Ecological seawall simulation model system

Abstract

This invention discloses an ecological seawall simulation model system, relating to the ecological construction of seawalls. The system includes: a data acquisition module for acquiring multi-source heterogeneous data of the target seawall, including geographic information data, hydrological monitoring data, ecological survey data, and remote sensing imagery data; a data fusion processing module connected to the data acquisition module for performing spatiotemporal registration, normalization, and missing data imputation on the multi-source heterogeneous data to generate a standardized fused dataset; and a dynamic simulation model construction module for constructing a three-dimensional digital model based on the fused dataset, including the main structure of the seawall, the shoreline in front of the seawall, and the ecological space behind the seawall, and setting hydrodynamic boundary conditions and ecological evolution rules. This invention, by quantitatively evaluating ecological seawall assessment indicators, helps improve the marine ecological environment, enhances the ecological service functions of ecological seawalls, and provides strong support for the sustainable development of coastal areas.

Description

Technical Field

[0001] This invention relates to the ecological construction of seawalls, and in particular to an ecological seawall simulation model system. Background Technology

[0002] Seawalls are crucial infrastructure for coastal areas to protect against natural disasters such as storm surges and typhoon waves. Traditional seawall construction often causes damage to the intertidal zone and natural coastlines, impacting the ecological functions of the coastline. Ecological seawalls, which combine storm surge prevention and disaster mitigation with the restoration of coastal ecological functions, have become an inevitable trend in seawall construction both domestically and internationally.

[0003] However, how to scientifically and systematically evaluate the effectiveness of ecological seawall construction is the main challenge currently facing ecological seawall construction. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention provides an ecological seawall simulation model system, comprising: The data acquisition module is used to acquire multi-source heterogeneous data of the target seawall, including geographic information data, hydrological monitoring data, ecological survey data, and remote sensing image data. The data fusion processing module, connected to the data acquisition module, is used to perform spatiotemporal registration, normalization processing, and missing data imputation on the multi-source heterogeneous data to generate a standardized fusion dataset. The dynamic simulation model construction module constructs a three-dimensional digital model based on the fused dataset, including the main structure of the seawall, the beach in front of the seawall, and the ecological space behind the seawall, and sets the hydrodynamic boundary conditions and ecological evolution rules. The indicator quantification and evaluation module has a pre-set comprehensive evaluation indicator system for ecological seawalls. It is used to receive the simulation results of the three-dimensional digital model under different simulation scenarios, and automatically calculate the safety indicator value, ecological indicator value and sustainability indicator value based on the comprehensive evaluation indicator system. The simulation analysis module is used to perform weighted fusion of the safety index value, ecological index value and sustainability index value according to the preset weight vector to generate a comprehensive ecological seawall index, and to classify and evaluate the performance of the target seawall according to the comprehensive index. The visualization and interaction module is used to visualize the three-dimensional digital model, simulation process, and evaluation results in three dimensions, and provides corresponding parameter adjustment interfaces so that users can modify the simulation conditions.

[0005] Preferably, the data acquisition module includes: The geographic information acquisition unit is used to collect and process lidar point cloud data, digital elevation model data and engineering geological survey data of the target seawall, and generate a high-precision topographic model. The real-time hydrological monitoring unit is used to acquire real-time monitoring data on tide level, waves, current velocity, and current direction through a sensor network deployed in the target seawall area, and to clean and calibrate abnormal data. The ecological remote sensing inversion unit is used to acquire multi-temporal high-resolution remote sensing images and extract water quality parameters, vegetation coverage, and intertidal substrate type information through remote sensing inversion algorithms. The historical data digitization unit is used to digitize historical hydrological and ecological paper data and establish a historical database.

[0006] Preferably, the dynamic simulation model construction module includes: The parametric modeling unit is used to construct the geometric model of the main structure of the seawall and the beach in front of the seawall using parametric methods based on the topographic and geological data in the fused dataset, and to set the material properties. The hydrodynamic numerical simulation unit establishes an unstructured grid coupled tidal wave numerical model based on the Navier-Stokes equations or shallow water equations to simulate the overtopping and scouring process of seawalls under the action of storm surges and typhoon waves with different return periods. An ecological process simulation unit, connected to the hydrodynamic numerical simulation unit, is used to construct a benthic organism habitat suitability model and a salt marsh vegetation dynamic succession model to simulate the impact of hydrodynamic changes on organism distribution and vegetation growth. The model coupling driving unit is used to couple the hydrodynamic numerical simulation unit with the ecological process simulation unit to achieve the output of bidirectional feedback dynamic simulation data of hydrodynamic-ecological processes.

[0007] Preferably, the comprehensive evaluation index system for the ecological seawall includes: The safety index subsystem includes the seawall crest elevation compliance rate, the risk of excessive wave overtopping, the overall stability safety factor of the seawall body, and the seepage stability safety factor. The ecological indicator subsystem includes the comprehensive water quality index, sediment environmental quality index, benthic biodiversity index, vegetation coverage, proportion of ecological space behind the dike, bank width, bank erosion and sedimentation rate, water-facing slope, water-facing slope surface porosity, and building material eco-friendliness score. The sustainable indicators subsystem includes the effectiveness of disaster prevention and mitigation functions, habitat maintenance function index, economic benefits of disaster prevention and mitigation, environmental cleanliness maintenance capacity, and the convenience of ecological maintenance and management.

[0008] Preferably, the indicator quantification and evaluation module includes: The index calculation unit is used to extract relevant parameters from the simulation results and calculate the values ​​of each index according to predefined formulas. The formula for calculating the Water Quality Index (WQI) is as follows: WQI is the comprehensive water quality index; C i To evaluate the concentration of factor i in the environment, S i The environmental quality standard concentration for evaluation factor i is given, and n is the number of all water quality items being evaluated. Formula for calculating the sediment environmental quality index: a i Let n be the exceedance rate of the i-th evaluation item, and n be the number of evaluation items; The formula for calculating the benthic biodiversity index is: S represents the number of macrobenthic species, P i is the ratio of the number of individuals of the i-th macrobenthic animal to the total number of macrobenthic animals; The rate of change in erosion and deposition of the beach was calculated by comparing the differences between the simulated topography and the initial topography at different time periods. The eco-friendliness score of building materials is based on a comprehensive assessment of material source, carbon footprint, and recyclability factors.

