Fabricated building assembly construction process detection method

Abstract

The invention relates to the technical field of image processing, in particular to a fabricated building assembly construction process detection method, which comprises the following steps: determining the probability that point cloud data of a position and other point cloud data belong to the same fabricated building component according to coordinate information and color information of point cloud data; the point cloud data of the determined position and other point cloud data belong to the same fabricated building component; when the number of the point cloud data divided into the same fabricated building component is smaller than a second threshold value, marking the point cloud data as noise information and removing the noise information; voxels corresponding to the point cloud data under the same fabricated building component are determined; and detecting the installation process of each fabricated building component in the fabricated building by using the point cloud data in the voxels. According to the invention, the denoising and redundancy reduction effects of the point cloud data are optimized, the original characteristics of the point cloud data are retained, and the quality of the point cloud data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method for detecting an assembly construction process of an assembled building. Background Art

[0002] Prefabricated buildings are a modern construction method that prefabricates the components or modules of a building in a factory and then transports these prefabricated parts to the construction site for assembly. With the rapid development of modern construction technology, prefabricated buildings have gradually become an important development direction of the construction industry with their high efficiency, environmental protection and energy saving. During the assembly and construction process of prefabricated buildings, point cloud data can be used to detect construction progress and details. Point cloud data is an advanced technical means that can provide high-precision three-dimensional information. Through data preprocessing, three-dimensional modeling, observation and analysis, etc., it can help to fully understand the actual situation and progress of the construction site, and provide strong support for construction management and decision-making.

[0003] Generally, point cloud data will contain noise and redundant data, so it is necessary to denoise and reduce redundancy of point cloud data. In the traditional process of denoising and reducing redundancy of point cloud data, uniform voxel filtering is often used to process point cloud data. However, since prefabricated buildings are made up of different building components, different building components have different characteristics. Therefore, the effect of denoising and reducing redundancy of point cloud data of prefabricated buildings using uniform voxel filtering is poor, which can easily lead to the loss of features of the original point cloud data, reduce the quality of point cloud data, and further lead to poor results of subsequent analysis and processing based on point cloud data. Summary of the invention

[0004] In order to solve the technical problem that the denoising and redundancy reduction processing of point cloud data is poor, resulting in the loss of features of the original point cloud data and reducing the quality of the point cloud data, the purpose of the present invention is to provide a method for detecting the assembly construction process of prefabricated buildings.

[0005] In order to solve the above technical problems, the technical solutions adopted are as follows: The embodiment of the present invention provides a method for detecting an assembly construction process of an assembled building, comprising: acquiring point cloud data of different positions of an assembled building during an assembly construction process; determining the probability that the point cloud data at the position and the other point cloud data belong to the same assembled building component according to coordinate information and color information of other point cloud data in a target space around the position and the point cloud data at the position; when the probability is greater than a first threshold, determining that the point cloud data at the position and the other point cloud data belong to the same assembled building component; when the number of point cloud data divided into the same assembled building component is less than a second threshold, marking it as noise information and removing it; when the number of point cloud data divided into the same assembled building component is not less than the second threshold, determining the voxel corresponding to the point cloud data under the same assembled building component according to the point cloud data in the same assembled building component, the average probability of the probability that adjacent point cloud data in the same assembled building component are divided into the same assembled building component, and the volume of the standard voxel; and detecting the installation process of each assembled building component in the assembled building using the point cloud data in the voxel.

[0006] Optionally, determining the probability that the point cloud data at the location and other point cloud data belong to the same prefabricated building component based on the coordinate information and color information of other point cloud data in the target space around the location and the point cloud data at the location includes: determining a first distance between the point cloud data at the location and the other point cloud data based on the coordinate information of other point cloud data in the target space around the location and the point cloud data at the location; determining the color difference between the point cloud data at the location and the other point cloud data based on the color information of other point cloud data in the target space around the location and the point cloud data at the location; and using the first distance and color difference to determine the probability that the point cloud data at the location and the other point cloud data belong to the same prefabricated building component.

[0007] Optionally, using the first distance and color difference to determine the probability that the point cloud data at the location and other point cloud data belong to the same prefabricated building component includes: calculating a first product between the first distance and the color difference; normalizing the first product to obtain the probability that the point cloud data at the location and other point cloud data belong to the same prefabricated building component.

