Flood disaster evolution method and system based on digital twinning technology

Abstract

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. The method comprises the following steps: collecting all point cloud data of a river basin terrain, constructing a three-dimensional terrain model, and obtaining reflection intensity and elevation data of each point cloud; clustering all point clouds corresponding to the ground; regarding the area where all point clouds corresponding to the ground in each cluster are located as a local area; calculating coplanar characteristic values, erosion evaluation values, instability risk values, soil consolidation evaluation values and terrain evolution factors of each local area; and combining the three-dimensional terrain model to build a digital twinning simulation platform of the river basin terrain, and simulating the flood disaster. The application can avoid the disadvantage that a static terrain model cannot respond to terrain evolution caused by a flood disaster, effectively reduce simulation errors generated by the digital twinning simulation platform in the flood disaster evolution process, and improve the accuracy of risk assessment of the flood disaster.

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 height 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 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 purpose, technical scheme 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 examples. It should be understood that the specific examples 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 the embodiment, the similarity degree is calculated by calculating the cosine similarity of the normal vectors of all the arbitrary two triangular meshes corresponding to the ground in each local region, wherein the calculation of the cosine similarity is a known technique and will not be described here.

[0088] The dispersion degree of the elevation data of all the point clouds corresponding to the ground in each local region is calculated, denoted as a first dispersion degree.

[0089] In the embodiment, the dispersion degree is measured by calculating the information entropy of the elevation data of all the point clouds corresponding to the ground in each local region, wherein the calculation of the information entropy is a known technique and will not be described here. As other embodiments, the implementer can use other methods such as variance, and the embodiment does not specially limit this.

[0090] The ratio of the accumulation sum and the first dispersion degree is taken as the coplanar representation value of each local region.

[0091] It should be noted that the greater the similarity degree is, the greater the accumulation sum is, which indicates that the included angle of the planes where the different triangular meshes corresponding to the ground are located is smaller, and the possibility of being in the same plane is greater. The greater the first dispersion degree is, the more significant the elevation mutation of the point clouds corresponding to the ground in the local region is, and the greater the obtained coplanar representation value is, which indicates that the terrain of the local region is more gentle, and the water flow erosion resistance of the terrain of the local region is stronger.

[0092] Secondly, the distance conditions of the different point clouds corresponding to the ground in the local region and the reflection intensities of the different point clouds corresponding to the ground are analyzed, and the terrain degradation degree is calculated, specifically as follows.

[0093] The average distance between all the arbitrary two point clouds corresponding to the ground in each local region is denoted as a local dispersion degree.

[0094] In the embodiment, the distance is measured by calculating the Euclidean distance, wherein the calculation of the Euclidean distance is a known technique and will not be described here.

[0095] The average reflection intensity of all the point clouds corresponding to the ground in each local region is denoted as an average intensity.

[0096] The ratio of the average intensity and the local dispersion degree is taken as the terrain degradation degree of each local region.

[0097] It should be noted that the smaller the local dispersion degree is, the more concentrated the distribution of the point clouds corresponding to the ground in the local region is, and the greater the average intensity is, which indicates that the reflection intensity of the point clouds corresponding to the ground in the local region is more significant. The greater the obtained terrain degradation degree is, which indicates that the surface fragmentation condition caused by the influence of the water flow erosion on the terrain of the local region is more serious.

[0098] Further, based on the coplanar representation value and the terrain degradation degree, an erosion evaluation value is determined, specifically:

[0099] The ratio of the terrain degradation degree and the coplanar representation value is taken as the erosion evaluation value of each local region;

[0100] It should be noted that the larger the erosion evaluation value is, the more significant the influence of the water flow erosion caused by the flood disaster occurring in the historical period on the topographic evolution of the local region, and the weaker the water flow erosion resistance is. The erosion evaluation value provided in the embodiment is obtained by the method as shown in the flowchart of the steps. Figure 2

[0101] It should be noted that when calculating the ratio, a preset value greater than 0 is added to the denominator to avoid a denominator of 0. In the present embodiment, the preset value greater than 0 is 0.1. As an alternative, the implementer can set it according to the actual situation.

[0102] At this point, the erosion evaluation value of each local region is obtained.

