BIM model management system for hydraulic engineering construction

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a BIM (Building Information Modeling) management system for hydraulic engineering construction, which comprises a point cloud data acquisition module for acquiring corresponding initial point cloud data after construction of a drainage pipeline of a hydraulic engineering is completed, and dividing the initial point cloud data into a plurality of point cloud sets; the anomaly detection module is used for performing distribution analysis and reflection analysis on the three-dimensional space points in each point cloud set, and determining the three-dimensional space points to be processed and the target anomaly degree of the three-dimensional space points to be processed; the exception handling module is used for determining an abnormal three-dimensional space point according to the target exception degree of the to-be-processed three-dimensional space point, and abandoning the abnormal three-dimensional space point to obtain target point cloud data; and the model construction module is used for constructing a BIM model corresponding to the drainage pipeline of the water conservancy project according to the target point cloud data. According to the method, electric digital data processing is carried out on the drainage pipeline after construction is completed, useful point cloud data are reserved, and the accuracy of the model is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a BIM model management system for water conservancy project construction. Background Art

[0002] During the construction of water conservancy projects, in order to prevent water from seeping into structures such as reservoirs or dams, drainage pipes are usually built to drain the seeping water and reduce the pressure on the structure. Therefore, it is very important to inspect and manage the drainage pipes.

[0003] When inspecting and managing drainage pipes, the BIM model (Building Information Modeling) of the drainage pipes can be constructed through the point cloud data of the drainage pipes to achieve inspection and management. After obtaining the point cloud data of the drainage pipes, in order to ensure the accurate management of the model data, it is usually necessary to perform anomaly detection on the point cloud data. However, when obtaining point cloud data through a 3D laser scanner, since the drainage pipe is a narrow and long enclosed space, during the propagation of the laser signal, if the wall structure inside the pipe is uneven, it will be reflected multiple times on the wall structure inside the pipe, resulting in a multi-path effect, resulting in duplicate points or false points in the point cloud data; at the same time, the direction of the drainage pipe will affect the distribution of the point cloud data, making the results of the point cloud data anomaly detection inaccurate, which in turn leads to a reduction in the accuracy of the drainage pipe model constructed with the point cloud data.

[0004] Therefore, how to improve the accuracy of drainage pipe models in water conservancy construction is an urgent problem that needs to be solved. Summary of the invention

[0005] In order to solve the technical problem of how to improve the accuracy of drainage pipe models in water conservancy construction, the purpose of the present invention is to provide a BIM model management system for water conservancy project construction. The technical solution adopted is as follows: In one embodiment of the present application, a BIM model management system for water conservancy project construction is provided, the system comprising: A point cloud data acquisition module is used to acquire initial point cloud data corresponding to the drainage pipe of the water conservancy project after the construction is completed, and divide the initial point cloud data into multiple point cloud sets; An anomaly detection module, used to perform distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets, determine the three-dimensional space points to be processed, and the target anomaly degree corresponding to the three-dimensional space points to be processed; An abnormality processing module is used to determine abnormal three-dimensional space points according to the target abnormality degree corresponding to the three-dimensional space points to be processed, and discard the abnormal three-dimensional space points to obtain target point cloud data; The model building module is used to build a BIM model corresponding to the drainage pipe of the water conservancy project according to the target point cloud data.

[0006] In one embodiment of the present application, the point cloud data acquisition module includes: A scanning parameter setting submodule, used for obtaining a construction drawing of the drainage pipe of the water conservancy project, setting a point cloud scanning path according to the construction drawing, and determining a scanning resolution and a scanning speed according to the point cloud scanning path; The point cloud data acquisition submodule is used to scan the drainage pipe of the water conservancy project after construction along the point cloud scanning path based on the scanning resolution and scanning speed to obtain the initial point cloud data.

[0007] In one embodiment of the present application, the point cloud data acquisition module further includes: The noise cleaning submodule is used to clean and preprocess the noise in the initial point cloud data to obtain the preprocessed initial point cloud data, wherein the preprocessed initial point cloud data is used to divide into a plurality of the point cloud sets.

[0008] In one embodiment of the present application, the distribution analysis and reflection analysis of the three-dimensional space points in each of the point cloud sets are performed to determine the three-dimensional space points to be processed and the target abnormality degree corresponding to the three-dimensional space points to be processed, including: For any of the three-dimensional space points in the point cloud set, a spatial position analysis is performed according to a preset neighborhood range to obtain an initial abnormality degree corresponding to the three-dimensional space point; Determining the three-dimensional space point to be processed according to the initial abnormality degree corresponding to the three-dimensional space point; According to the reflection change and direction of the three-dimensional space point in the point cloud set, an abnormality factor is determined, and the initial abnormality degree of the three-dimensional space point to be processed is corrected according to the abnormality factor to obtain the target abnormality degree corresponding to the three-dimensional space point to be processed.

[0009] In one embodiment of the present application, the spatial position analysis is performed according to a preset neighborhood range to obtain the initial abnormality degree corresponding to the three-dimensional space point, including: Taking the three-dimensional space point as a center point, and determining a plurality of adjacent three-dimensional space points around the center point and the number of the adjacent three-dimensional space points according to the preset neighborhood range; Obtaining the distance between the central point and each of the adjacent three-dimensional space points, and the neighborhood density between the central point and the adjacent three-dimensional space points; Determine the distribution regularity of the center point and the distribution regularity of each of the adjacent three-dimensional space points according to the number of the adjacent three-dimensional space points, the distance between the center point and each of the adjacent three-dimensional space points, and the neighborhood density of the center point and the adjacent three-dimensional space points; The initial abnormality degree corresponding to the three-dimensional space point is determined according to the distribution regularity degree of the central point and the distribution regularity degree of each of the adjacent three-dimensional space points.

[0010] In one embodiment of the present application, determining the initial abnormality degree corresponding to the three-dimensional space point according to the distribution regularity degree of the central point and the distribution regularity degree of each of the adjacent three-dimensional space points includes: Determining a difference in the degree of distribution regularity according to the degree of distribution regularity of the central point and the degree of distribution regularity of each of the adjacent three-dimensional space points; The initial abnormality degree corresponding to the three-dimensional space point is determined according to the number of the adjacent three-dimensional space points and the difference in the degree of the distribution regularity.

