A simulation system for effect of beach vegetation on beach fixation and wave dissipation based on multi-scale numbers

Abstract

The present application relates to the technical field of computer aided engineering, specifically to a simulation system for the effect of beach vegetation on beach stabilization and wave dissipation based on multi-scale numerical values, which comprises obtaining terrain basic data, boundary condition data, physical parameters of vegetation and ecological parameters of vegetation; using the terrain basic data to establish a numerical calculation grid and terrain, using the boundary condition data as a driving condition, updating the topographic elevation and hydrodynamic conditions based on the wave flow sediment coupling mechanism; calculating the flexible deformation of vegetation according to the vegetation mechanical model, combining the hydrodynamic conditions and the physical parameters of vegetation, and updating the hydraulic drag force parameters based on the flexible deformation of vegetation; evaluating the probabilistic trend of vegetation growth, decline and expansion according to the habitat suitability model; combining the physical process simulation and the topographic evolution calculation, introducing adaptive checking and resetting steps, and realizing the accelerated simulation of the beach vegetation on beach stabilization and wave dissipation. The present application realizes the accurate prediction of beach stabilization and wave dissipation through multi-scale simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer-aided engineering, in particular to a simulation system for the effect of beach vegetation in stabilizing beaches and reducing waves based on multi-scale numerical values. BACKGROUND

[0002] Beach vegetation is an important ecological barrier and plays a key role in stabilizing shorelines and reducing waves. To scientifically evaluate its protective effect, numerical simulation technology is usually used to reproduce physical processes such as hydrodynamics, sediment transport, and geomorphological evolution.

[0003] Existing process-based numerical simulation, which is usually based on two-dimensional or three-dimensional digital models, can well simulate short-term physical processes such as wave propagation, water flow movement, and sediment transport driven by them, providing important technical reference for engineering design. However, the long-term evolution of beach topography is essentially the result of the coupling of fast hydrodynamic processes and slow biological and geomorphological processes at multiple spatiotemporal scales. The current simulation method is still generally limited in improving the accuracy of long-term prediction, and the reliability of the prediction results is difficult to fully meet the needs of refined ecological evaluation and engineering design, resulting in the failure to accurately capture the evolution law of ecological geomorphological systems.

[0004] Therefore, a simulation system for the effect of beach vegetation in stabilizing beaches and reducing waves based on multi-scale numerical values is proposed. SUMMARY

[0005] The present application aims to provide a simulation system for the effect of beach vegetation in stabilizing beaches and reducing waves based on multi-scale numerical values, which realizes accurate prediction of beach stabilization and wave reduction through multi-scale simulation. The topographic basic data, boundary condition data, vegetation physical parameters, and vegetation ecological parameters are obtained. The numerical calculation grid and topography are established using the topographic basic data, and the boundary condition data is used as the driving condition to update the geomorphic elevation and hydrodynamic conditions based on the wave-flow-sediment coupling mechanism. The vegetation flexible deformation is calculated based on the vegetation mechanical model combined with the hydrodynamic conditions and vegetation physical parameters, and the hydrodynamic drag force parameters are updated based on the vegetation flexible deformation. The probabilistic trend of vegetation growth, decline, and expansion is evaluated based on the habitat suitability model. The physical process simulation and geomorphic evolution calculation are combined, and an adaptive checking and resetting step is introduced to realize accelerated simulation of beach vegetation in stabilizing beaches and reducing waves.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0007] A simulation system for the effect of beach vegetation in stabilizing beaches and reducing waves based on multi-scale numerical values, comprising:

[0008] A data acquisition module: obtaining topographic basic data, boundary condition data, vegetation physical parameters, and vegetation ecological parameters;

[0009] Simulation building module: using terrain basic data, establishing numerical calculation grid and terrain, using boundary condition data as driving condition, updating topographic elevation and hydrodynamic condition based on wave flow sediment coupling mechanism;

[0010] Vegetation response module: according to vegetation mechanics model, combining water dynamic condition and vegetation physical parameter to calculate vegetation flexible deformation, updating water drag force parameter through predetermined function relation based on said vegetation flexible deformation;

[0011] Topographic evolution module: according to habitat suitability model, combining vegetation growth, decay and expansion probability trend evaluated according to topographic elevation, water dynamic condition and vegetation ecological parameter, feeding back updated vegetation state to vegetation response module;

[0012] Accelerated simulation module: combining physical process simulation and topographic evolution calculation, introducing adaptive check and reset step, realizing accelerated simulation of beach vegetation in beach fixation and wave dissipation.

[0013] Preferably, when the simulation building module establishes initial numerical calculation grid and terrain, it uses high-precision water depth data in terrain basic data to establish digital elevation model, generates unstructured triangular grid covering the entire study area for complex coastline containing tidal creek and beach, and performs local grid densification processing on the areas of tidal creek, vegetation coverage area and engineering area of interest, interpolates elevation data contained in the digital elevation model to grid nodes to form initial terrain required for model calculation.

[0014] Preferably, in the simulation building module, the calculation flow of the wave flow sediment coupling mechanism executed in a coupling time step is: the water dynamic solver calculates the average flow field at the current time and passes it to the wave solver, the wave solver calculates the propagation, deformation and wave-induced radiation stress of the wave on this basis, then feeds back the wave-induced radiation stress to the water dynamic solver and the bed shear stress of the flow to superimpose, obtains the bed shear stress, and uses the bed shear stress to drive the sediment transport formula to calculate and update the topographic elevation change at the current time step.

[0015] Preferably, the simulation building module is further configured to call the data acquisition module to obtain physical property data including vegetation elastic modulus, and pass the elastic modulus as a core parameter to the cantilever beam mechanics model in the vegetation response module for calculating flexible bending deformation.

[0016] Preferably, the water dynamic load acting on the vegetation is calculated by using the Morison equation, the vegetation is mechanically modeled by using the cantilever beam theory, the load is applied to the model to obtain the bending displacement of the vegetation, and the bending displacement is taken as the dynamic deformation parameter; the bending displacement and the geometric and mechanical properties of the vegetation are input into an empirical function model representing the relationship between the dynamic bending of the vegetation and the water flow drag force to update the water drag force parameter; the empirical function model is established by fitting and back-calculation based on the dynamic deformation amount and total drag force data of the vegetation measured in a controlled physical model experiment.

[0017] Preferably, the geomorphologic evolution module evaluates the evolution trend of the vegetation by using a habitat suitability model, the habitat suitability model takes multiple environmental factors of the location where the vegetation is located as comprehensive inputs, including the average beach elevation, the average scouring rate and the average water flooding frequency, calculates a comprehensive habitat suitability index based on the comprehensive inputs, and determines the growth, death and spatial expansion trend of the vegetation in the next geomorphologic evolution time step in combination with a probability model according to the value of the habitat suitability index.

[0018] Preferably, the execution flow of the adaptive time step control in the accelerated simulation module is as follows: when each calculation time step is completed, the Courant-Friedrichs-Lewy number for judging the numerical stability is calculated in the whole calculation domain, if the Courant-Friedrichs-Lewy number is greater than a preset safety threshold, the calculation result of the current time step is rejected, and the step length of the next time step is shortened by a preset proportion and then the calculation is performed again.

