A method and related apparatus for tsunami simulation and rendering

Abstract

The application discloses a tsunami simulation and rendering method and related equipment, and the method comprises the following steps: acquiring seabed topography data and seismic source data; preprocessing the seabed topography data to obtain water depth data; based on the seismic source data and the water depth data, using a preset numerical model to quantitatively obtain tsunami metadata; based on the image channel corresponding to the tsunami metadata, a tsunami rendering image is obtained through color mapping rendering. The application automatically generates a standard format tsunami metadata image through a preset model, significantly simplifies the process, and improves the analysis efficiency; then, by using the color mapping technology, multi-dimensional information such as tsunami propagation time, dynamic wave height and maximum wave height is rendered into an intuitive and understandable graphical product, which greatly enhances the spatial performance of tsunami dynamics and risk, provides efficient support for disaster warning, emergency planning and public communication, and can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a tsunami simulation and rendering method and related equipment. Background Technology

[0002] Tsunamis, as a highly destructive marine phenomenon triggered by geological disasters such as undersea earthquakes, volcanic eruptions, or landslides, pose a serious threat to human life, property, and coastal infrastructure. Therefore, accurate and rapid simulation and visualization of the occurrence, propagation, and impact range of tsunamis are of vital value for disaster early warning, risk assessment, and emergency decision-making.

[0003] Currently, tsunami simulations primarily rely on complex hydrodynamic numerical models, such as shallow water wave equation models. These models typically require high-performance computing resources to perform high-precision processing and long-term iterative calculations on initial data such as seabed topography and source parameters before outputting data on the spatiotemporal evolution of tsunami waves. Existing technical solutions have several limitations: First, data processing and simulation calculations are often separated, resulting in cumbersome workflows and low efficiency in converting raw data into understandable disaster information. Second, simulation results are usually output in the form of professional numerical files or charts, such as multidimensional arrays and contour maps, whose spatial distribution characteristics are not intuitive enough, making it difficult for non-professionals (such as emergency commanders and the public) to quickly grasp the overall dynamics and key information of the tsunami (such as the estimated arrival time and maximum wave height along the coast). Third, there is a lack of a method that can efficiently integrate and visually represent multidimensional information such as the tsunami propagation process and spatiotemporal intensity, leading to low efficiency in information transmission for decision support. Summary of the Invention

[0004] The main objective of this invention is to provide a tsunami simulation and rendering method, apparatus, electronic device, storage medium, and program product, aiming to solve at least one problem of the prior art.

[0005] To achieve the above objectives, one aspect of this invention proposes a tsunami simulation and rendering method, the method comprising:

[0006] Acquire seabed topography data and seismic source data;

[0007] Preprocessing the seabed topography data yields water depth data;

[0008] Based on seismic source data and water depth data, tsunami metadata is obtained by quantification using a preset numerical model. The tsunami metadata includes tsunami propagation time, hourly tsunami wave height and maximum tsunami wave height, and the tsunami metadata is an image in a preset format.

[0009] Based on the image channels corresponding to the tsunami metadata, a tsunami rendering map is obtained through color mapping rendering; the tsunami rendering map includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map.

[0010] In some embodiments, the seabed topography data uses global seabed topography at a first spatial resolution as the basic data source. Preprocessing the seabed topography data to obtain water depth data includes the following steps:

[0011] Based on the geographical scope of the target area, terrain clipping is performed on the basic data source to obtain preliminary data;

[0012] The preliminary data is resampled to a second spatial resolution using bilinear interpolation and then converted into a preset file format to obtain water depth data.

[0013] In some embodiments, tsunami metadata is obtained by quantization using a preset numerical model based on seismic source data and water depth data, including the following steps:

[0014] Based on the latitude and longitude of the seismic source corresponding to the water depth data and the source data, the tsunami propagation time is obtained by using the diffusion cumulative cost quantification of the tsunami propagation time calculation model.

[0015] Based on the source data and water depth data, hourly tsunami wave height and maximum tsunami wave height were obtained by using a tsunami simulation numerical model.

[0016] In some embodiments, the water depth data includes the original water depth values ​​corresponding to each grid within the target area, and the focal latitude and longitude include the focal longitude and focal latitude. Based on the water depth data and the focal latitude and longitude corresponding to the focal data, the tsunami propagation time is obtained using a tsunami propagation time calculation model with diffusion cumulative cost quantification, including the following steps:

[0017] The original water depth value is negativeed to obtain the positive water depth value corresponding to each grid.

[0018] The square root of the product of the positive water depth and the gravitational acceleration is taken to obtain the tsunami propagation speed of each grid, and the reciprocal of the tsunami propagation speed is used as the grid cost.

[0019] The source column number is determined by dividing the absolute value of the difference between the longitude of the epicenter and the left longitude of the target area by the longitude direction pixel resolution of the grid, and then rounding it down.

[0020] The source row number is determined by dividing the absolute value of the difference between the source latitude and the top latitude of the target area by the pixel resolution of the grid in the latitudinal direction, and then rounding down.

[0021] Based on the source column number and source row number, determine the source grid in the target area and add the source grid to the min-heap list;

[0022] Set the first movement cost of the source grid to 0, and set the first movement cost of the remaining grids to infinity;

[0023] Take an element from the min-heap list as the move center and mark the access status of the move center as visited;

[0024] The second movement cost of the neighborhood grid is obtained by multiplying the mean of the grid costs of the movement center and its neighborhood grid by a distance coefficient.

[0025] The neighborhood grid includes direct neighborhood grids and oblique neighborhood grids. The distance coefficient of the direct neighborhood grid is 1 times the spatial distance of the grid, and the distance coefficient of the oblique neighborhood grid is 1 / 2 times the spatial distance of the grid. Double the spatial distance;

[0026] When the second move cost is less than the first move cost, update the first move cost using the second move cost.

[0027] Add the unvisited area grids to the min-heap list, then return to execute the step of taking an element from the min-heap list as the move center, until all grids in the target region have been traversed;

[0028] The final determined first movement cost for each grid is converted into a preset format image to obtain the tsunami propagation time for the corresponding grid.

[0029] In some embodiments, the water depth data includes water depth values ​​for each grid within the target area. Based on the source data and water depth data, hourly tsunami wave heights and maximum tsunami wave heights are obtained using a tsunami simulation numerical model, including the following steps:

[0030] The source data and water depth data are imported into the Cornell multi-grid coupled tsunami model. Based on the preset model step size configuration, the tsunami wave height corresponding to each grid in the target area is obtained by processing the shallow water wave equation, and then the maximum and minimum wave heights are determined.

