CN117934747B - Active construction landform three-dimensional model construction method based on laser point cloud data - Google Patents
Active construction landform three-dimensional model construction method based on laser point cloud data Download PDFInfo
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Abstract
The application provides a method for constructing a three-dimensional model of a moving construction landform based on laser point cloud data. Performing digital modeling by utilizing laser point cloud data to generate a first model of an active construction landform; determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data, obtaining a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model; determining a non-rigid transformation matrix of the laser point cloud data relative to the first model according to the deformation model; and generating a three-dimensional model of the movable construction landform according to the non-rigid body transformation matrix so as to carry out safety detection of the movable construction landform based on the three-dimensional model of the movable construction landform. The application is helpful for enhancing the monitoring and analysis of the movable structure landform and improving the prediction and control level of geological disasters.
Description
Technical Field
The application relates to the technical field of geological safety detection, in particular to a method for constructing a three-dimensional model of a moving structure landform based on laser point cloud data.
Background
Active formation topography refers to the surface topography features, such as fractures, folds, etc., caused by the movement of the geological formation. The method is used for accurately constructing and analyzing the three-dimensional model of the moving structure landform, and has important significance for predicting and preventing and controlling geological disasters.
At present, a commonly used method for constructing a three-dimensional model of the active construction landform is a method based on remote sensing data and ground measurement data. According to the method, the satellite remote sensing image, the aerial image or the ground photogrammetry data are obtained, the ground surface characteristics such as topographic relief, fracture zones and the like are extracted, and then the digital technology is utilized for modeling and analyzing the landform.
However, the prior art solutions have some drawbacks: limitation of data acquisition: acquisition of telemetry and ground survey data typically requires high cost and complex operations, limiting the overall observation and analysis of the active formation topography. Lack of fine surface feature extraction: the extraction of the surface features by the existing method is often based on a simplified algorithm and model, and complex deformation features of the movable structural landforms are difficult to accurately capture. Lack of dynamic monitoring capability: the existing method mainly relies on static data for modeling and analysis, and cannot monitor and evaluate the change and evolution process of the active construction landform in real time.
In view of the foregoing, the prior art solutions have some limitations and challenges in the construction of three-dimensional models of active-structure features, and further research and exploration of new technical solutions are needed to improve understanding and prediction capabilities of active-structure features.
Disclosure of Invention
The embodiment of the application provides a method for constructing a three-dimensional model of a movable construction landform based on laser point cloud data, which is used for solving the problem of poor effect on analysis and monitoring of the movable construction landform in the prior art.
In a first aspect, an embodiment of the present application provides a method for constructing a three-dimensional model of a feature based on active construction of laser point cloud data, including:
collecting laser point cloud data of a movable construction landform area, and preprocessing the data;
carrying out topographic feature extraction and segmentation according to the processed laser point cloud data, and carrying out digital modeling by utilizing the laser point cloud data to generate a first model of an active construction landform, wherein the first model comprises a landform structure of the laser point cloud data and a spatial position corresponding to each landform structure;
Determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data in the first model to obtain a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relationship between the laser point cloud data and the first model;
Determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model;
And transforming the corresponding space coordinates in the laser point cloud data to the space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate a movable constructed landform three-dimensional model, wherein the movable constructed landform three-dimensional model at least comprises a landform structure, the laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data.
Optionally, the establishing a deformation model corresponding between the laser point cloud data and the first model based on the matching result includes:
performing grid division on the first model to generate a grid model, wherein the grid model comprises a plurality of grid nodes;
And determining grid nodes corresponding to the laser point cloud data, and adjusting the positions and/or connection relations of the grid nodes in the grid model according to the laser point cloud data so as to generate a deformation model corresponding to the laser point cloud data and the first model.
Optionally, the determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model includes:
dispersing each laser point cloud data into a plurality of grid cells, wherein the grid cells comprise corresponding vertex coordinates and topological structures;
Obtaining deformation results of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells;
And generating a non-rigid body transformation matrix of the laser point cloud data relative to the first model according to deformation results of a plurality of grid cells.
Optionally, the deformation result includes a displacement field and a deformation field;
The obtaining the deformation result of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells comprises:
Obtaining displacement increment information of each grid cell by applying external force and/or constraint conditions to the grid cells;
calculating a corresponding displacement field according to the displacement increment information of each grid unit;
Calculating a corresponding deformation gradient tensor according to the displacement field of each grid unit;
Calculating a corresponding deformation field according to the deformation gradient tensor of each grid cell, wherein the deformation field is used for indicating the deformation condition of the grid cell;
and generating a deformation result of each grid cell according to the displacement field and the deformation field of each grid cell.
Optionally, in the first model, determining a spatial position corresponding to at least one relief structure matched with the relief feature of the laser point cloud data, to obtain a matching result, including:
Obtaining the geomorphic characteristics of the laser point cloud data and the position characteristics of the spatial positions corresponding to the geomorphic structures;
And determining a matching result between the laser point cloud data and the landform structure according to the landform characteristics and the similarity of the position characteristics.
