Method for realizing three-dimensional visualization of landslide catastrophe process

Abstract

The invention discloses a method for realizing three-dimensional visualization of a landslide catastrophe process, and belongs to the technical field of unmanned aerial vehicle measurement, and the method comprises the following steps: S1, data acquisition and preprocessing; s2, dynamically simulating a depression filling process; s3, performing multi-source data fusion modeling; and S4, displaying by a three-dimensional visualization system. According to the method for realizing three-dimensional visualization of the landslide catastrophe process, data consistency is improved, errors are reduced through mutual verification, errors caused by different data sources are effectively eliminated, a reliable basis is provided for subsequent analysis, dynamic simulation precision is improved, and the method is suitable for popularization and application. And a guarantee is provided for dynamic simulation and multi-source data fusion of the landslide depression filling process.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) measurement technology, and in particular to a method for realizing three-dimensional visualization of a landslide disaster process. Background Art

[0002] Traditional landslide monitoring methods primarily rely on ground-based measurements or single remote sensing images. These methods are not only inefficient and costly, but also struggle to obtain high-precision three-dimensional terrain data. This approach limits the effective monitoring and analysis of landslide dynamics. Most current three-dimensional landslide models are constructed based on static digital elevation model (DEM) data, lacking effective simulation of the dynamic evolution of landslides, such as sinkhole filling and changes in sliding paths. This static representation fails to fully capture the development process and potential risks of landslides. Accurate spatial fusion of geological borehole data and surface DEM data is a major challenge. Due to the different sources and technical processing methods of these two data types, their spatial fusion accuracy is low, making it difficult to accurately construct the subsurface structure, which is crucial for a deeper understanding of landslide mechanisms. Existing technologies often suffer from inefficiencies when rendering large-scale three-dimensional terrain, particularly with noticeable lags in the display of dynamic processes. This not only impacts the user experience but also reduces the effectiveness and reliability of hazard analysis. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for realizing three-dimensional visualization of landslide disaster process, improve data consistency, reduce errors through mutual verification, effectively eliminate errors introduced by different data sources, provide a reliable basis for subsequent analysis, improve the accuracy of dynamic simulation, and provide a guarantee for dynamic simulation of landslide filling process and multi-source data fusion.

[0004] To achieve the above object, the present invention provides a method for realizing three-dimensional visualization of landslide disaster process, comprising the following steps:

[0005] S1, data acquisition and preprocessing;

[0006] S2, dynamic simulation of the depression filling process;

[0007] S3, multi-source data fusion modeling;

[0008] S4, 3D visualization system display.

[0009] Preferably, S1 specifically includes the following steps:

[0010] S1.1. Data acquisition: Using drone oblique photogrammetry technology, we acquire multi-view surface images at different time points before and after the landslide, and construct a DEM digital elevation model. The images are then processed using software to generate high-resolution DEM data.

[0011] S1.2. Data preprocessing: Perform temporal and spatial registration of the DEM data before and after the landslide to eliminate differences in coordinate systems, resolution, and geometric deformation.

[0012] Preferably, S1.2 specifically includes the following steps:

[0013] S1.2.1. Data preparation: Input the pre-landslide DEM data DEM1, the post-landslide DEM data DEM2, and the orthorectified extent of the landslide body; ensure that DEM1 and DEM2 have the same geographic coordinate system, projection mode, and resolution; process the spatial offset caused by the different acquisition times of the UAV images; calculate and save the difference DEM raster data of DEMΔ = DEM2 - DEM1;

[0014] S1.2.2. Unify the coordinate systems of DEM data before and after the landslide: Check the coordinate systems of DEM data before and after the landslide to see if they are the same. If not, convert the coordinate systems.

[0015] S1.2.3. Match the DEM data resolution before and after the landslide: Check the DEM data resolution before and after the landslide to see if they have the same resolution. If not, resample the lower-resolution data.

[0016] S1.2.4. Resampling and matching: Use tools to adjust the resolution of DEM data;

[0017] S1.2.5. Correction and registration: mainly includes control point-based registration and feature point-based correction;

[0018] S1.2.6. Result verification: Use the orthophoto range of the landslide body, exclude the landslide area, perform difference calculation and root mean square error (RMSE) to verify the surrounding terrain to determine the matching accuracy.

