Flood disaster evolution method and system based on digital twinborn technology

Abstract

The invention relates to the technical field of digital twinning, in particular to a flood disaster evolution method and system based on the digital twinning technology, and the method comprises the steps: collecting all point cloud data of a river basin terrain, constructing a three-dimensional terrain model, obtaining the reflection intensity and elevation data of each point cloud, clustering all point clouds corresponding to the ground, and obtaining a three-dimensional model; recording the areas where all point clouds corresponding to the ground in each cluster are located as local areas; and calculating a coplanar characterization value, an erosion evaluation value, an instability risk value, a soil fixation evaluation value and a terrain evolution factor of each local area, and constructing a digital twinborn simulation platform of the river basin terrain in combination with the three-dimensional terrain model to perform analog simulation on flood disasters. The defect that a static terrain model cannot respond to terrain evolution caused by flood disasters can be avoided, simulation errors generated by a digital twin simulation platform in the flood disaster evolution process are effectively reduced, and the accuracy of risk assessment of the flood disasters is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of digital twinning, in particular to a flood disaster evolution method and system based on digital twinning technology. BACKGROUND

[0002] Flood disasters, as one of the most common natural disasters in the world, have caused great impact on human society and economy. With the intensification of climate change, the frequency and severity of flood events are on the rise. By applying digital twinning technology to flood disaster prevention and control, the application studies the use of three-dimensional dynamic modeling, simulation deduction and other technologies to construct a digital twin with the ability of flood disaster assessment, pre-visualization and prevention in river basins, and proposes a theoretical basis and solution for flood disaster assessment and prevention.

[0003] Precise simulation and risk assessment of flood disasters rely on high-precision topographic data and dynamic analysis of surface coverings. Traditional methods usually simulate flood evolution based on static topographic models, without considering the topographic changes caused by phenomena such as riverbed incision and bank collapse due to water erosion, as well as the impact of soil-retaining effects of vegetation on the bank slope of the river basin. This leads to distorted topographic modeling of the river basin and simulation errors in the evolution of flood disasters, and the accumulation of simulation errors will cause the dynamic deduction path of flood disasters to deviate, seriously affecting the accuracy of flood disaster risk assessment. SUMMARY

[0004] To solve the above technical problems, a flood disaster evolution method and system based on digital twinning technology are provided to solve the existing problems.

[0005] The application solves the technical problem by providing a flood disaster evolution method and system based on digital twinning technology, which includes the following steps:

[0006] In a first aspect, the application provides a flood disaster evolution method based on digital twinning technology, which includes the following steps:

[0007] Collect all point cloud data of the river basin terrain, construct a three-dimensional terrain model, and obtain the reflectivity and elevation data of each point cloud. All point clouds on the three-dimensional terrain model are divided into ground corresponding point clouds and non-ground corresponding point clouds;

[0008] Cluster all ground corresponding point clouds, and record the area where each cluster of ground corresponding point clouds is located as each local area. Project the non-ground corresponding point clouds onto the plane where the local area is located, and obtain the non-ground corresponding point clouds distributed in the local area;

[0009] Grid each local area of ground and non-ground corresponding point clouds, respectively, to obtain ground and non-ground corresponding triangular meshes, respectively.

[0010] The coplanar condition of different triangular meshes corresponding to the ground in each local area and the discrete condition of the elevation data of the ground corresponding point cloud are analyzed, the coplanar representation value of each local area is calculated, the erosion evaluation value of each local area is determined in combination with the distribution discrete condition of the ground corresponding point cloud and the average level of the reflection intensity;

[0011] The coplanar condition of the planes where the corresponding triangular meshes between the ground and the non-ground in each local area are located, and the proportion condition of the non-ground corresponding point cloud are calculated, the instability risk value of each local area is calculated, the soil fixation evaluation value of each local area is determined in combination with the difference condition of the reflection intensity of the corresponding point cloud between the ground and the non-ground, and the discrete condition of the elevation data of the non-ground corresponding point cloud;

[0012] Based on the erosion evaluation value and the soil fixation evaluation value, the topographic evolution factor of each local area is determined, and a digital twin simulation platform of the river basin terrain is built in combination with the three-dimensional terrain model to simulate the evolution of the flood disaster.

[0013] Preferably, all point clouds are divided into ground corresponding point clouds and non-ground corresponding point clouds by using cloth simulation filtering algorithm.

[0014] Preferably, the calculation of the coplanar representation value of each local area comprises:

[0015] The normal vector of the plane where each triangular mesh corresponding to the ground is located is calculated, and the accumulated sum of the similarity degree of the normal vectors of any two triangular meshes corresponding to the ground in each local area is calculated;

[0016] The discrete degree of the elevation data of all point clouds corresponding to the ground in each local area is calculated, and is recorded as a first discrete degree;

[0017] The coplanar representation value is the ratio of the accumulated sum to the first discrete degree.

