CN115655128A

CN115655128A - Steel structure deformation positioning method and device, computer equipment and storage medium

Info

Publication number: CN115655128A
Application number: CN202211229895.7A
Authority: CN
Inventors: 吴巧云; 张义; 董华; 汪俊; 易程; 王冰; 王得超
Original assignee: Nanjing University of Aeronautics and Astronautics; Anhui University; First Construction Co Ltd of China Construction Third Engineering Division
Current assignee: Nanjing University of Aeronautics and Astronautics; Anhui University; First Construction Co Ltd of China Construction Third Engineering Division
Priority date: 2022-10-09
Filing date: 2022-10-09
Publication date: 2023-01-31

Abstract

The application discloses a steel structure deformation positioning method, a device, a computer device and a storage medium, which relate to the technical field of computer application, the method comprises the steps of obtaining a sampling point set of a steel structure and a discrete point set of an axis model to which the steel structure belongs, calculating a rigid transformation matrix of the sampling point set according to the corresponding relation between each sampling measurement point and each reference point, determining the average distance between the sampling point set and the discrete point set according to the rigid transformation matrix, updating each sampling measurement point in the sampling point set based on the rigid transformation matrix, and returning to the step of calculating the rigid transformation matrix of the sampling point set until the difference value between the average distance obtained at present and the average distance obtained at last time is smaller than a preset difference threshold value, taking the sampling measurement point updated at present as a target sampling measurement point, determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model, and improving the efficiency and accuracy of large-span positioning.

Description

Steel structure deformation positioning method and device, computer equipment and storage medium

Technical Field

The application relates to the technical field of engineering structure deformation detection, in particular to a steel structure deformation positioning method and device, computer equipment and a storage medium.

Background

With the continuous development of cities, landmark buildings such as superstores, stadiums, exhibition halls and airports are increasing, and steel structures are widely applied to construction engineering structures due to the excellent properties of large strength, large span, good shaping, high temperature resistance and the like. However, because the steel structure engineering has a complex building shape and a large volume, the shape, the self-rigidity, the shaping and other characteristics of the steel structure in the construction process can be changed to different degrees along with the continuous promotion of the construction process and the continuous increase of the load, and therefore, how to rapidly, correctly, comprehensively and systematically master the deformation trend of the steel structure is an important work in the building construction and maintenance process.

To the holistic deformation detection of steel construction, total powerstation and GPS monitoring are the main in the traditional method more. These methods set up a plurality of fixed observation points on the observation object itself, and monitor the change of the object based on the periodic change of the fixed observation points. However, the data obtained with these methods are very limited and for objects with complex structures and no distinct feature points, these methods tend to be difficult to apply or laborious to work with.

Aiming at the problem that the deformation detection of the large-span steel structure is difficult to realize rapidly, comprehensively and accurately in the related technology, an effective solution is not provided at present.

Disclosure of Invention

The embodiment of the application aims to provide a steel structure deformation positioning method to solve the problems of low efficiency and low accuracy of the existing steel structure deformation measurement.

In order to solve the technical problem, an embodiment of the present application provides a method for positioning deformation of a steel structure, including the following steps:

acquiring a sampling point set of a steel structure, wherein the sampling point set comprises a plurality of sampling measurement points;

acquiring a discrete point set of an axis model to which a steel structure belongs, wherein the discrete point set comprises a plurality of reference points;

calculating a rigid body transformation matrix of the sampling point set according to the corresponding relation between each sampling measuring point and each reference point;

determining the average distance between the sampling point set and the discrete point set according to the rigid body transformation matrix;

updating each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and returning to the step of calculating the rigid body transformation matrix of the sampling point set, and taking the currently updated sampling measurement point as a target sampling measurement point when the difference value between the currently obtained average distance and the last obtained average distance is smaller than a preset difference threshold value;

and determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model.