[0009] Preferably, the simulation analysis module includes: The weight dynamic adjustment unit is used to store the weights of each indicator determined based on the analytic hierarchy process or the entropy weight method, and allows users to dynamically adjust the weights according to actual engineering needs. The comprehensive index generation unit, connected to the index quantification and evaluation module and the weight dynamic adjustment unit, is used to generate an index based on the formula: ECI stands for Ecological Seawall Index. i W represents the evaluation score of each of the i indicator factors in the indicator system. i represents the relative weight of the i-th indicator factor in the indicator system with respect to the overall objective; The rating unit is connected to the comprehensive index generation unit. It has a preset rating standard for comparing the calculated ecological seawall comprehensive index (ECI) with the standard threshold to determine the ecological quality level of the target seawall.

[0010] Preferably, the evaluation level classification standard maps the ECI value to a continuous interval of 0 to 100, and divides it into four intervals: (80, 100), (60, 80), (30, 60), and (0, 30), where: The area between (80, 100) is a high-quality ecological seawall; The area between (60, 80) represents a good ecological seawall; The interval (30, 60) represents a poor ecological seawall; The interval (0, 30) represents the worst ecological seawall.

[0011] As a preferred option, it also includes: The scheme comparison and optimization module is connected to the simulation analysis module. It is used to automatically generate multiple alternative seawall modification or repair schemes according to the preset optimization objectives, and drive the dynamic simulation model construction module and the index quantification and evaluation module to perform simulation evaluation on each alternative scheme, and finally output the optimal scheme or the scheme ranking list that meets the optimization objectives.

[0012] Preferably, the scheme comparison and optimization module includes: The parameterized scheme generation unit is used to generate a series of parameter combinations as alternative schemes by adjusting the seawall structure parameters and ecological restoration parameters; The multi-objective optimization solution unit is used to automatically generate Pareto front solution sets based on genetic algorithms or multi-objective particle swarm optimization algorithms, with the objectives of maximizing the comprehensive index of ecological seawalls and minimizing engineering costs. The scheme visualization comparison unit is used to visualize and compare the simulation evaluation results of different schemes, assisting decision-makers in selecting the final implementation scheme.

[0013] Preferably, the visualization and interaction module includes: The 3D scene rendering unit is used to render the seawall and its surrounding environment with high realism based on the Unity3D or UE4 engine, enabling immersive roaming. The dynamic process replay unit is used to replay the dynamic processes of storm surge evolution, shoreline evolution, and vegetation growth in a timeline format. The interactive parameter adjustment unit provides a graphical interface that allows users to adjust hydrological conditions and seawall structure parameters in real time, trigger a resimulation, and instantly view the effects of the adjustments.

[0014] The present invention has at least the following beneficial effects: The data fusion processing module performs spatiotemporal registration and interpolation on multi-source heterogeneous data to generate a standardized fusion dataset, providing accurate initial conditions for the construction of dynamic simulation models and improving the realism and reliability of simulation results.

[0015] The dynamic simulation model construction module couples hydrodynamic numerical simulation with ecological process simulation to achieve two-way feedback. It can accurately simulate the complex interaction between shoreline evolution and ecological response under storm surge, providing a scientific basis for the design of ecological seawalls.

[0016] It can not only automatically generate a comprehensive index based on a preset indicator system, but also supports users to dynamically adjust the indicator weights according to actual engineering needs, making the evaluation results more flexible and realistic.

[0017] The visualization and interaction module uses a high-fidelity rendering engine and interactive parameter adjustment, enabling users to intuitively observe the dynamic process and adjust parameters in real time to view the effects, thus improving the user experience and ease of use of the system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a module diagram provided in Embodiment 1 of the present invention; Figure 2 This is a unit architecture diagram of the data acquisition module provided in Embodiment 1 of the present invention; Figure 3 This is a unit architecture diagram of the dynamic simulation model construction module provided in Embodiment 1 of the present invention; Figure 4 This is a unit architecture diagram of the simulation analysis module provided in Embodiment 1 of the present invention; Figure 5 This is a unit architecture diagram of the scheme comparison and optimization module provided in Embodiment 1 of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "including," "having," and any variations are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] Example 1

[0023] This embodiment provides an ecological seawall simulation model system, such as Figure 1 As shown, it includes: a data acquisition module, a data fusion and processing module, an indicator quantification and evaluation module, a simulation analysis module, and a visualization and interaction module. Specifically: The data acquisition module is used to acquire multi-source heterogeneous data of the target seawall, including geographic information data, hydrological monitoring data, ecological survey data, and remote sensing image data.

[0024] Detailed, such as Figure 2 As shown, the data acquisition module includes: The geographic information acquisition unit is used to collect and process lidar point cloud data, digital elevation model data and engineering geological survey data of the target seawall, and generate a high-precision topographic model. The real-time hydrological monitoring unit is used to acquire real-time monitoring data on tide level, waves, current velocity, and current direction through a sensor network deployed in the target seawall area, and to clean and calibrate abnormal data. The ecological remote sensing inversion unit is used to acquire multi-temporal high-resolution remote sensing images and extract water quality parameters, vegetation coverage, and intertidal substrate type information through remote sensing inversion algorithms. The historical data digitization unit is used to digitize historical hydrological and ecological paper data and establish a historical database.

[0025] Specifically, the geographic information acquisition unit is primarily responsible for acquiring topographic and geological data of the seawall and its surrounding area. In terms of implementation, this unit typically uses a LiDAR system mounted on a UAV or manned aircraft to acquire high-precision point cloud data, generating a digital elevation model (DEM). Simultaneously, it combines this with a multibeam echo sounder system to acquire underwater topographic data, and uses engineering geological drilling to obtain stratigraphic structure and physical and mechanical parameters, ultimately forming a high-precision integrated land-sea topographic model. For example, when assessing the ecological transformation of an existing seawall, this unit can use a UAV LiDAR to acquire a centimeter-level accurate surface model of the seawall body and the area behind it, use a shipborne multibeam echo sounder to acquire the intertidal zone and nearshore underwater topography in front of the seawall, and combine this with geological borehole data to construct a complete three-dimensional geological model of the seawall's underwater front, seawall structure, and the land area behind it.

