High-order precision flood simulation method based on GPU acceleration

Abstract

The invention discloses a high-order precision flood simulation method based on GPU acceleration, and relates to application of a parallel computing technology utilizing a graphic processing unit and a high-order precision numerical discretization method in flood simulation. A WENO reconstruction technology, an implicit time discretization scheme, depth average model innovation, a GPU parallel computing strategy, a terrain adaptability algorithm, a dynamic source item processing mechanism and a multi-source item coupling simulation technology are combined; simulation efficiency can be remarkably improved while high calculation precision is kept, and the method is particularly suitable for flood simulation under complex terrain conditions; through strict model verification and calibration processes and user-friendly software implementation, the accuracy and reliability of the method in practical application are ensured, the limitation of an existing flood simulation technology under large-scale and complex terrain conditions is effectively solved, and the high-order precision flood simulation method based on GPU acceleration is introduced, so that the high-order precision flood simulation method has a good application prospect. The simulation precision and efficiency are remarkably improved, and the dynamic process of the flood event is effectively predicted and analyzed.

Description

Technical Field

[0001] The invention belongs to the technical field of computational fluid dynamics, and in particular relates to a high-order precision flood simulation method based on GPU acceleration. Background Art

[0002] 1. Current status of computational fluid dynamics (CFD):

[0003] Computational fluid dynamics is a science that uses numerical methods and computer technology to simulate and analyze flow phenomena. Although CFD has been widely used in aerospace, automotive design, environmental science and other fields, its application in flood simulation still faces challenges. Traditional CFD methods are often limited by computing resources and accuracy requirements when dealing with large-scale, complex terrain flood simulations.

[0004] 2. Development of GPU parallel computing technology:

[0005] In recent years, with the development of GPU technology, its application in scientific computing has become more and more extensive. The parallel processing capability of GPU enables it to perform well in handling complex computing tasks, especially in CFD problems that require a large number of parallel operations. However, applying GPU parallel computing technology to flood simulation requires solving a series of technical problems such as data transmission, memory management, and load balancing.

[0006] 3. Numerical discretization method:

[0007] Numerical discretization is the process of converting continuous differential equations into discrete algebraic equations. In flood simulation, commonly used numerical discretization methods include finite difference method, finite volume method and finite element method. These methods have their own advantages and disadvantages in terms of accuracy and stability. Choosing a suitable discretization method is crucial to improving simulation accuracy and computational efficiency.

[0008] 4. High-order precision numerical solution:

[0009] High-order numerical schemes, such as the WENO (Weighted Essentially Non-Oscillatory) scheme, can improve the accuracy of the numerical solution while maintaining the non-oscillatory characteristics by weighting different reconstruction points. These schemes perform well in dealing with fluid problems with complex wave characteristics, but implementing these schemes requires complex algorithm design and optimization.

[0010] 5. Challenges of flood simulation:

[0011] Flood simulation needs to consider multiple factors, such as rainfall, topography, and water flow dynamics. Existing flood simulation models often face problems of insufficient accuracy and low computational efficiency when dealing with complex terrain and rapidly changing water flow conditions. In addition, existing models also show certain limitations when simulating flood dynamics under extreme weather events.

[0012] 6. Treatment of rigid friction terms:

[0013] Friction is a key factor in flood simulation, especially when simulating fast-flowing water. The handling of rigid friction is crucial for the stability and accuracy of the model. Traditional explicit solutions may be limited by the time step when dealing with such problems, while implicit solutions can provide better stability but have higher computational complexity.

[0014] 7. Application of the deep average model:

[0015] The depth-averaged model is a method to simplify a three-dimensional flow problem into a two-dimensional problem. This method is very effective in dealing with flood simulations where the flow depth is much smaller than the horizontal scale. However, the existing depth-averaged models still need to be further improved and optimized when dealing with complex terrain and rapidly changing flow conditions.

[0016] 8. Complexity of source term processing:

[0017] Source terms, such as rainfall and infiltration, are an integral part of flood simulation. Accurately simulating these source terms is crucial to predicting the occurrence and evolution of floods. Existing source term processing methods often face challenges in accuracy and efficiency when dealing with spatial and temporal variations. Summary of the invention

[0018] The present invention aims to address the limitations of existing flood simulation technology in dealing with large-scale and complex terrain conditions. By introducing a high-precision flood simulation method based on GPU acceleration, the accuracy and efficiency of the simulation can be significantly improved, and the dynamic process of flood events can be effectively predicted and analyzed.

[0019] The present invention adopts the following technical solutions to solve the above technical problems:

[0020] A high-precision flood simulation method based on GPU acceleration, its innovation is reflected in the following key steps:

[0021] Step 1, data collection and preprocessing: Use advanced data acquisition technology to collect high-resolution terrain data, land use data, drainage system layout, etc. Through preprocessing techniques such as data fusion and interpolation methods, ensure that the data format and accuracy meet the high-precision requirements of the model and provide accurate initial conditions for subsequent simulations;

[0022] Step 2, model initialization: using the automated parameter identification algorithm, dynamically initialize the model parameters based on the collected data, and set the simulation time and space ranges to meet the needs of flood simulations of different scales and complexities;

[0023] Step 3, WENO reconstruction and implicit time discretization: Introduce weighted essentially non-oscillatory WENO reconstruction technology to reconstruct flow velocity and water level with high precision, significantly improving simulation accuracy. At the same time, use implicit time discretization schemes, backward Euler method or Crank-Nicolson scheme to effectively handle rigid terms in the model, improve the stability of the numerical solution, and allow the use of larger time steps, thereby improving computational efficiency.

[0024] Step 4, terrain adaptive grid adjustment: Develop a terrain adaptive grid generation algorithm to dynamically adjust the computational grid according to the terrain characteristics. For example, the grid is automatically refined in areas with drastic terrain changes to improve simulation accuracy, while the grid is appropriately coarsened in flat areas to optimize the allocation of computing resources and improve the adaptability of the model and the overall computing efficiency.

[0025] Step 5, dynamic source item processing: adopt the dynamic source item processing mechanism to realize the dynamic update mechanism of source items, and adjust the rainfall and infiltration source items according to the real-time data or prediction model;

[0026] Step 6, multi-source coupling simulation: In flood simulation, multiple source items that affect water flow are considered simultaneously, including rainfall, infiltration, drainage, and river leakage; these source items interact with each other and jointly determine the dynamic behavior of the flood;

[0027] Step 7, GPU parallel computing: Use GPU parallel computing capabilities to accelerate the simulation process and obtain fast simulation results;

[0028] Step 8, simulation results analysis: Analyze the simulation results to identify high-risk areas for waterlogging and drainage bottlenecks.

