Flood risk map result data coupling and management method

Abstract

The invention discloses a flood risk map result data coupling and management method, and relates to the technical field of hydrological information processing and geographic information systems, and the method comprises the following steps: obtaining hydrological data and topographic data required by flood risk assessment, including water level time sequence data, submerging depth data and TIN topographic data, and carrying out standardized conversion on data in different formats, and carrying out unified alignment based on a coordinate reference system. According to the method, the coupling precision of the hydrological data and the topographic data is improved through the adaptive Delaunay triangulation algorithm, and simulation distortion caused by time-space mismatching is avoided; the storage is optimized by adopting a topological optimization GeoJSON data structure, so that the query and management efficiency is improved; gPU acceleration rendering and LOD layering technologies are combined, the real-time visualization capability of flood simulation is improved, and efficient and accurate data support is provided for flood prevention emergency management and water conservancy decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological information processing and geographic information systems, and specifically relates to a method for coupling and managing flood risk map result data. Background Art

[0002] The coupling and management of flood risk map result data refers to the process of format conversion, scale matching, and structural optimization of heterogeneous data from different data sources (such as hydrological observation data, terrain data, model simulation results, etc.) during flood risk assessment, enabling it to be stored, queried, and visually displayed within a unified data framework. The present invention uses an adaptive Delaunay triangulation algorithm (ADT) to spatially couple hydrological section data with terrain TIN grids, realizing the dynamic expression of the two-dimensional water surface driven by water level changes. At the same time, a lightweight GeoJSON geographic entity data structure is designed to optimize the unified storage method of flood time series data and two-dimensional grid data, improving data storage efficiency and visualization loading speed. This method can not only improve the expression accuracy of flood simulation results but also optimize data query and rendering efficiency, providing more intelligent data management and decision-making support for flood control and drought relief command, water conservancy project management, and environmental monitoring.

[0003] The existing technology has the following deficiencies: During the coupling and management of flood risk map result data in the existing technology, there may be temporal and spatial mismatches between hydrological data and terrain data during scale conversion, resulting in distorted flood simulation results. Existing methods usually use linear interpolation or fixed-resolution grids for data coupling. However, the water level time series data output by the hydrodynamic model and the two-dimensional terrain TIN grid data are generated at different spatial scales and time steps. Without an accurate spatio-temporal alignment mechanism, it may lead to situations such as lagging water level changes and misjudgment of flood inundation areas. For example, in flood control decision-making, if the system calculates the flood diffusion trend based on incorrect data, it may misjudge the flood arrival time, causing emergency measures to be not executed in time, thus exacerbating disaster losses. The present invention ensures the dynamic consistency between time series data and spatial data through the adaptive Delaunay triangulation algorithm (ADT) and topologically optimized GeoJSON data structure, thereby improving the accuracy and reliability of data coupling and avoiding the occurrence of such disastrous errors.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a method for coupling and managing the result data of flood risk maps, which improves the coupling accuracy of hydrological data and terrain data through an adaptive Delaunay triangulation algorithm, avoiding simulation distortion caused by time and space mismatches; optimizes storage by adopting a topological optimized GeoJSON data structure to improve query and management efficiency; combines GPU-accelerated rendering and LOD hierarchical technology to enhance the real-time visualization ability of flood simulation, providing efficient and accurate data support for flood control emergency management and water conservancy decision-making, so as to solve the problems in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: A method for coupling and managing the result data of flood risk maps, comprising the following steps:

[0007] Obtain hydrological data and terrain data required for flood risk assessment, including water level time series data, inundation depth data and TIN terrain data, perform standardized conversion on data in different formats, and perform unified alignment based on the coordinate reference system;

[0008] Adopt an adaptive Delaunay triangulation algorithm to spatially couple the hydrological section data with the terrain TIN grid, generate a dynamic water surface TIN model, enable the water level change to drive the continuous adjustment of the water surface morphology, and improve the fine expression ability of flood risk data;

[0009] Construct a lightweight GeoJSON data structure to couple one-dimensional water level time series data with two-dimensional TIN grid data, perform data association storage based on a topological optimization algorithm, ensure the consistency of data at different time steps and spatial scales, and improve data management efficiency;

[0010] Based on the flood diffusion data calculated by the hydrodynamic model, use a multi-level indexing mechanism to match the water level time series with the TIN grid, realize dynamic data association, adopt a hierarchical loading strategy, and load data with different precisions as needed during the data visualization process to improve the system calculation efficiency;

[0011] Adopt a GPU-based rendering technology to optimize the dynamic rendering process of the water surface TIN grid through parallel computing, combine with a pipeline cache mechanism, improve the real-time visualization ability of large-scale flood risk data, and realize high-precision dynamic display of the flood inundation range and water level changes;

[0012] Perform consistency verification on the stored flood risk data, detect data anomalies based on time and space matching rules, ensure the integrity and accuracy of the data, and provide the coupled flood risk map result data to the flood control scheduling system to support disaster prevention and mitigation decision-making.