[0026] The real-time hydrological monitoring unit is responsible for dynamically acquiring hydrodynamic environmental data of the sea area where the seawall is located. This unit is implemented by deploying a sensor network in the target sea area, including pressure wave meters deployed at different water depths in front of the seawall to obtain wave elements, acoustic Doppler current profilers (ADCP) to obtain vertical current velocity distribution, and radar level gauges to monitor tidal changes in real time. All sensor data is transmitted to the data center in real time via 4G / 5G or BeiDou short message communication, and anomaly cleaning algorithms are built in, such as removing outlier data caused by sensor malfunctions and smoothing wave data using Kalman filtering. Taking the seawall safety assessment during a typhoon as an example, this unit can transmit back in real time the wave height changes during the approach of typhoon waves, the storm surge process, and the maximum current velocity at the toe of the seawall, providing boundary conditions for the dynamic simulation model.

[0027] The ecological remote sensing inversion unit and the historical data digitization unit jointly complete the collection of ecological data. The ecological remote sensing inversion unit utilizes multi-temporal, multispectral, or hyperspectral satellite remote sensing imagery (such as Sentinel-2, Landsat-8, and Gaofen series satellites) to extract large-scale ecological parameters through remote sensing inversion algorithms. For example, it inverts suspended sediment concentration based on the band ratio method to characterize water quality, calculates the vegetation cover of salt marshes behind and in front of the dike based on the Normalized Divided Vegetation Index (NDVI), and extracts the distribution of intertidal substrate types based on remote sensing interpretation. The historical data digitization unit is responsible for scanning, identifying, and vectorizing scattered, paper-based historical hydrological and ecological survey data to establish a long-term historical database. For example, when assessing the long-term ecological effects of a newly built ecological seawall, this unit can retrieve remote sensing imagery of the area over the past ten years to invert and analyze the evolution trend of the shoreline in front of the dike; simultaneously, it digitizes benthic organism sampling data from paper reports of previous marine environmental surveys to construct a time-series dataset of species lists and abundance.

[0028] The raw data collected by the three units exhibit significant differences in data format, spatiotemporal resolution, and coordinate system. Therefore, the multi-source heterogeneous data output by the data acquisition module is directly transmitted to the subsequent data fusion processing module. This module performs unified spatiotemporal registration, normalization, and missing data imputation, ultimately generating a standardized fusion dataset that is spatiotemporally consistent and formatted uniformly. This dataset provides accurate and reliable initialization parameters and boundary conditions for the dynamic simulation model construction module. Through this multi-level, multi-method collaborative data acquisition mode, the system can comprehensively depict the real state of the seawall and its surrounding environment, effectively overcoming the shortcomings of incomplete and inaccurate information from a single data source, and laying a solid data foundation for high-fidelity ecological seawall simulation.

[0029] The data fusion processing module, connected to the data acquisition module, is used to perform spatiotemporal registration, normalization, and missing data imputation on multi-source heterogeneous data to generate a standardized fusion dataset. Specifically, the data includes high-precision topographic data (point cloud, DEM) obtained through lidar and engineering surveys; hydrological monitoring data (tide level, wave, current velocity) collected in real time by a sensor network deployed in the seawall area; ecological data (water quality parameters, vegetation cover) obtained through satellite remote sensing inversion; and archival data digitized from historical paper documents. A significant characteristic of this data is its multi-source and heterogeneous nature—spatially, topographic data may use Gauss-Kruger projection coordinates, while remote sensing imagery is based on geographic coordinate systems, resulting in inconsistent spatial references; temporally, hydrological sensors collect data once per second, while satellite remote sensing imagery only passes through the area every few days, leading to vastly different sampling frequencies; and quantitatively, elevation is measured in meters, while water quality concentration is measured in milligrams per liter, resulting in significantly different numerical ranges.

[0030] To address the aforementioned issues, the data fusion processing module operates according to a three-stage processing flow: spatiotemporal alignment, normalization, and interpolation. First, spatiotemporal registration is performed. This process borrows the raster alignment concept from multimodal geospatial data processing, projecting all data onto the same spatiotemporal coordinate system using a unified spatiotemporal coding technique (such as STARE spatiotemporal adaptive resolution coding or the GeoHash algorithm). Specifically, the module resamples and registers the data using a preset spatiotemporal resolution grid (e.g., spatially divided into 10m × 10m grids, with a minimum time bucket of 1 hour), ensuring that each data point is accurately associated with a specific location and time point, thus resolving spatial misalignment and temporal inconsistencies. For example, discrete sensor point data is interpolated onto the entire raster surface, ensuring a one-to-one correspondence with pixels in the remote sensing imagery.

[0031] After registration, the module performs normalization. Since subsequent simulation analysis and index calculations usually rely on the relative relationships of values ​​rather than absolute values, the purpose of normalization is to eliminate the influence of different dimensions between indicators, so that the data falls into a uniform numerical range (such as 0~1). The module will adaptively select the normalization method according to the data distribution characteristics. For water quality parameters that conform to a normal or approximately normal distribution, Z-score normalization (also known as standardization) is usually used, so that its mean is 0 and its standard deviation is 1; for topographic slope data with clear boundaries and relatively uniform distribution, min-max normalization is used to linearly scale it to the (0,1) interval; for sensor data that is susceptible to outliers, such as extreme wave records, robust scaling (based on median and interquartile range) is used to improve the robustness of normalization.

[0032] Finally, to address data gaps caused by sensor malfunctions, signal obstruction, or missing historical archives during data acquisition, the module performs data imputation. The imputation strategy is not simply filling in the mean, but rather employs a model-based multiple imputation method (such as chain equation multiple imputation, MICE) to preserve the original distribution characteristics of the data to the greatest extent possible. This process utilizes the correlations between variables (such as the physical relationship between tide level and waves, and the association between vegetation cover and topography) to establish a regression model, randomly sampling values ​​from the conditional distribution predicted by the model for imputation. This is more effective at restoring the variability and uncertainty of the data than simple mean imputation or deterministic regression imputation. For example, when measured wave data for a certain period is missing, the module uses complete tide level data from the same period and historical data from the same period to establish a regression relationship between the two, introducing a random error term to generate imputed values ​​that conform to statistical laws.