[0019] Preferably, S1.2.6 specifically includes the following operations:

[0020] The difference calculation formula is as follows:

[0021] ΔZ(x,y)=Z DEM2 (x,y)-Z DEM1 (x,y);

[0022] Among them, Z DEM1 (x,y) and Z DEM2 (x,y) represents the elevation value DEMΔ of DEM1 and DEM2 at the grid point (x,y) respectively;

[0023] Visual validation and statistical validation;

[0024] Visual verification: Use the software to load the original DEM1, DEM2 and the registered DEM2 to observe whether there is any obvious misalignment;

[0025] Statistical verification: Calculate the overall root mean square error RMSE of DEM1 and DEM2:

[0026]

[0027] Among them, N is the number of sample points, that is, the number of all pixels involved in the calculation, ΔZ(x i ,y i ) is the elevation difference at the i-th pixel point, that is, the DEM before and after the landslide at position x i ,y i The difference in elevation.

[0028] Preferably, S2 specifically includes the following steps:

[0029] S2.1: Data preparation: Input the pre-landslide DEM data DEM1 and the post-landslide DEM data DEM2. Ensure that DEM1 and DEM2 have completed spatiotemporal registration, including coordinate system, resolution, and correction registration. Calculate the pixel-by-pixel difference between the pre- and post-landslide DEM data to generate a dynamic transition sequence for the sinkhole filling process.

[0030] S2.2, difference weight control;

[0031] S2.3, dynamic transition sequence generation;

[0032] S2.4. Implement time-series animation playback in the CesiumJS engine.

[0033] Preferably, S2.2 specifically includes the following operations:

[0034] S2.2.1. Difference calculation: Perform pixel-by-pixel difference calculation on DEM1 and DEM2 within the orthophoto range of the landslide body:

[0035] ΔZ(x,y)=Z DEM2 (x,y)-Z DEM1 (x,y);

[0036] Positive values ​​indicate depression-filled areas where the terrain is uplifted, and negative values ​​indicate landslide areas where the terrain is subsiding;

[0037] S2.2.2, Dynamic transition formula:

[0038] Use the time scale parameter t = [0,1] to control the dynamic changes of the filling process:

[0039] Z t (x,y)=Z DEM1(x,y)-t·ΔZ(x,y);

[0040] When t = 0, the result is the initial state DEM1; when t = 1, the result is the target state DEM2; when t = (0, 1), the result is the intermediate state of the filling process;

[0041] S2.2.3. Difference weighted smoothing: Combined with the inverse distance weighted difference method, the intermediate state is optimized to make the filling process smoother;

[0042] S2.3 specifically includes the following operations:

[0043] S2.3.1. Time segmentation: Divide the depression filling process into several equal time intervals, such as 10 frames or 20 frames, with each frame corresponding to a time ratio t;

[0044] S2.3.2. Generate intermediate DEM data: For each time scale t, calculate the corresponding difference result Z according to the formula t (x,y), generate a series of intermediate DEM data;

[0045] S2.3.3. Generate terrain tiles for each time period: Generate corresponding terrain slice files for each intermediate DEM to optimize the web rendering speed of large-scale terrain;

[0046] S2.4 specifically includes the following operations:

[0047] S2.4.1, data loading;

[0048] S2.4.2, animation playback;

[0049] S2.4.3. Visualization effect.

[0050] Preferably, S3 specifically includes the following operations:

[0051] S3.1. Data preparation: Input geological borehole data and surface DEM data. The geological borehole data includes the coordinates of the borehole location, depth, and properties of each layer of rock and soil. Output is a "surface-underground" integrated model that superimposes the DEM terrain model and the underground geological model.

[0052] S3.2. Use radial basis function (RBF) interpolation to process irregularly distributed data points.

[0053] S3.3. Construct an underground stratum structure model;

[0054] S3.4, 3D fusion modeling.

