CN116150855A - Building component assembling analysis method and system based on point cloud wire frame constraint - Google Patents
Building component assembling analysis method and system based on point cloud wire frame constraint Download PDFInfo
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Abstract
The invention discloses a building component assembling analysis method and system based on point cloud wire frame constraint, which relate to the related field of building construction pre-assembling and obtain position parameters of a sampling measurement device; the sampling measurement device is used for collecting data of building components to obtain a point cloud data set; obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set; constructing an assembled building BIM model according to the assembled building model component library and the processing point cloud data set; and importing the BIM model of the assembled building into Geomagic Control, comparing by setting a reference model, and outputting an assembly analysis result. The technical problems of high cost, long time consumption and low analysis precision in the process of pre-assembling analysis in the prior art are solved.
Description
Technical Field
The invention relates to the field of building construction pre-assembly, in particular to a building component assembly analysis method and system based on point cloud wire frame constraint.
Background
Fabricated buildings are buildings that are built up by machining components inside a factory and then transporting them to a construction site for installation. To ensure that the building elements are accurately installed in place after shipment to the site, it is generally necessary to pre-assemble the building elements at the factory to verify the assemblability of the structure.
The solid pre-assembly is carried out in factories, namely, the assembly type building components manufactured in sections are subjected to the operation process of integral or sectional layered temporary assembly before leaving factories. The adoption of entity pre-assembly not only needs to occupy a large area, but also has high cost, long time consumption and lower precision.
In the process of pre-assembly analysis, the prior art has the technical problems of high cost, long time consumption and low analysis precision.
Disclosure of Invention
The technical problems of high cost, long time consumption and low analysis precision in the pre-assembly analysis process in the prior art are solved by the building component assembly analysis method and system based on the point cloud wire frame constraint.
In view of the above problems, the application provides a building component assembling analysis method and system based on point cloud wire frame constraint.
In a first aspect, the present application provides a method for analyzing assembly of building elements based on point cloud wire frame constraints, the method being applied to an assembly analysis system of building elements, the assembly analysis system of building elements being communicatively connected to a sampling measurement device, the method comprising: obtaining a position parameter of the sampling measurement device; the sampling measurement device is used for collecting data of building components to obtain a point cloud data set; obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set; constructing an assembled building BIM model according to the assembled building model component library and the processing point cloud data set; and importing the BIM model of the assembled building into Geomagic Control, comparing by setting a reference model, and outputting an assembly analysis result.
In another aspect, the present application further provides a building element assembly analysis system based on point cloud wire frame constraints, the system being communicatively connected to a sampling measurement device, the system comprising: the position sampling module is used for obtaining the position parameters of the sampling measurement device; the point cloud sampling module is used for acquiring data of building components through the sampling measurement device to obtain a point cloud data set; the constraint processing module is used for obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set; the construction module is used for constructing an assembled building BIM according to the assembled building model component library and the processing point cloud data set; and the assembly analysis module is used for guiding the BIM model of the assembled building into Geomagic Control, comparing the BIM model with the Geomagic Control model by setting a reference model, and outputting an assembly analysis result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of reading position parameters through a sampling measurement device, collecting data of building construction through the sampling measurement device, obtaining a point cloud data set of building construction to be preassembled, carrying out coordinate identity on the basis of the point cloud data set, the position parameters and line frame data, completing constraint processing of the point cloud data, obtaining a processed point cloud data set, constructing an assembled building BIM (building information modeling) model according to an assembled building model component library and the processed point cloud data set, guiding the assembled building BIM model into Geomagic Control, comparing through a set reference model, outputting an assembling analysis result, carrying out modeling preassembly according to a sampling fitting result through sampling fitting of building components, and achieving the technical effects of reducing preassembly cost, reducing preassembly time and improving analysis precision.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
FIG. 1 is a schematic flow diagram of a method for analyzing assembly of building elements based on point cloud wire frame constraints;
FIG. 2 is a schematic flow chart of a method for analyzing the assembly of building elements based on the constraint of point cloud wire frames to obtain and process point cloud data sets;
fig. 3 is a schematic structural diagram of a construction element assembly analysis system based on point cloud wire frame constraint.
Reference numerals illustrate: the system comprises a position sampling module 1, a point cloud sampling module 2, a constraint processing module 3, a construction module 4 and an assembly analysis module 5.