[0018] Preferably, the determination of the erosion evaluation value of each local area comprises:

[0019] The average value of the distance between any two point clouds corresponding to the ground in each local area is recorded as a local discrete degree;

[0020] The average value of the reflection intensity of all point clouds corresponding to the ground in each local area is recorded as an average intensity, and the ratio of the average intensity to the local discrete degree is taken as the topographic degradation degree of each local area;

[0021] The erosion evaluation value is the ratio of the topographic degradation degree to the coplanar representation value.

[0022] Preferably, the calculation of the instability risk value of each local area comprises:

[0023] a mode of an angle between a normal vector of a plane where all the triangular meshes corresponding to the ground in each local region is located and a normal vector of the ground plane, denoted as a first angle;

[0024] determining a local normal vector of each local region according to normal vectors of planes where different triangular meshes corresponding to non-ground in each local region is located and areas of the triangular meshes;

[0025] denoting an angle between the local normal vector and the normal vector of the ground plane as a second angle;

[0026] calculating a cosine value of a difference between the first angle and the second angle;

[0027] counting a total number of all the point clouds corresponding to non-ground and all the point clouds corresponding to the ground in each local region; calculating a ratio of a number of all the point clouds corresponding to non-ground in each local region to the total number, denoted as a number ratio;

[0028] the instability risk value is a ratio of the cosine value and the number ratio.

[0029] Preferably, the determining of the local normal vector of each local region comprises:

[0030] calculating areas of each triangular mesh corresponding to non-ground in each local region;

[0031] performing a weighted average on the normal vectors of the planes where all the triangular meshes corresponding to non-ground in each local region is located with the areas as weight values, to obtain the local normal vector of each local region.

[0032] Preferably, the determining of the soil stabilization evaluation value of each local region comprises:

[0033] denoting a sum value of differences in reflection intensity between each point cloud corresponding to non-ground and all the point clouds corresponding to the ground in each local region as a relative difference; and denoting a sum of the relative differences of all the point clouds corresponding to non-ground in each local region as a local difference amount;

[0034] calculating a discrete degree of elevation data of all the point clouds corresponding to non-ground in each local region, denoted as a second discrete degree;

[0035] taking a product of the local difference amount and the second discrete degree as a hydrological separation degree of each local region;

[0036] the soil stabilization evaluation value is a product of the instability risk value and the hydrological separation degree.

[0037] Preferably, the terrain evolution factor is a normalized result of a product of the erosion evaluation value and the soil stabilization evaluation value.

[0038] Preferably, the digital twin simulation platform for building the terrain of the river basin simulates the evolution of the flood disaster, including:

[0039] Based on the three-dimensional terrain model and the terrain evolution factor, a digital twin bottom plate is built by using the digital twin technology;

[0040] Real-time hydro-meteorological data and engineering scheduling data are introduced to form a dynamic data lake, and through data mapping technology, the dynamic data lake is synchronized to the three-dimensional terrain model to build a digital twin simulation platform for the terrain of the river basin, and the digital twin simulation platform is used to simulate the evolution of the flood disaster.

[0041] In the second aspect, the embodiments of the present application also provide a flood disaster evolution system based on the digital twin technology, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of the flood disaster evolution method based on the digital twin technology in any of the above aspects are implemented.

[0042] The present application has at least the following beneficial effects:

[0043] The application calculates the coplanar representation value of each local area by considering the coplanar condition of different triangular meshes corresponding to the ground in the local area and the discrete condition of the elevation data of the ground corresponding point cloud, which has the beneficial effect of considering the flatness of the terrain to reflect the water flow erosion resistance of the local area terrain; secondly, the erosion evaluation value of each local area is determined, which has the beneficial effect of considering the distribution of the ground corresponding point cloud and the change of the reflectivity, reflecting the water flow erosion and the erosion resistance of the local area in the historical flood disaster, evaluating the significance of the terrain evolution of the local area caused by the water flow erosion; the instability risk value of each local area is calculated, which has the beneficial effect of considering the angle between the vegetation represented by the non-ground corresponding point cloud and the ground corresponding point cloud, reflecting the inclination degree of the vegetation canopy represented by the non-ground corresponding point cloud caused by the water flow erosion; the soil fixation evaluation value of each local area is determined, which has the beneficial effect of considering the reflectivity difference between the non-ground and ground corresponding point cloud and the elevation data fluctuation of the non-ground corresponding point cloud, reflecting the inhibition degree of the vegetation to the water flow erosion of the ground, i.e. the soil fixation ability of the vegetation, evaluating the terrain evolution of the local area caused by the vegetation resisting the water flow erosion, determining the terrain evolution factor of each local area, and combining the three-dimensional terrain model to build a digital twin simulation platform of the river basin terrain, and simulate the flood disaster, which has the beneficial effect of comprehensively evaluating the risk condition of the terrain evolution of the local area under the water flow erosion of the flood disaster, and then building a digital twin simulation platform of the river basin terrain, which can avoid the disadvantages of the static terrain model that cannot respond to the terrain evolution caused by the flood disaster, effectively reduce the simulation error of the digital twin simulation platform in the evolution process of the flood disaster, and improve the accuracy of the risk assessment of the flood disaster. BRIEF DESCRIPTION OF DRAWINGS

[0044] The flood disaster evolution method based on digital twin technology provided by the application will be further described in detail below in combination with the accompanying drawings.