[0029] As a further preferred solution of the high-order precision flood simulation method based on GPU acceleration of the present invention, in step 3, the WENO scheme is used to reconstruct the flow velocity and water level, and its weighted characteristics between different reconstruction points are used to achieve high-precision simulation, which is used to improve the simulation accuracy of the flow velocity and water level in the flood simulation, while maintaining the non-oscillation characteristics of the numerical solution;

[0030] The specific calculation is as follows:

[0031] Step 3.1, meshing: divide the computational domain into uniform or non-uniform grids;

[0032] Step 3.2, data structure: using a suitable data structure to store the values ​​of the grid points and the associated weight coefficients;

[0033] Step 3.3, WENO reconstruction implementation: For each grid point, WENO reconstruction is performed based on the values ​​of neighboring points:

[0034] According to the one-dimensional conservation law equation:

[0035] Among them, u is the conserved variable, f(u) is the corresponding flux function; t is time; x is distance; j and i are counting marks;

[0036] WENO reconstruction involves i A high-order approximation to u is:

[0037] in, is at the grid point x i The reconstruction value, w j is the weighting coefficient, k is the reconstruction order,

[0038] Weighting coefficient w j Based on the smoothness indicator β j Calculation to balance the accuracy and non-oscillatory properties of different reconstructions: in, C j is a positive constant, ε is a small positive number to ensure numerical stability; the smoothness indicator β j Used to measure the degree of smoothness within the reconstruction interval:

[0039] As a further preferred solution of the high-order precision flood simulation method based on GPU acceleration of the present invention, in step 3, the implicit time discretization scheme: adopting the backward Euler method or the Crank-Nicolson scheme for time discretization ensures the stability of the numerical solution, improves the stability of the model when processing rigid friction terms, and allows the use of a larger time step;

[0040] The details are as follows:

[0041] Step 3.4, time step selection: select an appropriate time step according to the time scale and stability requirements of the problem;

[0042] Step 3.5, discretization scheme selection: select a first-order or second-order implicit time discretization scheme according to the rigidity and accuracy requirements of the problem;

[0043] First-order implicit time discretization is the backward Euler method:

[0044] For the time derivative Discretize using the Backward Euler method:

[0045] Among them, U n+1 and Un are the solution after time step Δt and the current solution, S n+1 is the source term at the new time step;

[0046] Second-order implicit time discretization is the Crank-Nicolson scheme:

[0047] For the time derivative terms, the Crank-Nicolson scheme is used for discretization:

[0048] Step 3.6, implicit treatment of rigid source terms: For rigid source terms, implicit methods are used to ensure numerical stability;

[0049]

[0050] Among them, a(U n+1 ) is the solution-dependent friction coefficient;

[0051] Step 3.7, Linearization and iterative solution: For nonlinear problems, linearization techniques, including Newton's method, are used for iterative solution.

[0052] As a further preferred solution of the high-precision flood simulation method based on GPU acceleration of the present invention, in step 4, a depth average model is used to automatically adjust the model to adapt to different terrain features, including steep slopes and curvatures; specifically as follows:

[0053] Step 4.1, terrain data preprocessing: analyzing and smoothing the terrain data;

[0054] Step 4.2, model initialization: Initialize water depth, velocity and terrain data;

[0055] Step 4.3. Discretization of continuity and momentum equations: Use the finite volume method to discretize the continuity equation and momentum equation in space and time;

[0056] Among them, the continuity equation: In the depth average model, the continuity equation is expressed as:

[0057] Where h is the water depth, u is the horizontal velocity vector, S h is the source term, including rainfall and evaporation;

[0058] Momentum equation: The momentum equation describes the change of fluid momentum over time and its spatial distribution:

[0059]

[0060] Where g is the acceleration due to gravity, S m is the momentum source term, including friction;

[0061] Friction term: The friction term is usually expressed using the Manning formula:

[0062] Where ρ is the density of water, n is the Manning roughness coefficient;

[0063] Step 4.4. Terrain adaptability analysis: Use terrain adaptability algorithm to analyze terrain characteristics. Through terrain analysis and dynamic adjustment algorithm, ensure that the model can adapt to complex terrain changes; the details are as follows:

[0064] Terrain feature analysis: Use terrain data to calculate the slope and roughness characteristics of the terrain:

[0065] Adaptive Mesh Refinement: Dynamically refine or coarsen the mesh for computational efficiency and accuracy based on terrain characteristics:

[0066]

[0067] Where C is the base value of the grid size, ∈ is a small positive number to avoid division by zero errors;

[0068] Source term adjustment affected by terrain: Adjust the source terms, including rainfall infiltration and surface runoff, to reflect the impact of terrain: S = S0 f (terrain features). As a further preferred solution of the high-order precision flood simulation method based on GPU acceleration of the present invention, in step 5, a dynamic source term processing mechanism is adopted to realize a dynamic update mechanism of the source term, and the rainfall and infiltration source terms are adjusted according to real-time data or prediction models; specifically as follows:

[0069] Step 5.1, data collection: collect real-time hydrological and meteorological data, including rainfall and soil moisture;

[0070] Step 5.2, data preprocessing: cleaning and standardizing the collected data;

[0071] Step 5.3, prediction model integration: integrating rainfall prediction models with other hydrological and meteorological prediction tools;

[0072] Step 5.4, source item dynamic update algorithm: dynamically update source items according to real-time data or prediction results;

[0073] Dynamic source item update: Update the source item S according to the real-time data or prediction model: S(t) = f(real-time data or prediction model, t);

[0074] Among them, t represents time, f is the update function;

[0075] Rainfall intensity adjustment: Adjust the rainfall intensity according to radar rainfall data or weather forecast: I(t) = I0·(1+a·ΔP(t));

[0076] Where I0 is the baseline rainfall intensity, ΔP(t) is the rainfall intensity change, and a is the adjustment coefficient;

[0077] Infiltration rate update: Update the infiltration rate K according to soil moisture and rainfall intensity: K(t) = K0·e- β·土壤湿度(t) ;

[0078] Among them, K0 is the base infiltration rate, β is the soil moisture influence coefficient;

[0079] Step 5.5, model integration: Integrate the dynamic source term processing mechanism into the flood simulation model.

[0080] As a further preferred embodiment of the high-precision flood simulation method based on GPU acceleration of the present invention, in step 5, multi-source term coupling simulation refers to simultaneously considering multiple source terms that affect water flow in flood simulation, such as rainfall, infiltration, drainage, and river leakage; these source terms interact with each other and jointly determine the dynamic behavior of the flood;

[0081] The combined effect of source terms: All source terms are combined into a total source term S: S = S 降雨 +S 入渗 -S 排水 -S 渗漏 ;

[0082] Rainfall source term: The rainfall source term can be expressed as: S 降雨 =I·A;

[0083] Among them, I is the rainfall intensity and A is the rainfall area;

[0084] Infiltration source term: The infiltration source term can be expressed as: S 入渗 =K·(PP c );

[0085] Where K is the infiltration rate, P is the rainfall intensity, and P c is the water holding capacity of the soil;

[0086] Drainage and seepage source terms: Drainage and seepage are usually determined based on the capacity of the drainage system and soil characteristics.

[0087] As a further preferred solution of the high-precision flood simulation method based on GPU acceleration of the present invention, in step 7, the GPU parallel computing strategy utilizes the high parallel processing capability of the GPU to significantly improve the efficiency of the numerical simulation by distributing the computing tasks to multiple processing cores; specifically as follows:

[0088] Step 7.1, Data Parallelism: Data parallelism involves splitting the dataset into multiple small chunks, each of which is processed independently by different GPU threads or cores;

[0089] Step 7.2, Task Parallelism: Task parallelism involves breaking down a computational task into multiple subtasks, which can be executed simultaneously on different GPU threads or cores;

[0090] Step 7.3, load balancing: Ensure that the workload of all GPU cores is balanced to avoid the situation where some cores are idle while other cores are overloaded: Load balance = total workload / number of cores.