Optionally, the determining a matching result between the laser point cloud data and the geomorphic structure according to the similarity of the geomorphic feature and the position feature includes:
acquiring a first similarity and a first distance between the geomorphic features and the position features;
Determining the similarity of the landform features and the position features according to the first similarity and the weight corresponding to the first similarity, wherein the first distance and the weight corresponding to the first distance;
And if the similarity is greater than the set similarity, determining that the landform features of the laser point cloud data are matched with the landform structure.
In a second aspect, an embodiment of the present application provides an active-construction geomorphic three-dimensional model construction apparatus based on laser point cloud data, including:
collecting laser point cloud data of a movable construction landform area, and preprocessing the data;
carrying out topographic feature extraction and segmentation according to the processed laser point cloud data, and carrying out digital modeling by utilizing the laser point cloud data to generate a first model of an active construction landform, wherein the first model comprises a landform structure of the laser point cloud data and a spatial position corresponding to each landform structure;
Determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data in the first model to obtain a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relationship between the laser point cloud data and the first model;
Determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model;
Transforming corresponding space coordinates in the laser point cloud data to space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate a movable constructed landform three-dimensional model, wherein the movable constructed landform three-dimensional model at least comprises a landform structure, laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data;
in a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor is configured to run the computer program to execute the method for constructing an active construction relief three-dimensional model based on laser point cloud data according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, on which computer program instructions are stored, where the computer program instructions, when executed by a processor, implement the method for constructing a three-dimensional model of an active construction feature based on laser point cloud data according to the first aspect.
In the embodiment of the application, the laser point cloud data of the movable construction landform area is collected and data preprocessing is carried out; carrying out topographic feature extraction and segmentation according to the processed laser point cloud data, and carrying out digital modeling by utilizing the laser point cloud data to generate a first model of an active construction landform, wherein the first model comprises a landform structure of the laser point cloud data and a spatial position corresponding to each landform structure; determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data in the first model to obtain a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relationship between the laser point cloud data and the first model; determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model; and transforming the corresponding space coordinates in the laser point cloud data to the space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate a movable constructed landform three-dimensional model, wherein the movable constructed landform three-dimensional model at least comprises a landform structure, the laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data. .
The method for constructing the three-dimensional model of the active construction landform based on the laser point cloud data has the following beneficial effects:
Accurate topographic feature extraction: by extracting and dividing the topographic features through laser point cloud data, the topographic relief, fracture zone and other features of the movable structural topographic features can be accurately captured, and the accurate identification and extraction of the topographic features can be realized.
Efficient digital modeling: the laser point cloud data is utilized for digital modeling, so that a three-dimensional model of the movable structural landform can be quickly generated, the three-dimensional model comprises a landform structure and spatial position information, and modeling efficiency and accuracy are improved.
And (3) establishing a dynamic deformation model: and establishing a deformation model between the laser point cloud data and the first model based on the matching result, and reflecting the non-rigid deformation relation of the movable structural landform to realize dynamic landform change monitoring and analysis.
Integrated model presentation and security detection: the generation of the three-dimensional model of the movable structure landform comprises a landform structure, laser point cloud data and a virtual structure, the overall view of the movable structure landform is comprehensively displayed, meanwhile, the safety detection and evaluation of the movable structure landform can be carried out based on the three-dimensional model of the movable structure landform, and the geological disaster prediction and prevention capability is improved.
In conclusion, the method for constructing the three-dimensional model of the movable structure landform based on the laser point cloud data is beneficial to enhancing the monitoring and analysis of the movable structure landform and improving the prediction and control level of geological disasters.
Aspects and other aspects will be more readily apparent from the description of the embodiments that follows.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a flow chart of a method for constructing a three-dimensional model of a moving construction landform based on laser point cloud data;
Fig. 2 shows a schematic structural diagram of a device for constructing a three-dimensional model of a moving structure based on laser point cloud data;
Fig. 3 shows a schematic structural diagram of an electronic device provided by the application.
Detailed Description
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.
In some of the flows described in the specification and claims of the present application and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.
Fig. 1 is a flowchart of a method for constructing a three-dimensional model of a feature of an active construction based on laser point cloud data according to an embodiment of the present application, as shown in fig. 1, the method includes:
101. collecting laser point cloud data of a movable construction landform area, and preprocessing the data;
In the step, the step of collecting the laser point cloud data of the movable construction landform area refers to collecting the laser point cloud data on the ground through a laser radar device, and recording the space coordinate information of each data point. According to the embodiment of the application, the laser radar equipment can be utilized to scan the active construction landform area and record the space coordinate information of each laser point cloud data point. In the data preprocessing stage, denoising, registering and other processes are carried out on the acquired data so as to improve the accuracy and reliability of the data. By collecting and processing laser point cloud data, three-dimensional information of the movable construction landforms, including characteristics of landform fluctuation, fracture zones and the like, can be rapidly obtained, and basic data is provided for subsequent landform modeling and analysis. Meanwhile, the change of laser point cloud data is monitored in real time, so that the problems of geological disasters and the like can be found in time, and the monitoring and predicting capability of the movable structure landform is improved.
