A 3D point cloud-BIM fusion construction progress intelligent comparison method and system

Abstract

The application relates to a 3D point cloud-BIM fusion construction progress intelligent comparison method and system. The method comprises the following steps: acquiring 3D point cloud data of a construction site to obtain a site real scene state point cloud data set; based on the site real scene state point cloud data set, performing coordinate transformation processing on a BIM design model to obtain spatially aligned BIM design model data; dividing the spatially aligned BIM design model data and the site real scene state point cloud data into three-dimensional grid units of the same size to obtain point cloud density distribution characteristics in each grid unit; based on the point cloud density distribution characteristics in each grid unit, calculating geometric deviation values between the BIM design model and the real scene state through a layer-by-layer comparison analysis algorithm to obtain a progress comparison result. The method can realize high-precision, real-time and intelligent construction progress monitoring.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of building engineering, and particularly relates to a 3D point cloud-BIM fusion construction progress intelligent comparison method and system. BACKGROUND

[0002] With the expansion of the scale of building engineering construction and the progress of construction technology, the traditional manual monitoring method has been difficult to adapt to the requirements of modern construction management on efficiency and real-time. The construction stage is an important stage of the building life cycle, and if the construction progress is delayed, the cost and quality of the entire building engineering project will be affected. Therefore, construction progress monitoring is particularly important in the project construction site.

[0003] Traditional construction progress management relies on manual inspection and static comparison with BIM models, and has problems such as low efficiency, poor accuracy, and lagging dynamic update. The progress monitoring of the construction site involves multi-dimensional data collection, spatial registration, model comparison, and risk assessment, and how to realize high-precision, real-time, and intelligent construction progress monitoring has become a problem to be solved. SUMMARY

[0004] Therefore, it is necessary to provide a 3D point cloud-BIM fusion construction progress intelligent comparison method and system in view of the above technical problems.

[0005] In a first aspect, the application provides a 3D point cloud-BIM fusion construction progress intelligent comparison method, comprising:

[0006] Obtaining 3D point cloud data of the construction site to obtain a site real scene state point cloud data set;

[0007] Based on the site real scene state point cloud data set, performing coordinate transformation processing on the BIM design model to obtain a spatially aligned BIM design model data;

[0008] Dividing the spatially aligned BIM design model data and the site real scene state point cloud data into three-dimensional grid units of the same size to obtain point cloud density distribution characteristics in each grid unit;

[0009] Based on the point cloud density distribution characteristics in each grid unit, calculating the geometric deviation value between the BIM design model and the real scene state through layer-by-layer comparison and analysis to obtain a progress comparison result.

[0010] Further, the obtaining of the 3D point cloud data of the construction site to obtain the site real scene state point cloud data set comprises:

[0011] Obtaining 3D point cloud data of the construction site through multiple sensors;

[0012] Based on the 3D point cloud data, synchronously obtaining site geometric information through a laser scanner and a depth camera;

[0013] Based on the field geometric information, it is detected whether there is device shielding or light change interference, and a detection result is obtained.

[0014] If the detection result is that the device shielding or light change interference is detected, a denoising instruction is generated, the denoising instruction is used to remove the dust influence and noise interference through an adaptive filtering processing technology, and a field real scene state point cloud data set is obtained.

[0015] Further, based on the field real scene state point cloud data set, coordinate transformation processing is performed on the BIM design model to obtain a spatially aligned BIM design model data, including:

[0016] Feature point distribution information in the field real scene state point cloud data set is extracted, an iterative closest point registration algorithm is used to establish a spatial transformation matrix between the field coordinate system and the BIM design model coordinate system, and an initial registration result is obtained.

[0017] Based on the initial registration result, the matching value of the calculated feature point is compared with a preset threshold to obtain a comparison result.

[0018] If the comparison result is that the matching degree of the feature point reaches the preset threshold, the initial registration result is used to perform coordinate transformation processing on the BIM design model to obtain a standard BIM design model in a unified coordinate system with the field real scene state.

[0019] Or,

[0020] If the comparison result is that the matching degree of the feature point is lower than the preset threshold, a new registration reference point is selected to obtain new spatial registration parameters.

[0021] The BIM design model is subjected to coordinate transformation processing through the new spatial registration parameters to obtain a standard BIM design model.

[0022] It is judged whether the boundary of the standard BIM design model exceeds the field data range to obtain a judgment result.

[0023] If the judgment result is that the boundary exceeds the field data range, the spatial registration parameters are recalculated to obtain the spatially aligned BIM design model data.

[0024] Further, based on the point cloud density distribution characteristics in each grid unit, the geometric deviation value between the BIM design model and the real scene state is calculated through layer-by-layer comparison and analysis to obtain a progress comparison result, including:

[0025] According to the point cloud density distribution characteristics in each grid unit, the ratio relationship between the BIM design model point cloud density and the real scene point cloud density is calculated to obtain a density ratio parameter.

[0026] Based on the density ratio parameter, the construction progress in each grid unit is judged according to the construction state judgment rule, and a complete construction progress distribution map is obtained.

[0027] Based on the complete construction progress distribution map, the geometric deviation value between the BIM design model and the real scene state is calculated through layer-by-layer comparison and analysis.

[0028] It is judged whether the geometric deviation value exceeds the preset tolerance range, and a judgment result is obtained.

[0029] If the judgment result is that the geometric deviation value exceeds the preset tolerance range, a marking instruction is generated; the marking instruction is used to mark the relevant area as a construction abnormal area, and a progress comparison result is obtained.

[0030] Further, based on the complete construction progress distribution map, the geometric deviation value between the BIM design model and the real scene state is calculated through layer-by-layer comparison and analysis, including:

[0031] The Euclidean distance formula is used to calculate the distance between two points in the three-dimensional space of the BIM design model and the real scene state, and the spatial distance between the BIM design model and the real scene state is obtained:

[0032]

[0033] Wherein, R i is the spatial distance between the BIM design model and the real scene state, x i is the coordinate of the BIM design model in the three-dimensional space, and y i is the coordinate of the real scene state in the three-dimensional space.

[0034] Based on the spatial distance, the average deviation calculation formula is used to calculate the average deviation value of each layer of the BIM design model and the real scene state:

[0035]

[0036] Wherein, M i is the average deviation of each layer, m i is the number of sampling points of each layer, and E ij is the deviation value of each sampling point of each layer.