In order to solve the technical problem, an embodiment of the present application provides a steel structure deformation positioner, and steel structure deformation positioner includes:

the measuring point acquisition module is used for acquiring a sampling point set of the steel structure, wherein the sampling point set comprises a plurality of sampling measuring points;

the datum point acquisition module is used for acquiring a discrete point set of an axis model to which the steel structure belongs, wherein the discrete point set comprises a plurality of datum points;

the rigid body transformation module is used for calculating a rigid body transformation matrix of the sampling point set according to the corresponding relation between each sampling measuring point and each reference point;

the average distance acquisition module is used for determining the average distance between the sampling point set and the discrete point set according to the rigid body transformation matrix;

the iteration module is used for updating each sampling measurement point in the sampling point set based on the rigid body transformation matrix and returning to the step of calculating the rigid body transformation matrix of the sampling point set until the difference value between the currently obtained average distance and the last obtained average distance is smaller than a preset difference threshold value, and taking the currently updated sampling measurement point as a target sampling measurement point;

and the deformation positioning module is used for determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model.

In order to solve the technical problem, an embodiment of the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the steel structure deformation positioning method when executing the computer program.

In order to solve the technical problem, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of the above-mentioned steel structure deformation positioning method.

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

the method comprises the steps of obtaining a sampling point set of a steel structure and a discrete point set of an axis model to which the steel structure belongs, calculating a rigid body transformation matrix of the sampling point set according to the corresponding relation between each sampling measurement point and each reference point, determining the average distance between the sampling point set and the discrete point set according to the rigid body transformation matrix, updating each sampling measurement point in the sampling point set based on the rigid body transformation matrix, returning to the step of calculating the rigid body transformation matrix of the sampling point set, taking the currently updated sampling measurement point as a target sampling measurement point until the difference between the currently obtained average distance and the last obtained average distance is smaller than a preset threshold value, and determining the deformation position of the steel structure according to the distance between each target sampling measurement point and the axis model. On the one hand, under the corresponding relation between the sampling measuring point and the reference point of the steel structure, the registration accuracy between the points is improved in a mode of updating the sampling measuring point according to the rigid body transformation matrix, and then the deformation position of the steel structure is determined according to the distance from the obtained target sampling measuring point to the axis model, so that the position with larger deformation can be quickly positioned, the difficulty of deformation detection of the large-span steel structure is effectively reduced, and the efficiency and the accuracy of deformation positioning of the large-span steel structure are improved. On the other hand, compared with the traditional deformation detection method for the large-span steel structure, the method is applicable to the detection scene of the complex steel structure or the serious point cloud data noise, and has high robustness.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram to which the present application may be applied;

FIG. 2 is a schematic flow chart of a steel structure deformation positioning method provided in the embodiment of the present application;

FIG. 3 is a schematic three-dimensional point cloud of a steel structure of a roof of a building according to an embodiment of the present application;

FIG. 4 is a schematic view of discrete points of an axis model of a steel structure according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a second bounding box of an embodiment of the present application;

FIG. 6 is a schematic structural diagram of an embodiment of a steel structure deformation positioning device provided by the present application;

FIG. 7 is a schematic block diagram of one embodiment of a computer device provided herein.

Detailed Description

Unless defined otherwise, 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 belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that the steel structure deformation positioning method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the steel structure deformation positioning device is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

In the embodiment of the present application, as shown in fig. 2, fig. 2 is a schematic flow chart of a steel structure deformation positioning method provided in the embodiment of the present application, and the concrete implementation of the steel structure deformation positioning method includes:

s201: and acquiring a sampling point set of the steel structure, wherein the sampling point set comprises a plurality of sampling measurement points.

In the embodiment of the present application, as shown in fig. 3, fig. 3 is a schematic three-dimensional point cloud of a building roof steel structure according to the embodiment of the present application. The sampling point set acquisition mode of steel construction includes: three-dimensional point cloud data (namely a plurality of original three-dimensional measuring points) of a building roof steel structure are obtained through a three-dimensional laser measuring principle or a photogrammetric principle, and structural sampling is carried out on the three-dimensional point cloud data to obtain a three-dimensional point cloud data set, namely a sampling point set. Each sampling measurement point in the sampling point set is a three-dimensional space point sampled from the three-dimensional point cloud data, and the position information of the three-dimensional space point is space coordinate information. The three-dimensional point cloud data is a massive point set which expresses target space distribution and target surface characteristics under the same space reference system, namely a three-dimensional space point set is obtained after the space coordinate information of each three-dimensional space point on the surface of the steel structure is obtained.