[0055] Preferably, S3.3 specifically includes the following operations:

[0056] S3.3.1. Data preprocessing: Clean the geological drilling data to remove outliers and erroneous data;

[0057] S3.3.2, convert the data into RBF interpolation format;

[0058] S3.3.3, RBF interpolation implementation steps;

[0059] S3.3.4, construct a difference model;

[0060] S3.3.5. Generate a three-dimensional stratigraphic model;

[0061] S3.4 specifically includes the following operations:

[0062] S3.4.1. Data fusion: Fuse the generated underground stratigraphic model with the surface DEM data;

[0063] S3.4.2. Build an integrated model: Use CesiumJS to load the surface DEM and underground stratum model to create a comprehensive "surface-underground" integrated model.

[0064] Preferably, S4 specifically includes the following steps:

[0065] S4.1, stratigraphic section interaction;

[0066] S4.2, stratum blasting interaction;

[0067] S4.3, landslide-related overlay.

[0068] Therefore, the present invention adopts the above-mentioned method to realize three-dimensional visualization of landslide disaster process, improve data consistency, verify each other to reduce errors, effectively eliminate errors introduced by different data sources, provide a reliable basis for subsequent analysis, improve dynamic simulation accuracy, and provide guarantees for dynamic simulation of landslide filling process and multi-source data fusion.

[0069] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a three-dimensional visualization method for landslide disaster process of the present invention, which is a comparison diagram of the impact before and after the landslide, wherein: Figure 1 (a) is the image before the landslide. Figure 1 (b) in the figure is the image after the landslide;

[0071] Figure 2 This is an example diagram of terrain data display of a method for realizing three-dimensional visualization of landslide disaster process according to the present invention;

[0072] Figure 3It is a landslide disaster marking schematic diagram for realizing a three-dimensional visualization method of a landslide disaster process according to the present invention;

[0073] Figure 4 This is a schematic diagram of a three-dimensional fusion modeling process of geological drilling data for realizing a three-dimensional visualization method of a landslide disaster process according to the present invention, wherein: Figure 4 (a) is a three-dimensional display diagram of the borehole elevation and the borehole lithology elevation. Figure 4 (b) is a three-dimensional display of the continuous terrain of the surface calculated by interpolation based on the drilling elevation of a. Figure 4 (c) in the figure is a schematic diagram of dividing the unit space area based on (b) to construct a three-dimensional grid. Figure 4 (d) is a three-dimensional schematic diagram showing the spatial geological situation based on (c);

[0074] Figure 5 It is a three-dimensional stratum model diagram of a method for realizing three-dimensional visualization of landslide disaster process according to the present invention;

[0075] Figure 6 It is a topographic structure diagram of a method for realizing three-dimensional visualization of landslide disaster process according to the present invention;

[0076] Figure 7 It is a schematic diagram of the interactive stratum blasting for realizing a three-dimensional visualization method of a landslide disaster process according to the present invention;

[0077] Figure 8 It is a schematic diagram of underground water content for realizing a three-dimensional visualization method of a landslide disaster process according to the present invention;

[0078] Figure 9 This is a three-dimensional fusion diagram of stratum stress and displacement vector data for realizing a three-dimensional visualization method of landslide disaster process according to the present invention, wherein: Figure 9 (a) in the figure is the generalized shear strain cloud diagram. Figure 9 (b) in the figure is the displacement cloud map. DETAILED DESCRIPTION

[0079] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0080] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "include" or "comprise" mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0081] Example 1

[0082] The present invention provides a method for realizing three-dimensional visualization of a landslide disaster process, comprising the following steps:

[0083] S1. Data acquisition and preprocessing; specifically including the following steps:

[0084] S1.1. Data acquisition: Using drone oblique photogrammetry technology, we acquire multi-view surface images at different time points before and after the landslide, and construct a DEM digital elevation model. The images are then processed using software to generate high-resolution DEM data.