[0008] Optionally, determining the voxel corresponding to the point cloud data under the same prefabricated building component based on the point cloud data in the same prefabricated building component, the average probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, and the volume of the standard voxel includes: determining the density parameter of the point cloud data under the same prefabricated building component based on a first number of point cloud data in the same prefabricated building component, the average probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, the maximum distance between the point cloud data in the same prefabricated building component, the first volume of the largest cuboid wrapped by the first number of point cloud data, and the second volume of the largest cuboid wrapped by all point cloud data in the prefabricated building; determining the voxel corresponding to the point cloud data under the same prefabricated building component based on the density parameter of the point cloud data under the same prefabricated building component and the volume of the standard voxel.

[0009] Optionally, determining the density parameter of the point cloud data under the same prefabricated building component based on the first number of point cloud data in the same prefabricated building component, the average probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, the maximum distance between the point cloud data in the same prefabricated building component, the first volume of the largest cuboid enclosed by the first number of point cloud data, and the second volume of the largest cuboid enclosed by all point cloud data in the prefabricated building includes: calculating a first ratio between the average probability and the maximum distance, and a second ratio between the second volume and the first volume; and determining the second product of the first number, the first ratio and the second ratio as the density parameter of the point cloud data under the same prefabricated building component.

[0010] Optionally, determining the voxel corresponding to the point cloud data under the same prefabricated building component based on the density parameters of the point cloud data under the same prefabricated building component and the volume of the standard voxel includes: determining the volume of the standard voxel based on the point cloud data being in a uniform state; normalizing the density parameter to obtain a normalized density parameter; and determining the third product of the normalized density parameter and the volume as the voxel corresponding to the point cloud data under the same prefabricated building component.

[0011] Optionally, using point cloud data within a voxel to detect the installation process of each prefabricated building component in the prefabricated building includes: determining the average value of the point cloud data within the voxel as the target point cloud data corresponding to the voxel; and using the target point cloud data to detect the installation process of each prefabricated building component in the prefabricated building corresponding to the target point cloud data.

[0012] Optionally, using the target point cloud data to detect the installation process of each prefabricated building component corresponding to the target point cloud data in the prefabricated building includes: determining the target prefabricated building component whose position information of the target point cloud data at adjacent moments changes based on the target point cloud data of the prefabricated building component at adjacent moments; determining the vector direction of each target point cloud data in the target prefabricated building component in space based on the spatial coordinates of the target point cloud data in the target prefabricated building component at adjacent moments; and detecting the installation process of the target prefabricated building component using the vector direction of each target point cloud data in the target prefabricated building component in space.

[0013] Optionally, determining the vector direction of each target point cloud data in the target prefabricated building component in space based on the spatial coordinates of the target point cloud data at adjacent moments in the target prefabricated building component includes: calculating the difference between the spatial coordinates of the target point cloud data at adjacent moments as the spatial vector of the target point cloud data in space; and determining the vector direction of the spatial vector as the vector direction of the target point cloud data in space.

[0014] Optionally, the installation process of the target prefabricated building component is detected using the vector direction of each target point cloud data in the space in the target prefabricated building component, including: if the vector direction of each target point cloud data in the space is inconsistent, then it is determined that the installation process of the target prefabricated building component is abnormal; if the vector direction of each target point cloud data in the space is consistent, then the distribution direction of the corresponding target point cloud data is constructed along the vector direction; if the distribution direction is consistent with the vector direction of the corresponding target point cloud data, then it is determined that the installation process of the target prefabricated building component is good; if the distribution direction is inconsistent with the vector direction of the corresponding target point cloud data, then it is determined that the installation process of the target prefabricated building component is abnormal.

[0015] The present invention has the following beneficial effects: first, point cloud data of different positions of an assembled building during an assembly construction process are obtained; then, based on coordinate information and color information of other point cloud data in a target space around the position and the point cloud data of the position, the probability that the point cloud data of the position and other point cloud data belong to the same assembled building component is determined; when the probability is greater than a first threshold, it is determined that the point cloud data of the position and other point cloud data belong to the same assembled building component; when the number of point cloud data divided into the same assembled building component is less than a second threshold, it is marked as noise information and removed; when the number of point cloud data divided into the same assembled building component is not less than the second threshold, the voxel corresponding to the point cloud data under the same assembled building component is determined based on the point cloud data in the same assembled building component, the average probability of the probability that adjacent point cloud data in the same assembled building component are divided into the same assembled building component, and the volume of the standard voxel; and the point cloud data in the voxel is used to detect the installation process of each assembled building component in the assembled building.

