Anti-flood safety evaluation system for dike project

Abstract

The invention discloses an embankment project flood control safety evaluation system, and relates to the technical field of flood control safety, an initial position and boundary conditions of an embankment breach are set through a simulation scene construction module, and an MIKE 21 model is utilized to simulate an embankment damage and breach forming process; the data acquisition module records breach boundary and water flow dynamic data in real time and provides support for numerical stability analysis; the numerical stability analysis module evaluates the stability of the model according to the dynamic data and identifies an unstable region; the instability adjusting module adopts a self-adaptive grid refining technology, and optimizes model calculation by dynamically adjusting grid division; finally, the optimized model carries out embankment breach risk assessment through a risk analysis report generation module, an accurate risk analysis report is generated, the prediction accuracy and the numerical stability in the embankment breach simulation process are improved through the technology, and the reliability of risk assessment is remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of flood control safety, and in particular to a flood control safety evaluation system for a levee project. Background Art

[0002] Flood control safety evaluation of levee projects refers to a systematic assessment of the flood control function of levee projects to ensure that they can effectively prevent floods and protect people's lives and property and the integrity of the ecological environment when facing extreme weather conditions such as floods and rainstorms. Flood control safety evaluation usually involves a comprehensive consideration of the structural strength, stability, and anti-seepage performance of the levee, and evaluates its ability to withstand external hydrological conditions such as specific water levels and flow rates.

[0003] In existing technologies, flood control safety assessment is mainly carried out through hydrological and hydraulic model analysis, numerical simulation, geological survey and other methods. For example, commonly used techniques include simulating the bearing capacity of levees through finite element analysis (FEM), or using hydrodynamic simulation to assess the deformation and damage risk of levees under different water flow conditions. These methods can perform accurate safety assessments by inputting geological information, structural parameters and historical flood data of the levees. For example, the MIKE 21 hydrodynamic model is used to analyze the breach risk of levees to help decision makers formulate flood control measures.

[0004] The prior art has the following deficiencies: When a dike breach occurs, the free surface of the water flow shows a high degree of instability, and the expansion of the breach and the dynamic changes of the water flow are extremely complex. Although MIKE 21 provides a relatively powerful water flow simulation function, it may face numerical stability problems when facing dynamic free surface flow, especially when the dike breach begins to expand. In addition, the rapid changes in water flow and the irregular expansion of the breach boundary will bring highly nonlinear dynamic processes to the model, which may lead to numerical oscillations or non-convergence of the solution, thus affecting the accuracy of the simulation results and may cause serious deviations in risk assessment. Summary of the invention

[0005] The purpose of the present invention is to provide a flood control safety evaluation system for levee projects to address the deficiencies in the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a flood control safety evaluation system for levee projects, comprising a simulation scenario construction module, a data acquisition module, a numerical stability analysis module, an instability adjustment module and a risk analysis report generation module; Simulation scenario building module: In the MIKE 21 model, the initial location and boundary conditions of the breach are set, and the model is used to simulate the damage of the levee and the initial formation process of the breach; Data acquisition module: During the simulation process, the time stepping in MIKE 21 is used to record the changes in the breach boundary at each time step, and the extended dynamic data of the boundary shape during the breach expansion process are extracted; the free surface boundary of the water flow is set in the model, and the water level fluctuation data during the flood overflow process is output; Numerical stability analysis module: evaluates the numerical stability of the MIKE 21 model simulation process based on the extracted dynamic data of the boundary shape during the breach expansion process and the water level fluctuation data during the flood overflow process; Instability Adjustment Module: Based on the evaluation results, it identifies areas with numerical instability and uses dynamic mesh refinement technology to dynamically adjust the computational mesh according to the expansion speed of the breach through an adaptive meshing method. Risk analysis report generation module: After optimizing the MIKE 21 model, the prediction accuracy of the MIKE 21 model is determined, and the MIKE 21 model with high prediction accuracy is used for risk assessment of levee breaches, and a corresponding risk analysis report is generated.

