Three-dimensional display method, system and storage medium for landslide simulation

Abstract

The present application relates to the technical field of three-dimensional simulation, and in particular to a landslide simulation three-dimensional display method and system and a storage medium. The method comprises the following steps: obtaining multi-source monitoring data of a landslide area; performing noise filtering and spatial interpolation on the multi-source monitoring data of the landslide area to obtain pre-processed landslide monitoring data; performing time-space reference unification processing on the pre-processed landslide monitoring data to obtain time-space aligned monitoring data; performing NURBS surface modeling based on the time-space aligned monitoring data to obtain an initial landslide three-dimensional structure model; performing finite element grid self-adaptive subdivision on the initial landslide three-dimensional structure model to obtain a dynamic landslide body grid model; and obtaining current landslide displacement data. The present application effectively solves the problem of three-dimensional model update delay in the traditional method, and ensures the real-time and accuracy of landslide simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional simulation, in particular to a landslide simulation three-dimensional display method, system and storage medium. BACKGROUND

[0002] Traditional landslide simulation display methods mostly rely on two-dimensional maps or simple three-dimensional models. These methods have obvious shortcomings in displaying the dynamic process and complex topography of landslides. For example, in mountainous road construction and maintenance work, engineers need to accurately assess the impact of potential landslides on roads in order to take preventive measures in advance. However, existing two-dimensional maps cannot intuitively display the three-dimensional spatial changes of landslides, making it difficult for engineers to accurately determine the scope and impact of landslides, thereby increasing the risk of engineering design and construction.

[0003] In practical applications, although existing three-dimensional display technologies have improved the visualization effect of landslide simulation to some extent, there are still some technical difficulties. For example, landslide monitoring systems usually need to combine real-time landslide data with three-dimensional models to dynamically display the evolution process of landslides. However, existing three-dimensional display technologies have a delay problem in data updating and model dynamic adjustment. Specifically, when the monitoring system obtains new landslide data, the update speed of the three-dimensional model often cannot keep up with the changes in the data, resulting in a deviation between the displayed landslide dynamic process and the actual situation. This delay not only affects the accuracy of disaster warning, but also misleads decision-makers and emergency response personnel, thereby delaying the implementation of rescue and protection measures. SUMMARY

[0004] Therefore, it is necessary to provide a landslide simulation three-dimensional display method, system and storage medium to solve at least one of the above technical problems.

[0005] To achieve the above-mentioned purpose, a landslide simulation three-dimensional display method comprises the following steps:

[0006] Step S1: Obtain multi-source monitoring data of the landslide area; perform noise filtering and spatial interpolation on the multi-source monitoring data of the landslide area to obtain preprocessed landslide monitoring data; perform spatiotemporal reference unification processing on the preprocessed landslide monitoring data to obtain spatiotemporally aligned monitoring data;

[0007] Step S2: Based on the spatiotemporally aligned monitoring data, perform NURBS surface modeling to obtain an initial landslide three-dimensional structure model; perform finite element mesh adaptive subdivision on the initial landslide three-dimensional structure model to obtain a dynamic landslide body mesh model;

[0008] Step S3: Obtain current landslide displacement data; perform landslide simulation based on the dynamic landslide mesh model and the current landslide displacement data to obtain landslide motion simulation data; dynamically update the landslide motion simulation data to obtain real-time landslide evolution prediction data;

[0009] Step S4: GPU-accelerated rendering of real-time landslide evolution prediction data to obtain a landslide deformation visualization map; heat map overlay and VR interactive interface integration of the landslide deformation visualization map to obtain an immersive 3D landslide scene.

[0010] Step S5: Perform error inversion analysis based on the immersive 3D landslide scene and the current landslide displacement data to obtain optimized parameters for the landslide model; generate early warning maps based on the optimized parameters for the landslide model to obtain closed-loop landslide risk early warning data.

[0011] Preferably, the present invention provides a three-dimensional display system for landslide simulation, used to execute the three-dimensional display method for landslide simulation as described above, the three-dimensional display system for landslide simulation comprising:

[0012] The data preprocessing module is used to acquire multi-source monitoring data of the landslide area; perform noise filtering and spatial interpolation on the multi-source monitoring data of the landslide area to obtain preprocessed landslide monitoring data; and perform spatiotemporal benchmark unification processing on the preprocessed landslide monitoring data to obtain spatiotemporally aligned monitoring data.

[0013] The 3D modeling module is used to perform NURBS surface modeling based on spatiotemporal aligned monitoring data to obtain an initial 3D landslide structure model; the initial 3D landslide structure model is then subjected to adaptive finite element mesh generation to obtain a dynamic landslide body mesh model.

[0014] The motion simulation module is used to acquire current landslide displacement data; perform landslide simulation based on the dynamic landslide mesh model and the current landslide displacement data to obtain landslide motion simulation data; and dynamically update the landslide motion simulation data to obtain real-time landslide evolution prediction data.

[0015] The visualization rendering module is used to perform GPU-accelerated rendering of real-time landslide evolution prediction data to obtain a landslide deformation visualization map; the landslide deformation visualization map is overlaid with a heat map and integrated with a VR interactive interface to obtain an immersive 3D landslide scene.

[0016] The early warning module is used to perform error inversion analysis based on the immersive 3D landslide scene and the current landslide displacement data to obtain optimized parameters for the landslide model; and to generate early warning maps based on the optimized parameters for the landslide model to obtain closed-loop landslide risk early warning data.