102. Carrying out topographic feature extraction and segmentation according to the processed laser point cloud data, and carrying out digital modeling by utilizing the laser point cloud data to generate a first model of an active construction landform, wherein the first model comprises a landform structure of the laser point cloud data and a spatial position corresponding to each landform structure;
In this step, the first model includes the geomorphic structures of the laser point cloud data and the spatial positions corresponding to each of the geomorphic structures refer to a process of converting the geomorphic features into a digital model by extracting and dividing the topographic features of the processed laser point cloud data.
In this embodiment, by using professional geographic information system software, topographic feature extraction and segmentation are performed on the processed laser point cloud data, and a first model of an active construction topography is generated. The first model comprises a geomorphic structure, such as characteristics of relief, fracture zones and the like, and spatial position information corresponding to each geomorphic structure. And through digital modeling, the laser point cloud data collected in the field is converted into a visual three-dimensional model, so that the geological structure analysis and geological disaster prediction are convenient to carry out. The digital modeling method of the movable structure landform based on the laser point cloud data is beneficial to more accurately presenting the characteristics of the geological landform, improves the digital expression and analysis capability of the movable structure landform, and provides important support for geological exploration and disaster prevention.
103. Determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data in the first model to obtain a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relationship between the laser point cloud data and the first model;
In this step, according to the geomorphic features in the laser point cloud data, determining the spatial position of the matched geomorphic structure in the first model, and analyzing and calculating the matching result to establish a deformation model between the laser point cloud data and the first model for describing the non-rigid deformation relationship between the laser point cloud data and the first model. For example, assuming a region of active formation topography, by processing the laser point cloud data, topographical features of the region, such as topographical relief, fractured zones, etc., are extracted.
In the first model, a topographical structure, such as mountains, canyons, etc., is included that matches topographical features in the laser point cloud data. By comparing the topographical features in the laser point cloud data with the spatial locations of the topographical structures in the first model, a matching result can be determined and a deformation model can be established based on the matching result to describe a non-rigid deformation relationship between the laser point cloud data and the first model. The deformation model can be established by adopting a mathematical model or an interpolation algorithm, and deformation information such as displacement, rotation and the like of each point is obtained by analyzing and calculating the corresponding relation between the laser point cloud data and the first model, so that the alignment and the matching of the laser point cloud data and the first model are realized. By establishing a deformation model between the laser point cloud data and the first model, the change and evolution process of the landform features can be described more accurately, and powerful support is provided for analysis and research of the active construction landform.
104. Determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model;
In this step, according to the deformation model, determining a non-rigid transformation relation of the laser point cloud data relative to the first model, and further calculating a transformation matrix for describing the transformation relation of the laser point cloud data under different coordinate systems.
For example, assuming a section of an actively constructed geomorphic region, a first model is obtained by processing and digitally modeling laser point cloud data, the model containing the geomorphic features and spatial location information of the region. Meanwhile, the real-time monitoring equipment collects the temperature, humidity and other parameter data of the region, and the real-time monitoring data are required to be aligned and matched with the first model. According to the deformation model, the non-rigid transformation relation of the laser point cloud data relative to the first model can be determined, and a corresponding transformation matrix can be calculated. The transformation matrix can be calculated by adopting a linear algebra method, and the space positions of the laser point cloud data under different coordinate systems are transformed and aligned by matrix multiplication and other operations, so that the matching and alignment between the laser point cloud data and the first model are realized. By determining the non-rigid body transformation matrix of the laser point cloud data relative to the first model, the change and evolution process of the landform features can be described more accurately, and powerful support is provided for analysis and research of the active construction landform.
105. Transforming corresponding space coordinates in the laser point cloud data to space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate a movable constructed landform three-dimensional model, wherein the movable constructed landform three-dimensional model at least comprises a landform structure, laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data;
In this step, the spatial coordinates in the laser point cloud data are transformed according to the non-rigid transformation matrix to align with the spatial positions corresponding to the relief structure in the first model, thereby generating the movably constructed relief three-dimensional model. The movable structural relief three-dimensional model comprises a relief structure, laser point cloud data corresponding to the relief structure and a virtual structure generated based on the laser point cloud data. Through the step, laser point cloud data acquired in real time can be integrated with an existing geomorphic model to form a more comprehensive and accurate geomorphic model, wherein the geomorphic model not only comprises real laser point cloud data, but also comprises a virtual structure generated based on the data.
Assuming that a region with a moving structure is present, laser point cloud data of the region is obtained through a laser scanning technology. At the same time, we have built a first model of the region, including a digitized representation of the relief structure. By the non-rigid transformation matrix obtained in the previous step, we can transform the spatial coordinates in the laser point cloud data to align with the spatial positions corresponding to the geomorphic structure in the first model.