[0013] Preferably, the data acquisition and preprocessing step further includes abnormal data elimination and optimization processing, specifically including the following steps;

[0014] Denoise the original hydrological data, identify the mutation points in the water level data using the time series anomaly detection algorithm, and use the moving average smoothing method to reduce random errors to ensure data continuity;

[0015] Optimize the terrain TIN grid data, remove redundant triangular meshes using the redundant node removal method, improve the storage and calculation efficiency of terrain data, and ensure the expression accuracy of spatial data in the TIN model;

[0016] During the data coordinate alignment process, use the bilinear interpolation method to perform scale matching on geographical data from different sources, unify the projection coordinates, and avoid data offset problems caused by inconsistent coordinate systems;

[0017] For the inundation depth data output by the hydrodynamic model, perform boundary smoothing processing, reduce the mutation boundary through the gradient-based boundary correction method, and improve the smoothness and accuracy of data visualization expression.

[0018] Preferably, the steps of constructing the water surface TIN model by adaptive Delaunay triangulation further optimize the triangular mesh structure, specifically including the following steps;

[0019] Use the TIN triangulation algorithm based on weighted constraints to dynamically adjust the TIN model, making the density of triangular meshes proportional to the water level gradient change, ensuring that the meshes in the high water level change area are denser and improving the accuracy;

[0020] Set the minimum angle threshold to prevent the appearance of overly distorted narrow and long triangles, improve the mesh stability, and avoid rendering distortion;

[0021] During the water level data update process, automatically adjust the triangular mesh connection relationship based on the topological reconstruction algorithm, enabling the water surface TIN model to smoothly transition with the water level change and avoiding drastic jumps;

[0022] Adopt a step-by-step refinement interpolation method to insert auxiliary nodes in the local TIN grid, improve the simulation accuracy of the local area, and reduce calculation redundancy at the same time.

[0023] Preferably, the optimization calculation method of the adaptive Delaunay triangulation algorithm is as follows:

[0024] Calculate the water level change rate, and the calculation expression is as follows:

[0025] ,

[0026] In the formula, V w is the water level change rate, Δh is the water level height change amount, and Δt is the time interval;

[0027] Set the grid refinement factor, and the calculation expression is as follows:

[0028] ,

[0029] In the formula, R f is the local refinement factor of the TIN grid, e is the natural base, α is the adjustment parameter, and V c is the critical value of water level change;

[0030] Adjust the TIN grid based on the refinement factor, and the calculation expression is as follows:

[0031] A new = A old ·R f ,

[0032] In the formula, A new is the updated triangle area, and A old is the original triangle area.

[0033] Preferably, the topological optimization storage steps of time series data and spatial data include a data index optimization mechanism, specifically including the following steps;

[0034] When storing water level time series data, a multi-layer time index structure is adopted, including a long-term index layer and a short-term index layer, to improve the query efficiency of historical data and optimize the dynamic update ability of real-time data;

[0035] For TIN grid data, a spatial index method based on Morton code is adopted to map two-dimensional grid data to one-dimensional space, improve the query speed, and reduce the search range;

[0036] By constructing cross-scale matching rules, a fast mapping relationship is established between time series data and TIN grid data, so that the time complexity of data query is reduced to the O level and the data retrieval efficiency is improved.

[0037] Preferably, the topological optimization storage of time series data and spatial data adopts a dynamic matching algorithm, and the specific steps are as follows:

[0038] Calculate the interpolation function of the water level time series, and the calculation expression is as follows:

[0039] f(t)=a0 + a1t + a2t 2 +…+a n t n ,

[0040] In the formula, f(t) is the time series interpolation function, a0, a1, a2………a n are the coefficients of the polynomial, t is the time variable, and n is the polynomial order;

[0041] Calculate the optimal matching distance of TIN grid points, and the calculation expression is as follows:

[0042] ,

[0043] In the formula, d match is the optimal matching distance, represents finding the value of d that minimizes the error, f(t i ) represents the water level height of the i-th grid point at time t, N is the total number of data points, and h(d i ) is the water level height corresponding to the i-th grid point;

[0044] Adjust the storage index through least squares optimization, and the calculation expression is as follows:

[0045] ,

[0046] In the formula, S opt is the optimal storage index, represents finding the storage index S that minimizes the sum of squared errors, and S i is the stored data value.

[0047] Preferably, the real-time data matching and dynamic loading steps are further combined with an adaptive data caching mechanism, which specifically includes the following steps;

[0048] According to the water level change rate calculated by the hydrodynamic model, adaptively adjust the data cache update period to ensure the optimization of computing resource allocation;

[0049] Adopt a hierarchical loading strategy, determine the data loading accuracy according to the user view range, load low-resolution data in the far-distance area, and load high-resolution data in the near-distance area to reduce computing consumption;

[0050] For the rendering requirements of different perspectives, use the pre-computed LOD technology to optimize data expression at different zoom levels, and improve the rendering efficiency and system response speed.