In the embodiment of the application, a depth camera can be adopted to measure the three-dimensional point cloud data of the steel structure.

In some embodiments, obtaining a set of sampling points for a steel structure comprises:

acquiring a plurality of original three-dimensional measuring points of a steel structure;

determining a first bounding box according to the original three-dimensional measurement points, wherein the first bounding box comprises a plurality of grid cubic voxels;

sampling a grid cubic voxel comprising an original three-dimensional measuring point in a preset number to obtain a sampling measuring point;

and when the total number of the sampling measuring points is less than or equal to the preset number threshold, taking the original measuring three-dimensional points which are not sampled as new original three-dimensional measuring points, returning to the step of determining the first enclosure box according to the original three-dimensional measuring points, and stopping sampling until the total number of the sampling measuring points is greater than the preset number threshold, so as to obtain a sampling point set.

In the embodiment of the present application, a bounding algorithm is used to obtain the first bounding box. The bounding box algorithm is an algorithm for solving an optimal bounding space of a discrete point set, and the basic idea is to approximately replace a complex geometric object by a geometric body (called a bounding box) with a slightly larger volume and simple characteristics, so that the original three-dimensional measurement points can be converted into a first bounding box by the bounding box algorithm, namely the first bounding box disperses the original three-dimensional measurement points of a steel structure into a plurality of grid cubic voxels.

Specifically, the side length of the starting grid cube voxel (i.e., the first grid cube voxel) of the bounding box is set to the length L of the diagonal of the first bounding box

Wherein | P | represents the number of three-dimensional measurement points in the set P where the original three-dimensional measurement points are located. Establishing an association of each original three-dimensional measurement point with the grid cube voxel it is located in, e.g. three-dimensional measurement point p ₁ And grid cube voxel c ₁ Relating, three-dimensional measuring points p ₂ And grid cube voxel c ₂ Relating, three-dimensional measuring points p ₃ And grid cube voxel c ₃ Association, and the like. In each non-empty grid cube voxel, a preset number of original three-dimensional measurement points are selected for sampling, the sampled original three-dimensional measurement points are used as first-time sampling measurement points, and the preset number can be obtained according to an actual sampling experience, for example, the preset number can be 1, 2, 3, and the like, which is not limited herein. Taking the residual original three-dimensional measuring points which are not sampled as original three-dimensional measuring points for constructing the first bounding box for the second time, and setting the side length of the grid cubic voxel in the first bounding box constructed for the second time as the side length of the grid cubic voxel of the first grid cubic voxel

Dividing by 2, sampling original three-dimensional measurement points according to a preset number in the first bounding box constructed at the second time, and taking the sampled original three-dimensional measurement points as second-time sampling measurement points. Taking the residual original three-dimensional measuring points which are not sampled as original three-dimensional measuring points for reconstructing the first bounding box for the third time, and setting the side length of the grid cubic voxel in the first bounding box constructed for the third time as the side length of the grid cubic voxel of the second time

Dividing by 2, sampling original three-dimensional measuring points in a first bounding box constructed for the third time according to the preset number \8230 \ 8230, and repeating the above processes until the total number of the sampled measuring points is greater than the preset number threshold value, and stopping sampling. Compared with random sampling or farthest point sampling, the method can keep the geometric structure information of the target object to the maximum extent on the basis of ensuring the sampling efficiency, and is beneficial to reductionUnnecessary interference data are reduced, the calculated amount of deformation of the steel structure is reduced, and the registration and calculation efficiency of the steel structure is improved.