[0031] Based on the tsunami wave height, periodic sampling is performed in chronological order to obtain the hourly tsunami wave height;

[0032] Based on the tsunami wave height, the maximum tsunami wave height within a preset period is obtained by iterating and comparing along the time sequence.

[0033] The tsunami wave height output by the Cornell multigrid coupled tsunami model is a preset format image.

[0034] In some embodiments, when the tsunami metadata is the tsunami propagation time, a tsunami rendering image is obtained by color mapping based on the image channels corresponding to the tsunami metadata, including the following steps:

[0035] The target image channels of the tsunami propagation time are analyzed using shaders to obtain the first channel resolution value for each pixel in the target region;

[0036] The first channel's analytical value is normalized to a preset range to obtain the propagation time analytical value;

[0037] Based on the preset initial channel color, configure a color lookup mapping table between the preset range and the rendered color to obtain the gradient color strip;

[0038] The color of each pixel in the gradient color band is determined by the product of the interval ratio corresponding to the pixel and the initial channel color. The interval ratio is determined based on the relative position of the pixel in the gradient color band, and the initial channel color includes the initial colors of multiple channels.

[0039] Based on the propagation time resolution value, the color of each pixel in the target area is filled and rendered using gradient color bands to obtain the tsunami propagation time map of the target area.

[0040] In some embodiments, the tsunami metadata also includes a maximum wave height and a minimum wave height. When the tsunami metadata is an hourly tsunami wave height, a tsunami rendering image is obtained by color mapping based on the image channels corresponding to the tsunami metadata, including the following steps:

[0041] The target image channels of the hourly tsunami wave height are analyzed using shaders to obtain the second channel resolution value for each pixel in the target region;

[0042] Based on the second channel resolution value, the first wave height resolution value is obtained by numerical transformation by combining the maximum and minimum wave height values.

[0043] Based on the first high-resolution value, the first rendering color of each pixel is determined by looking up the mapping table through the preset color range, thus obtaining the hourly tsunami wave height map of the target area.

[0044] The color range lookup mapping table is configured by associating multiple numerical ranges with preset color values ​​based on the range intervals of the maximum and minimum wave height.

[0045] In some embodiments, the tsunami metadata also includes a maximum wave height and a minimum wave height. When the tsunami metadata represents the maximum tsunami wave height, a tsunami rendering image is obtained by color mapping based on the image channel corresponding to the tsunami metadata, including the following steps:

[0046] The target image channel with the maximum tsunami wave height is analyzed using a shader to obtain the third channel resolution value for each pixel in the target region;

[0047] Based on the third channel resolution value, the second wave height resolution value is obtained by combining the maximum and minimum wave height values ​​through numerical transformation.

[0048] Based on the second high-resolution value, the second rendering color of each pixel is determined by looking up the mapping table through the preset color range, thus obtaining the maximum tsunami wave height map of the target area.

[0049] The color range lookup mapping table is configured by associating multiple numerical ranges with preset color values ​​based on the range intervals of the maximum and minimum wave height.

[0050] To achieve the above objectives, another aspect of the present invention provides a tsunami simulation and rendering apparatus, the apparatus comprising:

[0051] The first module is used to acquire seabed topographic data and seismic source data;

[0052] The second module is used to preprocess seabed topography data to obtain water depth data;

[0053] The third module is used to quantify tsunami metadata based on seismic source data and water depth data using a preset numerical model. The tsunami metadata includes tsunami propagation time, hourly tsunami wave height and maximum tsunami wave height, and the tsunami metadata is a preset format image.

[0054] The fourth module is used to obtain a tsunami rendering map by color mapping based on the image channels corresponding to the tsunami metadata. The tsunami rendering map includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map.

[0055] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0056] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0057] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0058] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a tsunami simulation and rendering method, apparatus, electronic device, storage medium, and program product. This solution acquires seabed topographic data and seismic source data; preprocesses the seabed topographic data to obtain water depth data; and quantizes tsunami metadata based on the seismic source data and water depth data using a preset numerical model. The tsunami metadata includes tsunami propagation time, hourly tsunami wave height, and maximum tsunami wave height, and the tsunami metadata is a preset format image. Based on the image channels corresponding to the tsunami metadata, a tsunami rendering image is obtained through color mapping rendering. The tsunami rendering image includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map. This invention combines numerical simulation with image processing technology to achieve a direct and efficient conversion from raw data to decision-making information. Specifically, this invention automatically generates standard-format tsunami metadata images through a preset model, significantly simplifying the process and improving analysis efficiency. Furthermore, by utilizing color mapping technology, multi-dimensional information such as tsunami propagation time, dynamic wave height, and maximum wave height is rendered into intuitive and understandable graphical products, greatly enhancing the spatial representation of tsunami dynamics and risks, and providing efficient support for disaster early warning, emergency planning, and public communication. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of an implementation environment for the tsunami simulation and rendering method provided in this embodiment of the invention;

[0060] Figure 2 This is a flowchart illustrating a tsunami simulation and rendering method provided in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of the unfolding process of step S200 provided in the embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram illustrating the overall application process of the tsunami simulation and rendering method provided in this embodiment of the invention;

[0063] Figure 5 This is a schematic diagram of the structure of a tsunami simulation and rendering device provided in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0066] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0067] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0068] 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 this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0069] In related technologies, how to overcome the shortcomings of existing technologies such as lengthy processes, unintuitive results expression, and insufficient integration of multi-dimensional information, and provide a more efficient and intuitive tsunami simulation analysis and result presentation solution, has become an urgent problem to be solved.

[0070] In view of this, this embodiment of the invention provides a tsunami simulation and rendering method and related equipment. This scheme acquires seabed topography data and seismic source data; preprocesses the seabed topography data to obtain water depth data; and quantifies tsunami metadata based on the seismic source data and water depth data using a preset numerical model. The tsunami metadata includes tsunami propagation time, hourly tsunami wave height, and maximum tsunami wave height, and the tsunami metadata is a preset format image. Based on the image channels corresponding to the tsunami metadata, a tsunami rendering image is obtained through color mapping. The tsunami rendering image includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map. This invention combines numerical simulation with image processing technology to achieve a direct and efficient conversion from raw data to decision-making information. Specifically, this invention automatically generates standard-format tsunami metadata images through a preset model, significantly simplifying the process and improving analysis efficiency. Furthermore, by utilizing color mapping technology, multi-dimensional information such as tsunami propagation time, dynamic wave height, and maximum wave height is rendered into intuitive and understandable graphical products, greatly enhancing the spatial representation of tsunami dynamics and risks, and providing efficient support for disaster early warning, emergency planning, and public communication.

