Steel structure deformation positioning method and device, computer equipment and storage medium
By calculating the rigid transformation matrix of the steel structure and updating the sampling point set, the efficiency and accuracy problems of deformation detection of large-span steel structures are solved, and fast and accurate deformation positioning is achieved, which is suitable for complex structures and noisy data scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ANHUI UNIV
- Filing Date
- 2022-10-09
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies struggle to quickly, comprehensively, and accurately detect deformation in large-span steel structures, especially in complex structures and with noisy data, where traditional methods are inefficient and have low accuracy.
By acquiring the sampling point set of the steel structure and the discrete point set of the axis model, the rigid body transformation matrix is calculated, and the measurement points in the sampling point set are updated until the average distance difference is less than the threshold. The deformation position is then determined, and the registration accuracy between points is improved by using the rigid body transformation matrix.
It improves the efficiency and accuracy of deformation positioning in large-span steel structures, is suitable for complex structures and noisy data scenarios, reduces the difficulty of detection, and has high robustness.
Smart Images
Figure CN115655128B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of engineering structure deformation detection technology, and in particular to a method, device, computer equipment and storage medium for locating steel structure deformation. Background Technology
[0002] With the continuous development of cities, landmark buildings such as large shopping malls, stadiums, exhibition halls, and airports are increasing. Steel structures are widely used in building engineering structures due to their excellent properties such as high strength, large span, good plasticity, and high temperature resistance. However, due to the complex shape and huge size of steel structure buildings, the shape, stiffness, and plasticity of the steel structure will change to varying degrees during the construction process as the construction progresses and the load increases. Therefore, it is important to quickly, accurately, comprehensively, and systematically grasp the deformation trend of steel structures in the construction and maintenance process.
[0003] For deformation detection of steel structures, traditional methods mainly rely on total stations and GPS monitoring. These methods establish multiple fixed observation points on the object itself and monitor changes based on the periodic variations of these fixed observation points. However, the data obtained using these methods is very limited, and for complex structures or objects without obvious characteristic points, these methods are often difficult to apply or involve a heavy workload of fieldwork.
[0004] There is currently no effective solution to the problem of the difficulty in quickly, comprehensively, and accurately detecting deformation in large-span steel structures in related technologies. Summary of the Invention
[0005] The purpose of this application is to propose a method for locating deformation of steel structures, so as to solve the problems of low efficiency and low accuracy in current steel structure deformation measurement.
[0006] To address the aforementioned technical problems, this application provides a method for positioning steel structure deformation, comprising the following steps:
[0007] Obtain a set of sampling points for the steel structure, wherein the set of sampling points includes multiple sampling measurement points;
[0008] Obtain the discrete point set of the axis model to which the steel structure belongs, where the discrete point set includes multiple reference points;
[0009] Calculate the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point;
[0010] Determine the average distance between the set of sampling points and the set of discrete points based on the rigid body transformation matrix;
[0011] Based on the rigid body transformation matrix, update each sampling measurement point in the sampling point set, and return to the step of calculating the rigid body transformation matrix of the sampling point set until the difference between the current average distance and the previous average distance is less than the preset difference threshold, and then take the currently updated sampling measurement point as the target sampling measurement point.
[0012] The deformation location of the steel structure is determined based on the distance from each target sampling measurement point to the axis model.
[0013] To address the aforementioned technical problems, this application provides a steel structure deformation positioning device, which includes:
[0014] The measurement point acquisition module is used to acquire a set of sampling points for the steel structure, wherein the set of sampling points includes multiple sampling measurement points;
[0015] The benchmark point acquisition module is used to acquire the discrete point set of the axis model to which the steel structure belongs, wherein the discrete point set includes multiple benchmark points;
[0016] The rigid body transformation module is used to calculate the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point;
[0017] The average distance acquisition module is used to determine the average distance between the sampling point set and the discrete point set based on the rigid body transformation matrix.
[0018] The iterative module is used to update each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and return the step of performing the calculation of the rigid body transformation matrix of the sampling point set until the difference between the currently obtained average distance and the previously obtained average distance is less than a preset difference threshold, at which point the currently updated sampling measurement point is taken as the target sampling measurement point.