[0007] Preferably, in the data acquisition module, the abnormal index of the breach expansion rate is generated after analyzing the dynamic rate of the breach expansion. The method for obtaining the abnormal index of the breach expansion rate is: Collect time series data on breach growth rate ,in It represents the breach expansion rate at the nth time step. The density of a data point in the local area is measured by calculating the relative density of the data point and its neighbors. , whose local reachable density The calculation expression is: ; Among them, reachability distance refers to the distance between data points Among the k nearest neighbors of The distance to the nearest point; Comparing Data Points The density of its neighbors to determine whether it is an outlier , The value is calculated as: ;in: It is a data point The set of k nearest neighbors of Yes The local reachable density is used to calculate the abnormal index of breach expansion rate, and the expression is: ; In the formula, is the abnormal index of breach expansion rate, and Represents the local outliers of all data points X The minimum and maximum values.

[0008] Preferably, in the data acquisition module, the water level rise and fall trends at different locations and time points are analyzed to generate a water level rise and fall fluctuation index, and the method for obtaining the water level rise and fall fluctuation index is: Obtain the water level data W(t) of the time series, where t represents the time point and W(t) represents the water level at time t. Divide the water level time series into several time windows to obtain several subsequences. , at each time point Corresponding to a network node, the node weight is set to the water level value ; Calculate the similarity of water level between two time points. The expression is: ;in, Respectively at time points and The water level data at Indicates time point and According to the similarity between time points, an adjacency matrix A is constructed, where each element Indicates time point and The connection strength, degree The calculation expression is: ; Clustering coefficient It measures the connection density between neighbors of node p and indicates the local fluctuation trend of water level change. The calculation formula of clustering coefficient is: ; In the formula, is the degree of node p, It represents the number of triangles formed between the neighbors of node p. The water level fluctuation index is obtained by taking the weighted average sum of the calculated similarity and clustering coefficient.

[0009] Preferably, in the numerical stability analysis module, the breach expansion rate anomaly index and the water level rise and fall fluctuation index are normalized so that they are both between [0,1], and the normalized breach expansion rate anomaly index and the water level rise and fall fluctuation index are calculated by weighted average to obtain the numerical stability coefficient in the MIKE 21 model simulation process.

[0010] Preferably, in the instability adjustment module, the numerical stability coefficient obtained during the MIKE 21 model simulation process is compared with a numerical stability reference threshold value pre-set according to experimental data; if the numerical stability coefficient during the MIKE 21 model simulation process is greater than or equal to the pre-set numerical stability reference threshold value, it means that the numerical stability during the MIKE 21 model simulation process is high, and the area with high numerical stability is divided into a numerical stability area, and there is no need to adjust the grid; if the numerical stability coefficient during the MIKE 21 model simulation process is less than the pre-set numerical stability reference threshold value, it means that the numerical stability during the MIKE 21 model simulation process is low, and the area with low numerical stability is divided into a numerical instability area, and the grid needs to be refined.

[0011] Preferably, in the risk analysis report generation module, after optimizing the MIKE 21 model, the prediction accuracy of the MIKE 21 model is judged. When judging the prediction accuracy of the MIKE 21 model, multiple error analysis methods are used, including root mean square error RMSE and determination coefficient. , the root mean square error RMSE calculation formula is: ;in: is the value of the model prediction result at the bth time point, is the value of the actual observation data at the bth time point, b represents each specific time point in the calculation process, and E is the total number of time points; the coefficient of determination It is used to measure the quality of model fitting, and its calculation formula is: ;in: is the mean of the actual observed data.

[0012] Preferably, according to the numerical stability coefficient of the optimized MIKE 21 model and the improved grid refinement technology, the prediction accuracy of the model is judged by the calculated error index, specifically: If RMSE < 0.1, and >0.9, the prediction accuracy is considered high, and the MIKE 21 model with high prediction accuracy is used for risk assessment of levee breaches and to generate corresponding risk analysis reports; if RMSE ≥ 0.1, and <0.9, the prediction accuracy is considered to be low, and the MIKE 21 model is reoptimized.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention effectively improves the prediction accuracy of the model in the face of dynamic water flow and complex breach expansion by introducing a multi-module optimization process, including simulation scenario construction, data acquisition, numerical stability analysis, instability adjustment, and risk analysis report generation. In particular, in the data acquisition module, by calculating the breach expansion rate anomaly index and the water level fluctuation index, and combining the adaptive grid refinement technology, the stability of the model is optimized, avoiding the numerical oscillation problem caused by the irregularity of breach expansion and the dynamic changes of water flow, and ensuring the accuracy of risk assessment.