S202: obtaining a discrete point set of an axis model to which the steel structure belongs, wherein the discrete point set comprises a plurality of reference points.

The method comprises the steps of obtaining a design model of a steel structure, extracting an axis of the steel structure, and constructing the axis model, wherein the design model is a three-dimensional model. And carrying out discretization sampling on the point cloud data on each axis of the axis model to obtain each reference point of the discretization point set. The reference point is from a standard three-dimensional model, namely the reference point can be used for matching the reference standard of the sampling measuring point, so that the reliable matching between the reference point and the steel structure sampling measuring point is facilitated.

In the embodiment of the present application, obtaining a discrete point set of an axis model to which a steel structure belongs includes:

obtaining an axis model to which the steel structure belongs, wherein the axis model comprises a plurality of axes;

determining vertexes corresponding to each axis, wherein the vertexes comprise a first vertex and a second vertex;

extracting an axis point in the axis at preset intervals from the first vertex of each axis until the extracted axis point is the second vertex, and stopping the extraction operation;

the vertices and axis points on each axis are taken as reference points, and the reference points are merged into a discrete point set.

Specifically, a design model is obtained in modeling software through a design drawing of the steel structure to serve as a standard model of the actual steel structure. And extracting a plurality of axes of the steel structure from the design model to obtain an axis model, wherein the axis model is formed by the axes of the steel structure, and each point in the three-dimensional point cloud data with each discrete axis can be used as a reference point.

In the embodiment of the present application, for each axis l in the design model _i Respectively corresponding first vertexes a _i And a second vertex b _i I is a positive integer and the predetermined interval is

Starting from the first vertex of each axis, at axis l _i According to the interval

One axis point is extracted. As shown in FIG. 4, FIG. 4 is a schematic view of discrete points of an axis model of a steel structure according to an embodiment of the present invention, from a first vertex a _i At the beginning, according to

Along the axis l _i Sampling the axis point until sampling to b _i Finally, combining the vertex and the axis point on each axis as reference points into a discrete point set, wherein the discrete point set is

S203: and calculating a rigid body transformation matrix of the sampling point set according to the corresponding relation between each sampling measuring point and each reference point.

The corresponding relation refers to the matching relation between the sampling measuring points and the reference points, namely the registration between the three-dimensional point clouds. And (3) carrying out rigid body transformation processing on each sampling measurement point through a known matching relation to obtain a rigid body transformation matrix, and realizing the mapping from the sampling measurement point to the reference point. Where rigid body transformations can be decomposed into translation transformations, rotation transformations, and inversion (mirror) transformations.

In this embodiment of the present application, before calculating the rigid body transformation matrix of the sampling point set according to the corresponding relationship between each sampling measurement point and each reference point, the specific implementation of determining the corresponding relationship includes:

acquiring a first vector of each sampling measurement point in a preset second enclosing box;

acquiring a second vector of each reference point in a preset third bounding box;

and constructing the corresponding relation between each sampling measuring point and each reference point according to the first vector and the second vector.

In this embodiment of the present application, obtaining a first vector of each sampling measurement point in a preset second bounding box includes:

constructing a second enclosure box according to the sampling point set, wherein the second enclosure box comprises a plurality of key points carrying numbers;

respectively calculating the distance from each sampling measurement point in the sampling point set to a plurality of key points to obtain a plurality of first distances corresponding to each sampling measurement point;

and obtaining a first vector of each sampling measuring point according to the numbering sequence and the plurality of first distances.