[0091] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0092] 1. The present invention combines WENO reconstruction technology, implicit time discretization scheme, deep average model innovation, GPU parallel computing strategy, terrain adaptability algorithm, dynamic source term processing mechanism and multi-source term coupling simulation technology; through the integration of these technologies, the algorithm can significantly improve the simulation efficiency while maintaining high calculation accuracy, and is particularly suitable for flood simulation under complex terrain conditions; after a rigorous model verification and calibration process, and implemented through user-friendly software, its accuracy and reliability in practical applications are ensured;

[0093] 2. The high-order precision numerical discretization of the present invention: adopts high-order precision methods such as WENO to improve simulation accuracy; implicit time discretization: handles rigid friction terms and improves calculation stability; innovation of deep average model: adapts to complex terrain and improves the adaptability and accuracy of the model; GPU parallel computing strategy: optimizes GPU resource usage and realizes accelerated calculation of flood simulation; terrain adaptability algorithm: automatically adjusts the model to adapt to different terrain features; dynamic source term processing mechanism: adjusts source terms according to real-time or predicted hydrological conditions; multi-source term coupling simulation: comprehensively considers the influence of multiple source terms. Software implementation and optimization: provides a user-friendly operation interface, which is easy to operate and apply. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0095] Figure 1 It is a flow chart of a high-order precision flood simulation method based on GPU acceleration of the present invention;

[0096] Figure 2 This is a diagram of the GPU parallel computing strategy architecture of the present invention;

[0097] Figure 3 It is a multi-source term coupling simulation flow chart of the present invention;

[0098] Figure 4 It is a flow chart of the high-order precision WENO reconstruction technology of the present invention;

[0099] Figure 5 is a flow chart of the implicit time discretization scheme of the present invention;

[0100] Figure 6 is a flow chart of the depth average model of the present invention;

[0101] Figure 7 It is a flow chart of the GPU parallel computing strategy of the present invention;

[0102] Figure 8 is a flow chart of the terrain adaptability algorithm of the present invention;

[0103] Fig. 9 is a flow chart of the dynamic source item processing mechanism of the present invention;

[0104] Fig.10 It is a multi-source term coupling simulation flow chart of the present invention;

[0105] Fig.11 It is a flow chart of software implementation and optimization of the present invention;

[0106] Fig.12 It is a flow chart of model verification and calibration of the present invention. DETAILED DESCRIPTION

[0107] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0108] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments, and the purpose and effect of the present invention will become clearer. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0109] like Figures 1 to 3As shown, the present invention proposes a high-order precision flood simulation method based on GPU acceleration, aiming to solve the limitations of the prior art and provide an efficient and accurate flood simulation solution. The present invention belongs to the field of computational fluid dynamics (CFD), and in particular, relates to the application of parallel computing technology using graphics processing units (GPUs) and high-order precision numerical discretization methods in flood simulation. The core of this technical field is to develop and implement an efficient and high-precision flood simulation algorithm to predict and analyze the dynamic process of flood events.

[0110] Computational Fluid Dynamics (CFD): Computational fluid dynamics is a scientific computing method that uses numerical analysis and data structures to solve and analyze fluid flow problems. CFD technology is widely used in many fields such as aerospace, automotive design, environmental science, and medical research. In this invention, CFD technology is used to simulate the flow characteristics of floods, including the speed, direction, depth, etc. of water flow.

[0111] GPU parallel computing technology: Graphics processing units (GPUs) were originally designed to handle complex computing tasks in image and video rendering. In recent years, as GPU computing power has significantly improved, they are increasingly used for parallel processing tasks in scientific computing and engineering simulation. GPU parallel computing technology can significantly increase computing speed, especially when dealing with large-scale or complex computing problems. The present invention uses the parallel processing power of GPUs to accelerate the computational process of flood simulation.

[0112] High-order precision numerical discretization method: Numerical discretization is the process of converting a continuous mathematical model into a discrete numerical problem. In flood simulation, the choice of numerical discretization method directly affects the accuracy and stability of the simulation. High-order precision numerical discretization methods, such as the weighted essentially non-oscillatory (WENO) scheme, can provide higher simulation accuracy while maintaining computational efficiency. The present invention adopts high-order precision methods such as WENO to ensure the accuracy of flood simulation results.

[0113] Flood simulation application: Flood simulation is a key technology in hydrology and disaster management. It predicts the occurrence, development and impact range of floods by simulating the dynamic process of floods. Accurate flood simulation is of great significance for flood warning systems, urban planning, disaster prevention and emergency response. The flood simulation algorithm of the present invention is particularly targeted at flood events under complex terrain conditions, providing an efficient and reliable simulation tool.

[0114] The high-order precision numerical discretization of the present invention: adopts high-order precision methods such as WENO to improve simulation accuracy; implicit time discretization: handles rigid friction terms and improves calculation stability; innovation of depth average model: adapts to complex terrain and improves the adaptability and accuracy of the model; GPU parallel computing strategy: optimizes GPU resource usage and realizes accelerated calculation of flood simulation; terrain adaptability algorithm: automatically adjusts the model to adapt to different terrain features; dynamic source term processing mechanism: adjusts source terms according to real-time or predicted hydrological conditions. Multi-source term coupling simulation: comprehensively considers the influence of multiple source terms. Software implementation and optimization: provides a user-friendly operation interface, which is easy to operate and apply.

[0115] The present invention provides a high-order precision flood simulation algorithm based on GPU acceleration, which combines WENO reconstruction technology, implicit time discretization scheme, deep average model innovation, GPU parallel computing strategy, terrain adaptability algorithm, dynamic source term processing mechanism and multi-source term coupling simulation technology. Through the integration of these technologies, the algorithm can significantly improve the simulation efficiency while maintaining high computational accuracy, and is particularly suitable for flood simulation under complex terrain conditions. The algorithm undergoes a rigorous model verification and calibration process and is implemented through user-friendly software to ensure its accuracy and reliability in practical applications.

[0116] This invention aims to solve the following key technical problems to improve the accuracy and efficiency of flood simulation, especially in applications under complex terrain and extreme hydrological conditions:

[0117] Adaptability to complex terrain: Existing flood simulation models often fail to accurately capture flow characteristics when dealing with areas with steep slopes, curvatures, and variable terrain features. This invention improves the model's adaptability to complex terrain and simulation accuracy through an innovative depth-averaged model and terrain adaptability algorithm.

[0118] Stability issues of rigid friction terms: Under steep terrain conditions, the friction term may become very rigid, leading to stability issues in the numerical solution. The present invention uses an implicit time discretization scheme and a high-order precision numerical discretization method to improve the stability of the model when dealing with rigid friction terms.

[0119] High-order numerical discretization: Traditional numerical discretization methods may not provide sufficient accuracy in flood simulation, especially when simulating rapidly changing water flow conditions. The present invention adopts high-order numerical discretization methods such as WENO to improve the accuracy of simulation.

[0120] Optimization of GPU parallel computing: Although GPU parallel computing technology has great potential in improving computing efficiency, existing flood simulation algorithms do not fully utilize the parallel processing capabilities of GPUs. This paper optimizes the use of GPU resources by designing efficient data parallel and task parallel strategies to achieve accelerated computing of flood simulation.

[0121] Dynamic source term processing mechanism: Existing flood simulation models often lack flexibility and accuracy when dealing with dynamically changing source terms, such as rainfall and infiltration. The present invention implements a dynamic source term update mechanism to adjust the source terms according to real-time or predicted hydrological conditions, thereby improving the practicality of the model.