[0016] In this way, the embodiment of the present invention can first determine and remove noise information based on the number of point cloud data divided into the same prefabricated building component. Then, the voxel size suitable for each prefabricated building component is determined based on the point cloud data in each prefabricated building component, the volume of the standard voxel, and the average probability of each point cloud data. The voxel adapted to the prefabricated building component can be used to accurately process the data redundancy of the prefabricated building purchase. In this way, the effect of point cloud data denoising and redundancy reduction processing is optimized, the characteristics of the original point cloud data are retained, the quality of the point cloud data is improved, and the subsequent analysis and processing effect based on the point cloud data is further improved. Furthermore, the point cloud data after denoising and redundancy reduction processing is used to detect the installation process of each prefabricated building component in the prefabricated building, thereby improving the accuracy of the detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a method for detecting an assembly construction process of an assembled building provided by one embodiment of the present invention; Figure 2 A schematic structural diagram of an assembled building component provided by one embodiment of the present invention; Figure 3A schematic diagram of the vector direction of each point cloud data in a building component R provided by an embodiment of the present invention; Figure 4 A schematic diagram of the vector direction and distribution direction of each point cloud data in a building component R provided by an embodiment of the present invention; Figure 5 A schematic diagram of the vector direction and distribution direction of each point cloud data in a building component R provided by another embodiment of the present invention; Figure 6 A schematic structural diagram of a prefabricated building assembly construction process detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a prefabricated building assembly construction process detection method proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] 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 invention belongs.

[0021] The specific scheme of the detection method for the assembly construction process of an assembled building provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] Embodiment 1: See also Figure 1 , which shows a flow chart of a method for detecting the assembly construction process of an assembled building provided by an embodiment of the present invention, including: Step S101, obtaining point cloud data of different positions of the prefabricated building during the assembly construction process.

[0023] Specifically, the embodiment of the present invention can use a drone equipped with a high-precision laser radar scanner to scan the construction site of the prefabricated building during the assembly and construction process, and obtain the original data information of the prefabricated building during the assembly and construction process. The original data information includes but is not limited to GPS and INS data. The original data information is then processed and the return time of the laser pulse is accurately converted into point coordinates in three-dimensional space using GPS and INS data. Data is then used to generate preliminary point cloud data.

[0024] More specifically, the point cloud data collected in the above-mentioned embodiments of the present invention often contain a large amount of noise and redundant data, and need to be subjected to denoising, filtering and other processing to make the generated point cloud data more reliable. However, for prefabricated buildings, which are composed of multiple prefabricated building components, simple overall voxel filtering is not effective. Therefore, the embodiment of the present invention analyzes the characteristic performance of each prefabricated building component, analyzes voxels of different sizes for different prefabricated building components, and then performs denoising and redundancy reduction. The effect of such processing also retains the characteristics of the initial point cloud data. Then, according to the changes in the point cloud data before and after time, the installation angle of the building component being installed is analyzed to complete the synchronous detection of the assembly construction process.

[0025] Step S102, determining the probability that the point cloud data at the location and other point cloud data belong to the same prefabricated building component based on the coordinate information and color information of the point cloud data at the location and other point cloud data in the target space around the location.

[0026] Specifically, since prefabricated building components are usually large blocks, the three-dimensional point cloud data belonging to the prefabricated building components are clustered in space. This is mainly due to the characteristics of the components themselves. According to this characteristic, the embodiment of the present invention performs noise processing on the initially generated three-dimensional point cloud data. Among them, since the sizes and shapes of prefabricated building components are different, mainly because there are many components of prefabricated buildings and their sizes are diverse. In order to retain the original characteristics of prefabricated building components in the three-dimensional point cloud data, the embodiment of the present invention first distinguishes the point cloud data belonging to the same prefabricated building component, and then uses uneven voxels to perform voxel division on the three-dimensional point cloud data corresponding to the prefabricated building components. Exemplary, such as Figure 2 As shown, Figure 2 A schematic structural diagram of an assembled building component provided by an embodiment of the present invention, Figure 2 In the figure, there are building component 1, building component 2 and building component 3, and the sizes and shapes of the three building components are different.

[0027] More specifically, when determining the probability that the point cloud data at a location and other point cloud data belong to the same prefabricated building component, as an optional embodiment of the present invention, first, based on the coordinate information of the point cloud data at the location and other point cloud data in the target space around the location, the first distance between the point cloud data at the location and the other point cloud data is determined; then, based on the color information of the point cloud data at the location and other point cloud data in the target space around the location, the color difference between the point cloud data at the location and the other point cloud data is determined; finally, the first distance and the color difference are used to determine the probability that the point cloud data at the location and the other point cloud data belong to the same prefabricated building component.