Specifically, a second bounding box is constructed from each of the sampled measurement points in the set of sampled points, and the key points include eight corner points and a center point of the second bounding box. As shown in fig. 5, fig. 5 is a schematic diagram of a second bounding box according to an embodiment of the present application, and the eight corner points of the second bounding box are numbered in a certain order. For example, 8 corner points are numbered 1-8, respectively, and can be located at a distance o from the origin of the preset coordinate system _xyz The nearest corner point is marked with No. 1, and three corner points nearest to the corner point of No. 1 are respectively marked with No. 2, no. 3 and No. 4 in the anticlockwise direction. And marking the rest corner points connected with the corner point No. 1 with No. 5, marking the corner point connected with the corner point No. 2 with No. 6, marking the corner point connected with the corner point No. 3 with No. 7, and marking the corner point connected with the corner point No. 4 with No. 8. For each sample measurement point P in the set of sample points P _i Calculating each sampled measurement point p _i Sequentially obtaining 9 Euclidean distances from eight corner points (from No. 1 to No. 8) and a central point of a second bounding box of the sampling point set P to generate a 9-dimensional first vector, wherein the first vector is a sampling measurement point P _i Descriptor fp of _i 。

In the embodiment of the present application, the second vector of each reference point in the preset third bounding box is obtained in the same way as the first vector of each sampled measurement point in the preset second bounding box, i.e. the third bounding box is constructed from each reference point in the discrete point set. In the same way, each reference point Q in Q is collected by discrete points _i The Euclidean distances from eight angular points and the central point of the third bounding box are obtained to obtain 9 Euclidean distances, and one is generatedA 9-dimensional second vector which is the descriptor fq of the reference point _i 。

In the embodiment of the present application, constructing a corresponding relationship between each sampling measurement point and each reference point according to the first vector and the second vector includes:

calculating the similarity of the first vector of the sampling measuring point and the second vector of each reference point;

and determining the corresponding relation of the sampling measurement points according to the reference points corresponding to the maximum similarity.

In particular, from the first vector and the second vector, a correspondence between each sampled measurement point and each reference point is constructed, i.e. based on the descriptor fp _i And descriptor fq _i And establishing a corresponding relation between the sampling measuring point and the reference point. By the operator fp _i And descriptor fq _i Similarity measure of (3), i.e. similarity measure sim = fp _i ·fq _i Finding out the reference point Q with maximum sim value in the discrete point set Q _i While, i.e. sampling, the measuring point q _i And a reference point p _i And correspondingly.

Specifically, based on the corresponding relationship between points in the sampling point set P and the discrete point set Q, a covariance matrix is calculated

Wherein, the first and the second end of the pipe are connected with each other,

n and M are the total number of sampling measuring points in the sampling 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 to obtain [ U, S, V []= SVD (CV), where U and V represent two mutually orthogonal matrices and S represents a diagonal matrix. Further calculating through U and V to obtain a sampling measurement point p _i And a reference point q _i Wherein the rigid body transformation matrix comprises a rotation matrix R = VU ^T Translation vector t = -R × o _P +o _Q 。

S204: and determining the average distance between the sampling point set and the discrete point set according to the rigid body transformation matrix.

In particular, the rotation matrix R = VU corresponding to the sampling measurement points comprised by the rigid body transformation matrix ^T Translation vector t = -R x o _P +o _Q Calculating the average distance between the transformed sample point set and the discrete point set to determine the minimum average distance, i.e. the minimum average distance

S205: and updating each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and returning to the step of calculating the rigid body transformation matrix of the sampling point set until the difference value between the currently obtained average distance and the last obtained average distance is smaller than a preset difference threshold value, and taking the currently updated sampling measurement point as a target sampling measurement point.

In the examples of the present application, the minimum average distance is recorded

And then, obtaining a new sampling point set P' = R x P + t, namely updating each sampling measurement point in the current sampling point set, returning each updated sampling measurement point to the step of executing the step S203 and the step S204 to calculate the average distance corresponding to each transformation, finishing iteration when the difference value of the adjacent average distances is smaller than a preset difference threshold value, and taking the latest updated sampling measurement point as a target sampling measurement point. Wherein the preset difference threshold may be 1e ^-3 Specifically, the setting may be performed according to an actual scene, and is not limited herein. For example, the last obtained average distance d _k-1 And the currently obtained average distance d _k The calculated difference is less than the threshold value 1e ^-3 And then, finishing iteration, and realizing registration of the sampling measuring point of the steel structure and the reference point of the design model.

S206: and determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model.

