Methods, devices, computer equipment and storage media for dimensional reduction of curved curtain walls

Abstract

This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, and storage medium for dimensionality reduction of curved curtain walls. The method includes: acquiring curved curtain wall blocks of the curved curtain wall to be dimensionality reduced; determining the principal direction and normal vector corresponding to each curved curtain wall block; determining the projection plane corresponding to each curved curtain wall block according to the principal direction and normal vector; projecting each curved curtain wall block onto the corresponding projection plane to obtain dimensionality-reduced planar blocks; and stitching together the planar blocks to obtain the corresponding planar curtain wall of the curved curtain wall to be dimensionality reduced. This method can reduce the dimensionality of curved curtain walls.

Description

Technical Field

[0001] This application relates to the field of building information technology, and in particular to a method, apparatus, computer equipment, and storage medium for dimensionality reduction processing of curved curtain walls. Background Technology

[0002] With the development of the times, large or high-rise buildings often feature lightweight walls with decorative effects, namely curtain walls. In architectural design, these curtain walls can be designed and drawn according to actual application needs.

[0003] In traditional methods, the curtain walls drawn are usually three-dimensional curved surfaces. How to reduce the dimensionality of the resulting three-dimensional curved curtain walls has become an urgent problem to be solved at this stage. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for dimensionality reduction of curved curtain walls, which can address the aforementioned technical problems.

[0005] A method for dimensional reduction processing of curved curtain walls, the method comprising:

[0006] Obtain the curved curtain wall blocks to be reduced in dimension;

[0007] Based on each curved curtain wall block, determine the principal direction and normal vector of each curved curtain wall block;

[0008] Based on each principal direction and normal vector, determine the projection plane corresponding to each curved curtain wall block;

[0009] Each curved curtain wall block is projected onto its corresponding projection plane to obtain the dimensionality-reduced planar blocks.

[0010] By piecing together the various planar blocks, a planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension is obtained.

[0011] In one embodiment, each curved curtain wall block is projected onto a corresponding projection plane to obtain dimensionality-reduced planar blocks, including:

[0012] Establish the projection relationships between each curved curtain wall block and its corresponding projection plane;

[0013] Based on the projection relationships, each point of each curved curtain wall block is projected onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks.

[0014] In one embodiment, the planar blocks are spliced ​​together to obtain the planar curtain wall corresponding to the dimension-reduced curved surface curtain wall, including:

[0015] Obtain the connection relationships between the various curved curtain wall blocks;

[0016] Based on the connection relationship, the planar blocks are spliced ​​together to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension.

[0017] In one embodiment, based on each curved curtain wall segment, the principal direction and normal vector corresponding to each curved curtain wall segment are determined, including:

[0018] Obtain the coordinates of each key point of each curved curtain wall segment;

[0019] Based on the coordinates of each key point, determine the coordinates of the center point of each curved curtain wall block;

[0020] Based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block, construct the covariance matrix corresponding to each curved curtain wall block.

[0021] Based on the covariance matrix of each curved curtain wall segment, the principal direction and normal vector of each curved curtain wall segment are determined.

[0022] In one embodiment, based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall segment, a covariance matrix corresponding to each curved curtain wall segment is constructed, including:

[0023] Based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block, determine the coordinate difference between the key point coordinates and the corresponding center point coordinates of each curved curtain wall block.

[0024] Based on the coordinate differences of each curved curtain wall segment, a covariance matrix corresponding to each curved curtain wall segment is constructed.

[0025] In one embodiment, based on the covariance matrix of each curved curtain wall segment, the principal direction and normal vector of each curved curtain wall segment are determined, including:

[0026] Solve for each covariance matrix to determine the eigenvalues ​​and eigenvectors of each covariance matrix;

[0027] The eigenvector corresponding to the smallest eigenvalue of each covariance matrix is ​​determined as the normal vector of the corresponding curved curtain wall block;

[0028] The direction corresponding to the eigenvector of the largest eigenvalue of each covariance matrix is ​​determined as the main direction of the corresponding curved curtain wall block.

[0029] In one embodiment, obtaining the curved curtain wall blocks of the curved curtain wall to be dimension-reduced includes:

[0030] Obtain the curved curtain wall to be reduced in dimension;

[0031] The planar data of the reduced-dimensional curved curtain wall is generated by unfolding the planar data.

[0032] The planar data is segmented to obtain multiple planar curtain wall blocks;

[0033] Establish the mapping relationships between each planar curtain wall segment and the curved curtain wall to be reduced in dimension;

[0034] Based on each planar curtain wall block and its corresponding mapping relationship, construct the curved curtain wall block corresponding to the curved curtain wall to be reduced in dimension.

[0035] A curved curtain wall dimension reduction processing device, the device comprising:

[0036] The curved curtain wall block acquisition module is used to acquire curved curtain wall blocks of the curved curtain wall to be reduced in dimension;

[0037] The main direction and normal vector determination module is used to determine the main direction and normal vector of each curved curtain wall block based on each curved curtain wall block.