[0085] S1.2 Data preprocessing: Perform spatiotemporal registration of the DEM data before and after the landslide to eliminate differences in coordinate systems, resolution, and geometric deformation. This includes the following steps:

[0086] S1.2.1. Data preparation: Input the pre-landslide DEM data DEM1, the post-landslide DEM data DEM2, and the orthorectified extent of the landslide body; ensure that DEM1 and DEM2 have the same geographic coordinate system, projection mode, and resolution; process the spatial offset caused by the different acquisition times of the UAV images; calculate and save the difference DEM raster data of DEMΔ = DEM2 - DEM1;

[0087] S1.2.2. Unify the coordinate systems of the DEM data before and after the landslide: Check the coordinate systems of the DEM data before and after the landslide to determine whether they are the same. Use ArcMap or ArcGIS Pro in ArcGIS to load the two DEM data and check the coordinate system information of DEM1 and DEM2. If the coordinate systems are inconsistent, the coordinate systems need to be converted.

[0088] For data that needs to be converted, a projection transformation can be performed. Taking ArcGIS as an example, input DEM2 as the raster data to be processed into the ArcGIS ProjectRaster tool, set the output coordinate system to the coordinate system of DEM1, run the tool, generate a new DEM2 file, and convert DEM2 to the same coordinate system as DEM1.

[0089] S1.2.3. Match the DEM data resolution before and after the landslide: Check the DEM data resolution before and after the landslide. Check the resolution information (usually in meters or degrees) in the DEM layer properties to see if they have the same resolution. If not, resample the lower-resolution data.

[0090] S1.2.4. Resampling and matching: Use the Resample tool in ArcGIS to adjust the resolution of the DEM data;

[0091] Input DEM2 as the raster data to be processed, set the output pixel size to the target resolution (consistent with DEM1), set the resampling method (Bilinear or Cubic is recommended to preserve terrain details), run the tool, and generate a new DEM2 file.

[0092] S1.2.5. Correction and registration: mainly includes control point-based registration and feature point-based correction;

[0093] (1) Matching based on control points

[0094] Manually perform geometric correction on DEM2 using ArcGIS's Georeferencing tool. Load DEM1 and DEM2 into the same map window. Enable the Georeferencing toolbar and select DEM2 as the layer to be corrected. Add ground control points (GCPs) to DEM2 and align them to the corresponding positions of DEM1. Control points can be selected from obvious terrain features (such as mountain tops, valleys, etc.), and at least four control points are required to ensure accuracy. Set the transformation method (PolynomialTransformation is recommended). After completing the correction, save the alignment results.

[0095] (2) Matching based on feature points

[0096] If manual registration is inefficient, you can use ArcGIS's Image Analysis tool or a third-party plug-in (such as ENVI) for automatic registration. Automatic registration is usually based on feature point matching algorithms (such as SIFT or SURF).

[0097] S1.2.6. Result Verification: Use the orthophoto range of the landslide body, exclude the landslide area, perform difference calculation and root mean square error (RMSE) verification on the surrounding terrain to determine the matching accuracy. Specific operations include the following:

[0098] The difference calculation formula is as follows:

[0099] ΔZ(x,y)=Z DEM2 (x,y)-Z DEM1 (x,y);

[0100] Among them, Z DEM1 (x,y) and Z DEM2 (x, y) represents the elevation values ​​of DEM1 and DEM2 at the grid point (x, y), respectively. Using ArcToolbox, a difference grid DEMΔ is generated, the orthogonal range of the landslide is superimposed, and the landslide area is removed.

[0101] Visual validation and statistical validation;

[0102] Visual verification: Use ArcGIS software to load the original DEM1, DEM2 and the registered DEM2 to observe whether there is any obvious misalignment; use the Transparency function to overlay the two DEMs to visually check the registration effect.

[0103] Statistical verification: Calculate the overall root mean square error RMSE of DEM1 and DEM2:

[0104]

[0105] Among them, N is the number of sample points, that is, the number of all pixels involved in the calculation, ΔZ(x i ,y i ) is the elevation difference at the i-th pixel point, that is, the DEM before and after the landslide at position x i ,y i The difference in elevation.

[0106] If the RMSE is less than the preset threshold (such as 0.3 meters), the registration is successful. Figure 1 As shown, Figure 1 (a) is the image before the landslide. Figure 1 (b) in the figure is the image after the landslide.

[0107] S2. Dynamic simulation of the depression filling process, specifically including the following steps:

[0108] S2.1: Data preparation: Input the DEM data DEM1 before the landslide and the DEM data DEM2 after the landslide. Ensure that DEM1 and DEM2 have completed the temporal and spatial registration. The temporal and spatial registration includes the coordinate system, resolution and correction registration. Calculate the pixel-by-pixel difference of the DEM data before and after the landslide to generate a dynamic transition sequence of the depression filling process.