[0014] 2. The present invention performs weighted averaging of the breach expansion rate anomaly index and the water level fluctuation index after normalization, calculates the numerical stability coefficient, and refines the grid in the unstable area based on the coefficient, thereby improving the numerical stability of the model. The prediction accuracy of the MIKE 21 model is evaluated by using error analysis methods (such as root mean square error RMSE and determination coefficient), which further improves the reliability of the simulation results. If the prediction accuracy reaches a high standard, an accurate risk analysis report can be generated to assist in the risk assessment of dike breaches and ensure the scientificity and rationality of flood control decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of 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.

[0018] For examples, see Figure 1 As shown, a flood control safety evaluation system for levee projects described in this embodiment includes a simulation scenario construction module, a data acquisition module, a numerical stability analysis module, an instability adjustment module, and a risk analysis report generation module; Simulation scenario building module: In the MIKE 21 model, the initial location and boundary conditions of the breach are set, and the model is used to simulate the damage of the levee and the initial formation process of the breach; Data acquisition module: During the simulation process, the time stepping in MIKE 21 is used to record the changes in the breach boundary at each time step, and the extended dynamic data of the boundary shape during the breach expansion process are extracted; the free surface boundary of the water flow is set in the model, and the water level fluctuation data during the flood overflow process is output; Numerical stability analysis module: evaluates the numerical stability of the MIKE 21 model simulation process based on the extracted dynamic data of the boundary shape during the breach expansion process and the water level fluctuation data during the flood overflow process; Instability Adjustment Module: Based on the evaluation results, it identifies areas with numerical instability and uses dynamic mesh refinement technology to dynamically adjust the computational mesh according to the expansion speed of the breach through an adaptive meshing method. Risk analysis report generation module: After optimizing the MIKE 21 model, the prediction accuracy of the MIKE 21 model is determined, and the MIKE 21 model with high prediction accuracy is used for risk assessment of levee breaches, and a corresponding risk analysis report is generated.

[0019] In the MIKE 21 model, the initial position and boundary conditions of the levee breach are set to simulate the damage of the levee and the initial formation process of the breach. First, the terrain data around the levee must be obtained, including the shape of the levee, the surrounding river channel, the spillway, etc. The terrain data can usually be obtained through a digital elevation model (DEM) or ground measurement data. In MIKE 21, the terrain data is imported in the form of a grid or irregular grid. The structural parameters of the levee (such as the elevation, width, material type, etc. of the levee) also need to be input. The soil parameters of the levee (such as permeability, shear strength, etc.) have an important influence on the formation of the breach.

[0020] In MIKE 21, the simulation area can be discretized using a regular grid (traditional rectangular grid) or an irregular grid (Flexible Mesh). Depending on the size and complexity of the levee, grids of different resolutions can be selected to capture details. For the area where the levee breach occurs, the grid needs to be appropriately refined in order to accurately capture the process of breach expansion. In the levee model, boundary conditions such as inflow, outflow, and water level need to be set. Common boundary conditions include: Inflow boundary: simulates the water flow input conditions of rivers or other water sources to the levee, such as water level or flow rate. Outflow boundary: sets the outflow conditions of the simulation area, which can be fixed water level or flow conditions. Water level change: During the simulation, the water level change of the flood event can be set, especially the water level rise process before the breach occurs.

[0021] Initial water level of the water body: Set the initial water level of the simulation area, usually the normal water level of the river or water body. When a breach occurs, the water level will change dramatically. The initial condition is the benchmark for simulating water flow changes. Initial state of levee materials and structure: The structure of the levee and the initial state of the soil (such as dry or wet conditions, strength of the soil layer, etc.) also need to be set in the model. The material properties of the levee determine its durability under water erosion and affect the speed and scale of the breach.