[0038] The projection plane determination module is used to determine the projection plane corresponding to each curved curtain wall block based on each principal direction and normal vector.

[0039] The planar block generation module is used to project each curved curtain wall block onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks.

[0040] The splicing module is used to splice together the various planar blocks to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension.

[0041] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any of the above embodiments.

[0042] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0043] The aforementioned method, apparatus, computer equipment, and storage medium for dimensionality reduction of curved curtain walls acquire small curved curtain wall blocks of the curved curtain wall to be reduced in dimension. Then, based on each small curved curtain wall block, the principal direction and normal vector of that block are determined. Based on these principal directions and normal vectors, the corresponding projection plane for each small curved curtain wall block is determined. Each small curved curtain wall block is then projected onto its corresponding projection plane to obtain dimensionality-reduced planar blocks. These planar blocks are then stitched together to obtain the planar curtain wall of the curved curtain wall to be reduced in dimension. Thus, based on the acquired small curved curtain wall blocks, the principal direction and normal vector of each small curved curtain wall block are calculated to determine the projection plane. The small curved curtain wall blocks are then projected onto their corresponding projection planes to obtain dimensionality-reduced planar blocks. The stitching of these planar blocks yields the planar curtain wall of the curved curtain wall to be reduced in dimension, achieving dimensionality reduction of curved curtain walls. This solves the problem of incompatibility between curved curtain walls and engineering applications in traditional methods, improving the accuracy of the constructed curtain wall. Attached Figure Description

[0044] Figure 1 This is an application scenario diagram of the curved curtain wall dimensional reduction processing method in one embodiment;

[0045] Figure 2 This is a flowchart illustrating a method for dimensional reduction processing of curved curtain walls in one embodiment;

[0046] Figure 3 This is a schematic diagram of a curved curtain wall to be dimension-reduced in one embodiment;

[0047] Figure 4 This is a schematic diagram of multiple curved curtain wall blocks in one embodiment;

[0048] Figure 5 This is a schematic diagram of a curved curtain wall block in one embodiment;

[0049] Figure 6 This is a schematic diagram of planar data in one embodiment;

[0050] Figure 7 This is a schematic diagram of planar data in another embodiment;

[0051] Figure 8 This is a structural block diagram of a curved curtain wall dimension reduction processing device in one embodiment;

[0052] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] The dimensionality reduction method for curved curtain walls provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. Terminal 102 can receive user instructions and send them to server 104 to instruct the server to perform dimensionality reduction processing on the curved curtain wall. After receiving the user instructions, server 104 can obtain the curved curtain wall blocks to be reduced in dimensionality, and determine the principal direction and normal vector of each curved curtain wall block. Further, server 104 can determine the projection plane corresponding to each curved curtain wall block according to each principal direction and normal vector, and project each curved curtain wall block onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks. Then, server 104 can stitch together the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimensionality. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices, and server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0055] In one embodiment, such as Figure 2 As shown, a method for dimensional reduction of curved curtain walls is provided, which can prepare for construction. This method can be applied to... Figure 1 Taking the server in the example, the following steps are included:

[0056] Step S202: Obtain the curved curtain wall block of the curved curtain wall to be reduced in dimension.

[0057] Among them, the curved surface curtain wall to be reduced in dimension refers to the three-dimensional curved surface curtain wall (which can be a three-dimensional graphic) generated in 3D software, for reference. Figure 3 As shown in the figure, it can specifically be a three-dimensional curved surface.

[0058] In curtain wall design, what's needed isn't a single, monolithic curtain wall, but rather a segmented one. By assembling these segmented sections into a cohesive whole, a more realistic display effect can be achieved. Curved curtain wall modules refer to the individual pieces that make up the curved curtain wall, corresponding to the construction units used in later stages of construction. These modules can be generated by dividing the curved curtain wall into smaller sections. For details on curved curtain wall modules, please refer to... Figure 4 As shown, the small sections of the curved curtain wall can also be three-dimensional graphics.

[0059] In this embodiment, the server can obtain the curved curtain wall blocks to be reduced in dimension according to the terminal's instructions and perform subsequent processing.

[0060] Step S204: Based on each curved curtain wall block, determine the principal direction and normal vector of each curved curtain wall block.

[0061] In this embodiment, after the server obtains the curved curtain wall blocks, it can solve for the principal direction and normal vector of each curved curtain wall block.

[0062] Here, the principal direction refers to the principal direction of the curved curtain wall block relative to the reference coordinate system, while the normal vector refers to the normal vector of the plane corresponding to the curved curtain wall block.

[0063] In this embodiment, when the server determines the principal direction and normal vector of each curved curtain wall block, it can do so in parallel using multiple threads, and then perform subsequent processing.

[0064] Step S206: Determine the projection plane corresponding to each curved curtain wall block based on each principal direction and normal vector.