[0011] In one embodiment of the present application, determining the abnormal factor according to the reflection change and direction of the three-dimensional space point in the point cloud set includes: Acquire the to-be-processed three-dimensional space point and a plurality of adjacent three-dimensional space points within a preset neighborhood range of the to-be-processed three-dimensional space point, and merge the to-be-processed three-dimensional space point and the plurality of adjacent three-dimensional space points into a point set; Performing least squares fitting on all points in the point set to obtain an optimal plane equation; The abnormal factor of the three-dimensional space point to be processed is determined according to the normal vector of the plane corresponding to the optimal plane equation.

[0012] In one embodiment of the present application, determining the three-dimensional space point to be processed according to the initial abnormality degree corresponding to the three-dimensional space point includes: Comparing the initial abnormality degree corresponding to the three-dimensional space point with a first preset abnormality degree threshold; When the comparison result is that the initial abnormality degree corresponding to the three-dimensional space point is greater than or equal to the first preset abnormality degree threshold, the three-dimensional space point is used as the three-dimensional space point to be processed.

[0013] In one embodiment of the present application, determining the abnormal three-dimensional space point according to the target abnormality degree corresponding to the three-dimensional space point to be processed includes: Comparing the target abnormality degree corresponding to the three-dimensional space point to be processed with a second preset abnormality degree threshold; When the comparison result is that the target abnormality degree corresponding to the to-be-processed three-dimensional space point is greater than or equal to the second preset abnormality degree threshold, the to-be-processed three-dimensional space point is used as the abnormal three-dimensional space point.

[0014] In one embodiment of the present application, constructing a BIM model corresponding to a drainage pipe of a water conservancy project according to the target point cloud data includes: Importing the target point cloud data into a pre-configured BIM software; In the BIM software, a geometric shape corresponding to the drainage pipe of the water conservancy project is created according to the target point cloud data; The attribute information and accessory equipment information corresponding to the drainage pipe of the water conservancy project are obtained, the attribute information is configured on the geometric shape, and accessory equipment is added to the geometric shape according to the accessory equipment information to obtain the BIM model corresponding to the drainage pipe of the water conservancy project.

[0015] The present invention has the following beneficial effects: First, the point cloud data acquisition module obtains the initial point cloud data corresponding to the drainage pipe of the water conservancy project after the construction is completed, and divides the initial point cloud data into multiple point cloud sets; then, the anomaly detection module performs distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets to determine the three-dimensional space points to be processed and the target abnormality degree corresponding to the three-dimensional space points to be processed; then, the anomaly processing module determines the abnormal three-dimensional space points according to the target abnormality degree corresponding to the three-dimensional space points to be processed, and discards the abnormal three-dimensional space points to obtain the target point cloud data; finally, the model construction module constructs the BIM model corresponding to the drainage pipe of the water conservancy project according to the target point cloud data. In the present invention, the initial point cloud data is divided into multiple point cloud sets, and the large data set is divided into small data sets, which can reduce the amount of calculation and improve the processing efficiency. Each point cloud set can be analyzed in detail for a specific area, making it easier to find local anomalies. By analyzing the spatial distribution of point cloud data, abnormal point cloud clusters, such as duplicate points or false points, can be identified. False points generated by multiple reflections usually have abnormal reflection intensity. Reflection analysis can be used to judge the reliability of point cloud data. According to the target abnormality degree corresponding to the three-dimensional space point to be processed, the abnormal three-dimensional space point is determined, and discarding the abnormal three-dimensional space point can effectively remove noise and improve the purity of the point cloud data. Through accurate identification and removal of abnormal three-dimensional space points, useful point cloud data is retained to ensure the accuracy of the model. The BIM model constructed based on the purified point cloud data has higher accuracy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of an implementation environment of a BIM model management system for water conservancy project construction provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a BIM model management system for water conservancy project construction provided by one embodiment of the present invention; Figure 3 A schematic diagram of the structure of a point cloud data acquisition module provided by an embodiment of the present invention; Figure 4 A flowchart of a BIM model management method for water conservancy project construction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the BIM model management system for water conservancy project construction proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] The specific scheme of a BIM model management system for water conservancy project construction provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] See also Figure 1 , Figure 1 A schematic diagram of an implementation environment of a BIM model management system for water conservancy project construction provided by an embodiment of the present invention is shown as follows: Figure 1 As shown, the implementation environment includes a water conservancy project construction management terminal 101 and a drainage pipe 102. The water conservancy project construction management terminal 101 can be a terminal device configured with a target client, including but not limited to a laptop computer, tablet computer, handheld computer, PAD, desktop computer, etc. with local computing capabilities. The target client can be a video client, instant messaging client, browser client, etc. that supports BIM model management for water conservancy project construction; the water conservancy project construction management terminal 101 can be, but not limited to, modeling and managing the drainage pipe 102. The above-mentioned water conservancy project construction management terminal 101 can be, but not limited to, including a human-computer interaction screen, a processor, and a memory. The above-mentioned human-computer interaction screen can be, but not limited to, used to display the results of equipment fault judgment on the oil well repair machine. The above-mentioned processor can be, but not limited to, used to respond to human-computer interaction operations, execute corresponding operations, or generate corresponding instructions.

[0023] As an optional method, the water conservancy project construction management terminal 101 can also be a server, which can be a single server, a server cluster composed of multiple servers, or a cloud server. The above is only an example, and this embodiment does not make any limitation to this.

[0024] As an optional method, a BIM model management system for water conservancy project construction can be installed on the water conservancy project construction management terminal 101. The structure of the BIM model management system for water conservancy project construction is as follows: A point cloud data acquisition module is used to acquire initial point cloud data corresponding to the drainage pipe of the water conservancy project after the construction is completed, and divide the initial point cloud data into multiple point cloud sets; An anomaly detection module, used to perform distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets, determine the three-dimensional space points to be processed, and the target anomaly degree corresponding to the three-dimensional space points to be processed; An abnormality processing module is used to determine abnormal three-dimensional space points according to the target abnormality degree corresponding to the three-dimensional space points to be processed, and discard the abnormal three-dimensional space points to obtain target point cloud data; The model building module is used to build a BIM model corresponding to the drainage pipe of the water conservancy project according to the target point cloud data.