[0103] Step 3, by the coplanar condition of the corresponding triangular mesh plane between the ground and the non-ground in each local region, and the proportion of the non-ground corresponding point cloud, the instability risk value of each local region is calculated, and the soil stabilization evaluation value of each local region is determined by combining the difference of the reflection intensity of the corresponding point cloud between the ground and the non-ground, and the dispersion of the elevation data of the non-ground corresponding point cloud.

[0104] Further, when a flood disaster occurs, the point cloud data corresponding to the non-ground, i.e. the vegetation, has a resisting effect on the water flow erosion in the flood disaster, and the root system of the vegetation can increase the shear strength of the soil, thereby inhibiting the surface from being stripped by the water flow. When the vegetation soil stabilization effect is weakened, the soil structure will be loose, i.e. the surface is more easily eroded by the water flow, leading to intensified erosion, and after the vegetation is washed away by the flood, local obstacles will be formed, which will induce the phenomenon of intensified flood turbulence, further intensifying the destructive power of the flood disaster, thereby causing a cascading effect risk. Therefore, when simulating the evolution of the flood disaster by digital twinning, if the influence of the vegetation is not considered, there will be errors in the simulation process of the evolution of the flood disaster.

[0105] ​Secondly, when the basin terrain is more prone to evolution, the vegetation root system is less obvious in inhibiting the phenomenon of surface being washed away by water flow by increasing the soil shear strength, that is, the vegetation soil fixation effect will be weakened. In the area where the vegetation soil fixation ability is weak, the difference between the reflection intensity of the point cloud corresponding to the ground and the point cloud corresponding to the non-ground in the area where the vegetation root is located is larger, and the elevation inconsistency of the point cloud corresponding to the non-ground is more serious. At the same time, affected by the water flow scouring during the waterlogging disaster in the historical period, the angle between the plane where the point cloud corresponding to the non-ground is located and the water flow direction in each local area is smaller, and the distribution of the point cloud corresponding to the non-ground is sparse or uneven. The water flow is more likely to penetrate the vegetation area, and the possibility of vegetation being washed away by water flow during the flood disaster is higher.

[0106] Based on the above analysis, the angle between the plane where each triangular mesh corresponding to the ground in each local area is located and the ground plane is analyzed to evaluate the terrain slope of the local area, specifically:

[0107] The mode of the angle between the normal vector of the plane where all triangular meshes corresponding to the ground in each local area are located and the normal vector of the ground plane is calculated as the first angle of each local area.

[0108] In this embodiment, the normal vector corresponding to the ground plane is (0, 0, 1), and the normal vector is perpendicular to the ground plane. As other embodiments, the implementer can set it according to the actual situation. Secondly, the calculation process of the angle between the normal vectors is a known technology, which will not be repeated here.

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

[0110] Secondly, the angle between the plane where the point cloud corresponding to the non-ground is located and the ground plane is analyzed to reflect the inclination degree of the vegetation canopy, specifically:

[0111] The point cloud corresponding to the non-ground is projected into the plane of the local area to obtain the point cloud corresponding to the non-ground distributed in the local area.

[0112] Triangular meshing is performed on all point clouds corresponding to the non-ground in each local area to obtain triangular meshes corresponding to the non-ground.

[0113] In this embodiment, Delaunay triangulation algorithm is used for meshing processing, wherein the Delaunay triangulation algorithm is a known technology, which will not be repeated here.

[0114] The normal vector of the plane where each triangular mesh corresponding to the non-ground in each local area is located is calculated.

[0115] An area of each triangle mesh corresponding to a non-ground point in each local region is calculated, and a normal vector of a plane on which all triangle meshes corresponding to non-ground points in each local region are located is weightedly averaged with the area as a weight to obtain a local normal vector of each local region;

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

[0117] It should be noted that the larger the area is, the denser the vegetation canopy in the region where the triangle mesh corresponding to the non-ground point is located is, the thicker the branches of the vegetation are, the large-area vegetation structure dominates the overall direction, and the stronger the resistance or guiding effect of the water flow erosion is. Therefore, by evaluating the angle between the normal vector of the plane on which the triangle mesh is located and the ground plane, the inclination degree of the vegetation canopy represented by the point cloud corresponding to the non-ground point in the local region is reflected, so as to determine whether the point cloud is easy to be eroded by the water flow. The larger the second angle is, the more easily the vegetation represented by the point cloud corresponding to the non-ground point in the local region is eroded by the water flow, thereby increasing the risk of dumping.