Detailed Description
The technical problems of high cost, long time consumption and low analysis precision in the pre-assembly analysis process in the prior art are solved by the building component assembly analysis method and system based on the point cloud wire frame constraint. Embodiments of the present application are described below with reference to the accompanying drawings. As one of ordinary skill in the art can appreciate, with the development of technology and the appearance of new scenes, the technical solution provided in the present application is also applicable to similar technical problems.
The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the present application provides a building element assembly analysis method based on point cloud wire frame constraint, the method is applied to a building element assembly analysis system, the building element assembly analysis system is in communication connection with a sampling measurement device, and the method includes:
step S100: obtaining a position parameter of the sampling measurement device;
step S200: the sampling measurement device is used for collecting data of building components to obtain a point cloud data set;
specifically, the point cloud data is a set of vectors in one three-dimensional coordinate system, and color information exists in addition to the set position. The acquisition of the point cloud data always has a certain difficulty, for example, the scanned point cloud data usually contains a considerable number of missing areas, because of shielding of objects such as trees, vehicles and the like or imperfect scanning conditions, such as light problems, or the point cloud data is easy to receive noise infection in the acquisition process, so that the acquired point cloud is scattered and irregular. In addition, since the prefabricated building elements are numerous and the diversity of shapes is also complex, manually modeling the prefabricated building is a rather time-consuming process.
In order to acquire and construct more accurate point cloud data, first, setting of a sampling measurement device is performed before data acquisition is performed. The sampling measurement device is a device capable of performing data acquisition and construction, such as a hand-held Augmented Reality (AR) smart phone, a three-dimensional laser scanning (LiDAR) or other scanners. And carrying out data acquisition through the sampling measurement device, taking the directly measured data as a plurality of 2D images, fusing the data with a 3D scene through positioning and mapping equipment positions, and converting the images into 3D point clouds.
Step S300: obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set;
specifically, the wire frame data are CAD wire frame data of building components, CAD wire frame data of all assembled building components are obtained through observation of a total station, the wire frame data of the building components are matched according to components acquired by the point cloud data, and the point cloud constraint processing of the point cloud data set is performed according to the matched wire frame data.
Further, the processing includes a process of performing coordinate system integration and a process of point cloud constraint. The coordinate system is used for placing the point cloud data and the wire frame data under the same coordinate system so as to facilitate comparison and analysis, and further simplify the processing workload of point cloud constraint.
Further, step S300 of the present application further includes:
step S310: taking the position parameter as an origin of coordinates, taking the wire frame data as an absolute coordinate system, carrying out coordinate system unification of the point cloud data set and the wire frame data, and obtaining a processed point cloud data set according to a result of the coordinate system, wherein a calculation formula of the coordinate system is as follows:
wherein, (x, y) is the coordinate of the point cloud data before transformation, (-) is shown in the specification
,/>
) For the transformed point cloud data coordinates, θ is the rotation angle, λ is the scaling factor, Δx is the translation factor in the X direction, and Δy is the translation factor in the Y direction.
Further, as shown in fig. 2, step S300 of the present application further includes:
step S320: setting a distance constraint threshold n;
step S330: performing distance constraint comparison between the point cloud data set and the associated wire frame data through the distance constraint threshold n to obtain a distance constraint comparison result;
step S340: when the distance constraint comparison result is that the distance constraint threshold n is met, translating the point cloud data in the met point cloud data set to an associated wire frame, and removing the original point cloud data;
step S350: and when the distance constraint comparison result is that the distance constraint threshold value n is not met, eliminating the corresponding point cloud data, and carrying out the point cloud data set processing according to the elimination and translation to obtain the processing point cloud data set.
Specifically, the position parameter is used as the origin of coordinates, that is, the position of the acquisition and measurement device is used as the origin of coordinates, the X axis is in the horizontal scanning plane, the Y axis is perpendicular to the X axis in the horizontal scanning plane, the Z axis is perpendicular to the horizontal scanning plane, the CAD line frame data is the local absolute coordinate system, the point cloud data set and the CAD line frame data are projected to the O-XY plane, and the rotation is utilizedRotating, translating and zooming, unifying two groups of data coordinate systems,
wherein, (x, y) is the coordinate of the point cloud data before transformation, (-) is shown in the specification
,/>
) For the transformed point cloud data coordinates, θ is the rotation angle, λ is the scaling factor, Δx is the translation factor in the X direction, and Δy is the translation factor in the Y direction.