[0025] Based on the above method, the initial point cloud data is divided into multiple point cloud sets, and the large data set is divided into small data sets, which can reduce the amount of calculation and improve processing efficiency. Each point cloud set can be analyzed in detail for a specific area, making it easier to find local anomalies; by analyzing the spatial distribution of point cloud data, abnormal point cloud clusters, such as duplicate points or false points, can be identified; false points generated by multiple reflections usually have abnormal reflection intensity, and the reliability of point cloud data can be judged using reflection analysis; according to the target abnormality degree corresponding to the three-dimensional space point to be processed, the abnormal three-dimensional space point is determined, and discarding the abnormal three-dimensional space point can effectively remove noise and improve the purity of the point cloud data. Through accurate identification and removal of abnormal three-dimensional space points, useful point cloud data is retained to ensure the accuracy of the model. The BIM model constructed based on the purified point cloud data has higher accuracy and reliability.

[0026] As an optional example, this embodiment does not limit the host of the BIM model management system for the water conservancy project construction. The BIM model management system for the water conservancy project construction can be installed on the water conservancy project construction management terminal 101. For example, when the water conservancy project construction management terminal 101 is a desktop computer, some or all of the components of the BIM model management system for the water conservancy project construction can be installed on the desktop computer.

[0027] In one embodiment of the present application, a BIM model management system for water conservancy project construction is provided. Figure 2 A structural diagram of a BIM model management system for water conservancy project construction provided by an embodiment of the present invention is shown in FIG. Figure 2 The BIM model management system for water conservancy project construction includes the following modules 210 to 240: The point cloud data acquisition module 210 is used to acquire the initial point cloud data corresponding to the drainage pipe of the water conservancy project after the construction is completed, and divide the initial point cloud data into multiple point cloud sets.

[0028] Drainage pipes refer to pipe systems used to discharge rainwater, sewage or other liquids, and are commonly found in urban infrastructure, industrial facilities and water conservancy projects. Drainage pipes are usually long and narrow enclosed spaces, and their internal structures may be uneven, with complex shapes such as bends and branches.

[0029] The initial point cloud data refers to the set of three-dimensional coordinate points inside the drainage pipe obtained by a three-dimensional laser scanner or other three-dimensional scanning equipment. Each point in the initial point cloud data contains three-dimensional coordinates and possible attributes such as color information and reflection intensity. The initial point cloud data usually contains a large number of points, which may include noise points and false points.

[0030] The initial point cloud data is divided into multiple smaller point cloud sets for local processing and analysis. By dividing the point cloud sets, the amount of calculation can be reduced, the processing efficiency can be improved, and local anomalies can be more easily discovered and processed.

[0031] The anomaly detection module 220 is used to perform distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets to determine the three-dimensional space points to be processed and the target anomaly degree corresponding to the three-dimensional space points to be processed.

[0032] Distribution analysis refers to identifying abnormal distribution patterns in point cloud data by statistically analyzing the spatial distribution of point cloud data. Distribution analysis can detect abnormalities such as clustered points, sparse points, duplicate points, and false points in point cloud data. Common distribution analysis methods include point cloud density analysis and cluster analysis.

[0033] Reflection analysis refers to identifying abnormal reflection patterns in point cloud data by counting and analyzing the reflection intensity (echo intensity) of point cloud data. Through reflection analysis, abnormal reflection points caused by multiple reflections, surface material changes, etc. can be found. Common reflection analysis methods include histogram analysis, threshold segmentation, etc.

[0034] Among them, the 3D points to be processed refer to the 3D points that are identified as having possible problems during the distribution analysis and reflection analysis. These points may be noise points, false points, or abnormal 3D points caused by multiple reflections. The target abnormality level refers to the abnormality level of the 3D points to be processed, which is usually expressed by quantitative indicators. By evaluating the target abnormality level, it can be determined which points need to be further processed or deleted.

[0035] The abnormality processing module 230 is used to determine the abnormal three-dimensional space point according to the target abnormality degree corresponding to the three-dimensional space point to be processed, and discard the abnormal three-dimensional space point to obtain the target point cloud data.

[0036] Among them, according to the 3D space points to be processed and their target abnormality levels determined in the previous step, the abnormal 3D space points to be discarded are further screened out. By quantifying the abnormality level, it is ensured that only truly abnormal points are discarded, thereby retaining as much valid point cloud data as possible.

[0037] The model building module 240 is used to build a BIM model corresponding to the drainage pipe of the water conservancy project according to the target point cloud data.

[0038] Among them, the processed and purified target point cloud data is used to construct the building information model (BIM model) of the drainage pipes in the water conservancy project. By building the BIM model, the three-dimensional visualization, information management, simulation analysis and other functions of the drainage pipes can be realized, and the management level and efficiency of the engineering project can be improved. Common BIM model construction methods include point cloud data preprocessing, model construction, attribute assignment, etc.

[0039] In one embodiment of the present application, see Figure 3 , Figure 3 The structure diagram of a point cloud data acquisition module provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the point cloud data acquisition module 210 includes: The scanning parameter setting submodule 211 is used to obtain the construction drawing of the drainage pipe of the water conservancy project, set the point cloud scanning path according to the construction drawing, and determine the scanning resolution and scanning speed according to the point cloud scanning path.

[0040] Among them, the construction drawing of the drainage pipe of the water conservancy project refers to the design drawing drawn before the construction of the drainage pipe, which describes in detail the location, size, direction, connection method and other information of the pipe.

[0041] Among them, the point cloud scanning path refers to the route that the laser scanner moves during the scanning process, which determines the scope and order of the scanning. A reasonable scanning path can ensure that the scanner covers all important parts of the drainage pipe to avoid omissions.

[0042] Among them, scanning resolution refers to the number of three-dimensional points collected by the scanner within a unit length, usually expressed in points / meter or points / cm. Scanning speed refers to the speed at which the scanner moves along the scanning path, usually expressed in meters / second or centimeters / second. High resolution can provide more detailed point cloud data, but it will increase the amount of data and processing time. Reasonable scanning speed can balance data quality and collection efficiency, and avoid data loss or quality degradation due to excessive speed.