[0019] Preferably, the method of combining the physical process simulation and the geomorphologic evolution calculation includes: performing high-resolution physical process simulation of a representative period to calculate a net change trend field of the geomorphology; in a loop calculation step, applying the net change trend field to update the terrain in a macroscopic geomorphologic evolution time step; after the time step is completed, a preset key indicator of the system is checked, when the checking result of the key indicator meets a preset stability condition, the loop calculation step is repeatedly executed using the current net change trend field, and when the checking result does not meet the stability condition, the high-resolution physical process simulation is performed again to update the net change trend field.

[0020] Compared with the prior art, the method has the following beneficial effects:

[0021] 1、The method calculates the flexible deformation of the vegetation in real time, and dynamically updates the water drag force parameter based on the deformation, so that the model can more truly describe the resistance change process of the vegetation, and the simulation accuracy of physical effects such as wave breaking and siltation promotion is improved.

[0022] 2、The evaluation method based on habitat suitability index and probability model can comprehensively consider the coupling influence of multiple environmental factors, and make a probabilistic prediction on the evolution trend of vegetation.

[0023] 3、The "simulation-verification-reset" mechanism ensures that the evolution trend can be updated in time when the system state changes significantly by adaptive verification of the system state, thereby greatly improving the calculation efficiency while ensuring the stability and reliability of the long-term simulation results. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A beach vegetation solidification and wave dissipation effect simulation system structure schematic diagram based on multi-scale numerical values is provided for the embodiments of the present application.

[0025] Figure 2 A simulation construction module flowchart is provided for the embodiments of the present application.

[0026] Figure 3 A geomorphological evolution module flowchart is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0028] Embodiment one:

[0029] Please refer to Figures 1 to 3 The present application provides a beach vegetation solidification and wave dissipation effect simulation system based on multi-scale numerical values, and the technical solutions are as follows:

[0030] A beach vegetation solidification and wave dissipation effect simulation system based on multi-scale numerical values, comprising:

[0031] Data acquisition module: obtain terrain basic data, boundary condition data, vegetation physical parameters and vegetation ecological parameters.

[0032] Topographic data was used to construct the initial numerical computational grid and digital elevation model (DEM). This data includes the elevation, topographic features, and sediment information of the mudflat area. Data types include mudflat surface elevation, the location of tidal gullies and vegetation-covered areas, and sediment type (e.g., sandy or silty). Topographic data was obtained through a combination of field measurements and publicly available data. Field measurements used conventional distance measuring tools and poles to measure mudflat elevations at a grid spacing of approximately 5 m x 5 m, covering the entire study area. Tidal gully locations and depths were recorded manually, with a spacing of approximately 2 m between measurement points. Sediment samples were collected manually to record sediment types. For publicly available data, shoreline and vegetation distribution were extracted using freely available satellite imagery to supplement the field measurements. The collected elevation data was manually collated and converted to a unified planar coordinate system. Open-source GIS software was used to interpolate the elevation points to generate a DEM. Sediment data were classified to generate a distribution map. Outputs include a NetCDF DEM (including elevation and topographic features) and a Shapefile-based DEM (including sediment type).

[0033] Boundary condition data provide physical driving conditions for the numerical model, including hydrodynamic boundary conditions (tides, waves, wind fields) and sediment boundary conditions (suspended sediment concentration). Tidal data include tide level and flow velocity, wave data include wave height and wave period, wind field data include wind speed, and sediment data include offshore sediment concentration. Boundary condition data are obtained through a combination of field observations, public data, and auxiliary experiments to ensure that the data covers the dynamic characteristics of the study area.

[0034] Tidal data: Historical tidal data covering at least two complete tidal cycles were obtained from the National Oceanographic Data Center or local oceanographic stations. Field observations were conducted at 5–7 observation points at the boundary of the study area to measure tide levels and tidal current speeds, with records recorded six times daily for 25 days.

[0035] Wave data: Historical wave data (wave height, wave period, and wave direction) were obtained from oceanographic stations for 30 days, and field observations were conducted four times a day to record wave height, wave period, and wave direction for 20 days.

[0036] Wind data: Wind speed and direction data were collected from a nearby weather station every two hours for 30 days. On-site observations were conducted using weather stations deployed at 5-7 points along the border, continuously recording wind speed and direction every hour for 25 days.

[0037] Sediment boundary conditions: Seven sampling points were set up at the boundary of the study area, and water samples were collected four times a week (for 25 days). The suspended sediment concentration was measured and calibrated by filtration. The particle size distribution was recorded, and the sediment settling rate was determined by laboratory sedimentation experiments.

[0038] Tidal and wave data are interpolated using splines to generate continuous time series with a time resolution of 30 minutes. Wind field data are averaged over 1 hour and smoothed to ensure temporal continuity. Sediment concentration and settling velocity data are compiled into spatial distribution datasets and assigned to the model boundary nodes using Kriging interpolation. Output formats are:

[0039] Hydrodynamic boundary conditions: stored in CSV format, containing time, tidal level, tidal current velocity, wave height, wave period, wave direction, wind speed, and wind direction.

[0040] Sediment boundary conditions: stored in NetCDF format, containing spatial distributions of suspended sediment concentration and settling velocity.

[0041] Model validation data are used to calibrate and validate the accuracy of the numerical model, including hydrodynamic validation data (tidal level, current velocity, wave parameters), sediment transport validation data (deposition rate, topographic elevation change), and vegetation status data (distribution, density, height). Hydrodynamic validation: 3-5 validation points are selected within the study area, daily records of high and low tidal levels, current velocity, and wave parameters are measured, data are cross-validated with public tide tables and wave records to ensure consistency.

[0042] Sediment transport validation: 3 sediment collection points are set up in vegetated and non-vegetated areas, sediment deposition is measured once a week, elevation change is measured by pole, and beach elevation change is recorded.

[0043] Vegetation status validation: vegetation data are obtained through ground surveys and image analysis, ground surveys cover 30 sampling points, vegetation type, coverage, and average height are recorded, hydrodynamic data are compiled into time series, average values and standard deviations are calculated for comparison with model output. Sediment transport data are used to calculate deposition rate and elevation change, and spatial distribution maps are generated. Vegetation status data are compiled into coverage and height distribution maps, stored as Shapefile format.

[0044] Simulation construction module: using the terrain base data, an initial numerical calculation grid and terrain are established; using the boundary condition data as the driving condition for physical process calculation; in a unified numerical model, based on the wave-flow-sediment coupling mechanism, the sediment transport induced by wave and current interaction is calculated, and the topographic elevation is updated according to the spatial gradient of the sediment transport flux; using the model validation data, the calculation results are calibrated and verified.