[0019] The deformation positioning module is used to determine the deformation position of the steel structure based on the distance from each target sampling measurement point to the axis model.
[0020] To address the aforementioned technical problems, this application also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned steel structure deformation positioning method.
[0021] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned steel structure deformation positioning method.
[0022] Compared with the prior art, the embodiments of this application have the following main advantages:
[0023] By acquiring the sampling point set of the steel structure and the discrete point set of the axis model to which the steel structure belongs, and calculating the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point, the average distance between the sampling point set and the discrete point set is determined based on the rigid body transformation matrix. Based on the rigid body transformation matrix, each sampling measurement point in the sampling point set is updated, and the process of calculating the rigid body transformation matrix of the sampling point set is repeated until the difference between the currently obtained average distance and the previously obtained average distance is less than a preset difference threshold. At this point, the currently updated sampling measurement point is used as the target sampling measurement point. The deformation position of the steel structure is determined based on the distance from each target sampling measurement point to the axis model. On the one hand, by updating the sampling measurement points according to the rigid body transformation matrix under the correspondence between the sampling measurement points and the reference points of the steel structure, the registration accuracy between points is improved. Furthermore, by determining the deformation position of the steel structure by the distance from the obtained target sampling measurement point to the axis model, the location of large deformations can be quickly located, effectively reducing the difficulty of deformation detection of large-span steel structures and improving the efficiency and accuracy of deformation positioning of large-span steel structures. On the other hand, compared with traditional deformation detection methods for large-span steel structures, this solution is applicable to complex steel structures or detection scenarios with severe point cloud data noise, and has high robustness. Attached Figure Description
[0024] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0025] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;
[0026] Figure 2 This is a flowchart illustrating the steel structure deformation positioning method provided in the embodiments of this application;
[0027] Figure 3 This is a three-dimensional point cloud diagram of the building roof steel structure according to an embodiment of this application;
[0028] Figure 4 This is a schematic diagram of discrete points of the axis model to which the steel structure belongs in an embodiment of this application;
[0029] Figure 5 This is a schematic diagram of the second bounding box according to an embodiment of this application;
[0030] Figure 6 This is a schematic diagram of a structure of one embodiment of the steel structure deformation positioning device provided in this application;
[0031] Figure 7 This is a schematic diagram of the structure of one embodiment of the computer device provided in this application. Detailed Implementation
[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.
[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0034] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
[0035] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.
[0036] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.
[0037] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.
[0038] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.
[0039] It should be noted that the steel structure deformation positioning method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the steel structure deformation positioning device is generally installed in the server / terminal device.
[0040] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.
[0041] In the embodiments of this application, such as Figure 2 As shown, Figure 2 This is a flowchart illustrating the steel structure deformation positioning method provided in this application embodiment. The specific implementation of the steel structure deformation positioning method includes:
[0042] S201: Obtain the sampling point set of the steel structure, wherein the sampling point set includes multiple sampling measurement points.
[0043] In the embodiments of this application, such as Figure 3 As shown, Figure 3 This is a schematic diagram of a three-dimensional point cloud of a building roof steel structure according to an embodiment of this application. The method for acquiring the sampling point set of the steel structure includes: acquiring three-dimensional point cloud data (i.e., multiple original three-dimensional measurement points) of the building roof steel structure through three-dimensional laser measurement principles or photogrammetry principles; performing structural sampling on the three-dimensional point cloud data to obtain a three-dimensional point cloud dataset, i.e., a sampling point set. Each sampling measurement point in the sampling point set is a three-dimensional spatial point sampled from the three-dimensional point cloud data, and the position information of the three-dimensional spatial point is spatial coordinate information. The three-dimensional point cloud data is a massive set of points representing the spatial distribution and surface characteristics of the target under the same spatial reference frame; that is, after acquiring the spatial coordinate information of each three-dimensional spatial point on the steel structure surface, a three-dimensional spatial point set is obtained.
[0044] In this embodiment of the application, a depth camera can be used to measure the three-dimensional point cloud data of the steel structure.