[0017] Preferably, the present invention also provides a computer-readable storage medium storing a program that can be loaded by a processor and executed as described above for a three-dimensional representation method of landslide simulation.

[0018] The beneficial effects of this invention are as follows:

[0019] On the one hand, by acquiring multi-source monitoring data and performing noise filtering and spatial interpolation, the spatiotemporal alignment of landslide area data was achieved, providing accurate basic data for subsequent 3D modeling and effectively solving the modeling error problem caused by inconsistent spatiotemporal benchmarks in traditional methods.

[0020] On the other hand, by using NURBS surface modeling and adaptive finite element mesh generation, a three-dimensional structural model that can dynamically reflect the changes in the landslide body is generated, which effectively solves the problem of three-dimensional model update delay in traditional methods and ensures the real-time performance and accuracy of landslide simulation.

[0021] On the other hand, by using GPU-accelerated rendering and heatmap overlay, combined with VR interactive interface integration, not only is the efficiency of landslide deformation visualization improved, but the user's immersion is also enhanced, making the dynamic process of landslides more intuitive and further improving the visualization effect of landslide simulation. Attached Figure Description

[0022] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description with reference to the accompanying drawings:

[0023] Fig. 1 A flowchart illustrating the steps of a three-dimensional representation method for landslide simulation according to one embodiment is shown.

[0024] Fig. 2 A detailed flowchart of step S23 of one embodiment is shown.

[0025] Fig. 3 The illustration shows a three-dimensional landslide scene of one embodiment. Detailed Implementation

[0026] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0028] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] To achieve the above objectives, please refer to Figs. 1 to 3 This invention provides a three-dimensional visualization method for landslide simulation, comprising the following steps:

[0030] Step S1: Obtain multi-source monitoring data of the landslide area; perform noise filtering and spatial interpolation on the multi-source monitoring data of the landslide area to obtain preprocessed landslide monitoring data; perform spatiotemporal benchmark unification processing on the preprocessed landslide monitoring data to obtain spatiotemporally aligned monitoring data.

[0031] Step S2: Based on the spatiotemporal aligned monitoring data, perform NURBS surface modeling to obtain the initial three-dimensional landslide structure model; perform finite element mesh adaptive subdivision on the initial three-dimensional landslide structure model to obtain the dynamic landslide body mesh model;

[0032] Step S3: Obtain current landslide displacement data; perform landslide simulation based on the dynamic landslide mesh model and the current landslide displacement data to obtain landslide motion simulation data; dynamically update the landslide motion simulation data to obtain real-time landslide evolution prediction data;

[0033] Step S4: GPU-accelerated rendering of real-time landslide evolution prediction data to obtain a landslide deformation visualization map; heat map overlay and VR interactive interface integration of the landslide deformation visualization map to obtain an immersive 3D landslide scene.

[0034] Step S5: Perform error inversion analysis based on the immersive 3D landslide scene and the current landslide displacement data to obtain optimized parameters for the landslide model; generate early warning maps based on the optimized parameters for the landslide model to obtain closed-loop landslide risk early warning data.

[0035] Preferably, step S1 includes:

[0036] Step S11: Perform a three-dimensional scan of the landslide area using lidar to obtain initial terrain point cloud data;

[0037] In this embodiment of the invention, a lidar system, such as the Riegl VZ-4000, is used to perform a three-dimensional scan of the landslide area. This system has high precision and long-range scanning capabilities, and can generate high-density point cloud data. During the scanning process, the lidar is mounted on a tripod, and the scanning range is set to cover the entire landslide area. The scanning resolution parameters are adjusted, for example, the point spacing is set to 0.1 meters. After the scan is completed, the lidar system outputs initial terrain point cloud data, which contains the three-dimensional coordinate information of each scanned point.

[0038] Step S12: High-frequency noise is filtered out from the initial terrain point cloud data to obtain denoised terrain point cloud data; spatial completion is performed on the denoised terrain point cloud data to obtain complete terrain elevation data.

[0039] In this embodiment of the invention, the Point Cloud Library (PCL) is used to process the initial terrain point cloud data. A statistical noise filtering method is employed, with a neighborhood search radius of 0.2 meters. By calculating the average distance to the neighboring points of each point, points whose distances are significantly greater than the average are identified and removed; these points are considered noise points. After noise removal, a K-nearest neighbor-based spatial interpolation method is used to spatially complete the point cloud data. Specifically, for each missing elevation data point, its five nearest neighbors are found, and the elevation value of that point is calculated using a weighted average, with the weights calculated based on the reciprocal of the distance. Through these steps, complete and smooth terrain elevation data is generated.

[0040] Step S13: Collect real-time landslide displacement data in the landslide area through a ground displacement sensor network; perform time-series smoothing on the real-time landslide displacement data to obtain optimized landslide displacement data;

[0041] In this embodiment of the invention, a network of multiple GPS displacement sensors is used. These sensors are installed at key locations in the landslide area, such as the leading edge, middle, and trailing edge of the landslide. Each sensor collects landslide displacement data in real time at a sampling frequency of 1 minute. A moving average filtering method is used to smooth the real-time landslide displacement data. Specifically, the moving average window size is set to 5 data points, and the smoothed displacement data is obtained by calculating the average value of the data points within each window.