[0045] Figure 1 The step flow chart of the flood disaster evolution method based on digital twin technology provided by the embodiment of the application;

[0046] Figure 2 The step flow chart of the erosion evaluation value acquisition method provided by the embodiment of the application;

[0047] Figure 3 The step flow chart of the soil fixation evaluation value acquisition method provided by the embodiment of the application. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the present application clearer, the method and system for flood disaster evolution based on digital twin technology provided by the present application are further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0050] Please refer to Figure 1 which shows the step flow chart of the method for flood disaster evolution based on digital twin technology provided by an embodiment of the present application, which comprises the following steps:

[0051] Step 1, collect all point cloud data of the river basin terrain, construct a three-dimensional terrain model, and obtain the reflectivity and elevation data of each point cloud, and divide all point clouds on the three-dimensional terrain model into ground corresponding point clouds and non-ground corresponding point clouds.

[0052] Through the depth camera and laser radar scanner carried by the unmanned aerial vehicle, fly over the basin prone to flood disasters to collect all point cloud data of the river basin terrain and the reflectivity of each point cloud;

[0053] It should be noted that the flight height of the unmanned aerial vehicle in the flat basin area is set to 500m, and the flight height in the complex terrain basin area is set to 300m, the heading overlap of the unmanned aerial vehicle is set to ≥80%, and the lateral overlap is set to ≥60%, to ensure that the data between adjacent flight lines have enough overlapping areas, to ensure the continuity of the point cloud data, to use the GNSS / IMU combined positioning system to record the POS data of the unmanned aerial vehicle in real time, i.e. position and attitude data, and to use the GPS clock to align the POS data and the three-dimensional point cloud data, to ensure the time synchronization of the data, to eliminate the point position deviation caused by the vibration of the unmanned aerial vehicle, and as other implementation manners, the implementer can set it according to the actual situation.

[0054] Preprocess all point cloud data, specifically:

[0055] The average distance between each point cloud and all other point clouds in its neighborhood is denoted as the average distance;

[0056] In this embodiment, the neighborhood radius is 1m, i.e. taking any point cloud as the center, the area where all point clouds with a distance less than or equal to 1m from the point cloud are located is denoted as the neighborhood of the point cloud, and as other implementation manners, the implementer can set it according to the actual situation; the distance is measured by calculating the Euclidean distance between each point cloud and all other point clouds in its preset neighborhood, wherein the calculation of the Euclidean distance is a known technology, which will not be described here.

[0057] The Z-score algorithm is used to calculate the Z-score of the average distance of each point cloud, and the point cloud with an absolute value of the Z-score greater than a preset value is removed;

[0058] In this embodiment, the preset value is 3, wherein the Z-score algorithm is a known technology and will not be described here again, and the Z-score Z of the average distance of each point cloud is calculated according to the following formula: i x i is the average distance of the i-th point cloud, μ is the mean value of the average distance of all point clouds, σ is the standard deviation of the average distance of all point clouds, wherein the preset value is 3, which is set according to the Laiyida criterion, that is, to judge whether the value of x i is distributed in the range of (μ-3σ, μ+3σ), that is, Z i is greater than 3, which indicates that the point cloud is abnormal, wherein the Laiyida criterion is a known technology and will not be described here again.

[0059] The elevation data of each point cloud is extracted by using a digital elevation model;

[0060] It should be noted that the digital elevation model is a known technology and will not be described here again.

[0061] The point cloud corresponding to the minimum elevation data in the neighborhood of each point cloud is selected and recorded as the lowest point; the distance between each point cloud and the lowest point in the neighborhood is recorded as the interval distance;

[0062] In this embodiment, the Euclidean distance between each point cloud and the lowest point in the neighborhood is recorded as the interval distance.

[0063] The difference between the elevation data of each point cloud and the elevation data of the lowest point in the neighborhood is calculated and recorded as the elevation difference;

[0064] In this embodiment, the absolute value of the difference between the elevation data of each point cloud and the elevation data of the lowest point in the neighborhood is calculated and recorded as the elevation difference.

[0065] The angle value corresponding to the arctangent function of the ratio of the elevation difference to the interval distance is calculated;

[0066] The difference between the elevation data of each point cloud and the mean value of the elevation data of all point clouds in the neighborhood is calculated and recorded as the elevation deviation;

[0067] In this embodiment, the absolute value of the difference between the elevation data of each point cloud and the mean value of the elevation data of all point clouds in the neighborhood is calculated and recorded as the elevation deviation.

[0068] The point cloud with an angle value greater than or equal to a preset angle and an elevation deviation greater than or equal to a preset deviation value is removed; ​

[0069] In the embodiment, the preset angle value is 45 degrees, and the preset deviation value is 1. As other implementation manners, the implementer can set them according to actual conditions; thereby the preprocessing process of the point cloud data is completed.

