A Component Stability Analysis Method and System Based on BIM Technology

Abstract

This application relates to the field of component stability analysis technology, specifically to a component stability analysis method and system based on BIM technology. The method includes: collecting point cloud data for each set of components; dividing the local region of each data point in each set of point cloud data using its nearest neighbor data points; obtaining the salient feature value of each data point in its local region and the distance judgment value between different data points in each set of point cloud data; clustering each set of point cloud data to obtain the adjustment coefficient of the corresponding data point in each cluster, thus obtaining the spatial weight of each data point in each cluster; performing noise reduction processing on all data points in each cluster; and finally performing stability analysis on the component. This application improves the accuracy of component stability analysis.

Description

Technical Field

[0001] This application relates to the field of component stability analysis technology, specifically to a component stability analysis method and system based on BIM technology. Background Technology

[0002] In the construction process, component stability is crucial for safe construction. Traditional component stability analysis suffers from large information errors and difficulty in achieving dynamic assessment. However, BIM technology, through the creation of three-dimensional digital models of components, can integrate information on the geometric parameters, material properties, and mechanical performance of components during construction. This provides accurate and efficient technical support for stability analysis during construction, enabling the rapid identification of potential safety hazards and ensuring the safety of the construction process.

[0003] However, in practical applications, the quality of the collected component point cloud data is poor due to differences in the structural characteristics of different components and varying degrees of environmental interference. Fluctuations in temperature and humidity, random shifts in the point cloud caused by dust and moisture in the construction environment, and the formation of localized holes in the point cloud by direct sunlight all contribute to reduced quality. Furthermore, traditional noise reduction methods do not adequately consider the differences between local and overall interference effects on components in different scenarios, resulting in poor processing of component point cloud data. This, in turn, affects the accuracy of scatter matrix feature vector extraction, making it difficult to accurately capture the overall trend characteristics of the component. Consequently, the parameters extracted from the component's geometric features deviate from reality, ultimately impacting the accuracy of component stability analysis based on BIM technology and reducing the precision of component safety status assessment. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a component stability analysis method and system based on BIM technology. The specific technical solution adopted is as follows:

[0005] This application provides a component stability analysis method based on BIM technology, including the following steps:

[0006] Collect point cloud data for each component;

[0007] For each set of point cloud data, a nearest neighbor search is performed to obtain the nearest neighbor data points of each data point in each set of point cloud data. The region where each data point and its nearest neighbor data points are located is taken as the local region of each data point. Based on the feature distribution of each data point in its local region, the significant feature value of each data point in its local region is obtained. Then, combined with the degree of difference between different data points in each set of point cloud data, the distance judgment value between different data points in each set of point cloud data is obtained to perform clustering and division of each set of point cloud data.

[0008] By analyzing the distribution of significant feature values ​​of all data points in each cluster of each set of point cloud data, the adjustment coefficient of each data point in each cluster of each set of point cloud data is obtained. Then, by combining the differences between different data points in each cluster of each set of point cloud data, the spatial weight of each data point in each cluster of each set of point cloud data is obtained, so as to perform noise reduction processing on all data points in each cluster of each set of point cloud data.

[0009] Stability analysis of the component is performed based on the degree of dispersion of all point cloud data in the component after noise reduction.

[0010] Preferably, the method for calculating the salient feature value of each data point in its local region in each group of point cloud data is as follows:

[0011] ;

[0012] In the formula, For each set of point cloud data, the first... The salient feature values ​​of each data point in its local region; For each set of data, the first... Density characteristic values ​​of data points in their local regions; For each set of point cloud data, the first... The first data point Density characteristic values ​​of neighboring data points in their local regions; For each set of point cloud data, the first... The data point and its first The KL divergence value of the probability distribution curves in the corresponding local regions between the nearest neighbor data points; This represents the number of neighboring data points.

[0013] Preferably, the method for obtaining the density feature value is as follows:

[0014] The mean of all Euclidean distances between each data point and its nearest neighbor data points in each set of point cloud data is used as the density feature value of each data point in its local region.