[0028] Specifically, in the embodiment of the present invention, the target space can be the area around the point cloud data. Of course, the size of the target space can also be determined according to actual conditions, and the embodiment of the present invention does not limit this. The coordinate information of the point cloud data is , based on Point cloud data, judge around it The first of the other point cloud data in the space The distance between the point cloud data is calculated by the following formula in the embodiment of the present invention: Point cloud data and the target space The first distance between point cloud data: In the above formula, Indicates Point cloud data and the target space The first distance between point cloud data. Indicated in Point cloud data is located The target space The spatial coordinates of the point cloud data. Indicated in The spatial coordinates of the point cloud data.

[0029] More specifically, for the same prefabricated building component, its color expression is unified. And the point cloud data contains color values. In the embodiment of the present invention, the first The color information of the point cloud data is , based on Point cloud data, judge around it In the space of The color difference between the point cloud data. Represents the The color value of the RGB channel of the point cloud data is calculated by the following formula: Point cloud data and the target space Color difference between point cloud data: In the above formula, Indicates Point cloud data and the target space The color difference between the point cloud data. Indicates Point cloud data is located The first The color information of the point cloud data. Indicates The color information of the point cloud data.

[0030] More specifically, when calculating the probability that the point cloud data at a certain location and other point cloud data belong to the same prefabricated building component, as an optional embodiment of the present invention, the first product between the first distance and the color difference is first calculated; then the first product is normalized to obtain the probability that the point cloud data at the location and other point cloud data belong to the same prefabricated building component.

[0031] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the above probability: In the above formula, Indicates Point cloud data and the target space The probability that the point cloud data belongs to the same prefabricated building component. Indicates Point cloud data and the target space The first distance between point cloud data. Indicates Point cloud data and the target space The color difference between the point cloud data. Represents the inverse normalization function, which is used to For point cloud data, when the differences in position and color between two adjacent point cloud data are small, the possibility that they belong to the same prefabricated building component is greater.

[0032] Step S103: when the probability is greater than the first threshold, it is determined that the point cloud data at the position and other point cloud data belong to the same prefabricated building component.

[0033] Specifically, the first threshold can be determined according to actual conditions, and in the embodiment of the present invention, the value is 0.7. , indicating that this Point cloud data and the target space The point cloud data belong to the same prefabricated building component. All the point cloud data are traversed and all the point cloud data are used to complete the division of the prefabricated building components.

[0034] Step S104: When the number of point cloud data classified into the same prefabricated building component is less than a second threshold, mark it as noise information and remove it; when the number of point cloud data classified into the same prefabricated building component is not less than the second threshold, determine the voxel corresponding to the point cloud data under the same prefabricated building component based on the point cloud data in the same prefabricated building component, the average probability of adjacent point cloud data in the same prefabricated building component being classified into the same prefabricated building component, and the volume of the standard voxel.

[0035] Specifically, for prefabricated building components, their volume is generally large. Therefore, when very few point cloud data are classified into the same prefabricated building component, or when only a single point cloud data is classified together, it is marked as noise information and removed. The second threshold can be determined according to actual conditions, and the embodiment of the present invention is not limited here.

[0036] More specifically, when the number of point cloud data divided into the same prefabricated building component is not less than the second threshold, it is necessary to reduce the redundancy of the point cloud data in the same prefabricated building component. In order to ensure the characteristic expression of the original point cloud data, the embodiment of the present invention uses different voxels to divide the point cloud data belonging to different prefabricated building components. Among them, the division of uneven voxels needs to be based on the specific expression of the point cloud data belonging to different prefabricated building components. When the point cloud data belonging to a certain prefabricated building component is sparse, it is divided by a larger voxel unit; when the point cloud data is dense, it is divided by a smaller voxel unit.

[0037] More specifically, when determining the voxels corresponding to the point cloud data under the same prefabricated building component, as an optional embodiment of the present invention, first, according to the first number of point cloud data in the same prefabricated building component, the average probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, the maximum distance between the point cloud data in the same prefabricated building component, the first volume of the largest cuboid wrapped by the first number of point cloud data, and the second volume of the largest cuboid wrapped by all point cloud data in the prefabricated building, the density parameters of the point cloud data under the same prefabricated building component are determined; then, according to the density parameters of the point cloud data under the same prefabricated building component and the volume of the standard voxel, the voxels corresponding to the point cloud data under the same prefabricated building component are determined.