[0070] All the point cloud data of the pretreated river basin terrain is subjected to three-dimensional modeling to construct a three-dimensional terrain model.

[0071] In the embodiment, the modeling software can use 3dsmax, blender, C4D and other modeling software capable of making three-dimensional models. The construction of the three-dimensional terrain model is a known technology, and will not be described here.

[0072] All the point clouds on the three-dimensional terrain model are subjected to inverse distance weighted interpolation to restore terrain details, and are divided by using cloth simulation filtering algorithm to obtain ground corresponding point clouds and non-ground corresponding point clouds.

[0073] It should be noted that the inverse distance weighted interpolation and the cloth simulation filtering algorithm are known technologies, and will not be described here.

[0074] Thus, all the ground corresponding point clouds and non-ground corresponding point clouds in the river basin terrain, as well as the reflectivity and elevation data of each point cloud, are obtained.

[0075] Step 2, analyze the coplanar condition of different triangular meshes corresponding to the ground in each local area and the discrete condition of the elevation data of the ground corresponding point clouds, calculate the coplanar representation value of each local area, and determine the erosion evaluation value of each local area in combination with the distribution discrete condition of the ground corresponding point clouds and the average level of the reflectivity.

[0076] In the process of flood disaster, the point clouds corresponding to the ground and the point clouds corresponding to the non-ground such as vegetation in the river basin terrain can reflect the influence degree of the water flow erosion caused by the flood disaster in the basin terrain area on the terrain evolution. The water flow erosion can cause the river channel to widen and the riverbed to incise, and the change condition of the elevation data and the point cloud density of the ground point cloud corresponding point clouds in the basin terrain area can reflect the intensity of the water flow erosion in the historical period of flood disaster and the erosion resistance of the basin terrain. Secondly, the point clouds corresponding to the non-ground contain vegetation information. The vegetation has a strong resistance to flood disaster. For example, the higher the vegetation coverage rate, the stronger the water and soil conservation ability, and the stronger the anti-flood disaster ability. The point clouds corresponding to the non-ground can effectively reflect the vegetation coverage rate and the vegetation lodging degree to evaluate the water flow erosion condition of the basin terrain and the anti-flood disaster intensity.

[0077] Secondly, when the basin terrain is more prone to evolution, the basin terrain is more strongly eroded by the water flow of historical flood disasters, the high-intensity erosion of the water flow in the basin terrain region causes the surface to be more severely broken, and the distribution density of the point cloud is greater; and the rough rock layer or cementing layer exposed after the erosion of the strong erosion-resistant surface will increase the laser reflection surface, so that the reflection intensity of the corresponding point cloud of the ground is significantly improved; at the same time, when the anti-erosion ability of the basin terrain is stronger, the condition of the elevation data of the point cloud in the basin terrain region is less likely to mutate, and the anti-erosion ability of the terrain is stronger to the random erosion of the water flow, so that the corresponding point cloud of the ground is more likely to be on the same plane, that is, the region where the corresponding point cloud of the ground is located is more gentle.

[0078] Based on the above analysis, the entire basin is divided into different local regions by clustering the point cloud corresponding to the ground, specifically:

[0079] All point cloud data corresponding to the ground on the three-dimensional terrain model are clustered, and the region where all point clouds corresponding to the ground in each cluster are located is recorded as each local region.

[0080] In this embodiment, the K-mediods clustering algorithm is used for clustering, the number of cluster clusters is determined by the elbow rule, and the absolute value of the difference between the elevation data of any two point cloud data corresponding to the ground in the ground point cloud data is used as the distance, wherein the K-mediods clustering algorithm is a known technology and will not be described here.

[0081] Further, the coplanar condition of the point cloud corresponding to the ground in the local region and the fluctuation condition of the elevation data corresponding to the point cloud are analyzed, and a coplanar representation value is calculated, specifically:

[0082] Triangular meshing is performed on all point clouds corresponding to the ground in each local region to obtain triangular meshes corresponding to the ground.

[0083] In this embodiment, the Delaunay triangulation algorithm is used for meshing processing, wherein the Delaunay triangulation algorithm is a known technology and will not be described here.

[0084] The normal vector of the plane where each triangular mesh corresponding to the ground is located is calculated.

[0085] It should be noted that the calculation of the normal vector is a known technology and will not be described here.

[0086] The accumulation sum of the similarity of the normal vectors of any two triangular meshes corresponding to the ground in each local region is calculated.

[0087] In this embodiment, the correlation is calculated by measuring the cosine similarity of the normal vectors of all two triangular meshes corresponding to the ground in each local area. The calculation of cosine similarity is a well-known technique and will not be described in detail here.

[0088] Calculate the degree of dispersion of the elevation data of all point clouds corresponding to the ground in each local area, and denote it as the first degree of dispersion;

[0089] In this embodiment, the degree of dispersion is measured by calculating the information entropy of the elevation data of all point clouds corresponding to the ground in each local area. The calculation of information entropy is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of existing technology, such as variance, etc. This embodiment does not impose any special restrictions on this.