[0015] Preferably, the probability distribution curve further includes:

[0016] For each data point in each set of point cloud data, a surface is fitted to each data point and its nearest neighbor data points, and the curvature value of each data point and its nearest neighbor data points at the corresponding positions in the fitted surface is calculated.

[0017] After statistically analyzing the probability of all curvature values ​​of each data point and its nearest neighbor data points in each set of point cloud data, curve fitting is performed to obtain the probability distribution curve of each data point in its local region.

[0018] Preferably, the method for calculating the distance judgment value between different data points in each group of point cloud data is as follows:

[0019] ;

[0020] In the formula, For each set of point cloud data, the first... The data point and the Distance judgment value between data points; Indicates the first point in each set of point cloud data. The salient feature values ​​of each data point in its local region; Indicates the first point in each set of point cloud data. The salient feature values ​​of each data point in its local region; Indicates the first point in each set of point cloud data. The data point and the Euclidean distance between data points.

[0021] Preferably, the method for calculating the adjustment coefficient of the corresponding data point in each cluster under each set of point cloud data is as follows:

[0022] ;

[0023] In the formula, For each set of point cloud data, the first... The adjustment coefficient for the corresponding data point in each cluster; For each set of point cloud data, the first... The mean of the significant feature values ​​corresponding to all data points in each cluster; This represents the number of all clusters in each set of point cloud data. For each set of point cloud data, the first... The cluster and the first The crossover and union ratio of the salient eigenvalue ranges of all data points among the clusters.

[0024] Preferably, the method for obtaining the range of significant feature values ​​is as follows:

[0025] Probabilistic statistics are performed on the significant feature values ​​corresponding to all data points in each cluster under each set of point cloud data to obtain the range of significant feature values ​​for all data points in each cluster under each set of point cloud data.

[0026] Preferably, the method for calculating the spatial weight of each data point in each cluster under each set of point cloud data is as follows:

[0027] ;

[0028] In the formula, The spatial weight of each data point in each cluster under each set of point cloud data; The mean of all Euclidean distances between each data point and other data points in each cluster of each point cloud data set; This represents the normalized result of the adjustment coefficient for the corresponding data point in each cluster under each set of point cloud data.

[0029] Preferably, the dispersion of all point cloud data in the component after noise reduction further includes:

[0030] Divergence analysis was performed on the center point cloud coordinates of all point cloud data of the component after noise reduction to obtain the degree of dispersion of all point cloud data of the component in the three dimensions of x, y, and z.

[0031] This application also provides a component stability analysis system based on BIM technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described component stability analysis methods based on BIM technology.

[0032] As can be seen from the above, the component stability analysis method and system based on BIM technology provided in this application have at least the following beneficial effects:

[0033] This application fully considers the complex construction site environment and significant differences in component bonding characteristics, leading to substantial local interference variations in the actual collected point cloud data. This affects the processing and analysis results of the point cloud data, causing significant deviations between the parameters used for stability analysis and actual parameters, thus reducing the accuracy of stability analysis. Therefore, by collecting point cloud data and performance parameters of components, and combining the differences in the impact of environmental interference on the local structure of components during the actual collection process, the data distribution differences of local point cloud data at different displacements are compared. This allows for precise analysis of the significant characteristic values ​​of local interference effects in the collected component point cloud data. Furthermore, all point cloud data are divided, and based on the divided data, grouped filtering and adjustment processing of the component point cloud data is performed. This, combined with the differences in the impact of environmental interference on the local structure of components during actual collection, achieves precise filtering processing of the component's local point cloud data, improves the accuracy of component geometric parameter extraction, and ultimately enhances the stability analysis results of components during actual construction based on BIM technology. Attached Figure Description

[0034] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating the steps of the component stability analysis method based on BIM technology provided in this application;

[0036] Figure 2 A flowchart illustrating the steps of the method for obtaining significant feature values ​​provided in this application;

[0037] Figure 3 A block diagram of a component stability analysis system based on BIM technology provided for this application. Detailed Implementation

[0038] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the component stability analysis method and system based on BIM technology proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0040] The following, in conjunction with the accompanying drawings, details the specific scheme of the component stability analysis method and system based on BIM technology provided in this application.