[0071] It is understood that the tsunami simulation and rendering method provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0072] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0073] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0074] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0075] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0076] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a tsunami simulation and rendering method. The following description uses the application of this tsunami simulation and rendering method in server 101 as an example. It can be understood that the tsunami simulation and rendering method can also be applied to terminal 102.

[0077] Reference Figure 2 , Figure 2 This is an optional flowchart of the tsunami simulation and rendering method provided in the embodiments of the present invention. The execution subject of the tsunami simulation and rendering method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S400.

[0078] Step S100: Obtain seabed topography data and seismic source data;

[0079] For example, in some specific implementations, seabed topography in TIFF format with the highest global 15 arcsecond spatial resolution (approximately 416 meters / pixel) from Gebco can be downloaded as the basic data source for data processing. In addition, seismic source data for the corresponding region can be acquired simultaneously (e.g., through the Global Seismic Data Center and Database).

[0080] Step S200: Preprocess the seabed topography data to obtain water depth data;

[0081] It should be noted that the seafloor topography data uses global seafloor topography at the first spatial resolution as the basic data source. In some embodiments, such as... Figure 3 As shown, step S200 may include the following steps: S210, based on the geographical range of the target area, perform terrain clipping on the basic data source to obtain preliminary data; S220, resample the preliminary data to the second spatial resolution using bilinear interpolation, and then convert it into a preset file format to obtain water depth data.

[0082] For example, in some specific implementations, taking seabed topography data using Gebco's highest global 15 arcsecond spatial resolution (approximately 416 meters / pixel) TIFF format as an example, the topography can first be cropped according to a preset range, such as 100°E to 100°W and 60°S to 65°N. This range covers the entire Pacific Ocean and some countries and regions along its east and west coasts. The topography grid size is 2401×1501 (columns × rows). The file generated in this step is called pacific_dem5arcmin.tif. Then, pacific_dem5arcmin.tif is subjected to bilinear interpolation to balance accuracy and efficiency, resampled and downscaled to a 5 arcminute spatial resolution (approximately 8 km / pixel), and converted to .xyz format. The generated pacific_dem5arcmin.xyz is used as the water depth data.

[0083] The resampling principle of bilinear interpolation is as follows:

[0084] The interpolation point's pixel value is weighted according to its distance from the four nearest measured pixels. This means the interpolation point's pixel value contributes to the coefficients based on four different weights, as shown in the formula: z = f(x, y) = a0 + a1x + a2y + a3xy. By substituting the x and y coordinates (x1 to x4 and y1 to y4) and elevation z of the four points into the formula, a system of four equations with four unknowns is formed, yielding the coefficients a0, a1, a2, and a3. This system of equations can be written in matrix form as follows:

[0085]

[0086] Step S300: Based on the source data and water depth data, the tsunami metadata is obtained by quantification using a preset numerical model;

[0087] The tsunami metadata includes tsunami propagation time, hourly tsunami wave height and maximum tsunami wave height, and the tsunami metadata is a preset format image;

[0088] It should be noted that in some embodiments, step S300 may include the following steps: S310, based on the latitude and longitude of the source corresponding to the water depth data and the source data, the tsunami propagation time is obtained by using the diffusion cumulative cost quantification using the tsunami propagation time calculation model; S320, based on the source data and water depth data, the hourly tsunami wave height and the maximum tsunami wave height are obtained by using the tsunami simulation numerical model.

[0089] For example, in some specific implementations, the latitude and longitude coordinates of the epicenter and the pacific_dem5arcmin.tif file can be input into a preset tsunami propagation time calculation model for data logic processing. Then, the information such as the series of image addresses, maximum and minimum values, time, latitude and longitude range, and image width and height can be stored in JSON and sent to the front end for calling. In addition, water depth data (such as pacific_dem5arcmin.xyz) can be placed in the Comcot (Cornell Multi-grid Coupled Tsunami Model) model reading path, and the contents of the Comcot.ctl file can be edited to modify parameters such as epicenter coordinates, depth, fault length and width, strike, dip angle, slip angle, displacement, the path of the first layer grid water depth topography file, and the simulated latitude and longitude range to run the model to generate hourly tsunami wave height and maximum tsunami wave height.

[0090] It should be noted that the water depth data includes the original water depth value corresponding to each grid in the target area, and the source latitude and longitude include the source longitude and source latitude. In some embodiments, step S310 may include the following steps: negating the original water depth value to obtain a positive water depth value corresponding to each grid; taking the square root of the product of the positive water depth value and the gravitational acceleration to obtain the tsunami propagation speed of each grid, and using the reciprocal of the tsunami propagation speed as the grid cost; determining the source column number by dividing the absolute value of the difference between the source longitude and the left longitude of the target area by the grid's longitude direction pixel resolution, and then rounding the result; dividing the absolute value of the difference between the source latitude and the top latitude of the target area by the grid's latitude... The latitudinal pixel resolution is determined, and the source row number is determined by rounding. Based on the source column number and source row number, a source grid is determined in the target area, and the source grid is added to a min-heap list. The first movement cost of the source grid is set to 0, and the first movement cost of the remaining grids is set to infinity. An element is taken from the min-heap list as the movement center, and its access status is marked as visited. The average grid cost of the movement center and its neighboring grids is multiplied by a distance coefficient to obtain the second movement cost of the neighboring grid. The neighboring grid includes direct neighboring grids and oblique neighboring grids. The distance coefficient of the direct neighboring grid is 1 times the spatial distance of the grid, and the distance coefficient of the oblique neighboring grid is 1 / 2 times the spatial distance of the grid. Double the spatial distance; when the second movement cost is less than the first movement cost, update the first movement cost using the second movement cost; add the unvisited area grids to the min-heap list, return to execute the step of taking an element from the min-heap list as the movement center, until all grids in the target area have been traversed; convert the final determined first movement cost of each grid into a preset format image to obtain the tsunami propagation time of the corresponding grid.