[0022] In MIKE 21, levee failure can be simulated by setting the erosion characteristics and stress conditions of the levee materials. Common failure mechanisms include: Scour erosion: The scouring action of water flow will cause the loss of the surface soil of the levee, which will then form a breach. In MIKE 21, hydrodynamic modules (such as MIKE 21 Flow Model) are usually used to simulate the scouring of the levee surface by water flow. When the water level continues to rise, the permeability of the levee may increase, and the water flow will penetrate through the bottom or side of the levee, resulting in the weakening of the levee structure and further promoting the formation of levee breaches. Stress-strain model: The strength of the levee material can be described by a stress-strain model. In this model, the deformation of the levee surface under the action of water flow and the expansion of cracks are dynamic, and the finite element analysis (FEA) module may be required to deal with the elastic-plastic behavior of the soil.

[0023] Once the levee begins to be eroded by water flow, the erosion force of the water flow on the levee will increase, gradually weakening the stability of the levee. When simulating the initial formation of a levee breach, the hydrodynamic module can be used to simulate the velocity distribution of the water flow on the levee surface to determine which areas are likely to be damaged first.

[0024] At the beginning of levee failure, the breach boundary usually expands in an irregular manner. The initial breach may be localized, but as the water continues to scour, the breach will gradually expand. At this time, the breach expansion model or erosion model can be used to simulate the levee's destruction process and the expansion of the breach.

[0025] In the MIKE 21 model, the function of the data acquisition module is to extract and record important data such as breach expansion, boundary changes, and water flow fluctuations during the simulation process. Specifically, the time stepping function of the model can be used to record and extract these data at each time step for subsequent analysis and evaluation. The following are the specific steps of the data acquisition module: In MIKE 21, the time-stepping function is used to simulate the development of a dike breach step by step. At each time step, the model calculates the evolution of the breach area and records the changes in the breach boundaries based on water flow, soil properties and external conditions.

[0026] In the MIKE 21 model, the time step setting is very important, as it determines the accuracy and stability of the simulation. The time step needs to be set according to the changes in water flow, the speed of the breach development process, etc. The time step can be set to a fixed value through the model, or it can be automatically adjusted according to the simulation process to better capture data in rapidly changing processes such as breach expansion.

[0027] MIKE 21 will regularly record breach boundary data during the simulation process, including: Boundary morphology: the geometry, location, and expansion direction of the breach. Boundary size: the width and height of the breach. Breach expansion speed: the expansion rate and progress of the breach. These data will be saved at each time step for subsequent analysis of the breach expansion trend. The model provides an output function that can save these boundary change data in the form of time series data or spatial data sets. Users can extract breach boundary data at specific time points as needed for further analysis.

[0028] The MIKE 21 model can generate dynamic data on breach boundary changes, which is convenient for analyzing the specific evolution of the breach expansion process. Breach expansion data includes changes in the damaged area of ​​the levee at different time steps, which helps to determine the evolution trend and potential risks of the breach. At each time step, the model calculates and outputs the morphology and location data of the breach area. Users can extract this data and display the expansion process of the breach in the form of graphs or data tables. These data can help analyze: The path of breach expansion: how the breach expands along the levee boundary and whether there is a specific expansion direction. The rate of breach expansion: The rate of breach expansion at different time steps may be related to factors such as water flow rate and levee structure. The model can also output the boundary change rate, that is, the dynamic rate of breach expansion, to help evaluate how fast the breach progresses.

[0029] MIKE 21 provides visualization tools that can display breach boundary data in the form of dynamic graphics. Through these graphics, users can intuitively see the expansion and changes of breach boundaries. Data at different time steps during the breach development process can be observed through layer overlays or time series graphs.

[0030] After analyzing the dynamic rate of breach expansion, the breach expansion rate anomaly index is generated. The method for obtaining the breach expansion rate anomaly index is as follows: Collect time series data of breach expansion rate, representing the expansion rate at different time steps (or spatial positions). Set these data to be expressed as ,in It represents the breach expansion rate at the nth time step. The density of the data point in the local area is measured by calculating the relative density of the data point and its neighbors. For a data point , whose local reachable density The calculation expression is: ; Among them, reachability distance refers to the distance between data points Among the k nearest neighbors of The distance to the nearest point.