[0033] After the above three steps, raw data from different sources, formats, and scales are integrated into a standardized fusion dataset. This dataset has a consistent spatiotemporal benchmark, unified dimensions, and continuous and complete records, providing a high-quality data foundation for subsequent dynamic simulation model construction. For example, in a practical application scenario, when Typhoon Huayan makes landfall, the module can access real-time wind field data from weather forecasts, precipitation data retrieved from radar, tide data transmitted from on-site sensors, and shoreline images taken by drones after the disaster. Through spatiotemporal registration, these data with different temporal resolutions (seconds vs. hours) and spatial scales (kilometer-level meteorological grids vs. centimeter-level drone images) are uniformly aligned to the fine grid of the seawall area; through normalization processing, physical quantities such as tide level, wave height, and rainfall intensity are converted into dimensionless standardized features; and through missing data imputation, missing data during periods of sensor disconnection caused by extreme weather are supplemented.

[0034] The dynamic simulation model construction module constructs a three-dimensional digital model based on the fused dataset, which includes the main structure of the seawall, the beach in front of the seawall, and the ecological space behind the seawall, and sets the hydrodynamic boundary conditions and ecological evolution rules. The aforementioned dynamic simulation model construction module includes (such as) Figure 3 (as shown) The parametric modeling unit is used to construct the geometric model of the main structure of the seawall and the beach in front of the seawall using parametric methods based on the topographic and geological data in the fused dataset, and to set the material properties. The hydrodynamic numerical simulation unit establishes an unstructured grid coupled tidal wave numerical model based on the Navier-Stokes equations or shallow water equations to simulate the overtopping and scouring process of seawalls under the action of storm surges and typhoon waves with different return periods. The ecological process simulation unit, connected to the hydrodynamic numerical simulation unit, is used to construct a benthic organism habitat suitability model and a salt marsh vegetation dynamic succession model to simulate the impact of hydrodynamic changes on organism distribution and vegetation growth. The model coupling driving unit is used to couple the hydrodynamic numerical simulation unit with the ecological process simulation unit to achieve the output of bidirectional feedback dynamic simulation data of hydrodynamic-ecological processes.

[0035] In its specific implementation, the module first calls the parametric modeling unit to receive lidar point cloud data, digital elevation model, and engineering geological survey data from the fused dataset. It then uses a non-uniform rational B-spline parametric modeling method to automatically construct a three-dimensional geometric model of the main structure of the seawall and the beach in front of the seawall. Based on the geological data, it assigns material properties to different areas of the seawall body and foundation. Simultaneously, based on the remote sensing image interpretation results and ecological survey data, it performs semantic segmentation and object-oriented modeling of the ecological space behind the seawall in the model. Ecological elements such as salt marsh vegetation and benthic habitats are embedded into the model in the form of parametric objects, forming a full-element three-dimensional digital model that includes physical structure and ecological elements.

[0036] Based on the established geometric model, the hydrodynamic numerical simulation unit uses an unstructured grid to spatially discretize the computational domain and establishes a numerical model of tidal current and wave coupling based on the Navier-Stokes equations. The unit extracts hydrological monitoring data from the fused dataset as model boundary conditions, including real-time tide data obtained through sensor networks, storm surge process lines with different return periods retrieved based on historical typhoon data, and deep-water wave elements calculated using wave models. During the simulation, the unit can accurately calculate the deformation, breaking, and overtopping processes of waves in front of the dike, as well as the impact of overtopping water on the dike crest and the area behind the dike. At the same time, it simulates the bottom shear stress and sediment transport processes of the beach in front of the dike under the combined action of waves and currents.

[0037] The ecological process simulation unit is responsible for constructing a benthic habitat suitability model and a salt marsh vegetation dynamic succession model. The benthic habitat suitability model is based on macrobenthic animal survey data to establish the response relationship between different species and environmental factors such as water depth, substrate type, inundation time, and sediment organic matter content, and generates a habitat suitability index distribution map. The salt marsh vegetation dynamic succession model is based on the principle of cellular automata, which correlates vegetation growth rules with hydrodynamic factors such as elevation, flooding frequency, and wave energy to simulate the germination, growth, expansion, or degradation process of vegetation.

[0038] The model-coupled driving unit is key to realizing the hydrodynamic-ecological two-way feedback simulation, employing a hybrid strategy combining loose and tight coupling. In terms of time stepping, this unit first drives the hydrodynamic numerical simulation unit to calculate the flow field, wave field, and sediment transport results within one tidal cycle, and then transmits the calculation results to the ecological process simulation unit to update habitat suitability and vegetation distribution. Subsequently, the ecological process simulation unit feeds back the updated parameters such as bed roughness and vegetation resistance to the hydrodynamic numerical simulation unit, adjusting the momentum equation source term for the next time period. This iterative process realizes how hydrodynamic conditions change the shoreline landform and ecological pattern, and how the evolution of the ecological pattern, in turn, affects the dynamic coupling simulation of hydrodynamic processes.

[0039] Taking the simulation evaluation of an ecological seawall renovation project as an example: the system collects multi-temporal remote sensing images and historical hydrological data of the seawall area, and constructs a three-dimensional digital model including the existing seawall, the silty tidal flat in front of the seawall, and the reed wetland behind the seawall; it sets a 50-year typhoon wave as the hydrodynamic boundary condition to simulate the scouring process of the beach surface in front of the seawall during the passage of a typhoon; the ecological process simulation unit recalculates the habitat suitability index of benthic shellfish based on the topography and water depth conditions after scouring, and predicts the inundation stress range of the reed community; the model coupling driving unit feeds back the resistance changes after vegetation degradation to the hydrodynamic model, recalculates the overtopping volume and the scouring depth at the seawall toe, and finally outputs the coupled simulation results considering ecological degradation, accurately revealing the interaction mechanism between seawall safety and ecosystem response under extreme hydrological conditions.