Further, after unifying the coordinate system, the data constraint of the point cloud data set is performed through a distance constraint threshold n, and taking a point in one point cloud data set as an example, the specific constraint is as follows: setting a distance constraint threshold n, obtaining distance information of the point and a corresponding wire frame straight line, judging the relation between the distance information and the distance constraint threshold n, and if the distance information is larger than the distance constraint threshold n, not processing the point; and when the distance information is smaller than the distance constraint threshold value n, the point is considered to be on the line frame straight line, the point is translated to the corresponding line frame straight line, and the current point is removed from the point cloud data set. And updating data after the translation of the removed points, and directly removing the points which are not processed as noise points.
And performing the processing on all the points in the point cloud data set, and after all the points are traversed, obtaining the processed point cloud data set according to the removing, eliminating and data updating results.
By processing the point cloud data set, the obtained point cloud data is lower in noise and more accurate in data, and a foundation is tamped for the follow-up accurate assembly fitting.
Step S400: constructing an assembled building BIM model according to the assembled building model component library and the processing point cloud data set;
step S500: and importing the BIM model of the assembled building into Geomagic Control, comparing by setting a reference model, and outputting an assembly analysis result.
Specifically, the building in Building (BIM) model is a pre-assembled model constructed based on the collected processing point cloud data set and the building model component library. The component representations in the fabricated building model component library are xi= (cl, l, s, r), where cl represents component category (e.g., prefabricated staircase), l represents position, s represents scaling, r represents rotation, typically about the z-axis.
Further, step S400 of the present application further includes:
calculating root mean square error functions of the processing point cloud data set and the assembly building BIM model, wherein the calculation formula is as follows:
wherein ,
is root mean square error function>
For inputting point cloud->
Representing a point cloud acquired by a BIM model surface and having the same point density as the input point cloud, nndist represents Euclidean distance, and BIM represents a BIM surface determined by X.
Further, step S400 of the present application further includes:
(a) Traversing the components Xi in the component library of the assembled building model, and performing value decoding Revit on the components Xi;
(b) Modifying an ith component based on the value decoding Revit through a Revit API, sampling the BIM model surface of the modified ith component, and obtaining a point cloud
；
(c) Input point clouds are input to the (i+1) th module through voxelization in sequence
Sum point cloud->
Sampling, and calculating to obtain the minimum RMSE of the sampling result according to the sampling result and the root mean square error function, wherein a component X corresponding to the minimum RMSE is an optimized component under the automatic modeling condition; />
(d) Updating the covariance matrix of the minimum RMSE through a core algorithm CMA-ES and evolving a search strategy;
(e) Repeating the steps a-d until the whole building block BIM model and the topology thereof are built gradually from the model component library, and completing the building of the building block BIM model.
Specifically, a function f of the root mean square error RMSE between the building in assembly BIM model and the input point cloud is calculated as follows:
wherein ,
is root mean square error function>
For inputting point cloud->
Representing a point cloud acquired by a BIM model surface and having the same point density as the input point cloud, nndist represents Euclidean distance, and BIM represents a BIM surface determined by X.
After the root mean square error function is calculated, the COBIMG-Revit is run. The COBIMG-Revit is a model generation plug-in based on a BIM platform, and comprises three modules, namely, a module 1: the core algorithm module is CMA-ES integrated in COBIMG, module 2: RMSE computation module, module based on f (X) computation, module 3: the component mounting module is a component mounting module extended from cobimag.
The operating steps for operating the COBIMG-Revit are as follows:
(a) Traversing all components Xi that satisfy the condition, the component assembly module decodes the value of Xi into Revit family (cl), location (l), scale(s), and rotation (r).
(b) Modifying the ith component based on the decoded parameters through a Revit API, and sampling the BIM model surface of the modified ith component by a component assembly module to obtain a point cloud
。
(c) Input point clouds are input to the (i+1) th module through voxelization in sequence
Sum point cloud->
Sampling is carried out, the minimum RMSE of the sampling result is obtained according to the sampling result and the root mean square error function, and the minimum RMSE is returned to the first module of the core algorithm.
(d) The core algorithm CMA-ES updates its covariance matrix and evolves the search strategy.