In the process, the geomorphic characteristics in the laser point cloud data are matched with the geomorphic structure in the first model, and the laser point cloud data are adjusted according to the non-rigid transformation matrix so as to be aligned with the geomorphic structure. Finally, a three-dimensional model of the movable construction landform is generated. Through the three-dimensional model of the movable structure landform, the landform characteristics of the region can be more comprehensively known, the real laser point cloud data and the virtual structure are displayed, and a more accurate and visual tool is provided for researching and analyzing the movable structure landform.
In the embodiment of the application, firstly, laser point cloud data of a movable structural geomorphic region is collected, and data preprocessing is carried out, including steps of noise removal, point cloud registration and the like. And carrying out topographic feature extraction and segmentation according to the processed laser point cloud data to generate a first model of the movable structural landform, wherein the first model comprises a landform structure and a spatial position corresponding to each landform structure. And in the first model, determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic characteristics of the laser point cloud data, obtaining a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model to indicate a non-rigid deformation relationship between the laser point cloud data and the first model. A non-rigid body transformation matrix of the laser point cloud data relative to the first model is determined from the deformation model. And transforming the corresponding space coordinates in the laser point cloud data to the space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate the movable constructed landform three-dimensional model. The movable structural landform three-dimensional model comprises a landform structure, laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data.
For example:
Assume that we perform laser scanning on a moving structure geomorphic region, and the obtained point cloud data includes different elevation and shape information of the earth surface. Firstly, processing the point cloud data, extracting topographic features, and generating a first model, namely a corresponding relation between a topographic structure and a spatial position, according to the features.
Then, in the first model, we find the landform structure that best matches the laser point cloud data, and build a deformation model, determine the deformation relationship between the laser point cloud data and the first model. And converting the spatial coordinates of the laser point cloud data into the spatial positions of the geomorphic structure in the first model through a non-rigid body transformation matrix.
Finally, according to the conversion, an actively constructed three-dimensional model of the relief is generated, which includes the relief structure, the laser point cloud data, and the virtual structure generated from the point cloud data. The three-dimensional model can better show the characteristics of the movable structural landform, and support is provided for further analysis and safety detection.
Further, after step 105, the method further includes:
106. And displaying the movable structural landform three-dimensional model, and carrying out safety detection on the movable structural landform according to the movable structural landform three-dimensional model.
In this step, the movable construction topography three-dimensional model is displayed, i.e., the movable construction topography three-dimensional model is visually displayed, so that the characteristics and the change conditions of the movable construction topography can be more intuitively observed and analyzed. Meanwhile, safety detection of the movable structural landform is carried out according to the movable structural landform three-dimensional model, namely, the stability and safety of the landform area are judged through analysis and comparison of the movable structural landform three-dimensional model.
In step 105, we have successfully integrated the laser point cloud data with the first model, resulting in an actively constructed three-dimensional model of the topography. In step 106, we need to visually display the three-dimensional model of the movable structure and perform security detection on the movable structure.
For example, the three-dimensional model of the movable construction landform can be visually displayed by utilizing the technologies of GIS, BIM and the like, so that the characteristics and the change conditions of the landform can be more intuitively observed and analyzed. Meanwhile, the stability and safety of the landform area can be judged by analyzing and comparing the three-dimensional model of the movable structure landform. For example, we can compare the three-dimensional models of the movable construction topography at different time periods and observe the change condition of the topography features, so as to judge whether the region has the risk of the movable construction topography. If the risk exists, measures can be timely taken, and the safety and stability of the area are guaranteed.
Through the step, the characteristics and the change conditions of the movable structure landforms can be more comprehensively known, potential geological disaster risks can be early warned and processed in time, and the safety of lives and properties of people is ensured.
In an embodiment of the present application, the step 106 displaying process may include the following processes:
displaying the three-dimensional model of the movable structural landform: and displaying the generated three-dimensional model of the movable structure landform through three-dimensional visualization software, wherein the three-dimensional model comprises a landform structure, laser point cloud data and a virtual structure. By rotation, scaling, translation, etc., the topographical features can be more clearly observed.
Safety detection of movable structural landforms: and carrying out safety detection according to the three-dimensional model of the movable construction landform. The specific process comprises the following steps:
detecting the deformation of the geomorphic structure: and comparing the current landform state with the historical data by utilizing the actual landform structure and the laser point cloud data in the three-dimensional model, and detecting whether the landform structure is deformed or not.
Analyzing the topography change: by comparing the laser point cloud data with the virtual structure, the change condition of the terrain, including the conditions of earth surface subsidence, ground cracks and the like, is analyzed.
Predicting geological disasters: and combining the three-dimensional model of the landform with geological information to predict and evaluate geological disasters, such as landslide, debris flow and the like.
Making safety measures: and (3) according to the safety detection result, corresponding safety measures and emergency plans are formulated so as to ensure the safety of the movable construction landform area.