[0038] The point cloud data is three-dimensional data. In the coordinate system, all the point cloud data can form a minimum rectangle, and the minimum rectangle contains all the point cloud data. When determining the density parameter, first calculate the first ratio between the average probability and the maximum distance, and the second ratio between the second volume and the first volume; then determine the second product of the first number, the first ratio and the second ratio as the density parameter of the point cloud data under the same prefabricated building component.

[0039] More specifically, the embodiment of the present invention takes the k-th prefabricated building component as an example, and specifically uses the following formula to calculate the density parameter of the point cloud data in the k-th prefabricated building component: In the above formula, Indicates Density parameters of point cloud data in prefabricated building components. Indicates belonging to The first amount of point cloud data under prefabricated building components. It refers to The size of the first volume of the largest cuboid that is completely enclosed by the point cloud data of the prefabricated building component. It refers to the size of the second volume of the rectangle that can completely enclose all the point cloud data in the prefabricated building. Indicates that it belongs to this The average probability of the adjacent point cloud data under the prefabricated building components being divided together. Indicates that in this The maximum distance between the point cloud data under the prefabricated building components.

[0040] Among them, for the point cloud data under the prefabricated building components, the smaller the overall space proportion of its distribution, the more point cloud data, which means that the complexity of this prefabricated building component is greater, the distribution density is greater, and it needs to be finely divided, that is, using smaller voxel units. In addition, the greater the possibility that these point cloud data belong together, the smaller the maximum distance between the point cloud data, which means that these point cloud data are more concentrated and the distribution density is greater.

[0041] More specifically, when determining the voxel corresponding to the point cloud data under the same prefabricated building component, as an optional embodiment of the present invention, the volume of the standard voxel is first determined according to the state that the point cloud data is in a uniform state; then the density parameter is normalized to obtain the normalized density parameter; finally, the third product between the normalized density parameter and the volume is determined to be the voxel corresponding to the point cloud data under the same prefabricated building component.

[0042] Specifically, the embodiment of the present invention first determines the voxel size of the entire prefabricated building in a uniform state, specifically by counting the maximum and minimum values ​​of the X, Y, and Z coordinate values ​​of the point cloud data of the entire prefabricated building, and determining the row number, column number, and layer number according to the divided grid size L, thereby dividing it into multiple grids, thereby dividing each point cloud data into a corresponding grid, and the volume of the grid is the volume of the standard voxel. It is worth noting that the voxels in the embodiment of the present invention are small cubes. In order to prevent out-of-bounds situations, it is usually necessary to appropriately expand the maximum and minimum values ​​of the X, Y, and Z coordinate values ​​of the point cloud data to ensure that all point cloud data can be correctly divided into voxels.

[0043] In this embodiment of the present invention, the k-th prefabricated building component is taken as an example, and the following formula is specifically used to calculate the voxel corresponding to the point cloud data under the k-th prefabricated building component: In the above formula, Represents the voxel corresponding to the point cloud data under the k-th prefabricated building component. Represents the volume of a standard voxel. Indicates Density parameters of point cloud data in prefabricated building components. is the inverse normalization function, which is used to Normalization is performed. For data-intensive areas, the embodiment of the present invention uses smaller voxel units for division. Since voxel division is performed on point cloud data belonging to different prefabricated building components, and a single prefabricated building component is smaller than the entire prefabricated building, the volume of the voxel for dividing a single prefabricated building component is smaller than the volume of the standard voxel for dividing the entire prefabricated building.

[0044] Furthermore, the point cloud data in each voxel can be represented by its average value, thereby reducing the redundancy of the data. So far, the embodiment of the present invention has completed the denoising and redundancy reduction of the point cloud data.

[0045] Step S105, using the point cloud data in the voxel to detect the installation process of each prefabricated building component in the prefabricated building.

[0046] Specifically, the above embodiments of the present invention complete the denoising and redundancy reduction of the acquired point cloud data. For the assembly construction process of prefabricated buildings, since these prefabricated building components are generally finished products assembled from large construction materials, in order to ensure the quality of the building during assembly, the components should be kept stable in the vertical or horizontal direction during installation without skew and the like. When skew occurs, it may cause the prefabricated building components to get stuck, resulting in poor assembly construction results of the prefabricated building, which requires adjustment.