[0065] In this embodiment, after obtaining the principal direction and normal vector corresponding to the curved curtain wall block, the server can determine the projection plane of each curtain wall block based on the obtained principal direction and normal vector.

[0066] Step S208: Project each curved curtain wall block onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks.

[0067] Specifically, the server can establish the projection relationship between each point in the curved curtain wall block and the projection plane, and then project each point in the curved curtain wall block onto the projection plane based on the projection relationship to generate the corresponding planar block of the curved curtain wall block, thus realizing the dimensionality reduction process from curved surface to plane.

[0068] Step S210: The planar blocks are spliced ​​together to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension.

[0069] In this embodiment, the server can stitch together the obtained planar blocks to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension.

[0070] Those skilled in the art will understand that the assembled planar curtain wall is essentially a curved curtain wall, but not entirely a curved surface. Each small section of the planar curtain wall is a small plane rather than a curved surface, thus ensuring that the final planar curtain wall meets the actual application requirements of the project, guiding construction and improving the accuracy of the constructed curtain wall.

[0071] The aforementioned method for dimensionality reduction of curved curtain walls involves acquiring small curved curtain wall blocks to be reduced in dimensionality. Then, based on each block, the principal direction and normal vector are determined. Following these principal directions and normal vectors, the corresponding projection plane for each block is determined. Each block is then projected onto its corresponding projection plane to obtain dimensionality-reduced planar blocks. These planar blocks are then stitched together to obtain the planar curtain wall of the curved curtain wall to be reduced in dimensionality. Thus, based on the acquired curved curtain wall blocks, the principal direction and normal vector of each block are calculated to determine the projection plane. The block is then projected onto its corresponding projection plane to obtain the dimensionality-reduced planar blocks. The stitching of these planar blocks yields the planar curtain wall of the curved curtain wall to be reduced in dimensionality, achieving dimensionality reduction of the curved curtain wall. This solves the problem of incompatibility between curved curtain walls and engineering applications in traditional methods, improving the accuracy of the constructed curtain wall.

[0072] In one embodiment, projecting each curved curtain wall block (three-dimensional graphic) onto the corresponding projection plane to obtain each planar block after dimensionality reduction may include: establishing projection relationships between each curved curtain wall block and the corresponding projection plane; and based on the projection relationships, projecting each point of each curved curtain wall block onto the corresponding projection plane to obtain each planar block after dimensionality reduction.

[0073] In this embodiment, each curved curtain wall segment may include corresponding key points, for example, referring to... Figure 5 In (a), for a small triangular curved surface curtain wall block, the corresponding key points are three vertices, namely points A1, A2, and A3. (Refer to...) Figure 5 In (b), for a small quadrilateral curved curtain wall block, the corresponding key points are the four vertices, namely points B1, B2, B3, and B4.

[0074] In this embodiment, the server establishes the projection relationship between each curved curtain wall block and its corresponding projection plane based on the key points corresponding to each curved curtain wall block. Then, based on each projection relationship, each point of each curved curtain wall block is projected onto the corresponding projection plane to obtain each planar block after dimensionality reduction.

[0075] Specifically, the server can traverse each point on the curved curtain wall block based on the projection relationship, and project each point onto the projection plane to obtain the dimensionality-reduced planar block.

[0076] In one embodiment, splicing together the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension can include: obtaining the connection relationship between the curved curtain wall blocks; and splicing together the planar blocks based on the connection relationship to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension.

[0077] In this embodiment, there are certain connections between the curved curtain wall segments to be reduced in dimension, and there should also be corresponding connections between the converted planar segments. For example, continue to refer to... Figure 4 If curved curtain wall block 1 and curved curtain wall block 2 are connected, then their corresponding planar blocks should also be connected.

[0078] In this embodiment, the server can obtain the connection relationship between each curved curtain wall block and, based on the connection relationship, splice the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension.

[0079] In this embodiment, the server can also stitch together the generated planar blocks based on the key point coordinates of each block to obtain the planar curtain wall after dimensionality reduction processing of the curved surface curtain wall to be reduced.

[0080] In one embodiment, determining the principal direction and normal vector of each curved curtain wall segment based on each curved curtain wall segment may include: obtaining the coordinates of each key point of each curved curtain wall segment; determining the coordinates of the center point of each curved curtain wall segment based on the coordinates of each key point; constructing the covariance matrix of each curved curtain wall segment based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall segment; and determining the principal direction and normal vector of each curved curtain wall segment based on the covariance matrix of each curved curtain wall segment.

[0081] As mentioned earlier, curved curtain wall blocks can include corresponding key points. For triangular curved curtain wall blocks, the corresponding key points are three vertices, and for quadrilateral curved curtain wall blocks, the corresponding key points are four vertices.

[0082] In this embodiment, the server can obtain the coordinates of each key point of each curved curtain wall block, and determine the coordinates of the center point of each curved curtain wall block based on the coordinates of each key point. For example, for a triangular curved curtain wall block, the server can add the vertex coordinates of the three vertices and calculate the average value to obtain the coordinates of the center point of the triangular curved curtain wall block. The same applies to the quadrilateral curved curtain wall block, and will not be elaborated here.