[0045] In some implementations, obtaining a set of sampling points for the steel structure includes:
[0046] Obtain multiple original three-dimensional measurement points of the steel structure;
[0047] The first bounding box is determined based on the original three-dimensional measurement points, wherein the first bounding box comprises multiple grid cube elements;
[0048] A preset number of grid cube elements, including the original three-dimensional measurement points, are sampled to obtain sampled measurement points;
[0049] When the total number of sampled measurement points is less than or equal to the preset number threshold, the unsampled original 3D measurement points are used as new original 3D measurement points, and the process returns to the step of determining the first bounding box based on the original 3D measurement points. Sampling stops when the total number of sampled measurement points is greater than the preset number threshold, and a set of sampled points is obtained.
[0050] In this embodiment, a bounding box algorithm is used to obtain the first bounding box. The bounding box algorithm is an algorithm for solving the optimal bounding space of a discrete point set. The basic idea is to use a geometric object with a slightly larger volume and simpler characteristics (called a bounding box) to approximate a complex geometric object. Therefore, the bounding box algorithm can be used to transform the original three-dimensional measurement points into the first bounding box, that is, the first bounding box discretizes the original three-dimensional measurement points of the steel structure into multiple grid cube elements.
[0051] Specifically, the side length of the starting grid cube element (i.e., the first grid cube element) of the bounding box is set to the diagonal length L of the first bounding box. The ratio, where |P| represents the number of 3D measurement points in the set P containing the original 3D measurement points. An association is established between each original 3D measurement point and its corresponding raster cube element; for example, 3D measurement point p1 is associated with raster cube element c1, 3D measurement point p2 with raster cube element c2, 3D measurement point p3 with raster cube element c3, and so on. Within each non-empty raster cube element, a preset number of original 3D measurement points are selected for sampling. These sampled original 3D measurement points are used as the first set of sampling measurement points. The preset number can be determined based on actual sampling experience; for example, the preset number could be 1, 2, 3, etc., and is not limited here. The remaining unsampled original 3D measurement points are used as the original 3D measurement points for constructing the first bounding box in the second construction. The side length of the raster cube elements in the second constructed first bounding box is set to the side length of the first raster cube elements. Divide by 2, and sample the original 3D measurement points in the first bounding box constructed in the second time according to a preset number. Use the sampled original 3D measurement points as the second sampling measurement points. Use the remaining unsampled original 3D measurement points as the original 3D measurement points for the third reconstruction of the first bounding box. Set the side length of the grid cube element in the first bounding box constructed in the third time to the side length of the grid cube element in the second time. Divide by 2, and sample the original 3D measurement points in the first bounding box constructed in the third time according to the preset number... Repeat the above process until the total number of sampled measurement points is greater than the preset threshold, then stop sampling. Compared with random sampling or farthest point sampling, this method can preserve the geometric structural information of the target object to the maximum extent while ensuring sampling efficiency. It helps to reduce unnecessary interference data, thereby reducing the amount of calculation of steel structure deformation and improving the registration and calculation efficiency of steel structures.
[0052] S202: Obtain the discrete point set of the axis model to which the steel structure belongs, where the discrete point set includes multiple reference points.
[0053] The process involves acquiring a design model of the steel structure and extracting its axes to construct an axis model. The design model is a three-dimensional model. Point cloud data along each axis of the axis model is discretized and sampled to obtain reference points for a discrete point set. Since these reference points originate from a standard three-dimensional model, they can be used as a reference standard for matching the sampled measurement points, facilitating reliable matching with the steel structure's sampled measurement points later.
[0054] In this embodiment of the application, obtaining the discrete point set of the axis model to which the steel structure belongs includes:
[0055] Obtain the axis model to which the steel structure belongs, where the axis model includes multiple axes;
[0056] Identify the vertices corresponding to each axis, where the vertices include the first vertex and the second vertex;
[0057] Starting from the first vertex of each axis, extract one axis point at preset intervals along the axis until the extracted axis point becomes the second vertex, then stop the extraction operation;
[0058] The vertices and axis points on each axis are used as reference points, and these reference points are merged into a discrete point set.