[0109] S2.2. Difference weight control, specifically including the following operations:

[0110] S2.2.1. Difference calculation: Perform pixel-by-pixel difference calculation on DEM1 and DEM2 within the orthophoto range of the landslide body:

[0111] ΔZ(x,y)=Z DEM2 (x,y)-Z DEM1 (x,y);

[0112] A positive difference indicates a depression-filled area where the terrain is uplifted, and a negative difference indicates a landslide area where the terrain is subsiding.

[0113] S2.2.2, Dynamic transition formula:

[0114] Use the time scale parameter t = [0,1] to control the dynamic changes of the filling process:

[0115] Z t (x,y)=Z DEM1 (x,y)-t·ΔZ(x,y);

[0116] When t = 0, the result is the initial state DEM1; when t = 1, the result is the target state DEM2; when t = (0, 1), the result is the intermediate state of the filling process.

[0117] S2.2.3. Difference weighted smoothing: Combined with the inverse distance weighted difference method, the intermediate state is optimized to make the filling process smoother; as shown below:

[0118]

[0119] Among them, d i is the distance between the current position and the sampling point, p is the power index, Z t (x,y) is the interpolation result of the target position, w i is the weight of the i-th sampling point, Z i is the actual measurement value of the i-th sampling point.

[0120] S2.3. Dynamic transition sequence generation, including the following operations:

[0121] S2.3.1. Time segmentation: Divide the depression filling process into several equal time intervals, such as 10 frames or 20 frames, where each frame corresponds to a time ratio t, t = [0.0, 0.1, 0.2, ..., 1.0];

[0122] S2.3.2. Generate intermediate DEM data: For each time scale t, calculate the corresponding difference result Z according to the formula t (x,y), generate a series of intermediate DEM data, and save the intermediate DEM data of each frame as an independent file;

[0123] S2.3.3. Generate terrain tiles for each time period: Generate corresponding terrain slice files (.terrain) for each intermediate DEM, reduce network transmission data, and optimize the web rendering speed of large-scale terrain;

[0124] Terrain is a terrain data format designed specifically for Cesium, based on Quantized Mesh technology. This format compresses and quantizes terrain data, greatly reducing the storage space of terrain data and improving loading and rendering performance. Cesium uses this format to process large terrain datasets, especially terrain tiles that require efficient streaming loading. Examples of terrain data display are as follows: Figure 2 shown.

[0125] S2.4. Implementing sequential animation playback in the CesiumJS engine; specifically, the following operations are involved:

[0126] S2.4.1. Data loading: Use the CesiumJS engine to load the generated intermediate DEM data sequence.

[0127] S2.4.2. Animation playback: Use CesiumJS's Clock module to implement time-series animation playback. Based on the set event length and playback speed, load the current time-series DEM data frame by frame and remove the previous time-series data. This creates a time-series terrain change animation. Use CesiumJS's built-in clock component to implement animation pausing and playback control.

[0128] S2.4.3, Visualization effect;

[0129] Frame-by-frame display: CesiumJS is used to render the intermediate DEM data of each frame to create a continuous dynamic transition effect.

[0130] Color mapping: Use different colors to represent terrain height changes and enhance visual expression.

[0131] Overlay satellite images: The filling process can be overlaid with satellite images to further enhance the sense of reality. The landslide hazard marking diagram is as follows: Figure 3shown.

[0132] S3. Multi-source data fusion modeling: Combined with geological drilling data, the radial basis function (RBF) interpolation method is used to fit the underground stratum structure of the landslide area. The stratum model is fused with the surface DEM in three dimensions to construct an integrated "surface-underground" model of the landslide body, such as Figure 4 As shown, Figure 4 (a) is a three-dimensional display diagram of the borehole elevation and the borehole lithology elevation. Figure 4 (b) is a three-dimensional display of the continuous terrain of the surface calculated by interpolation based on the drilling elevation of a. Figure 4 (c) in the figure is a schematic diagram of dividing the unit space area based on (b) to construct a three-dimensional grid. Figure 4 (d) is a three-dimensional schematic diagram showing the spatial geological conditions based on c.