[0042] Step S14: Acquire multispectral image data of the landslide area;

[0043] In this embodiment of the invention, a drone equipped with a multispectral camera, such as the DJI Mavic 2 Enterprise Advanced, is used to conduct aerial photography of the landslide area. This multispectral camera can simultaneously acquire image data in four bands: red, green, blue, and near-infrared. During aerial photography, the drone follows a preset flight path, flying at an altitude of 10 meters with a 50% image overlap rate. After photography, professional image processing software, such as Pix4Dmapper, is used to stitch and correct the acquired multispectral image data, generating a multispectral image map covering the entire landslide area.

[0044] Step S15: Spatially align the complete terrain elevation data, optimized landslide displacement data, and multispectral image data to obtain spatially unified monitoring data;

[0045] In this embodiment of the invention, the GIS software QGIS is used to spatially align complete topographic elevation data, optimized landslide displacement data, and multispectral image data. The topographic elevation data is imported into QGIS, and its coordinate system is set to WGS 84 / UTM zone 33N. The optimized landslide displacement data and multispectral image data are imported separately, and their coordinate systems are aligned to WGS 84 / UTM zone 33N using a coordinate transformation tool. Using QGIS's alignment tool, the multispectral image data is spatially corrected by matching feature points in the topographic elevation data with ground feature points in the multispectral image data. Finally, the optimized landslide displacement data and the corrected multispectral image data are spatially overlaid to generate unified spatial monitoring data.

[0046] Step S16: Perform time-series synchronization on the unified spatial monitoring data to obtain spatiotemporally aligned monitoring data.

[0047] In this embodiment of the invention, the Python programming language combined with the Pandas library is used to perform time-series synchronization processing on spatially unified monitoring data. The timestamps of topographic elevation data, optimized landslide displacement data, and multispectral image data are extracted and stored in a Pandas DataFrame. Through timestamp alignment, the timestamps from different data sources are unified to a common time reference. Specifically, the timestamp of the landslide displacement data is selected as the reference, and time interpolation is performed on other data sources to ensure that all data have corresponding values ​​at the same time point. Through the above steps, spatiotemporally aligned monitoring data is generated.

[0048] Preferably, step S2 includes:

[0049] Step S21: Extract terrain elevation feature points based on spatiotemporal aligned monitoring data to obtain a landslide terrain feature point set;

[0050] In this embodiment of the invention, the GIS software QGIS is used to process the spatiotemporal alignment monitoring data. Topographic elevation data is imported into QGIS, and its coordinate system is set to WGS 84 / UTM zone 33N. Using QGIS's "Extract Topographic Feature Points" tool, areas with an elevation change rate greater than 10% are selected as the threshold for feature point extraction. By setting these parameters, QGIS can automatically identify and extract points with significant topographic elevation changes; these points are typically located at landslide boundaries, cracks, and steep slopes. Finally, the generated landslide topographic feature point set contains the coordinates and elevation information of these key locations.

[0051] Step S22: Perform three-dimensional surface modeling based on the landslide topographic feature point set to obtain the initial landslide surface model;

[0052] In this embodiment of the invention, the 3D modeling software Cloud Compare is used to perform 3D surface modeling on a set of landslide topographic feature points. The landslide topographic feature point set is imported into Cloud Compare, and the "Surface Reconstruction" function is selected. When setting the parameters, the "Poisson Reconstruction" algorithm is selected, which can generate a smooth 3D surface based on point cloud data. The reconstruction resolution parameter is set to 0.5 meters. Through these operations, Cloud Compare generates an initial landslide surface model, which can intuitively display the topography of the landslide area.

[0053] Step S23: Identify key terrain feature regions in the initial landslide surface model to obtain landslide feature region identifiers;

[0054] Step S24: Based on the landslide feature area identifier, densify the point cloud of the corresponding key areas in the initial landslide surface model to obtain optimized landslide point cloud data;

[0055] In this embodiment of the invention, the point cloud processing library PCL is used to densify the point cloud of key areas in the initial landslide surface model. The initial landslide surface model and landslide feature region identifiers are imported into PCL. The "voxel mesh filtering" method is selected to densify the key areas, and the voxel size is set to 0.1 meters. Finally, the generated optimized landslide point cloud data includes the densified point cloud of the key areas.

[0056] Step S25: Reconstruct the quadratic surface based on the optimized landslide point cloud data to obtain the three-dimensional structure model of the landslide; perform finite element mesh generation on the three-dimensional structure model of the landslide to obtain the basic landslide mesh model;

[0057] In this embodiment of the invention, the 3D modeling software Cloud Compare is used to perform quadratic surface reconstruction on optimized landslide point cloud data. The optimized landslide point cloud data is imported into Cloud Compare, and the "Surface Reconstruction" function is selected. When setting parameters, the "Poisson Reconstruction" algorithm is selected, and the reconstruction resolution parameter is set to 0.2 meters. Through these operations, Cloud Compare generates a 3D structural model of the landslide. The finite element mesh generation tool Gmsh is used to mesh the 3D structural model of the landslide. The "Tetrahedral Mesh" type is selected, and the mesh size is set to 0.5 meters, which is not overly complex. Finally, the generated basic landslide mesh model contains the finite element mesh of the landslide area.

[0058] Step S26: Refine the feature region of the basic landslide mesh model to obtain the dynamic landslide body mesh model.

[0059] In this embodiment of the invention, the basic landslide mesh model and landslide feature area identifiers are imported into Gmsh. The "Local Refinement" function is selected to refine the mesh in key terrain feature areas. The mesh size of the refined area is set to 0.2 meters. Through these operations, Gmsh generates a dynamic landslide mesh model.