[0037] Based on the average deviation, the following formula is used to calculate the geometric deviation value between the BIM design model and the real scene state:

[0038]

[0039] Wherein, ΔG is the geometric deviation value, n is the number of comparison points, M i is the coordinate value of each point in the BIM design model, and R i is the coordinate value of the corresponding point in the real scene state.

[0040] Further, the method further comprises:

[0041] Based on the progress comparison result, a time series analysis method is used to predict the deviation trend of the subsequent construction stage, and a deviation prediction value of the subsequent construction stage is obtained;

[0042] It is judged whether the deviation prediction value shows an increasing trend, and a judgment result is obtained;

[0043] If the judgment result is that the deviation prediction value shows an increasing trend, a construction progress risk assessment report is generated;

[0044] According to the key risk point position information in the construction progress risk assessment report, a progress monitoring interface containing three-dimensional visual annotation is obtained;

[0045] If the construction situation is changed in the progress monitoring interface, a progress update instruction is generated, which is used to update the display content in real time, and an intelligent progress monitoring result is obtained.

[0046] Further, based on the progress comparison result, a time series analysis method is used to predict the deviation trend of the subsequent construction stage, and a deviation prediction value of the subsequent construction stage is obtained, comprising:

[0047] Obtain the deviation value sequence of each time node in the progress comparison result, construct the time series data set of deviation change according to time sequence, and obtain the deviation time series data set;

[0048] Based on the deviation time series data set, the evolution characteristics of the deviation are analyzed, and a complete deviation evolution trajectory is obtained;

[0049] According to the deviation evolution trajectory, a sliding window method is used to calculate the deviation growth rate in a continuous time period;

[0050] It is judged whether the deviation growth rate continuously exceeds the preset threshold value in a preset period, and a judgment result is obtained;

[0051] If the judgment result is that the deviation growth rate continuously exceeds the preset threshold value in a preset period, a deviation trend abnormal result is obtained;

[0052] The future trend of the deviation trend abnormal result is analyzed through a time series prediction model, and a deviation prediction value of the subsequent construction stage is obtained.

[0053] Further, according to the key risk point position information in the construction progress risk assessment report, a progress monitoring interface containing three-dimensional visual annotation is obtained, comprising:

[0054] Based on the progress monitoring interface, the change data of the construction site is obtained in real time;

[0055] The change data is compared with the original position data to obtain a comparison result;

[0056] If the comparison result is that the change data is inconsistent with the original position data, an interface update instruction is generated, the interface update instruction is used to start an interface update mechanism, and an updated monitoring display interface is obtained;

[0057] According to the updated monitoring display interface, the coverage of the original position data and the change data is compared with a preset threshold to obtain a comparison result;

[0058] If the comparison result is that the coverage is lower than the preset threshold, an abnormal marking instruction is generated, the abnormal marking instruction is used to mark the related area as an abnormal state, and a distribution of the abnormal area is obtained;

[0059] The distribution of the abnormal area is subjected to data aggregation processing, an affected construction progress node is identified, and a progress influence range is obtained;

[0060] According to the progress influence range, the abnormal area is dynamically marked in combination with a three-dimensional visualization model, and a progress monitoring interface containing three-dimensional visualization marking is obtained.

[0061] In a second aspect, the present application also provides a 3D point cloud-BIM fusion construction progress intelligent comparison system, comprising:

[0062] A data acquisition module is configured to acquire 3D point cloud data of a construction site to obtain a site real scene state point cloud data set;

[0063] A coordinate alignment module is configured to perform coordinate transformation processing on a BIM design model based on the site real scene state point cloud data set to obtain a spatially aligned BIM design model data;

[0064] A unit division module is configured to divide the spatially aligned BIM design model data and the site real scene state point cloud data into three-dimensional grid units of the same size to obtain point cloud density distribution characteristics in each grid unit;

[0065] A data processing module is configured to calculate a geometric deviation value between the BIM design model and the real scene state by layer-by-layer comparison and analysis based on the point cloud density distribution characteristics in each grid unit to obtain a progress comparison result.

[0066] In a third aspect, the present application also provides a computer device comprising a memory and a processor, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement any one of the 3D point cloud-BIM fusion construction progress intelligent comparison methods described in the embodiments of the present application.

[0067] Fourthly, this application also provides a computer-readable storage medium storing at least one piece of program code, which is loaded and executed by a processor to implement a 3D point cloud-BIM fusion construction progress intelligent comparison method as described in any of the embodiments of this application.

[0068] The above-mentioned intelligent construction progress comparison method and system integrating 3D point cloud and BIM takes into account the requirements of efficiency and real-time performance in modern construction management. By combining 3D point cloud and BIM technologies, it compares the construction progress of the building construction site through multi-dimensional data collection, spatial registration, model comparison and risk assessment, and achieves high-precision, real-time and intelligent construction progress monitoring. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying 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.

[0070] Figure 1 This is a flowchart illustrating a method for intelligent comparison of construction progress using 3D point cloud-BIM fusion in one embodiment.

[0071] Figure 2 This is a schematic diagram of the process for obtaining 3D point cloud data of a construction site in one embodiment;

[0072] Figure 3 This is a schematic diagram of the structure of a 3D point cloud-BIM fusion intelligent construction progress comparison system in one embodiment. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0074] In one embodiment, such as Figure 1 As shown, a method for intelligent comparison of construction progress based on 3D point cloud-BIM fusion is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0075] Step S101: Obtain 3D point cloud data of the construction site to obtain a real-world point cloud dataset.

[0076] The 3D point cloud data is a set of discrete points in a three-dimensional space collected by sensors or generated by algorithms, and each discrete point can include coordinate information, color, intensity, and normal vector.

[0077] Specifically, the construction site can be scanned comprehensively by laser scanners and unmanned aerial vehicle oblique photography devices, and original point cloud data of the construction site can be collected to generate 3D point cloud data containing the actual status of the construction site, thereby forming a point cloud data set of the real scene status covering the construction area.

[0078] In step S102, the BIM design model is subjected to coordinate transformation based on the point cloud data set of the real scene status, and a spatially aligned BIM design model data is obtained.

[0079] The BIM design model is a three-dimensional model based on digital technology, which contains the geometric shape of the construction site building and integrates the semantic information and logical relationship of the building life cycle.