[0122] Multi-source coupling simulation: In flood simulation, it is usually necessary to consider the impact of multiple source items at the same time, such as rainfall, infiltration, drainage, etc. Existing models may have difficulties in integrating these source items. The present invention develops a multi-source coupling simulation technology to comprehensively consider the impact of multiple source items and improve the comprehensiveness of the simulation.

[0123] Automation of model verification and calibration: Model verification and calibration are important steps to ensure the accuracy of simulation results, but some methods may rely too much on manual operation. The present invention improves the reliability of the model and the convenience of user operation by automating the verification and calibration process.

[0124] A high-precision flood simulation method based on GPU acceleration, its innovation is reflected in the following key steps:

[0125] Step 1, data collection and preprocessing: Use advanced data acquisition technology to collect high-resolution terrain data, land use data, drainage system layout, etc. Through preprocessing techniques such as data fusion and interpolation methods, ensure that the data format and accuracy meet the high-precision requirements of the model and provide accurate initial conditions for subsequent simulations;

[0126] Step 2, model initialization: using the automated parameter identification algorithm, dynamically initialize the model parameters based on the collected data, and set the simulation time and space ranges to meet the needs of flood simulations of different scales and complexities;

[0127] Step 3, WENO reconstruction and implicit time discretization: Introduce weighted essentially non-oscillatory WENO reconstruction technology to reconstruct flow velocity and water level with high precision, significantly improving simulation accuracy. At the same time, use implicit time discretization schemes, backward Euler method or Crank-Nicolson scheme to effectively handle rigid terms in the model, improve the stability of the numerical solution, and allow the use of larger time steps, thereby improving computational efficiency.

[0128] Step 4, terrain adaptive grid adjustment: Develop a terrain adaptive grid generation algorithm to dynamically adjust the computational grid according to the terrain characteristics. For example, the grid is automatically refined in areas with drastic terrain changes to improve simulation accuracy, while the grid is appropriately coarsened in flat areas to optimize the allocation of computing resources and improve the adaptability of the model and the overall computing efficiency.

[0129] Step 5, dynamic source item processing: adopt the dynamic source item processing mechanism to realize the dynamic update mechanism of source items, and adjust the rainfall and infiltration source items according to the real-time data or prediction model;

[0130] Step 6, multi-source coupling simulation: In flood simulation, multiple source items that affect water flow are considered simultaneously, including rainfall, infiltration, drainage, and river leakage; these source items interact with each other and jointly determine the dynamic behavior of the flood;

[0131] Step 7, GPU parallel computing: Use GPU parallel computing capabilities to accelerate the simulation process and obtain fast simulation results;

[0132] Step 8, simulation results analysis: Analyze the simulation results to identify high-risk areas for waterlogging and drainage bottlenecks.

[0133] The specific embodiments are as follows:

[0134] 1. High-order precision WENO reconstruction technology:

[0135] Purpose: To improve the simulation accuracy of flow velocity and water level in flood simulations while maintaining the non-oscillatory nature of the numerical solution.

[0136] Implementation method: The WENO scheme is used to reconstruct the flow velocity and water level, and its weighted characteristics between different reconstruction points are utilized to achieve high-precision simulation.

[0137] (I) Principle: WENO (Weighted Essentially Non-Oscillatory) reconstruction technology is a high-order numerical method for solving conservation law equations, especially in fluid dynamics. The key to the WENO scheme is to achieve high-precision numerical solutions while ensuring non-oscillatory characteristics. It does this by weighting different linear reconstructions to accommodate local smoothness or discontinuity.

[0138] The WENO scheme is used to reconstruct the flow velocity and water level, and its weighted characteristics between different reconstruction points are used to achieve high-precision simulation, which is used to improve the simulation accuracy of flow velocity and water level in flood simulation while maintaining the non-oscillatory characteristics of the numerical solution;

[0139] The specific calculation is as follows:

[0140] According to the one-dimensional conservation law equation:

[0141] Among them, u is the conserved variable, f(u) is the corresponding flux function; t is time; x is distance; j and i are counting marks;

[0142] WENO reconstruction involves i A high-order approximation to u is:

[0143] in, is at the grid point x i The reconstruction value, w j is the weighting coefficient, k is the order of reconstruction;

[0144] Weighting coefficient w j Based on the smoothness indicator β j Calculation to balance the accuracy and non-oscillatory properties of different reconstructions: in, C j is a positive constant, ε is a small positive number to ensure numerical stability; the smoothness indicator β j Used to measure the degree of smoothness within the reconstruction interval:

[0145] like Figure 4 As shown:

[0146] Meshing: Divide the computational domain into uniform or non-uniform grids.

[0147] Data structure design: Design a suitable data structure to store the values ​​of grid points and the associated weight coefficients.

[0148] WENO reconstruction implementation: For each grid point, WENO reconstruction is performed based on the values ​​of neighboring points.

[0149] Time step advancement: Use a suitable RK (Runge-Kutta) method or other time integration method, combined with WENO reconstruction, to advance the solution.

[0150] Boundary condition processing: Boundary conditions are taken into account during the reconstruction process to ensure that high-precision numerical solutions can be obtained near the boundaries.

[0151] Stability and accuracy testing: The WENO solution is tested for stability and accuracy to ensure reliable results under various conditions.

[0152] Parallel computing optimization: Optimize the WENO reconstruction algorithm for parallel computing platforms such as GPU to improve computing efficiency.

[0153] Software Integration: Integrate the WENO reconstruction algorithm into flood simulation software, providing user configuration options and result output.

[0154] Actual case verification: Use actual flood event data to verify the effectiveness and superiority of WENO reconstruction technology.

[0155] End: Complete model development and validation.

[0156] 2. Implicit time discretization scheme:

[0157] Purpose: Improve the stability of the model when dealing with rigid friction terms, allowing the use of larger time steps.

[0158] Implementation method: Use the backward Euler method or Crank-Nicolson scheme for time discretization to ensure the stability of the numerical solution.

[0159] Rationale: Implicit time discretization schemes are mainly used to deal with stiff problems in numerical computations, especially when the system dynamics strongly depend on rapidly changing processes. In flood simulations, stiff problems are often associated with friction terms or other strongly nonlinear source terms. Implicit schemes allow the use of larger time steps without sacrificing numerical stability, which is not possible with explicit schemes.

[0160] like Figure 5 As shown, the details are as follows:

[0161] Time step selection: Choose an appropriate time step based on the time scale and stability requirements of the problem;

[0162] Discretization scheme selection: Select a first-order or second-order implicit time discretization scheme based on the rigidity and accuracy requirements of the problem;

[0163] First-order implicit time discretization is the backward Euler method:

[0164] For the time derivative Discretize using the Backward Euler method:

[0165] Among them, U n+1 and U n are the solution after time step Δt and the current solution, S n+1 is the source term at the new time step.

[0166] Second-order implicit time discretization is the Crank-Nicolson scheme:

[0167] For the time derivative terms, the Crank-Nicolson scheme is used for discretization:

[0168] Step 3.6, implicit treatment of rigid source terms: For rigid source terms, implicit methods are used to ensure numerical stability;

[0169]

[0170] Among them, a(U n+1 ) is the solution-dependent friction coefficient;

[0171] Linearization and iterative solution: For nonlinear problems, linearization techniques, including Newton's method, are used for iterative solution.

[0172] Application of boundary conditions and initial conditions: During the implicit solution process, the implementation of boundary conditions and initial conditions is considered.

[0173] Numerical stability analysis: Perform stability analysis on implicit solutions to ensure stability under various conditions.