For example:
Let us assume that we construct a three-dimensional model of relief using the above-mentioned activities, revealing the relief structure, laser point cloud data and virtual structure. During the course of the presentation we can observe details and features of the terrain, such as relief of the terrain, fault location etc.
Next, we perform security detection of the active build topography. By comparing the current laser point cloud data with the historical data, whether the geomorphic structure is deformed or not can be detected. Meanwhile, the change condition of the terrain, such as whether the earth surface subsides or the occurrence of ground cracks, is analyzed. By means of prediction and evaluation of geological disasters, preparation can be made in advance, and safety of the active construction landform area is ensured.
Finally, according to the safety detection result, corresponding safety measures such as reinforcing a geological structure, setting up monitoring points and the like can be formulated so as to cope with possible geological disasters and ensure the safety of the movable structural landform area. In this way, we can better perform geological security management and risk control with data support.
Optionally, in the embodiment of the present application, as a possible implementation manner, in step 103, "in the first model, determining a spatial location corresponding to at least one relief structure that matches a relief feature of the laser point cloud data, a process of obtaining a matching result" may include: obtaining the geomorphic characteristics of the laser point cloud data and the position characteristics of the spatial positions corresponding to the geomorphic structures; and determining a matching result between the laser point cloud data and the landform structure according to the landform characteristics and the similarity of the position characteristics.
Specifically, the process of determining a matching result between the laser point cloud data and the geomorphic structure according to the geomorphic feature and the similarity of the position feature may include: acquiring a first similarity and a first distance between the geomorphic features and the position features; determining the similarity of the landform features and the position features according to the first similarity and the weight corresponding to the first similarity, wherein the first distance and the weight corresponding to the first distance; and if the similarity is greater than the set similarity, determining that the landform features of the laser point cloud data are matched with the landform structure.
In this embodiment, during the processing of the laser point cloud data of the active configuration geomorphic region, we need to determine the spatial position corresponding to the geomorphic structure matching the geomorphic feature of the laser point cloud data. This process may be accomplished by:
And obtaining the landform features of the laser point cloud data, such as information of landform relief, gradient, elevation and the like, and simultaneously obtaining the position features of the space positions corresponding to the landform structures, such as the landform features, geographic coordinates and the like.
And determining a matching result between the laser point cloud data and the landform structure according to the similarity of the landform features and the position features. This process may include the steps of:
A first similarity and a first distance between the topographical features and the positional features are obtained.
And determining the similarity of the landform features and the position features according to the first similarity and the weight corresponding to the first distance.
And if the similarity is larger than the set similarity threshold, determining that the landform features of the laser point cloud data are matched with the landform structure.
Through the steps, the landform features in the laser point cloud data can be accurately matched with the landform structures, so that the spatial positions of the laser point cloud data corresponding to the landform structures in the first model are determined. Thus, an accurate three-dimensional model of the movable construction landform can be better constructed, and a reliable foundation is provided for subsequent display and safety detection.
Optionally, in the embodiment of the present application, as a possible implementation manner, the process of "establishing a deformation model corresponding between the laser point cloud data and the first model based on the matching result" in step 103 may include:
1031. Performing grid division on the first model to generate a grid model, wherein the grid model comprises a plurality of grid nodes;
1032. And determining grid nodes corresponding to the laser point cloud data, and adjusting the positions and/or connection relations of the grid nodes in the grid model according to the laser point cloud data so as to generate a deformation model corresponding to the laser point cloud data and the first model.
In this embodiment, during the process of constructing the three-dimensional model of the active feature, based on the matching result, we need to build a deformation model corresponding between the laser point cloud data and the first model. This process may be accomplished by:
And performing grid division on the first model to generate a grid model, wherein the grid model comprises a plurality of grid nodes. This step is to digitize the first model for subsequent data interfacing and processing.
And determining grid nodes corresponding to the laser point cloud data, and adjusting the positions and/or connection relations of the grid nodes in the grid model according to the laser point cloud data to generate a deformation model corresponding to the laser point cloud data and the first model. This process may include the steps of:
and determining grid nodes corresponding to the laser point cloud data, namely finding out the nodes which are matched with the laser point cloud data in the grid model.
And according to the information of the laser point cloud data, adjusting the positions and/or connection relations of the corresponding nodes in the grid model to enable the positions and/or connection relations to be more consistent with the laser point cloud data.
And generating a deformation model corresponding to the laser point cloud data and the first model by adjusting the nodes in the grid model.
Through the steps, a deformation model between the laser point cloud data and the first model can be established, so that accurate modeling of the three-dimensional model of the movable structural landform is realized. This will help more accurately demonstrate the relief feature and carry out the security detection, improve the efficiency of geological disaster early warning and reply.
Optionally, in an embodiment of the present application, step 104 may include:
1041. dispersing each laser point cloud data into a plurality of grid cells, wherein the grid cells comprise corresponding vertex coordinates and topological structures;
1042. Obtaining deformation results of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells;
1043. and generating a non-rigid body transformation matrix of the laser point cloud data relative to the first model according to deformation results of a plurality of grid cells.