[0059] Specifically, a design model is obtained from the steel structure design drawings in modeling software, serving as the standard model for the actual steel structure. Multiple axes of the steel structure are extracted from the design model to obtain the axis model, which is composed of the axes of the steel structure. Each point in the discrete 3D point cloud data of each axis can be used as a reference point.
[0060] In this embodiment of the application, for each axis l in the design model i The corresponding first vertex a i Second vertex b i i is a positive integer, and the preset interval is . Starting from the first vertex of each axis, on axis l i In accordance with every Extract an axis point from the distance. For example... Figure 4 As shown, Figure 4 This is a schematic diagram of discrete points of the axis model of the steel structure in this application embodiment, starting from the first vertex a. i Begin, according to The interval along axis l i Sampling axis points, until sampled to b i Finally, the vertices and axis points on each axis are merged into a discrete point set as reference points, resulting in the discrete point set.
[0061] S203: Calculate the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point.
[0062] The correspondence relationship refers to the matching relationship between the sampled measurement points and the reference points, i.e., the registration between 3D point clouds. Using the known matching relationship, each sampled measurement point undergoes rigid body transformation to obtain a rigid body transformation matrix, thus achieving the mapping from the sampled measurement points to the reference points. The rigid body transformation can be decomposed into translation transformation, rotation transformation, and inversion (mirror) transformation.
[0063] In this embodiment of the application, before calculating the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point, the specific implementation of determining the correspondence includes:
[0064] Obtain the first vector of each sampling measurement point within a preset second bounding box;
[0065] Obtain the second vector of each reference point within a preset third bounding box;
[0066] Based on the first and second vectors, construct the correspondence between each sampling measurement point and each reference point.
[0067] In this embodiment of the application, obtaining the first vector of each sampling measurement point within a preset second bounding box includes:
[0068] A second bounding box is constructed based on the set of sampling points, wherein the second bounding box includes multiple key points carrying numbers;
[0069] Calculate the distance from each sampling measurement point in the sampling point set to multiple key points to obtain multiple first distances corresponding to each sampling measurement point;
[0070] Based on the numbering order and multiple first distances, the first vector of each sampling measurement point is obtained.
[0071] Specifically, a second bounding box is constructed based on each sampling measurement point in the sampling point set. Key points include the eight corner points and the center point of the second bounding box. For the eight corner points of the second bounding box, such as... Figure 5 As shown, Figure 5 This is a schematic diagram of the second bounding box according to an embodiment of this application, numbered in a certain order. For example, the eight corner points are labeled with numbers 1-8 respectively, which can be located at a distance of o from the origin of the preset coordinate system. xyz The nearest corner point is labeled as 1. The three corner points closest to corner point 1 are labeled as 2, 3, and 4 respectively, in a counter-clockwise direction. The remaining corner points connected to corner point 1 are labeled as 5, those connected to corner point 2 as 6, those connected to corner point 3 as 7, and those connected to corner point 4 as 8. For each sampling measurement point p in the sampling point set P... i Calculate p for each sampling measurement point i The Euclidean distances to the eight corner points (from 1 to 8) and the center point of the second bounding box of the sampling point set P are sequentially used to obtain nine Euclidean distances, generating a 9-dimensional first vector. This first vector represents the sampling measurement point p. i descriptor fp i .
[0072] In this embodiment, the method for obtaining the second vector of each reference point in the preset third bounding box is the same as the method for obtaining the first vector of each sampled measurement point in the preset second bounding box, that is, constructing the third bounding box based on each reference point in the discrete point set. Similarly, each reference point q in the discrete point set Q is used... i The Euclidean distances to the eight corner points and the center point of the third bounding box are calculated to obtain nine Euclidean distances, generating a 9-dimensional second vector. This second vector is the descriptor fq of the reference point. i .
[0073] In this embodiment of the application, the correspondence between each sampling measurement point and each reference point is constructed based on the first vector and the second vector, including:
[0074] Calculate the similarity between the first vector of the sampled measurement points and the second vector of each reference point;
[0075] The correspondence between the sampling measurement points is determined based on the benchmark point corresponding to the maximum similarity.