[0041] Please see Figure 1 It illustrates a flowchart of the component stability analysis method based on BIM technology provided in an embodiment of this application, including the following steps:

[0042] Step 1: Collect point cloud data for each component.

[0043] During actual construction, the load conditions of the current construction stage are obtained, including but not limited to the self-weight of the formwork, the impact load of concrete pouring, and the construction live load. A 3D laser scanner is used to collect point cloud data for each component, and this point cloud data is used as a set of point cloud data. Furthermore, a total station is used to calibrate the coordinate system of the 3D scanner to ensure the accuracy of the spatial positioning of the point cloud data. For precise correction and adjustment, the total station has an angle measurement accuracy of 0.5s and a distance measurement accuracy of 1mm + 1ppm. On the other hand, for the collection of actual component performance data, a rebound hammer is used to collect rebound values ​​at different locations on the component. Each component is divided into 10 testing zones, with 16 testing points evenly distributed within each zone, to calculate the compressive strength data at different locations on the component. An ultrasonic testing instrument is used to collect ultrasonic signals from the component, further calculating the internal density data. Temperature and humidity data of the environment in which the component is located are collected using temperature and humidity sensors. The collected data is then transmitted to the monitoring and analysis center. Preferably, in this embodiment, the processes of obtaining compressive strength data with a rebound hammer and obtaining internal solidity data with ultrasonic testing are well-known technologies, and the specific details will not be elaborated further.

[0044] Step 2: Perform nearest neighbor search on each set of point cloud data to obtain the nearest neighbor data points of each data point in each set of point cloud data. The region where each data point and its nearest neighbor data points are located is taken as the local region of each data point. Based on the feature distribution of each data point in its local region, obtain the significant feature value of each data point in its local region. Then, combined with the degree of difference between different data points in each set of point cloud data, obtain the distance judgment value between different data points in each set of point cloud data to perform clustering and division of each set of point cloud data.

[0045] Due to significant differences in the geometric features of components and the varying interference characteristics of their environments during actual data acquisition, the point cloud data collected from the components is subject to varying degrees of interference, leading to a decrease in the accuracy of extracting and analyzing the key stress locations of the components. Therefore, the monitoring and analysis center performs feature analysis and processing on the received data to reduce the impact of interference on geometric feature extraction. Simultaneously, considering the influence of component surface features and environmental variations such as dust, water vapor, and temperature and humidity during actual data acquisition, the collected point cloud data may exhibit defects such as localized sparse point clouds, concentrated noise interference, and blurred edge structures. This affects the accuracy of point cloud data processing, resulting in deviations between the collected component data and the actual data, and reducing the accuracy of component stability analysis during actual construction. Therefore, considering the impact of interference characteristics caused by differences in component geometric features and their environments on component feature extraction during BIM-based component stability analysis, targeted optimization of component data processing is performed to improve the accuracy of component feature extraction.

[0046] During the acquisition of point cloud data for each component, differences in geometric features and varying environmental interference characteristics lead to significant differences in the impact of interference on local areas of the component. This can result in substantial differences in the processing effectiveness of local areas during filtering, thus affecting the analysis of the overall geometric trend characteristics of the component. Therefore, for each set of point cloud data for the component, the K-nearest neighbor algorithm is used to obtain the K nearest neighbor data points for each data point in each set of point cloud data. The region containing each data point and its nearest neighbor data points is then considered as the local region of each data point in each set of point cloud data. In this embodiment, K is set to 20 to fully reflect the differences in point cloud distribution caused by interference in local areas of the component. Preferably, the K-nearest neighbor algorithm is a well-known technique, and its specific process will not be elaborated further.

[0047] Furthermore, the mean of all Euclidean distances between each data point and its nearest neighbors in each set of point cloud data is used as the density feature value of each data point in its local region. Meanwhile, environmental interference such as dust and moisture may cause random shifts in local data points within the point cloud data, resulting in differences in the density distribution of local data points.