[0083] Furthermore, after determining the center point coordinates of each curtain wall segment, the server can construct the covariance matrix of each curved curtain wall segment based on the center point coordinates and the key point coordinates of each curved curtain wall segment.

[0084] In this embodiment, the server can construct the covariance matrix corresponding to each curved curtain wall block, and determine the principal direction and normal vector of each curved curtain wall block based on the covariance matrix.

[0085] In one embodiment, constructing a covariance matrix for each curved curtain wall block based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block may include: determining the coordinate difference between the coordinates of the key points of each curved curtain wall block and the coordinates of the corresponding center point based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block; and constructing a covariance matrix for each curved curtain wall block based on the coordinate difference between each curved curtain wall block.

[0086] In this embodiment, after the server obtains the coordinates of the center point and the coordinates of each key point, it can calculate the difference between the coordinates of each key point and the coordinates of the center point.

[0087] In this embodiment, the curved curtain wall blocks are three-dimensional curved surfaces, and the coordinates of their corresponding center points and key points are all three-dimensional coordinates. The server can obtain the three-dimensional coordinate difference between each key point and the center point.

[0088] Furthermore, the server can construct the covariance matrix of the corresponding curved curtain wall block based on the obtained coordinate difference.

[0089] In one embodiment, determining the principal direction and normal vector of each curved curtain wall block based on the covariance matrix of each curved curtain wall block may include: solving for each covariance matrix to determine the eigenvalues ​​and eigenvectors of each covariance matrix; determining the eigenvector corresponding to the smallest eigenvalue of each covariance matrix as the normal vector of the corresponding curved curtain wall block; and determining the direction corresponding to the eigenvector corresponding to the largest eigenvalue of each covariance matrix as the principal direction of the corresponding curved curtain wall block.

[0090] In this embodiment, after the server constructs the covariance matrix corresponding to each curved curtain wall block, it can solve the covariance matrix by using the singular value decomposition method to obtain the multiple eigenvalues ​​and eigenvectors corresponding to each covariance matrix.

[0091] For example, if the constructed covariance matrix is ​​represented as H, the server can solve for the eigenvectors using the following formula (1).

[0092] [USV]=SVD(H) (1)

[0093] Where U, S, and V represent the eigenvectors of the corresponding covariance matrix H. The specific decomposition method will be explained in detail below.

[0094] In this embodiment, the covariance matrix H is a 2*3 matrix, defined as H = USV. T .

[0095] Where U is a 2x2 matrix; S is a 2x3 matrix, with all elements except those on the main diagonal being 0, and each element on the main diagonal is called a singular value; V is a 3x3 matrix. Both U and V are unitary matrices, meaning that V satisfies... T V = I, U T U = I.

[0096] Furthermore, we can perform matrix multiplication between the transpose of H and H, which will result in a 3x3 square matrix H. T H.

[0097] Furthermore, regarding the H formation T H is subjected to eigenvalue decomposition, and the square matrix H is used. T The eigenvalues ​​and eigenvectors of H satisfy the following formula (2).

[0098] (H T H)v i =λ i v i (2)

[0099] Based on the above formula (2), the corresponding square matrix H can be obtained. T H has three eigenvectors v.

[0100] In this embodiment, the server can transmit the array H T The three eigenvectors of H form a 3*3 matrix V, which is the matrix V in formula (1).

[0101] Furthermore, the server can perform matrix multiplication on H and the transpose of H to obtain a 2x2 square matrix HH. T .

[0102] In this embodiment, the server can perform HH on the array. T Perform eigenvalue decomposition to obtain the corresponding eigenvalues ​​and eigenvectors. For details, please refer to the following formula (3).

[0103] (HH T )u i =λ i u i (3)

[0104] Based on the above formula (3), the corresponding square matrix HH can be obtained. T The two feature vectors u.

[0105] In this embodiment, the server can transmit the array HH T The two eigenvectors form a 2*2 matrix U, which is the matrix U in formula (1).

[0106] Furthermore, the server can substitute matrices U and V into the above formula (1) to obtain the corresponding S.

[0107] In this embodiment, after the server determines the eigenvalues ​​and eigenvectors of the corresponding covariance matrix, it can sort the solved eigenvalues, determine the eigenvectors corresponding to the eigenvalues ​​that satisfy the first preset condition as the first eigenvectors, and determine the direction corresponding to the first eigenvectors as the main direction of the surface graphic.

[0108] Similarly, the server can determine the eigenvector corresponding to the eigenvalue that satisfies the first preset condition as the second eigenvector, and determine the second eigenvector as the normal vector of the surface graph.

[0109] In this embodiment, the first preset condition is that the feature value is the largest, and the second preset condition is that the feature value is the smallest. In other embodiments, other determination methods may also be used, and this application does not limit them.