[0076] Specifically, based on the first and second vectors, a correspondence is constructed between each sampling measurement point and each reference point, that is, based on the descriptor fp. i and descriptor fq i Establish the correspondence between sampling measurement points and reference points. This is done using the operator fp. i and descriptor fq i The similarity, i.e., the similarity measure sim = fp i ·fq i Find the reference point q in the discrete point set Q that maximizes the sim value. i At that time, i.e., sampling measurement point q i With reference point p i Correspondingly.
[0077] Specifically, the covariance matrix is calculated based on the correspondence between points in the sampling point set P and the discrete point set Q. in, N and M represent the total number of sampled measurement points in the sampled point set P and the total number of reference points in the discrete point set Q, respectively. Singular Value Decomposition (SVD) is performed on the covariance matrix CV, yielding [U,S,V] = SVD(CV), where U and V represent two mutually orthogonal matrices, and S represents a diagonal matrix. The sampled measurement points p are further calculated using U and V. i and reference point q i The rigid body transformation matrix, wherein the rigid body transformation matrix includes the rotation matrix R = VU T Translation vector t = -R*o P +o Q .
[0078] S204: Determine the average distance between the sampling point set and the discrete point set based on the rigid body transformation matrix.
[0079] Specifically, based on the rotation matrix R = VU corresponding to the sampling measurement points included in the rigid body transformation matrix. T Translation vector t = -R*o P +o Q Calculate the average distance between the transformed set of sampled points and the set of discrete points to determine the minimum average distance, i.e., the minimum average distance.
[0080] S205: Based on the rigid body transformation matrix, update each sampling measurement point in the sampling point set, and return to the step of calculating the rigid body transformation matrix of the sampling point set until the difference between the currently obtained average distance and the previously obtained average distance is less than the preset difference threshold, and then take the currently updated sampling measurement point as the target sampling measurement point.
[0081] In this embodiment of the application, the minimum average distance is recorded. Then, a new set of sampling points P′=R*P+t is obtained, that is, each sampling measurement point in the current sampling point set is updated, and each updated sampling measurement point is returned to the steps S203 and S204 to calculate the average distance corresponding to each transformation. When the difference between adjacent average distances is less than a preset gap threshold, the iteration ends, and the latest updated sampling measurement point is taken as the target sampling measurement point. The preset gap threshold can be 1e -3 The specific settings can be adjusted according to the actual scenario; no limitations are set here. For example, the average distance d obtained last time... k-1 and the currently obtained average distance d k The calculated difference is less than the threshold 1e -3 At this point, the iteration ends, achieving the registration of the sampling measurement points of the steel structure with the reference points of the design model.
[0082] S206: Determine the deformation location of the steel structure based on the distance from each target sampling measurement point to the axis model.
[0083] Specifically, after calculating the distance from each target sampling measurement point to each axis after iterative transformation, a color difference map can be used to display different distances, so as to quickly locate the deformation position with large deformation.
[0084] In this embodiment of the application, the deformation location of the steel structure is determined based on the distance from each target sampling measurement point to the axis model, including:
[0085] Based on the distance from the target sampling measurement point to each axis, multiple first distances to the target sampling measurement point are obtained;
[0086] The minimum first distance is taken as the distance from the target sampling measurement point to the axis model;
[0087] Based on the distance from each target sampling measurement point to the axis model, determine the various deformations of the steel structure;
[0088] The deformation location of the steel structure is determined based on the target sampling measurement point corresponding to the largest deformation.
[0089] Specifically, for each target, sample and measure p′ i Based on the point-to-line distance formula, calculate the first distance. In the axis model Find the axis l that minimizes the dist value. j The first and last vertices of this axis are a and a, respectively. j and b j The minimum distance at this time is dist min That is, the target sampling measurement point pi The distance to the axis model. Based on all the calculated first distances, the various deformations of the steel structure are represented. Each target sampling measurement point is colored, with the point having a larger corresponding first distance showing a darker color. Based on the above steps, the target sampling measurement points with darker colors can be quickly located. According to the spatial coordinates of the target sampling measurement points, the location of the largest deformation in the large-span steel structure can be determined.