[0051] Preferably, the visualization rendering optimization step based on GPU is further combined with an adaptive dynamic grid segmentation technology, which specifically includes the following steps;

[0052] Adopt the GPU parallel computing method to perform streaming processing on the water surface TIN grid, so that the dynamic changes of the water surface morphology can be rendered in real time;

[0053] During the real-time rendering process, use the grid optimization algorithm to reduce redundant calculations, optimize the rendering speed by removing invisible triangular regions, and improve the frame rate;

[0054] Combined with the physically based rendering method, a lighting calculation model is used to improve the visual effect of water bodies, making the dynamic visualization of flood diffusion more realistic.

[0055] Preferably, the data consistency verification and output steps are further combined with deep learning anomaly detection technology, specifically including the following steps;

[0056] Adopt a time series anomaly detection algorithm based on LSTM to automatically identify abnormal data in the water level time series and correct it;

[0057] Analyze TIN grid data through a convolutional neural network, detect irregular grid structures, and automatically adjust to optimize grid quality;

[0058] Combine historical flood data for comparison and analysis, calculate data deviation through the error regression method, and perform local correction on high-error areas to improve data consistency.

[0059] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0060] Through the adaptive Delaunay triangulation algorithm (ADT), the present invention realizes the high-precision coupling of hydrological data and terrain data, ensuring that the water level time series data can dynamically drive the morphological changes of the water surface TIN grid, thereby improving the simulation accuracy of the flood inundation area. In the prior art, since the water level data and terrain data output by the hydrodynamic model usually have different spatial resolutions and time steps, if linear interpolation or fixed grid matching is directly performed, it is easy to cause water level change lag or spatial mismatch, thereby affecting the accuracy of flood prediction. Through the adaptive grid optimization strategy, the present invention automatically increases the grid density in areas with large water level changes, making the dynamic changes in the flood inundation area more refined, and adopts a topological reconstruction algorithm to ensure that the adjustment of the TIN grid can maintain data continuity and avoid unstable calculation results caused by mutations. This method not only improves the accuracy of hydrodynamic simulation, but also optimizes the utilization rate of computing resources, providing more reliable decision-making support data for flood risk assessment.

[0061] The present invention adopts a topologically optimized GeoJSON data structure and combines it with the Morton code spatial indexing technology to achieve efficient storage and management of hydrological data and terrain data, improving the query efficiency and data consistency of the system. In traditional flood risk data management methods, water level time series data is usually stored in a relational database, while terrain data is stored in a rasterized or TIN grid format. This approach makes the data access and matching process relatively complex, especially during large-scale data calculations, where the query time increases significantly. Through a GeoJSON data organization method based on a tree structure, the present invention directly associates water level time series data with TIN grid data, avoiding the complex query connection operations in traditional methods and improving data retrieval efficiency. In addition, by using the Morton code index, spatial data can be mapped into a one-dimensional sequence, optimizing the query speed of spatial data. Combining data compression and hierarchical storage strategies reduces storage space occupancy and improves data transmission efficiency. This method ensures the efficiency and flexibility of data storage, making the management of flood risk map result data more intelligent and convenient.

[0062] The present invention adopts a visualization rendering technology based on GPU acceleration, combines a physically based rendering (PBR) method and a LOD (Level of Detail) hierarchical rendering strategy, significantly enhancing the dynamic display effect of the flood inundation process and making flood control emergency decision-making more intuitive and efficient. In existing visualization methods, the rendering of flood inundation areas is often restricted by computing resources, resulting in low frame rates and severe interaction delays, making it difficult to meet the requirements of real-time monitoring and emergency response. Through the CUDA parallel computing framework, the present invention transfers the rendering calculation task of the water surface TIN grid from the CPU to the GPU and adopts a streaming computing method, enabling calculations to be performed during data transmission, significantly improving the rendering efficiency. In addition, the LOD technology can automatically adjust the rendering accuracy according to the user's perspective, reducing the amount of detailed calculations in long-distance views and enhancing the rendering quality in close-up views, ensuring that the system can maintain a high frame rate while presenting fine water body dynamic changes. Ultimately, this method optimizes the visualization expression ability of flood risk data, providing more accurate and efficient technical support for flood control emergency management and water conservancy decision-making. Brief Description of the Drawings

[0063] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0064] Figure 1 It is a method flow chart of a method for coupling and managing flood risk map result data of the present invention. Detailed implementation manners

[0065] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

[0066] The present invention provides a method for coupling and managing flood risk map result data as shown in Figure 1 and includes the following steps:

[0067] Obtain hydrological data and topographic data required for flood risk assessment, including water level time series data, inundation depth data, and TIN (Triangulated Irregular Network) topographic data, perform standardized conversion on data in different formats, and perform unified alignment based on the coordinate reference system;

[0068] The data collection and preprocessing step further includes abnormal data elimination and optimization processing, specifically including the following steps;

[0069] Perform denoising processing on the original hydrological data, use a time series anomaly detection algorithm to identify mutation points in the water level data, and use a moving average smoothing method to reduce random errors and ensure data continuity;

[0070] Optimize the topographic TIN grid data, use a redundant node elimination method to remove redundant triangular grids, improve the storage and calculation efficiency of topographic data, and ensure the expression accuracy of spatial data in the TIN model;

[0071] In the process of data coordinate alignment, use bilinear interpolation to perform scale matching on geographic data from different sources, unify the projection coordinates, and avoid data offset problems caused by inconsistent coordinate systems;

[0072] For the inundation depth data output by the hydrodynamic model, perform boundary smoothing processing, reduce the mutation boundary through a gradient-based boundary correction method, and improve the smoothness and accuracy of data visualization expression.