[0060] Preferably, step S23 includes:

[0061] Step S231: Perform surface curvature analysis on the initial landslide surface model to obtain landslide surface curvature distribution data;

[0062] In this embodiment of the invention, the initial landslide surface model is imported into Cloud Compare. The "Curvature Analysis" function is selected, and the analysis radius is set to 0.5 meters to ensure that local curvature changes on the surface can be captured. Cloud Compare calculates the principal curvature value for each point and generates a curvature distribution map. This map displays regions with different curvature values ​​using color coding, with high curvature areas typically represented in red and low curvature areas in blue. Ultimately, the generated landslide surface curvature distribution data includes the curvature value for each point.

[0063] Step S232: Extract the high curvature region from the initial landslide surface model based on the landslide surface curvature distribution data to obtain potential feature region data;

[0064] In this embodiment of the invention, landslide surface curvature distribution data is imported into PCL. The "Threshold Extraction" function is selected, and a curvature threshold of 0.1 is set to extract points with curvature greater than this threshold. These points are typically located in landslide cracks, steep slopes, and other areas with significant topographic changes. Through the above steps, PCL can automatically identify and extract point cloud data from high-curvature areas, generating potential feature region data. This data includes the point cloud coordinates and curvature values ​​of the high-curvature areas.

[0065] Step S233: Perform edge enhancement on the potential feature region data to obtain enhanced feature edge data; perform feature region clustering on the enhanced feature edge data to obtain a candidate feature region set;

[0066] In this embodiment of the invention, the image processing library OpenCV is used to perform edge enhancement and feature region clustering on latent feature region data. The latent feature region data is converted to a grayscale image. Using OpenCV's "Edge Enhancement" function, the Sobel operator is selected, and the kernel size is set to 3 to enhance the edge features in the image. The "Region Growing" algorithm is used to cluster the enhanced edge features, identifying continuous feature regions. By setting the growth threshold to 0.05 and the minimum region area to 50 square meters, OpenCV can automatically identify and cluster multiple candidate feature regions. Finally, the generated candidate feature region set contains the location, extent, and area information of these regions.

[0067] Step S234: Based on the candidate feature region set, calculate the area and continuity index of each region to generate feature region evaluation data;

[0068] In this embodiment of the invention, a set of candidate feature regions is imported into QGIS. Using QGIS's "Region Analysis" tool, the area and continuity index of each region are calculated. The continuity index is evaluated by calculating the connectivity of points within the region, specifically by calculating the average distance and standard deviation of points within the region. An area threshold of 100 square meters and a continuity index threshold of 0.8 are set to filter out regions with sufficiently large areas and good continuity. Through these operations, QGIS generates feature region evaluation data, which includes the area, continuity index, and other relevant attributes of each candidate feature region.

[0069] Step S235: Determine key feature areas based on feature area assessment data to obtain landslide feature area identifiers.

[0070] In this embodiment of the invention, the feature region evaluation data is imported into a Pandas DataFrame. By setting filtering conditions, areas with an area greater than 100 square meters and a continuity index greater than 0.8 are selected as key feature regions. These regions are located at the boundaries, cracks, and steep slopes of the landslide, and play an important indicative role in the dynamic changes of the landslide. Finally, the generated landslide feature region identifier contains the location, extent, and attribute information of these key regions.

[0071] Preferably, step S3 includes:

[0072] Step S31: Collect current landslide displacement data through a ground displacement sensor network;

[0073] In this embodiment of the invention, a network of multiple high-precision GPS displacement sensors is used. These sensors are installed at key locations in the landslide area, such as the leading edge, middle, and trailing edge of the landslide mass. Each sensor collects landslide displacement data in real time at a sampling frequency of 1 minute. The sensors transmit the data to a central data acquisition system via a wireless communication module. This system automatically synchronizes the data every 10 minutes to ensure the real-time nature and integrity of the data. Through the above steps, real-time displacement data of the landslide area can be obtained.

[0074] Step S32: Denoise the current landslide displacement data to obtain optimized current displacement data;

[0075] In this embodiment of the invention, the collected displacement data is imported into a Pandas DataFrame for data manipulation. The `scipy.signal.savgol_filter` function from the SciPy library is used to perform Savitzky-Golay filtering on the displacement data. The filter window length is set to 11 data points, and the polynomial order is 2 to smooth short-term fluctuations and noise in the data. Through the above steps, random noise in the data can be effectively removed, while preserving the long-term trend of landslide displacement, thereby obtaining optimized current displacement data.

[0076] Step S33: Extract the mechanical parameters of the grid nodes of the dynamic landslide mesh model to obtain the mechanical property data of the landslide body;

[0077] In this embodiment of the invention, a dynamic landslide mesh model is imported into Gmsh. Using Gmsh's "Mesh Node Attribute Extraction" function, the mechanical parameters of each mesh node are extracted, including elastic modulus, Poisson's ratio, and density. These parameters can be set according to the geological material properties of the landslide. For example, for a typical clay landslide, the elastic modulus is set to 10 MPa, the Poisson's ratio to 0.3, and the density to 1.8 g / cm³. Through these operations, Gmsh can generate a data file containing the mechanical parameters of each mesh node.

[0078] Step S34: Based on the optimized current displacement data and landslide mechanical property data, the kinematic equations are solved using the finite element method to obtain preliminary landslide motion field data;

[0079] In this embodiment of the invention, optimized current displacement data and landslide mechanical property data are imported into Open FOAM. Using Open FOAM's "kinematic equation solver," the time step is set to 0.1 seconds, and the total simulation time is 100 seconds. The kinematic equations are solved using the finite element method, calculating the displacement, velocity, and acceleration of each grid node at different time steps. Ultimately, the generated preliminary landslide motion field data contains information on the dynamic changes of the landslide body during the simulation time.