In this step, the objective of the computation of the non-rigid body transformation matrix is to obtain the transformation relationship of the whole non-rigid body object based on the combination and interpolation of the rigid body transformation matrices. The following is an example of a formula for weighted averaging:
；
Wherein T is a non-rigid transformation matrix, ti is each rigid transformation matrix, namely Ti is each grid unit generated after laser point cloud data are scattered, and w is a weight which can be set according to requirements.
In the above step, the deformation result includes a displacement field and a deformation field;
specifically, step 1042 may comprise: obtaining displacement increment information of each grid cell by applying external force and/or constraint conditions to the grid cells;
Wherein the displacement increment information is denoted as deltau (x, y, z) and represents the displacement variation of each grid cell in space after external force and/or constraint conditions are applied. The displacement increment information is calculated by applying external force and/or constraint conditions and is used for describing the displacement change condition of the grid cells.
Further, according to the displacement increment information of each grid unit, calculating a corresponding displacement field;
Wherein the above procedure may be based on the formula: u (x, y, z) =u0 (x, y, z) +Δu (x, y, z) is calculated, where u (x, y, z) is the displacement field describing the displacement vector of each grid cell in space, i.e. the final displacement after external forces and/or constraints. The displacement field is a superposition of initial position and displacement delta information representing the final position distribution of the grid cells. u0 (x, y, z) is the initial position of the grid cell, and Δu (x, y, z) is the displacement increment information.
Further, according to the displacement field of each grid unit, calculating a corresponding deformation gradient tensor;
wherein the above procedure may be based on the formula: And (5) calculating to obtain the product. In this formula,/> Representing the gradient of the displacement field u (x, y, z) in space. Wherein/>、/>、/>Representing the partial derivatives of the displacement field in the x, y and z directions respectively, i.e. representing the rate of change of the displacement field in each direction respectively. The gradient represents the rate and direction of change in space of a scalar field, and for a displacement field, the gradient tensor describes the change in space of the displacement field.
In this formula, althoughIt appears that the displacement field is graded, but in practice the displacement field u (x, y, z) here is a vector field instead of a scalar field. Thus, gradient tensor here/>Representing gradient information of the displacement field in space, i.e. the rate of change of the displacement field in various directions.
In this case, the calculation of the gradient tensor can describe the change situation of the displacement field more accurately than just the change rate in one direction. Therefore, the gradient calculation of the displacement field can obtain more comprehensive displacement information, and the deformation condition of the object in space can be further analyzed and understood.
Further, according to the deformation gradient tensor of each grid cell, calculating a corresponding deformation field, wherein the deformation field is used for indicating the deformation condition of the grid cell;
wherein the above procedure may be based on the formula: Epsilon (x, y, z) is a deformation field calculated from the deformation gradient tensor and is used for indicating the deformation condition of the grid cell. The deformation field describes the deformation of the grid cells in space, being a symmetric part of the gradient tensor of the displacement field. Wherein/> Is a transpose of the deformation gradient tensor.
And generating a deformation result of each grid cell according to the displacement field and the deformation field of each grid cell.
Fig. 2 is a schematic structural diagram of an active configuration landform three-dimensional model building device based on laser point cloud data according to an embodiment of the present application, where, as shown in fig. 2, the device includes:
the acquisition module 21 is used for acquiring laser point cloud data of the movable construction landform area and preprocessing the data;
The generating module 22 is configured to extract and divide topographic features according to the processed laser point cloud data, and digitally model the processed laser point cloud data to generate a first model of an active configuration topographic feature, where the first model includes a topographic feature structure of the laser point cloud data and a spatial position corresponding to each topographic feature structure;
a processing module 23, configured to determine, in the first model, a spatial position corresponding to at least one relief structure that matches a relief feature of the laser point cloud data, obtain a matching result, and establish a deformation model corresponding between the laser point cloud data and the first model based on the matching result, where the deformation model is used to indicate a non-rigid deformation relationship between the laser point cloud data and the first model;
A determining module 24 for determining a non-rigid transformation matrix of laser point cloud data relative to the first model from the deformation model;
The generating module 22 is further configured to transform, according to the non-rigid transformation matrix, the corresponding spatial coordinate in the laser point cloud data to a spatial position corresponding to a geomorphic structure in the first model, so as to generate an active constructed three-dimensional geomorphic model, where the active constructed three-dimensional model at least includes a geomorphic structure, laser point cloud data corresponding to the geomorphic structure, and a virtual structure generated according to the laser point cloud data.
In an embodiment of the present application, optionally, the apparatus further includes:
The processing module 23 is further configured to perform mesh division on the first model, and generate a mesh model, where the mesh model includes a plurality of mesh nodes; and determining grid nodes corresponding to the laser point cloud data, and adjusting the positions and/or connection relations of the grid nodes in the grid model according to the laser point cloud data so as to generate a deformation model corresponding to the laser point cloud data and the first model.