[0073] Adopt an adaptive Delaunay triangulation algorithm to spatially couple the hydrological section data with the topographic TIN grid to generate a dynamic water surface TIN model, enabling the water level change to drive the continuous adjustment of the water surface morphology and improving the fine expression ability of flood risk data;

[0074] The step of constructing the water surface TIN model by adaptive Delaunay triangulation further optimizes the triangular grid structure, specifically including the following steps;

[0075] A TIN triangulation algorithm based on weighted constraints is used to dynamically adjust the TIN model so that the density of the triangle mesh is proportional to the change in the water level gradient, ensuring a denser mesh in areas with high water level changes and improving accuracy.

[0076] Set a minimum angle threshold to prevent overly distorted narrow triangles, improve mesh stability, and avoid rendering artifacts.

[0077] During the water level data update process, the triangular mesh connection relationship is automatically adjusted based on the topology reconstruction algorithm, so that the water surface TIN model can transition smoothly with the water level changes and avoid drastic jumps;

[0078] A step-by-step refinement interpolation method is used to insert auxiliary nodes into the local TIN grid to improve the simulation accuracy of the local area while reducing computational redundancy.

[0079] The optimization calculation method of the adaptive Delaunay triangulation algorithm (ADT) is as follows:

[0080] Calculate the water level change rate using the following expression:

[0081] ,

[0082] Where V w is the water level change rate, Δh is the change in water level height, and Δt is the time interval;

[0083] Set the mesh refinement factor, and the calculation expression is as follows:

[0084] ,

[0085] Where R f is the local refinement factor of the TIN grid, e is the natural base, α is the adjustment parameter, V c is the critical value of water level change;

[0086] Adjust the TIN grid based on the refinement factor. The calculation expression is as follows:

[0087] A new =A old ·R f

[0088] , where A new is the updated triangle area, A old is the area of the original triangle.

[0089] Construct a lightweight GeoJSON data structure to couple one-dimensional water level time series data with two-dimensional TIN grid data, and perform data association storage based on a topology optimization algorithm to ensure the consistency of data at different time steps and spatial scales, and improve data management efficiency;

[0090] The topology optimization storage steps for time series data and spatial data include a data index optimization mechanism, which specifically includes the following steps;

[0091] When storing water level time series data, a multi-layer time index structure is adopted, including a long-term index layer and a short-term index layer, to improve the query efficiency of historical data and optimize the dynamic update ability of real-time data;

[0092] For TIN grid data, a spatial index method based on Morton code is adopted to map two-dimensional grid data to one-dimensional space, improve the query speed, and reduce the search range;

[0093] By constructing cross-scale matching rules, a fast mapping relationship is established between time series data and TIN grid data, reducing the time complexity of data query to the O(logn) level and improving data retrieval efficiency.

[0094] The topology optimization storage of time series data and spatial data adopts a dynamic matching algorithm, and the specific steps are as follows:

[0095] Calculate the interpolation function of the water level time series, and the calculation expression is as follows:

[0096] f(t) = a0 + a1t + a2t 2 +…+ a n t n

[0097] , where f(t) is the time series interpolation function used to describe the change trend of water level over time. The method of nth-degree polynomial interpolation is adopted to make the water level data smoothly transition in the continuous time domain. a0, a1, a2………a n are the coefficients of the polynomial, obtained by fitting historical water level data, used to approximate the change of water level over time. t is the time variable, and n is the polynomial order, representing the highest order of polynomial interpolation;

[0098] Calculate the optimal matching distance of TIN grid points, and the calculation expression is as follows:

[0099] ,

[0100] where d match is the optimal matching distance, referring to the spatial position in the TIN grid that best matches the time series data, Denote finding the value of d that minimizes the error, i.e., finding the terrain grid point that best matches the water level time series, f(t i ) represents the water level height at the i-th grid point at time t, N is the total number of data points, h(d i ) is the water level height corresponding to the i-th grid point;

[0101] Adjust the storage index through least squares optimization, and the calculation expression is as follows:

[0102] ,

[0103] In the formula, S opt is the optimal storage index, which is used to adjust the data storage structure to make the storage method of time series data and spatial data reach the optimal state. Denote finding the storage index S to minimize the sum of squared errors, i.e., finding the optimal data storage structure, S i is the stored data value, which refers to the data value under the current storage structure and is compared with the time series data to optimize the storage method.