[0045] First, import the digital elevation model and substrate distribution data generated by the data acquisition module. The digital elevation model is stored in NetCDF format, containing elevation and terrain feature data of the tidal flat area. The substrate distribution data is stored in Shapefile format, recording sediment type information. The system parses the NetCDF file, extracts the elevation data and checks its integrity to ensure consistent coordinate systems. The substrate data is loaded through spatial query tools to verify data format and range. The system automatically defines the boundary of the calculation domain based on the spatial range of the elevation data, generating a polygon boundary file containing the shoreline and open boundary. The boundary file is stored in Shapefile format for subsequent grid generation.

[0046] For complex coastlines containing tidal creeks and tidal flats, use the open-source mesh generation tool to generate unstructured triangular meshes covering the entire calculation domain. Load the boundary file, set the initial mesh parameters, generate uniform triangular meshes, identify the tidal creek locations in the elevation data, extract the tidal creek centerline using spatial analysis tools, and apply local mesh refinement to the tidal creek area based on the centerline and manually recorded tidal creek locations to reduce mesh edge length and improve resolution. Perform spatial queries on vegetation-covered areas and engineering areas (such as breakwaters) to determine their boundary ranges, and apply local mesh refinement to ensure smooth transition between encrypted and non-encrypted areas. Check mesh quality, optimize triangle interior angles and skewness, adjust node positions to eliminate distorted elements, and store the generated mesh as a compressed file containing node coordinates, element connection information, and encryption area markers.

[0047] Interpolate high-precision bathymetric data to unstructured triangular mesh nodes to form an initial digital elevation model. Load the NetCDF format digital elevation model, extract the elevation data, and use Kriging interpolation to assign elevation values to mesh nodes. Apply bilinear interpolation to the tidal creek area to ensure smooth depth changes. Load the Shapefile format substrate distribution data and assign corresponding substrate types to each grid cell through spatial queries. Extract substrate-related physical parameters, including sediment grain size, density, and critical incipient stress, from the pre-set parameter library and associate them with the grid cells. Check the continuity and accuracy of the initial digital elevation model and correct abnormal values. The generated initial digital elevation model is stored in NetCDF format, containing node coordinates, elevation values, and substrate parameters, ensuring complete matching with the unstructured triangular mesh.

[0048] By generating unstructured triangular meshes and locally refining the meshes in the gully, vegetation-covered areas, and engineering areas, the model can accurately adapt to the topographic features of complex coastlines, improve the simulation resolution in key areas, and more accurately capture the spatial variations of water flow and sediment transport. The initial digital elevation model is formed by high-precision interpolation, ensuring the long-term stability of the terrain details and providing a reliable spatial foundation for predicting the evolution of ecological geomorphological systems and laying a foundation for improving the accuracy of long-term predictions.

[0049] The boundary condition data provided by the data acquisition module includes hydrodynamic boundary conditions and sediment boundary conditions. The hydrodynamic boundary conditions are stored in CSV format and include time series of tidal level, tidal current velocity, wave height, wave period, wave direction, wind speed, and wind direction data. The CSV file is parsed, and the data is extracted line by line. A continuous time series is generated by spline interpolation and assigned to the nodes of the open boundary of the model to ensure that the time step is synchronized with the model calculation. The sediment boundary conditions are stored in NetCDF format and include spatial distribution data of suspended sediment concentration and settling velocity. The NetCDF file is loaded, and the concentration and settling velocity are assigned to the boundary nodes using inverse distance weighted interpolation. The shoreline boundary is set as a non-slip boundary, and the wave reflection is handled using Neumann conditions. The wind field data is converted to surface stress using the Charnock formula. The boundary condition data set is stored in NetCDF format, including time series and spatial distribution, and is used to drive subsequent physical process calculations.

[0050] In the unified numerical model, a two-dimensional calculation framework is constructed based on the finite volume method using unstructured triangular meshes. Adaptive time steps are set to ensure numerical stability. The boundary condition data is loaded, and the hydrodynamic module is started. The two-dimensional shallow water equations are solved to calculate the water level and flow velocity distribution in the entire calculation domain. The Godunov-type format is used to handle the convection term, and the PISO algorithm is used to solve the pressure-velocity coupling. The input data includes tidal level, tidal current velocity, and wind field stress. The calculation process considers the influence of the initial digital elevation model on the bottom boundary. The calculation results are stored as temporary variables, including water depth and flow velocity field, for subsequent wave module use. The water flow field data is stored in NetCDF format, recording the dynamic changes of each grid node.

[0051] The water level and flow velocity field computed by the hydrodynamic module are passed to the wave module. To handle the significant time scale difference between the wave field and the flow field, the coupling mechanism of the present embodiment employs a fractional step marching strategy. Specifically, the computational time step adopted by the wave solver is set to be less than one tenth of the time step of the hydrodynamic solver, ensuring that the rapid oscillation process of the wave can be fully analyzed within each hydrodynamic calculation step, thereby providing a stable and accurate time-averaged wave-induced radiation stress field for the hydrodynamic module, ensuring the stability and physical reality of the entire coupled system in long-term simulation. Based on the wave action balance equation, the propagation, refraction, breaking and energy dissipation of the wave are calculated. The input data includes the boundary wave height, wave period and wave direction. The spectral method is used to solve the wave field and calculate the wave-induced radiation stress. The water depth and flow velocity of the initial digital elevation model are considered to affect the wave propagation, and the wave height and wave period distribution are updated. The wave calculation results are fed back to the hydrodynamic module to adjust the flow field, and the wave field data are output in NetCDF format, including wave height, wave period and radiation stress, for subsequent bottom shear force calculation.

[0052] The flow velocity field of the integrated hydrodynamic module and the wave-induced radiation stress of the wave module are combined to calculate the total bed shear stress. The bed shear stress generated by the flow is calculated using the Manning formula based on the roughness parameters of the underlying grid cells in the initial digital elevation model. The bottom orbital velocity of the wave module is extracted to calculate the wave-induced shear stress. A nonlinear superposition method is used to combine the flow and wave shear stresses to generate the total bed shear stress. The Soulsby formula based on wave-flow interaction is used to correct the shear stress, taking into account nonlinear effects. The superposition process is designed to reflect the dynamic characteristics of wave-flow coupling. The total bed shear stress data is output in NetCDF format, containing the shear stress values for each grid cell, which is used for sediment transport calculations. To calculate the sediment transport rate driven by the total bed shear stress, a composite sediment transport calculation module is used, which integrates transport formulas for different types of underlying materials. Based on the underlying material distribution map obtained from the data acquisition module, the grid cells in the calculation domain are calculated accordingly. For non-cohesive sediment areas on sandy beds, the system calls the van Rijn transport formula for calculation. For cohesive sediment areas on silt beds, the transport formula based on the Partheniades-Krone theory is called to simulate the erosion and deposition processes, respectively. This calculation strategy based on material zoning and calling different physical mechanism formulas ensures the physical authenticity and accuracy of the geomorphological evolution simulation. To ensure the numerical stability and physical immediacy of the above coupling calculations, the wave-flow sediment coupling mechanism also includes an in-step iteration feedback loop. After calculating the sediment transport flux at the current time step, the system does not immediately update the topography, but first forms a temporary topographic change based on the flux. This temporary topography will act back on the hydrodynamic calculations, forming an iterative cycle until the hydrodynamic and topographic changes converge within a time step. The iterative mechanism can capture the immediate negative feedback of topographic changes on water and sediment dynamics, avoiding overestimation of the erosion-deposition rate under strong dynamic conditions, and significantly enhancing the stability and physical authenticity of the model simulation in extreme events.