[0133] The specific operations include:

[0134] S3.1. Data preparation: Input geological borehole data and surface DEM data. The geological borehole data includes the coordinates of the borehole location, depth, and the properties of each layer of rock and soil (such as thickness and type). Output is a "surface-underground" integrated model that superimposes the DEM terrain model and the underground geological model.

[0135] S3.2. Use radial basis function (RBF) interpolation to process irregularly distributed data points.

[0136] Radial basis function (RBF) interpolation is a gridless interpolation method commonly used for data interpolation in multidimensional space. Its basic concept is to predict the value of an unknown point using the value and location of a known point. For modeling the subsurface structure of landslide areas, RBF can effectively handle irregularly distributed data points.

[0137] The basic form of RBF interpolation is:

[0138]

[0139] Where f(x) is the predicted value; α i is the weight coefficient; φ() is the radial basis function; p(x) is the polynomial part, usually a first-order or second-order polynomial, used to ensure the smoothness of the interpolation result; N is the number of known sample points; x i and x represent the positions of known sample points and predicted points respectively. Commonly used radial basis functions include Gaussian function, multiquadratic surface function, etc.

[0140] S3.3. Construct an underground stratum structure model; specifically, the following operations are included:

[0141] S3.3.1. Data preprocessing: Clean the geological drilling data to remove outliers and erroneous data; convert the drilling data into a form suitable for RBF interpolation, that is, determine the coordinates, depth and corresponding stratigraphic information of each borehole.

[0142] S3.3.2, convert the data into RBF interpolation format;

[0143] Construct 3D point cloud data, convert the stratigraphic information at the depth of each borehole into 3D spatial points (X, Y, Z), and attach corresponding attribute values.

[0144] The sandstone layer at a depth of 5 m in borehole BK-001 has three-dimensional coordinates (100.0, 200.0, -5.0), and attribute values: lithology type = 2 (sandstone), and permeability = 0.1 cm / s.

[0145] Borehole BK-002 at a depth of 3m in the silt clay layer: three-dimensional coordinates (150.0, 250.0, -3.0), attribute values: lithology type = 3, permeability = 0.005cm / s.

[0146] Data formatting: organize the data into an input format suitable for RBF interpolation: input matrix: contains the three-dimensional coordinates (X, Y, Z) of all points, output vector: the attribute value of the corresponding point (such as lithology type, permeability).

[0147] S3.3.3, RBF interpolation implementation steps;

[0148] Common RBF types include:

[0149] Gaussian function:

[0150] Multiple quadratic functions:

[0151] Thin plate spline: φ(r) = r 2 ln(r).

[0152] S3.3.4, construct a difference model;

[0153] Use Python's scipy.interpolate.Rbf to implement RBF interpolation.

[0154] S3.3.5. Generate a three-dimensional stratigraphic model;

[0155] like Figure 5 As shown, the prediction results are converted into a 3D model (such as a format supported by VTK, GeoJSON or Cesium), and the vtu model is converted into gltf format to facilitate the overlay display of cesiumJS and DEM on the web side.

[0156] S3.4. 3D fusion modeling; specifically includes the following operations:

[0157] S3.4.1. Data fusion: Fuse the generated underground stratum model with the surface DEM data; use a direct overlay method, with the surface DEM as the top layer and the underground stratum model arranged below it in sequence.

[0158] S3.4.2. Build an integrated model: Use CesiumJS to load the surface DEM and underground stratum model to create a comprehensive "surface-underground" integrated model.

[0159] In the visualization environment, the viewing angle can be adjusted dynamically to observe the stratigraphic structure at different levels and analyze the overall characteristics of the landslide. The topographic structure diagram is as follows: Figure 6 shown.

[0160] S4. 3D visualization system display: In the 3D visualization system of the landslide disaster process, based on the original dynamic simulation of the filling process, further superimposing the data of the stratum geological model, a more comprehensive and in-depth spatial geographic analysis can be achieved. Specifically, the following steps are included:

[0161] S4.1, stratigraphic section interaction;

[0162] Stratigraphic cross-sections: By adding a stratigraphic cross-section feature to the 3D visualization system, users can select specific locations for vertical cutting to view the stratigraphic structure at different depths. This helps understand the complexity of the geological structure within the landslide area and its impact on landslide activity.