[0080] Specifically, control points with obvious features are selected from the point cloud data set of the real scene status, such as the corners of buildings and the intersection points of axes, and corresponding control points are also determined in the BIM design model. Then, an iterative closest point registration algorithm is used to find the optimal rigid transformation between the two sets of point clouds, i.e., the best rotation matrix R and translation vector t, to construct a coordinate transformation matrix. The BIM design model is transformed using the matrix to convert it from its own coordinate system to the same coordinate system as the point cloud data set of the real scene status, so that the two are aligned in space.

[0081] In step S103, the spatially aligned BIM design model data and the point cloud data of the real scene status are divided into three-dimensional grid cells of the same size, and the point cloud density distribution characteristics in each grid cell are obtained.

[0082] The point cloud density is the ratio of the number of point clouds to the volume of the grid. Specifically, according to the range and accuracy requirements of the data, a suitable three-dimensional grid size is set, and the spatially aligned BIM design model data and the point cloud data of the real scene status are divided according to the size to form three-dimensional grid cells, the number of point clouds contained therein is counted, the point cloud density is calculated, and the point cloud density distribution characteristics are obtained.

[0083] In step S104, based on the point cloud density distribution characteristics in each grid cell, the geometric deviation value between the BIM design model and the real scene status is calculated by layer-by-layer comparison and analysis, and a progress comparison result is obtained.

[0084] The layer-by-layer comparison analysis is a method of finding differences, rules or problems by comparing and analyzing the model or object in different levels. Specifically, according to the point cloud density distribution characteristics in the grid cell, the grid cell is divided into different levels. Starting from the bottom layer, the geometric deviation values of the BIM design model and the on-site scene state point cloud data in each grid cell are compared layer by layer upwards, and the geometric deviation values of all grid cells are summarized and analyzed to comprehensively judge the differences between the construction progress and the design progress.

[0085] The 3D point cloud-BIM fusion construction progress intelligent comparison method provided in the embodiment realizes the quantitative evaluation of the construction progress and can perform high-precision construction progress monitoring by spatially aligning the BIM design model and the on-site scene state point cloud data, calculating the geometric deviation between the BIM design model and the on-site scene, and comparing the construction progress.

[0086] In one of the embodiments, as shown in Figure 2 The 3D point cloud data of the construction site is obtained to obtain an on-site scene state point cloud data set, which includes:

[0087] In step S201, the 3D point cloud data of the construction site is obtained by a plurality of sensors.

[0088] The plurality of sensors can be a laser scanner, an unmanned aerial vehicle oblique photography, a depth camera, a GNSS positioning device, an inertial measurement unit, etc. Specifically, a laser scanner can be deployed at the construction site to obtain a large-range high-precision geometric profile, a depth camera can supplement details and textures, a GNSS and an inertial measurement unit can assist in spatial positioning, and finally the 3D point cloud data of the construction site is fused and generated.

[0089] In step S202, the on-site geometric information is synchronously obtained by the laser scanner and the depth camera based on the 3D point cloud data.

[0090] Specifically, after obtaining the 3D point cloud data of the construction site, the laser scanner and the depth camera are synchronized within less than 10 ms by a hardware synchronization line or a software timer to scan the same region of the site, unify the coordinate system, associate the laser point cloud with the RGB value, form a three-dimensional point with color, correct the geometric distortion of the depth camera, and complete the synchronous acquisition of the on-site geometric information.

[0091] In step S203, whether there is device occlusion or light change interference is detected based on the on-site geometric information to obtain a detection result.

[0092] Specifically, whether there is device occlusion or light change interference is detected by the density change, geometric shape anomaly and color information mutation of the 3D point cloud data of the construction site to obtain a detection result.

[0093] Step S204, if the detection result is that the device is blocked or the light change interference is detected, a denoising instruction is generated, the denoising instruction is used to remove the dust influence and noise interference by an adaptive filtering processing technology to obtain the on-site real scene state point cloud data set.

[0094] The adaptive filtering processing technology is an intelligent signal processing method capable of automatically adjusting filtering parameters according to input signals or environmental characteristics. Specifically, when it is detected that there is device blocking or light change interference, the adaptive filtering processing technology is automatically started by the system. For the discrete noise points caused by dust influence, the neighborhood point information is counted to judge and eliminate abnormal points. For the noise caused by light change, the disturbed area is smoothed by combining the point cloud reflection intensity and the image color information to obtain the on-site real scene state point cloud data set.

[0095] In this embodiment, by effectively eliminating the noise interference caused by device blocking, light change, dust and other factors, the quality of the point cloud data is improved, and the point cloud more truly reflects the actual situation of the construction site.

[0096] In one of the embodiments, based on the on-site real scene state point cloud data set, a coordinate transformation process is performed on the BIM design model to obtain a spatially aligned BIM design model data, including:

[0097] Step S301, feature point distribution information in the on-site real scene state point cloud data set is extracted, and an iterative closest point registration algorithm is used to establish a spatial transformation matrix between the on-site coordinate system and the BIM design model coordinate system to obtain an initial registration result.

[0098] The iterative closest point registration algorithm is to find the optimal rigid body transformation between two sets of point clouds by iterative calculation to minimize the distance between their corresponding points. In the core iterative process of the algorithm, the step of solving the optimal rigid body transformation matrix depends on the least square method.

[0099] Specifically, the feature point distribution information in the on-site real scene state point cloud data set is extracted, and the initial transformation parameters of the BIM design model and the on-site point cloud are set, which are usually unit matrices. The nearest neighbor points of each feature point of the BIM design model in the on-site point cloud are found, and the optimal transformation parameters are calculated by the least square method to minimize the root mean square error of the two sets of feature points:

[0100]

[0101] Wherein, R is a 3x3 rotation matrix, t is a 3x1 translation vector, P is a point cloud to be transformed, Q is a reference point cloud, which corresponds to the points in P one by one. The BIM design model is transformed by applying the calculated new parameters, and the nearest neighbor point is found and the minimum root mean square error is calculated repeatedly for each feature point until the minimum root mean square error converges or the maximum number of iterations is reached, obtaining the initial spatial registration parameters [R|t], thereby obtaining the initial registration spatial transformation matrix T=[R|t], and the BIM design model is transformed from the original coordinate system to the site coordinate system.