[0104] This method ensures a high-precision match between time series data and spatial data and improves data storage efficiency.

[0105] Based on the flood diffusion data calculated by the hydrodynamic model, use the multi-level index mechanism to match the water level time series with the TIN grid, realize dynamic data association, adopt the hierarchical loading strategy, and load data with different precisions as needed during the data visualization process to improve the system calculation efficiency;

[0106] The real-time data matching and dynamic loading steps are further combined with the adaptive data caching mechanism, which specifically includes the following steps;

[0107] According to the water level change rate calculated by the hydrodynamic model, adaptively adjust the data cache update period to ensure the optimization of computing resource allocation;

[0108] Adopt the hierarchical loading strategy, determine the data loading precision according to the user view range, load low-resolution data in the far-distance area and high-resolution data in the near-distance area to reduce computing consumption;

[0109] For the rendering requirements of different perspectives, use the pre-computed LOD (Level of Detail) technology to optimize data representation at different zoom levels to improve rendering efficiency and system response speed.

[0110] Adopt the GPU-based rendering technology, optimize the dynamic rendering process of the water surface TIN grid through parallel computing, and combine the pipeline caching mechanism to improve the real-time visualization ability of large-scale flood risk data and realize the high-precision dynamic display of the flood inundation range and water level changes;

[0111] The GPU-based visual rendering optimization steps are further combined with the adaptive dynamic mesh segmentation technology, specifically including the following steps;

[0112] Adopt the GPU parallel computing method to perform streaming processing on the water surface TIN mesh, enabling real-time rendering of the dynamic changes in the water surface morphology;

[0113] During the real-time rendering process, use the mesh optimization algorithm to reduce redundant calculations, optimize the rendering speed by removing invisible triangle regions, and increase the frame rate;

[0114] Combine the physically based rendering method (PBR), use the lighting calculation model to improve the visual effect of the water body, and make the dynamic visualization of flood diffusion more realistic.

[0115] Perform consistency verification on the stored flood risk data, detect data anomalies based on the time-space matching rules, ensure the integrity and accuracy of the data, and provide the coupled flood risk map result data to the flood control and dispatching system to support disaster prevention and mitigation decision-making;

[0116] The data consistency verification and output steps are further combined with the deep learning anomaly detection technology, specifically including the following steps;

[0117] Adopt the time series anomaly detection algorithm based on LSTM (Long Short-Term Memory Network) to automatically identify the abnormal data in the water level time series and perform correction;

[0118] Analyze the TIN mesh data through the convolutional neural network (CNN), detect the irregular mesh structure, and automatically adjust to optimize the mesh quality;

[0119] Combine the historical flood data for comparison and analysis, calculate the data deviation through the error regression method, and perform local correction on the high-error regions to improve the data consistency.

[0120] Embodiment 1: During the flood risk assessment process, the dynamic changes in the water surface morphology are one of the key factors affecting the accuracy of flood simulation. Traditional methods mainly use linear interpolation or fixed-resolution grids for data coupling, but these methods often have problems such as poor adaptability and low computational efficiency in complex terrain environments, resulting in distorted simulation results of the flood inundation area. In this embodiment, the water surface TIN mesh is constructed through the adaptive Delaunay triangulation algorithm (ADT) to realize the dynamic water surface reconstruction driven by water level changes, so as to improve the accuracy of flood risk assessment.

[0121] First, obtain high-precision topographic data of the study area, including river cross-section data, lake water boundary information, etc. The topographic data is usually stored in the form of a Triangulated Irregular Network (TIN) structure, which has better adaptability and can adaptively adjust the density of triangular meshes according to topographic features. In the data preprocessing stage, data standardization is required, including coordinate system unification, error correction, and outlier removal, to ensure the consistency of topographic data and hydrological data.

[0122] Secondly, use the adaptive Delaunay triangulation algorithm to construct the water surface TIN mesh. This algorithm has stronger flexibility and adaptability compared to traditional methods. Specifically, by dynamically adjusting the triangulation density, the mesh in the high water level gradient area is finer, while the mesh in the area with small water level changes is sparser, thereby improving the calculation efficiency and reducing redundant calculations. During the triangulation process, mesh optimization rules need to be set to ensure that the minimum angle of the triangle is not less than a specific threshold (such as 30°) to avoid the influence of overly distorted triangular meshes on the rendering effect.

[0123] In addition, to ensure the dynamic continuity of the water surface morphology, this embodiment adopts a topological reconstruction algorithm. When the water level data is updated, the topological relationship of the TIN mesh is automatically adjusted to enable the water surface morphology to smoothly transition with the water level change. For example, when the flood rises, the system will automatically insert new TIN nodes to expand the water surface boundary, and when the water level drops, the redundant TIN nodes will be removed to reduce calculation redundancy.

[0124] Finally, the constructed water surface TIN model can be used for real-time rendering of the flood inundation area and fused with the inundation depth data output by the hydrodynamic model to improve the accuracy of data expression and calculation efficiency. This method not only optimizes the accuracy of flood risk assessment but also improves the utilization rate of computing resources, providing more reliable data support for flood control emergency management.