[0043] The point cloud data acquisition submodule 212 is used to scan the drainage pipe of the water conservancy project after construction along the point cloud scanning path based on the scanning resolution and scanning speed to obtain the initial point cloud data.

[0044] Among them, the point cloud data acquisition submodule can be a three-dimensional laser scanner.

[0045] Exemplarily, the construction drawing of the drainage pipe of the water conservancy project is obtained, and the point cloud scanning path of the 3D laser scanner is arranged according to the construction drawing. At the same time, the scanning resolution and scanning speed of the 3D laser scanner are planned according to the point cloud scanning path. The above parameters will affect the distribution density and accuracy of the point cloud data. This embodiment does not impose fixed restrictions on the specific scanning resolution and scanning speed, which can be manually set according to actual needs to ensure that high-quality point cloud data of the drainage pipe can be obtained. Then, the drainage pipe of the water conservancy project is scanned along the point cloud scanning path using a 3D laser scanner according to the set scanning resolution and scanning speed to obtain initial point cloud data, and the initial point cloud data is stored in a memory. The initial point cloud data contains multiple three-dimensional space points, each of which has a three-dimensional coordinate and a laser reflection intensity.

[0046] In this embodiment, a reasonable scanning path is planned according to the construction drawing to ensure that the scanner covers all important parts of the drainage pipe to avoid omissions. The scanning resolution and speed are reasonably set to ensure high data accuracy and acquisition efficiency. The scanning path is automatically generated according to the construction drawing to reduce manual intervention and improve work efficiency. By reasonably setting the scanning resolution and speed, the data quality and acquisition time are balanced to improve the overall efficiency. High-resolution point cloud data provides a solid foundation for subsequent data processing and model building. Reasonable scanning path and parameter settings can reduce the generation of noise points and improve the purity of the data.

[0047] In one embodiment of the present application, the point cloud data acquisition module further includes: The noise cleaning submodule is used to clean and preprocess the noise in the initial point cloud data to obtain the preprocessed initial point cloud data, wherein the preprocessed initial point cloud data is used to divide into a plurality of the point cloud sets.

[0048] Among them, cleaning preprocessing refers to the denoising and filtering of the initial point cloud data to remove abnormal three-dimensional spatial points and noise in the data and improve the quality and availability of the data. When denoising, random noise points in the point cloud data can be removed. These noise points may be caused by factors such as the scanning environment and equipment errors. When filtering, the point cloud data can be smoothed to reduce the fluctuation and irregularity of the data, making the data smoother and more continuous.

[0049] For example, since the amount of point cloud data of the drainage pipe is large and the distribution of point cloud data at different positions of the drainage pipe is also different, if the overall data is directly analyzed, there will be a large error, so it is necessary to divide the initial point cloud data of the drainage pipe. According to the preset division ratio coefficient, the initial point cloud data of the drainage pipe is divided to obtain multiple point cloud sets.

[0050] In one embodiment of the present application, the distribution analysis and reflection analysis of the three-dimensional space points in each of the point cloud sets are performed to determine the three-dimensional space points to be processed and the target abnormality degree corresponding to the three-dimensional space points to be processed, including: For any of the three-dimensional space points in the point cloud set, a spatial position analysis is performed according to a preset neighborhood range to obtain an initial abnormality degree corresponding to the three-dimensional space point; Determining the three-dimensional space point to be processed according to the initial abnormality degree corresponding to the three-dimensional space point; According to the reflection change and direction of the three-dimensional space point in the point cloud set, an abnormality factor is determined, and the initial abnormality degree of the three-dimensional space point to be processed is corrected according to the abnormality factor to obtain the target abnormality degree corresponding to the three-dimensional space point to be processed.

[0051] The preset neighborhood range refers to the area within a fixed radius around each 3D spatial point when performing spatial position analysis. It is used to calculate the local density of 3D spatial points, the distribution of neighboring points, etc., to help identify abnormal 3D spatial points. Assume that the preset neighborhood range is 1 meter, that is, within a 1-meter range around each point.

[0052] Among them, spatial position analysis refers to identifying the degree of abnormality of three-dimensional space points by statistically analyzing the distribution of points in the neighborhood of three-dimensional space points. Through spatial position analysis, abnormalities such as clustered points, sparse points, duplicate points, and false points in point cloud data can be found. Common spatial position analysis methods include point cloud density analysis, local density analysis, etc.

[0053] Among them, reflection change refers to the change in the reflection intensity of a three-dimensional space point; trend refers to the distribution trend of a three-dimensional space point in space. By analyzing the reflection change and trend, abnormal three-dimensional space points caused by multiple reflections, surface material changes, etc. can be identified. Common analysis methods include reflection intensity histogram analysis, local reflection intensity change rate analysis, etc.

[0054] Among them, the anomaly factor refers to the weight coefficient used to correct the initial anomaly degree of the 3D space point. By introducing the anomaly factor, the anomaly degree of the 3D space point can be more accurately evaluated, and the accuracy of the abnormal 3D space point identification can be improved. The anomaly factor is usually determined based on the reflection change and direction of the 3D space point.

[0055] In this embodiment, the point cloud density is calculated to identify abnormal three-dimensional space points in the point cloud data. The abnormal three-dimensional space points are further confirmed by analyzing the reflection intensity and spatial distribution trend. By introducing the abnormal factor, the initial abnormal degree is corrected to improve the accuracy of abnormal three-dimensional space point identification. The identified abnormal three-dimensional space points are removed to improve the purity of the point cloud data. Through denoising, the number of invalid data points is reduced, reducing the burden of subsequent data processing. Pure point cloud data is easier to perform subsequent processing, such as segmentation, registration and other operations, which improves the overall processing speed.