In an embodiment of the present application, optionally, the apparatus further includes:
The processing module 23 is further configured to discretize each of the laser point cloud data into a plurality of grid cells, where the plurality of grid cells includes corresponding vertex coordinates and a topology structure; obtaining deformation results of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells; and generating a non-rigid body transformation matrix of the laser point cloud data relative to the first model according to deformation results of a plurality of grid cells.
In an embodiment of the present application, optionally, the deformation result includes a displacement field and a deformation field, and the apparatus further includes:
the processing module 23 is also used for; obtaining displacement increment information of each grid cell by applying external force and/or constraint conditions to the grid cells; calculating a corresponding displacement field according to the displacement increment information of each grid unit; calculating a corresponding deformation gradient tensor according to the displacement field of each grid unit; calculating a corresponding deformation field according to the deformation gradient tensor of each grid cell, wherein the deformation field is used for indicating the deformation condition of the grid cell; and generating a deformation result of each grid cell according to the displacement field and the deformation field of each grid cell.
In an embodiment of the present application, optionally, the apparatus further includes:
The processing module 23 is further configured to obtain a geomorphic feature of the laser point cloud data and a position feature of a spatial position corresponding to the geomorphic structure; and determining a matching result between the laser point cloud data and the landform structure according to the landform characteristics and the similarity of the position characteristics.
In an embodiment of the present application, optionally, the apparatus further includes:
The processing module 23 is further configured to obtain a first similarity and a first distance between the geomorphic feature and the position feature; determining the similarity of the landform features and the position features according to the first similarity and the weight corresponding to the first similarity, wherein the first distance and the weight corresponding to the first distance; and if the similarity is greater than the set similarity, determining that the landform features of the laser point cloud data are matched with the landform structure.
The device for constructing the three-dimensional model of the active construction feature based on the laser point cloud data shown in fig. 2 may execute the method for constructing the three-dimensional model of the active construction feature based on the laser point cloud data shown in the embodiment shown in fig. 1, and its implementation principle and technical effects are not repeated. The specific manner in which the respective modules and units perform the operations in the apparatus for constructing the active construction relief three-dimensional model based on the laser point cloud data in the above-described embodiments has been described in detail in the embodiments related to the method, and will not be described in detail here.
In one possible design, the active construction topography three-dimensional model building apparatus based on laser point cloud data of the embodiment shown in fig. 2 may be implemented as an electronic device, which may include a storage component 31 and a processing component 32, as shown in fig. 3;
the storage component 31 stores one or more computer instructions for execution by the processing component 32.
The processing component 32 is configured to: collecting laser point cloud data of a movable construction landform area, and preprocessing the data; carrying out topographic feature extraction and segmentation according to the processed laser point cloud data, and carrying out digital modeling by utilizing the laser point cloud data to generate a first model of an active construction landform, wherein the first model comprises a landform structure of the laser point cloud data and a spatial position corresponding to each landform structure; determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data in the first model to obtain a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relationship between the laser point cloud data and the first model; determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model; transforming corresponding space coordinates in the laser point cloud data to space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate a movable constructed landform three-dimensional model, wherein the movable constructed landform three-dimensional model at least comprises a landform structure, laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data; and displaying the movable structural landform three-dimensional model, and carrying out safety detection on the movable structural landform according to the movable structural landform three-dimensional model.
Wherein the processing component 32 may include one or more processors to execute computer instructions to perform all or part of the steps of the methods described above. Of course, the processing component may also be implemented as one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements for executing the methods described above.
The storage component 31 is configured to store various types of data to support operations at the terminal. The memory component may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
Of course, the computing device may necessarily include other components, such as input/output interfaces, communication components, and the like.
The input/output interface provides an interface between the processing component and a peripheral interface module, which may be an output device, an input device, etc.
The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, and the like.
The computing device may be a physical device or an elastic computing host provided by the cloud computing platform, and at this time, the computing device may be a cloud server, and the processing component, the storage component, and the like may be a base server resource rented or purchased from the cloud computing platform.
The embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program can realize the method for constructing the three-dimensional model of the active construction landform based on the laser point cloud data in the embodiment shown in the figure 1 when being executed by a computer.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (8)
1. The method for constructing the three-dimensional model of the moving construction landform based on the laser point cloud data is characterized by comprising the following steps of:
collecting laser point cloud data of a movable construction landform area, and preprocessing the data;
carrying out topographic feature extraction and segmentation according to the processed laser point cloud data, and carrying out digital modeling by utilizing the laser point cloud data to generate a first model of an active construction landform, wherein the first model comprises a landform structure of the laser point cloud data and a spatial position corresponding to each landform structure;
Determining a spatial position corresponding to at least one geomorphic structure matched with the geomorphic features of the laser point cloud data in the first model to obtain a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relationship between the laser point cloud data and the first model;
Determining a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model;
Transforming corresponding space coordinates in the laser point cloud data to space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate a movable constructed landform three-dimensional model, wherein the movable constructed landform three-dimensional model at least comprises a landform structure, laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data;
The determining a non-rigid body transformation matrix of laser point cloud data relative to the first model according to the deformation model comprises:
dispersing each laser point cloud data into a plurality of grid cells, wherein the grid cells comprise corresponding vertex coordinates and topological structures;
Obtaining deformation results of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells;
generating a non-rigid transformation matrix of the laser point cloud data relative to the first model according to deformation results of a plurality of grid cells;
Wherein the non-rigid body transformation matrix is represented by the formula: t= Σ (w×ti)/Σw, calculated from the combination and interpolation of a plurality of the mesh units, where T is the non-rigid transformation matrix, ti is each of the mesh units, and w is a weight.