[0031] Comparing Data Points The density of a point is compared with that of its neighbors to determine whether it is an outlier. If the density of a point is significantly lower than the density of its neighbors, then the point is considered an outlier. , The value is larger, The value is calculated as: ;in: It is a data point The set of k nearest neighbors of Yes The local reachable density of . ≈1, indicating The density of is similar to that of its neighbors and is a normal point. >1, means The density of is significantly lower than that of its neighbors and is an outlier. <1, means The density of is significantly higher than that of its neighbors and may belong to the center of the cluster. The abnormal index of breach expansion rate is calculated as follows: ; In the formula, is the abnormal index of breach expansion rate, which indicates the abnormal degree of breach expansion rate at a specific time step (or location). and Represents the local outliers of all data points X The minimum and maximum values.

[0032] The closer the value is to 1, the more abnormal the point is, that is, the change in the breach expansion rate is significantly different from the surrounding data points. The value of is close to 0, indicating that the point is relatively normal and the change in the breach expansion rate conforms to the normal pattern of the surrounding area.

[0033] In MIKE 21, setting the free surface boundary is an important step in simulating water flow fluctuations during breach. The free surface refers to the boundary where the water level changes over time. Especially during flood overflow, the fluctuation of water level is crucial to the damage of levees and the formation of breaches.

[0034] In MIKE 21, the free surface boundaries of the water flow can be defined by setting boundary conditions. Usually include: Water level boundary conditions: set the water level conditions at the inflow and outflow ends of the simulation area, especially for the fluctuation of the breach water level during the occurrence and development of the breach. Water flow rate boundary conditions: In the breach area, the water flow speed and flow direction also have an impact on the damage of the levee. By setting the flow rate boundary, the model can simulate the scouring effect of water flow on the levee. These boundary conditions will affect the formation of the levee breach, and the water level fluctuations at the boundaries will change depending on the water flow.

[0035] During the simulation, MIKE 21 will continuously monitor and output water level fluctuation data. These data include: Water level change curve: water level data of each measuring point in each time step, recording water level fluctuations during flood overflow. Water level fluctuation trend: records the rise and fall trend of water levels at different locations and time points to help analyze the impact of floods on levees. In addition to water level fluctuation data, MIKE 21 can also output dynamic change data such as flow rate and water depth. These data are crucial for evaluating the degree of damage to levees and the development of breaches during flood overflows. These data can be collected by setting sensor locations and time intervals to gain a detailed understanding of the flow characteristics during floods.

[0036] After the MIKE 21 model outputs data, users can further process and analyze it through built-in data analysis tools or external data analysis software (such as MATLAB, Excel, etc.): Time series analysis: Analyze the time series data of breach boundary changes, expansion rate and water level fluctuations. Spatial data analysis: Through spatial analysis tools, observe the spatial changes of breach boundaries and evaluate breach risks in different areas.

[0037] After analyzing the water level fluctuation trends at different locations and time points, the water level fluctuation index is generated. The method for obtaining the water level fluctuation index is as follows: Obtain the water level data W(t) of the time series, where t represents the time point and W(t) represents the water level at time t. Divide the water level time series into several time windows to obtain several subsequences. , at each time point Corresponding to a network node, the node weight is set to the water level value ; Calculate the similarity of water level between two time points. The expression is: ;in, Respectively at time points and The water level data at Indicates time point and According to the similarity between time points, an adjacency matrix A is constructed, where each element Indicates time point and The connection strength, degree The calculation expression is: ; Clustering coefficient It measures the connection density between neighbors of node p and indicates the local fluctuation trend of water level change. The calculation formula of clustering coefficient is: ; In the formula, is the degree of node p, It represents the number of triangles formed between the neighbors of node p. The water level fluctuation index is obtained by taking the weighted average sum of the calculated similarity and clustering coefficient.

[0038] In the numerical stability analysis module, the breach expansion rate anomaly index and the water level fluctuation index are normalized so that they are both between [0,1]. The normalized breach expansion rate anomaly index and the water level fluctuation index are calculated by weighted average to obtain the numerical stability coefficient of the MIKE 21 model simulation process.