[0102] Step S302, based on the initial registration result, the matching value of the calculated feature point is compared with the preset threshold to obtain a comparison result.

[0103] Wherein, the preset threshold is set according to the construction accuracy requirement, for example, the matching degree of the main structure registration should be ≥0.8, and the decoration stage should be ≥0.95. Specifically, based on the initial registration result, the matching degree of the feature points of the registered BIM design model and the corresponding points of the site point cloud is calculated:

[0104]

[0105] Wherein, d i is the distance error of the ith pair of feature points, d max is the maximum allowed distance error (such as 5cm); θ i is the normal angle, and θ max is the maximum allowed angle, which can be 5°. The calculated matching degree is compared with the preset threshold to obtain a comparison result.

[0106] Step S303, if the comparison result is that the matching degree of the feature point reaches the preset threshold, the initial registration result is used to perform coordinate transformation processing on the BIM design model to obtain a standard BIM design model in a unified coordinate system with the site real scene state.

[0107] Specifically, when the matching degree is ≥ the preset threshold, the coordinate transformation of the BIM design model is directly applied by the transformation matrix T to obtain a standard BIM design model in a unified coordinate system with the site real scene state.

[0108] Step S304, if the comparison result is that the matching degree of the feature point is lower than the preset threshold, then the registration reference point is reselected to obtain new spatial registration parameters.

[0109] Specifically, when the matching degree is < the preset threshold, the feature points with larger matching errors are manually or automatically removed, and more reliable reference points are reselected, and the spatial registration parameters [R'|t'] are re-calculated based on the new reference points.

[0110] Step S305, the coordinate transformation processing of the BIM design model is performed through the new spatial registration parameters to obtain a standard BIM design model.

[0111] Specifically, the standard BIM design model is obtained by performing coordinate transformation on the BIM design model according to the recalculated spatial registration parameters [R' | t'].

[0112] In step S306, it is judged whether the boundary of the standard BIM design model exceeds the range of the field data, and a judgment result is obtained.

[0113] Specifically, the judgment result is obtained by checking whether the geometric boundary of the standard BIM design model exceeds the spatial range of the field point cloud data.

[0114] In step S307, if the judgment result is that the boundary exceeds the range of the field data, the spatial registration parameters are recalculated to obtain the spatially aligned BIM design model data.

[0115] Specifically, if the boundary exceeds the range of the field data, the transformation parameters are further optimized, the translation component is constrained, or multi-scale registration is introduced to obtain the spatially aligned BIM design model data.

[0116] In this embodiment, the spatial alignment of the BIM design model and the field point cloud is realized through feature point registration and iterative optimization, the data reference is unified, and the communication cost and rework risk caused by coordinate deviation are reduced.

[0117] In one of the embodiments, based on the point cloud density distribution characteristics in each grid cell, the geometric deviation value between the BIM design model and the actual scene state is calculated through layer-by-layer comparison and analysis to obtain a progress comparison result, which includes:

[0118] In step S401, the density ratio of the BIM design model point cloud density and the actual scene point cloud density is calculated according to the point cloud density distribution characteristics in each grid cell to obtain a density ratio parameter.

[0119] Specifically, for each grid cell, the BIM design model point cloud density and the actual scene point cloud density are counted respectively, and the density ratio parameter of the two is calculated through the formula density ratio = actual scene point cloud density / design point cloud density.

[0120] In step S402, based on the density ratio parameter, the construction progress in each grid cell is judged according to a construction state judgment rule to obtain a complete construction progress distribution map.

[0121] The construction state judgment rule is: density ratio < 0.2: indicating that the region has not started construction; 0.2 < density ratio < 0.8: indicating that construction is in progress; and density ratio > 0.8: indicating that construction has been completed.

[0122] Specifically, according to the density ratio parameter of each grid unit, the construction state determination rule is used to mark each grid as "not started", "in progress", "completed" and the like, and a complete construction progress distribution map is obtained.

[0123] In step S403, based on the complete construction progress distribution map, the geometric deviation value between the BIM design model and the real scene state is calculated by layer-by-layer comparison and analysis.

[0124] Specifically, based on the construction progress distribution map, the three-dimensional space is divided into multiple layers along the height direction, and the average deviation of all grid units in each layer is calculated to obtain the geometric deviation value of the layer.

[0125] In step S404, it is determined whether the geometric deviation value exceeds the preset tolerance range, and a judgment result is obtained.

[0126] The preset tolerance range is set according to the construction specification or design requirement. Specifically, the calculated overall deviation statistical value is compared with the set tolerance range to determine whether it exceeds the threshold value, and a judgment result is obtained.

[0127] In step S405, if the judgment result is that the geometric deviation value exceeds the preset tolerance range, a marking instruction is generated; the marking instruction is used to mark the related area as a construction abnormal area, and a progress comparison result is obtained.

[0128] If the calculated overall deviation statistical value exceeds the tolerance range, the related area is automatically marked as a construction abnormal area, and the remaining area is a normal area, and a progress comparison result is obtained.

[0129] In this embodiment, the distribution map generated by the numerical deviation value can quickly locate the construction lagging or leading area, which is convenient for the management personnel to adjust the resources and improve the efficiency and accuracy of progress evaluation.

[0130] In one of the embodiments, based on the complete construction progress distribution map, the geometric deviation value between the BIM design model and the real scene state is calculated by layer-by-layer comparison and analysis, which includes:

[0131] In step S501, the Euclidean distance formula is used to calculate the distance between two points in the three-dimensional space of the BIM design model and the real scene state, and the spatial distance between the BIM design model and the real scene state is obtained:

[0132]

[0133] wherein R i is the spatial distance between the BIM design model and the real scene state, x i is the coordinate of the BIM design model in the three-dimensional space, and y i is the coordinate of the real scene state in the three-dimensional space.

[0134] The spatial distance between the BIM design model and the actual scene state is used to accurately measure the geometric difference between the virtual design and the physical construction in the three-dimensional digital modeling and construction quality monitoring. Specifically, the shortest geometric distance between the surface of the BIM design model and the point cloud or scanned surface of the actual scene is calculated by using the Euclidean distance formula.