Specifically, the distance from each target sampling measurement point after iterative transformation to each axis is calculated, and different distances can be displayed by using a color difference map, so that a deformation position with large deformation can be quickly positioned.

In the embodiment of the application, the determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model comprises:

obtaining a plurality of first distances of the target sampling measuring point according to the distance from the target sampling measuring point to each axis;

taking the minimum first distance as the distance from the target sampling measurement point to the axis model;

determining each deformation quantity of the steel structure according to the distance from each target sampling measurement point to the axis model;

and determining the deformation position of the steel structure according to the target sampling measurement point corresponding to the maximum deformation.

Specifically, p 'is measured for each target sample' _i Calculating a first distance based on a point-to-line distance formula

On-axis model

Find the axis l that minimizes the dist value _j The head and tail vertexes of the axis are a respectively _j And b _j Minimum distance dist at this time _min I.e. the target sampling measurement point p _i Distance to the axis model. And all the first distances obtained based on calculation are all deformation quantities of the steel structure. And coloring and displaying each target sampling measuring point, wherein the color of the corresponding point with the larger first distance is darker. Based on the steps, the target sampling measuring point with the dense color can be quickly positioned, and the position with the large deformation in the large-span steel structure can be positioned according to the space coordinate of the target sampling measuring point.

The method comprises the steps of obtaining a sampling point set of a steel structure and a discrete point set of an axis model to which the steel structure belongs, calculating a rigid transformation matrix of the sampling point set according to the corresponding relation between each sampling measurement point and each reference point, determining the average distance between the sampling point set and the discrete point set according to the rigid transformation matrix, updating each sampling measurement point in the sampling point set based on the rigid transformation matrix, returning to the step of calculating the rigid transformation matrix of the sampling point set, taking the currently updated sampling measurement point as a target sampling measurement point when the difference value between the currently obtained average distance and the last obtained average distance is smaller than a preset difference threshold value, and determining the deformation position of the steel structure according to the distance between each target sampling measurement point and the axis model. On one hand, under the corresponding relation between the sampling measuring points and the reference points of the steel structure, the registration accuracy between the points is improved in a mode of updating the sampling measuring points according to the rigid body transformation matrix, and then the deformation position of the steel structure is determined according to the distance from the obtained target sampling measuring points to the axis model, so that the position with large deformation can be quickly positioned, the difficulty of deformation detection of the large-span steel structure is effectively reduced, and the efficiency and the accuracy of deformation positioning of the large-span steel structure are improved. On the other hand, compared with the traditional deformation detection method for the large-span steel structure, the method is applicable to the detection scene of the complex steel structure or the serious point cloud data noise, and has high robustness.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 6, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a steel structure deformation positioning apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.

As shown in fig. 6, for the structural schematic diagram of an embodiment of the steel structure deformation positioning device provided by the present 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 localization module 66. Wherein the content of the first and second substances,

the measuring point acquisition module 61 is used for acquiring a sampling point set of the steel structure, wherein the sampling point set comprises a plurality of sampling measuring points;

the datum point acquisition module 62 is configured to acquire a discrete point set of an axis model to which the steel structure belongs, where the discrete point set includes a plurality of datum points;

the rigid body transformation module 63 is used for calculating a rigid body transformation matrix of the sampling point set according to the corresponding relation between each sampling measurement point and each reference point;

an average distance obtaining module 64, configured to determine an average distance between the sampling point set and the discrete point set according to the rigid body transformation matrix;

an iteration module 65, configured to update each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and return to the step of executing the rigid body transformation matrix of the sampling point set, and take the currently updated sampling measurement point as a target sampling measurement point until a difference between the currently obtained average distance and the last obtained average distance is smaller than a preset difference threshold;

and the deformation positioning module 66 is used for determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model.