[0048] Therefore, for each data point in each set of point cloud data, a surface fitting algorithm is used to obtain the fitted surface of each data point in its local region, and the curvature value of each data point and its nearest neighbor data points in the fitted surface is calculated. Normally, the surface features of a component's local region are consistent, but under the influence of interference, the local structural features of the component will shift, potentially leading to concentrated noise interference, missing local data, etc., resulting in significant differences in the impact of interference on the component's local region. The surface fitting algorithm includes, but is not limited to, the least squares method and quadratic surface fitting. This embodiment uses the least squares method, and the specific process will not be elaborated further.

[0049] Furthermore, considering the impact of environmental interference on the structural characteristics of different components, there are differences in the local regions of each set of point cloud data under different displacements. The characteristics of each data point in each set of point cloud data and its nearest neighbor data points in their local regions are compared. For each set of point cloud data for a component, probability statistics are performed on all curvature values ​​of each data point and its nearest neighbor data points in each set of point cloud data. After obtaining a histogram, the least squares method is used for curve fitting to obtain the probability distribution curve of each data point in each set of point cloud data in its local region. Preferably, in this embodiment, the least squares method is a well-known technique, and the specific process will not be elaborated further.

[0050] Based on the above analysis, and considering the data distribution of each data point in its local region within each set of point cloud data, and combined with the differences in the feature distribution of each data point in its local region within each set of point cloud data, the significant feature value of each data point in its local region within each set of point cloud data is calculated. In this embodiment, the specific calculation formula is as follows:

[0051] ;

[0052] In the formula, For each set of point cloud data, the first... The salient feature values ​​of each data point in its local region; For each set of data, the first... Density characteristic values ​​of data points in their local regions; For each set of point cloud data, the first... The first data point Density characteristic values ​​of neighboring data points in their local regions; For each set of point cloud data, the first... The data point and its first The KL divergence value of the probability distribution curves in the corresponding local regions between the nearest neighbor data points; This represents the number of neighboring data points.

[0053] Among them, the larger the salient feature value, the greater the difference in the geometric features of the components and the interference differences in the environment in which the components are located during the comprehensive construction process, and the greater the difference in the local interference influence of the point cloud data; the larger the KL divergence value, the greater the difference in the characteristic distribution of the interference influence in the local area; the larger the density feature value, the more concentrated the data distribution of the corresponding data points in the local area of ​​each group of point cloud data. In addition, the flowchart of the method for obtaining the salient feature value provided in this embodiment is as follows. Figure 2 As shown.

[0054] Therefore, taking into full account the differences in the environment in which the components are located during construction, as well as the differences in the geometric characteristics of the components themselves, and the structural offset characteristics of the component point cloud data caused by the interference, we analyze the characteristic differences of the component point cloud data under different degrees of displacement, and then accurately analyze the significant characteristic values ​​of the local data points of the component point cloud data affected by the interference.

[0055] Furthermore, when clustering the collected component point cloud data, traditional clustering methods rely solely on the distance and density of data points, neglecting the biases caused by interference in the point cloud data distribution. This makes it difficult to accurately reflect the true differences between data points affected by interference. Therefore, this paper analyzes the characteristics of interference affecting point cloud data of components with different geometric features, and combines this with the judgment criteria of distance and interference feature differences to optimize and adjust the point cloud data segmentation process.

[0056] Based on the above analysis, and considering the differences in significant feature values ​​of different data points in their local regions within each set of point cloud data, and combined with the differences between different data points in each set of point cloud data, the distance judgment value between different data points in each set of point cloud data is calculated. In this embodiment, the specific calculation formula is as follows:

[0057] ;

[0058] In the formula, For each set of point cloud data, the first... The data point and the Distance judgment value between data points; Indicates the first point in each set of point cloud data. The salient feature values ​​of each data point in its local region; Indicates the first point in each set of point cloud data. The salient feature values ​​of each data point in its local region; Indicates the first point in each set of point cloud data. The data point and the Euclidean distance between data points.