2. The method of claim 1, wherein the establishing a deformation model of the correspondence between the laser point cloud data and the first model based on the matching result comprises:
performing grid division on the first model to generate a grid model, wherein the grid model comprises a plurality of grid nodes;
And determining grid nodes corresponding to the laser point cloud data, and adjusting the positions and/or connection relations of the grid nodes in the grid model according to the laser point cloud data so as to generate a deformation model corresponding to the laser point cloud data and the first model.
3. The method of claim 1, wherein the deformation results comprise a displacement field and a deformation field;
The obtaining the deformation result of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells comprises:
Obtaining displacement increment information of each grid cell by applying external force and/or constraint conditions to the grid cells;
calculating a corresponding displacement field according to the displacement increment information of each grid unit;
Calculating a corresponding deformation gradient tensor according to the displacement field of each grid unit;
Calculating a corresponding deformation field according to the deformation gradient tensor of each grid cell, wherein the deformation field is used for indicating the deformation condition of the grid cell;
and generating a deformation result of each grid cell according to the displacement field and the deformation field of each grid cell.
4. The method of claim 1, wherein determining, in the first model, a spatial location corresponding to at least one geomorphic structure that matches a geomorphic feature of the laser point cloud data, resulting in a matching result, comprises:
Obtaining the geomorphic characteristics of the laser point cloud data and the position characteristics of the spatial positions corresponding to the geomorphic structures;
And determining a matching result between the laser point cloud data and the landform structure according to the landform characteristics and the similarity of the position characteristics.
5. The method of claim 4, wherein the determining a matching result between the laser point cloud data and the geomorphic structure according to the similarity of the geomorphic features and the position features comprises:
acquiring a first similarity and a first distance between the geomorphic features and the position features;
Determining the similarity of the landform features and the position features according to the first similarity and the weight corresponding to the first similarity, wherein the first distance and the weight corresponding to the first distance;
And if the similarity is greater than the set similarity, determining that the landform features of the laser point cloud data are matched with the landform structure.
6. An active construction relief three-dimensional model construction device based on laser point cloud data is characterized by comprising:
the acquisition module is used for acquiring laser point cloud data of the movable construction landform area and preprocessing the data;
The generation module is used for extracting and dividing the topographic features according to the processed laser point cloud data, and performing digital modeling by utilizing the laser point cloud data to generate a first model of an active construction topographic form, wherein the first model comprises a topographic form structure of the laser point cloud data and a space position corresponding to each topographic form structure;
The processing module is used for determining a space position corresponding to at least one geomorphic structure matched with the geomorphic characteristics of the laser point cloud data in the first model, obtaining a matching result, and establishing a deformation model corresponding to the laser point cloud data and the first model based on the matching result, wherein the deformation model is used for indicating a non-rigid deformation relation between the laser point cloud data and the first model;
A determining module, configured to determine a non-rigid transformation matrix of laser point cloud data relative to the first model according to the deformation model;
the generation module is further used for transforming corresponding space coordinates in the laser point cloud data to space positions corresponding to the landform structures in the first model according to the non-rigid body transformation matrix so as to generate an active construction landform three-dimensional model, and the active construction landform three-dimensional model at least comprises a landform structure, the laser point cloud data corresponding to the landform structure and a virtual structure generated according to the laser point cloud data;
The determining, according to the deformation model, a non-rigid transformation matrix of laser point cloud data relative to the first model specifically includes: dispersing each laser point cloud data into a plurality of grid cells, wherein the grid cells comprise corresponding vertex coordinates and topological structures; obtaining deformation results of each grid cell by applying external force and/or constraint conditions to the plurality of grid cells; generating a non-rigid transformation matrix of the laser point cloud data relative to the first model according to deformation results of a plurality of grid cells;
Wherein the non-rigid body transformation matrix is represented by the formula: t= Σ (w×ti)/Σw, calculated from the combination and interpolation of a plurality of the mesh units, where T is the non-rigid transformation matrix, ti is each of the mesh units, and w is a weight.
7. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the active construction relief three-dimensional model construction method based on laser point cloud data as claimed in any of claims 1 to 5.
8. A computer storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of constructing a three-dimensional model of an active construction relief based on laser point cloud data as claimed in any one of claims 1 to 5.
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