[0039] For example, the present invention can use the following formula to calculate the numerical stability coefficient in the MIKE 21 model simulation process, and the calculation expression is: ; In the formula, is the numerical stability coefficient of the MIKE 21 model during simulation, is the abnormal index of breach expansion rate, is the water level fluctuation index, is the weight coefficient of the breach expansion rate anomaly index and the water level fluctuation index (which can be optimized based on experimental experience or machine learning), and Both are greater than 0.

[0040] Instability Adjustment Module: Based on the evaluation results, the area of ​​numerical instability is identified, and the dynamic mesh refinement technology is used to dynamically adjust the computational mesh according to the expansion speed of the breach through the adaptive meshing method.

[0041] The obtained numerical stability coefficient of the MIKE 21 model simulation process is compared with the numerical stability reference threshold value pre-set according to the experimental data. If the numerical stability coefficient of the MIKE 21 model simulation process is greater than or equal to the pre-set numerical stability reference threshold value, it means that the numerical stability of the MIKE 21 model simulation process is high, and the area with high numerical stability is divided into a numerical stability area, and there is no need to adjust the grid; if the numerical stability coefficient of the MIKE 21 model simulation process is less than the pre-set numerical stability reference threshold value, it means that the numerical stability of the MIKE 21 model simulation process is low, and the area with low numerical stability is divided into a numerical instability area, and the grid needs to be refined.

[0042] For the numerically unstable areas, dynamic mesh refinement technology is used to improve the numerical accuracy and stability of the simulation. The steps are as follows: By observing the initial formation and expansion process of the breach, the expansion speed of the breach can be determined. Calculated based on the time step and the spatial variation of the breach boundary: ;in, It is the change of the breach boundary within the time step Δt.

[0043] Based on the breach expansion speed , dynamically adjust the mesh division: The calculated breach expansion speed The breach expansion speed is compared with the preset breach expansion speed threshold. If the breach expansion speed is greater than or equal to the preset breach expansion speed threshold, it is divided into a high expansion speed area. If the breach expansion speed is less than the preset breach expansion speed threshold, it is divided into a low expansion speed area.

[0044] High expansion rate area: In the area where the breach expansion rate is high, the mesh should be finer to ensure the accuracy of the simulation results. This area requires a smaller calculation unit. Low expansion rate area: In the area where the breach expansion rate is low, a larger calculation unit can be used to reduce the amount of calculation. Through adaptive mesh technology, the mesh will automatically adjust according to the breach expansion rate, so that the area with high numerical instability can obtain higher calculation accuracy.

[0045] Define a grid resolution function for each time step , this function adjusts the degree of mesh refinement according to the breach expansion speed, and the expression is: ;in, is a fitness function, when When it is higher, the mesh will be refined; when When lower, the mesh will be coarser.

[0046] At each time step of the model, the distribution of grid nodes is adjusted, and the grid is refined in the numerically unstable area according to the current breach expansion speed and numerical stability coefficient. For the high expansion speed area, the grid unit is refined to increase the calculation accuracy; for the stable area, the grid unit is appropriately coarsened to reduce the consumption of computing resources.

[0047] Risk analysis report generation module: After optimizing the MIKE 21 model, the prediction accuracy of the MIKE 21 model is determined, and the MIKE 21 model with high prediction accuracy is used for risk assessment of levee breaches, and a corresponding risk analysis report is generated.

[0048] There are a number of error analysis methods that can be used to determine the accuracy of the MIKE 21 model predictions. Commonly used methods include the root mean square error (RMSE) and the coefficient of determination. Among them, RMSE is widely used in simulation prediction accuracy assessment because it can quantify the difference between simulation results and actual observation data.

[0049] The root mean square error RMSE calculation formula is: ;in: is the value of the model prediction result at the bth time point, is the value of the actual observation data at the bth time point, b represents each specific time point in the calculation process, and E is the total number of time points; the coefficient of determination It is used to measure the quality of model fitting, and its calculation formula is: ;in: is the mean of the actual observed data. The closer the value is to 1, the higher the prediction accuracy of the model.

[0050] According to the numerical stability coefficient of the optimized MIKE 21 model and the improved mesh refinement technology, the RMSE and Indicators are used to quantify the prediction accuracy of the model. Generally, lower RMSE and higher The value indicates that the optimized model has a higher prediction accuracy.