[0053] By implementing the wave-flow sediment coupling mechanism based on the Soulsby formula, the bed shear stress changes under the combined action of waves and flow can be accurately simulated, improving the reliability of sediment transport calculations. This method improves the prediction accuracy of long-term geomorphological evolution by considering nonlinear wave-flow interactions, meeting the needs of refined ecological assessment and engineering design.

[0054] Load the model verification data, including hydrodynamic verification data, sediment transport verification data, and vegetation state data, run the model, cover the observation time period of the verification data. Extract the tide level, flow velocity and wave height time series output by the model at the internal verification points, compare with the hydrodynamic verification data, adjust the bed roughness coefficient, optimize the hydrodynamic simulation results. Compare the sedimentation rate and elevation change of the model with the sediment transport verification data, adjust the settling velocity and incipient velocity parameters in the sediment transport formula, ensure the consistency of the erosion and deposition trend.

[0055] Through the multi-source data fusion verification method, the hydrodynamic, sediment transport and vegetation state data are used to comprehensively evaluate the performance of the model, and the weighted average error analysis is used to enhance the optimization effect of the model parameters, ensure that the simulation results are more stable and reliable in long-term prediction, meet the requirements of fine ecological evaluation and engineering design.

[0056] Use independent verification data set, compare the model output of tide level, flow velocity, wave height, sedimentation rate and elevation change with the measured data, calculate the correlation coefficient, evaluate the prediction ability of the model, check the sediment deposition distribution in the vegetation area, verify whether the simulation results reflect the resistance effect of vegetation on sediment transport, perform statistical analysis, generate time series comparison chart and spatial distribution comparison chart, store the verification results, error index and correlation coefficient as CSV format.

[0057] Vegetation response module: receiving the verified real-time hydrodynamic conditions, and calculating the flexible bending deformation of the vegetation according to the preset vegetation mechanics model, updating the hydraulic drag force parameter representing the comprehensive resistance effect of the vegetation to water flow based on the posture after deformation.

[0058] Start the vegetation response module, load the verified real-time hydrodynamic condition data output by the simulation construction module, the hydrodynamic condition data is stored in NetCDF format, including time series of water level, flow velocity, wave height, wave period and wave-induced radiation stress. The system parses the hydrodynamic data file, extracts the flow velocity field and wave field data of each time step, checks the integrity and time continuity of the data. Use spatial query tool to distribute hydrodynamic condition data to the nodes of unstructured triangular mesh, ensure the spatial position matching with the vegetation distribution area, perform time interpolation on flow velocity and wave data by spline interpolation method, generate time series data set synchronized with vegetation mechanics calculation, store as temporary variable.

[0059] The vegetation state data provided by the data acquisition module is loaded, including vegetation distribution, density, initial height, and type information, and the vegetation state data is stored in a Shapefile format file. The vegetation state data file is parsed, and the boundary, type (such as reed, salt marsh grass), coverage rate, and initial height data of the vegetation covered area are extracted. Through spatial query, the vegetation data is distributed to the cells of the unstructured triangular mesh, and a vegetation attribute table for each mesh cell is generated. The vegetation mechanics model parameters are loaded, and the biomechanical properties, including elastic modulus, cross-sectional moment of inertia, stem diameter, bending stiffness, and leaf area, are extracted from the parameter library, which is stored in JSON format and contains parameters for common vegetation types, such as the typical mechanical properties of reed and salt marsh grass. The completeness of the vegetation data and parameters is checked, and missing or abnormal values are corrected to generate an initial state data set for the vegetation, which includes the vegetation attributes and mechanical parameters of the mesh cells, and the data set is stored in NetCDF format.

[0060] The vegetation mechanics model is started, and the beam theory is adopted to consider the vegetation stem as a flexible cantilever beam subjected to the action of water flow and waves, fixed at the bottom and free at the top. The input data includes the flow velocity field of the hydrodynamic module, the bottom track velocity of the wave field, and the mechanical parameters of the vegetation (such as elastic modulus, cross-sectional moment of inertia, leaf area). For each time step, the Morison equation is used to calculate the hydrodynamic load acting on the vegetation stem and leaves, which is decomposed into drag force and inertial force. The drag force is based on the flow velocity and the projected area of the vegetation (combined with the stem and leaf area), and the inertial force is based on the wave acceleration and the vegetation mass. The finite difference method is used to solve the beam theory model to calculate the bending deformation of the stem along the height direction, considering the additional resistance effect of the leaves. The deformation curve of each mesh cell is output and stored in NetCDF format, recording the displacement of the stem top, bending angle, and leaf inclination angle.

[0061] Based on the spatial distribution of the vegetation, the spatial density coefficient between the vegetation is calculated, and the DBSCAN clustering algorithm is used to iteratively merge the dense vegetation areas to generate a synthetic vegetation area. The neighborhood radius of the DBSCAN algorithm is in the range of 5-15 times the average vegetation plant spacing, and the minimum neighborhood data point is in the range of 10-30. The neighborhood radius is determined by analyzing the k-distance graph and selecting the inflection point of the curve, and the k value is set according to the typical vegetation patch size of the study area, and a typical reed community k value can be set to 20. The spatial density coefficient is determined by analyzing the spatial distance matrix of the vegetation coverage rate between the grid cells, and the distance threshold is dynamically adjusted according to the vegetation type and water depth. The DBSCAN algorithm merges the dense vegetation cells into a bounding box, which is defined as a synthetic vegetation area and stored in NetCDF format, containing boundary coordinates (minimum x, y and maximum x, y) and average vegetation density within the area.

[0062] After the synthetic vegetation area is determined, the system dynamically adjusts and updates the comprehensive hydrodynamic drag force parameter, i.e. the drag coefficient Cd, in each area. The drag coefficient Cd is determined as a function of multiple vegetation morphologies and hydrodynamic parameters, the input of which includes the spatial density, stem diameter and elastic modulus of the vegetation, and the dynamic deformation parameter of the stem bending displacement calculated by the model in real time. To establish the function model representing the relationship between the hydrodynamic drag coefficient and the dynamic response of the vegetation, first, the basic data is obtained through controlled flume physical model experiments, systematically changing the flow conditions and the physical parameters of the vegetation (such as stiffness, density), and using high-speed cameras and force sensors to measure the dynamic bending deformation of the vegetation and the total flow drag force it is subjected to. Second, based on the experimental data, a function model is constructed. Specifically, in each working condition, according to the measured total drag force, current flow rate, water density and vegetation frontal area, the hydrodynamic drag coefficient under the current deformation state is inversely solved based on the standard definition of drag force in fluid dynamics. Finally, a number of sets of relative bending of the vegetation and the hydrodynamic drag coefficient calculated by the above method are correlated, and an empirical function model reflecting the change of the coefficient with the bending deformation of the vegetation is established through curve fitting. Subsequently, based on the updated drag coefficient Cd and the real-time flow velocity, the system calculates the hydrodynamic influence index, which is defined as the spatially unevenly distributed momentum sink (i.e. resistance term) generated by the vegetation to the flow, to accurately quantify the energy dissipation effect of the vegetation on the flow.