[0091] For example, in some specific implementations, the principle of the tsunami propagation time calculation model is as follows: simulating the shortest cumulative distance from the earthquake source point to all grids as it gradually spreads from the origin. The greater the water depth, the faster the tsunami wave propagates. The calculation start point is determined by the row and column numbers of the water depth terrain corresponding to the earthquake source point. Eight neighborhood pixels around it are taken, and the cumulative distance is calculated based on the distance in the horizontal, vertical, and diagonal directions of the pixels. By comparing the cumulative cost of all possible paths, the minimum value of moving the current point to the next point is added to the min-heap. The top of the min-heap (i.e., the minimum cumulative cost) is popped and updated with the minimum cumulative cost path of the current step and the center point of the next determined eight neighborhood pixels. The above operation is iterated until there are no elements in the min-heap and all pixels have been calculated. Finally, the time accumulation of the earthquake tsunami wave propagation process through all pixels is obtained.

[0092] The specific calculation steps can be implemented as follows:

[0093] 1. Calculate the reciprocal of the tsunami propagation speed for each grid cell: speed_inverse =

[0094] (g is the acceleration due to gravity. Since the initial water depth is negative, ...) To include the grid of negative terrain in the calculation, a positive water depth h is obtained. The reason for taking the reciprocal is that the higher the velocity, the smaller the cell value. The next step is to calculate and compare the minimum value of the neighboring cells to update the cumulative value.

[0095] 2. Calculation of the source row and column number:

[0096] Column number (col_num) = [ |(Earthquake focal longitude - water depth, topography, left longitude) | / longitude direction pixel size]

[0097] Row number (row_num) = [ |(source latitude - water depth top latitude)| / pixel size in the latitude direction]

[0098] Where: "[expression]" is for rounding, and "|expression|" is for taking the absolute value. Since the starting point of latitude and longitude is the bottom left corner, while the starting point of row and column numbers is the top left corner, the latitude of the top side is taken. The pixel unit is expressed in degrees (°).

[0099] 3. Cumulative cost distance algorithm and cumulative cost calculation:

[0100] The algorithm program, developed using the Python gdal library, reads the input source row and column numbers and water depth / topography in TIFF format. It then calculates the cumulative time grid of tsunami propagation based on the principle of the tsunami propagation time calculation model.

[0101] The speed of tsunami waves is determined by the water depth; the deeper the water, the faster the wave propagates, and the shallower the water, the slower the wave propagates.

[0102] Cost definition: The time dt for the tsunami to propagate through each water depth grid, and the velocity dv = If the distance in the grid space is equal in the x and y directions, and the distance in the grid space is represented by dx, then time is equal to distance divided by velocity, i.e., dt = dx / dv.

[0103] Motion cost definition: This refers to the time cost of moving from one pixel to another. Motion cost = (Current grid cost + Neighboring grid cost) / 2 × Distance coefficient. The distance coefficient for the horizontal and vertical directions of the neighboring pixel cost is 1, and the diagonal distance coefficient is √2. The costs for the top, left, right, and bottom of the 8 pixels in the center point's neighborhood are respectively... ×dx, the costs for the top left, top right, bottom left, and bottom right are respectively .

[0104] Supplementary explanation of the calculation process:

[0105] 1. First, calculate the reciprocal of the water depth propagation velocity as the grid cost, which serves as the basis for calculating the movement cost.

[0106] 2. The initial value of the moving cost grid is infinity.

[0107] 3. Convert the latitude and longitude coordinates of the epicenter into grid row and column numbers. The pixel at this location serves as the source point for the initial movement cost calculation, with a movement cost of 0 for the source point. Begin calculating the movement cost of pixels surrounding the source point, comparing the values ​​of the movement cost grid corresponding to neighboring pixels. If the value is less, update the value of the movement cost grid. Finally, add the row and column numbers and the calculation results to a min-heap list, marking the source point as visited (a min-heap list is a data structure sorted in ascending order).

[0108] 4. If the min-heap list is not empty, the loop continues. A new element is popped from the min-heap list, which is used as the center for calculating the current movement cost. The movement cost from the current center to the surrounding pixels is calculated plus the movement cost of the previous moving pixel. This is then compared with the value of the corresponding movement cost grid. If it is less, it is updated; otherwise, it is skipped. Finally, the row and column numbers of the cells that were updated and the calculation results are added to the min-heap list, and the current point is marked as visited.

[0109] 5. Through the iteration of step 4, until the contents of the min-heap list are popped and cleared and the loop is exited, the moving cost grid will be updated to obtain a cumulative moving cost grid from the source point to all cell points.

[0110] It should be noted that the water depth data includes the water depth value of each grid in the target area. In some embodiments, step S320 may include the following steps: importing the source data and water depth data into the Cornell multi-grid coupled tsunami model; processing the shallow water wave equation based on the preset model step size configuration to obtain the tsunami wave height corresponding to each grid in the target area, and then determining the maximum and minimum wave heights; performing periodic sampling along the time sequence based on the tsunami wave height to obtain the hourly tsunami wave height; and performing traversal comparison along the time sequence based on the tsunami wave height to obtain the maximum tsunami wave height within the preset period. The tsunami wave height output by the Cornell multi-grid coupled tsunami model is a preset format image.

[0111] For example, in some specific implementations, the principle of the tsunami numerical simulation model is as follows:

[0112] The Comcot (Cornell Multi-grid Coupled Tsunami Model) is a multi-grid nested finite difference numerical model developed by Cornell University. It simulates the propagation of tsunami waves triggered by earthquakes, submarine landslides, and other events, and can predict the inundation wave height and extent in coastal areas. It supports selecting multiple nested subgrids in spherical or Cartesian coordinate systems and choosing linear or nonlinear shallow water wave equations for calculation. The following is a brief introduction.

[0113] 1. Governing equations:

[0114] Linear shallow water wave equations in spherical coordinates:

[0115]

[0116] in It is water surface displacement. and Let represent the Earth's latitude and longitude, R represent the Earth's radius, t represent the simulation time, P and Q represent the horizontal and vertical volume flux (the product of water depth and gravitational acceleration), g is the gravitational acceleration, h is the water depth, and f is the Coriolis force coefficient caused by the Earth's rotation, f= ( (This refers to the Earth's rotation rate) This is the effect of seabed landslides.