[0163] Interactive Exploration: Allows users to freely select the sectioning location and direction and observe the changes in the strata in real time. This interactive approach makes the analysis process more intuitive and flexible.

[0164] S4.2, stratum blasting interaction;

[0165] Stratum explosion view: Provides an "explosion" view mode, allowing users to see the distribution of underground layers more clearly. The interactive diagram of the stratum explosion is as follows Figure 7 shown.

[0166] S4.3, landslide-related overlay;

[0167] Overlay of related data: Combine geological attribute data (such as lithology, water content, etc.) and the above-ground and underground analysis results of the landslide body to provide users with detailed underground information. This not only improves the practicality of the model, but also enhances the user's overall understanding of the above-ground and underground characteristics of the landslide body. Figure 8 、 Figure 9 As shown, Figure 9(a) in the figure is the generalized shear strain cloud diagram. Figure 9 (b) in the figure is the displacement cloud map.

[0168] Therefore, the present invention adopts the above-mentioned method to realize three-dimensional visualization of landslide disaster process, improve data consistency, verify each other to reduce errors, effectively eliminate errors introduced by different data sources, provide a reliable basis for subsequent analysis, improve dynamic simulation accuracy, and provide guarantees for dynamic simulation of landslide filling process and multi-source data fusion.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for realizing three-dimensional visualization of landslide disaster process, characterized by: The following steps are involved: S1, data acquisition and preprocessing; S2, dynamic simulation of the depression filling process; S3, multi-source data fusion modeling; S4, 3D visualization system display.

2. The method for realizing three-dimensional visualization of landslide disaster process according to claim 1, characterized in that: S1 specifically includes the following steps: S1.

1. Data acquisition: Using drone oblique photogrammetry technology, we acquire multi-view surface images at different time points before and after the landslide, and construct a DEM digital elevation model. The images are then processed using software to generate high-resolution DEM data. S1.

2. Data preprocessing: Perform temporal and spatial registration of the DEM data before and after the landslide to eliminate differences in coordinate systems, resolution, and geometric deformation.

3. The method for realizing three-dimensional visualization of landslide disaster process according to claim 2, characterized in that: S1.2 specifically includes the following steps: S1.2.

1. Data preparation: Input the pre-landslide DEM data DEM1, the post-landslide DEM data DEM2, and the orthorectified extent of the landslide body; ensure that DEM1 and DEM2 have the same geographic coordinate system, projection mode, and resolution; process the spatial offset caused by the different acquisition times of the UAV images; calculate and save the difference DEM raster data of DEMΔ = DEM2 - DEM1; S1.2.

2. Unify the coordinate systems of DEM data before and after the landslide: Check the coordinate systems of DEM data before and after the landslide to see if they are the same. If not, convert the coordinate systems. S1.2.

3. Match the DEM data resolution before and after the landslide: Check the DEM data resolution before and after the landslide to see if they have the same resolution. If not, resample the lower-resolution data. S1.2.

4. Resampling and matching: Use tools to adjust the resolution of DEM data; S1.2.

5. Correction and registration: mainly includes control point-based registration and feature point-based correction; S1.2.

6. Result verification: Use the orthophoto range of the landslide body, exclude the landslide area, perform difference calculation and root mean square error (RMSE) to verify the surrounding terrain to determine the matching accuracy.

4. The method for realizing three-dimensional visualization of landslide disaster process according to claim 3, characterized in that: S1.2.6 specifically includes the following operations: The difference calculation formula is as follows: ΔZ(x,y)=Z DEM2 (x,y)-Z DEM1 (x,y); Among them, Z DEM1 (x,y) and Z DEM2 (x,y) represents the elevation value DEMΔ of DEM1 and DEM2 at the grid point (x,y) respectively; Visual validation and statistical validation; Visual verification: Use the software to load the original DEM1, DEM2 and the registered DEM2 to observe whether there is any obvious misalignment; Statistical verification: Calculate the overall root mean square error RMSE of DEM1 and DEM2: Among them, N is the number of sample points, that is, the number of all pixels involved in the calculation, ΔZ(x i ,y i ) is the elevation difference at the i-th pixel point, that is, the DEM before and after the landslide at position x i ,y i The difference in elevation.