(e) Repeating the steps a-d until the whole building block BIM model and the topology thereof are built gradually from the model component library, and completing the building of the building block BIM model.
Further, the BIM model of the assembled building is imported into Geomagic Control, comparison is carried out through setting a reference model, and an assembly analysis result is output, namely the BIM model of the assembled building is output as a model to be tested, a BIM theoretical model of pre-assembled building is taken as the reference model, and the model to be tested is imported into Geomagic Control software. The method comprises the steps of coloring a model to be tested, secondarily reducing noise, removing external orphan points and the like, setting a reference model as a digital fitting standard model, setting digital fitting parameters, and comparing the model to be tested with the reference model.
Example two
Based on the same inventive concept as the building element assembling analysis method based on the point cloud wire frame constraint in the foregoing embodiment, the present invention further provides a building element assembling analysis system based on the point cloud wire frame constraint, as shown in fig. 3, the system is in communication connection with a sampling measurement device, and the system includes:
the position sampling module 1 is used for obtaining the position parameters of the sampling measurement device;
the point cloud sampling module 2 is used for acquiring data of building components through the sampling measurement device to obtain a point cloud data set;
the constraint processing module 3 is used for obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set;
the construction module 4 is used for constructing an assembled building BIM according to the assembled building model component library and the processing point cloud data set;
and the assembly analysis module 5 is used for leading the BIM model of the assembled building into Geomagic Control, comparing the BIM model with the Geomagic Control model by setting a reference model, and outputting an assembly analysis result.
Further, the constraint processing module 3 is further configured to:
taking the position parameter as an origin of coordinates, taking the wire frame data as an absolute coordinate system, carrying out coordinate system unification of the point cloud data set and the wire frame data, and obtaining a processed point cloud data set according to a result of the coordinate system, wherein a calculation formula of the coordinate system is as follows:
wherein, (x, y) is the coordinate of the point cloud data before transformation, (-) is shown in the specification
,/>
) For the transformed point cloud data coordinates, θ is the rotation angle, λ is the scaling factor, Δx is the translation factor in the X direction, and Δy is the translation factor in the Y direction.
Further, the constraint processing module 3 is further configured to:
setting a distance constraint threshold n;
performing distance constraint comparison between the point cloud data set and the associated wire frame data through the distance constraint threshold n to obtain a distance constraint comparison result;
when the distance constraint comparison result is that the distance constraint threshold n is met, translating the point cloud data in the met point cloud data set to an associated wire frame, and removing the original point cloud data;
and when the distance constraint comparison result is that the distance constraint threshold value n is not met, eliminating the corresponding point cloud data, and carrying out the point cloud data set processing according to the elimination and translation to obtain the processing point cloud data set.
Further, the construction module 4 is further configured to:
calculating root mean square error functions of the processing point cloud data set and the assembly building BIM model, wherein the calculation formula is as follows:
wherein ,
is root mean square error function>
For inputting point cloud->
Representing a point cloud acquired by a BIM model surface and having the same point density as the input point cloud, nndist represents Euclidean distance, and BIM represents a BIM surface determined by X.
Further, the construction module 4 is further configured to:
(a) Traversing the components Xi in the component library of the assembled building model, and performing value decoding Revit on the components Xi;
(b) Modifying an ith component based on the value decoding Revit through a Revit API, sampling the BIM model surface of the modified ith component, and obtaining a point cloud
；
(c) Input point clouds are input to the (i+1) th module through voxelization in sequence
Sum point cloud->
Sampling, and calculating to obtain the minimum RMSE of the sampling result according to the sampling result and the root mean square error function;
(d) Updating the covariance matrix of the minimum RMSE through a core algorithm CMA-ES and evolving a search strategy;
(e) Repeating the steps a-d until the whole building block BIM model and the topology thereof are built gradually from the model component library, and completing the building of the building block BIM model.
The foregoing various modifications and specific examples of a building element assembling analysis method based on the point cloud wire frame constraint in the first embodiment of fig. 1 are equally applicable to a building element assembling analysis system based on the point cloud wire frame constraint in this embodiment, and by the foregoing detailed description of a building element assembling analysis method based on the point cloud wire frame constraint, those skilled in the art can clearly know the implementation method of a building element assembling analysis system based on the point cloud wire frame constraint in this embodiment, so that, for brevity of description, details will not be described herein.