[0117] Nonlinear shallow water wave equations in spherical coordinates:

[0118]

[0119] in, It is water surface displacement. and Let represent the Earth's latitude and longitude, R represent the Earth's radius, t represent the simulation time, P and Q represent the horizontal and vertical volume flux (the product of water depth and gravitational acceleration), g is the gravitational acceleration, h is the water depth, and f is the Coriolis force coefficient caused by the Earth's rotation, f= ( (where H is the Earth's rotational speed), and H is the total water depth, H = h + , For the effect of seabed landslides, Fx and Fy represent bottom friction, which can be calculated using the Manning formula:

[0120]

[0121] Where n is the Manning coefficient.

[0122] Linear shallow water wave equations in Cartesian coordinates:

[0123]

[0124] in This represents water surface displacement. In Cartesian coordinates, x represents the horizontal coordinate of the water depth grid, y represents the vertical coordinate of the water depth grid, t represents the simulation time, P and Q represent the volume flux (the product of water depth and gravitational acceleration) in the horizontal and vertical directions, respectively, g is the gravitational acceleration, and h is the water depth. This is the effect of seabed landslides.

[0125] Nonlinear shallow water wave equations in Cartesian coordinates:

[0126]

[0127] in This represents water surface displacement. In Cartesian coordinates, x represents the horizontal coordinate of the water depth grid, y represents the vertical coordinate of the water depth grid, t represents the simulation time, P and Q represent the volume flux (the product of water depth and gravitational acceleration) in the horizontal and vertical directions, respectively, R represents the Earth's radius, g is the gravitational acceleration, h is the water depth, and f is the Coriolis force coefficient caused by the Earth's rotation, f = ( (where H is the Earth's rotational speed), and H is the total water depth, H = h + , For the effect of seabed landslides, Fx and Fy represent bottom friction, which can be calculated using the Manning formula:

[0128]

[0129] Where n is the Manning coefficient.

[0130] The following CFL conditions must be met when setting the model step size:

[0131] For grid size, The maximum static water depth is set. If a step size exceeding the maximum depth is set, the model will automatically modify to the maximum allowed time step size.

[0132] Multi-level mesh nesting:

[0133] Comcot allows for nested calculations using multiple mesh levels. For ocean-scale tsunami wave propagation, a lower-resolution water depth topography mesh can be used, while a higher-resolution mesh can be employed for calculations focusing on localized details. For example, in the case of a tsunami triggered by a large earthquake in the western Pacific volcanic and seismic belt, the tsunami waves travel a significant distance and time to reach the eastern coast. Therefore, a lower-resolution water depth mesh can be selected. However, for calculations focusing on the extent of tsunami inundation along the coast, a high-resolution water depth topography mesh can be used, balancing computational efficiency with the accuracy of predicting the tsunami inundation area.

[0134] Parameter control file:

[0135] Comcot.ctl is the entry file for controlling various parameters of the simulated tsunami scenario, such as: source coordinates (latitude and longitude), depth, fault length and width, strike, dip angle, slip angle, displacement, path of the first-layer grid water depth topography file, and the simulated latitude and longitude range.

[0136] Step S400: Based on the image channels corresponding to the tsunami metadata, a tsunami rendering image is obtained through color mapping rendering;

[0137] The tsunami rendering includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map.

[0138] It should be noted that in some embodiments, when the tsunami metadata is the tsunami propagation time, step S400 may include the following steps: using a shader to parse the target image channel of the tsunami propagation time to obtain the first channel resolution value corresponding to each pixel in the target area; normalizing the first channel resolution value to a preset interval range to obtain the propagation time resolution value; configuring a color lookup mapping table between the preset interval range and the rendering color based on the preset initial channel color to obtain a gradient color band; wherein, the color of each pixel in the gradient color band is determined by the product of the interval ratio corresponding to the pixel and the initial channel color, the interval ratio is determined based on the relative position of the pixel in the gradient color band, and the initial channel color includes the initial colors of multiple channels; based on the propagation time resolution value, color filling rendering of each pixel in the target area is performed through the gradient color band to obtain the tsunami propagation time map of the target area.

[0139] For example, in some specific implementations, the tsunami wave propagation time map rendering method can be implemented as follows:

[0140] 1. Original high-precision numerical restoration: The GPU parses (and the WebGL shader parses and calculates the texture) the r and g channels of the PNG image, calculates the 16-bit value, and then divides it by 65535.0. The calculation formula is: highPrecisionValue = (rValue × 256.0 + gValue) / 65535.0 (r and g channels are in the range of 0-255, and highPrecisionValue is between 0 and 1).

[0141] 2. Use highPrecisionValue as texture coordinates to sample a custom gradient color map (i.e., a color lookup map) created in Python to obtain the corresponding rendering color.

[0142] The custom gradient color strip is a function that generates a one-dimensional texture. The generation method can be implemented as follows:

[0143] 1. Define a list of relative positions (between 0 and 1) and a list of corresponding RGB / hexadecimal colors to control the relative positions in the gradient color strip.

[0144] 2. For each pixel coordinate x of the gradient color band, from 0 to width (color band width) - 1, calculate the relative position of x in the overall width pos = x / (width - 1). Traverse the list in 1 to find the coordinate intervals of the two endpoints of the relative position of the current pos in the list (positions[i], positions[i+1]) and the color interval (colors[i], colors[i+1]).

[0145] 3. Calculate the precise proportion for the defined color range: precision_pos = (pos-positions[i]) / (positions[i+1]- positions[i]).

[0146] 4. Color Interpolation Calculation. Determine the RGB value for each relative position within the interval: r = int(colors[i][0]×1.01 × (1 - precision_pos) + colors[i + 1][0]×1.01 × precision_pos), g = int(colors[i][1]×1.01 × (1 - precision_pos) + colors[i + 1][1]×1.01 × precision_pos), b = int(colors[i][2]×1.01 × (1 - precision_pos) + colors[i + 1][2]××1.01 × segment_pos). int(...) is the floor function, where colors[i][0], colors[i][1], and colors[i][2] are the initial colors of the r, g, and b channels, respectively.

[0147] 5. Fill the gradient color strip with the rbg calculated in step 4 according to the coordinates (w, h), where w is between 0 and width-1 and h is between 0 and height-1.

[0148] For example, in the WebGL shader, the highPrecisionValue between (0-1) is obtained by parsing and calculating the tsunami propagation time map. Then, the color obtained by sampling the one-dimensional gradient color strip texture with highPrecisionValue as the horizontal coordinate of the texture is used as the final rendering color.