[0110] In one embodiment, obtaining the curved curtain wall blocks of the dimensionality-reduced curved curtain wall may include: obtaining the dimensionality-reduced curved curtain wall; unfolding the dimensionality-reduced curved curtain wall into a plane to generate corresponding planar data; segmenting the planar data to obtain multiple planar curtain wall blocks; establishing mapping relationships between each planar curtain wall block and the dimensionality-reduced curved curtain wall; and constructing the corresponding curved curtain wall blocks of the dimensionality-reduced curved curtain wall based on each planar curtain wall block and its corresponding mapping relationships.

[0111] In this embodiment, the server can obtain the curved curtain wall to be reduced in dimension based on the dimensionality reduction command sent by the terminal. The curved curtain wall to be reduced in dimension obtained by the server is a complete three-dimensional curved curtain wall.

[0112] In this embodiment, after the server obtains the complete curved curtain wall to be reduced in dimension, it can unfold it into a planar curtain wall, that is, unfold it into planar data.

[0113] In this embodiment, the server can use a planar parametric method to unfold the three-dimensional curved curtain wall into planar data, that is... Figure 3 The curved curtain wall shown unfolds as Figure 6 The plane shown.

[0114] In this embodiment, depending on different application requirements, the server can use the area preservation method, that is, to ensure that the area error is minimized when unfolding the plane, so as to obtain the planar data of the corresponding curved curtain wall.

[0115] Furthermore, the server can use triangulation to divide the unfolded planar data into multiple triangular faces corresponding to the planar data. For example... Figure 7 As shown.

[0116] In this embodiment, since the planar data is two-dimensional data, the resulting triangular faces are also two-dimensional planar data.

[0117] In this embodiment, the server can perform segmentation based on user needs or the needs of specific applications. For example, it can split planar data into a preset number of triangular faces or into triangular faces of a certain size.

[0118] Furthermore, the server can divide multiple triangular faces according to preset rules to form multiple planar curtain wall blocks.

[0119] Here, a planar curtain wall panel can refer to a module composed of at least one triangular facet, for example, see [reference to...]. Figure 7 The small blocks of the planar curtain wall can be modules composed of points L0, L1, L2, L3, etc. in the figure, or modules corresponding to the triangular faces of points M0, M1, M2.

[0120] In this embodiment, the server can preset segmentation rules and, based on these rules, segment the triangular faces to generate corresponding planar curtain wall blocks. For example, the triangular faces can be segmented according to their area or the number of triangular faces.

[0121] Furthermore, the server can form a segmented coordinate library based on multiple planar curtain wall blocks, and establish mapping relationships between each planar curtain wall block and the curved curtain wall to be reduced in dimension based on the segmented coordinate library.

[0122] Specifically, after the server splits the curtain wall into individual planar blocks, it can obtain the location of feature points of each planar curtain wall block, such as the coordinates of the vertices, and store them in the database to form a segmentation coordinate library.

[0123] Furthermore, the server can establish corresponding mapping relationships based on the locations of each feature point stored in the segmentation coordinate library.

[0124] In this embodiment, for each planar curtain wall segment, a mapping relationship can be established between it and the three-dimensional curved surface curtain wall to be reduced in dimension; that is, one planar curtain wall segment corresponds to one mapping relationship. In other embodiments, other mapping relationships can also be used. For example, all mapping relationships can be weighted and summed to obtain the final mapping relationship, where there is only one mapping relationship between all planar curtain wall segments and the surface to be segmented.

[0125] In this embodiment, after establishing the mapping relationships, the server can construct the segmented curved curtain wall corresponding to the dimensionality-reduced curved curtain wall based on each planar curtain wall block and the corresponding mapping relationships, thus obtaining multiple curved curtain wall blocks.

[0126] In this embodiment, after establishing the mapping relationship between planar curtain wall blocks and the curved curtain wall to be reduced in dimension, the server can map the planar curtain wall blocks onto the curved curtain wall to be reduced in dimension based on each mapping relationship. Based on the segmented surfaces of each corresponding planar curtain wall block obtained from the mapping, the server obtains the segmented curved curtain wall corresponding to the curved curtain wall to be reduced in dimension, thus obtaining multiple curved curtain wall blocks, as shown below. Figure 4 As shown.

[0127] In this embodiment, the mapping of each planar curtain wall block can be performed in parallel. That is, the server can map multiple planar curtain wall blocks in parallel and then generate segmented curved curtain wall blocks to improve the segmentation efficiency of the dimensionality-reduced curved curtain wall.

[0128] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0129] In one embodiment, such as Figure 8 As shown, a curved curtain wall dimensionality reduction processing device is provided, including: a curved curtain wall small block acquisition module 100, a main direction and normal vector determination module 200, a projection plane determination module 300, a planar small block generation module 400, and a splicing module 500, wherein:

[0130] The curved curtain wall block acquisition module 100 is used to acquire the curved curtain wall blocks of the curved curtain wall to be reduced in dimension.