[0051] According to the calculated error index, the prediction accuracy of the model is judged, specifically: If RMSE < 0.1, and >0.9, the prediction accuracy is considered high, if RMSE ≥ 0.1, and <0.9, the prediction accuracy is considered to be low, and the MIKE 21 model is reoptimized.

[0052] After confirming that the optimized MIKE 21 model has high prediction accuracy, the model can be used for levee breach risk assessment. The following are the specific steps for levee breach risk assessment: The optimized MIKE 21 model is used to simulate different scenarios of levee breaches. For example, the breach process of the levee under different water levels and flow conditions is simulated. The bearing capacity of the levee structure is evaluated through data such as breach expansion rate and flood overflow water level fluctuation.

[0053] According to the simulation results, combined with the risk analysis method, the risk of levee breach is graded: High-risk area: The breach expands quickly, the flood fluctuation is large, and the levee may be damaged. Medium-risk area: The breach expands slowly, the water level fluctuation is moderate, but vigilance is still required. Low-risk area: The breach expands slowly, the water level fluctuation is small, and the levee is relatively stable.

[0054] Based on the above assessment results, a risk analysis report is generated. The report content includes: the risk level of the dike breach. The numerical stability coefficient of each area, the breach expansion rate, and the flood fluctuation. Suggestions for unstable areas (such as strengthening dike reinforcement, adjusting breach boundaries, etc.). The content of the report should include the following aspects: Model Overview: Introduction to the optimized MIKE 21 model, simulation techniques used, data sources, and analysis methods. Forecast Accuracy Evaluation: Based on RMSE and , providing a detailed analysis of the model's prediction accuracy. Risk assessment results: Based on the optimized simulation results, the risk of levee breaches is analyzed and graded. Recommendations and measures: Based on the risk assessment results, targeted disaster prevention and mitigation measures are proposed, such as levee reinforcement and early warning system optimization.

[0055] In this embodiment, first, in the simulation scenario construction module, the initial position and boundary conditions of the breach are set, and the MIKE 21 model is used to simulate the damage of the levee and the initial formation process of the breach. Next, the data acquisition module records the changes in the breach boundary and extracts extended dynamic data through time stepping, while monitoring water level fluctuations. Based on these data, the numerical stability analysis module evaluates the numerical stability of the model and determines whether there are unstable areas. If instability is found, the instability adjustment module uses dynamic grid refinement technology to adaptively adjust the computational grid to optimize the model. Finally, the risk analysis report generation module evaluates the prediction accuracy of the model, conducts risk assessment of the levee breach based on the optimized high-accuracy model, and finally generates a detailed risk analysis report to provide a basis for decision-making.

[0056] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A flood control safety evaluation system for dike projects, characterized by: It includes simulation scenario construction module, data acquisition module, numerical stability analysis module, instability adjustment module and risk analysis report generation module; Simulation scenario building module: In the MIKE 21 model, the initial location and boundary conditions of the breach are set, and the model is used to simulate the damage of the levee and the initial formation process of the breach; Data acquisition module: During the simulation process, the time stepping in MIKE 21 is used to record the changes in the breach boundary at each time step, and the extended dynamic data of the boundary shape during the breach expansion process are extracted; the free surface boundary of the water flow is set in the model, and the water level fluctuation data during the flood overflow process is output; Numerical stability analysis module: evaluates the numerical stability of the MIKE 21 model simulation process based on the extracted dynamic data of the boundary shape during the breach expansion process and the water level fluctuation data during the flood overflow process; Instability Adjustment Module: Based on the evaluation results, it identifies areas with numerical instability and uses dynamic mesh refinement technology to dynamically adjust the computational mesh according to the expansion speed of the breach through an adaptive meshing method. Risk analysis report generation module: After optimizing the MIKE 21 model, the prediction accuracy of the MIKE 21 model is determined, and the MIKE 21 model with high prediction accuracy is used for risk assessment of levee breaches, and a corresponding risk analysis report is generated.

