urban rainstorm flood simulation based on point cloud data, characterized in that, Including: Obtain the point cloud data of the target building; Perform eigenvalue enhancement normalization on the point cloud data and set grids to tile on the normalized point cloud data to obtain the rasterized point cloud data; Use the adaptive neighborhood search algorithm to perform neighborhood search on all rasterized point cloud data to obtain the set of neighboring points for each rasterized point cloud data, construct a covariance matrix based on the set of neighboring points, and obtain the normal vector of each rasterized point cloud data by performing eigenvalue decomposition on the covariance matrix; Determine the planar feature points and inclined plane feature points by extracting the vertical components of the normal vectors of each rasterized point cloud data, and then count the number of planar feature points and inclined plane feature points to determine the building type; Project the point cloud data of the target building onto the XOY plane, extract the minimum X-axis / Y-axis boundaries of the point cloud data to generate the minimum bounding rectangle as the regularized contour boundary; Model according to the type of the target building: If it is a flat-roof building, merge the regularized contour boundary and the preset ground surface model to obtain the building model; If it is a pitched-roof building, perform feature enhancement, resampling, and regularization operations on the point cloud data of the target building to obtain the regularized roof points to construct a triangular grid, and use the regularized contour boundary to merge the triangular grid and the preset ground surface model to obtain the building model.

2. The building modeling method for urban rainstorm and flood simulation based on point cloud data according to claim 1, wherein After merging the regularized contour boundary and the preset ground surface model, it further includes dividing the preset ground surface model into different sub-regions and setting reflection boundaries for the regularized contour boundary.

3. The building modeling method for urban stormwater simulation based on point cloud data according to claim 1, wherein After merging the triangular grid and the preset ground surface model according to the regularized contour boundary, set interaction boundaries for the regularized contour boundary.

4. The building modeling method for urban rainstorm and flood simulation based on point cloud data according to claim 1, characterized in that Performing eigenvalue enhancement normalization on the point cloud data and setting grids to tile on the normalized point cloud data to obtain the rasterized point cloud data includes: Normalize the point cloud data and set grids to tile on the normalized point cloud data; Enlarge the Z-axis coordinate value of the point cloud data with the largest Z-axis coordinate value in each grid according to a preset proportional multiple; Take the grid center as the center coordinate of the point cloud data with the largest Z-axis coordinate value to obtain the rasterized point cloud data.

5. The building modeling method for urban rainstorm flood simulation based on point cloud data according to claim 1, characterized in that, Using the adaptive neighborhood search algorithm to perform neighborhood search on all rasterized point cloud data to obtain the set of neighboring points for each rasterized point cloud data, construct a covariance matrix based on the set of neighboring points, and obtain the normal vector of each rasterized point cloud data by performing eigenvalue decomposition on the covariance matrix includes: Preset a search radius according to the density of the rasterized point cloud data and a proximity point quantity threshold for each rasterized point cloud data , where ; Sequentially traverse all rasterized point cloud data: If the number of neighboring points is less than or equal to the preset threshold of the number of neighboring points for each rasterized point cloud data , then use the KNN search algorithm to obtain neighboring points to obtain a set of neighboring points; If the number of neighboring points is greater than the preset threshold of the number of neighboring points for each rasterized point cloud data , then search for neighboring points using a search radius to obtain a set of neighboring points; Based on the set of neighboring points, calculate the centroid of all neighboring points of each rasterized point cloud data and construct a covariance matrix; Perform eigenvalue decomposition on the covariance matrix to obtain the normal vector of each rasterized point cloud data.

6. The building modeling method for urban rainstorm and flood simulation based on point cloud data according to claim 1, wherein The method for extracting the vertical component of the normal vector of each rasterized point cloud data includes: Predefined global upward vector ; Take the dot product of the normal vector of each rasterized point cloud data and the search global upward vector and take the absolute value as the vertical component of the normal vector of each rasterized point cloud data.

7. The building modeling method for urban rainstorm and flood simulation based on point cloud data according to claim 6, characterized in that The method for determining the planar feature points and inclined plane feature points includes: Preset critical value of the normal vector of the sloping roof Preset critical value of the normal vector of the flat roof , where ; If the vertical component of the normal vector is greater than or equal to the critical value of the normal vector of the flat roof and less than or equal to 1, it is determined as a planar feature point; If the vertical component of the normal vector is greater than the critical value of the normal vector of the pitched roof and less than the critical value of the normal vector of the flat roof , it is determined as an inclined plane feature point; If the vertical component of the normal vector is greater than or equal to 0 and less than or equal to the critical value of the normal vector of the pitched roof , it is determined as a wall surface or an abnormal point.

8. The building modeling method for urban stormwater simulation based on point cloud data according to claim 7, wherein Determine the building type by counting the number of planar feature points and the number of inclined-plane feature points, including: Ignore walls or abnormal points and count the number of planar feature points and the number of inclined-plane feature points; If the ratio of the number of planar feature points to the sum of the number of planar feature points and the number of inclined-plane feature points is greater than a preset ratio threshold, it is determined as a flat-roof building; If the ratio of the number of planar feature points to the sum of the number of planar feature points and the number of inclined-plane feature points is less than or equal to the preset ratio threshold, it is determined as a pitched-roof building.

9. The building modeling method for urban rainstorm flood simulation based on point cloud data according to claim 1, characterized in that, Project the point cloud data of the target building onto the XOY plane, and extract the minimum X-axis / Y-axis boundaries of the point cloud data to generate a minimum bounding rectangle as the regularized contour boundary, including: Set a grid according to the density of the point cloud data of the target building; Use the grid to perform duplicate removal and downsampling operations on the point cloud data of the target building to obtain the gridded X-axis coordinates and Y-axis coordinates of the point cloud data of the target building; Traverse all the gridded X-axis coordinates and Y-axis coordinates, and record the maximum and minimum values of the X-axis coordinates and the maximum and minimum values of the Y-axis coordinates respectively to obtain the bounding rectangle boundary range of the point cloud data of the target building; Construct four sets of corner coordinates according to the bounding rectangle boundary range of the point cloud data of the target building to generate a minimum bounding rectangle as the regularized contour boundary; The four sets of corner coordinates include the lower-left corner coordinates, the upper-left corner coordinates, the lower-right corner coordinates, and the upper-right corner coordinates.

10. The building modeling method for urban stormwater simulation based on point cloud data according to claim 1, wherein Obtain the regularized roof points through feature enhancement, resampling, and regularization operations on the point cloud data of the target building, including: Use a grid to extract the Z-axis coordinate values of the point cloud data of the target building, magnify the Z-axis coordinate values according to a preset ratio multiple, and use the center of the grid as the center coordinate of the point cloud data with the largest Z-axis coordinate value to obtain the gridded point cloud data; Use the principal component analysis method to identify the ridge line direction according to the gridded point cloud data; Identify the connection line of the grid center points parallel to the ridge line direction according to the ridge line direction; Construct an index sequence that extends from the ridge line to both sides of the connection line; Traverse the Z-axis coordinate values of the grid center points in the order of the index sequence: Reset the Z-axis coordinate values in the same index sequence to the average value of the Z-axis coordinates of the grid center points in the index sequence as the regularized roof points.

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