[0040] The indicator quantification and evaluation module has a pre-set comprehensive evaluation indicator system for ecological seawalls. It is used to receive the simulation results of the three-dimensional digital model under different simulation scenarios, and automatically calculate the safety indicator value, ecological indicator value and sustainability indicator value based on the comprehensive evaluation indicator system. The aforementioned comprehensive evaluation index system for ecological seawalls includes: The safety index subsystem includes the seawall crest elevation compliance rate, the risk of excessive wave overtopping, the overall stability safety factor of the seawall body, and the seepage stability safety factor. The ecological indicator subsystem includes the comprehensive water quality index, sediment environmental quality index, benthic biodiversity index, vegetation coverage, proportion of ecological space behind the dike, bank width, bank erosion and sedimentation rate, water-facing slope, water-facing slope surface porosity, and building material eco-friendliness score. The sustainable indicators subsystem includes the effectiveness of disaster prevention and mitigation functions, habitat maintenance function index, economic benefits of disaster prevention and mitigation, environmental cleanliness maintenance capacity, and the convenience of ecological maintenance and management.

[0041] Furthermore, the aforementioned indicator quantification and evaluation module includes: The index calculation unit is used to extract relevant parameters from the simulation results and calculate the values ​​of each index according to predefined formulas. The formula for calculating the Water Quality Index (WQI) is as follows: WQI is the comprehensive water quality index; C i To evaluate the concentration of factor i in the environment, S i The environmental quality standard concentration for evaluation factor i is given, and n is the number of all water quality items being evaluated. Formula for calculating the sediment environmental quality index: a i Let n be the exceedance rate of the i-th evaluation item, and n be the number of evaluation items; The formula for calculating the benthic biodiversity index is: S represents the number of macrobenthic species, P i is the ratio of the number of individuals of the i-th macrobenthic animal to the total number of macrobenthic animals; The rate of change in erosion and deposition of the beach was calculated by comparing the differences between the simulated topography and the initial topography at different time periods. The eco-friendliness score of building materials is based on a comprehensive assessment of material source, carbon footprint, and recyclability factors.

[0042] Specifically, the comprehensive evaluation index system for ecological seawalls extracts features and quantifies the physical field data and ecological response data generated from simulations. This module integrates an index calculation engine, which first receives simulation results from the dynamic simulation model construction module under different simulation scenarios through a pre-defined data interface. For example, for storm surge scenarios, it receives physical field data such as seawall overtopping volume, seawall stress distribution, and shoreline erosion and deposition changes; for normal scenarios, it receives ecological data such as water quality parameter distribution, benthic habitat suitability distribution, and vegetation cover. The pre-defined index system within the module is specifically manifested as a safety index subsystem, an ecological index subsystem, and a sustainability index subsystem, each containing multiple quantifiable underlying indicators. During the calculation process, the index quantification and evaluation module automatically identifies the simulation scenario type, calls the corresponding index calculation algorithm, and quantifies each indicator.

[0043] For example, when the dynamic simulation model building module simulates a once-in-a-century typhoon storm surge scenario, the index quantification and evaluation module first extracts the instantaneous flow process line at the seawall overpass from the simulation results, calculates the cumulative overpass volume, and compares it with the preset permissible overpass volume threshold for the revetment to obtain the overpass volume exceeding the standard risk index; simultaneously, it extracts stress and strain data at different locations on the seawall body and calculates the overall stability safety factor. For the quantification of ecological indicators, the module, based on the flow field distribution and water quality diffusion results output from the simulation, uses the following formula: ; Calculate the comprehensive water quality index—for example, COD concentration C under a certain simulation scenario.COD The actual measured value was 20 mg / L, while the standard S COD The concentration of ammonia nitrogen is 30 mg / L, C. NH3 It is 1.5 mg / L, standard S NH3 If the concentration is 2.0 mg / L, then WQI = 20 / 30 + 1.5 / 2.0 = 0.67 + 0.75 = 1.42, which reflects the water quality status.

[0044] The benthic biodiversity index is based on species distribution simulation data output by the ecological process simulation unit, according to... The calculation assumes that a certain cross-section simulation yields three benthic animal species with individual population proportions of 0.5, 0.3, and 0.2, respectively. Therefore, H' = -(0.5 × ln0.5 + 0.3 × ln0.3 + 0.2 × ln0.2) = 1.03. The rate of change in shoreline erosion and sedimentation is calculated by comparing simulated topographic data before and after storm surge, determining the net erosion or sedimentation thickness per unit width of shoreline. For example, if the elevation of a certain shoreline section is 2.5m before simulation and 2.3m after storm surge, the rate of change is -0.2m. For the ecological scoring of building materials, the module calculates the weighted average based on the material attribute data input during model construction—such as using ecological blocks (score 90 points) or ordinary concrete (score 50 points)—combined with the material usage ratio. Through the above automated index calculation process, the index quantification and evaluation module transforms complex physical and ecological simulation results into standardized, comparable safety, ecological, and sustainability index values, providing accurate input data for the subsequent comprehensive evaluation and scheme optimization of the intelligent simulation analysis module.

[0045] The simulation analysis module is used to weight and fuse safety index values, ecological index values, and sustainability index values ​​according to a preset weight vector to generate a comprehensive ecological seawall index, and to classify and evaluate the performance of the target seawall based on the comprehensive index. Furthermore, the aforementioned simulation analysis module includes (such as...) Figure 4 (as shown) The weight dynamic adjustment unit is used to store the weights of each indicator determined based on the analytic hierarchy process or the entropy weight method, and allows users to dynamically adjust the weights according to actual engineering needs. The comprehensive index generation unit, connected to the indicator quantification and evaluation module and the weight dynamic adjustment unit, is used to generate an index based on the formula: ECI stands for Ecological Seawall Index. i W represents the evaluation score of each of the i indicator factors in the indicator system. i represents the relative weight of the i-th indicator factor in the indicator system with respect to the overall objective; The rating unit is connected to the comprehensive index generation unit. It has a preset rating rating standard, which is used to compare the calculated ecological seawall comprehensive index (ECI) with the standard threshold to determine the ecological quality level of the target seawall.

[0046] It should be noted that the evaluation grading standard maps ECI values ​​to a continuous interval of 0 to 100, and divides it into four intervals: (80, 100), (60, 80), (30, 60), and (0, 30). The area between (80, 100) is a high-quality ecological seawall; The area between (60, 80) represents a good ecological seawall; The interval (30, 60) represents a poor ecological seawall; The interval (0, 30) represents the worst ecological seawall.