[0149] It should be noted that in some embodiments, the tsunami metadata also includes a maximum wave height and a minimum wave height. When the tsunami metadata is an hourly tsunami wave height, step S400 may include the following steps: using a shader to parse the target image channel of the hourly tsunami wave height to obtain a second channel resolution value for each pixel in the target area; based on the second channel resolution value, combining the maximum and minimum wave heights to obtain a first wave height resolution value through numerical conversion; based on the first wave height resolution value, determining the first rendering color of each pixel through a preset color range lookup mapping table to obtain an hourly tsunami wave height map of the target area; wherein, the color range lookup mapping table is configured based on multiple numerical ranges associated with preset color values, divided by the range intervals of the maximum and minimum wave heights.

[0150] For example, in some specific implementations, the method for rendering tsunami wave height animation (i.e., hourly tsunami wave height map) every hour can be implemented as follows:

[0151] 1. Original high-precision numerical restoration: Calculate the 16-bit values ​​in the r and g channels of the GPU-parsed PNG image and then divide by 65535.0. The calculation formula is: highPrecisionValue = (rValue × 256.0 + gValue) / 65535.0 × (max_height - min_height) + (min_height) (where r and g channels are in the range of 0-255).

[0152] 2. Use the maximum and minimum wave height values ​​to define a color range lookup table, and then restore the actual values ​​to find the corresponding range of colors for rendering.

[0153] 3. By dragging or playing the timeline, you can switch between hourly wave height charts for analysis and rendering, thus displaying the tsunami propagation animation. A custom color range lookup map can be used to achieve the following (see Table 1 for illustrative purposes only):

[0154] Table 1

[0155]

[0156] It should be noted that in some embodiments, the tsunami metadata also includes a maximum wave height and a minimum wave height. When the tsunami metadata is the maximum tsunami wave height, step S400 may include the following steps: using a shader to parse the target image channel of the maximum tsunami wave height to obtain the third channel resolution value corresponding to each pixel in the target area; based on the third channel resolution value, combining the maximum and minimum wave heights to obtain a second wave height resolution value through numerical transformation; based on the second wave height resolution value, determining the second rendering color of each pixel through a preset color range lookup mapping table to obtain the maximum tsunami wave height map of the target area; wherein, the color range lookup mapping table is configured based on multiple numerical ranges associated with preset color values, divided by the range intervals of the maximum and minimum wave heights.

[0157] For example, in some specific implementations, the maximum tsunami wave height map rendering method can be implemented as follows:

[0158] 1. Original high-precision numerical restoration: In the GPU parsing of the PNG image's r and g channels, calculate the 16-bit value and then divide it by 65535.0. The calculation formula is: highPrecisionValue=(rValue×256.0+gValue) / 65535.0×(max_height - min_height)+(min_height) (r and g channels are both in the range of 0-255), where rValue is the R channel parsing value, gValue is the G channel parsing value, max_height is the maximum wave height, and min_height is the minimum wave height.

[0159] 2. Use the maximum and minimum wave height values ​​to look up the color range in the mapping table. For example, if the maximum value is 8.1 and the minimum value is 0, based on the statistical analysis of the array value range, 90% of the pixel wave height values ​​fall between 0 and 1 meter. Therefore, a smaller range between 0 and 1 meter and a slightly larger range for values ​​greater than 1 meter will result in better rendering. The corresponding color range is then found and rendered into the image by restoring the actual values.

[0160] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0161] To address the shortcomings of existing technologies, such as Figure 4 As shown in the figure, this invention provides a tsunami simulation and rendering method. In some specific application scenarios, the data processing flow of this invention can be implemented as follows:

[0162] 1.1 Seafloor Topography Preprocessing: The highest global 15 arcsecond spatial resolution (approximately 416 meters / pixel) TIFF format seafloor topography data from Gebco was downloaded as the basic data source for data processing. The topography was cropped according to the range of 100°E to 100°W and 60°S to 65°N, covering the entire Pacific Ocean and parts of countries and regions along its east and west coasts. The topography grid size was 2401×1501 (columns×rows). The file generated in this step was named pacific_dem5arcmin.tif.

[0163] 1.2 Generation of water depth topography format: The pacific_dem5arcmin.tif file is bilinearly interpolated to balance accuracy and efficiency, and then resampled and downscaled to a spatial resolution of 5 arcminutes (approximately 8 km / pixel) to be converted into the .xyz format. The generated pacific_dem5arcmin.xyz file is used as the input for Comcot's water depth topography, taking into account the computational efficiency of Comcot's large-scale ocean propagation of tsunami waves.

[0164] 1.3 Generation of raw tsunami wave height maps and maximum tsunami wave height data every 1 hour: Place pacific_dem5arcmin.xyz in the Comcot model reading path, edit the contents of the Comcot.ctl file, modify parameters such as source coordinates, depth, fault length and width, strike, dip angle, slip angle, displacement, path of the first layer grid water depth topography file, and the simulated latitude and longitude range, and run the model to generate z_01_xxxx.dat and zmax_layer01.dat (xxxx represents the step time).

[0165] 1.4 Parsing the raw hourly tsunami wave height map data into PNG: The DAT text for all time periods is parsed using Python NumPy. The maximum and minimum values ​​of all files are calculated and normalized to 0-1. To restore higher-precision data during GPU parsing, the normalized array is mapped to a 16-bit range of 0-65535. The R channel of the PNG image stores the 16-bit array divided by 256, and the G channel stores the 16-bit array modulo 256. Finally, the image is stored in JSON format, with hourly time units, and includes information such as image address, maximum and minimum values, time, latitude and longitude range, and image width and height. The specific structure is described in Table 2 below.

[0166] Table 2

[0167]

[0168] 1.5 Parsing the original maximum tsunami wave height data into PNG: Using Python NumPy to parse the DAT text, the maximum and minimum values ​​are calculated to normalize it to 0-1. To restore higher precision data during GPU parsing, the normalized array is mapped to a 16-bit range of 0-65535. The R channel of the PNG image stores the 16-bit array divided by 256, and the G channel stores the 16-bit array modulo 256. The image is stored in JSON format, containing information such as the series of image addresses, maximum and minimum values, time, latitude and longitude range, and image width and height, which is then passed to the front-end for processing. The specific structure is described in Table 3 below.