[0090] By acquiring the sampling point set of the steel structure and the discrete point set of the axis model to which the steel structure belongs, and calculating the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point, the average distance between the sampling point set and the discrete point set is determined based on the rigid body transformation matrix. Based on the rigid body transformation matrix, each sampling measurement point in the sampling point set is updated, and the process of calculating the rigid body transformation matrix of the sampling point set is repeated until the difference between the currently obtained average distance and the previously obtained average distance is less than a preset difference threshold. At this point, the currently updated sampling measurement point is used as the target sampling measurement point. The deformation position of the steel structure is determined based on the distance from each target sampling measurement point to the axis model. On the one hand, by updating the sampling measurement points according to the rigid body transformation matrix under the correspondence between the sampling measurement points and the reference points of the steel structure, the registration accuracy between points is improved. Furthermore, by determining the deformation position of the steel structure by the distance from the obtained target sampling measurement point to the axis model, the location of large deformations can be quickly located, effectively reducing the difficulty of deformation detection of large-span steel structures and improving the efficiency and accuracy of deformation positioning of large-span steel structures. On the other hand, compared with traditional deformation detection methods for large-span steel structures, this solution is applicable to complex steel structures or detection scenarios with severe point cloud data noise, and has high robustness.
[0091] 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. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).
[0092] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures 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, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.
[0093] Further reference Figure 6 As a response to the above Figure 2 The present application provides an embodiment of a steel structure deformation positioning device to implement the method shown. This embodiment of the device is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.
[0094] like Figure 6 The diagram shown is a structural schematic of an embodiment of the steel structure deformation positioning device provided in this application. The steel structure deformation positioning device further includes: a measurement point acquisition module 61, a reference point acquisition module 62, a rigid body transformation module 63, an average distance acquisition module 64, an iteration module 65, and a deformation positioning module 66.
[0095] The measurement point acquisition module 61 is used to acquire a set of sampling points for the steel structure, wherein the set of sampling points includes multiple sampling measurement points;
[0096] The benchmark point acquisition module 62 is used to acquire the discrete point set of the axis model to which the steel structure belongs, wherein the discrete point set includes multiple benchmark points;
[0097] Rigid body transformation module 63 is used to calculate the rigid body transformation matrix of the sampling point set based on the correspondence between each sampling measurement point and each reference point;
[0098] The average distance acquisition module 64 is used to determine the average distance between the sampling point set and the discrete point set based on the rigid body transformation matrix.
[0099] The iteration module 65 is used to update each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and return the step of performing the calculation of the rigid body transformation matrix of the sampling point set until the difference between the current average distance and the previous average distance is less than the preset difference threshold, and then the currently updated sampling measurement point is taken as the target sampling measurement point.
[0100] The deformation positioning module 66 is used to determine the deformation position of the steel structure based on the distance from each target sampling measurement point to the axis model.
[0101] In some implementations, the measurement point acquisition module 61 includes:
[0102] The first acquisition submodule is used to acquire multiple original three-dimensional measurement points of the steel structure;
[0103] The first confirmation submodule is used to determine the first bounding box based on the original three-dimensional measurement points, wherein the first bounding box includes multiple grid cube elements;
[0104] The sampling submodule is used to sample a preset number of grid cube elements, including the original three-dimensional measurement points, to obtain sampled measurement points.
[0105] The iterative submodule is used to take the unsampled original 3D measurement points as new original 3D measurement points when the total number of sampled measurement points is less than or equal to a preset number threshold, and return to execute the step of determining the first bounding box based on the original 3D measurement points, until the total number of sampled measurement points is greater than the preset number threshold, at which point sampling stops and a set of sampled points is obtained.
[0106] In some implementations, the reference point acquisition module 62 includes:
[0107] The second acquisition submodule is used to acquire the axis model to which the steel structure belongs, wherein the axis model includes multiple axes;
[0108] The second determination submodule is used to determine the vertex corresponding to each axis, wherein the vertex includes the first vertex and the second vertex;
[0109] The extraction submodule is used to extract axis points from the first vertex of each axis at preset intervals until the extracted axis point is the second vertex, at which point the extraction operation stops.
[0110] The merge submodule is used to take the vertices and axis points on each axis as reference points and merge the reference points into a discrete set of points.