[0080] Step S35: Perform particle flow simulation based on preliminary landslide motion field data to obtain landslide motion trajectory data;

[0081] In this embodiment of the invention, the particle flow simulation software YADE (Yet Another Discrete Element Library) is used to simulate the particle flow of the preliminary landslide motion field data. The preliminary landslide motion field data is imported into YADE. The physical properties of the particles, such as diameter, density, and friction coefficient, are set. For example, for typical landslide particles, the diameter is set to 0.1 meters, the density to 2.5 g / cm³, and the friction coefficient to 0.5. Using YADE's "particle flow simulator," the simulation time is set to 100 seconds, and the time step is 0.01 seconds. Through simulation, the position and trajectory of each particle at different time steps are calculated. Finally, the generated landslide trajectory data can intuitively demonstrate the dynamic evolution process of the landslide.

[0082] Step S36: Perform dynamic extrapolation based on the landslide trajectory data to obtain real-time landslide evolution prediction data.

[0083] Preferably, step S36 includes:

[0084] Step S361: Extract time-series displacement features based on landslide motion trajectory data to obtain a landslide displacement time-series feature set;

[0085] In this embodiment of the invention, landslide trajectory data is imported into a Pandas DataFrame, including the position coordinates of each particle at different time points. The displacement of each particle within each time step is calculated by calculating the Euclidean distance between adjacent time points. Displacement features of each particle are extracted, including maximum displacement, average displacement, and standard deviation of displacement. These features reflect the dynamic changes of the landslide at different time points. Finally, the generated landslide displacement time-series feature set contains the displacement features of each particle within different time steps.

[0086] Step S362: Perform short-term displacement prediction based on the landslide displacement time series feature set to obtain preliminary displacement prediction data;

[0087] In this embodiment of the invention, the landslide displacement time-series feature set is imported into a Pandas DataFrame. The first 10 data points of the time series are selected as training data, and the last 5 data points are selected as test data. A linear regression model is used to predict the displacement of each particle. The input features of the model include the time step, maximum displacement, and average displacement. By training the model, the displacement within the next few time steps is predicted. Finally, the generated preliminary displacement prediction data contains the predicted displacement of each particle at future time points.

[0088] Step S363: Based on the preliminary displacement prediction data, perform medium- and long-term trend prediction to obtain landslide movement trend prediction data;

[0089] In this embodiment of the invention, preliminary displacement prediction data is imported into a Pandas DataFrame, with the time step set as the independent variable and the displacement amount as the dependent variable. The Prophet model is used to predict the displacement trend of each particle, with the prediction time range set to the next 30 time steps. The model's seasonality and trend parameters are adjusted. Ultimately, the generated landslide movement trend prediction data includes the displacement trend of each particle within the next 30 time steps.

[0090] Step S364: Correct the prediction results of the landslide movement trend prediction data to obtain optimized displacement prediction data;

[0091] In this embodiment of the invention, landslide movement trend prediction data is imported into a Pandas DataFrame. The error at each time step is calculated by comparing the predicted data with the actual observed data. A moving average method is used to smooth the error, with the moving average window size set to 5 time steps. By adjusting the predicted data, the impact of the error is reduced, resulting in optimized displacement prediction data. Ultimately, the generated optimized displacement prediction data is closer to the actual observed values.

[0092] Step S365: Reconstruct the displacement field of the entire region based on the optimized displacement prediction data to obtain the landslide displacement field prediction data;

[0093] In this embodiment of the invention, optimized displacement prediction data is imported into a Pandas DataFrame. NumPy interpolation functions are used to reconstruct the displacement field of the entire region. The interpolation method is set to cubic spline interpolation, and the displacement field of the entire landslide area at each time step is calculated using known particle displacement data. Finally, the generated landslide displacement field prediction data can intuitively display the displacement distribution of the landslide body at different time points.

[0094] Step S366: Calculate the velocity of each grid point in the dynamic landslide mesh model based on the landslide displacement field prediction data to obtain the landslide velocity field prediction data;

[0095] In this embodiment of the invention, landslide displacement field prediction data is imported into a Pandas DataFrame. The velocity of each grid point is obtained by calculating the displacement difference between adjacent time steps. With a time step set to 0.1 seconds, the velocity value of each grid point is obtained by dividing the displacement difference by the time step. Ultimately, the generated landslide velocity field prediction data contains the velocity information of each grid point within different time steps.

[0096] Step S367: Perform spatiotemporal feature fusion on the landslide velocity field prediction data to obtain real-time landslide evolution prediction data.

[0097] In this embodiment of the invention, landslide velocity field prediction data is imported into a Pandas DataFrame. The spatiotemporal characteristics of the velocity field are obtained by calculating the statistical features of the velocity field within each time step, such as average velocity, maximum velocity, and standard deviation of velocity. These features are then visualized using the Matplotlib library to generate a spatiotemporal distribution map of the velocity field.

[0098] Preferably, step S4 includes:

[0099] Step S41: Extract the displacement of the three-dimensional grid vertices from the real-time landslide evolution prediction data to obtain the landslide deformation characteristic data;

[0100] In this embodiment of the invention, real-time landslide evolution prediction data is imported into a Pandas DataFrame. This data contains displacement information for each grid point within different time steps. The displacement of each grid vertex is extracted by calculating the difference between vertex positions within adjacent time steps. Specifically, the time step is set to 0.1 seconds, and the landslide deformation characteristic data are obtained by calculating the displacement vector of each vertex within each time step.