[0169] Table 3

[0170]

[0171] 1.6 Tsunami Propagation Time Map Generation: The source coordinates (latitude and longitude) and the pacific_dem5arcmin.tif file are input into the developed tsunami propagation time calculation model. The model stores a series of image addresses, maximum and minimum values, time, latitude and longitude ranges, and image width and height information in JSON format, which is then sent to the front-end for processing. The specific structure is described in Table 4 below:

[0172] Table 4

[0173]

[0174] In summary, to address the shortcomings of existing technologies, this invention provides a tsunami simulation and rendering method that can significantly improve the efficiency and interpretability of tsunami disaster analysis and assessment. Specific beneficial effects include:

[0175] Improving data processing and information extraction efficiency: By uniformly preprocessing seabed topographic data and directly combining it with seismic source parameters, and using a pre-defined optimized numerical model for quantitative calculation, standard-format tsunami metadata images are automatically generated. This method standardizes and automates the complex simulation process, avoiding frequent data format conversions and manual post-processing in traditional methods. It significantly shortens the time from raw data to the output of key disaster information, improving the overall efficiency and repeatability of the process.

[0176] Achieving integrated and intuitive visualization of multidimensional disaster information: By defining core physical quantities such as tsunami propagation time, hourly wave height, and maximum wave height as images with a preset format (i.e., "tsunami metadata"), structured encapsulation of spatiotemporal multidimensional data on a unified spatial grid is achieved. Furthermore, based on image channels, color mapping technology is used for rendering, directly generating tsunami propagation time maps, hourly tsunami wave height maps, and maximum tsunami wave height maps. This method of converting data into images and rendering them allows the dynamic propagation process, spatiotemporal distribution of intensity, and final impact range of the tsunami to be clearly presented, greatly enhancing the intuitiveness and readability of the results.

[0177] Strengthening decision support and public communication capabilities: The generated tsunami renderings are not only scientific analysis tools but also efficient communication mediums. Tsunami propagation timelines clearly indicate warning times and sequences; hourly wave height maps dynamically display disaster evolution; and maximum wave height maps can be directly used for risk assessment and delineation of key disaster prevention areas. This intuitive graphical output is particularly beneficial for emergency management departments to quickly develop evacuation plans and also facilitates the dissemination of easily understandable warning information to the public, thereby improving overall disaster response effectiveness.

[0178] like Figure 5 As shown, this embodiment of the invention also provides a tsunami simulation and rendering device 900, which can implement the above-described method. This device may include:

[0179] The first module, 910, is used to acquire seabed topographic data and seismic source data.

[0180] The second module 920 is used to preprocess the seabed topography data to obtain water depth data;

[0181] The third module 930 is used to quantify tsunami metadata based on seismic source data and water depth data using a preset numerical model. The tsunami metadata includes tsunami propagation time, hourly tsunami wave height and maximum tsunami wave height, and the tsunami metadata is a preset format image.

[0182] The fourth module 940 is used to obtain a tsunami rendering map by color mapping based on the image channels corresponding to the tsunami metadata; the tsunami rendering map includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map.

[0183] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0184] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0185] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0186] like Figure 6 As shown, Figure 6 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes:

[0187] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0188] The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001.

[0189] Input / output interface 1003 is used to implement information input and output;

[0190] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0191] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0192] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0193] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0195] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0196] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0197] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0198] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0199] The tsunami simulation and rendering method, apparatus, electronic device, storage medium, and program product provided in this invention acquire seabed topographic data and seismic source data; preprocess the seabed topographic data to obtain water depth data; and quantize tsunami metadata based on the seismic source data and water depth data using a preset numerical model. The tsunami metadata includes tsunami propagation time, hourly tsunami wave height, and maximum tsunami wave height, and is a preset format image. Based on the image channels corresponding to the tsunami metadata, a tsunami rendering image is obtained through color mapping. The tsunami rendering image includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map. This invention combines numerical simulation with image processing technology to achieve a direct and efficient conversion from raw data to decision-making information. Specifically, this invention automatically generates standard-format tsunami metadata images through a preset model, significantly simplifying the process and improving analysis efficiency. Furthermore, by utilizing color mapping technology, multi-dimensional information such as tsunami propagation time, dynamic wave height, and maximum wave height is rendered into intuitive and understandable graphical products, greatly enhancing the spatial representation of tsunami dynamics and risks, and providing efficient support for disaster early warning, emergency planning, and public communication.

[0200] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0201] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0202] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0204] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for simulating and rendering tsunamis, characterized in that, The method includes the following steps: Acquire seabed topography data and seismic source data; The seabed topography data is preprocessed to obtain water depth data; Based on the earthquake source data and the water depth data, tsunami metadata is obtained by quantification using a preset numerical model; wherein, the tsunami metadata includes tsunami propagation time, hourly tsunami wave height and maximum tsunami wave height, and the tsunami metadata is an image in a preset format; Based on the image channels corresponding to the tsunami metadata, a tsunami rendering image is obtained through color mapping rendering; wherein, the tsunami rendering image includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map; The step of quantifying tsunami metadata based on the seismic source data and the water depth data using a preset numerical model includes the following steps: Based on the latitude and longitude of the earthquake source corresponding to the water depth data and the earthquake source data, the tsunami propagation time is obtained by using the diffusion cumulative cost quantification using the tsunami propagation time calculation model. Based on the earthquake source data and the water depth data, the hourly tsunami wave height and the maximum tsunami wave height are obtained by processing with a tsunami simulation numerical model. The water depth data includes the water depth value of each grid within the target area. The step of obtaining the hourly tsunami wave height and the maximum tsunami wave height based on the seismic source data and the water depth data using a tsunami simulation numerical model includes the following steps: The source data and water depth data are imported into the Cornell multigrid coupled tsunami model. Based on the preset model step size configuration, the tsunami wave height corresponding to each grid in the target area is obtained by processing the shallow water wave equation, and then the maximum and minimum wave heights are determined. Based on the tsunami wave height, periodic sampling is performed in chronological order to obtain the hourly tsunami wave height; Based on the tsunami wave height, the maximum tsunami wave height within a preset period is obtained by traversing and comparing in chronological order. The tsunami wave height output by the Cornell multigrid coupled tsunami model is the preset format image.

2. The method according to claim 1, characterized in that, The seabed topography data uses global seabed topography at a first spatial resolution as the basic data source. The preprocessing of the seabed topography data to obtain water depth data includes the following steps: Based on the geographical scope of the target area, the basic data source is cropped to obtain preliminary data. The preliminary data is resampled to a second spatial resolution using bilinear interpolation and then converted into a preset file format to obtain the water depth data.