[0047] Specifically, the intelligent simulation analysis module integrates three collaborative sub-units: a dynamic weight adjustment unit, a comprehensive index generation unit, and a rating unit. In the dynamic weight adjustment unit, the system does not employ fixed weights but provides a flexible weight management mechanism. This unit pre-stores initial weight vectors determined using scientific methods such as the analytic hierarchy process (AHP) or entropy weighting, reflecting the fundamental importance of each indicator in the comprehensive evaluation. Taking the ecological seawall assessment of a coastal city as an example, the initial weights might be set as follows: ecological indicator weight 0.5, safety indicator weight 0.3, and sustainability indicator weight 0.2, which aligns with the design philosophy of ecological seawalls, which prioritizes ecological restoration. More importantly, this unit provides a user interface, allowing project decision-makers or review experts to dynamically adjust the weights based on the specific needs of the project, regional characteristics, or policy guidance. For example, if the assessment object is located in a densely populated core urban area, where safety requirements are given the highest priority, users can use a graphical interface to increase the safety indicator weight to 0.5 while correspondingly decreasing the weights of the ecological and sustainability indicators. After the weights are adjusted, the system will automatically normalize the weight vector to ensure that the sum of all indicator weights is 1, thus preparing for subsequent calculations.

[0048] The comprehensive index generation unit is the executor of the weighted fusion algorithm. This unit receives the final weight vector from the dynamic weight adjustment unit and the score values ​​of each indicator from the indicator quantification and evaluation module. Its core algorithm employs a weighted summation model, i.e., according to the formula... Calculate the Ecological Seawall Index (ECI). In this formula, ECI... i W represents the score of the i-th indicator, which has been standardized and mapped to the range of 0-100; iThis represents the weight corresponding to the indicator. For example, suppose that in a simulation evaluation, after weight adjustment, the overall stability safety coefficient score of the levee under the safety indicator is 90 points (weight 0.15), and the risk score of exceeding the wave limit is 85 points (weight 0.15); the benthic biodiversity index score under the ecological indicator is 70 points (weight 0.25), and the vegetation coverage score is 80 points (weight 0.25); the disaster prevention and mitigation function effectiveness score under the sustainability indicator is 95 points (weight 0.1), and the habitat maintenance function index score is 75 points (weight 0.1). The comprehensive index generation unit will automatically perform the calculation: (90×0.15)+(85×0.15)+(70×0.25)+(80×0.25)+(95×0.1)+(75×0.1)=13.5+12.75+17.5+20+9.5+7.5=80.75 points. This score of 80.75 is the Ecological Dike Index (ECI) of the target seawall in this simulation evaluation.

[0049] Finally, the rating unit is responsible for semantically interpreting the calculated ECI value. This unit has a fixed set of scientific evaluation rating standards, dividing the continuous score range of 0-100 into four clearly defined levels: (80, 100) for excellent ecological seawalls, (60, 80) for good ecological seawalls, (30, 60) for poor ecological seawalls, and (0, 30) for very poor ecological seawalls. The rating unit automatically compares the 80.75 score output by the comprehensive index generation unit with the above thresholds, determining that it falls within the (80, 100) range. Therefore, it ultimately outputs the rating result of excellent ecological seawall and sends this result, along with the comprehensive index value, to the visualization module for presentation.

[0050] The visualization and interaction module is used to visualize the 3D digital model, simulation process, and evaluation results in 3D, and provides corresponding parameter adjustment interfaces so that users can modify the simulation conditions.

[0051] Furthermore, the aforementioned visualization and interaction modules include: The 3D scene rendering unit is used to render the seawall and its surrounding environment with high realism based on the Unity3D or UE4 engine, enabling immersive roaming. The dynamic process replay unit is used to replay the dynamic processes of storm surge evolution, shoreline evolution, and vegetation growth in a timeline format. The interactive parameter adjustment unit provides a graphical interface that allows users to adjust hydrological conditions and seawall structure parameters in real time, trigger a resimulation, and instantly view the effects of the adjustments.

[0052] Specifically, commercial game engines such as Unreal Engine or Unity3D are used as the underlying graphics rendering platform, leveraging their advanced rendering pipelines to achieve highly realistic visual presentation. Physically Based Rendering (PBR) technology simulates the optical properties of the seawall surface material, dynamic lighting algorithms recreate changes in light and shadow under different sunlight and weather conditions, and particle systems simulate natural phenomena such as wave spray and swaying vegetation, ultimately constructing an immersive virtual seawall environment. Regarding dynamic process playback, the module operates based on a time-series data-driven mechanism: simulation calculation results stored in the system's backend are converted into keyframe sequences, and the 3D scene transitions smoothly along the timeline using interpolation algorithms. This allows for a visual timeline-based playback of the storm surge's evolution, including tide level rise, wave volume changes, shoreline erosion and sedimentation, and the phased growth of salt marsh vegetation, enabling users to intuitively understand the complex hydrodynamic-ecological coupling mechanism. For the core function of interactive parameter adjustment, the module adopts a "data-view two-way binding" architecture: the front end provides interactive controls such as sliders, knobs, and input boxes through a graphical interface. When users adjust hydrological parameters (such as return period wave height and tide level) or seawall structural parameters (such as seawall crest elevation, water-facing slope, and surface porosity), the front end captures the parameter changes in real time and encapsulates them into a JSON request, which is then sent to the simulation analysis engine. The engine triggers the dynamic simulation model in the background to recalculate, pushes the updated 3D model data and evaluation results back to the front end, and the scene is then refreshed and presented. To achieve a smooth real-time interactive experience, the system uses a WebSocket long connection to maintain communication between the front end and the back end, while using WebWorker for lightweight data preprocessing to avoid blocking the main thread. For example, in practical engineering applications, designers can adjust the slope of a section of seawall from 1:3 to 1:5 by dragging a slider in a 3D scene. The system can then resimulate the impact of this adjustment on the overtopping volume and the suitability of benthic habitats within seconds, and display the changed shoreline morphology and biological distribution density as a chromatogram overlaid on the 3D model. Users can also click on specific components in the model and directly modify their building material ecological ratings in the pop-up parameter panel. The system automatically recalculates the comprehensive index and updates the evaluation level. This WYSIWYG interactive method transforms multi-scheme comparison and parameter optimization from a traditional offline calculation mode to a real-time online interactive mode, significantly improving the scientific nature and work efficiency of ecological seawall design.