[0090] The ratio of the summation to the first discreteness is used as the coplanarity representation value of each local region;

[0091] It should be noted that the greater the similarity, the greater the sum, indicating that the smaller the angle between the planes containing the different triangular grids on the ground, the greater the probability that they are on the same plane. The greater the first dispersion, the more significant the elevation change of the point cloud corresponding to the ground in the local area. The larger the obtained coplanar characterization value, the more gentle the terrain in the local area, and the stronger the resistance of the terrain to water erosion.

[0092] Secondly, the distances to different point clouds corresponding to the ground in a local area are analyzed, as well as the reflection intensities of different point clouds corresponding to the ground, to calculate the terrain degradation degree, specifically:

[0093] The mean distance between any two point clouds corresponding to the ground in each local region is denoted as the local dispersion.

[0094] In this embodiment, the distance is measured by calculating the Euclidean distance, which is a well-known technique and will not be described in detail here.

[0095] The average intensity is the mean value of the reflection intensity of all point clouds corresponding to the ground in each local area.

[0096] The ratio of the average intensity to the local dispersion is used as the degree of terrain deterioration in each local area;

[0097] It should be noted that the smaller the local dispersion, the more concentrated the distribution of point clouds corresponding to the ground in that local area; the greater the average intensity, the more significant the reflection intensity of the point clouds corresponding to the ground in that local area; and the greater the resulting terrain deterioration, the more severe the surface fragmentation caused by water erosion in that local area.

[0098] Furthermore, based on the coplanar characterization value and the topographic deterioration degree, an erosion assessment value is determined, specifically as follows:

[0099] The ratio of the terrain deterioration degree to the coplanar characterization value is used as the erosion assessment value for each local area;

[0100] It should be noted that the higher the erosion assessment value, the more significant the impact of historical floods on the topographic evolution of the local area, and the weaker its resistance to water erosion. The flowchart of the method for obtaining the erosion assessment value provided in this embodiment is shown below. Figure 2 As shown.

[0101] It should be noted that, in order to avoid the denominator being 0 when calculating the ratio, a preset value greater than 0 is added to the denominator. In this embodiment, the preset value greater than 0 is 0.1. As for other implementation methods, the implementer can set it according to the actual situation.

[0102] Thus, the erosion assessment values ​​for each local area were obtained.

[0103] Step 3: Calculate the instability risk value of each local area by considering the coplanarity of the planes containing the corresponding triangular grids between the ground and non-ground areas, as well as the proportion of the corresponding point cloud in the non-ground area. Combine the differences in reflection intensity between the corresponding point clouds between the ground and non-ground areas, and the dispersion of the elevation data of the corresponding point cloud in the non-ground area, to determine the soil stabilization assessment value of each local area.

[0104] Furthermore, during floods, non-ground point cloud data, specifically vegetation, plays a crucial role in resisting water erosion. Vegetation roots increase soil shear strength, thus inhibiting surface erosion. However, when the soil-stabilizing effect of vegetation weakens, soil structure loosens, making the surface more susceptible to erosion. This exacerbates erosion, and the resulting localized obstacles created by fallen vegetation can intensify flood turbulence, further aggravating the destructive power of floods and creating a cascading effect. Therefore, failing to consider the impact of vegetation when using digital twins to simulate flood evolution can lead to errors in the simulation process.

[0105] Secondly, when the topography of a watershed is more prone to change, the effect of vegetation roots in increasing the shear strength of the soil and inhibiting the erosion and stripping of the surface by water flow is less pronounced. In other words, the soil-fixing effect of vegetation weakens. In areas where the soil-fixing capacity of vegetation is weaker, the difference in reflection intensity between the point cloud corresponding to the ground where the vegetation roots are located and the point cloud corresponding to the non-ground location is greater, and the inconsistency in the elevation of the point cloud corresponding to the non-ground location is more severe. At the same time, affected by the erosion of water flow during floods in historical periods, the angle between the plane of the point cloud corresponding to the non-ground location and the direction of water flow is smaller, and the distribution of the point cloud corresponding to the non-ground location is more sparse or uneven. Water flow can penetrate the vegetation area more easily, and the vegetation is more likely to be eroded and toppled during floods.

[0106] Based on the above analysis, the terrain slope of each local area is assessed by analyzing the angle between the plane containing each triangular grid and the ground plane. Specifically:

[0107] Calculate the mode of the angle between the normal vector of the plane containing all the triangular meshes on the ground in each local area and the normal vector of the ground plane, and use it as the first angle of each local area;

[0108] In this embodiment, the normal vector corresponding to the ground plane is (0,0,1), so the normal vector is perpendicular to the ground plane. As for other implementation methods, the implementer can set it according to the actual situation. Secondly, the calculation process of the angle between the normal vectors is a well-known technology and will not be described in detail here.

[0109] It should be noted that the larger the first angle, the higher the slope of the terrain in that local area.