[0047] More specifically, when detecting the installation process of each prefabricated building component in a prefabricated building, as an optional embodiment of the present invention, the average value of the point cloud data within the voxel is first determined as the target point cloud data corresponding to the voxel; then the target point cloud data is used to detect the installation process of each prefabricated building component in the prefabricated building corresponding to the target point cloud data.

[0048] Among them, the analysis of synchronous detection during the assembly construction process of prefabricated buildings needs to be analyzed under time conditions. Since the position of the installed prefabricated building components is fixed, the corresponding point cloud data of the prefabricated building components being installed will change in space under time. Therefore, the embodiment of the present invention detects the installation process of the prefabricated building components according to the target point cloud data at adjacent moments. Among them, as an optional embodiment of the present invention, first, according to the target point cloud data of the prefabricated building components at adjacent moments, the target prefabricated building components whose position information of the target point cloud data at adjacent moments is changed are determined; then, according to the spatial coordinates of the target point cloud data at adjacent moments in the target prefabricated building components, the vector direction of each target point cloud data in the target prefabricated building components in space is determined; finally, the installation process of the target prefabricated building components is detected using the vector direction of each target point cloud data in the target prefabricated building components in space.

[0049] Specifically, observe the prefabricated building components in Moment under and The distribution of the target point cloud data in space after denoising and redundancy reduction at each moment is as follows: First, obtain The point cloud data after denoising and redundancy reduction is obtained at the same time The point cloud data after denoising and redundancy reduction at the moment. Match the two sets of point cloud data: Since the position of the prefabricated building components that have been installed remains unchanged, the position information of the corresponding point cloud data does not change. Compare the two sets of point cloud data at adjacent moments, and combine the prefabricated building components to which the data in the two sets of point cloud data belong, to obtain the target prefabricated building components corresponding to the point cloud data whose position information has changed. . Further, the target prefabricated building components The area size will not change, that is, the number of corresponding point cloud data will remain unchanged in the previous and next time. The distribution on Moment and Moment target prefabricated building components The corresponding point cloud data is matched.

[0050] More specifically, when determining the vector direction of each target point cloud data in the target prefabricated building component in space, as an optional embodiment of the present invention, the difference between the spatial coordinates of the target point cloud data at adjacent moments is first calculated as the spatial vector of the target point cloud data in space; then the vector direction of the spatial vector is determined as the vector direction of the target point cloud data in space.

[0051] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the spatial vector of the target point cloud data in space: In the above formula, Represents the target prefabricated building component The i-th target point cloud data in The space vector of time. express Moment target prefabricated building components The corresponding The spatial coordinates of the point cloud data. express Moment target prefabricated building components The corresponding The spatial coordinates of the point cloud data. The direction is the vector direction of the target point cloud data in space.

[0052] More specifically, the embodiment of the present invention obtains the vector direction of each point cloud data in space in the above manner. When detecting the installation process of the target prefabricated building component, if the vector direction of each target point cloud data in space is inconsistent, it is determined that the installation process of the target prefabricated building component is abnormal; if the vector direction of each target point cloud data in space is consistent, the distribution direction of the corresponding target point cloud data is constructed along the vector direction. If the distribution direction is consistent with the vector direction of the corresponding target point cloud data, it is determined that the installation process of the target prefabricated building component is good. If the distribution direction is inconsistent with the vector direction of the corresponding target point cloud data, it is determined that the installation process of the target prefabricated building component is abnormal.

[0053] Specifically, if Figure 3 As shown, Figure 3 A schematic diagram of the vector direction of each point cloud data in a building component R provided by an embodiment of the present invention. Figure 3 In , there are three target point cloud data, and the overall vector directions of the three target point cloud data in space are inconsistent. Therefore, it is determined that the installation process of the target prefabricated building component is abnormal. Figure 4 and Figure 5 As shown, Figure 4A schematic diagram of the vector direction and distribution direction of each point cloud data in a building component R provided by an embodiment of the present invention, Figure 5 A schematic diagram of the vector direction and distribution direction of each point cloud data in a building component R provided in another embodiment of the present invention. Figure 4 In the figure, the vector direction of each target point cloud data in space and the distribution direction of the target point cloud data are consistent, indicating that the installation angle of the prefabricated building components is good. Figure 5 In the figure, the vector direction of each target point cloud data in space is consistent, but the distribution direction of the target point cloud data is inconsistent, indicating that there is a problem with the installation angle of the prefabricated building components.