[0063] To specifically define the empirical function model, in this embodiment, the function is constructed as a second-order polynomial function that correlates the relative bending of the vegetation and the hydrodynamic drag force. The specific form of the function is as follows:

[0064] ;

[0065] represents the updated hydrodynamic drag coefficient after flexible deformation correction, represents the initial hydrodynamic drag coefficient of the vegetation before bending deformation, represents the bending displacement of the top of the vegetation calculated in real time by the vegetation mechanics model, represents the natural height of the vegetation, and represents the dimensionless empirical coefficient obtained by fitting and inverse calculation of the data obtained from the controlled physical model experiment. When the simulation object is typical tidal reed, according to the experimental data, the empirical coefficient can be taken as =-0.5, =0.15.

[0066] The prediction accuracy of the polynomial function relationship is closely related to the range of experimental calibration data used to establish the relationship. In this embodiment, the preferred application range of the function relationship is limited to a specific interval of experimental calibration conditions (such as flow rate and water depth). For example, when the hydrodynamic conditions encountered in the simulation exceed 30% of the calibration range, the system will use a linear interpolation or extrapolation strategy based on boundary values to estimate the drag force parameters and output a warning message. In addition, the system monitors the duration of conditions that exceed the applicable range. If conditions that exceed the applicable range occur continuously for multiple macro-geomorphological evolution time steps, the reset mechanism in the accelerated simulation module is forcibly triggered, returning to execute high-resolution physical process simulation to ensure that the model regains a reliable parameterization scheme under the new dynamic environment.

[0067] By calculating the spatial density coefficient between vegetation and generating synthetic vegetation areas using dynamic clustering, the real dense distribution of vegetation in space can be accurately reflected, and the area boundaries closely related to hydrodynamics and sediment transport can be generated. By calculating the hydraulic influence index to dynamically adjust the drag force parameters, the resistance effect of vegetation on water flow can be accurately quantified, and the adaptive behavior of vegetation under complex hydrodynamic environment can be truly reflected, enhancing the long-term stability of hydrodynamic and sediment transport simulation, and providing reliable support for capturing the evolution law of ecological geomorphological system and fine ecological assessment.

[0068] Integrating the flexible bending deformation of vegetation, the boundaries of synthetic vegetation areas, the updated hydraulic drag force coefficients, and the calculated hydraulic influence index, a vegetation dynamic response dataset is generated. This dataset is stored in NetCDF format and contains time-series deformation curves, area boundaries, drag force coefficients, and hydraulic influence index, among other key information. The system checks the consistency of the data to ensure that it completely matches the time step of the hydrodynamic conditions and can generate corresponding visualization files, such as spatial distribution maps and time series graphs of vegetation bending state, synthetic vegetation areas, and hydraulic influence index. The hydraulic influence index in this vegetation dynamic response dataset will be directly fed back to the fluid dynamics equations in the simulation construction module for calculation, and the entire dataset will also be delivered to the geomorphological evolution module for analysis of the long-term impact of vegetation on geomorphological evolution.

[0069] The vegetation state verification data provided by the data collection module is loaded, stored in Shapefile format, containing measured vegetation height, coverage, leaf state and hydraulic resistance data. Run the vegetation response module to cover the verification data time period. Extract the synthetic vegetation area, hydraulic drag force parameters and hydraulic influence index output by the model and compare them with the measured data. Calculate the deviation between the simulation value and the measured value, adjust the parameters of the vegetation mechanics model, including elastic modulus, drag coefficient and leaf resistance coefficient, iterate and optimize until the deviation is minimized, verify whether the synthetic vegetation area accurately reflects the dense distribution, check the influence of the hydraulic influence index on the hydrodynamic force and sediment transport, confirm that the flow velocity field and sedimentation rate output by the simulation construction module are consistent with the measured values, and store the verification results in CSV format, including error indicators and correlation coefficients. The adjusted parameters are stored in JSON format, updating the parameter library to ensure the reliability of the model.

[0070] The geomorphological evolution module: based on the habitat suitability model, combined with the probabilistic trend of vegetation growth, decline and expansion evaluated according to geomorphic elevation, hydrodynamic conditions and vegetation ecological parameters, the updated vegetation state is fed back to the vegetation response module.

[0071] After the geomorphological evolution module is started, the geomorphic elevation and hydrodynamic condition data output by the simulation construction module are loaded, both stored in NetCDF format. The geomorphic elevation data contains the elevation values of the unstructured triangular grid nodes, reflecting the erosion and deposition changes caused by sediment transport, and the hydrodynamic condition data includes time series of water level, flow velocity, wave height, wave period and wave-induced radiation stress. The system parses the NetCDF file, extracts the geomorphic elevation and hydrodynamic field data at each time step, checks the data integrity and time continuity, uses spatial query tools to distribute the data to unstructured triangular grid nodes, ensuring spatial position matching with vegetation distribution area. Generate a synchronized time series data set stored as a temporary variable for subsequent habitat suitability assessment.

[0072] Load the initial vegetation state data provided by the data collection module, stored in Shapefile format, containing vegetation distribution boundaries, coverage, initial height, stem density and type information. Parse the Shapefile file, extract vegetation attributes, and distribute them to unstructured triangular grid cells through spatial queries to generate a vegetation attribute table. Load the preset habitat suitability model parameters, stored in JSON format, which mainly include the weights and coefficients of each environmental factor for the logistic regression model. Check the integrity of the vegetation data and habitat parameters, correct abnormal values. Generate vegetation initial state and habitat suitability parameter data sets, containing grid cell vegetation attributes and model weights and coefficients, store these data in NetCDF format.

[0073] A logistic regression model is used to evaluate the evolutionary trend of vegetation based on a habitat suitability index. The output probability of the logistic regression model is a sigmoid function value based on a linear combination of environmental factors. The linear combination includes a base intercept term and weight coefficients corresponding to beach elevation, erosion rate, and flooding frequency, respectively. In a typical embodiment for reed, the base intercept term is 0.5, the weight coefficient of beach elevation is 2.5, the weight coefficient of erosion rate is -1.5, and the weight coefficient of flooding frequency is -0.8, which are obtained by fitting historical data of the region.