[0135] In step S502, the average deviation value of each layer of the BIM design model and the actual scene state is calculated based on the spatial distance by using the average deviation calculation formula:

[0136]

[0137] wherein M i is the average deviation of each layer, m i is the number of sampling points of each layer, and E ij is the deviation value of each sampling point of each layer.

[0138] The average deviation value is a statistical index for measuring the overall deviation degree of the spatial distance between the BIM design model and the actual scene state. Specifically, the average deviation value of each layer of the BIM design model and the actual scene state is calculated according to the calculated spatial distance.

[0139] In step S503, the geometric deviation value between the BIM design model and the actual scene state is calculated based on the average deviation by using the following formula:

[0140]

[0141] wherein ΔG is the geometric deviation value, n is the number of comparison points, M i is the coordinate value of each point in the BIM design model, and R i is the coordinate value of the corresponding point in the actual scene state.

[0142] Specifically, the geometric deviation value is calculated by using the calculated average deviation of each layer of the BIM design model and the actual scene state, which is used to represent the construction progress difference between the BIM design model and the actual scene state.

[0143] In this embodiment, the geometric deviation value between the BIM design model and the actual scene point cloud is calculated systematically, which provides a quantitative basis for construction quality control and improves the accuracy and efficiency of construction quality control.

[0144] In one of the embodiments, the construction progress intelligent comparison method provided by the embodiment further comprises:

[0145] In step S601, the deviation trend of the subsequent construction stage is predicted by using the time sequence analysis method based on the progress comparison result, and the deviation prediction value of the subsequent construction stage is obtained.

[0146] The time series analysis method is a technology for trend prediction and anomaly detection on a data sequence arranged in chronological order. Specifically, the geometric deviation values of each construction stage are extracted from the progress comparison result, and the time series analysis method is used to predict the deviation trend of the subsequent construction stage to obtain the deviation prediction value of the subsequent construction stage.

[0147] In step S602, it is judged whether the deviation prediction value shows an increasing trend to obtain a judgment result.

[0148] Specifically, whether the deviation prediction value shows an increasing trend can be judged by calculating the daily growth rate of the deviation prediction value at adjacent time points, judging whether the daily growth rate at consecutive time points is positive and exceeds a threshold value, and obtaining a judgment result.

[0149] In step S603, if the judgment result is that the deviation prediction value shows an increasing trend, a construction progress risk assessment report is generated.

[0150] Specifically, if the daily growth rate at consecutive time points is positive and exceeds a threshold value, it is judged that the deviation prediction value shows an increasing trend, and a construction progress risk assessment report is generated.

[0151] In step S604, according to the key risk point position information in the construction progress risk assessment report, a progress monitoring interface containing three-dimensional visual annotations is obtained.

[0152] Specifically, the key risk point coordinates in the risk assessment report are mapped to a BIM or point cloud model, and different colors and symbols are used to annotate the risk level to obtain a progress monitoring interface containing three-dimensional visual annotations.

[0153] In step S605, if the construction situation in the progress monitoring interface is changed, a progress update instruction is generated, which is used to update the display content in real time to obtain an intelligent progress monitoring result.

[0154] Specifically, when the construction site detects progress changes through sensors, such as component installation completion, the construction state in the three-dimensional model is automatically refreshed, the risk prediction is dynamically adjusted, and a change log is generated for intelligent progress monitoring.

[0155] In this embodiment, a dynamic monitoring closed loop is formed through data collection, deviation analysis, risk prediction, and rectification feedback to ensure the timeliness of the monitoring information, the system automatically identifies changes and adjusts the prediction, and the management efficiency is improved.

[0156] In one of the embodiments, based on the progress comparison result, the time series analysis method is used to predict the deviation trend of the subsequent construction stage to obtain the deviation value of the subsequent construction stage, which includes:

[0157] Step S701, obtain the deviation value sequence of each time node in the progress comparison result, construct the time sequence data set of deviation change according to time sequence, and obtain the deviation time sequence data set.

[0158] Specifically, the geometric deviation value or the density ratio value parameter of each day or each week is extracted from the progress comparison result, arranged in time sequence, and a deviation sequence data set is constructed.

[0159] Step S702, based on the deviation time sequence data set, analyze the deviation evolution characteristics, and obtain the complete deviation evolution trajectory.

[0160] Specifically, the long-term change trend is identified by polynomial fitting, whether it is continuously increasing or periodically fluctuating. The time point when the deviation value suddenly exceeds the change is identified. Whether there is seasonal change is detected by Fourier transform. The Fourier transform is to decompose the frequency components in the signal to reveal the periodicity hidden in the complex data. Specifically, according to the above analysis, a curve of the deviation value changing with time is drawn, key feature points are marked, and a complete deviation evolution trajectory is formed.

[0161] Step S703, according to the deviation evolution trajectory, the deviation growth rate in the continuous time period is calculated by using the sliding window method.

[0162] Specifically, the window size is defined, and for each window data [d i , d i+1 , …, d i+n ], the deviation growth rate of adjacent time points is calculated:

[0163]

[0164] Wherein, r j is the deviation growth rate, j = i, i+1, …, i+w-1.

[0165] Step S704, judge whether the deviation growth rate exceeds the preset threshold for a continuous preset period, and obtain a judgment result.

[0166] Specifically, the calculated deviation growth rate is compared with the preset growth rate threshold, whether the preset growth rate threshold is exceeded for three continuous periods is judged, and a judgment result is obtained.

[0167] Step S705, if the judgment result is that the deviation growth rate exceeds the preset threshold for a continuous preset period, a deviation trend abnormal result is obtained.

[0168] Specifically, if the deviation growth rate exceeds the preset growth rate threshold for three continuous periods, an abnormal result report is automatically generated, and a deviation trend abnormal result is obtained.

[0169] Step S706, the bias trend anomaly result is analyzed by the time series prediction model to obtain the bias prediction value of the subsequent construction stage.

[0170] Specifically, the time series prediction model is used to predict the bias values of the subsequent construction periods by taking the abnormal results such as the growth rate and the mutation point as conditions.

[0171] In this embodiment, a complete chain is formed from obtaining, feature analysis, anomaly detection to trend prediction of the bias data set, and the construction process is dynamically optimized.

[0172] In one of the embodiments, the progress monitoring interface including three-dimensional visual annotations is obtained according to the key risk point position information in the construction progress risk assessment report, including:

[0173] Step S801, real-time acquisition of change data of the construction site based on the progress monitoring interface.