[0111] In some embodiments, the steel structure deformation positioning device further includes:
[0112] The first acquisition module is used to acquire the first vector of each sampling measurement point in a preset second bounding box;
[0113] The second acquisition module is used to acquire the second vector of each reference point in a preset third bounding box;
[0114] The module is used to construct the correspondence between each sampled measurement point and each reference point based on the first vector and the second vector.
[0115] In some implementations, the first acquisition module includes:
[0116] A construction submodule is used to construct a second bounding box based on the sampling point set, wherein the second bounding box includes multiple key points carrying numbers;
[0117] The first calculation submodule is used to calculate the distance from each sampling measurement point in the sampling point set to multiple key points, and obtain multiple first distances corresponding to each sampling measurement point;
[0118] The numbering submodule is used to obtain the first vector for each sampling measurement point based on the numbering order and multiple first distances.
[0119] In some implementations, the building module includes:
[0120] The second calculation submodule is used to calculate the similarity between the first vector of the sampled measurement points and the second vector of each reference point;
[0121] The third determination submodule is used to determine the correspondence between sampling measurement points based on the benchmark point corresponding to the maximum similarity.
[0122] In some embodiments, the deformation positioning module 66 includes:
[0123] The distance acquisition submodule is used to obtain multiple first distances of the target sampling measurement point based on the distance from the target sampling measurement point to each axis.
[0124] The fourth determination submodule is used to take the minimum first distance as the distance from the target sampling measurement point to the axis model;
[0125] The fifth submodule is used to determine the various deformations of the steel structure based on the distance from each target sampling measurement point to the axis model;
[0126] The sixth determination submodule is used to determine the deformation location of the steel structure based on the target sampling measurement point corresponding to the largest deformation.
[0127] Regarding the steel structure deformation positioning device in the above embodiments, the specific methods by which each module performs its operation have been described in detail in the embodiments related to the method, and will not be elaborated here.
[0128] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 7 , Figure 7 This is a basic structural block diagram of the computer device in this embodiment.
[0129] The computer device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected via a system bus. It should be noted that only the computer device 7 with components 71-73 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.
[0130] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.
[0131] The memory 71 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or SD steel structure deformation positioning memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 7. Of course, the memory 71 may include both the internal storage unit and its external storage device of the computer device 7. In this embodiment, the memory 71 is typically used to store the operating system and various application software installed on the computer device 7, such as the program code of the steel structure deformation positioning method. In addition, the memory 71 can also be used to temporarily store various types of data that have been output or will be output.
[0132] In some embodiments, the processor 72 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 72 is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is used to run program code stored in the memory 71 or process data, for example, to run the program code for the steel structure deformation positioning method.
[0133] The network interface 73 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 7 and other electronic devices.
[0134] This application also provides another embodiment, namely, a computer-readable storage medium storing a steel structure deformation positioning program, which can be executed by at least one processor to perform the steps of the steel structure deformation positioning method as described above.
[0135] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0136] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.
Claims
1. A method of positioning a steel structure by deformation, characterized in that, The method includes: Obtain a sampling point set for the steel structure, wherein the sampling point set includes multiple sampling measurement points; Obtain the discrete point set of the axis model to which the steel structure belongs, wherein the discrete point set includes multiple reference points; A second bounding box is constructed based on the set of sampling points, wherein the second bounding box includes multiple key points carrying numbers; Calculate the distance from each of the sampling measurement points in the sampling point set to the multiple key points to obtain multiple first distances corresponding to each of the sampling measurement points; Based on the numbering order and multiple first distances, a first vector is obtained for each of the sampling measurement points; a second vector for each reference point in a preset third bounding box is obtained; Based on the first vector and the second vector, a correspondence is constructed between each sampling measurement point and each reference point; Based on the correspondence between each of the sampling measurement points and each of the reference points, calculate the rigid body transformation matrix of the sampling point set; The average distance between the sampling point set and the discrete point set is determined based on the rigid body transformation matrix. Based on the rigid body transformation matrix, update each sampling measurement point in the sampling point set, and return to the step of calculating the rigid body transformation matrix of the sampling point set until the difference between the currently obtained average distance and the previously obtained average distance is less than a preset difference threshold, and then take the currently updated sampling measurement point as the target sampling measurement point. The deformation location of the steel structure is determined based on the distance from each target sampling measurement point to the axis model.