[0054] It is worth noting that when the above-mentioned embodiment analyzes that there are problems in the installation process of the prefabricated building components being installed, feedback alarms are promptly provided to relevant staff, so as to adjust the installation of the prefabricated building components, ensure the project quality during the assembly construction of the prefabricated building, and also complete synchronous detection during the construction process.

[0055] The embodiment of the present invention can first determine and remove noise information based on the number of point cloud data divided into the same prefabricated building component. Then, the voxel size suitable for each prefabricated building component is determined based on the point cloud data in each prefabricated building component, the volume of the standard voxel, and the average probability of each point cloud data. The voxel adapted to the prefabricated building component can be used to accurately process the data redundancy of the prefabricated building purchase. In this way, the effect of point cloud data denoising and redundancy reduction processing is optimized, the characteristics of the original point cloud data are retained, the quality of the point cloud data is improved, and the subsequent analysis and processing effect based on the point cloud data is further improved. Furthermore, the point cloud data after denoising and redundancy reduction processing is used to detect the installation process of each prefabricated building component in the prefabricated building, thereby improving the accuracy of the detection.

[0056] Embodiment 2: Corresponding to the prefabricated building assembly construction process detection method provided in the above embodiment, based on the same technical concept, the embodiment of the present invention also provides a prefabricated building assembly construction process detection system, which is used to execute the prefabricated building assembly construction process detection method. Figure 6 A schematic diagram of a structural diagram of a prefabricated building assembly construction process detection system for implementing various embodiments of the present invention is shown in FIG. Figure 6As shown in the hardware level, the assembly construction process detection system of the prefabricated building includes a processor, and optionally, an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the assembly construction process detection system of the prefabricated building may also include hardware required for other businesses.

[0057] The processor, network interface and memory can be interconnected through an internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0058] The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation commands. The memory may include internal memory and non-volatile memory, and provide instructions and data to the processor.

[0059] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for locating the specified user at the logical level. The processor executes the program stored in the memory and is specifically used to execute: Figure 1 The method disclosed in the illustrated embodiment implements the functions and beneficial effects of each method in the previous method embodiments, which will not be described in detail here.

[0060] It should be noted that the prefabricated building assembly construction process detection system provided by the embodiment of the present invention and the prefabricated building assembly construction process detection method provided by the embodiment of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned prefabricated building assembly construction process detection method, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0061] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for detecting the assembly construction process of an assembled building, characterized in that: The method for detecting the assembly construction process of the prefabricated building comprises: Obtain point cloud data of different positions of prefabricated buildings during the assembly and construction process; Determine the probability that the point cloud data at the location and the other point cloud data belong to the same prefabricated building component based on the coordinate information and color information of other point cloud data in the target space around the location and the point cloud data at the location; When the probability is greater than a first threshold, determining that the point cloud data at the position and the other point cloud data belong to the same prefabricated building component; When the number of point cloud data classified into the same prefabricated building component is less than a second threshold, marking it as noise information and removing it; When the number of point cloud data classified into the same prefabricated building component is not less than a second threshold, determining the voxel corresponding to the point cloud data under the same prefabricated building component according to the point cloud data in the same prefabricated building component, the average probability of the probability that adjacent point cloud data in the same prefabricated building component are classified into the same prefabricated building component, and the volume of the standard voxel; The point cloud data in the voxel is used to detect the installation process of each prefabricated building component in the prefabricated building.

2. The method for detecting the assembly construction process of prefabricated buildings according to claim 1, characterized in that: Determining the probability that the point cloud data at the location and the other point cloud data belong to the same prefabricated building component based on the coordinate information and color information of the other point cloud data in the target space around the location and the point cloud data at the location includes: Determine a first distance between the point cloud data at the position and the other point cloud data according to coordinate information of other point cloud data and the point cloud data at the position in the target space around the position; Determine the color difference between the point cloud data at the position and the other point cloud data according to color information of other point cloud data and the point cloud data at the position in the target space around the position; The probability that the point cloud data at the position and the other point cloud data belong to the same prefabricated building component is determined by using the first distance and the color difference.

3. The method for detecting the assembly construction process of prefabricated buildings according to claim 2, characterized in that: The probability that the point cloud data at the position determined by using the first distance and the color difference and the other point cloud data belong to the same prefabricated building component includes: calculating a first product between the first distance and the color difference; The first product is normalized to obtain a probability that the point cloud data at the position and the other point cloud data belong to the same prefabricated building component.