[0131] The main direction and normal vector determination module 200 is used to determine the main direction and normal vector of each curved curtain wall block based on each curved curtain wall block.

[0132] The projection plane determination module 300 is used to determine the projection plane corresponding to each curved curtain wall block based on each principal direction and normal vector.

[0133] The planar block generation module 400 is used to project each curved curtain wall block onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks.

[0134] The splicing module 500 is used to splice together the various planar blocks to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension.

[0135] In one embodiment, the planar block generation module 400 may include:

[0136] The projection relationship establishment submodule is used to establish the projection relationships between each curved curtain wall block and its corresponding projection plane.

[0137] The planar block generation submodule is used to project each point of each curved curtain wall block onto the corresponding projection plane based on the projection relationship, so as to obtain the dimensionality-reduced planar blocks.

[0138] In one embodiment, the splicing module 500 may include:

[0139] The connection relationship acquisition submodule is used to obtain the connection relationships between each curved curtain wall block.

[0140] The planar curtain wall generation submodule is used to splice together the planar blocks based on the connection relationship to obtain the planar curtain wall corresponding to the dimensionality reduction curved surface curtain wall.

[0141] In one embodiment, the principal direction and normal vector determination module 200 may include:

[0142] The key point coordinate acquisition submodule is used to obtain the coordinates of each key point of each curved curtain wall block.

[0143] The center point coordinate determination submodule is used to determine the center point coordinates of each curved curtain wall block based on the coordinates of each key point.

[0144] The covariance matrix construction submodule is used to construct the covariance matrix of each curved curtain wall block based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block.

[0145] The main direction and normal vector determination submodule is used to determine the main direction and normal vector of each curved curtain wall block based on the covariance matrix of each curved curtain wall block.

[0146] In one embodiment, the covariance matrix construction submodule may include:

[0147] The coordinate difference determination unit is used to determine the coordinate difference between the key point coordinates and the corresponding center point coordinates of each curved curtain wall block, based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block.

[0148] The covariance matrix construction unit is used to construct the covariance matrix corresponding to each curved curtain wall block based on the coordinate difference between each curved curtain wall block.

[0149] In one embodiment, the principal direction and normal vector determination submodule may include:

[0150] The eigenvalue and eigenvector determination unit is used to solve for each covariance matrix and determine the corresponding eigenvalues ​​and eigenvectors of each covariance matrix.

[0151] The normal vector determination unit is used to determine the eigenvector corresponding to the smallest eigenvalue of each covariance matrix as the normal vector of the corresponding curved curtain wall block.

[0152] The main direction determination unit is used to determine the direction corresponding to the eigenvector of the largest eigenvalue of each covariance matrix as the main direction of the corresponding curved curtain wall block.

[0153] In one embodiment, the curved curtain wall small block acquisition module 100 includes:

[0154] The submodule for obtaining the curved curtain wall to be reduced in dimension is used to obtain the curved curtain wall to be reduced in dimension.

[0155] The planar unfolding submodule is used to unfold the curved curtain wall to be reduced in dimension into a planar shape and generate the corresponding planar data.

[0156] The segmentation submodule is used to segment the planar data to obtain multiple planar curtain wall blocks.

[0157] The mapping relationship establishment submodule is used to establish the mapping relationships between each planar curtain wall block and the curved curtain wall to be reduced in dimension.

[0158] The curved curtain wall block generation submodule is used to construct the curved curtain wall blocks corresponding to the curved curtain wall to be reduced in dimension based on each planar curtain wall block and its corresponding mapping relationship.

[0159] Specific limitations regarding the curved curtain wall dimensional reduction processing device can be found in the limitations of the curved curtain wall dimensional reduction processing method described above, and will not be repeated here. Each module in the aforementioned curved curtain wall dimensional reduction processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0160] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data such as curved curtain wall segments, principal directions, normal vectors, projection planes, and planar curtain walls. The network interface communicates with external terminals via a network. When executed by the processor, the computer program implements a dimensionality reduction method for curved curtain walls.

[0161] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0162] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: obtaining curved curtain wall blocks of the curved curtain wall to be dimension-reduced; determining the principal direction and normal vector of each curved curtain wall block based on each curved curtain wall block; determining the projection plane corresponding to each curved curtain wall block according to each principal direction and normal vector; projecting each curved curtain wall block onto the corresponding projection plane to obtain each planar block after dimension reduction; and splicing the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be dimension-reduced.

[0163] In one embodiment, when the processor executes a computer program, it projects each curved curtain wall block onto a corresponding projection plane to obtain each planar block after dimensionality reduction. This can include: establishing projection relationships between each curved curtain wall block and its corresponding projection plane; and based on these projection relationships, projecting each point of each curved curtain wall block onto its corresponding projection plane to obtain each planar block after dimensionality reduction.