[0101] Step S42: Update the vertex coordinates of the dynamic landslide mesh model based on the landslide deformation characteristic data to obtain the updated landslide mesh model;

[0102] In this embodiment of the invention, landslide deformation characteristic data is imported into Blender. This data includes the displacement vector of each grid vertex. Using Blender's "Vertex Editing" function, the coordinates of each vertex are updated based on the displacement vector. Specifically, the "Vertex Translation" tool is selected, the displacement vector of each vertex is input, and the vertex positions are updated one by one. Through the above steps, Blender generates an updated landslide mesh model.

[0103] Step S43: Apply surface coloring to the updated landslide mesh model to obtain a 3D rendering of the landslide foundation;

[0104] In this embodiment of the invention, Blender is used to color the surface of the updated landslide mesh model. The updated landslide mesh model is imported into Blender. The "Material Editing" function is selected to add colored materials to the model surface. The color mapping of the material is set, with the color gradually changing from blue (small displacement) to red (large displacement) according to the magnitude of the displacement. Specifically, using Blender's "Color Gradient" tool, the color mapping range is set to 0 to 10 meters of displacement. Through the above steps, Blender generates a 3D rendering of the landslide foundation, intuitively showing the degree of deformation of the landslide body.

[0105] Step S44: Render a real-time deformation animation based on the 3D rendering of the landslide foundation to obtain dynamic visualization data of the landslide;

[0106] In this embodiment of the invention, a 3D rendering of the landslide foundation is imported into Blender. Using Blender's "Animation Rendering" function, the animation time range is set to 0 to 100 seconds, with a frame rate of 30 frames per second. The dynamic deformation process of the landslide body is generated by updating the vertex coordinates of the mesh model frame by frame. Specifically, using Blender's "Keyframe Animation" tool, keyframes are set for the mesh model at each time step, automatically generating transition animations for intermediate frames. Finally, Blender generates dynamic visualization data of the landslide.

[0107] Step S45: Convert the displacement data in the landslide dynamic visualization data into thermal values ​​to obtain the overlay data of the thermal map;

[0108] In this embodiment of the invention, landslide dynamic visualization data is imported into a Pandas DataFrame. This data contains displacement information of grid points within each time step. The Matplotlib `imshow` function is used to map the displacement values ​​to a color range in the heatmap. The color mapping is set to "viridis", mapping the displacement values ​​from 0 to 10 meters to the color range. Through the above steps, overlaid heatmap data is generated.

[0109] Step S46: Construct a virtual scene based on the heatmap overlay data to obtain the basic data for the VR scene;

[0110] In this embodiment of the invention, heatmap overlay data, containing displacement distribution information of the landslide body, is imported into Unity3D. Using Unity3D's "Terrain Editing" function, virtual terrain is constructed based on the heatmap data. The terrain's resolution and height range are set, and the terrain's height and color are adjusted according to the displacement. Specifically, Unity3D's "Terrain Texture" tool is used to map the heatmap data onto the terrain surface, generating a virtual scene with a heatmap effect. Finally, Unity3D generates the basic data for the VR scene.

[0111] Step S47: Integrate gesture recognition and head tracking interaction functions based on VR scene basic data to obtain an immersive landslide 3D scene.

[0112] In this embodiment of the invention, basic VR scene data is imported into Unity3D. The Leap Motion plugin is installed and configured for gesture recognition. The recognition range and sensitivity parameters of Leap Motion are set to ensure accurate recognition of the user's gestures. Next, the Oculus Rift plugin is installed and configured for head tracking. The tracking range and refresh rate of the Oculus Rift are set to ensure real-time tracking of the user's head position and orientation. By integrating these devices, users can interact with the virtual scene through gestures and head movements, generating an immersive 3D landslide scene.

[0113] Preferably, step S5 includes:

[0114] Step S51: Extract displacement features from the immersive 3D landslide scene visualization model to obtain the model's predicted displacement data;

[0115] In this embodiment of the invention, an immersive 3D landslide scene is imported into Unity3D. Using Unity3D's "Model Analysis" tool, the displacement features of each mesh vertex in the scene are extracted. Specifically, the analysis time step is set to 0.1 seconds, and the displacement vector of the vertex within each time step is extracted. Through the above steps, Unity3D generates model prediction displacement data, which contains the displacement information of each mesh vertex within different time steps.

[0116] Step S52: Standardize the current landslide displacement data to obtain standard current displacement data;

[0117] In this embodiment of the invention, the current landslide displacement data is imported into a Pandas DataFrame. The displacement data is standardized using the `scipy.stats.zscore` function from the SciPy library. Specifically, the Z-score for each displacement data point is calculated, which is the difference between the data point and the mean divided by the standard deviation. Through these steps, the displacement data is converted to a standard normal distribution, yielding standard current displacement data. These data have a mean of 0 and a standard deviation of 1.

[0118] Step S53: Calculate the error between the model-predicted displacement data and the standard current displacement data to obtain the displacement error distribution data;

[0119] In this embodiment of the invention, the model-predicted displacement data and the standard current displacement data are imported into a Pandas DataFrame. The difference between the predicted displacement and the actual displacement of each grid vertex within each time step is calculated to obtain the displacement error. Specifically, the Pandas DataFrame.subtract method is used to calculate the point-by-point difference between the two datasets. Through the above steps, displacement error distribution data is generated, which reflects the deviation between model predictions and actual observations.