[0059] Therefore, based on the comparison results of local region data point features of different point cloud data in different displacement directions, the distance judgment in the point cloud data segmentation process is optimized, and the density peak clustering algorithm is used to cluster each group of point cloud data to avoid data with large differences in local features being classified into the same category due to differences in the influence of local interference, which would affect the judgment of local interference processing and adjustment. Preferably, in this embodiment, the density peak clustering algorithm is a well-known technology, and the specific process will not be described in detail.

[0060] Step 3: By analyzing the distribution of significant feature values ​​of all data points in each cluster of each point cloud data set, obtain the adjustment coefficients of the corresponding data points in each cluster of each point cloud data set. Then, by combining the differences between different data points in each cluster of each point cloud data set, obtain the spatial weight of each data point in each cluster of each point cloud data set, so as to perform noise reduction processing on all data points in each cluster of each point cloud data set.

[0061] Furthermore, for each data point in each cluster after the point cloud data set is divided, probabilistic statistics are performed on the significant feature values ​​corresponding to all data points in each cluster under each point cloud data set. Based on the probabilistic statistics results, the range of significant feature values ​​for all data points in each cluster under each point cloud data set is obtained; and the mean of the significant feature values ​​corresponding to all data points in each cluster under each point cloud data set is calculated. Therefore, based on the interference influence characteristics and distribution characteristics of the local regions of the components corresponding to the data points in different clusters of each point cloud data set, the parameters in the filtering process are adjusted.

[0062] Based on the above analysis, and considering the average level of the significant feature values ​​of all data points in each cluster under each set of point cloud data, and taking into account the overlap of the significant feature value ranges of all data points between different clusters under each set of point cloud data, the adjustment coefficient of the corresponding data point in each cluster under each set of point cloud data is calculated. In this embodiment, the specific calculation formula is as follows:

[0063] ;

[0064] In the formula, For each set of point cloud data, the first... The adjustment coefficient for the corresponding data point in each cluster; For each set of point cloud data, the first... The mean of the significant feature values ​​corresponding to all data points in each cluster; This represents the number of all clusters in each set of point cloud data. For each set of point cloud data, the first... The cluster and the first The crossover and union ratio of the salient eigenvalue ranges of all data points among the clusters.

[0065] The smaller the intersection-union ratio (IUGR), the greater the difference in the impact of interference on the local area of ​​the corresponding component; the larger the mean value, the greater the possibility that the data points within the structural area of ​​each cluster in the point cloud data are affected by environmental interference and thus deviate. To further highlight the local features of the component, it is necessary to better reduce noise interference and avoid distortion of the local features of the component due to interference, thereby affecting the accuracy of the geometric feature judgment of the component during feature extraction.

[0066] Based on the above analysis, noise reduction processing is performed on all data points in different clusters within each set of point cloud data. For regions where interference is more significant in the local area of ​​the component, the filtering effect is enhanced to reduce the impact of interference on the local structural features of the component; for regions with less interference, the filtering effect is appropriately reduced to avoid distortion of the component structure. Simultaneously, considering the noise suppression effects of spatial weights and range weights during point cloud data processing, and combining the characteristics of the component's geometric local interference, the parameters for filtering point cloud data in different regions are determined.

[0067] Based on the above analysis, and considering the differences between different data points in each cluster of each set of point cloud data, and combined with the normalized results of the adjustment coefficients of the corresponding data points in each cluster of each set of point cloud data, the spatial weight of each data point in each cluster of each set of point cloud data is calculated. In this embodiment, the specific calculation formula is as follows:

[0068] ;

[0069] In the formula, The spatial weight of each data point in each cluster under each set of point cloud data; The mean of all Euclidean distances between each data point and other data points in each cluster of each point cloud data set; This represents the normalized result of the adjustment coefficient for the corresponding data point in each cluster under each set of point cloud data.

[0070] The larger the adjustment coefficient, the greater the impact of interference on the local structure of the corresponding component; correspondingly, a larger spatial weight is set for that local area. To further suppress the impact of noise interference on the component structure, the ratio of spatial weight to range weight is set to 6:1 in this embodiment; the implementer can adjust the ratio of spatial weight to range weight according to the accuracy requirements of the component. After determining the weights, a bilateral filtering algorithm is used to denoise all data points in each cluster under each group of point cloud data of the component. Preferably, in this embodiment, the bilateral filtering algorithm is a known technology, and the specific process will not be described in detail.