[0164] In one embodiment, when the processor executes a computer program, it splices together the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension. This may include: obtaining the connection relationship between the curved curtain wall blocks; and splicing together the planar blocks based on the connection relationship to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension.

[0165] In one embodiment, when the processor executes the computer program, it determines the principal direction and normal vector of each curved curtain wall block based on each curved curtain wall block. This may include: obtaining the coordinates of each key point of each curved curtain wall block; determining the coordinates of the center point of each curved curtain wall block based on the coordinates of each key point; constructing the covariance matrix of each curved curtain wall block based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block; and determining the principal direction and normal vector of each curved curtain wall block based on the covariance matrix of each curved curtain wall block.

[0166] In one embodiment, when the processor executes the computer program, it constructs a covariance matrix corresponding to each curved curtain wall block based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block. This may include: determining the coordinate difference between the coordinates of the key points of each curved curtain wall block and the coordinates of the corresponding center point based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block; and constructing a covariance matrix corresponding to each curved curtain wall block based on the coordinate difference of each curved curtain wall block.

[0167] In one embodiment, when the processor executes the computer program, it implements the determination of the principal direction and normal vector of each curved curtain wall block based on the covariance matrix of each curved curtain wall block. This may include: solving for each covariance matrix to determine the eigenvalues ​​and eigenvectors of each covariance matrix; determining the eigenvector corresponding to the smallest eigenvalue of each covariance matrix as the normal vector of the corresponding curved curtain wall block; and determining the direction corresponding to the eigenvector corresponding to the largest eigenvalue of each covariance matrix as the principal direction of the corresponding curved curtain wall block.

[0168] In one embodiment, when the processor executes a computer program to acquire curved curtain wall blocks of the curved curtain wall to be dimension-reduced, it may include: acquiring the curved curtain wall to be dimension-reduced; unfolding the curved curtain wall to be dimension-reduced into a plane to generate corresponding planar data; segmenting the planar data to obtain multiple planar curtain wall blocks; establishing mapping relationships between each planar curtain wall block and the curved curtain wall to be dimension-reduced; and constructing the corresponding curved curtain wall blocks of the curved curtain wall to be dimension-reduced based on each planar curtain wall block and the corresponding mapping relationships.

[0169] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it performs the following steps: obtaining curved curtain wall blocks of the curved curtain wall to be dimension-reduced; determining the principal direction and normal vector of each curved curtain wall block based on each curved curtain wall block; determining the projection plane corresponding to each curved curtain wall block according to each principal direction and normal vector; projecting each curved curtain wall block onto the corresponding projection plane to obtain each planar block after dimension reduction; and splicing the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be dimension-reduced.

[0170] In one embodiment, when the computer program is executed by the processor, it projects each curved curtain wall block onto the corresponding projection plane to obtain each planar block after dimensionality reduction. This can include: establishing projection relationships between each curved curtain wall block and the corresponding projection plane; and based on the projection relationships, projecting each point of each curved curtain wall block onto the corresponding projection plane to obtain each planar block after dimensionality reduction.

[0171] In one embodiment, when the computer program is executed by the processor, it splices together the planar blocks to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension. This may include: obtaining the connection relationship between the curved curtain wall blocks; and splicing together the planar blocks based on the connection relationship to obtain the planar curtain wall corresponding to the curved curtain wall to be reduced in dimension.

[0172] In one embodiment, when the computer program is executed by the processor, it determines the principal direction and normal vector of each curved curtain wall block based on each curved curtain wall block. This can include: obtaining the coordinates of each key point of each curved curtain wall block; determining the coordinates of the center point of each curved curtain wall block based on the coordinates of each key point; constructing the covariance matrix of each curved curtain wall block based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block; and determining the principal direction and normal vector of each curved curtain wall block based on the covariance matrix of each curved curtain wall block.

[0173] In one embodiment, when the computer program is executed by the processor, it constructs a covariance matrix corresponding to each curved curtain wall block based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block. This may include: determining the coordinate difference between the coordinates of the key points of each curved curtain wall block and the coordinates of the corresponding center point based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block; and constructing a covariance matrix corresponding to each curved curtain wall block based on the coordinate difference of each curved curtain wall block.

[0174] In one embodiment, when the computer program is executed by the processor, it determines the principal direction and normal vector of each curved curtain wall block based on the covariance matrix of each curved curtain wall block. This may include: solving for each covariance matrix to determine the eigenvalues ​​and eigenvectors of each covariance matrix; determining the eigenvector corresponding to the smallest eigenvalue of each covariance matrix as the normal vector of the corresponding curved curtain wall block; and determining the direction corresponding to the eigenvector corresponding to the largest eigenvalue of each covariance matrix as the principal direction of the corresponding curved curtain wall block.