[0125] Embodiment 2: In flood risk assessment, the storage and management of multi-source heterogeneous data are one of the key factors affecting system performance. Traditional data storage methods usually adopt relational databases (such as SQL) or rasterized data storage methods, but these methods have great limitations in storing water level time series data and topographic data. For example, although relational databases can effectively store structured data, their query efficiency is low when dealing with spatial data, while the rasterized data storage method, although widely used in GIS applications, has high data redundancy and is difficult to adjust dynamically. This embodiment adopts a topologically optimized GeoJSON data structure to optimize the storage method of flood risk data and improve data management efficiency.

[0126] First, classify and store the data according to its characteristics. The water level time series data is stored in a lightweight GeoJSON format, which has good scalability and can organize the time series data in a tree structure and establish a direct association with the terrain data. Specifically, the data for each time step is stored as a JSON object and contains corresponding water level information, timestamps, spatial indices, etc., for easy querying and retrieval.

[0127] Secondly, to improve the data query efficiency, this embodiment uses the Morton code (Z-order curve) for spatial index optimization. The Morton code is an indexing method based on a recursive space-filling curve that can map two-dimensional spatial data into one-dimensional data, thus improving the query efficiency. When storing TIN grid data, the system automatically calculates the Morton code for each grid point and stores the data in the order of the Morton code, making adjacent geographical spatial data as close as possible in the storage structure and improving the data access efficiency.

[0128] In addition, this embodiment adopts a dynamic storage allocation strategy to automatically adjust the storage weights according to the historical access frequencies of the data. For example, data with high access frequencies is stored in the cache layer to improve the query speed, while data with low access frequencies is stored in the long-term storage layer to reduce the consumption of computing resources. At the same time, data compression algorithms (such as Gzip or Snappy) are used to compress the stored data to reduce the storage occupancy and improve the data transmission efficiency.

[0129] Finally, to ensure data consistency, this embodiment introduces a data verification mechanism to automatically detect the matching situation between the time series data and the TIN grid data during the storage process, ensuring their consistency in both the time and space dimensions. If mismatched data is found, the system will automatically perform data resampling or interpolation processing to ensure the integrity and accuracy of the data. This method not only optimizes the storage structure of flood risk data but also improves the data query efficiency, providing efficient and reliable data support for flood risk assessment.

[0130] Embodiment 3: The visual display of flood simulation data is crucial for flood control decision-making. However, due to the high computational complexity, traditional rendering methods often struggle to meet the requirements of real-time interaction. This embodiment uses a GPU-accelerated dynamic flood visualization rendering technology to optimize the rendering process of the flood inundation area and water surface morphology through parallel computing, improving the rendering efficiency and interaction performance.

[0131] First, utilize the CUDA parallel computing framework to transfer the rendering calculation tasks from the CPU to the GPU, and adopt the stream computing method to enable calculations during data transmission, thereby improving data processing efficiency. During the TIN grid rendering process, the system automatically splits large-scale data into multiple small tasks and accelerates the rendering process through the parallel computing ability of the GPU, thus reducing the computing burden on the CPU and improving the system response speed.

[0132] Secondly, during the rendering process, this embodiment adopts the physically based rendering (PBR) technology. By simulating physical characteristics such as lighting and reflection, it improves the water body rendering effect and makes the visualization of the flood inundation area more realistic. For example, calculate the water surface reflectivity and refractive index based on the PBR method, and enhance the water surface detail performance through the ambient occlusion (AO) technology, making the dynamic changes of the water body more in line with real physical laws.

[0133] In addition, to optimize the rendering performance, this embodiment introduces the LOD (Level of Detail) technology to dynamically adjust the rendering precision according to the user's perspective. In the long-distance view, the system automatically reduces the grid details to reduce the calculation amount, while in the close-up view, high-precision grids are used for rendering to ensure the balance between visual effects and data accuracy. Finally, this method optimizes the real-time visualization ability of flood risk data and provides an intuitive and accurate dynamic display of flood inundation for flood control emergency command.

[0134] The present invention realizes the high-precision coupling of hydrological data and terrain data through the adaptive Delaunay triangulation algorithm (ADT), ensuring that the water level time series data can dynamically drive the morphological changes of the water surface TIN grid, thereby improving the simulation accuracy of the flood inundation area. In the prior art, since the water level data output by the hydrodynamic model and the terrain data usually have different spatial resolutions and time steps, if linear interpolation or fixed grid matching is directly performed, it is easy to cause water level change lag or spatial mismatch, thereby affecting the accuracy of flood prediction. The present invention adopts an adaptive grid optimization strategy to automatically increase the grid density in areas with large water level changes, making the dynamic changes of the flood inundation area more refined, and uses a topological reconstruction algorithm to ensure that the adjustment of the TIN grid can maintain data continuity and avoid unstable calculation results caused by mutations. This method not only improves the accuracy of hydrodynamic simulation but also optimizes the utilization rate of computing resources, providing more reliable decision-making support data for flood risk assessment.