[0071] Step 4: Perform stability analysis on the component based on the degree of dispersion of all point cloud data in the component after noise reduction.

[0072] For all point cloud data of the component after noise reduction, the geometric features of the point cloud data space are calculated using a divergence matrix. Specifically, the mean of the coordinates in each dimension of all point cloud data is calculated to obtain the center point cloud coordinates, and the divergence matrix of the center point cloud coordinates reflects the dispersion of the component's point cloud data in the x, y, and z dimensions. For example, for the point cloud data of a cuboid beam component, its corresponding divergence matrix, after eigenvalue decomposition, will show that the largest eigenvalue corresponds to the beam length direction, and the other two eigenvalues ​​correspond to the width and height directions of the cross-section, respectively. Preferably, in this embodiment, the divergence matrix is ​​a well-known technique, and the specific process will not be described in detail.

[0073] Furthermore, based on the extracted geometric features, the geometric shape and spatial orientation of the components are analyzed. Specifically, when analyzing the geometric features of the components through the divergence matrix, the vector corresponding to the largest eigenvalue in the divergence matrix is ​​consistent with the direction of the component's main axis. The vector corresponding to the actual main axis direction of the component is compared with the axis vector of the standard component model in the BIM design to calculate the deviation during the actual construction process. The deviation includes, but is not limited to, the verticality deviation angle of the column and the horizontal offset of the beam axis.

[0074] Preferably, in this embodiment, all point cloud data, geometric feature parameters, material performance parameters, current construction stage load conditions, and stress distribution, deformation, and safety factor threshold standards from the "Code for Design of Concrete Structures" are used as input to obtain the component stability analysis results through a BIM platform. The geometric feature parameters include, but are not limited to, the column verticality deviation angle and the beam axis horizontal offset; the material performance parameters include concrete compressive strength and internal density; the current construction stage load conditions include, but are not limited to, formwork self-weight, concrete pouring impact load, and construction live load; the stress distribution includes, but is not limited to, the maximum tensile stress at the mid-span of the beam and the maximum compressive stress at the bottom of the column; the deformation includes, but is not limited to, deflection and lateral displacement; and the safety factor is the ratio of the actual bearing capacity to the design value.

[0075] Specifically, based on all point cloud data and geometric feature parameters of the component after noise reduction, a precise 3D model of the component is generated using the 3D reconstruction module of the BIM platform. Then, the stress distribution, deformation, and safety factor of the component are obtained through the finite element analysis module. Combined with the standard thresholds for component stability requirements during construction, the stability of the component is assessed, and the stability analysis results are obtained. Finally, the 3D model of the component is rendered using the visualization module of the BIM platform, enabling dynamic demonstration of stress color marking and deformation in the 3D model. The rendering results are then output and presented as a reference for determining whether the component is in a stable state during construction and for any necessary construction adjustments. The stability analysis results include, but are not limited to, marking of stress exceeding limits, whether deformation meets standards, and whether the safety factor is satisfied.

[0076] Based on the same inventive concept as the above method, this application also provides a component stability analysis system based on BIM technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described component stability analysis methods based on BIM technology.

[0077] Specifically, the component stability analysis system based on BIM technology in this embodiment includes a 3D reconstruction module, a finite element analysis module, and a visualization processing module. The block diagram of the component stability analysis system based on BIM technology is shown below. Figure 3 As shown.