[0053] Example 2

[0054] Based on the above embodiment one, this embodiment aims to provide a scheme comparison and optimization module, which is connected to the simulation analysis module. It is used to automatically generate multiple alternative seawall modification or repair schemes according to the preset optimization objectives, and drive the dynamic simulation model construction module and the index quantification and evaluation module to perform simulation evaluation on each alternative scheme, and finally output the optimal scheme or scheme ranking list that meets the optimization objectives.

[0055] It includes (such as) Figure 5 (as shown) The parameterized scheme generation unit is used to generate a series of parameter combinations as alternative schemes by adjusting the seawall structure parameters and ecological restoration parameters; The multi-objective optimization solution unit is used to automatically generate Pareto front solution sets based on genetic algorithms or multi-objective particle swarm optimization algorithms, with the objectives of maximizing the comprehensive index of ecological seawalls and minimizing engineering costs. The scheme visualization comparison unit is used to visualize and compare the simulation evaluation results of different schemes, assisting decision-makers in selecting the final implementation scheme.

[0056] Specifically, after a user or system sets an optimization goal, the scheme comparison and optimization module first activates its internal parameterized scheme generation unit. This unit stores key parameter variables of the seawall structure (such as seawall crest elevation, water-facing slope gradient, slope porosity, and building material type) and key parameter variables of ecological restoration (such as the width of the ecological space behind the seawall, the amount of sand replenishment on the shore, and the types and density of vegetation). These parameters do not exist in isolation but constitute a multi-dimensional parameter space. The parameterized scheme generation unit samples within this parameter space through a preset step size or by using experimental design methods, automatically generating a series of parameter combinations. Each parameter combination represents an alternative seawall modification or repair scheme. For example, for a section of rigid seawall that requires ecological modification, the system can automatically generate multiple alternative schemes such as Scheme 1 (slope 1:3, porosity 20%, planting Suaeda salsa), Scheme 2 (slope 1:4, porosity 30%, planting Reeds), and Scheme 3 (slope 1:3, porosity 30%, mixed planting of Suaeda salsa and Reeds).

[0057] Subsequently, these alternative schemes are input one by one into the dynamic simulation model construction module, triggering the reconstruction of the corresponding 3D digital model and hydrodynamic-ecological coupled simulation. The simulation results are then processed by the index quantification and evaluation module to calculate the safety, ecological, and sustainability index values ​​of each scheme. Finally, the intelligent simulation analysis module generates a comprehensive ecological seawall index for each scheme. At this point, the scheme comparison and optimization module can quantitatively evaluate and rank all alternative schemes, outputting a ranked scheme list, which in itself has significant practical value.

[0058] This elevates the intelligence level of the scheme comparison and optimization module to a higher level. It introduces a multi-objective optimization algorithm, enabling the system to achieve global optimization. Its core lies in the implementation of the multi-objective optimization solution unit, which typically employs heuristic intelligent algorithms such as genetic algorithms or multi-objective particle swarm optimization. Its operating principle can be compared to the evolutionary process in nature: First, the initial parameter combination generated by the parameterized scheme generation unit serves as the first generation population. Then, for each individual in this population (i.e., each scheme), the simulation evaluation described above is performed to calculate the corresponding two key objective function values—the comprehensive ecological seawall index (which needs to be maximized) and the engineering cost (which needs to be minimized). Next, according to the principle of survival of the fittest, those superior individuals with high comprehensive index and low cost are selected; The parameters of these superior individuals are cross-referenced (i.e., the advantages of different schemes are combined) and mutated (i.e., the parameters are randomly fine-tuned) to generate a new generation of offspring; this process is repeated continuously to iterate and evolve.

[0059] After several generations of evolution, the system converges to a series of solutions that achieve an optimal balance between the two conflicting objectives of high ecological comprehensive index and low engineering cost—the Pareto front solution set. Each solution in this set is not absolutely optimal, but rather emphasizes one aspect over the other. For example, solution A has the lowest cost but slightly lower ecological benefits, solution B has the highest ecological benefits but slightly higher costs, and solution C falls somewhere in between. Finally, the solution visualization and comparison unit presents these Pareto front solution sets to decision-makers in visual formats such as scatter plots and parallel coordinate graphs, and supports comparative displays of 3D models. For example, in the ecological transformation project of a seawall in a coastal new city in East China, the planning department used this system, setting the dual objectives of maximizing the ecological comprehensive index and minimizing engineering investment. After 50 generations of genetic algorithm iterations and optimizations, the system not only eliminated mediocre solutions that were expensive or had poor ecological effects, but also automatically discovered several golden ratio solutions that the designers had not thought of. For example, by locally increasing the porosity of the slope and combining it with specific vegetation combinations, the comprehensive ecological index was improved by 20% with only a 5% increase in cost, and it was finally adopted as the implementation plan.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An ecological seawall simulation model system, characterized in that, include: The data acquisition module is used to acquire multi-source heterogeneous data of the target seawall, including geographic information data, hydrological monitoring data, ecological survey data, and remote sensing image data. The data fusion processing module, connected to the data acquisition module, is used to perform spatiotemporal registration, normalization processing, and missing data imputation on the multi-source heterogeneous data to generate a standardized fusion dataset. The dynamic simulation model construction module constructs a three-dimensional digital model based on the fused dataset, including the main structure of the seawall, the beach in front of the seawall, and the ecological space behind the seawall, and sets the hydrodynamic boundary conditions and ecological evolution rules. The indicator quantification and evaluation module has a pre-set comprehensive evaluation indicator system for ecological seawalls. It is used to receive the simulation results of the three-dimensional digital model under different simulation scenarios, and automatically calculate the safety indicator value, ecological indicator value and sustainability indicator value based on the comprehensive evaluation indicator system. The simulation analysis module is used to perform weighted fusion of the safety index value, ecological index value and sustainability index value according to the preset weight vector to generate a comprehensive ecological seawall index, and to classify and evaluate the performance of the target seawall according to the comprehensive index. The visualization and interaction module is used to visualize the three-dimensional digital model, simulation process, and evaluation results in three dimensions, and provides corresponding parameter adjustment interfaces so that users can modify the simulation conditions.