[0135] The present invention adopts a topologically optimized GeoJSON data structure, combined with the Morton code spatial indexing technology, to achieve efficient storage and management of hydrological data and terrain data, improving the query efficiency and data consistency of the system. In traditional flood risk data management methods, water level time series data is usually stored in a relational database, while terrain data is stored in a rasterized or TIN grid format. This approach makes the data access and matching process relatively complex, especially during large-scale data calculations, where the query time increases significantly. Through a tree-based GeoJSON data organization method, the present invention directly associates water level time series data with TIN grid data, avoiding the complex query join operations in traditional methods and improving data retrieval efficiency. In addition, by using the Morton code index, spatial data can be mapped to a one-dimensional sequence, optimizing the query speed of spatial data. Combined with data compression and hierarchical storage strategies, it reduces storage space occupancy and improves data transmission efficiency. This method ensures the efficiency and flexibility of data storage, making the management of flood risk map result data more intelligent and convenient.

[0136] The present invention adopts a visualization rendering technology based on GPU acceleration, combined with a physically based rendering (PBR) method and a level of detail (LOD) hierarchical rendering strategy, significantly enhancing the dynamic display effect of the flood inundation process and making flood control emergency decision-making more intuitive and efficient. In existing visualization methods, the rendering of flood inundation areas is often limited by computing resources, resulting in low frame rates and serious interaction delays, making it difficult to meet the needs of real-time monitoring and emergency response. Through the CUDA parallel computing framework, the present invention transfers the rendering calculation task of the water surface TIN grid from the CPU to the GPU and adopts a streaming computing method, enabling calculations to be performed during data transmission, significantly improving the rendering efficiency. In addition, the LOD technology can automatically adjust the rendering accuracy according to the user's perspective, reducing the amount of detail calculation in long-distance views and enhancing the rendering quality in close-up views, ensuring that the system can maintain a high frame rate while presenting fine water body dynamic changes. Ultimately, this method optimizes the visualization expression ability of flood risk data, providing more accurate and efficient technical support for flood control emergency management and water conservancy decision-making.

[0137] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0138] Only some exemplary embodiments of the present invention have been described by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0139] It should be noted that in this text, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0140] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0141] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0142] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist physically alone for each unit, or two or more units may be integrated in one unit.

[0145] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, and all should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0146] Only some exemplary embodiments of the present invention have been described above by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A method for coupling and managing flood risk map result data, characterized in that, It includes the following steps: Obtain the hydrological data and topographic data required for flood risk assessment, including water level time series data, inundation depth data, and TIN topographic data, perform standardized conversion on data in different formats, and perform unified alignment based on the coordinate reference system; Adopt the adaptive Delaunay triangulation algorithm to spatially couple the hydrological section data with the topographic TIN grid, generate a dynamic water surface TIN model, enable the water level change to drive the continuous adjustment of the water surface morphology, and improve the fine expression ability of flood risk data; Construct a lightweight GeoJSON data structure, couple the one-dimensional water level time series data with the two-dimensional TIN grid data, and perform data association storage based on the topological optimization algorithm to ensure the consistency of data at different time steps and spatial scales and improve data management efficiency; Based on the flood diffusion data calculated by the hydrodynamic model, use the multi-level index mechanism to match the water level time series with the TIN grid, achieve dynamic data association, and adopt a hierarchical loading strategy to load data with different precisions as needed during the data visualization process to improve the system calculation efficiency; Adopt GPU-based rendering technology, optimize the dynamic rendering process of the water surface TIN grid through parallel computing, and combine the pipeline caching mechanism to improve the real-time visualization ability of large-scale flood risk data and achieve high-precision dynamic display of the flood inundation range and water level changes; Perform consistency verification on the stored flood risk data, detect data anomalies based on the time-space matching rules, ensure the integrity and accuracy of the data, and provide the coupled flood risk map result data to the flood control and dispatching system to support disaster prevention and mitigation decision-making.

2. The method for coupling and managing flood risk map result data according to claim 1, characterized in that The data collection and preprocessing steps further include abnormal data elimination and optimization processing, specifically including the following steps; Denoise the original hydrological data, use the time series anomaly detection algorithm to identify the mutation points in the water level data, and use the moving average smoothing method to reduce the random error to ensure data continuity; Optimize the topographic TIN grid data, use the redundant node elimination method to remove redundant triangular grids, improve the storage and calculation efficiency of the topographic data, and ensure the expression accuracy of the spatial data in the TIN model; During the data coordinate alignment process, use the bilinear interpolation method to perform scale matching on geographic data from different sources, unify the projection coordinates, and avoid data offset problems caused by inconsistent coordinate systems; For the inundation depth data output by the hydrodynamic model, perform boundary smoothing processing, reduce the mutation boundary through the gradient-based boundary correction method, and improve the smoothness and accuracy of the data visualization expression.