3. The method according to claim 1, characterized in that, The water depth data includes the original water depth value corresponding to each grid within the target area. The focal latitude and longitude include focal longitude and focal latitude. Based on the water depth data and the focal latitude and longitude corresponding to the focal data, the tsunami propagation time is obtained using a tsunami propagation time calculation model with diffusion cumulative cost quantification, including the following steps: The original water depth value is negativeed to obtain a positive water depth value corresponding to each grid. The square root of the product of the positive water depth and the gravitational acceleration is taken to obtain the tsunami propagation speed of each grid, and the reciprocal of the tsunami propagation speed is used as the grid cost. The source column number is determined by dividing the absolute value of the difference between the longitude of the seismic source and the left longitude of the target area by the longitude direction pixel resolution of the grid, and then rounding up. The source row number is determined by dividing the absolute value of the difference between the latitude of the seismic source and the top latitude of the target area by the pixel resolution of the latitudinal direction of the grid, and then rounding down. Based on the source column number and the source row number, a source grid is determined in the target area, and the source grid is added to a min-heap list; Set the first movement cost of the source grid to 0, and set the first movement cost of the remaining grids to infinity; Take an element from the min-heap list as the move center, and mark the access status of the move center as visited. The second movement cost of the neighborhood grid is obtained by multiplying the mean of the grid costs of the movement center and its neighborhood grid by a distance coefficient. The neighborhood grid includes a direct neighborhood grid and an oblique neighborhood grid. The distance coefficient of the direct neighborhood grid is 1 times the spatial distance of the grid, and the distance coefficient of the oblique neighborhood grid is [missing value]. Double the spatial distance; When the second movement cost is less than the first movement cost, update the first movement cost using the second movement cost; Add the unvisited grids in the domain to the min-heap list, and return to the step of taking an element from the min-heap list as the move center, until all grids in the target region have been traversed; The first movement cost finally determined for each grid is converted into the preset format image to obtain the tsunami propagation time for the corresponding grid.

4. The method according to claim 1, characterized in that, When the tsunami metadata is the tsunami propagation time, the process of obtaining a tsunami rendering image based on the image channel corresponding to the tsunami metadata through color mapping includes the following steps: The target image channels of the tsunami propagation time are analyzed using a shader to obtain the first channel resolution value for each pixel in the target region. The first channel's parsed value is normalized to a preset range to obtain the propagation time parsed value; Based on the preset initial channel color, configure the preset range and the color lookup mapping table of the rendered color to obtain the gradient color band; The color of each pixel in the gradient color band is determined by the product of the interval ratio corresponding to the pixel and the initial channel color. The interval ratio is determined based on the relative position of the pixel in the gradient color band. The initial channel color includes the initial colors of multiple channels. Based on the propagation time resolution value, the color of each pixel in the target area is filled and rendered using the gradient color band to obtain the tsunami propagation time map of the target area.

5. The method according to claim 1, characterized in that, The tsunami metadata also includes a maximum wave height and a minimum wave height. When the tsunami metadata is the hourly tsunami wave height, the step of obtaining a tsunami rendering image based on the image channel corresponding to the tsunami metadata through color mapping includes the following steps: The target image channel of the hourly tsunami wave height is analyzed using a shader to obtain the second channel resolution value for each pixel in the target region. Based on the second channel resolution value, the first wave height resolution value is obtained by combining the maximum wave height value and the minimum wave height value through numerical conversion. Based on the first wave height resolution value, the first rendering color of each pixel is determined by looking up a preset color range mapping table to obtain the hourly tsunami wave height map of the target area. The color range lookup mapping table is configured by associating multiple numerical ranges with preset color values ​​based on the range intervals of the maximum and minimum wave heights.

6. The method according to claim 1, characterized in that, The tsunami metadata also includes a maximum wave height and a minimum wave height. When the tsunami metadata is the maximum tsunami wave height, the process of obtaining a tsunami rendering image based on the image channel corresponding to the tsunami metadata through color mapping includes the following steps: The target image channel with the maximum tsunami wave height is analyzed using a shader to obtain the third channel resolution value for each pixel in the target region; Based on the third channel resolution value, the second wave height resolution value is obtained by combining the maximum wave height value and the minimum wave height value through numerical transformation. Based on the second high-resolution value, the second rendering color of each pixel is determined by looking up a preset color range mapping table to obtain the maximum tsunami wave height map of the target area; The color range lookup mapping table is configured by associating multiple numerical ranges with preset color values ​​based on the range intervals of the maximum and minimum wave heights.

7. A tsunami simulation and rendering device, characterized in that, The device includes: The first module is used to acquire seabed topographic data and seismic source data; The second module is used to preprocess the seabed topography data to obtain water depth data; The third module is used to quantify and obtain tsunami metadata based on the seismic source data and the water depth data using a preset numerical model; wherein, the tsunami metadata includes tsunami propagation time, hourly tsunami wave height and maximum tsunami wave height, and the tsunami metadata is a preset format image; The fourth module is used to obtain a tsunami rendering image by color mapping based on the image channels corresponding to the tsunami metadata; wherein the tsunami rendering image includes a tsunami propagation time map, an hourly tsunami wave height map, and a maximum tsunami wave height map; The step of quantifying tsunami metadata based on the seismic source data and the water depth data using a preset numerical model includes the following steps: Based on the latitude and longitude of the earthquake source corresponding to the water depth data and the earthquake source data, the tsunami propagation time is obtained by using the diffusion cumulative cost quantification using the tsunami propagation time calculation model. Based on the earthquake source data and the water depth data, the hourly tsunami wave height and the maximum tsunami wave height are obtained by processing with a tsunami simulation numerical model. The water depth data includes the water depth value of each grid within the target area. The step of obtaining the hourly tsunami wave height and the maximum tsunami wave height based on the seismic source data and the water depth data using a tsunami simulation numerical model includes the following steps: The source data and water depth data are imported into the Cornell multigrid coupled tsunami model. Based on the preset model step size configuration, the tsunami wave height corresponding to each grid in the target area is obtained by processing the shallow water wave equation, and then the maximum and minimum wave heights are determined. Based on the tsunami wave height, periodic sampling is performed in chronological order to obtain the hourly tsunami wave height; Based on the tsunami wave height, the maximum tsunami wave height within a preset period is obtained by traversing and comparing in chronological order. The tsunami wave height output by the Cornell multigrid coupled tsunami model is the preset format image.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

Images