[0174] Efficiency optimization: Optimize the iterative solution process, reduce the amount of calculation and improve efficiency.

[0175] Parallel computing adaptation: Adapt the implicit solution to parallel computing architectures, such as GPU, to improve computing efficiency.

[0176] Software Implementation: Integration of implicit time discretization scheme into flood simulation software.

[0177] Real-case validation: The implicit time discretization scheme is validated and calibrated using real data.

[0178] 3. Deep average model innovation:

[0179] Purpose: To adapt to complex terrain and improve the adaptability and accuracy of the model.

[0180] How it is implemented: A novel depth-averaged model is designed that automatically adjusts the model to different terrain features, including steep slopes and curvature.

[0181] Principle: The Depth-Averaged Model is a method that simplifies a three-dimensional flow problem into a two-dimensional problem by considering the average effect of the entire water depth to reduce the dimensionality of the problem. This model is particularly suitable for situations where the flow depth is much smaller than the horizontal scale, such as shallow water flow. In flood simulations, the depth-averaged model can effectively capture a wide range of flow characteristics while reducing the amount of calculation.

[0182] like Figure 6 As shown, the details are as follows:

[0183] Terrain data preprocessing: analyze and smooth terrain data;

[0184] Model initialization: Initialize water depth, speed and terrain data;

[0185] Discretization of continuity and momentum equations: Discretization of continuity and momentum equations in space and time using finite volume method numerical methods;

[0186] Among them, the continuity equation: In the depth average model, the continuity equation is expressed as:

[0187] Where h is the water depth, u is the horizontal velocity vector, S h is the source term, including rainfall and evaporation;

[0188] Momentum equation: The momentum equation describes the change of fluid momentum over time and its spatial distribution:

[0189]

[0190] Where g is the acceleration due to gravity, S m is the momentum source term, including friction;

[0191] Friction term: The friction term is usually expressed using the Manning formula:

[0192] Where ρ is the density of water, n is the Manning roughness coefficient;

[0193] like Figure 7 As shown, terrain adaptability analysis: analyze terrain characteristics and adjust model parameters to adapt to complex terrain.

[0194] Numerical Solver Development: Develop numerical solvers for deep averaging models.

[0195] Boundary conditions and initial conditions setting: Set the boundary conditions and initial conditions of the model.

[0196] Model solving and iteration: Perform model solving, including time step advancement and spatial updating.

[0197] Stability and accuracy analysis: Perform stability and accuracy analysis on the model solving process.

[0198] Parallel computing optimization: optimize the model solving process and adapt to the parallel computing architecture.

[0199] Software Integration and User Interface Design: Integrate the deep averaging model into the software and design the user interface.

[0200] Real case verification: The effectiveness of the model is verified using actual flood event data.

[0201] Model calibration and parameter adjustment: Model calibration and parameter adjustment are performed based on the verification results.

[0202] End: Complete model development and validation.

[0203] 4. Terrain adaptability analysis: Use terrain adaptability algorithms to analyze terrain characteristics, and ensure that the model can adapt to complex terrain changes through terrain analysis and dynamic adjustment algorithms;

[0204] Terrain Adaptive Algorithm

[0205] Purpose: Automatically adjust the model to suit different terrain features.

[0206] Implementation method: Through terrain analysis and dynamic adjustment algorithms, the model can be ensured to adapt to complex terrain changes.

[0207] Principle: Terrain adaptability algorithms are designed to improve the adaptability of flood simulation models to different terrain features. The algorithm analyzes terrain data and dynamically adjusts simulation parameters and grid structure to ensure accurate simulation results even in complex terrain conditions. This is particularly important for simulating flood flows in variable terrains, such as mountainous areas, urban areas, or mixed areas of different landforms.

[0208] Terrain feature analysis: Use terrain data to calculate the slope and roughness characteristics of the terrain:

[0209] Adaptive Mesh Refinement: Dynamically refine or coarsen the mesh for computational efficiency and accuracy based on terrain characteristics:

[0210]

[0211] Where C is the base value of the grid size, ∈ is a small positive number to avoid division by zero errors;

[0212] Source term adjustment for terrain effects: Adjust the source terms, including rainfall infiltration and surface runoff, to reflect the effects of terrain: S = S0·f(terrain characteristics).

[0213] like Figure 8As shown in the figure, terrain data preprocessing: loading and processing terrain data, such as DEM, to extract terrain features; terrain feature extraction: calculating key features such as slope and roughness of the terrain; grid adaptive adjustment: dynamically adjusting the grid size according to terrain features; model parameter adjustment: adjusting model parameters, such as Manning coefficient, to adapt to terrain changes; source term and terrain coupling: coupling terrain features with source terms and adjusting source term distribution; numerical solver configuration: configuring numerical solvers to handle adaptive grids and parameter adjustments; simulation execution: performing flood simulation on adaptive grids; result analysis and verification: analyzing simulation results and comparing them with actual data for verification; parameter optimization: optimizing model parameters and grid configuration based on result analysis; parallel computing adaptation: adapting the algorithm to the parallel computing architecture to improve computing efficiency; software implementation: integrating the terrain adaptability algorithm into the simulation software; user interface design: designing a user interface that allows users to input terrain data and adjust parameters; actual case testing: testing the effectiveness of the algorithm under actual terrain conditions; performance evaluation: evaluating the performance of the algorithm to ensure computing efficiency. End: completing algorithm development and testing. End: completing algorithm development and testing.

[0214] 5. Dynamic source item processing mechanism

[0215] Purpose: To improve the usefulness of the model by adjusting source terms based on real-time or predicted hydrological conditions.

[0216] Implementation method: Implement a dynamic update mechanism for source items, and adjust source items such as rainfall and infiltration based on real-time data or prediction models.

[0217] Principle: The dynamic source term processing mechanism refers to the dynamic adjustment of source terms in the model input, such as rainfall, infiltration, etc., according to real-time or predicted hydrological and meteorological data during flood simulation. This mechanism can significantly improve the accuracy and applicability of simulations, especially under rapidly changing and unpredictable hydrological conditions.

[0218] like Fig. 9 As shown, data collection: collect real-time hydrological and meteorological data, including rainfall and soil moisture;

[0219] Data preprocessing: cleaning and standardizing the collected data;

[0220] Forecast model integration: Integrate rainfall forecast models and other hydrological and meteorological forecasting tools;

[0221] Source item dynamic update algorithm: dynamically update source items based on real-time data or prediction results;

[0222] Dynamic source item update: Update the source item S according to the real-time data or prediction model: S(t) = f(real-time data or prediction model, t);

[0223] Among them, t represents time, f is the update function;

[0224] Rainfall intensity adjustment: Adjust the rainfall intensity according to radar rainfall data or weather forecast: I(t) = I0·(1+a·ΔP(t));

[0225] Where I0 is the baseline rainfall intensity, ΔP(t) is the rainfall intensity change, and a is the adjustment coefficient;

[0226] Infiltration rate update: Update the infiltration rate K according to soil moisture and rainfall intensity: K(t) = K0·e -β·土壤湿度(t) ;

[0227] Among them, K0 is the base infiltration rate, β is the soil moisture influence coefficient;

[0228] Model integration: Integrate dynamic source term processing mechanisms into flood simulation models.

[0229] Real-time data interface development: Develop interfaces to receive and process external data in real time.

[0230] Simulation Execution and Monitoring: Execute simulations and monitor the impact of updates to source items on simulation results.