5. The method for realizing three-dimensional visualization of landslide disaster process according to claim 4, characterized in that: S2 specifically includes the following steps: S2.1: Data preparation: Input the pre-landslide DEM data DEM1 and the post-landslide DEM data DEM2. Ensure that DEM1 and DEM2 have completed spatiotemporal registration, including coordinate system, resolution, and correction registration. Calculate the pixel-by-pixel difference between the pre- and post-landslide DEM data to generate a dynamic transition sequence for the sinkhole filling process. S2.2, difference weight control; S2.3, dynamic transition sequence generation; S2.

4. Implement time-series animation playback in the CesiumJS engine.

6. The method for realizing three-dimensional visualization of landslide disaster process according to claim 5, characterized in that: S2.2 specifically includes the following operations: S2.2.

1. Difference calculation: Perform pixel-by-pixel difference calculation on DEM1 and DEM2 within the orthophoto range of the landslide body: ΔZ(x,y)=Z DEM2 (x,y)-Z DEM1 (x,y); Positive values ​​indicate depression-filled areas where the terrain is uplifted, and negative values ​​indicate landslide areas where the terrain is subsiding; S2.2.2, Dynamic transition formula: Use the time scale parameter t = [0,1] to control the dynamic changes of the filling process: Z t (x,y)=Z DEM1 (x,y)-t·ΔZ(x,y); When t = 0, the result is the initial state DEM1; when t = 1, the result is the target state DEM2; when t = (0, 1), the result is the intermediate state of the filling process; S2.2.

3. Difference weighted smoothing: Combined with the inverse distance weighted difference method, the intermediate state is optimized to make the filling process smoother; S2.3 specifically includes the following operations: S2.3.

1. Time segmentation: Divide the depression filling process into several equal time intervals, such as 10 frames or 20 frames, with each frame corresponding to a time ratio t; S2.3.

2. Generate intermediate DEM data: For each time scale t, calculate the corresponding difference result Z according to the formula t (x,y), generate a series of intermediate DEM data; S2.3.

3. Generate terrain tiles for each time period: Generate corresponding terrain slice files for each intermediate DEM to optimize the web rendering speed of large-scale terrain; S2.4 specifically includes the following operations: S2.4.1, data loading; S2.4.2, animation playback; S2.4.

3. Visualization effect.

7. The method for realizing three-dimensional visualization of landslide disaster process according to claim 6, characterized in that: S3 specifically includes the following operations: S3.

1. Data preparation: Input geological borehole data and surface DEM data. The geological borehole data includes the coordinates of the borehole location, depth, and properties of each layer of rock and soil. Output is a "surface-underground" integrated model consisting of a DEM terrain model superimposed on the underground geological model. S3.

2. Use radial basis function (RBF) interpolation to process irregularly distributed data points. S3.

3. Construct an underground stratum structure model; S3.4, 3D fusion modeling.

8. The method for realizing three-dimensional visualization of landslide disaster process according to claim 7, characterized in that: S3.3 specifically includes the following operations: S3.3.

1. Data preprocessing: Clean the geological drilling data to remove outliers and erroneous data; S3.3.2, convert the data into RBF interpolation format; S3.3.3, RBF interpolation implementation steps; S3.3.4, construct a difference model; S3.3.

5. Generate a three-dimensional stratigraphic model; S3.4 specifically includes the following operations: S3.4.

1. Data fusion: Fuse the generated underground stratigraphic model with the surface DEM data; S3.4.

2. Build an integrated model: Use CesiumJS to load the surface DEM and underground stratum model to create a comprehensive "surface-underground" integrated model.

9. The method for realizing three-dimensional visualization of landslide disaster process according to claim 8, characterized in that: S4 specifically includes the following steps: S4.1, stratigraphic section interaction; S4.2, stratum blasting interaction; S4.3, landslide-related overlay.

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