The foregoing description is only a preferred embodiment of the technical solution of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (8)
1. The method is applied to a building component assembly analysis system which is in communication connection with a sampling measurement device, and comprises the following steps:
obtaining a position parameter of the sampling measurement device;
the sampling measurement device is used for collecting data of building components to obtain a point cloud data set;
obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set;
constructing an assembled building BIM model according to the assembled building model component library and the processing point cloud data set;
and importing the BIM model of the assembled building into Geomagic Control, comparing by setting a reference model, and outputting an assembly analysis result.
2. The method of claim 1, wherein the method further comprises:
taking the position parameter as an origin of coordinates, taking the wire frame data as an absolute coordinate system, carrying out coordinate system unification of the point cloud data set and the wire frame data, and obtaining a processed point cloud data set according to a result of the coordinate system, wherein a calculation formula of the coordinate system is as follows:
wherein, (x, y) is the coordinate of the point cloud data before transformation, (-) is shown in the specification
, />
) For transforming the coordinates of the point cloud data, θ is the rotation angle, λFor scaling coefficients, Δx is the translational coefficient in the X direction and Δy is the translational coefficient in the Y direction.
3. The method of claim 2, wherein the method further comprises:
setting a distance constraint threshold n;
performing distance constraint comparison between the point cloud data set and the associated wire frame data through the distance constraint threshold n to obtain a distance constraint comparison result;
when the distance constraint comparison result is that the distance constraint threshold n is met, translating the point cloud data in the met point cloud data set to an associated wire frame, and removing the original point cloud data;
and when the distance constraint comparison result is that the distance constraint threshold value n is not met, eliminating the corresponding point cloud data, and carrying out the point cloud data set processing according to the elimination and translation to obtain the processing point cloud data set.
4. The method of claim 1, wherein the method further comprises:
calculating root mean square error functions of the processing point cloud data set and the assembly building BIM model, wherein the calculation formula is as follows:
wherein ,
is root mean square error function>
For inputting point cloud->
Represents the point cloud acquired by the BIM model surface and having the same point density as the input point cloud, nndist represents Euclidean distance, BIM represents XA defined BIM surface.
5. The method of claim 4, wherein constructing the building-from-building model component library and the process point cloud data set, further comprises:
(a) Traversing the components Xi in the component library of the assembled building model, and performing value decoding Revit on the components Xi;
(b) Modifying an ith component based on the value decoding Revit through a Revit API, sampling the BIM model surface of the modified ith component, and obtaining a point cloud
；
(c) Input point clouds are input to the (i+1) th module through voxelization in sequence
And Point cloud->
Sampling, and calculating to obtain the minimum RMSE of the sampling result according to the sampling result and the root mean square error function;
(d) Updating the covariance matrix of the minimum RMSE through a core algorithm CMA-ES and evolving a search strategy;
(e) Repeating the steps a-d until the whole building block BIM model and the topology thereof are built gradually from the model component library, and completing the building of the building block BIM model.
6. The method of claim 5, wherein the member X corresponding to the minimum RMSE is an optimized member under automatic modeling conditions.
7. The method of claim 1, wherein the fabricated building BIM model is imported into geomic Control, the fabricated building BIM model is colored, noise is reduced secondarily, external orphan points are eliminated, comparison is performed by setting a reference model, and a splicing analysis result is output.
8. A building element assembly analysis system based on point cloud wire frame constraints, the system being in communication with a sampling measurement device, the system comprising:
the position sampling module is used for obtaining the position parameters of the sampling measurement device;
the point cloud sampling module is used for acquiring data of building components through the sampling measurement device to obtain a point cloud data set;
the constraint processing module is used for obtaining wire frame data of the building component, and performing point cloud constraint processing according to the point cloud data set, the wire frame data and the position parameters to obtain a processed point cloud data set;
the construction module is used for constructing an assembled building BIM according to the assembled building model component library and the processing point cloud data set;
and the assembly analysis module is used for guiding the BIM model of the assembled building into Geomagic Control, comparing the BIM model with the Geomagic Control model by setting a reference model, and outputting an assembly analysis result.
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| CN117726625B (en) * | 2024-02-08 | 2024-05-24 | 深圳大学 | Building component assembly quality control method and system based on point cloud wire frame constraint |
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