[0074] Based on the value of the habitat suitability index, the system combines a probability model of random forest algorithm to predict the growth, decline, and spatial expansion trend of vegetation in the next geomorphic evolution time step. The random forest model includes 100 decision trees with a maximum depth of 10, and the main feature for division is the suitability index of the current cell and the average vegetation coverage of 8 neighboring cells. The system then selects the trend with the highest probability as the final evolutionary direction of the cell and updates its vegetation state accordingly. Specifically, if the predicted trend is growth, the vegetation coverage in the cell will increase by a fixed growth rate percentage for the species extracted from the parameter library. Correspondingly, if the predicted trend is decline, its coverage will decrease by a fixed decline rate percentage for the species extracted from the parameter library. For the expansion trend, the system will establish new vegetation in adjacent blank cells that meet the habitat suitability conditions with an initial seed coverage rate preset from the parameter library. The fixed growth rate, fixed decline rate, and initial seed coverage rate are all physical parameters that can be determined through conventional experiments in the field. To further improve the authenticity of the simulation, for the growth trend, the vegetation coverage and stem density are increased based on the preset growth rate extracted from the parameter library, for the decline trend, the coverage is reduced or the vegetation is removed, and the height is adjusted to zero; for the expansion trend, a spatial diffusion model is used to expand the vegetation distribution to adjacent grid cells, and the initial coverage and height of the new grid cells are set. Through specific physical parameters and spatial models, the dynamic process of vegetation growth, decline, and expansion is finely described. This makes the ecological succession simulation closer to the natural law, and improves the authenticity and prediction accuracy of the entire system.

[0075] The updated vegetation spatial distribution and physiological parameters (coverage, height, stem density) are fed back to the vegetation response module for recalculating its hydraulic impact index, realizing the two-way feedback of biophysical processes. To ensure the reliability of the probability model and avoid producing non-physical prediction results near the ecological critical point, the historical observation or experimental data set used to train the random forest model should preferably contain a sufficient number of samples covering the critical interval of the habitat suitability index. For example, samples with a suitability index between 0.3 and 0.7, the number of which is recommended to be no less than 15% of the total sample size, to ensure that the model has sufficient learning and recognition ability for the critical behavior of vegetation expansion and decline. To enhance the long-term adaptability of ecological evolution prediction, the random forest model is configured to automatically perform model recalibration after a certain length of simulation, and the recalibration process uses the topography and vegetation state data generated in the past simulation year as a new training set to re-optimize the internal parameters of the random forest model, enabling the probability model to adapt to changes in weight relationships due to vegetation topography system evolution.

[0076] By integrating the average beach elevation, erosion rate, and flooding frequency based on the multi-environment factor habitat suitability model, the comprehensive suitability index is calculated, and the random forest probability model is used to evaluate the growth, decline, and spatial expansion trend of vegetation, simulating the evolution behavior of vegetation in a dynamic environment. Combined with the two-way feedback mechanism, the updated vegetation distribution and parameters are fed back to the vegetation response module, significantly improving the realism of biophysical process coupling and enhancing the prediction accuracy of long-term ecological topography evolution, providing reliable support for ecological assessment and engineering design.

[0077] Integrate the updated vegetation spatial distribution, physiological parameters (coverage, height, stem density), synthetic vegetation area, and hydraulic impact index to generate biogeomorphic evolution data sets. Verify the time step consistency of the data with the hydrodynamic conditions and topographic elevation, generate visualization results including vegetation coverage spatial distribution map, suitability index distribution map, and evolution trend (growth, decline, expansion) probability time series chart, and the results are fed back to the simulation construction module to update the hydrodynamic and sediment transport parameters, and stored as input for subsequent ecological topography analysis.

[0078] The vegetation state verification data provided by the data collection module is loaded, which is stored in Shapefile format and contains the measured vegetation coverage, height and stem density. Run the geomorphological evolution module, cover the verification data time period, extract the output vegetation distribution, suitability index and physiological parameters, compare them with the measured data, calculate the deviation, adjust the factor weight in the habitat suitability model and the parameters of the random forest model, iterate optimization to minimize the error, verify the influence of vegetation distribution on hydrodynamics and sediment transport, confirm the consistency of the flow field and sedimentation rate output by the simulation construction module, store the verification results, error indicators and correlation coefficients in CSV format, store the adjusted parameters in JSON format, update the parameter library to ensure the reliability of the model.

[0079] The acceleration simulation module combines physical process simulation with geomorphological evolution calculation, introduces adaptive verification and reset steps, and realizes the accelerated simulation of beach vegetation in beach fixation and wave dissipation.

[0080] The acceleration simulation module is started, and the current state output data from the simulation construction module, the vegetation response module and the geomorphological evolution module are loaded and integrated, including the current geomorphological elevation, vegetation spatial distribution, physiological parameters and hydrodynamic drag force parameters; the control parameters of the acceleration simulation are initialized, which are stored in JSON format, and the contents include the total time scale of the simulation, the macro geomorphological evolution time step and the system key indicators for state verification and their respective stable state thresholds.

[0081] The execution of the acceleration simulation starts with high-resolution physical process simulation, which continuously runs the simulation construction module, the vegetation response module and the geomorphological evolution module in fully coupled mode within a representative hydrodynamic period to calculate the net geomorphological change trend field caused by the combined action of physical and biological processes within the period. The representative hydrodynamic period is preferably a period that can reflect the main tidal dynamic characteristics, such as a complete tidal period containing spring tide and neap tide (about 15 days), to ensure that the calculated geomorphological change trend is statistically representative. To ensure numerical stability, the Courant-Friedrichs-Lewy number (CFL number) is checked at each calculation time step of the high-resolution simulation to adaptively adjust the step size.

[0082] To ensure numerical stability of the high-resolution physical process simulation, the system performs adaptive step control based on the Courant-Friedrichs-Lewy (CFL) condition at each computational time step. The control logic is as follows: at the beginning of each time step, the system calculates the CFL number for each computational grid cell based on the size of the cell, the current flow velocity, and the selected time step. The CFL number represents the ratio of the distance that information propagates in a single time step to the size of the grid. Then, the system finds the maximum CFL number in the entire computational domain and compares it with a pre-set safety threshold that is strictly less than 1.

[0083] If the maximum CFL number does not exceed the safety threshold, the current time step is considered stable, and the calculation continues. Otherwise, if the maximum CFL number breaks through the safety threshold, it means that the current time step is too long and may lead to numerical divergence. At this time, the system will automatically reject the calculation of the current step, reduce the time step by a pre-set reduction ratio, and then re-calculate the step using the new, shorter time step. This process will be repeated until the maximum CFL number meets the stability condition. This adaptive step control method can effectively avoid numerical instability and ensure the reliability of the simulation results.