[0120] Step S54: Perform model parameter inversion on the dynamic landslide mesh model based on the displacement error distribution data to obtain preliminary model correction parameters;

[0121] In this embodiment of the invention, displacement error distribution data is imported into a Pandas DataFrame. A linear regression model is used, with displacement error as the dependent variable and the parameters of the grid model (such as elastic modulus, Poisson's ratio, etc.) as independent variables, for regression analysis. Specifically, the training data for the model is set to the first 80% of the time steps, and the test data is set to the last 20% of the time steps. By training the model, preliminary model correction parameters are obtained.

[0122] Step S55: Filter the parameters of the preliminary model correction to obtain the optimized model parameter set; generate early warning color marks based on the optimized model parameter set to obtain the preliminary early warning map;

[0123] In this embodiment of the invention, preliminary model correction parameters are imported into a Pandas DataFrame. Valid correction parameters are selected based on the significance level of the parameters (e.g., p-value less than 0.05) and the amount of error reduction. Specifically, the Pandas DataFrame.query method is used to select parameters that meet the criteria. Based on the optimized model parameter set, warning color scales are generated. Matplotlib's color map function is used to map the displacement error to a color range, generating a preliminary warning map.

[0124] Step S56: Reconstruct the entire risk field based on the preliminary early warning map to obtain closed-loop landslide risk early warning data.

[0125] In this embodiment of the invention, the preliminary early warning map is imported into a Pandas DataFrame. NumPy interpolation functions are used to reconstruct the risk field of the entire landslide area. Specifically, cubic spline interpolation is set as the interpolation method, and the risk distribution of the entire area is calculated using known risk point data. Through the above steps, closed-loop landslide risk early warning data is generated.

[0126] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0127] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A three-dimensional display method for landslide simulation, characterized in that, Includes the following steps: Step S1: Obtain multi-source monitoring data of the landslide area; Noise filtering and spatial interpolation were performed on the multi-source monitoring data of the landslide area to obtain preprocessed landslide monitoring data; The pre-processed landslide monitoring data is subjected to spatiotemporal benchmark unification processing to obtain spatiotemporally aligned monitoring data; Step S2: Perform NURBS surface modeling based on spatiotemporal aligned monitoring data to obtain the initial three-dimensional structure model of the landslide; The initial three-dimensional landslide structure model was adaptively meshed using the finite element method to obtain a dynamic landslide mesh model. Step S3: Obtain current landslide displacement data; perform landslide simulation based on the dynamic landslide mesh model and the current landslide displacement data to obtain landslide motion simulation data; dynamically update the landslide motion simulation data to obtain real-time landslide evolution prediction data; Step S4: GPU-accelerated rendering of real-time landslide evolution prediction data to obtain a landslide deformation visualization map; Step S4 involves overlaying a heatmap onto a landslide deformation visualization map and integrating it with a VR interactive interface to obtain an immersive 3D landslide scene. Step S41: Extract the displacement of the three-dimensional grid vertices from the real-time landslide evolution prediction data to obtain the landslide deformation characteristic data; Step S42: Update the vertex coordinates of the dynamic landslide mesh model based on the landslide deformation characteristic data to obtain the updated landslide mesh model; Step S43: Apply surface coloring to the updated landslide mesh model to obtain a 3D rendering of the landslide foundation; Step S44: Render a real-time deformation animation based on the 3D rendering of the landslide foundation to obtain dynamic visualization data of the landslide; Step S45: Convert the displacement data in the landslide dynamic visualization data into thermal values ​​to obtain the overlay data of the thermal map; Step S46: Construct a virtual scene based on the heatmap overlay data to obtain the basic data for the VR scene; Step S47: Integrate gesture recognition and head tracking interaction functions based on VR scene basic data to obtain an immersive landslide 3D scene; Step S5: Perform error inversion analysis based on the immersive 3D landslide scene and the current landslide displacement data to obtain optimized parameters for the landslide model; generate early warning maps based on the optimized parameters to obtain closed-loop landslide risk early warning data. Step S5 includes: Step S51: Extract displacement features from the immersive 3D landslide scene visualization model to obtain the model's predicted displacement data; Step S52: Standardize the current landslide displacement data to obtain standard current displacement data; Step S53: Calculate the error between the model-predicted displacement data and the standard current displacement data to obtain the displacement error distribution data; Step S54: Perform model parameter inversion on the dynamic landslide mesh model based on the displacement error distribution data to obtain preliminary model correction parameters; Step S55: Filter the parameters of the preliminary model correction to obtain the optimized model parameter set; generate early warning color marks based on the optimized model parameter set to obtain the preliminary early warning map; Step S56: Reconstruct the entire risk field based on the preliminary early warning map to obtain closed-loop landslide risk early warning data.

2. The three-dimensional display method for landslide simulation according to claim 1, characterized in that, Step S1 includes: Step S11: Perform a three-dimensional scan of the landslide area using lidar to obtain initial terrain point cloud data; Step S12: High-frequency noise is filtered out from the initial terrain point cloud data to obtain denoised terrain point cloud data; spatial completion is performed on the denoised terrain point cloud data to obtain complete terrain elevation data. Step S13: Collect real-time landslide displacement data in the landslide area through a ground displacement sensor network; perform time-series smoothing on the real-time landslide displacement data to obtain optimized landslide displacement data; Step S14: Acquire multispectral image data of the landslide area; Step S15: Spatially align the complete terrain elevation data, optimized landslide displacement data, and multispectral image data to obtain spatially unified monitoring data; Step S16: Perform time-series synchronization on the unified spatial monitoring data to obtain spatiotemporally aligned monitoring data.