3. A method for coupling and managing flood risk map result data according to claim 1, characterized in that The steps of constructing the water surface TIN model by adaptive Delaunay triangulation further optimize the triangular grid structure, specifically including the following steps; Adopt the TIN triangulation algorithm based on weighted constraints to dynamically adjust the TIN model, make the density of the triangular grid proportional to the water level gradient change, ensure that the grid in the high water level change area is denser, and improve the accuracy; Set the minimum angle threshold to prevent the appearance of overly distorted narrow and long triangles, improve the grid stability, and avoid rendering distortion; During the water level data update process, the connection relationship of triangular meshes is automatically adjusted based on the topological reconstruction algorithm, enabling the water surface TIN model to smoothly transition with the change of water level and avoiding drastic jumps; Adopt a step-by-step refinement interpolation method to insert auxiliary nodes in the local TIN grid, improving the simulation accuracy of the local area while reducing computational redundancy.

4. A method for coupling and managing flood risk map result data according to claim 1, characterized in that The optimization calculation method of the adaptive Delaunay triangulation algorithm is as follows: Calculate the water level change rate, and the calculation expression is as follows: ， where V w is the rate of water level change, Δh is the change in water level height, and Δt is the time interval; Set the grid refinement factor, and the calculation expression is as follows: ， where R f is the local refinement factor of the TIN grid, e is the natural base, α is the adjustment parameter, and V c is the critical value of water level change; Adjust the TIN grid based on the refinement factor, and the calculation expression is as follows: A new = A old ·R f , where A new is the updated triangle area, and A old is the original triangle area.

5. A method for coupling and managing flood risk map result data according to claim 1, characterized in that The topological optimization storage steps of time series data and spatial data include a data index optimization mechanism, which specifically includes the following steps; When storing water level time series data, adopt a multi-layer time index structure, including a long-term index layer and a short-term index layer, to improve the query efficiency of historical data and optimize the dynamic update ability of real-time data; For TIN grid data, adopt a spatial index method based on Morton codes to map two-dimensional grid data to one-dimensional space, improving the query speed and reducing the search range; By constructing cross-scale matching rules, establish a fast mapping relationship between time series data and TIN grid data, reducing the time complexity of data query to the O level and improving data retrieval efficiency.

6. A method for coupling and managing flood risk map result data according to claim 1, characterized in that The topological optimization storage of time series data and spatial data adopts a dynamic matching algorithm, and the specific steps are as follows: Calculate the interpolation function of the water level time series, and the calculation expression is as follows: f(t) = a0 + a1t + a2t 2 + … + a n t n , Where f(t) is the time series interpolation function, a0, a1, a2………a n are the coefficients of the polynomial, t is the time variable, and n is the polynomial order; Calculate the optimal matching distance of TIN grid points, and the calculation expression is as follows: ， where d match is the optimal matching distance, means finding the value of d that minimizes the error, f(t i ) represents the water level height at the i-th grid point at time t, N is the total number of data points, h(d i ) is the water level height corresponding to the i-th grid point; Adjust the storage index through least squares optimization, and the calculation expression is as follows: ， Where S opt is the optimal storage index, means to find the storage index S to minimize the sum of squared errors, and S i is the stored data value.

7. A method for coupling and managing flood risk map result data according to claim 1, characterized in that The real-time data matching and dynamic loading steps further combine an adaptive data caching mechanism, which specifically includes the following steps; According to the water level change rate calculated by the hydrodynamic model, adaptively adjust the data cache update period to ensure the optimization of computing resource allocation; Adopt a hierarchical loading strategy to determine the data loading accuracy according to the user view range, loading low-resolution data in the far-distance area and high-resolution data in the near-distance area to reduce computational consumption; For the rendering requirements of different perspectives, use the pre-computed LOD technology to optimize data expression at different zoom levels, improving the rendering efficiency and system response speed.

8. A method for coupling and managing flood risk map result data according to claim 1, characterized in that The visualization rendering optimization steps based on GPU further combine an adaptive dynamic grid segmentation technology, which specifically includes the following steps; Adopt the GPU parallel computing method to perform streaming processing on the water surface TIN grid, enabling the dynamic changes of the water surface morphology to be rendered in real time; During the real-time rendering process, use the grid optimization algorithm to reduce redundant calculations, optimize the rendering speed by culling invisible triangular regions, and improve the frame rate; Combined with the physically based rendering method, use the lighting calculation model to improve the visual effect of the water body, making the dynamic visualization of flood diffusion more realistic.

9. A method for coupling and managing flood risk map result data according to claim 1, characterized in that, The data consistency verification and output steps further combine deep learning anomaly detection technology, which specifically includes the following steps; Adopt a time series anomaly detection algorithm based on LSTM to automatically identify abnormal data in the water level time series and perform correction; Analyze TIN grid data through a convolutional neural network, detect irregular grid structures, and automatically adjust to optimize grid quality; Combine historical flood data for comparison and analysis, calculate data deviation through an error regression method, and perform local correction on high-error regions to improve data consistency.

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