2. The method of claim 1, wherein The acquisition of the sampling point set for the steel structure includes: Obtain multiple original three-dimensional measurement points of the steel structure; The first bounding box is determined based on the original three-dimensional measurement points, wherein the first bounding box comprises a plurality of grid cube elements; A preset number of samples are taken from the grid cube elements including the original three-dimensional measurement points to obtain sampled measurement points; When the total number of sampled measurement points is less than or equal to a preset number threshold, the unsampled original 3D measurement points are used as new original 3D measurement points, and the process returns to the step of determining the first bounding box based on the original 3D measurement points. Sampling stops when the total number of sampled measurement points is greater than the preset number threshold, and a set of sampled points is obtained.
3. The method of claim 1, wherein The step of obtaining the discrete point set of the axis model to which the steel structure belongs includes: obtaining the axis model to which the steel structure belongs, wherein the axis model includes multiple axes; Determine the vertex corresponding to each of the axes, wherein the vertex includes a first vertex and a second vertex; Starting from the first vertex of each axis, an axis point is extracted at preset intervals along the axis until the extracted axis point is the second vertex, at which point the extraction operation stops. The vertices and axis points on each axis are used as reference points, and these reference points are merged into a discrete point set.
4. The method of claim 1, wherein The step of constructing the correspondence between each sampling measurement point and each reference point based on the first vector and the second vector includes: Calculate the similarity between the first vector of the sampled measurement points and the second vector of each of the reference points; The correspondence between the sampling measurement points is determined based on the benchmark point corresponding to the maximum similarity.
5. The method of claim 2, wherein Determining the deformation location of the steel structure based on the distance from each target sampling measurement point to the axis model includes: Based on the distance from the target sampling measurement point to each of the axes, a plurality of second distances to the target sampling measurement point are obtained; The minimum second distance is taken as the distance from the target sampling measurement point to the axis model; Based on the distance from each target sampling measurement point to the axis model, the various deformations of the steel structure are determined; The deformation location of the steel structure is determined based on the target sampling measurement point corresponding to the largest deformation.
6. A steel structure deformation positioning device, characterized by, The steel structure deformation positioning device includes: The measurement point acquisition module is used to acquire a set of sampling points for the steel structure, wherein the set of sampling points includes multiple sampling measurement points; The reference point acquisition module is used to acquire a discrete point set of the axis model to which the steel structure belongs, wherein the discrete point set includes multiple reference points; A first acquisition module is used to acquire a first vector of each sampling measurement point in a preset second bounding box; wherein, the first acquisition module includes a construction submodule, used to construct a second bounding box based on the sampling point set, wherein the second bounding box includes multiple key points carrying numbers; a first calculation submodule, used to calculate the distance from each sampling measurement point in the sampling point set to the multiple key points respectively, to obtain multiple first distances corresponding to each sampling measurement point; and a numbering submodule, used to obtain a first vector of each sampling measurement point based on the numbering order and the multiple first distances; The second acquisition module is used to acquire the second vector of each of the reference points in a preset third bounding box; A construction module is used to construct a correspondence between each of the sampling measurement points and each of the reference points based on the first vector and the second vector; The rigid body transformation module is used to calculate the rigid body transformation matrix of the sampling point set based on the correspondence between each of the sampling measurement points and each of the reference points; The average distance acquisition module is used to determine the average distance between the sampling point set and the discrete point set based on the rigid body transformation matrix. The iterative module is used to update each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and return to the step of calculating the rigid body transformation matrix of the sampling point set until the difference between the currently obtained average distance and the previously obtained average distance is less than a preset difference threshold, and then the currently updated sampling measurement point is taken as the target sampling measurement point. The deformation positioning module is used to determine the deformation position of the steel structure based on the distance from each target sampling measurement point to the axis model.
7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the steel structure deformation positioning method as described in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the steel structure deformation positioning method in any one of claims 1 to 5.