3. The three-dimensional display method for landslide simulation according to claim 1, characterized in that, Step S2 includes: Step S21: Extract terrain elevation feature points based on spatiotemporal aligned monitoring data to obtain a landslide terrain feature point set; Step S22: Perform three-dimensional surface modeling based on the landslide topographic feature point set to obtain the initial landslide surface model; Step S23: Identify key terrain feature regions in the initial landslide surface model to obtain landslide feature region identifiers; Step S24: Based on the landslide feature area identifier, densify the point cloud of the corresponding key areas in the initial landslide surface model to obtain optimized landslide point cloud data; Step S25: Reconstruct the quadratic surface based on the optimized landslide point cloud data to obtain the three-dimensional structure model of the landslide; perform finite element mesh generation on the three-dimensional structure model of the landslide to obtain the basic landslide mesh model; Step S26: Refine the feature region of the basic landslide mesh model to obtain the dynamic landslide body mesh model.

4. The three-dimensional display method for landslide simulation according to claim 3, characterized in that, Step S23 includes: Step S231: Perform surface curvature analysis on the initial landslide surface model to obtain landslide surface curvature distribution data; Step S232: Extract the high curvature region from the initial landslide surface model based on the landslide surface curvature distribution data to obtain potential feature region data; Step S233: Perform edge enhancement on the potential feature region data to obtain enhanced feature edge data; perform feature region clustering on the enhanced feature edge data to obtain a candidate feature region set; Step S234: Based on the candidate feature region set, calculate the area and continuity index of each region to generate feature region evaluation data; Step S235: Determine key feature areas based on feature area assessment data to obtain landslide feature area identifiers.

5. The three-dimensional display method for landslide simulation according to claim 1, characterized in that, Step S3 includes: Step S31: Collect current landslide displacement data through a ground displacement sensor network; Step S32: Denoise the current landslide displacement data to obtain optimized current displacement data; Step S33: Extract the mechanical parameters of the grid nodes of the dynamic landslide mesh model to obtain the mechanical property data of the landslide body; Step S34: Based on the optimized current displacement data and landslide mechanical property data, the kinematic equations are solved using the finite element method to obtain preliminary landslide motion field data; Step S35: Perform particle flow simulation based on preliminary landslide motion field data to obtain landslide motion trajectory data; Step S36: Perform dynamic extrapolation based on the landslide trajectory data to obtain real-time landslide evolution prediction data.

6. The three-dimensional display method for landslide simulation according to claim 5, characterized in that, Step S36 includes: Step S361: Extract time-series displacement features based on landslide motion trajectory data to obtain a landslide displacement time-series feature set; Step S362: Perform short-term displacement prediction based on the landslide displacement time series feature set to obtain preliminary displacement prediction data; Step S363: Based on the preliminary displacement prediction data, perform medium- and long-term trend prediction to obtain landslide movement trend prediction data; Step S364: Correct the prediction results of the landslide movement trend prediction data to obtain optimized displacement prediction data; Step S365: Reconstruct the displacement field of the entire region based on the optimized displacement prediction data to obtain the landslide displacement field prediction data; Step S366: Calculate the velocity of each grid point in the dynamic landslide mesh model based on the landslide displacement field prediction data to obtain the landslide velocity field prediction data; Step S367: Perform spatiotemporal feature fusion on the landslide velocity field prediction data to obtain real-time landslide evolution prediction data.

7. A three-dimensional display system for landslide simulation, characterized in that, The three-dimensional display system for landslide simulation as described in claim 1, used to perform the landslide simulation three-dimensional display method, comprises: The data preprocessing module is used to acquire multi-source monitoring data of the landslide area; perform noise filtering and spatial interpolation on the multi-source monitoring data of the landslide area to obtain preprocessed landslide monitoring data; and perform spatiotemporal benchmark unification processing on the preprocessed landslide monitoring data to obtain spatiotemporally aligned monitoring data. The 3D modeling module is used to perform NURBS surface modeling based on spatiotemporal aligned monitoring data to obtain an initial 3D landslide structure model; the initial 3D landslide structure model is then subjected to adaptive finite element mesh generation to obtain a dynamic landslide body mesh model. The motion simulation module is used to acquire current landslide displacement data; perform landslide simulation based on the dynamic landslide mesh model and the current landslide displacement data to obtain landslide motion simulation data; and dynamically update the landslide motion simulation data to obtain real-time landslide evolution prediction data. The visualization rendering module is used to perform GPU-accelerated rendering of real-time landslide evolution prediction data to obtain a landslide deformation visualization map; the landslide deformation visualization map is overlaid with a heat map and integrated with a VR interactive interface to obtain an immersive 3D landslide scene. The early warning module is used to perform error inversion analysis based on the immersive 3D landslide scene and the current landslide displacement data to obtain optimized parameters for the landslide model; and to generate early warning maps based on the optimized parameters for the landslide model to obtain closed-loop landslide risk early warning data.

8. A computer-readable storage medium, characterized in that, The program contains a program capable of being loaded by a processor and executed as described in any one of claims 1 to 6 for the three-dimensional representation of landslide simulation.

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