[0056] In one embodiment of the present application, the spatial position analysis is performed according to a preset neighborhood range to obtain the initial abnormality degree corresponding to the three-dimensional space point, including: Taking the three-dimensional space point as a center point, and determining a plurality of adjacent three-dimensional space points around the center point and the number of the adjacent three-dimensional space points according to the preset neighborhood range; Obtaining the distance between the central point and each of the adjacent three-dimensional space points, and the neighborhood density between the central point and the adjacent three-dimensional space points; Determine the distribution regularity of the center point and the distribution regularity of each of the adjacent three-dimensional space points according to the number of the adjacent three-dimensional space points, the distance between the center point and each of the adjacent three-dimensional space points, and the neighborhood density of the center point and the adjacent three-dimensional space points; The initial abnormality degree corresponding to the three-dimensional space point is determined according to the distribution regularity degree of the central point and the distribution regularity degree of each of the adjacent three-dimensional space points.

[0057] The center point refers to the three-dimensional space point currently being analyzed, which serves as the reference point for the analysis and is used to calculate the neighborhood information around it.

[0058] Among them, the adjacent three-dimensional space points refer to the three-dimensional space points adjacent to the center point within a preset neighborhood range, which are used to calculate the point distribution around the center point.

[0059] The distance between the center point and each adjacent three-dimensional space point refers to the Euclidean distance from the center point to each adjacent three-dimensional space point, which is used to evaluate the uniformity and density of the point distribution around the center point.

[0060] Among them, neighborhood density refers to the ratio of the number of points in a preset neighborhood to the neighborhood volume, which is used to evaluate the density of point cloud data in a local area.

[0061] Among them, the degree of distribution regularity refers to whether the distribution of three-dimensional space points in space is uniform and orderly. It is used to evaluate the degree of abnormality of three-dimensional space points. The more irregular the distribution, the higher the degree of abnormality.

[0062] For example, since the drainage pipe is a narrow and long closed space, during the propagation process of the laser signal of the three-dimensional laser scanner, if the wall structure inside the pipe is uneven, it will be reflected multiple times on the wall structure inside the pipe, resulting in a multipath effect, resulting in duplicate points or false points in the point cloud data. These duplicate points or false points will be abnormal in position distribution. For example, the point cloud distribution is relatively uniform, but suddenly a local position appears concentrated or sparse. Since the abnormal three-dimensional space points are mainly duplicate points or false points, there is a certain regularity difference between them and the normally distributed point cloud. Therefore, the spatial position relationship between each three-dimensional space point and the neighboring points in the point cloud set and the density difference of the three-dimensional space points are first analyzed to obtain the distribution regularity of each three-dimensional space point in the point cloud set. Any point cloud set is taken as a point cloud set, and any three-dimensional space point in the point cloud set is analyzed. With the current three-dimensional space point as the center point, the neighborhood range is preset to be a spherical range determined by the radius r as the neighborhood range of the current three-dimensional space point, where r is set to 1 in this embodiment.

[0063] Exemplarily, the distribution regularity degree may be expressed as: ; in, is the degree of distribution regularity; is the number of adjacent three-dimensional space points; is the distance between the center point and the ath adjacent three-dimensional space point; is the mean of the distances between the center point and all adjacent three-dimensional space points; is the difference between the neighborhood density of the center point and the neighborhood density of the ath adjacent three-dimensional space point, where the neighborhood density difference specifically refers to the difference in the number of three-dimensional space points within the neighborhood range; The distance distribution between the center point and the adjacent three-dimensional space points is extremely poor; It is the natural exponential function in mathematical calculation.

[0064] in, It represents the difference between the distance between the center point and the ath adjacent three-dimensional space point and the mean distance between the center point and all adjacent three-dimensional space points. The larger the difference, the lower the distribution regularity of the center point in the point cloud set and its surrounding adjacent three-dimensional space points.

[0065] in, It means that the greater the difference between the center point and the ath adjacent three-dimensional space point, the greater the difference, the lower the distribution regularity of the center point in the point cloud set and the adjacent three-dimensional space points around it.

[0066] in, The smaller it is, the lower the distribution regularity between the center point in the point cloud set and its surrounding three-dimensional space points. The larger the value, the higher the distribution regularity between the center point in the point cloud set and the adjacent 3D space points around it. In normal point cloud data, the distribution distance and neighborhood density between 3D space points are relatively stable and will not change too much, so the distribution regularity is relatively high.

[0067] In this embodiment, the distribution regularity of the point cloud data is evaluated by calculating the distance between the center point and the adjacent points and the neighborhood density, and abnormal three-dimensional space points are identified. The lower the distribution regularity, the higher the abnormality of the point, which helps to accurately identify abnormal three-dimensional space points. By identifying and removing abnormal three-dimensional space points, the purity of the point cloud data is improved and noise interference is reduced. The integrity and consistency of the point cloud data are ensured, providing a high-quality data foundation for subsequent data processing and analysis.

[0068] In one embodiment of the present application, determining the initial abnormality degree corresponding to the three-dimensional space point according to the distribution regularity degree of the central point and the distribution regularity degree of each of the adjacent three-dimensional space points includes: Determining a difference in the degree of distribution regularity according to the degree of distribution regularity of the central point and the degree of distribution regularity of each of the adjacent three-dimensional space points; The initial abnormality degree corresponding to the three-dimensional space point is determined according to the number of the adjacent three-dimensional space points and the difference in the degree of the distribution regularity.

[0069] The distribution regularity difference refers to the difference between the distribution regularity of the center point and the distribution regularity of each adjacent three-dimensional space point. By calculating the distribution regularity difference, the degree of abnormality of the center point relative to other points in its neighborhood can be evaluated. The greater the difference, the higher the degree of abnormality of the center point.

[0070] For example, the distribution regularity between normal three-dimensional space points in a point cloud set should be relatively similar, while the distribution regularity of abnormal three-dimensional space points and adjacent three-dimensional space points in the neighborhood are less similar. By analyzing the similarity of the distribution regularity of three-dimensional space points, the initial abnormality degree of each point in the point cloud set can be obtained.

[0071] The initial abnormality degree may be expressed as: ; in, is the initial abnormality degree corresponding to the three-dimensional space point; is the number of adjacent three-dimensional space points; is the distribution regularity of the center point; is the distribution regularity of the bth adjacent three-dimensional space point; in, It is the difference in the degree of distribution regularity, reflecting the similarity of the distribution regularity between the center point and the bth adjacent three-dimensional space point in the neighborhood. When the difference in the degree of distribution regularity is smaller, it means that the distribution regularities of the two are similar, and the anomaly of the three-dimensional space point is smaller.