[0118] Further, by analyzing the difference between the first angle and the second angle in the local region and the proportion of the number of the point cloud corresponding to the non-ground point, a risk value of instability is calculated, specifically:

[0119] A cosine value of the difference between the first angle and the second angle is calculated.

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

[0121] The total number of all point clouds corresponding to non-ground points and all point clouds corresponding to the ground in each local region is counted.

[0122] A ratio of the number of all point clouds corresponding to non-ground points in each local region to the total number is calculated and is recorded as a proportion of the number.

[0123] A ratio of the cosine value to the proportion of the number is taken as a risk value of instability of each local region.

[0124] It should be noted that the greater the cosine value, the smaller the angle between the plane where the vegetation canopy is located and the plane where the local region is located, the greater the cosine value, the smaller the angle between the plane where the non-ground corresponding point cloud is located and the plane where the ground corresponding point cloud is located, reflecting that the direction of vegetation inclination is more consistent with the direction of water flow erosion on the slope of the ground, the water flow maximizes the pushing force on the vegetation, the shear stress on the root of the vegetation is maximized, and the vegetation is easy to be uprooted or broken; the smaller the quantity ratio, the sparser the vegetation in the local region, the lower the resistance to water flow erosion, the greater the instability risk value, indicating that the vegetation represented by the non-ground corresponding point cloud in the local region has a greater risk of collapse, and after the vegetation collapses, the ground surface may be exposed, increasing the possibility of triggering a chain of erosion.

[0125] Further, by analyzing the difference in reflectivity intensity between the ground corresponding point cloud and the non-ground corresponding point cloud where the vegetation root is located, and the inconsistency of the elevation data of the non-ground corresponding point cloud, the hydrological separation degree is calculated to evaluate the state in which the water transport, erosion resistance or mechanical connectivity between the surface cover layer, such as the vegetation root layer, the litter layer and the underlying soil or bedrock, is significantly reduced during the water flow erosion process, specifically:

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

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

[0128] The sum of the relative difference of all point clouds corresponding to the non-ground in each local region is denoted as the local difference amount.

[0129] The dispersion degree of the elevation data of all point clouds corresponding to the non-ground in each local region is calculated and denoted as the second dispersion degree.

[0130] In this embodiment, the dispersion degree is measured by calculating the variance of the elevation data of all point clouds corresponding to the non-ground in each local region. As an alternative, the implementer can use other methods in the prior art, such as standard deviation, etc., which are not specifically limited in this embodiment.

[0131] The product of the local difference amount and the second dispersion degree 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 weaker the soil fixation ability of the vegetation in the area, the more irregular the elevation distribution of the vegetation, the higher the unstable situation of the elevation data of the non-ground corresponding point cloud in the local area, the greater the hydrological separation degree, and the weaker the soil fixation ability of the vegetation in the local area, resulting in more serious soil structure damage.

[0133] Further, based on the instability risk value and the hydrological separation degree, a soil fixation evaluation value is determined, specifically:

[0134] The product of the instability risk value and the hydrological separation degree is taken as the soil fixation evaluation value of each local area.

[0135] It should be noted that the greater the soil fixation evaluation value, the weaker the soil fixation ability of the vegetation in the local area, the more easily the water flow penetrates the vegetation area, and the higher the possibility of vegetation being washed away by the water flow in the flood disaster. The method for obtaining the soil fixation evaluation value provided in the embodiment is shown in the flowchart of steps. Figure 3

[0136] At this point, the soil fixation evaluation value of each local area is obtained.

[0137] Step 4, based on the erosion evaluation value and the soil fixation evaluation value, a topographic evolution factor of each local area is determined, and a digital twin simulation platform of the river basin terrain is built by combining the three-dimensional terrain model to simulate the evolution of the flood disaster.