In some embodiments, the measurement point acquisition module 61 includes:

the first acquisition submodule is used for acquiring a plurality of original three-dimensional measurement points of the steel structure;

the first confirming submodule is used for determining a first bounding box according to the original three-dimensional measuring points, wherein the first bounding box comprises a plurality of grid cubic voxels;

the sampling submodule is used for sampling the grid cubic voxels including the original three-dimensional measuring points in a preset number to obtain sampling measuring points;

and the iteration submodule is used for taking the original measuring three-dimensional points which are not sampled as new original three-dimensional measuring points when the total number of the sampling measuring points is less than or equal to the preset number threshold, returning to execute the step of determining the first enclosure box according to the original three-dimensional measuring points, and stopping sampling until the total number of the sampling measuring points is greater than the preset number threshold to obtain a sampling point set.

In some embodiments, the fiducial point acquisition module 62 includes:

the second obtaining submodule is used for obtaining an axis model to which the steel structure belongs, wherein the axis model comprises a plurality of axes;

the second determining submodule is used for determining vertexes corresponding to each axis, wherein the vertexes comprise a first vertex and a second vertex;

the extraction submodule is used for extracting an axis point in the axis at preset intervals from the first vertex of each axis until the extracted axis point is the second vertex, and stopping extraction operation;

and the merging submodule is used for taking the vertex and the axis point on each axis as reference points and merging the reference points into a discrete point set.

In some embodiments, the steel structure deformation positioning device further comprises:

the first acquisition module is used for acquiring a first vector of each sampling measurement point in a preset second enclosing box;

the second acquisition module is used for acquiring a second vector of each reference point in a preset third bounding box;

and the construction module is used for constructing the corresponding relation between each sampling measuring point and each reference point according to the first vector and the second vector.

In some embodiments, the first obtaining module comprises:

the construction submodule is used for constructing a second enclosure box according to the sampling point set, wherein the second enclosure box comprises a plurality of key points carrying numbers;

the first calculation submodule is used for calculating the distance from each sampling measuring point in the sampling point set to a plurality of key points respectively to obtain a plurality of first distances corresponding to each sampling measuring point;

and the numbering submodule is used for obtaining a first vector of each sampling measuring point according to the numbering sequence and the plurality of first distances.

In some embodiments, the building block comprises:

the second calculation submodule is used for calculating the similarity of the first vector of the sampling measuring point and the second vector of each datum point;

and the third determining submodule is used for determining the corresponding relation of the sampling measuring points according to the reference point corresponding to the maximum similarity.

In some embodiments, the deformation-locating module 66 includes:

the distance acquisition submodule is used for acquiring a plurality of first distances of the target sampling measuring point according to the distance from the target sampling measuring point to each axis;

a fourth determining submodule, which is used for taking the minimum first distance as the distance from the target sampling measuring point to the axis model;

the fifth determining submodule is used for determining each deformation quantity of the steel structure according to the distance from each target sampling measuring point to the axis model;

and the sixth determining submodule is used for determining the deformation position of the steel structure according to the target sampling measurement point corresponding to the maximum deformation.

With regard to the steel structure deformation positioning device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.

In order to solve the technical problem, the embodiment of the application further provides computer equipment. Referring to fig. 7 in particular, fig. 7 is a block diagram of a basic structure of a computer device according to the embodiment.

The computer device 7 comprises a memory 71, a processor 72, a network interface 73, which are communicatively connected to each other via a system bus. It is noted that only a computer device 7 having components 71-73 is shown in the figure, but it is to be understood that not all of the shown components are required to be implemented, and that more or less components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 71 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or D steel structure shape change positioning memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 71 may be an internal storage unit of the computer device 7, such as a hard disk or a 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, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 7. Of course, the memory 71 may also comprise both an internal storage unit of the computer device 7 and an external storage device thereof. In this embodiment, the memory 71 is generally used for storing an operating system installed in the computer device 7 and various types of application software, such as program codes of a steel structure deformation positioning method. Further, the memory 71 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 72 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 72 is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is configured to run a program code stored in the memory 71 or process data, for example, a program code for running the steel structure deformation positioning method.