[0072] Among them, the smaller the initial abnormality degree corresponding to the three-dimensional space point is, the less likely the three-dimensional space point is to be a duplicate point or a false point.

[0073] In this embodiment, by calculating the difference in the degree of distribution regularity between the center point and the adjacent points, the degree of abnormality of the center point can be more accurately evaluated. The greater the difference, the higher the degree of abnormality of the center point. By comprehensively considering the number of adjacent points and the difference in the degree of distribution regularity, the degree of abnormality of the center point can be more comprehensively evaluated, thereby improving the accuracy of abnormal three-dimensional spatial point recognition.

[0074] In one embodiment of the present application, determining the abnormal factor according to the reflection change and direction of the three-dimensional space point in the point cloud set includes: Acquire the to-be-processed three-dimensional space point and a plurality of adjacent three-dimensional space points within a preset neighborhood range of the to-be-processed three-dimensional space point, and merge the to-be-processed three-dimensional space point and the plurality of adjacent three-dimensional space points into a point set; Performing least squares fitting on all points in the point set to obtain an optimal plane equation; The abnormal factor of the three-dimensional space point to be processed is determined according to the normal vector of the plane corresponding to the optimal plane equation.

[0075] Among them, the least squares fitting method is a mathematical method used to find the best fitting curve or plane for a set of data, and find the optimal solution by minimizing the sum of square errors between the data points and the fitting curve or plane. In point cloud data processing, the least squares fitting method can be used to determine the best plane equation for a set of points, thereby evaluating the distribution of these points. By solving a system of linear equations, the parameters that minimize the sum of squared errors are found.

[0076] The optimal plane equation refers to the plane equation obtained by least squares fitting that can best fit a given set of points. The general form is ax+by+cz+d=0. The optimal plane equation can be used to describe the distribution trend of points in three-dimensional space and evaluate the flatness of point cloud data.

[0077] The normal vector of the plane corresponding to the optimal plane equation refers to the vector formed by the coefficients (a, b, c) in the plane equation ax+by+cz+d=0. The normal vector can be used to describe the direction of the plane and evaluate the distribution direction and consistency of points in three-dimensional space.

[0078] For example, since the structure of the drainage pipe is not designed along a straight line, in order to adapt to the terrain or avoid obstacles, the design direction of the pipe usually turns. When the drainage pipe turns, since the inner radius of the pipe is shorter and the outer radius is longer, when the point cloud data is obtained by the laser scanner, the inner point cloud data will be denser, while the outer point cloud data will be sparser. At this time, the point cloud data is unevenly distributed, and the distribution distance and neighborhood density of the three-dimensional space points in the point cloud data and the surrounding adjacent three-dimensional space points vary greatly. Judging the abnormality only by the degree of distribution law analyzed above is not accurate. Although the point cloud distribution law of the drainage pipe at the bend is somewhat similar to the distribution performance of the abnormal three-dimensional space points, during the propagation of the laser signal of the three-dimensional laser scanner, if the laser signal is reflected multiple times on the wall structure inside the pipe, it will cause the laser intensity of the three-dimensional space points in the point cloud data to be greatly attenuated. Obtain the point set consisting of the i-th three-dimensional space point to be processed in the point cloud set and the adjacent three-dimensional space points in the neighborhood range. , point set The least squares method is used to fit all the points in the , and the best plane equation is fitted. The normal vector of the plane determined by the plane equation is used as the centripetal vector of the i-th three-dimensional space point to be processed.

[0079] Exemplarily, the abnormal factor may be expressed as: ; in, is the abnormal factor of the i-th 3D space point to be processed in the point cloud set; is a linear normalization function used for normalization; It is the natural exponential function in mathematical calculation; is the number of adjacent three-dimensional space points within the neighborhood of the i-th three-dimensional space point to be processed in the point cloud set; is the angle between the centripetal vector of the i-th 3D space point to be processed in the point cloud set and the c-th adjacent 3D space point in the neighborhood; is the angle between the centripetal vector of the i-th 3D space point to be processed in the point cloud set and the d-th adjacent 3D space point in the neighborhood; is the laser reflection intensity of the i-th 3D space point to be processed in the point cloud set; It is the mean value of the laser reflection intensity of all three-dimensional space points in the neighborhood of the i-th three-dimensional space point to be processed in the point cloud set.

[0080] in, It reflects the change in the angle between the i-th three-dimensional space point to be processed and the centripetal vectors of different adjacent three-dimensional space points in the neighborhood. If the drainage pipe does not turn, the angle change is small. Otherwise, the angle change is more obvious. If a turn occurs, the initial abnormality degree should be corrected to reduce the abnormality degree, and the abnormal factor should also be smaller.

[0081] in, It reflects the change of the laser reflection intensity of the i-th three-dimensional space point to be processed. If the laser reflection intensity decays significantly, it will change greatly compared with the mean. The difference is mapped and amplified by the exponential function, and the larger the corresponding abnormal factor.

[0082] The larger the abnormality factor is, the greater the degree of correction required for the initial abnormality is.

[0083] Exemplarily, the target abnormality degree may be expressed as follows: ; in, is the target abnormality degree of the i-th 3D space point to be processed in the point cloud set; is the initial abnormality degree of the i-th 3D space point to be processed in the point cloud set; is the abnormal factor of the i-th 3D space point to be processed in the point cloud set. The greater the target abnormality, the more likely the i-th 3D space point to be processed is an abnormal 3D space point.

[0084] In this embodiment, the best plane equation of the three-dimensional space point can be accurately determined by least squares fitting, and the flatness of the point cloud data can be evaluated. The normal vector can describe the direction of the plane, evaluate the distribution direction and consistency of the three-dimensional space point, and thus more accurately identify abnormal three-dimensional space points. By calculating the abnormal factor, the degree of abnormality of the three-dimensional space point can be more comprehensively evaluated, and the accuracy of abnormal three-dimensional space point identification can be improved.