[0174] Specifically, the dynamic change data of the construction site, such as the component installation position and the completion state, is collected in real time by the sensor.

[0175] Step S802, comparison of the change data with the original position data to obtain a comparison result.

[0176] Specifically, the component or area position in the change data is matched with the original position data, the Euclidean distance and the state difference of the two are calculated, and the comparison result is obtained.

[0177] Step S803, if the comparison result is that the change data is inconsistent with the original position data, an interface update instruction is generated, the interface update instruction is used to start the interface update mechanism, and an updated monitoring display interface is obtained.

[0178] Specifically, if the change data is inconsistent with the original position data, the interface update mechanism is automatically started, the three-dimensional model display in the progress monitoring interface is dynamically refreshed, the time axis data is updated, and the monitoring display interface is updated.

[0179] Step S804, comparison of the coverage rate of the original position data and the change data with a preset threshold according to the updated monitoring display interface to obtain a comparison result.

[0180] The coverage rate is a measure of the coverage of the change data on the original position data, reflecting the degree of plan execution or the influence range of the change. According to the demand of construction progress monitoring, the coverage rate can be calculated by integrating the time dimension and the space dimension.

[0181] Specifically, the coverage rate of the change data and the original position data is calculated according to the updated monitoring display interface:

[0182]

[0183] Coverage = w1 x Time Coverage + w2 x Space Coverage

[0184] wherein w1 is a time coverage weight coefficient, and w2 is a space weight coefficient, which are set according to project requirements. The calculated coverage is compared with a set threshold to obtain a comparison result.

[0185] In step S805, if the comparison result is that the coverage is lower than the preset threshold, an abnormal marking instruction is generated, the abnormal marking instruction is used to mark the related area as an abnormal state, and a distribution of the abnormal area is obtained.

[0186] Specifically, when the coverage is lower than the preset threshold, the related area is automatically marked as an abnormal state, the abnormal area can be highlighted in the three-dimensional model, and the management personnel is prompted to re-allocate resources or optimize the process.

[0187] In step S806, data aggregation processing is performed on the distribution of the abnormal area, an affected construction progress node is identified, and a range of progress influence is obtained.

[0188] Specifically, the data aggregation processing is performed on the distribution of the abnormal area, and a high-frequency abnormal area is identified. The frequency of abnormal occurrence is counted according to a time window, and an abnormal high-occurrence period is identified. The abnormal area is associated with a specific node in the progress plan, the number or duration of abnormalities corresponding to each node is counted, the indirect influence of the abnormality on upstream and downstream nodes is inferred, and the range of progress influence is determined.

[0189] In step S807, according to the range of progress influence, the abnormal area is dynamically marked in combination with the three-dimensional visual model, and a progress monitoring interface containing three-dimensional visual marking is obtained.

[0190] Specifically, according to the obtained range of progress influence, the abnormal areas are distinguished by different colors in the three-dimensional model, red color represents serious, yellow color represents general, and a progress monitoring interface containing three-dimensional visual marking is obtained.

[0191] In the embodiment, by collecting dynamic change data of the construction site in real time, the three-dimensional model display of the monitoring interface is automatically refreshed, the management personnel is prompted to timely rectify, the closed loop of “monitoring-rectification-prevention” is realized, and the controllability of the construction project is improved.

[0192] It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least some of the other steps or steps or stages in other steps.

[0193] Based on the same inventive concept, the embodiments of the present application also provide a 3D point cloud-BIM fusion construction progress intelligent comparison system. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more 3D point cloud-BIM fusion construction progress intelligent comparison system embodiments provided below can refer to the limitations of the 3D point cloud-BIM fusion construction progress intelligent comparison method in the above text, and will not be repeated here.

[0194] In one exemplary embodiment, as shown in Figure 3 A 3D point cloud-BIM fusion construction progress intelligent comparison system 300 is provided, comprising:

[0195] A data acquisition module 301 is configured to acquire 3D point cloud data of a construction site to obtain a site real scene state point cloud data set;

[0196] A coordinate alignment module 302 is configured to perform coordinate transformation processing on a BIM design model based on the site real scene state point cloud data set to obtain a spatially aligned BIM design model data;

[0197] A unit division module 303 is configured to divide the spatially aligned BIM design model data and the site real scene state point cloud data into three-dimensional grid units of the same size to obtain point cloud density distribution characteristics in each grid unit;

[0198] A data processing module 304 is configured to calculate geometric deviation values between the BIM design model and the real scene state by layer-by-layer comparison and analysis based on the point cloud density distribution characteristics in each grid unit to obtain a progress comparison result.

[0199] In one embodiment, the data acquisition module 301 is further configured to:

[0200] acquire 3D point cloud data of a construction site through multiple sensors;

[0201] Based on 3D point cloud data, the geometric information of the scene is synchronously acquired by a laser scanner and a depth camera;

[0202] Based on the geometric information of the scene, it is detected whether there is device shielding or light change interference, and a detection result is obtained;

[0203] If the detection result is that the device shielding or light change interference is detected, a denoising instruction is generated, which is used to remove the dust influence and noise interference by an adaptive filtering processing technology, and a scene real scene state point cloud data set is obtained.

[0204] In one of the embodiments, the coordinate alignment module 302 is further configured to:

[0205] Extract the feature point distribution information in the scene real scene state point cloud data set, and use an iterative closest point registration algorithm to establish a spatial transformation matrix between the scene coordinate system and the BIM design model coordinate system, and obtain an initial registration result;

[0206] Based on the initial registration result, the matching value of the calculated feature points is compared with a preset threshold value, and a comparison result is obtained;

[0207] If the comparison result is that the matching degree of the feature points reaches the preset threshold value, the initial registration result is used for coordinate transformation processing of the BIM design model, and a standard BIM design model in a unified coordinate system of the scene real scene state is obtained;

[0208] Or,

[0209] If the comparison result is that the matching degree of the feature points is lower than the preset threshold value, a new registration reference point is selected, and a new spatial registration parameter is obtained;

[0210] The BIM design model is subjected to coordinate transformation processing through the new spatial registration parameter, and a standard BIM design model is obtained;

[0211] It is judged whether the boundary of the standard BIM design model exceeds the scene data range, and a judgment result is obtained;

[0212] If the judgment result is that the boundary exceeds the scene data range, the spatial registration parameter is recalculated, and a spatially aligned BIM design model data is obtained.