The network interface 73 may comprise a wireless network interface or a wired network interface, and the network interface 73 is generally used for establishing a communication connection between the computer device 7 and other electronic devices.

The present application further provides another embodiment, which is to provide a computer readable storage medium storing a steel structure deformation positioning program, where the steel structure deformation positioning program is executable by at least one processor to cause the at least one processor to execute the steps of the steel structure deformation positioning method as described above.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (such as a ROM/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It should be understood that the above-described embodiments are merely exemplary of some, and not all, embodiments of the present application, and that the drawings illustrate preferred embodiments of the present application without limiting the scope of the claims appended hereto. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A steel structure deformation positioning method is characterized by comprising the following steps:

obtaining a discrete point set of an axis model to which the steel structure belongs, wherein the discrete point set comprises a plurality of reference points;

updating each sampling measurement point in the sampling point set based on the rigid body transformation matrix, and returning to the step of calculating the rigid body transformation matrix of the sampling point set until the difference value between the average distance obtained currently and the average distance obtained last time is smaller than a preset difference threshold value, and taking the sampling measurement point after current updating as a target sampling measurement point;

2. The method for positioning deformation of a steel structure according to claim 1, wherein the acquiring of the sampling point set of the steel structure comprises:

determining a first bounding box from the raw three-dimensional measurement points, wherein the first bounding box comprises a plurality of grid cube voxels;

sampling the grid cubic voxels including the original three-dimensional measurement points in a preset number to obtain sampling measurement points;

and when the total number of the sampling measuring points is less than or equal to the preset number threshold, taking the original measuring three-dimensional points which are not sampled as new original three-dimensional measuring points, and returning to execute the step of determining the first enclosure box according to the original three-dimensional measuring points, and stopping sampling until the total number of the sampling measuring points is greater than the preset number threshold, so as to obtain a sampling point set.

3. The method for positioning deformation of a steel structure according to claim 1, wherein the obtaining of the discrete point set of the axis model to which the steel structure belongs comprises:

determining a vertex corresponding to each axis, wherein the vertex comprises a first vertex and a second vertex;

and taking the vertex and the axis point on each axis as reference points, and combining the reference points into a discrete point set.

4. A method for locating deformation of a steel structure according to claim 1, wherein before said calculating a rigid body transformation matrix for said set of sampling points based on a correspondence between each of said sampled measurement points and each of said reference points, said method further comprises:

acquiring a first vector of each sampling measuring point in a preset second enclosing box;

5. The method for positioning deformation of a steel structure according to claim 4, wherein the obtaining a first vector of each sampling measurement point in a preset second bounding box comprises:

constructing a second bounding box according to the sampling point set, wherein the second bounding box comprises a plurality of key points carrying numbers;

respectively calculating the distance from each sampling measuring point in the sampling point set to a plurality of key points to obtain a plurality of first distances corresponding to each sampling measuring point;

6. The method for positioning deformation of a steel structure according to claim 4, wherein the constructing a corresponding relationship between each sampling measurement point and each reference point according to the first vector and the second vector comprises:

calculating a similarity of the first vector of the sampled measurement points and a second vector of each of the reference points;

and determining the corresponding relation of the sampling measuring points according to the reference points corresponding to the maximum similarity.

7. The method for positioning deformation of a steel structure according to claim 2, wherein the determining the deformation position of the steel structure according to the distance from each target sampling measurement point to the axis model comprises:

determining deformation quantities of the steel structure according to the distance from each target sampling measurement point to the axis model;

8. The utility model provides a steel construction deformation positioner, its characterized in that, steel construction deformation positioner includes:

the iteration module is used for updating each sampling measurement point in the sampling point set based on the rigid body transformation matrix and returning to the step of calculating the rigid body transformation matrix of the sampling point set, and the current updated sampling measurement point is used as a target sampling measurement point until the difference value between the current obtained average distance and the last obtained average distance is smaller than a preset difference threshold value;

9. Computer apparatus comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the steel structure deformation positioning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the steel structure deformation positioning method according to any one of claims 1 to 7.