[0085] In one embodiment of the present application, determining the three-dimensional space point to be processed according to the initial abnormality degree corresponding to the three-dimensional space point includes: Comparing the initial abnormality degree corresponding to the three-dimensional space point with a first preset abnormality degree threshold; When the comparison result is that the initial abnormality degree corresponding to the three-dimensional space point is greater than or equal to the first preset abnormality degree threshold, the three-dimensional space point is used as the three-dimensional space point to be processed.

[0086] Exemplarily, the first preset abnormality level threshold is 0.4, and the three-dimensional space points whose initial abnormality levels are greater than or equal to the first preset abnormality level threshold are used as the three-dimensional space points to be processed.

[0087] In one embodiment of the present application, determining the abnormal three-dimensional space point according to the target abnormality degree corresponding to the three-dimensional space point to be processed includes: Comparing the target abnormality degree corresponding to the three-dimensional space point to be processed with a second preset abnormality degree threshold; When the comparison result is that the target abnormality degree corresponding to the to-be-processed three-dimensional space point is greater than or equal to the second preset abnormality degree threshold, the to-be-processed three-dimensional space point is used as the abnormal three-dimensional space point.

[0088] In one embodiment of the present application, constructing a BIM model corresponding to a drainage pipe of a water conservancy project according to the target point cloud data includes: Importing the target point cloud data into a pre-configured BIM software; In the BIM software, a geometric shape corresponding to the drainage pipe of the water conservancy project is created according to the target point cloud data; The attribute information and accessory equipment information corresponding to the drainage pipe of the water conservancy project are obtained, the attribute information is configured on the geometric shape, and accessory equipment is added to the geometric shape according to the accessory equipment information to obtain the BIM model corresponding to the drainage pipe of the water conservancy project.

[0089] Among them, BIM software is a software tool for creating and managing building information models. BIM software not only supports three-dimensional modeling, but also manages various attribute information and documents related to the model, providing powerful data management and analysis functions. Importing the processed and purified target point cloud data into the pre-configured BIM software for subsequent model creation and management can ensure that the point cloud data can be correctly displayed and processed in the BIM software, providing basic data for model creation. In the BIM software, the three-dimensional geometric shape of the drainage pipe is created based on the imported target point cloud data, and a three-dimensional model of the drainage pipe can be generated, providing a basis for subsequent attribute configuration and accessory equipment addition.

[0090] Among them, attribute information includes detailed information such as the material, specification, length, diameter, wall thickness, etc. of the drainage pipe. Accessory equipment information includes the type, location, specifications, and other information of accessory equipment such as valves, pumps, and sensors on the drainage pipe. Attribute information and accessory equipment information provide detailed engineering data for the model to support subsequent engineering management and maintenance. In the BIM software, the acquired attribute information is configured to the geometric shape of the drainage pipe to ensure that each geometric shape in the model is accompanied by detailed attribute information to facilitate engineering management and query. In the BIM software, according to the accessory equipment information, the corresponding accessory equipment model is added to the geometric shape of the drainage pipe to ensure that the model fully reflects the actual structure of the drainage pipe and supports engineering management and maintenance.

[0091] In this embodiment, the point cloud data is correctly displayed and processed in the BIM software to provide high-quality basic data for model creation; the precise geometric shape of the drainage pipe is generated based on the point cloud data to ensure the geometric accuracy of the model. Detailed attribute information is configured on the geometric shape to ensure that the model contains rich engineering data and supports subsequent engineering management and maintenance. The corresponding equipment model is added according to the attached equipment information to ensure that the model fully reflects the actual structure of the drainage pipe.

[0092] Based on the above embodiments, it can be seen that dividing the initial point cloud data into multiple point cloud sets and dividing the large data set into small data sets can reduce the amount of calculation and improve processing efficiency. Each point cloud set can be analyzed in detail for a specific area, making it easier to find local anomalies; by analyzing the spatial distribution of point cloud data, abnormal point cloud clusters can be identified, such as duplicate points or false points; false points generated by multiple reflections usually have abnormal reflection intensity, and the reliability of point cloud data can be judged using reflection analysis; according to the target abnormality degree corresponding to the three-dimensional space point to be processed, the abnormal three-dimensional space point is determined, and discarding the abnormal three-dimensional space point can effectively remove noise and improve the purity of the point cloud data. Through accurate identification and removal of abnormal three-dimensional space points, useful point cloud data is retained to ensure the accuracy of the model. The BIM model constructed based on the purified point cloud data has higher accuracy and reliability.

[0093] In one embodiment of the present application, a BIM model management method for water conservancy project construction is also provided. Figure 4 A flow chart of a BIM model management method for water conservancy project construction provided by an embodiment of the present invention is shown in FIG. Figure 4 , the method comprises the following steps: S410, obtaining initial point cloud data corresponding to the drainage pipe of the water conservancy project after the construction is completed, and dividing the initial point cloud data into multiple point cloud sets; S420, performing distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets to determine the three-dimensional space points to be processed and the target abnormality degree corresponding to the three-dimensional space points to be processed; S430, determining abnormal three-dimensional space points according to the target abnormality degree corresponding to the three-dimensional space points to be processed, and discarding the abnormal three-dimensional space points to obtain target point cloud data; S440: Construct a BIM model corresponding to the drainage pipe of the water conservancy project according to the target point cloud data.

[0094] Through the BIM model management method for water conservancy project construction in the embodiment of the present application, the initial point cloud data is divided into multiple point cloud sets, and the large data set is divided into small data sets, which can reduce the amount of calculation and improve processing efficiency. Each point cloud set can be analyzed in detail for a specific area, making it easier to find local anomalies; by analyzing the spatial distribution of point cloud data, abnormal point cloud clusters, such as duplicate points or false points, can be identified; false points generated by multiple reflections usually have abnormal reflection intensity, and the reliability of point cloud data can be judged using reflection analysis; according to the target abnormality degree corresponding to the three-dimensional space point to be processed, the abnormal three-dimensional space point is determined, and discarding the abnormal three-dimensional space point can effectively remove noise and improve the purity of the point cloud data. Through accurate identification and removal of abnormal three-dimensional space points, useful point cloud data is retained to ensure the accuracy of the model. The BIM model constructed based on the purified point cloud data has higher accuracy and reliability.