2. A flood control safety evaluation system for levee projects according to claim 1, characterized in that: In the data acquisition module, the dynamic rate of breach expansion is analyzed to generate the breach expansion rate anomaly index. The method for obtaining the breach expansion rate anomaly index is as follows: Collect time series data on breach growth rate ,in It represents the breach expansion rate at the nth time step. The density of a data point in the local area is measured by calculating the relative density of the data point and its neighbors. , whose local reachable density The calculation expression is: ; Among them, reachability distance refers to the distance between data points Among the k nearest neighbors of The distance to the nearest point; Comparing Data Points The density of its neighbors to determine whether it is an outlier , The value is calculated as: ;in: It is a data point The set of k nearest neighbors of Yes The local reachable density is used to calculate the abnormal index of breach expansion rate, and the expression is: ; In the formula, is the abnormal index of breach expansion rate, and Represents the local outliers of all data points X The minimum and maximum values.

3. A flood control safety evaluation system for levee projects according to claim 2, characterized in that: In the data acquisition module, the water level fluctuation trend at different locations and time points is analyzed to generate a water level fluctuation index. The method for obtaining the water level fluctuation index is as follows: Obtain the water level data W(t) of the time series, where t represents the time point and W(t) represents the water level at time t. Divide the water level time series into several time windows to obtain several subsequences. , at each time point Corresponding to a network node, the node weight is set to the water level value ; Calculate the similarity of water levels between two time points. The expression is: ;in, Respectively at time points and The water level data at Indicates time point and According to the similarity between time points, an adjacency matrix A is constructed, where each element Indicates time point and The connection strength, degree The calculation expression is: ; Clustering coefficient It measures the connection density between neighbors of node p and indicates the local fluctuation trend of water level change. The calculation formula of clustering coefficient is: ; In the formula, is the degree of node p, It represents the number of triangles formed between the neighbors of node p. The water level fluctuation index is obtained by taking the weighted average sum of the calculated similarity and clustering coefficient.

4. A flood control safety evaluation system for dike engineering according to claim 3, characterized in that: In the numerical stability analysis module, the breach expansion rate anomaly index and the water level fluctuation index are normalized so that they are both between [0,1]. The normalized breach expansion rate anomaly index and the water level fluctuation index are calculated by weighted average to obtain the numerical stability coefficient of the MIKE 21 model simulation process.

5. A flood control safety evaluation system for dike engineering according to claim 4, characterized in that: In the instability adjustment module, the numerical stability coefficient obtained during the simulation of the MIKE 21 model is compared with the numerical stability reference threshold value pre-set according to the experimental data. If the numerical stability coefficient during the simulation of the MIKE 21 model is greater than or equal to the pre-set numerical stability reference threshold value, it means that the numerical stability during the simulation of the MIKE 21 model is high, and the area with high numerical stability is divided into a numerical stability area, and there is no need to adjust the grid; if the numerical stability coefficient during the simulation of the MIKE 21 model is less than the pre-set numerical stability reference threshold value, it means that the numerical stability during the simulation of the MIKE 21 model is low, and the area with low numerical stability is divided into a numerical instability area, and the grid needs to be refined.

6. A flood control safety evaluation system for levee projects according to claim 5, characterized in that: In the risk analysis report generation module, after optimizing the MIKE 21 model, the prediction accuracy of the MIKE 21 model is judged. When judging the prediction accuracy of the MIKE 21 model, a variety of error analysis methods are used, including root mean square error RMSE and determination coefficient , the root mean square error RMSE calculation formula is: ;in: is the value of the model prediction result at the bth time point, is the value of the actual observation data at the bth time point, b represents each specific time point in the calculation process, and E is the total number of time points; the coefficient of determination It is used to measure the quality of model fitting, and its calculation formula is: ;in: is the mean of the actual observed data.

7. A flood control safety evaluation system for levee projects according to claim 6, characterized in that: According to the numerical stability coefficient of the optimized MIKE 21 model and the improved mesh refinement technology, the prediction accuracy of the model is judged by the calculated error index, which is as follows: If RMSE < 0.1, and >0.9, the prediction accuracy is considered high, and the MIKE 21 model with high prediction accuracy is used for risk assessment of levee breaches and to generate corresponding risk analysis reports; if RMSE ≥ 0.1, and <0.9, the prediction accuracy is considered to be low, and the MIKE 21 model is reoptimized.

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