4. The method for detecting the assembly construction process of prefabricated buildings according to claim 1, characterized in that: The step of determining the voxel corresponding to the point cloud data under the same prefabricated building component according to the point cloud data in the same prefabricated building component, the average probability of the probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, and the volume of the standard voxel comprises: Determine a density parameter of the point cloud data under the same prefabricated building component according to a first number of point cloud data in the same prefabricated building component, an average probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, a maximum distance between point cloud data in the same prefabricated building component, a first volume of a maximum cuboid wrapped by the first number of point cloud data, and a second volume of a maximum cuboid wrapped by all point cloud data in the prefabricated building; The voxel corresponding to the point cloud data under the same prefabricated building component is determined according to the density parameter of the point cloud data under the same prefabricated building component and the volume of the standard voxel.

5. The method for detecting the assembly construction process of prefabricated buildings according to claim 4, characterized in that: The method of determining the density parameter of the point cloud data under the same prefabricated building component according to the first number of point cloud data in the same prefabricated building component, the average probability of adjacent point cloud data in the same prefabricated building component being divided into the same prefabricated building component, the maximum distance between point cloud data in the same prefabricated building component, the first volume of the largest cuboid wrapped by the first number of point cloud data, and the second volume of the largest cuboid wrapped by all point cloud data in the prefabricated building comprises: calculating a first ratio between the average probability and the maximum distance, and a second ratio between the second volume and the first volume; A second product of the first number, the first ratio, and the second ratio is determined as a density parameter of the point cloud data of the same prefabricated building component.

6. The method for detecting the assembly construction process of prefabricated buildings according to claim 4, characterized in that: The step of determining the voxel corresponding to the point cloud data of the same prefabricated building component according to the density parameter of the point cloud data of the same prefabricated building component and the volume of the standard voxel comprises: Determine the volume of the standard voxel according to the point cloud data being in a uniform state; Normalizing the density parameter to obtain a normalized density parameter; A third product of the normalized density parameter and the volume is determined as a voxel corresponding to the point cloud data of the same prefabricated building component.

7. The method for detecting the assembly construction process of prefabricated buildings according to claim 1, characterized in that: The detecting the installation process of each prefabricated building component in the prefabricated building by using the point cloud data in the voxel includes: Determine the average value of the point cloud data in the voxel as the target point cloud data corresponding to the voxel; The target point cloud data is used to detect the installation process of each prefabricated building component corresponding to the target point cloud data in the prefabricated building.

8. The method for detecting the assembly construction process of prefabricated buildings according to claim 7, characterized in that: The detecting the installation process of each prefabricated building component corresponding to the target point cloud data in the prefabricated building by using the target point cloud data includes: Determine, based on the target point cloud data of the prefabricated building component at adjacent moments, a target prefabricated building component whose position information of the target point cloud data at adjacent moments changes; Determine the vector direction of each target point cloud data in the target prefabricated building component in space according to the spatial coordinates of the target point cloud data at adjacent moments in the target prefabricated building component; The installation process of the target prefabricated building component is detected by using the vector direction of each target point cloud data in the target prefabricated building component in space.

9. The method for detecting the assembly construction process of prefabricated buildings according to claim 8, characterized in that: The step of determining the vector direction of each target point cloud data in the target prefabricated building component in space according to the spatial coordinates of the target point cloud data at adjacent moments in the target prefabricated building component comprises: Calculate the difference between the spatial coordinates of the target point cloud data at adjacent moments as the spatial vector of the target point cloud data in space; The vector direction of the space vector is determined to be the vector direction of the target point cloud data in space.

10. The method for detecting the assembly construction process of prefabricated buildings according to claim 8, characterized in that: The detecting the installation process of the target prefabricated building component by using the vector direction of each target point cloud data in the space of the target prefabricated building component comprises: If the vector directions of each of the target point cloud data in space are inconsistent, it is determined that the installation process of the target prefabricated building component is abnormal; If the vector direction of each target point cloud data in space is consistent, the distribution direction of the corresponding target point cloud data is constructed along the vector direction; if the distribution direction is consistent with the vector direction of the corresponding target point cloud data, it is determined that the installation process of the target prefabricated building component is good; if the distribution direction is inconsistent with the vector direction of the corresponding target point cloud data, it is determined that the installation process of the target prefabricated building component is abnormal.

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