[0095] The specific implementation example of the BIM model management method for water conservancy project construction in this application can refer to the example shown in the above-mentioned BIM model management system for water conservancy project construction, which will not be repeated here in this example.

[0096] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A BIM model management system for water conservancy project construction, characterized in that: The system comprises: A point cloud data acquisition module is used to acquire initial point cloud data corresponding to the drainage pipe of the water conservancy project after the construction is completed, and divide the initial point cloud data into multiple point cloud sets; An anomaly detection module, used to perform distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets, determine the three-dimensional space points to be processed, and the target anomaly degree corresponding to the three-dimensional space points to be processed; An abnormality processing module is used to determine abnormal three-dimensional space points according to the target abnormality degree corresponding to the three-dimensional space points to be processed, and discard the abnormal three-dimensional space points to obtain target point cloud data; A model building module, used to build a BIM model corresponding to the drainage pipe of the water conservancy project according to the target point cloud data; The performing distribution analysis and reflection analysis on the three-dimensional space points in each of the point cloud sets to determine the three-dimensional space points to be processed and the target abnormality degree corresponding to the three-dimensional space points to be processed includes: For any of the three-dimensional space points in the point cloud set, a spatial position analysis is performed according to a preset neighborhood range to obtain an initial abnormality degree corresponding to the three-dimensional space point; Determining the three-dimensional space point to be processed according to the initial abnormality degree corresponding to the three-dimensional space point; Determine an abnormality factor according to the reflection change and direction of the three-dimensional space point in the point cloud set, and correct the initial abnormality degree of the three-dimensional space point to be processed according to the abnormality factor to obtain the target abnormality degree corresponding to the three-dimensional space point to be processed; The performing of spatial position analysis according to the preset neighborhood range to obtain the initial abnormality degree corresponding to the three-dimensional space point includes: Taking the three-dimensional space point as a center point, and determining a plurality of adjacent three-dimensional space points around the center point and the number of the adjacent three-dimensional space points according to the preset neighborhood range; Obtaining the distance between the central point and each of the adjacent three-dimensional space points, and the neighborhood density between the central point and the adjacent three-dimensional space points; Determine the distribution regularity of the center point and the distribution regularity of each of the adjacent three-dimensional space points according to the number of the adjacent three-dimensional space points, the distance between the center point and each of the adjacent three-dimensional space points, and the neighborhood density of the center point and the adjacent three-dimensional space points; Determining the initial abnormality degree corresponding to the three-dimensional space point according to the distribution regularity degree of the central point and the distribution regularity degree of each of the adjacent three-dimensional space points; Determining the abnormal factor according to the reflection change and direction of the three-dimensional space points in the point cloud set includes: Acquire the to-be-processed three-dimensional space point and a plurality of adjacent three-dimensional space points within a preset neighborhood range of the to-be-processed three-dimensional space point, and merge the to-be-processed three-dimensional space point and the plurality of adjacent three-dimensional space points into a point set; Performing least squares fitting on all points in the point set to obtain an optimal plane equation; The abnormal factor of the three-dimensional space point to be processed is determined according to the normal vector of the plane corresponding to the optimal plane equation.

2. The BIM model management system for water conservancy project construction according to claim 1 is characterized in that: The point cloud data acquisition module includes: A scanning parameter setting submodule, used for obtaining a construction drawing of the drainage pipe of the water conservancy project, setting a point cloud scanning path according to the construction drawing, and determining a scanning resolution and a scanning speed according to the point cloud scanning path; The point cloud data acquisition submodule is used to scan the drainage pipe of the water conservancy project after construction along the point cloud scanning path based on the scanning resolution and scanning speed to obtain the initial point cloud data.

3. The BIM model management system for water conservancy project construction according to claim 2 is characterized in that: The point cloud data acquisition module also includes: The noise cleaning submodule is used to clean and preprocess the noise in the initial point cloud data to obtain the preprocessed initial point cloud data, wherein the preprocessed initial point cloud data is used to divide into a plurality of the point cloud sets.

4. The BIM model management system for water conservancy project construction according to claim 1 is characterized in that: Determining the initial abnormality degree corresponding to the three-dimensional space point according to the distribution regularity degree of the central point and the distribution regularity degree of each of the adjacent three-dimensional space points includes: Determining a difference in the degree of distribution regularity according to the degree of distribution regularity of the central point and the degree of distribution regularity of each of the adjacent three-dimensional space points; The initial abnormality degree corresponding to the three-dimensional space point is determined according to the number of the adjacent three-dimensional space points and the difference in the degree of the distribution regularity.

5. The BIM model management system for water conservancy project construction according to claim 1 is characterized in that: The step of determining the three-dimensional space point to be processed according to the initial abnormality degree corresponding to the three-dimensional space point comprises: Comparing the initial abnormality degree corresponding to the three-dimensional space point with a first preset abnormality degree threshold; When the comparison result is that the initial abnormality degree corresponding to the three-dimensional space point is greater than or equal to the first preset abnormality degree threshold, the three-dimensional space point is used as the three-dimensional space point to be processed.

6. The BIM model management system for water conservancy project construction according to claim 1 is characterized in that: The step of determining the abnormal three-dimensional space point according to the target abnormality degree corresponding to the three-dimensional space point to be processed includes: Comparing the target abnormality degree corresponding to the three-dimensional space point to be processed with a second preset abnormality degree threshold; When the comparison result is that the target abnormality degree corresponding to the to-be-processed three-dimensional space point is greater than or equal to the second preset abnormality degree threshold, the to-be-processed three-dimensional space point is used as the abnormal three-dimensional space point.

7. The BIM model management system for water conservancy project construction according to claim 1 is characterized in that: The step of constructing a BIM model corresponding to a drainage pipe of a water conservancy project according to the target point cloud data includes: Importing the target point cloud data into a pre-configured BIM software; In the BIM software, a geometric shape corresponding to the drainage pipe of the water conservancy project is created according to the target point cloud data; The attribute information and accessory equipment information corresponding to the drainage pipe of the water conservancy project are obtained, the attribute information is configured on the geometric shape, and accessory equipment is added to the geometric shape according to the accessory equipment information to obtain the BIM model corresponding to the drainage pipe of the water conservancy project.

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