2. The ecological seawall simulation model system according to claim 1, characterized in that, The data acquisition module includes: The geographic information acquisition unit is used to collect and process lidar point cloud data, digital elevation model data and engineering geological survey data of the target seawall, and generate a high-precision topographic model. The real-time hydrological monitoring unit is used to acquire real-time monitoring data on tide level, waves, current velocity, and current direction through a sensor network deployed in the target seawall area, and to clean and calibrate abnormal data. The ecological remote sensing inversion unit is used to acquire multi-temporal high-resolution remote sensing images and extract water quality parameters, vegetation coverage, and intertidal substrate type information through remote sensing inversion algorithms. The historical data digitization unit is used to digitize historical hydrological and ecological paper data and establish a historical database.

3. The ecological seawall simulation model system according to claim 1, characterized in that, The dynamic simulation model construction module includes: The parametric modeling unit is used to construct the geometric model of the main structure of the seawall and the beach in front of the seawall using parametric methods based on the topographic and geological data in the fused dataset, and to set the material properties. The hydrodynamic numerical simulation unit establishes an unstructured grid coupled tidal wave numerical model based on the Navier-Stokes equations or shallow water equations to simulate the overtopping and scouring process of seawalls under the action of storm surges and typhoon waves with different return periods. An ecological process simulation unit, connected to the hydrodynamic numerical simulation unit, is used to construct a benthic organism habitat suitability model and a salt marsh vegetation dynamic succession model to simulate the impact of hydrodynamic changes on organism distribution and vegetation growth. The model coupling driving unit is used to couple the hydrodynamic numerical simulation unit with the ecological process simulation unit to achieve the output of bidirectional feedback dynamic simulation data of hydrodynamic-ecological processes.

4. The ecological seawall simulation model system according to claim 1, characterized in that, The comprehensive evaluation index system for ecological seawalls includes: The safety index subsystem includes the seawall crest elevation compliance rate, the risk of excessive wave overtopping, the overall stability safety factor of the seawall body, and the seepage stability safety factor. The ecological indicator subsystem includes the comprehensive water quality index, sediment environmental quality index, benthic biodiversity index, vegetation coverage, proportion of ecological space behind the dike, bank width, bank erosion and sedimentation rate, water-facing slope, water-facing slope surface porosity, and building material eco-friendliness score. The sustainable indicators subsystem includes the effectiveness of disaster prevention and mitigation functions, habitat maintenance function index, economic benefits of disaster prevention and mitigation, environmental cleanliness maintenance capacity, and the convenience of ecological maintenance and management.

5. The ecological seawall simulation model system according to claim 4, characterized in that, The indicator quantification and evaluation module includes: The index calculation unit is used to extract relevant parameters from the simulation results and calculate the values ​​of each index according to predefined formulas. The formula for calculating the Water Quality Index (WQI) is as follows: WQI is the comprehensive water quality index; C i To evaluate the concentration of factor i in the environment, S i The environmental quality standard concentration for evaluation factor i is given, and n is the number of all water quality items being evaluated. Formula for calculating the sediment environmental quality index: a i Let n be the exceedance rate of the i-th evaluation item, and n be the number of evaluation items; The formula for calculating the benthic biodiversity index is: S represents the number of macrobenthic species, P i is the ratio of the number of individuals of the i-th macrobenthic animal to the total number of macrobenthic animals; The rate of change in erosion and deposition of the beach was calculated by comparing the differences between the simulated topography and the initial topography at different time periods. The eco-friendliness score of building materials is based on a comprehensive assessment of material source, carbon footprint, and recyclability factors.

6. The ecological seawall simulation model system according to claim 1, characterized in that, The simulation analysis module includes: The weight dynamic adjustment unit is used to store the weights of each indicator determined based on the analytic hierarchy process or the entropy weight method, and allows users to dynamically adjust the weights according to actual engineering needs. The comprehensive index generation unit, connected to the index quantification and evaluation module and the weight dynamic adjustment unit, is used to generate an index based on the formula: ECI stands for Ecological Seawall Index. i W represents the evaluation score of each of the i indicator factors in the indicator system. i represents the relative weight of the i-th indicator factor in the indicator system with respect to the overall objective; The rating unit is connected to the comprehensive index generation unit. It has a preset rating standard for comparing the calculated ecological seawall comprehensive index (ECI) with the standard threshold to determine the ecological quality level of the target seawall.

7. The ecological seawall simulation model system according to claim 6, characterized in that, The evaluation grading standard maps ECI values ​​to a continuous interval of 0 to 100, and divides it into four intervals: (80, 100), (60, 80), (30, 60), and (0, 30). The area between (80, 100) is a high-quality ecological seawall; The area between (60, 80) represents a good ecological seawall; The interval (30, 60) represents a poor ecological seawall; The interval (0, 30) represents the worst ecological seawall.

8. The ecological seawall simulation model system according to claim 1, characterized in that, Also includes: The scheme comparison and optimization module is connected to the simulation analysis module. It is used to automatically generate multiple alternative seawall modification or repair schemes according to the preset optimization objectives, and drive the dynamic simulation model construction module and the index quantification and evaluation module to perform simulation evaluation on each alternative scheme, and finally output the optimal scheme or the scheme ranking list that meets the optimization objectives.

9. The ecological seawall simulation model system according to claim 8, characterized in that, The scheme comparison and optimization module includes: The parameterized scheme generation unit is used to generate a series of parameter combinations as alternative schemes by adjusting the seawall structure parameters and ecological restoration parameters; The multi-objective optimization solution unit is used to automatically generate Pareto front solution sets based on genetic algorithms or multi-objective particle swarm optimization algorithms, with the objectives of maximizing the comprehensive index of ecological seawalls and minimizing engineering costs. The scheme visualization comparison unit is used to visualize and compare the simulation evaluation results of different schemes, assisting decision-makers in selecting the final implementation scheme.

10. The ecological seawall simulation model system according to claim 1, characterized in that, The visualization and interaction module includes: The 3D scene rendering unit is used to render the seawall and its surrounding environment with high realism based on the Unity3D or UE4 engine, enabling immersive roaming. The dynamic process replay unit is used to replay the dynamic processes of storm surge evolution, shoreline evolution, and vegetation growth in a timeline format. The interactive parameter adjustment unit provides a graphical interface that allows users to adjust hydrological conditions and seawall structure parameters in real time, trigger a resimulation, and instantly view the effects of the adjustments.

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