[0213] In one of the embodiments, the data processing module 304 is further configured to:

[0214] According to the point cloud density distribution characteristics in each grid element, the ratio relationship between the BIM design model point cloud density and the real scene point cloud density is calculated, and a density ratio parameter is obtained;

[0215] Based on the density ratio parameter, the construction progress in each grid element is judged according to the construction state judgment rule, and a complete construction progress distribution map is obtained.

[0216] Based on the complete construction progress distribution map, the geometric deviation value between the BIM design model and the real scene state is calculated by layer-by-layer comparison analysis;

[0217] It is judged whether the geometric deviation value exceeds the preset tolerance range, and a judgment result is obtained;

[0218] If the judgment result is that the geometric deviation value exceeds the preset tolerance range, a marking instruction is generated; the marking instruction is used to mark the relevant area as a construction abnormal area, and a progress comparison result is obtained.

[0219] In one of the embodiments, the data processing module 304 is further configured to:

[0220] The Euclidean distance formula is used to calculate the distance between two points in the three-dimensional space of the BIM design model and the real scene state, and the spatial distance between the BIM design model and the real scene state is obtained:

[0221]

[0222] Wherein, R i is the spatial distance between the BIM design model and the real scene state, x i is the coordinate of the BIM design model in the three-dimensional space, and y i is the coordinate of the real scene state in the three-dimensional space;

[0223] Based on the spatial distance, the average deviation calculation formula is used to calculate the average deviation value of each layer of the BIM design model and the real scene state:

[0224]

[0225] Wherein, M i is the average deviation of each layer, m i is the number of sampling points of each layer, and E ij is the deviation value of each sampling point of each layer;

[0226] Based on the average deviation, the geometric deviation value between the BIM design model and the real scene state is calculated by the following formula:

[0227]

[0228] Wherein, ΔG is the geometric deviation value, n is the number of comparison points, M i is the coordinate value of each point in the BIM design model, and R i is the coordinate value of the corresponding point in the real scene state.

[0229] In an exemplary embodiment, the 3D point cloud-BIM fusion construction progress intelligent comparison system further comprises:

[0230] The deviation prediction module 401 is configured to predict a deviation trend of a subsequent construction stage based on the progress comparison result by using a time series analysis method, and obtain a deviation prediction value of the subsequent construction stage.

[0231] The judgment module 402 is configured to judge whether the deviation prediction value presents an increasing trend, and obtain a judgment result.

[0232] The report generation module 403 is configured to generate a construction progress risk assessment report if the judgment result is that the deviation prediction value presents an increasing trend.

[0233] The risk marking module 404 is configured to obtain a progress monitoring interface containing three-dimensional visual marking according to key risk point position information in the construction progress risk assessment report.

[0234] The instruction generation module 405 is configured to generate a progress update instruction if a construction condition in the progress monitoring interface is found to be changed, the progress update instruction being used to update display content in real time, and obtain an intelligent progress monitoring result.

[0235] In one of the embodiments, the deviation prediction module 401 is further configured to:

[0236] Obtain a deviation numerical sequence of each time node in the progress comparison result, construct a time series data set of deviation change according to time sequence, and obtain a deviation time series data set.

[0237] Based on the deviation time series data set, analyze a deviation evolution feature, and obtain a complete deviation evolution trajectory.

[0238] According to the deviation evolution trajectory, a sliding window method is used to calculate a deviation growth rate in a continuous time period.

[0239] Judge whether the deviation growth rate continuously exceeds a preset threshold value in a preset period, and obtain a judgment result.

[0240] If the judgment result is that the deviation growth rate continuously exceeds the preset threshold value in the preset period, obtain a deviation trend abnormal result.

[0241] Perform future trend analysis on the deviation trend abnormal result by using a time series prediction model, and obtain a deviation prediction value of a subsequent construction stage.

[0242] In one of the embodiments, the risk marking module 404 is further configured to:

[0243] Obtain change data of a construction site in real time based on the progress monitoring interface.

[0244] Compare the change data with original position data, and obtain a comparison result.

[0245] If the comparison result is that the changed data is inconsistent with the original position data, an interface update instruction is generated, the interface update instruction is used to start an interface update mechanism, and an updated monitoring display interface is obtained;

[0246] According to the updated monitoring display interface, the coverage of the original position data and the changed data is compared with a preset threshold, and a comparison result is obtained;

[0247] If the comparison result is that the coverage is lower than the preset threshold, an abnormal marking instruction is generated, the abnormal marking instruction is used to mark the related area as an abnormal state, and a distribution of the abnormal area is obtained;

[0248] The distribution of the abnormal area is subjected to data aggregation processing, an affected construction progress node is identified, and a progress influence range is obtained;

[0249] According to the progress influence range, the abnormal area is dynamically marked in combination with the three-dimensional visual model, and a progress monitoring interface containing three-dimensional visual marking is obtained.

[0250] In one embodiment, a computer device is provided, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the 3D point cloud-BIM fusion construction progress intelligent comparison method as described above when executing the computer program.

[0251] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0252] For the device embodiment, since it basically corresponds to the method embodiment, the related parts are described in the method embodiment. The above described device embodiment is only illustrative, and the components described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, they can be located in one place or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the present disclosure. Those skilled in the art can understand and implement it without creative labor.

[0253] The above described embodiments only express several implementation manners of the present application, the description is more specific and detailed, but it cannot be understood as a limitation on the patent scope of the application. It should be noted that, for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the protection scope of the present application.