[0231] Result analysis and evaluation: Analyze the simulation results and evaluate the effect of dynamic source term processing.

[0232] User interface design: Design a user interface that enables users to view and adjust feed item update settings.

[0233] Performance optimization: optimize algorithm performance to ensure real-time performance.

[0234] Software implementation and testing: Implement the mechanism as software and test it.

[0235] Practical case application: Apply the dynamic source term processing mechanism in actual flood simulation cases.

[0236] Feedback and adjustment: Provide feedback and adjustments based on application results.

[0237] End: Completed the development and testing of the dynamic source item processing mechanism.

[0238] 7. Multiple source coupling simulation

[0239] Purpose: To comprehensively consider the impact of multiple source terms and improve the comprehensiveness of the simulation.

[0240] Implementation method: Develop multi-source coupling simulation technology to simultaneously consider the impact of multiple source terms such as rainfall, infiltration, and drainage.

[0241] Principle: Multi-source coupled simulation refers to the simultaneous consideration of multiple source items that affect water flow in flood simulation, such as rainfall, infiltration, drainage, river leakage, etc. These source items interact with each other and jointly determine the dynamic behavior of floods. The purpose of coupled simulation is to accurately simulate these complex hydrological processes under a unified framework.

[0242] Multi-source coupling simulation refers to the simultaneous consideration of multiple source items that affect water flow in flood simulation, such as rainfall, infiltration, drainage, and river leakage; these source items interact with each other and jointly determine the dynamic behavior of floods;

[0243] The combined effect of source terms: All source terms are combined into a total source term S: S = S 降雨 +S 入渗 -S 排水 -S 渗漏 ;

[0244] Rainfall source term: The rainfall source term can be expressed as: S 降雨 =I·A;

[0245] Among them, I is the rainfall intensity and A is the rainfall area;

[0246] Infiltration source term: The infiltration source term can be expressed as: S 入渗 =K·(PP c );

[0247] Where K is the infiltration rate, P is the rainfall intensity, and P c is the water holding capacity of the soil;

[0248] Drainage and seepage source terms: Drainage and seepage are usually determined based on the capacity of the drainage system and soil characteristics.

[0249] like Fig.10As shown, source term identification and analysis: determine the key source terms that affect flood simulation; physical process modeling establishes corresponding physical models for each source term. Coupling scheme design: design a coupling scheme to integrate different source terms into the flood simulation model; numerical method selection: select a suitable numerical method to discretize the coupling model; coupling model solver development: develop a solver to solve the impact of all source terms at the same time; boundary conditions and initial conditions setting: set the boundary conditions and initial conditions of the model; model solution and iteration: perform model solution, including time step advancement and space update; stability and accuracy analysis: analyze the stability and accuracy of the coupling model; parallel computing adaptation: adapt the coupling model to the parallel computing architecture; software implementation and user interface design: integrate the coupling model into the software and design the user interface; actual case verification: use actual flood event data to verify the effectiveness of the coupling model; model calibration and parameter adjustment: perform model calibration and parameter adjustment based on the verification results; performance evaluation: evaluate the performance of the coupling model to ensure computational efficiency; end: complete the development and testing of the coupling model.

[0250] 8. GPU parallel computing strategy:

[0251] Purpose: To optimize the use of GPU resources and achieve accelerated calculation of flood simulation.

[0252] Implementation method: Develop efficient data parallel and task parallel strategies to utilize the parallel processing capability of GPU to improve computing speed.

[0253] Principle: The GPU parallel computing strategy takes advantage of the high parallel processing capabilities of the GPU to significantly improve the efficiency of numerical simulation by distributing computing tasks to multiple processing cores. This strategy is particularly suitable for tasks that require a lot of computing resources, such as flood simulation, because it can speed up the numerical solution process, especially when dealing with large-scale or high-resolution models.

[0254] The GPU parallel computing strategy utilizes the high parallel processing capability of the GPU to significantly improve the efficiency of numerical simulation by distributing computing tasks to multiple processing cores. The details are as follows:

[0255] Data parallelism: Data parallelism involves splitting a dataset into multiple small chunks, each of which is processed independently by a different GPU thread or core;

[0256] Task parallelism: Task parallelism involves breaking down a computational task into multiple subtasks that can be executed simultaneously on different GPU threads or cores;

[0257] Load balancing: Ensure that all GPU cores have a balanced workload, avoiding situations where some cores are idle while others are overloaded: Load balance = total workload / number of cores.

[0258] like Figure 7 As shown in the figure, parallel computing demand analysis: analyze the computing requirements of the flood simulation algorithm and determine the computing tasks that can be parallelized; data and task division: divide the data set and computing tasks into subtasks suitable for GPU parallel processing; GPU core mapping: map the divided tasks to multiple cores or thread blocks of the GPU; memory management optimization: optimize the storage and access mode of data in the GPU memory to reduce memory access latency; synchronization and communication mechanism: design a suitable synchronization mechanism to ensure the consistency of data between different processing units; load balancing strategy: implement a dynamic load balancing strategy to ensure that all GPU cores work efficiently; error detection and recovery: design error detection and recovery mechanisms to ensure the robustness of parallel computing; performance analysis and tuning: use performance analysis tools to identify bottlenecks and tune parallel algorithms; software implementation: integrate parallel algorithms into flood simulation software and provide necessary user configuration options; testing and verification: test parallel programs on GPU and perform sufficient verification to ensure correctness and performance; actual application deployment: deploy the optimized parallel algorithm to the actual flood simulation application; performance monitoring and optimization: monitor performance in actual applications and perform further optimization as needed; conclusion: complete the development, testing and deployment of parallel computing strategies.

[0259] 9. Software implementation and optimization

[0260] Purpose: To provide a user-friendly interface, easy to operate and apply.

[0261] Implementation method: Integrate the algorithm into user-friendly software, provide an interface that is easy to operate and apply, and optimize performance.

[0262] Principle: The software implementation and optimization phase is to integrate the flood simulation algorithms and models developed previously into a user-friendly software platform for easy use and promotion. This phase includes not only the coding implementation of the algorithm, but also the performance optimization of the software to ensure that the software runs efficiently and stably and provides a good user experience.

[0263] Performance optimization indicators: Performance can be measured by a variety of indicators, such as execution time T, memory usage M, etc.:

[0264] performance = f(T,M,...);

[0265] User experience evaluation: User experience can be evaluated by indicators such as interface usability and functional completeness:

[0266] User experience = g (ease of use of interface, functional completeness, ...).

[0267] like Fig.11As shown, demand analysis: determine the target users and functional requirements of the software; system design: design the software's architecture, modules and interfaces; coding implementation: implement the software's coding work according to the design; algorithm integration: integrate the developed flood simulation algorithm into the software; user interface development: develop an intuitive and easy-to-use user interface; performance evaluation: evaluate the performance of the software and identify bottlenecks; performance optimization: optimize software performance, including algorithm optimization and code optimization; functional testing: test the various functions of the software; user testing: let the target users test the software and collect feedback; defect fixing: fix software defects based on test feedback; document writing: write user manuals and development documents; version control: implement version control and manage different versions of the software; deployment and maintenance: deploy the software to the user environment and provide continuous maintenance and updates; performance monitoring: monitor software performance in actual use; user feedback: collect user feedback after use; continuous optimization: continuous optimization based on user feedback; end: complete the entire process of software implementation and optimization.