[0175] In one embodiment, when the computer program is executed by the processor, it acquires the curved curtain wall blocks of the curved curtain wall to be dimension-reduced. This can include: acquiring the curved curtain wall to be dimension-reduced; unfolding the curved curtain wall to be dimension-reduced into a plane to generate corresponding planar data; segmenting the planar data to obtain multiple planar curtain wall blocks; establishing mapping relationships between each planar curtain wall block and the curved curtain wall to be dimension-reduced; and constructing the corresponding curved curtain wall blocks of the curved curtain wall to be dimension-reduced based on each planar curtain wall block and the corresponding mapping relationships.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for dimensional reduction processing of curved curtain walls, characterized in that, The method includes: Obtain the curved curtain wall blocks of the curved curtain wall to be reduced in dimension; the curved curtain wall to be reduced in dimension is a three-dimensional curved curtain wall, and the curved curtain wall blocks refer to the construction units of splicing construction. The curved curtain wall blocks are generated by dividing the curved curtain wall to be reduced in dimension. Based on each curved curtain wall block, determine the principal direction and normal vector of each curved curtain wall block; Based on the principal directions and normal vectors, determine the projection plane corresponding to each curved curtain wall block; Each of the curved curtain wall blocks is projected onto its corresponding projection plane to obtain the dimensionality-reduced planar blocks. The planar blocks are spliced ​​together to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension; the planar curtain wall refers to a three-dimensional curved surface structure obtained by splicing together multiple planar blocks.

2. The method according to claim 1, characterized in that, The step of projecting each of the curved curtain wall blocks onto its corresponding projection plane to obtain the dimensionality-reduced planar blocks includes: Establish the projection relationships between each of the curved curtain wall blocks and its corresponding projection plane; Based on the projection relationships, each point of each curved curtain wall block is projected onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks.

3. The method according to claim 1, characterized in that, The step of splicing together the planar blocks to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension includes: Obtain the connection relationships between the various curved curtain wall blocks; Based on the connection relationship, the planar blocks are spliced ​​together to obtain the planar curtain wall corresponding to the curved surface curtain wall to be reduced in dimension.

4. The method according to claim 1, characterized in that, The process of determining the principal direction and normal vector for each curved curtain wall segment, based on each curved curtain wall segment, includes: Obtain the coordinates of each key point of each curved curtain wall segment; Based on the coordinates of each key point, determine the coordinates of the center point of each curved curtain wall block; Based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block, construct the covariance matrix corresponding to each curved curtain wall block. Based on the covariance matrix of each curved curtain wall segment, the principal direction and normal vector of each curved curtain wall segment are determined.

5. The method according to claim 4, characterized in that, The construction of covariance matrices for each curved curtain wall segment based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall segment includes: Based on the coordinates of each center point and the coordinates of each key point of each curved curtain wall block, determine the coordinate difference between the key point coordinates and the corresponding center point coordinates of each curved curtain wall block. Based on the coordinate differences between the various curved curtain wall blocks, a covariance matrix is ​​constructed for each curved curtain wall block.

6. The method according to claim 4, characterized in that, The determination of the principal direction and normal vector for each curved curtain wall segment based on its covariance matrix includes: Solve for each of the covariance matrices to determine the eigenvalues ​​and eigenvectors of each covariance matrix; The eigenvector corresponding to the smallest eigenvalue of each covariance matrix is ​​determined as the normal vector of the corresponding curved curtain wall block; The direction corresponding to the eigenvector of the largest eigenvalue of each covariance matrix is ​​determined as the main direction of the corresponding curved curtain wall block.

7. The method according to claim 1, characterized in that, The process of obtaining the curved curtain wall blocks to be dimension-reduced includes: Obtain the curved curtain wall to be reduced in dimension; The curved surface curtain wall to be reduced in dimension is unfolded in plane to generate corresponding planar data; The planar data is segmented to obtain multiple planar curtain wall blocks; Establish the mapping relationships between each of the planar curtain wall blocks and the curved curtain wall to be reduced in dimension; Based on each of the planar curtain wall blocks and the corresponding mapping relationships, construct the curved curtain wall blocks corresponding to the curved curtain wall to be reduced in dimension.

8. A dimension reduction processing device for curved curtain walls, characterized in that, The device includes: The curved curtain wall block acquisition module is used to acquire curved curtain wall blocks of the curved curtain wall to be reduced in dimension; the curved curtain wall to be reduced in dimension is a three-dimensional curved curtain wall, and the curved curtain wall block refers to the construction unit of splicing construction. The curved curtain wall block is generated by segmenting the curved curtain wall to be reduced in dimension. The main direction and normal vector determination module is used to determine the main direction and normal vector of each curved curtain wall block based on each curved curtain wall block. The projection plane determination module is used to determine the projection plane corresponding to each of the curved curtain wall blocks based on the principal directions and normal vectors. The planar block generation module is used to project each of the curved curtain wall blocks onto the corresponding projection plane to obtain the dimensionality-reduced planar blocks. The splicing module is used to splice the various planar blocks to obtain the planar curtain wall corresponding to the dimensional reduction curved surface curtain wall; the planar curtain wall refers to a three-dimensional curved surface structure obtained by splicing multiple planar blocks.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images