Claims

1. A 3D point cloud-BIM fusion construction progress intelligent comparison method, characterized in that, The method comprises: acquiring 3D point cloud data of a construction site to obtain a site real scene state point cloud data set; performing coordinate transformation processing on a BIM design model based on the site real scene state point cloud data set to obtain spatially aligned BIM design model data; dividing the spatially aligned BIM design model data and the site real scene state point cloud data into three-dimensional grid units of the same size to obtain point cloud density distribution characteristics in each grid unit; calculating a ratio relationship between BIM design model point cloud density and real scene point cloud density according to the point cloud density distribution characteristics in each grid unit to obtain a density ratio parameter; judging the construction progress in each grid unit based on the density ratio parameter according to a construction state judgment rule to obtain a complete construction progress distribution map; calculating the distance between two points in the BIM design model and the real scene state three-dimensional space by using the Euclidean distance formula to obtain the spatial distance between the BIM design model and the real scene state: ； wherein, is a spatial distance between the BIM design model and the as-built state, is a coordinate of the BIM design model in three-dimensional space, is a coordinate of the as-built state in three-dimensional space; calculating the average deviation value of each layer of the BIM design model and the real scene state by using the average deviation calculation formula based on the spatial distance: ； wherein, is the average deviation for each layer, is the number of sampling points for each layer, is the deviation value for each sampling point of each layer; calculating the geometric deviation value between the BIM design model and the real scene state based on the average deviation by using the following formula: ； Wherein, ΔG is a geometric deviation value, n is the number of matching points, is a coordinate value of each point in the BIM design model, is a coordinate value of the corresponding point in the real scene state. judging whether the geometric deviation value exceeds a preset tolerance range to obtain a judgment result; if the judgment result is that the geometric deviation value exceeds the preset tolerance range, generating a marking instruction; the marking instruction is used to mark the relevant area as an abnormal construction area to obtain a progress comparison result.

2. The method of claim 1, wherein, The method comprises: acquiring 3D point cloud data of a construction site to obtain a site real scene state point cloud data set; acquiring 3D point cloud data of a construction site to obtain a site real scene state point cloud data set; based on the 3D point cloud data, synchronously acquiring site geometric information by using a laser scanner and a depth camera; based on the site geometric information, detecting whether there is equipment shielding or light change interference, to obtain a detection result; 3. The method of claim 1, wherein, if the detection result is that equipment shielding or light change interference is detected, generating a denoising instruction, which is used to remove dust influence and noise interference by using an adaptive filtering processing technology to obtain the site real scene state point cloud data set. The method comprises: extracting feature point distribution information in the site real scene state point cloud data set, and using an iterative closest point registration algorithm to establish a spatial transformation matrix between a site coordinate system and a BIM design model coordinate system to obtain an initial registration result; based on the initial registration result, comparing the matching value of the calculated feature points with a preset threshold to obtain a comparison result; if the comparison result is that the matching degree of the feature points reaches the preset threshold, performing coordinate transformation processing on the BIM design model using the initial registration result to obtain a standard BIM design model in a unified coordinate system of the site real scene state; or, if the comparison result is that the matching degree of the feature points is lower than the preset threshold, then reselecting a registration reference point to obtain new spatial registration parameters; The BIM design model is subjected to coordinate transformation processing through the new spatial registration parameter, to obtain the standard BIM design model; It is judged whether the boundary of the standard BIM design model exceeds the range of the field data, to obtain a judgment result; If the judgment result is that the boundary exceeds the range of the field data, the spatial registration parameter is recalculated, to obtain a spatially aligned BIM design model data.

4. The method of claim 1, wherein, The method further comprises: Based on the progress comparison result, a time series analysis method is used to predict the deviation trend of the subsequent construction stage, to obtain a deviation prediction value of the subsequent construction stage; It is judged whether the deviation prediction value shows an increasing trend, to obtain a judgment result; If the judgment result is that the deviation prediction value shows an increasing trend, a construction progress risk assessment report is generated; According to the key risk point position information in the construction progress risk assessment report, a progress monitoring interface containing three-dimensional visual annotations is obtained; If the construction situation in the progress monitoring interface is monitored to have changed, a progress update instruction is generated, which is used to update the display content in real time, to obtain an intelligent progress monitoring result.

5. The method of claim 4, wherein, The method of predicting the deviation trend of the subsequent construction stage based on the progress comparison result, to obtain a deviation prediction value of the subsequent construction stage, comprises: The deviation value sequence of each time node in the progress comparison result is obtained, and a time series data set of deviation change is constructed in time sequence, to obtain a deviation time series data set; Based on the deviation time series data set, the deviation evolution characteristics are analyzed, to obtain a complete deviation evolution trajectory; According to the deviation evolution trajectory, a sliding window method is used to calculate the deviation growth rate in a continuous time period; It is judged whether the deviation growth rate exceeds a preset threshold for a continuous preset period, to obtain a judgment result; If the judgment result is that the deviation growth rate exceeds the preset threshold for a continuous preset period, an abnormal deviation trend result is obtained; The time series prediction model is used to analyze the future trend of the abnormal deviation trend result, to obtain a deviation prediction value of the subsequent construction stage.

6. The method of claim 4, wherein, The method of obtaining a progress monitoring interface containing three-dimensional visual annotations according to the key risk point position information in the construction progress risk assessment report, comprises: Based on the progress monitoring interface, change data of the construction site is obtained in real time; The change data and the original position data are compared, to obtain a comparison result; If the comparison result is that the change data is inconsistent with the original position data, an interface update instruction is generated, which is used to start an interface update mechanism, to obtain an updated monitoring display interface; According to the updated monitoring display interface, the coverage rate of the original position data and the change data is compared with a preset threshold, to obtain a comparison result; If the comparison result is that the coverage rate is lower than the preset threshold, an abnormal marking instruction is generated, which is used to mark the related area as an abnormal state, to obtain the distribution of the abnormal area; The distribution of the abnormal area is subjected to data aggregation processing, to identify the affected construction progress nodes, to obtain the scope of progress influence; According to the range of the progress influence, a dynamic label is given to the abnormal area in combination with the three-dimensional visual model, and a progress monitoring interface containing three-dimensional visual labeling is obtained.

7. A 3D point cloud-BIM fusion intelligent construction progress comparison system, characterized in that, The system is used to implement the steps of the method of any one of claims 1 to 6, comprising: a data acquisition module configured to acquire 3D point cloud data of a construction site to obtain a site real scene state point cloud data set; a coordinate alignment module configured to perform coordinate transformation processing on a BIM design model based on the site real scene state point cloud data set to obtain a spatially aligned BIM design model data; a unit division module configured to divide the spatially aligned BIM design model data and the site real scene state point cloud data into three-dimensional grid units of the same size to obtain point cloud density distribution characteristics in each grid unit; a data processing module configured to calculate geometric deviation values between the BIM design model and the real scene state by layer-by-layer comparison and analysis based on the point cloud density distribution characteristics in each grid unit to obtain a progress comparison result.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.

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