[0110] Secondly, the angle between the plane containing the point cloud (not on the ground) and the ground plane is analyzed to reflect the degree of inclination of the vegetation canopy. Specifically:

[0111] Project the point cloud that does not correspond to the ground onto the plane of the local area to obtain the point cloud that does not correspond to the ground distributed in the local area.

[0112] Triangulate all point clouds that are not on the ground within each local area to obtain each triangular mesh corresponding to the non-ground area.

[0113] In this embodiment, the Delaunay triangulation algorithm is used for meshing. The Delaunay triangulation algorithm is a well-known technique and will not be described in detail here.

[0114] Calculate the normal vector of the plane containing each triangular mesh that is not on the ground within each local region;

[0115] Calculate the area of ​​each triangular mesh corresponding to the non-ground area in each local region, and use the area as the weight to calculate the weighted average of the normal vectors of the plane containing all the triangular meshes corresponding to the non-ground area in each local region, so as to obtain the local normal vector of each local region.

[0116] The angle between the local normal vector and the normal vector of the ground plane is taken as the second angle of each local region;

[0117] It should be noted that the larger the area, the denser the vegetation canopy and the thicker the vegetation branches in the area where the triangular grid corresponding to the ground is located. The large area of ​​vegetation structure dominates the overall direction and has a stronger resistance or guiding effect against water erosion. Therefore, by evaluating the angle between the weighted average of the area and the normal vector of the plane where the triangular grid is located and the ground plane, the degree of inclination of the vegetation canopy represented by the point cloud corresponding to the ground in this local area is reflected, thereby determining whether it is easily eroded by water flow. The larger the second angle, the more easily the vegetation represented by the point cloud corresponding to the ground in this local area is eroded by water flow, thus increasing the risk of tipping over.

[0118] Furthermore, by analyzing the differences between the first and second angles within a local area, and the proportion of non-ground corresponding point clouds, the instability risk value is calculated, specifically as follows:

[0119] Calculate the cosine of the difference between the first angle and the second angle;

[0120] In this embodiment, the cosine of the absolute value of the difference between the first angle and the second angle is calculated.

[0121] Count the total number of point clouds corresponding to all non-ground areas and the total number of point clouds corresponding to all ground areas within each local region;

[0122] Calculate the ratio of the number of all point clouds corresponding to non-ground areas in each local region to the total number, and record it as the number percentage;

[0123] The ratio of the cosine value to the proportion of the quantity is used as the instability risk value of each local region;

[0124] It should be noted that a larger cosine value indicates a smaller angle between the plane containing the vegetation canopy and the plane containing the local area. A larger cosine value indicates a smaller angle between the plane containing the non-ground corresponding point cloud and the plane containing the ground corresponding point cloud. This reflects that the vegetation tilt direction is more consistent with the direction of water flow eroding the surface slope, maximizing the thrust of the water flow on the vegetation, and maximizing the shear stress on the vegetation roots, making the vegetation more susceptible to uprooting or breaking. A smaller percentage indicates sparser vegetation in the local area, lower resistance to water erosion, and a higher instability risk value. This indicates a greater risk of vegetation falling over in the local area, and after the vegetation falls over, it may expose the surface, increasing the possibility of chain erosion.

[0125] Furthermore, by analyzing the differences in reflection intensity between the point cloud corresponding to the ground where vegetation roots are located and the corresponding point cloud not on the ground, as well as the inconsistencies in the elevation data of the corresponding point cloud not on the ground, the hydrological separation degree is calculated to assess the state in which water transport, erosion resistance, or mechanical connectivity between the surface cover layer, such as the vegetation root layer and litter layer, and the underlying soil or bedrock is significantly reduced during water erosion. Specifically:

[0126] The sum of the differences in reflection intensity between each point cloud corresponding to the non-ground area and all point clouds corresponding to the ground area within each local region is denoted as the relative difference.

[0127] In this embodiment, the sum of the absolute values ​​of the differences in reflection intensity between each point cloud corresponding to the non-ground area and all point clouds corresponding to the ground area within each local region is denoted as the relative difference.

[0128] The sum of the relative differences of all point clouds that are not on the ground within each local region is denoted as the local difference quantity.

[0129] Calculate the degree of dispersion of the elevation data of all point clouds corresponding to non-ground areas in each local region, and denote it as the second degree of dispersion;

[0130] In this embodiment, the degree of dispersion is measured by calculating the variance of the elevation data of all point clouds corresponding to non-ground areas in each local region. As other implementation methods, implementers may use other methods of the prior art, such as standard deviation, etc. This embodiment does not impose any special restrictions on this.

[0131] The product of the local difference and the second dispersion is taken as the hydrological separation degree of each local region.