[0138] When the river basin terrain area is subjected to a higher water flow erosion intensity in the historical period, the water flow erosion resistance is weaker, and the inhibitory effect of the vegetation on the water flow erosion of the ground is lighter, the cascade risk of the vegetation collapse is higher, that is, the erosion evaluation value and the soil fixation evaluation value are greater, and the possibility of the river basin terrain being subjected to the flood water flow erosion to produce topographic evolution is greater. Therefore, based on the erosion evaluation value and the soil fixation evaluation value, a topographic evolution factor is determined, specifically:

[0139] The calculation formula of the topographic evolution factor of the mth local area is:

[0140] Y m = norm(Q m × G m )

[0141] Where Y m is the topographic evolution factor of the mth local area, Q m is the erosion evaluation value of the mth local area, and G m ​The soil fixation evaluation value of the mth local area is obtained by using a normalization function, and in this embodiment, a sigmoid function is used for normalization. The sigmoid function is a known technique and will not be described here. As other embodiments, the implementer can use other methods of existing technology, such as a softmax function, a tanh function, etc. This embodiment does not make special restrictions.

[0142] It should be noted that the greater the topographic evolution factor, the greater the impact of water erosion on the local area in the watershed topography, the weaker the erosion resistance of the watershed topography in the local area, the worse the inhibitory effect of the vegetation root system on the surface being stripped by the flood water flow, and the higher the cascade risk caused by the vegetation dumping.

[0143] Further, based on the topographic evolution factor, a digital twin simulation platform of the watershed topography is built, specifically:

[0144] Based on the three-dimensional topographic model corresponding to the watershed topography and the topographic evolution factor, a digital twin bottom plate is built using digital twin technology.

[0145] It should be noted that the digital twin bottom plate can be updated in real time or periodically, can reflect the dynamic evolution of the topography, and more accurately predict the evolution path and impact range of the flood disaster, thereby improving the accuracy of risk assessment.

[0146] Real-time hydro-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 the three-dimensional topographic model to build a digital twin simulation platform of the watershed topography, and the digital twin simulation platform is used to simulate the evolution of the flood disaster.

[0147] It should be noted that the hydro-meteorological data includes rainfall, soil moisture, watershed water level, and watershed flow velocity, and the engineering scheduling data includes reservoir discharge capacity, etc. The data mapping technology and the construction of the digital twin simulation platform are known techniques and will not be described here.

[0148] The specific simulation process is as follows: by simulating the input of hydro-meteorological data and engineering scheduling data under different flood disaster scenarios, as well as the topographic evolution factor, using hydrological and hydraulic models, combining 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 flow velocity of the flood disaster are rendered in real time through the digital twin simulation platform, realizing the simulation evolution of the flood disaster.

[0149] It should be noted that the hydrological and hydraulic models are known techniques and will not be described here.

[0150] Based on the same inventive concept as the above method, the embodiment of the present application also provides 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, wherein the processor executes the computer program to implement the steps of any one of the above flood disaster evolution methods based on digital twin technology.

[0151] It should be understood that, although Figure 1 The steps in the flowchart of the above embodiment are displayed in sequence according to the arrows, but these steps are not necessarily executed in sequence according to the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, Figure 1 At least part of the steps in the above embodiment can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0152] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0153] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation of the present application. It should be pointed out that, for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solution of the present application, all belong to the protection scope of the technical solution of the present application.

Claims

1. A flood disaster evolution method based on digital twin technology, characterized in that, The method comprises the following steps: Collecting all point cloud data of the river basin terrain, constructing a three-dimensional terrain model, and obtaining the reflection intensity and elevation data of each point cloud, and distinguishing all point clouds on the three-dimensional terrain model into ground corresponding point clouds and non-ground corresponding point clouds; Clustering all ground corresponding point clouds, and recording the area where all ground corresponding point clouds in each cluster are located as each local area; projecting the non-ground corresponding point clouds onto the plane where the local area is located, and obtaining the non-ground corresponding point clouds distributed in the local area; Respectively, griding all ground and non-ground corresponding point clouds in each local area, and respectively obtaining ground and non-ground corresponding triangular meshes; Analyzing 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, calculating the coplanar representation value of each local area, and combining the distribution discrete condition of the ground corresponding point clouds and the average level of the reflection intensity to determine the erosion evaluation value of each local area; Calculating the instability risk value of each local area through the coplanar condition of the plane 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 clouds, and combining the difference condition of the reflection intensity of the corresponding point clouds between the ground and the non-ground, and the discrete condition of the elevation data of the non-ground corresponding point clouds to determine the soil fixation evaluation value of each local area; Based on the erosion evaluation value and the soil fixation evaluation value, the terrain evolution factor of each local area is determined, and a digital twin simulation platform of the river basin terrain is built by combining the three-dimensional terrain model to simulate the evolution of the flood disaster.