[0084] After obtaining the net change trend field, the system enters the loop of geomorphic evolution process and performs adaptive state check and reset after the end of each macroscopic time step. The check logic is achieved by monitoring a set of pre-set system key indicators, which specifically include: the average beach elevation change of the whole domain, the total vegetation coverage area, and the wave height attenuation rate of the key section. The stability condition is judged by a dynamic relative change threshold, and the specific method is: after the end of each macroscopic time step, the system calculates the value of each key indicator and compares it with the value of the last macroscopic time step. If the relative change rate of any indicator exceeds the pre-set threshold percentage, it is considered that the system state has changed significantly, and the current net change trend field is no longer applicable. At this time, the system will automatically interrupt the acceleration cycle and return to the latest terrain based on the latest terrain and vegetation state, and re-execute the high-resolution physical process simulation to generate an updated net change trend field. The determination rule of the pre-set threshold percentage is that the system selects the specific reset threshold percentage according to the pre-set interval of the characteristic parameter representing the geomorphic activity (for example, the historical annual scouring and silting rate). The mapping relationship between the interval and the threshold is pre-defined as follows: if the annual scouring and silting rate is less than 5 cm / year, the threshold is 5%; if the rate is between 5 and 20 cm / year, the threshold is 15%; if the rate is greater than 20 cm / year, the threshold is 25%. In the adaptive check and reset step, in addition to judging the change amount of the key indicators, the direction of the change of the indicators is also judged. The system records whether the change of the key indicators in the last few consecutive time steps is increasing or decreasing to form a change direction sequence. When the change direction sequence presents a continuous alternating inversion mode (for example, "increase-decrease-increase" appears continuously), it is determined that the simulation process has non-physical numerical oscillation, and the reset step is triggered. This method can identify and suppress numerical oscillation problems, monitor the behavior pattern of change, and thus can capture the signs of simulation instability earlier and more accurately, ensuring the convergence of long-term evolution prediction results and the rationality of physical meaning.

[0085] By repeatedly executing the above loop process until the set total simulation time scale is reached, the system completes the long-term evolution simulation. After the end of each geomorphic evolution time step, the system stores the geomorphic elevation, vegetation state and key hydrodynamic conditions at that time to record the complete dynamic evolution process. After the simulation is completed, all time step data is integrated to generate a comprehensive data set reflecting long-term geomorphic evolution and vegetation effect. The data set is stored in NetCDF format and can generate corresponding visualization results. The final data is fed back to other modules to support subsequent analysis and prediction.

[0086] The net change trend field is generated by high-resolution physical process simulation within a representative period, and the terrain is dynamically updated by combining key indicators for verification in the cycle calculation, which can efficiently simulate the dynamic process of long-term geomorphology evolution and vegetation sedimentation and wave dissipation effect. The self-adaptive verification and reset mechanism ensures the applicability of the trend field under environmental changes, improves the prediction accuracy of multi-scale coupling, and provides reliable support for refined ecological assessment and engineering design.

[0087] The verification data provided by the data acquisition module is loaded, which is stored in Shapefile and NetCDF formats, and contains measured topographic elevation changes, vegetation distribution and hydraulic effect data. The accelerated simulation module is run to cover the time period of the verification data, the sedimentation effect (based on long-term trend of sedimentation amount change) and the wave dissipation effect (based on long-term amplitude of wave height attenuation) of the model output are extracted, compared with the measured data, and the deviation between the simulation value and the measured value is calculated. The root mean square error and correlation coefficient are used to evaluate the model accuracy. Through iterative adjustment of the geomorphology evolution time step, the stable threshold of the system key indicators, the CFL safety threshold and the time step shortening ratio, the model is optimized until the error converges. The verification results are stored in CSV format to confirm that the model can accurately capture the long-term sedimentation and wave dissipation effect, and ensure the reliability of long-term prediction.

[0088] Through the collaborative work of data acquisition, simulation construction, vegetation dynamic response, biological geomorphology evolution and accelerated simulation module, multi-scale coupling of fast hydrodynamic process and slow biological-geomorphology process is realized, which significantly improves the long-term prediction accuracy. The system uses unstructured triangular mesh and wave flow sediment coupling mechanism to accurately simulate the sediment transport and geomorphology evolution of complex coastlines; based on habitat suitability model and vegetation mechanics model, the growth, decline and hydraulic effect of vegetation are truly reflected; through adaptive time step control and key indicator verification, the stability and efficiency of long-term evolution simulation are optimized. Compared with traditional methods, the present application effectively captures the evolution law of ecological geomorphology system, meets the needs of refined ecological assessment and engineering design, and provides reliable support for scientific evaluation of beach vegetation protection effect.

[0089] Example two:

[0090] A multi-scale numerical-based simulation system for the effect of beach vegetation on beach fixation and wave dissipation is started. The effect of reed vegetation in beach area B on beach fixation and wave dissipation under the action of tides for 5 years is simulated. The terrain basic data, boundary condition data and initial state data of the vegetation provided by the data collection module are stored in the NetCDF and Shapefile formats. The terrain basic data includes a digital elevation model (average elevation 0.5 m, bottom material is silt), the elevation and bottom material distribution are extracted by analyzing the NetCDF file, an unstructured triangular mesh is generated by using an open source mesh generation tool, the mesh is locally encrypted in the tidal ditch and reed coverage area to improve the resolution, the elevation data is distributed to the mesh nodes by using the Kriging interpolation method, the initial digital elevation model is generated, the boundary condition data includes the tidal flow velocity and wave height, the CSV and NetCDF files are analyzed, the 30-minute resolution time series is generated by using the spline interpolation, the time series is distributed to the mesh boundary nodes, the initial state data of the vegetation is stored in the Shapefile format, the reed distribution is distributed to the mesh cells by spatial query, and the vegetation attribute table is generated; the parameter library is loaded, the factor weight and coefficient of the habitat suitability model of the reed are obtained, the consistency of the obtained reed data is checked and processed, the initial data set is generated, and the initial data set is stored in the NetCDF format.

[0091] Further, the vegetation response module is started, the hydrodynamic condition data output by the simulation construction module is loaded, the initial state data of the reed is loaded, the reed is regarded as a flexible cantilever beam by using the beam theory, the water dynamic load is calculated by using the Morison equation, the stem bending deformation is calculated based on the flow velocity and the wave track velocity, and the deformation curve is generated. The DBSCAN clustering algorithm is used, the composite vegetation area is generated based on the coverage rate and the distance between the meshes, the spatial density coefficient is calculated, the hydrodynamic drag coefficient is calculated by using the pre-calibration function based on the deformation curve and the vegetation attribute, and the hydrodynamic influence index is generated. In this embodiment, at a certain moment in the simulation, the system monitors the reed in a mesh cell, the natural height H of the reed is 1.8 m, and the initial hydrodynamic drag coefficient is 1.1. After calculation, the bending displacement of the top end of the stem is 0.36 m at this moment, and the relative bending degree is 0.2. According to the empirical function model and by using the preset empirical coefficient of the reed =-0.5, =0.15) of the present application, the updated hydrodynamic drag coefficient is 0.997. Through this calculation, the system obtains that the drag coefficient of the reed in the cell at this moment is dynamically updated from the initial 1.1 to about 0.997, and the result is subsequently used to generate the hydrodynamic influence index. The generated data is stored in the NetCDF format and is fed back to the simulation construction module to adjust the flow field calculation.

[0092] Start the geomorphological evolution module, load the topography elevation and hydrodynamic conditions from the simulation construction module, and the reed distribution and hydraulic impact index from the vegetation response module, all stored in NetCDF format; load the reed niche parameters, based on the habitat suitability model, extract the average beach elevation, average erosion rate, and average flooding frequency for each grid cell, and use the logistic regression method to calculate the comprehensive suitability index. Use the random forest model, input the suitability index and coverage of adjacent grids, to predict the growth, decline, or expansion trend of reed. For growing grids, increase the coverage and height; for declining grids, reduce the coverage or remove the vegetation; for expanding grids, use the spatial diffusion model to expand to adjacent grids. Update the vegetation distribution and physiological parameters to the vegetation response module to generate new synthetic vegetation area and hydraulic impact index, stored in NetCDF format.