[0268] 10. Model validation and calibration:

[0269] Purpose: To ensure the accuracy and reliability of simulation results.

[0270] Implementation: Automated validation and calibration process to validate and calibrate the model using real observation data

[0271] Rationale: Model validation and calibration are key steps to ensure the accuracy and reliability of flood simulation models. This process involves comparing model predictions with actual observations, evaluating model performance, and adjusting model parameters as needed to improve prediction results.

[0272] Error metrics: Calculate the error between the model prediction value P and the actual observation value O. Commonly used error metrics include root mean square error (RMSE) and mean absolute error (MAE):

[0273] Coefficient of Certainty: Use the coefficient of certainty (R2) to measure the goodness of fit of the model:

[0274] in, is the mean of the observations.

[0275] Parameter adjustment: According to the error analysis results, adjust the model parameters θ to minimize the error:

[0276] Among them, a is the learning rate, is the gradient of the error function E with respect to the parameter θ.

[0277] like Fig.12As shown, data collection: collect actual observation data for model validation and calibration; model prediction: run the model and generate prediction results; error analysis: calculate the error between model prediction and actual observation; performance evaluation: evaluate model performance using error metrics and certainty coefficients; parameter sensitivity analysis: determine the parameters that have the greatest impact on model prediction. Parameter adjustment strategy formulation: formulate parameter adjustment strategies to minimize prediction errors; model calibration: update model parameters according to the adjustment strategy; iterative verification: repeat model prediction, error analysis, and performance evaluation steps until the model performance reaches acceptable standards; calibration result recording: record calibrated model parameters and performance indicators; user feedback: obtain user feedback on model performance; model optimization: further optimize the model based on user feedback and performance evaluation results. Documentation: record the detailed process and results of model validation and calibration; end: complete the model validation and calibration process.

[0278] Example 1: Rainwater waterlogging control in Yanshan Road, Jianye District, Nanjing

[0279] Background: The Yanshan Road area in Jianye District, Nanjing City is often flooded during heavy rains due to its low-lying terrain and imperfect drainage system. In order to improve this situation, the flood simulation algorithm of the present invention is used to plan and design flood control.

[0280] Implementation steps:

[0281] 1. Data collection and preprocessing: Collect terrain data (DEM), land use data, drainage system layout, etc. in the Yanshan Road area. Preprocess the data to ensure that the data format and accuracy meet the model requirements.

[0282] 2. Model initialization: Initialize model parameters based on the collected data and set the time and space range of the simulation.

[0283] 3. WENO reconstruction and implicit time discretization: WENO reconstruction technology is used to reconstruct the flow velocity and water level with high accuracy. The implicit time discretization scheme is used to deal with the rigid terms in the model.

[0284] 4. Terrain-adaptive grid adjustment: The computational grid is adjusted according to the terrain characteristics to ensure that the model can accurately simulate waterlogging in low-lying areas.

[0285] 5. Dynamic source item processing: Dynamically adjust source items according to real-time rainfall data to simulate the impact of rainfall on waterlogging.

[0286] 6.Multi-source coupling simulation: Consider multiple source items such as rainfall and drainage system emissions, and perform coupled simulation.

[0287] 7. GPU parallel computing: Use GPU parallel computing capabilities to accelerate the simulation process and obtain fast simulation results.

[0288] 8. Analysis of simulation results: Analyze simulation results to identify high-risk areas for waterlogging and drainage bottlenecks.

[0289] 9. Control scheme design: Design a rainwater waterlogging control scheme based on simulation results, including drainage system optimization and emergency measures.

[0290] 10. Implementation and Monitoring: Implement the treatment plan and monitor during the rainstorm to verify the treatment effect.

[0291] Effect evaluation:

[0292] Through the flood simulation algorithm of the present invention, the waterlogging control plan in the Yanshan Road area has been scientifically designed and effectively implemented, which significantly reduces the risk of waterlogging.

[0293] Example 2: Flood warning in the mountainous area of ​​Kaihua County, Quzhou City, Zhejiang Province

[0294] Background: The mountainous area of ​​Kaihua County, Quzhou City, Zhejiang Province has complex terrain and is vulnerable to floods. In order to improve the accuracy and timeliness of flood warning, the flood simulation algorithm of the present invention is used to develop a flood warning system.

[0295] Implementation steps:

[0296] 1. Topographic and meteorological data collection: Collect topographic data, historical flood data and meteorological forecast data of Kaihua County.

[0297] 2. Model parameter setting: Set the model parameters based on the collected data, including rainfall patterns, terrain characteristics, and watershed characteristics.

[0298] 3. Application of depth average model: The depth average model is used to simplify three-dimensional flow problems and adapt to complex mountainous terrain.

[0299] 4. GPU parallel computing strategy: Use GPU parallel computing strategy to quickly simulate floods and provide time advantages for early warning.

[0300] 5. Dynamic source term and multiple source term coupling simulation: Dynamically adjust the rainfall source term to take into account the temporal and spatial changes of rainfall in mountainous areas. Coupled simulation of multiple source terms such as rainfall, surface runoff and groundwater flow.

[0301] 6. Simulation result output: Output simulation results, including flood evolution path, possible flooded areas and risk levels.

[0302] 7. Early warning system integration: Integrate simulation results into flood early warning systems to provide real-time flood risk assessment.

[0303] 8. Warning information release: Based on simulation results and risk assessment, timely release of flood warning information to guide residents and relevant departments to take response measures.

[0304] 9. Effect verification and feedback: After a flood occurs, verify the accuracy of the early warning system and collect feedback for model calibration and optimization.

[0305] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention should be included in the protection scope of the invention. All technical features in this embodiment can be freely combined according to actual needs.

[0306] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-precision flood simulation method based on GPU acceleration, characterized by: The specific steps include: Step 1, data collection and preprocessing: Data acquisition technology is used to collect high-resolution terrain data, land use data, and drainage system layout. Preprocessing technology, including data fusion and interpolation methods, is used to ensure that the data format and accuracy meet the high-precision requirements of the model; Step 2, model initialization: using the automated parameter identification algorithm, dynamically initialize the model parameters based on the collected data, and set the simulation time and space ranges to meet the needs of flood simulations of different scales and complexities; Step 3, WENO reconstruction and implicit time discretization: Introduce weighted intrinsically non-oscillatory WENO reconstruction technology to reconstruct flow velocity and water level with high precision. At the same time, use implicit time discretization scheme, backward Euler method or Crank-Nicolson scheme to deal with rigid terms in the model, improve the stability of the numerical solution, and allow the use of larger time steps, thereby improving computational efficiency. Step 4, terrain adaptive grid adjustment: Develop a terrain adaptive grid generation algorithm to dynamically adjust the computational grid according to the terrain characteristics. For example, the grid is automatically refined in areas with drastic terrain changes to improve simulation accuracy, while the grid is appropriately coarsened in flat areas to optimize the allocation of computing resources and improve the adaptability and overall computing efficiency of the model. Step 5, dynamic source item processing: adopt the dynamic source item processing mechanism to realize the dynamic update mechanism of source items, and adjust the rainfall and infiltration source items according to the real-time data or prediction model; Step 6, multi-source coupling simulation: In flood simulation, multiple source items that affect water flow are considered simultaneously, including rainfall, infiltration, drainage, and river leakage; these source items interact with each other and jointly determine the dynamic behavior of the flood; Step 7, GPU parallel computing: Use GPU parallel computing capabilities to accelerate the simulation process and obtain fast simulation results; Step 8, simulation results analysis: Analyze the simulation results to identify high-risk areas for waterlogging and drainage bottlenecks.