2. The flood disaster evolution method based on digital twin technology according to claim 1, characterized in that, All point clouds are divided into ground corresponding point clouds and non-ground corresponding point clouds by using cloth simulation filtering algorithm.

3. The flood disaster evolution method based on digital twin technology according to claim 1, characterized in that, The calculation of the coplanar representation value of each local area comprises: Calculating the normal vector of the plane where each triangular mesh corresponding to the ground is located; calculating the cumulative sum of the similarity degree of the normal vectors of any two triangular meshes corresponding to the ground in each local area; Calculating the discrete degree of the elevation data of all point clouds corresponding to the ground in each local area, which is recorded as the first discrete degree; The coplanar representation value is the ratio of the cumulative sum to the first discrete degree.

4. The flood disaster evolution method based on digital twin technology according to claim 1, wherein, The determination of the erosion evaluation value of each local area comprises: Recording the average distance between any two point clouds corresponding to the ground in each local area as the local discrete degree; Recording the average intensity of the reflection intensity of all point clouds corresponding to the ground in each local area as the average intensity; and taking the ratio of the average intensity to the local discrete degree as the terrain degradation degree of each local area; The erosion evaluation value is the ratio of the terrain degradation degree to the coplanar representation value.

5. The flood disaster evolution method based on digital twin technology according to claim 1, characterized in that, The calculation of the instability risk value of each local area comprises: Calculating the mode of the included angle between the normal vector of the plane where all triangular meshes corresponding to the ground in each local area are located and the normal vector of the ground plane, which is recorded as the first angle; Determining the local normal vector of each local area according to the normal vector of the plane where different triangular meshes corresponding to the non-ground in each local area are located and the area of the triangular mesh; Recording the included angle between the local normal vector and the normal vector of the ground plane as the second angle; calculate a cosine value of a difference between the first angle and the second angle; count a total number of all point clouds corresponding to non-ground and all point clouds corresponding to ground in each local region; and calculate a ratio of the number of all point clouds corresponding to non-ground in each local region to the total number, denoted as a number ratio; the instability risk value is a ratio of the cosine value to the number ratio.

6. The flood disaster evolution method based on digital twin technology according to claim 5, characterized in that, The method further includes: calculating an area of each triangular mesh corresponding to non-ground in each local region; performing a weighted average on normal vectors of planes on which all triangular meshes corresponding to non-ground in each local region are located, to obtain a local normal vector of each local region, with the area as a weight.

7. The flood disaster evolution method based on digital twin technology according to claim 1, characterized in that, The method further includes: calculating a sum value of differences in reflection intensity between each point cloud corresponding to non-ground and all point clouds corresponding to ground in each local region, denoted as a relative difference; and calculating a sum of the relative differences of all point clouds corresponding to non-ground in each local region, denoted as a local difference amount; calculating a second dispersion degree of elevation data of all point clouds corresponding to non-ground in each local region; multiplying the local difference amount and the second dispersion degree as a hydrological separation degree of each local region; the soil stabilization evaluation value is a product of the instability risk value and the hydrological separation degree.

8. The flood disaster evolution method based on digital twin technology according to claim 1, characterized in that, The terrain evolution factor is a normalized result of a product of the erosion evaluation value and the soil stabilization evaluation value.

9. The flood disaster evolution method based on digital twin technology according to claim 1, wherein, The method further includes: building a digital twin simulation platform of a river basin terrain based on the three-dimensional terrain model and the terrain evolution factor; real-time introduction of hydro-meteorological data and engineering scheduling data to form a dynamic data lake, synchronization of the dynamic data lake to the three-dimensional terrain model through data mapping technology, building of the digital twin simulation platform of the river basin terrain, and simulation and evolution of the flood disaster by using the digital twin simulation platform. 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, The processor implements the steps of the flood disaster evolution method based on the digital twin technology according to any one of claims 1-9 when executing the computer program.

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