[0093] Start the accelerated simulation module, load the output data from the simulation construction module, vegetation response module, and geomorphological evolution module, initialize the simulation parameters for a 5-year time scale, macro time step 1 month, run high-resolution physical process simulation within a tidal cycle, calculate the net change trend field of topography. At each calculation time step, solve the CFL number, if greater than the safety threshold 0.9, shorten the step by a proportion of 0.8 to recalculate, ensure numerical stability, in the loop calculation, update the terrain with the net change trend field for a macro time step of 1 month, at the end of each step, check the key indicators, including elevation change rate, coverage change rate, and hydraulic impact index change rate, if the stability condition is met, continue the loop; otherwise, re-run the high-resolution simulation, update the trend field, after 5 years of simulation, output the topography elevation change, reed distribution, and hydraulic impact index, stored in NetCDF format.

[0094] Integrate the accelerated simulation results to generate a comprehensive data set for a 5-year time scale, including topography elevation change (reflecting erosion and deposition trend), reed distribution (coverage, height, reflecting the beach-fixing effect), and hydraulic impact index (reflecting the wave-damping effect), verify the data consistency with the time steps of each module, generate visualization results, including the spatial distribution map of topography elevation change, reed distribution map, and hydraulic impact index time series graph, the results are fed back to the simulation construction module to update the terrain, the vegetation response module to update the drag force parameters, and the geomorphological evolution module to update the vegetation distribution, stored as input for ecological topography analysis.

[0095] Load the validation data, including topographic elevation change, reed coverage, shoaling effect, and wave dissipation effect. Run the acceleration simulation module, extract the output, and compare it with the measured data to calculate the root mean square error. Adjust the factor weights in the habitat suitability model, as well as the classification rule parameters, CFL threshold, step shortening ratio, and key indicator threshold in the probability model, and iterate optimization to minimize the error. The validation results are stored in CSV format (including error indicators and correlation coefficients), and the adjusted parameters are stored in JSON format, updating the parameter library to ensure model reliability.

[0096] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those of ordinary skill in the art that various changes, modifications, replacements, and variations can be made thereto without departing from the principles and spirit of the application, the scope of which is defined by the following claims and their equivalents.

Claims

1. A multi-scale numerical simulation system for the beach vegetation consolidation and wave-breaking effect, characterized in that: include: Data acquisition module: obtains terrain basic data, boundary condition data, vegetation physical parameters and vegetation ecological parameters; Simulation construction module: Use the basic terrain data to establish the numerical calculation grid and terrain, use the boundary condition data as the driving condition, and update the landform elevation and hydrodynamic conditions based on the wave-current-sediment coupling mechanism; Vegetation response module: Calculates vegetation flexible deformation based on vegetation mechanical model, combined with hydrodynamic conditions and vegetation physical parameters, and updates hydraulic drag force parameters based on the vegetation flexible deformation through a predetermined functional relationship; Landform Evolution Module: Based on the habitat suitability model, the module evaluates the probabilistic trends of vegetation growth, decline, and expansion based on landform elevation, hydrodynamic conditions, and vegetation ecological parameters, and feeds the updated vegetation status back to the Vegetation Response Module. Accelerated simulation module: combines physical process simulation with landform evolution calculation, introduces adaptive verification and reset steps, and realizes accelerated simulation of tidal flat vegetation in beach consolidation and wave dissipation.

2. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: When the simulation construction module establishes the initial numerical calculation grid and terrain, it uses the high-precision water depth data in the terrain basic data to establish a digital elevation model. For complex coastlines containing tidal gullies and mudflats, it generates an unstructured triangular mesh covering the entire study area, and performs local mesh encryption on the tidal gullies, vegetation-covered areas, and areas of key concern in the engineering area. The elevation data contained in the digital elevation model is interpolated onto the grid nodes to form the initial terrain required for model calculation.

3. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: In the simulation construction module, the calculation process executed by the wave-flow-sediment coupling mechanism within a coupling time step is as follows: the hydrodynamic solver calculates the average water flow field at the current moment and transmits it to the wave solver. The wave solver calculates the wave propagation, deformation and wave-induced radiation stress on this basis. The wave-induced radiation stress is then fed back to the hydrodynamic solver and superimposed with the bed shear stress of the water flow to obtain the bottom bed shear stress. The bottom bed shear stress is then used to drive the sediment transport formula to calculate and update the landform elevation change at the current time step.

4. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: The simulation construction module is further configured to call the data acquisition module to obtain physical property data including vegetation elastic modulus, and pass the elastic modulus as a core parameter to the cantilever beam mechanical model in the vegetation response module for calculating flexible bending deformation.

5. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: The hydrodynamic load acting on the vegetation is calculated using the Morrison equation, and the vegetation is mechanically modeled using the cantilever beam theory. The load is applied to the model to obtain the vegetation bending displacement, which is used as a dynamic deformation parameter. The bending displacement and the geometric and mechanical properties of the vegetation are input into an empirical function model that characterizes the relationship between the dynamic bending of vegetation and the water drag force to update the hydraulic drag force parameter. The empirical function model is established through fitting and inverse calculation based on the dynamic deformation and total drag force data of vegetation measured in a controlled physical model experiment.

6. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: The landform evolution module evaluates the evolution trend of vegetation through a habitat suitability model. The habitat suitability model takes multiple environmental factors of the vegetation location, including average beach elevation, average scour rate and average flooding frequency, as comprehensive inputs, and calculates a comprehensive habitat suitability index based on the comprehensive inputs. According to the value of the habitat suitability index, combined with the probability model, the growth, decline and spatial expansion trends of the vegetation in the next landform evolution time step are determined.

7. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: The accelerated simulation module also includes an adaptive time step control. The execution process of the adaptive time step control is as follows: when each calculation time step is completed, the Courant-Friedrich-Low dimension used to judge numerical stability is calculated in the entire calculation domain. If the Courant-Friedrich-Low dimension is greater than a preset safety threshold, the calculation result of the current time step is rejected, and the step size of the next time step is shortened by a preset ratio and then recalculated.

8. The multi-scale numerical simulation system for beach vegetation consolidation and wave elimination according to claim 1 is characterized in that: The method for combining physical process simulation with landform evolution calculation includes: performing high-resolution physical process simulation of a representative period to calculate a net change trend field of the landform; in a cyclic calculation step, applying the net change trend field to update the terrain within a macroscopic landform evolution time step; after the time step ends, verifying the preset system key indicators; when the verification results of the key indicators meet the preset stability conditions, continuing to use the current net change trend field to repeat the cyclic calculation steps; when the verification results do not meet the stability conditions, re-performing the high-resolution physical process simulation to update the net change trend field.

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