2. The high-precision flood simulation method based on GPU acceleration according to claim 1, characterized in that: In step 3, the WENO scheme is used to reconstruct the flow velocity and water level. The weighted characteristics between different reconstruction points are used to achieve high-precision simulation, which is used to improve the simulation accuracy of flow velocity and water level in flood simulation while maintaining the non-oscillatory characteristics of the numerical solution. The specific calculation is as follows: Step 3.1, meshing: divide the computational domain into uniform or non-uniform grids; Step 3.2, data structure: using a suitable data structure to store the values ​​of the grid points and the associated weight coefficients; Step 3.3, WENO reconstruction implementation: For each grid point, WENO reconstruction is performed based on the values ​​of neighboring points: According to the one-dimensional conservation law equation: Among them, u is the conserved variable, f(u) is the corresponding flux function; t is time; x is distance; j and i are counting marks; WENO reconstruction involves i A high-order approximation to u is: in, is at the grid point x i The reconstruction value, w j is the weighting coefficient, k is the order of reconstruction; Weighting coefficient w j Based on the smoothness indicator β j Calculation to balance the accuracy and non-oscillatory properties of different reconstructions: in, C j is a positive constant, ε is a small positive number to ensure numerical stability; the smoothness indicator β j Used to measure the degree of smoothness within the reconstruction interval:

3. The high-precision flood simulation method based on GPU acceleration according to claim 1 is characterized in that: In step 3, implicit time discretization scheme: the backward Euler method or Crank-Nicolson scheme is used for time discretization to ensure the stability of the numerical solution, improve the stability of the model when dealing with rigid friction terms, and allow the use of larger time steps; The details are as follows: Step 3.4, time step selection: select an appropriate time step according to the time scale and stability requirements of the problem; Step 3.5, discretization scheme selection: select a first-order or second-order implicit time discretization scheme according to the rigidity and accuracy requirements of the problem; First-order implicit time discretization is the backward Euler method: For the time derivative Discretize using the Backward Euler method: Among them, U n+1 and U n are the solution after time step Δt and the current solution, S n+1 is the source term at the new time step; Second-order implicit time discretization is the Crank-Nicolson scheme: For the time derivative terms, the Crank-Nicolson scheme is used for discretization: Step 3.6, implicit treatment of rigid source terms: For rigid source terms, implicit methods are used to ensure numerical stability; Among them, a(U n+1 ) is the solution-dependent friction coefficient; Step 3.7, Linearization and iterative solution: For nonlinear problems, linearization techniques, including Newton's method, are used for iterative solution.

4. The high-precision flood simulation method based on GPU acceleration according to claim 1 is characterized in that: In step 4, a depth-averaged model is used to automatically adjust the model to accommodate different terrain features, including steep slopes and curvature; specifically, as follows: Step 4.1, terrain data preprocessing: analyzing and smoothing the terrain data; Step 4.2, model initialization: Initialize water depth, velocity and terrain data; Step 4.

3. Discretization of continuity and momentum equations: Use the finite volume method to discretize the continuity equation and momentum equation in space and time; Among them, the continuity equation: In the depth average model, the continuity equation is expressed as: Where h is the water depth, u is the horizontal velocity vector, S h is the source term, including rainfall and evaporation; Momentum equation: The momentum equation describes the change of fluid momentum over time and its spatial distribution: Where g is the acceleration due to gravity, S m is the momentum source term, including friction; Friction term: The friction term is usually expressed using the Manning formula: Where ρ is the density of water, n is the Manning roughness coefficient; Step 4.

4. Terrain adaptability analysis: Use terrain adaptability algorithm to analyze terrain characteristics. Through terrain analysis and dynamic adjustment algorithm, ensure that the model can adapt to complex terrain changes; the details are as follows: Terrain feature analysis: Use terrain data to calculate the slope and roughness characteristics of the terrain: Adaptive Mesh Refinement: Dynamically refine or coarsen the mesh for computational efficiency and accuracy based on terrain characteristics: Where C is the base value of the grid size, ∈ is a small positive number to avoid division by zero errors; Source term adjustment for terrain effects: Adjust the source terms, including rainfall infiltration and surface runoff, to reflect the effects of terrain: S = S0·f(terrain characteristics).

5. The high-precision flood simulation method based on GPU acceleration according to claim 1 is characterized in that: In step 5, a dynamic source term processing mechanism is used to implement a dynamic update mechanism for source terms, and the rainfall and infiltration source terms are adjusted according to real-time data or prediction models; the details are as follows: Step 5.1, data collection: collect real-time hydrological and meteorological data, including rainfall and soil moisture; Step 5.2, data preprocessing: cleaning and standardizing the collected data; Step 5.3, prediction model integration: integrating rainfall prediction models with other hydrological and meteorological prediction tools; Step 5.4, source item dynamic update algorithm: dynamically update source items according to real-time data or prediction results; Dynamic source item update: Update the source item S according to the real-time data or prediction model: S(t) = f(real-time data or prediction model, t); Among them, t represents time, f is the update function; Rainfall intensity adjustment: Adjust the rainfall intensity according to radar rainfall data or weather forecast: I(t) = I0·(1+a·ΔP(t)); Where I0 is the baseline rainfall intensity, ΔP(t) is the rainfall intensity change, and a is the adjustment coefficient; Infiltration rate update: Update the infiltration rate K according to soil moisture and rainfall intensity: K(t) = K0·e -β·土壤湿度(t) ; Among them, K0 is the base infiltration rate, β is the soil moisture influence coefficient; Step 5.5, model integration: Integrate the dynamic source term processing mechanism into the flood simulation model.

6. The high-precision flood simulation method based on GPU acceleration according to claim 1, characterized in that: In step 5, multi-source coupling simulation refers to the simultaneous consideration of multiple source items that affect water flow in flood simulation, such as rainfall, infiltration, drainage, and river leakage; these source items interact with each other and jointly determine the dynamic behavior of the flood; The combined effect of source terms: All source terms are combined into a total source term S: S = S 降雨 +S 入渗 -S 排水 -S 渗漏 ; Rainfall source term: The rainfall source term can be expressed as: S 降雨 =I·A; Among them, I is the rainfall intensity and A is the rainfall area; Infiltration source term: The infiltration source term can be expressed as: S 入渗 =K·(PP c ); Where K is the infiltration rate, P is the rainfall intensity, and P c is the water holding capacity of the soil; Drainage and seepage source terms: Drainage and seepage are usually determined based on the capacity of the drainage system and soil characteristics.

7. The high-precision flood simulation method based on GPU acceleration according to claim 1, characterized in that: In step 7, the GPU parallel computing strategy utilizes the high parallel processing capability of the GPU to significantly improve the efficiency of the numerical simulation by distributing the computing tasks to multiple processing cores; the details are as follows: Step 7.1, Data Parallelism: Data parallelism involves splitting the dataset into multiple small chunks, each of which is processed independently by different GPU threads or cores; Step 7.2, Task Parallelism: Task parallelism involves breaking down a computational task into multiple subtasks, which can be executed simultaneously on different GPU threads or cores; Step 7.3, load balancing: Ensure that the workload of all GPU cores is balanced to avoid the situation where some cores are idle while other cores are overloaded: Load balance = total workload / number of cores.

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