[0078] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0080] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1.A method of analyzing stability of a component based on BIM technology, characterized by, The method comprises the following steps: Collecting each set of point cloud data of the component; Performing a near neighbor search on each set of point cloud data to obtain the near neighbor data points of each data point in each set of point cloud data, and taking the region where each data point and its near neighbor data points are located as the local region of each data point, obtaining the significant feature value of each data point in each set of point cloud data in its local region according to the feature distribution of each data point in each set of point cloud data in its local region, and further obtaining the distance judgment value between different data points in each set of point cloud data in combination with the difference between different data points in each set of point cloud data, to cluster and divide each set of point cloud data; Obtaining the adjustment coefficient of the corresponding data points in each cluster under each set of point cloud data according to the distribution of the significant feature values of all data points in each cluster under each set of point cloud data, and further obtaining the spatial weight of each data point in each cluster under each set of point cloud data in combination with the difference between different data points in each cluster under each set of point cloud data, to perform noise reduction processing on all data points in each cluster under each set of point cloud data; Performing stability analysis on the component according to the dispersion degree of all point cloud data in the component after noise reduction processing. 2.The BIM technology-based component stability analysis method of claim 1, wherein, The calculation method of the significant feature value of each data point in each set of point cloud data in its local region is: ； In the formula, For each set of point cloud data, the first... The salient feature values ​​of each data point in its local region; For each set of data, the first... Density characteristic values ​​of data points in their local regions; For each set of point cloud data, the first... The first data point Density characteristic values ​​of neighboring data points in their local regions; For each set of point cloud data, the first... The data point and its first The KL divergence value of the probability distribution curves in the corresponding local regions between the nearest neighbor data points; This represents the number of neighboring data points. 3.The BIM technology-based component stability analysis method of claim 2, wherein, The method for obtaining the density feature value is: Taking the mean value of all Euclidean distances between each data point and its near neighbor data points in each set of point cloud data as the density feature value of each data point in its local region. 4.The BIM technology-based component stability analysis method of claim 2, wherein, The probability distribution curve further comprises: Performing surface fitting on each data point and its near neighbor data points in each set of point cloud data, and counting the curvature values of each data point and its near neighbor data points at the corresponding positions in the fitted surface in each set of point cloud data; After probability statistics of all curvature values of each data point and its near neighbor data points in each set of point cloud data, curve fitting is performed to obtain the probability distribution curve of each data point in its local region in each set of point cloud data. 5.The BIM technology-based component stability analysis method of claim 1, wherein, The calculation method of the distance judgment value between different data points in each set of point cloud data is: ； In the formula, is a distance judgment value between the i-th data point and the j-th data point in each group of point cloud data; is a significant feature value of the i-th data point in its local region in each group of point cloud data; is a Euclidean distance between the i-th data point and the j-th data point in each group of point cloud data; ​​​​​​​ 6.The BIM technology-based component stability analysis method of claim 1, wherein, The calculation method of the adjustment coefficient of the corresponding data points in each cluster under each set of point cloud data is: ； In the formula, is the adjustment coefficient of the corresponding data point in the th clustering cluster under each group of point cloud data; is the mean value of the significant feature value corresponding to all data points in the th clustering cluster under each group of point cloud data; is the number of all clustering clusters in each group of point cloud data; is the intersection-union ratio of the significant feature value range of all data points between the th clustering cluster and the th clustering cluster under each group of point cloud data. 7.The BIM technology-based component stability analysis method of claim 6, wherein, The method for obtaining the significant feature value range is: Probability statistics is performed on the significant feature values corresponding to all data points in each cluster under each set of point cloud data to obtain the significant feature value range of all data points in each cluster under each set of point cloud data. 8.The BIM technology-based component stability analysis method of claim 1, wherein, The calculation method of the spatial weight of each data point in each cluster under each set of point cloud data is: ； In the formula, is a spatial weight of each data point in each clustering cluster under each set of point cloud data; is a mean value of all Euclidean distances between each data point and other data points in each clustering cluster under each set of point cloud data; is a normalization result of the adjustment coefficient of the corresponding data point in each clustering cluster under each set of point cloud data. 9.The BIM technology-based component stability analysis method of claim 1, wherein, The dispersion degree of all point cloud data in the component after noise reduction processing further comprises: Divergence analysis is performed on the center point cloud coordinates in all point cloud data in the component after noise reduction processing to obtain the dispersion degree of all point cloud data of the component in x, y and z dimensions. 10.A system for analyzing stability of a component based on BIM technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program comprises the following steps of: The processor implements the steps of the component stability analysis method based on BIM technology according to any one of claims 1-9 when executing the computer program.

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