[0132] It should be noted that the greater the local difference, the greater the difference in reflection intensity between the non-ground corresponding point cloud and the ground corresponding point cloud in the local area. The greater the second dispersion, the more likely the vegetation is to be eroded by water flow in areas with weak vegetation soil-fixing capacity, resulting in a more irregular elevation distribution. The higher the instability of the elevation data of the non-ground corresponding point cloud in the local area, the greater the hydrological separation, indicating that the watershed topography in the local area is less affected by the vegetation soil-fixing capacity represented by the non-ground corresponding point cloud, leading to more severe soil structure damage.

[0133] Furthermore, based on the instability risk value and the hydrological separation degree, the soil stabilization assessment value is determined, specifically as follows:

[0134] The product of the instability risk value and the hydrological separation degree is used as the soil stabilization assessment value for each local area.

[0135] It should be noted that the higher the soil stabilization assessment value, the weaker the soil stabilization capacity of the vegetation in that local area, the easier it is for water to penetrate the vegetation area, and the higher the possibility of the vegetation being eroded and toppled during floods. The flowchart of the method for obtaining the soil stabilization assessment value provided in this embodiment is shown below. Figure 3 As shown.

[0136] Thus, the soil stabilization assessment values ​​for each local area are obtained.

[0137] Step 4: Based on the erosion assessment value and the soil stabilization assessment value, determine the topographic evolution factors of each local area, and combine the three-dimensional topographic model to build a digital twin simulation platform for the river basin topography to simulate the evolution of flood disasters.

[0138] When the intensity of historical water erosion within a watershed's topographic region is higher, its resistance to water erosion is weaker, and the inhibitory effect of vegetation on surface erosion is less pronounced, leading to a higher cascading risk from vegetation collapse—that is, when the erosion assessment value and soil stabilization assessment value are larger—the watershed's topography is more likely to undergo topographic evolution due to flooding. Therefore, based on the aforementioned erosion assessment value and soil stabilization assessment value, a topographic evolution factor is determined, specifically as follows:

[0139] The formula for calculating the topographic evolution factor of the m-th local region is:

[0140] Y m =norm(Q) m ×G m )

[0141] Among them, Y m Let Q be the topographic evolution factor for the m-th local region. m G is the erosion assessment value for the m-th local region. mLet m be the soil stabilization assessment value of the m-th local region, and norm() be the normalization function. In this embodiment, the sigmoid function is used for normalization. The sigmoid function is a well-known technique and will not be described in detail here. As other implementation methods, implementers can use other methods of the prior art, such as the softmax function, tanh function, etc. This embodiment does not impose any special restrictions on this.

[0142] It should be noted that the larger the topographic evolution factor, the greater the impact of water erosion on the local area of ​​the watershed topography. In this case, the watershed topography has a weaker resistance to erosion in the local area, the worse the effect of vegetation roots on inhibiting the stripping of the surface by floodwaters, and the higher the cascading risk caused by vegetation collapse.

[0143] Furthermore, based on the aforementioned topographic evolution factors, a digital twin simulation platform for the watershed topography is constructed, specifically as follows:

[0144] Based on the three-dimensional terrain model corresponding to the watershed topography and the terrain evolution factors, a digital twin base is built using digital twin technology;

[0145] It should be noted that digital twin baseboards can be updated in real time or periodically, reflecting the dynamic evolution of the terrain and more accurately predicting the evolution path and impact range of floods, thereby improving the accuracy of risk assessment.

[0146] Hydrological and meteorological data and engineering scheduling data are introduced in real time to form a dynamic data lake. Through data mapping technology, the dynamic data lake is synchronized to a three-dimensional terrain model to build a digital twin simulation platform for the watershed terrain. The digital twin simulation platform is then used to simulate the evolution of flood disasters.

[0147] It should be noted that hydrological and meteorological data include rainfall, soil moisture content, watershed water level, and watershed flow velocity, while engineering scheduling data includes reservoir discharge. The data mapping technology and the construction of the digital twin simulation platform are well-known technologies and will not be elaborated upon here.

[0148] The specific simulation process is as follows: by simulating the input of hydrological and meteorological data and engineering scheduling data under different flood disaster scenarios, as well as topographic evolution factors, using hydrological and hydraulic models, combined with topographic information, the instantaneous flow and inundation depth of the flood disaster in each local area are calculated, the inundation path of the flood disaster is dynamically simulated, and the inundation range and water flow velocity of the flood disaster are rendered in real time through a digital twin simulation platform to realize the simulation evolution of the flood disaster.

[0149] It should be noted that hydrological and hydraulic models are well-known technologies and will not be elaborated upon here.

[0150] Based on the same inventive concept as the above methods, this application also provides a flood disaster evolution system based on digital twin technology, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described flood disaster evolution methods based on digital twin technology.

[0151] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A flood disaster evolution method based on digital twin technology, characterized in that, The method includes the following steps: Collect all point cloud data of the river basin topography, construct a three-dimensional terrain model, and obtain the reflection intensity and elevation data of each point cloud. Divide all point clouds on the three-dimensional terrain model into point clouds corresponding to the ground and point clouds corresponding to non-ground areas. Cluster all point clouds corresponding to the ground, and denote the regions where all point clouds corresponding to the ground are located within each cluster as local regions; project the point clouds not corresponding to the ground onto the plane of the local regions to obtain the point clouds not corresponding to the ground distributed within the local regions. Mesh all point clouds corresponding to the ground and non-ground areas in each local region, and obtain each triangular mesh corresponding to the ground and non-ground areas respectively; The coplanarity of different triangular grids corresponding to the ground in each local area is analyzed, as well as the dispersion of elevation data of the corresponding point cloud. The coplanarity characterization value of each local area is calculated. Combined with the distribution dispersion of the corresponding point cloud and the average level of reflection intensity, the erosion assessment value of each local area is determined. By considering the coplanarity of the planes containing the corresponding triangular grids between the ground and non-ground areas in each local region, as well as the proportion of the corresponding point cloud in the non-ground area, the instability risk value of each local region is calculated. Combined with the difference in reflection intensity between the corresponding point clouds between the ground and non-ground areas, and the dispersion of the elevation data of the corresponding point clouds in the non-ground area, the soil stabilization assessment value of each local region is determined. Based on erosion assessment values ​​and soil stabilization assessment values, the topographic evolution factors of each local area are determined. Combined with a three-dimensional topographic model, a digital twin simulation platform for river basin topography is built to simulate the evolution of flood disasters.

2. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, Using a cloth-based simulation filtering algorithm, all point clouds are divided into point clouds corresponding to the ground and point clouds corresponding to non-ground objects.

3. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, The calculation of the coplanar characterization values ​​of each local region includes: Calculate the normal vector of the plane containing each triangular mesh corresponding to the ground; calculate the sum of the similarity of the normal vectors of all any two triangular meshes corresponding to the ground within each local region; Calculate the degree of dispersion of the elevation data of all point clouds corresponding to the ground in each local area, and denote it as the first degree of dispersion; The coplanar characterization value is the ratio of the accumulated sum to the first discreteness.

4. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, The determination of erosion assessment values ​​for each local area includes: The mean distance between any two point clouds corresponding to the ground in each local region is denoted as the local dispersion. The average intensity is the mean of the reflection intensity of all point clouds corresponding to the ground in each local area; the ratio of the average intensity to the local dispersion is taken as the terrain deterioration degree of each local area. The erosion assessment value is the ratio of the terrain deterioration degree to the coplanar characterization value.

5. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, The calculation of the instability risk value for each local region includes: Calculate the mode of the angle between the normal vector of the plane containing all the triangular grids on the ground in each local area and the normal vector of the ground plane, and denote it as the first angle; The local normal vector of each local region is determined based on the normal vector of the plane containing the different triangular meshes corresponding to the non-ground areas within each local region and the area of ​​the triangular meshes. The angle between the local normal vector and the normal vector of the ground plane is denoted as the second angle; Calculate the cosine of the difference between the first angle and the second angle; Count the total number of all point clouds corresponding to non-ground surfaces and the total number of point clouds corresponding to ground surfaces in each local area; calculate the ratio of the number of all point clouds corresponding to non-ground surfaces in each local area to the total number, and record it as the quantity percentage. The instability risk value is the ratio of the cosine value to the quantity proportion.

6. The flood disaster evolution method based on digital twin technology as described in claim 5, characterized in that, Determining the local normal vectors of each local region includes: Calculate the area of ​​each triangular grid corresponding to the non-ground region within each local area; Using the area as the weight, the normal vectors of the planes containing all triangular meshes that are not on the ground within each local area are weighted and averaged to obtain the local normal vectors of each local area.

7. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, The determination of soil stabilization assessment values ​​for each local area includes: The sum of the differences in reflection intensity between each point cloud corresponding to the non-ground surface and all point clouds corresponding to the ground surface within each local region is denoted as the relative difference; the sum of the relative differences of all point clouds corresponding to the non-ground surface within each local region is denoted as the local difference quantity. Calculate the degree of dispersion of the elevation data of all point clouds corresponding to non-ground areas in each local region, and denote it as the second degree of dispersion; The product of the local difference and the second dispersion is taken as the hydrological separation degree of each local region. The soil stabilization assessment value is the product of the instability risk value and the hydrological separation degree.

8. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, The topographic evolution factor is the normalized result of the product of the erosion assessment value and the soil stabilization assessment value.

9. The flood disaster evolution method based on digital twin technology as described in claim 1, characterized in that, The aforementioned digital twin simulation platform for river basin topography is used to simulate the evolution of flood disasters, including: Based on the three-dimensional terrain model and the terrain evolution factors, a digital twin base is constructed using digital twin technology; Real-time hydrological and meteorological data and engineering scheduling data are introduced to form a dynamic data lake. Through data mapping technology, the dynamic data lake is synchronized to a three-dimensional terrain model to build a digital twin simulation platform for the river basin terrain. The digital twin simulation platform is then used to simulate the evolution of flood disasters.

10. A flood disaster evolution system based on digital twin technology, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the flood disaster evolution method based on digital twin technology as described in any one of claims 1-9.

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