Simulation and prediction method and system for diffusion and deposition amount of dredged objects in port channel area

Abstract

The invention provides a method and a system for simulating and predicting diffusion and deposition amount of dredged objects in a port channel area. The method comprises the steps that a condition parameter database is constructed as an initial condition, a basic database is constructed as a boundary condition, a multi-field coupled hydrodynamic model is solved, and hydrodynamic field parameters are obtained; constructing a dredged object migration-sedimentation model including diffusion, sedimentation and scouring-sedimentation processes; parameters of the hydrodynamic model and the dredged object migration-deposition model are corrected through a Kalman filtering algorithm; simulating and predicting the diffusion range and the deposition amount of the dredged objects on the basis of the corrected model; and verifying a simulation prediction result, generating a dredged material diffusion range diagram and a deposition amount statistical report, and performing visual display. According to the method, the multi-field coupled hydrodynamic model and the dredged object migration-deposition model are constructed, and model parameters are dynamically corrected in combination with real-time monitoring data, so that the diffusion range and the deposition amount of the dredged objects are accurately and efficiently simulated and predicted.

Description

Technical Field

[0001] This invention belongs to the field of port and waterway engineering technology, specifically relating to a method and system for simulating and predicting the diffusion of dredged materials and the amount of siltation in port and waterway areas. Background Technology

[0002] Port channels, as crucial hubs in waterway transportation, directly impact regional economic development due to their navigation capacity. Siltation easily occurs in port channels due to natural and human factors such as sediment deposition from water flows and disturbances from ship navigation, leading to insufficient channel depth and affecting safe navigation. Therefore, regular dredging operations are necessary to ensure unobstructed navigation. During dredging, the dredged material is dispersed by water flow and wind, and some of it re-accumulates in the channel or surrounding waters. This not only reduces the effectiveness of dredging operations but may also impact the port's ecological environment. Therefore, accurate simulation and prediction of the dispersion range and accumulation volume of dredged material are crucial for optimizing dredging plans, determining dumping sites, assessing environmental impacts, and ensuring channel navigation capacity.

[0003] In existing technologies, simulation and prediction methods for dredged material diffusion and sedimentation are mostly based on empirical formulas or traditional hydrodynamic models. Empirical formula methods rely on fitting historical data and do not fully consider the complex hydrodynamic conditions of ports and waterways (such as the coupling effects of tidal currents, waves, and runoff) and the dynamic changes in dredging operation parameters (such as dredging intensity and operation point location), resulting in low prediction accuracy and limited applicability. While traditional hydrodynamic models can consider some hydrodynamic factors, they do not pay sufficient attention to the differences in particle size distribution, settling characteristics, and physicochemical changes during diffusion of dredged material. Furthermore, the fixed parameter settings during simulation make it difficult to respond in real time to changes in operating conditions and environmental factors, making it difficult to balance simulation efficiency and prediction accuracy. Physical model testing, on the other hand, is costly, time-consuming, and cannot fully reproduce the complex conditions of real-world scenarios.

[0004] For example, Chinese invention patent application CN108665224A discloses a dredged material diffusion simulation method based on MIKE21. This method only simulates dredged material diffusion based on tidal field, without considering other hydrodynamic factors such as waves and runoff, and does not perform differentiated simulation based on the particle characteristics of dredged material, resulting in significant prediction errors in complex port and waterway environments. Patent CN113806851A discloses a method for predicting channel siltation caused by hydrodynamic changes in dredging. It focuses on rivers with predominantly alluvial sediment bed formation and calculates siltation by deriving the unbalanced sediment transport equation. However, this method is applicable to specific river types, and the model needs to be rebuilt as the channel shape changes during dredging, leading to high costs, low efficiency, and difficulty in guaranteeing accuracy. A search of relevant published inventions and papers has revealed no method that can comprehensively, efficiently, and accurately simulate and predict dredged material diffusion and siltation in port and waterway areas. Therefore, this invention aims to fill this technological gap.

[0005] Therefore, there is an urgent need for a simulation and prediction method for dredged material diffusion and siltation in port and waterway areas that can comprehensively consider the influence of multiple factors, dynamically adapt to operating conditions, and balance simulation accuracy and efficiency, so as to solve the shortcomings of existing technologies. Summary of the Invention

[0006] To overcome the problems of low accuracy, poor adaptability, and insufficient efficiency in the simulation and prediction of dredged material diffusion and siltation in existing technologies, this invention provides a method and system for simulating and predicting dredged material diffusion and siltation in port and waterway areas. The aim is to construct a multi-field coupled hydrodynamic-dredged material transport model and dynamically correct the model parameters by combining real-time monitoring data, so as to achieve accurate and efficient simulation and prediction of the diffusion range and siltation, and provide a scientific basis for the optimized design and decision-making of port and waterway dredging projects.

[0007] To achieve the above objectives, the present invention provides the following solution: A method for simulating and predicting the diffusion and siltation of dredged materials in a port channel area, the method comprising: Based on historical data of port and waterway areas and multi-source satellite data of sea surface sediment concentration, we obtained overall hydrodynamic and sediment characteristics data of the sea area and constructed a condition parameter database. A basic database is constructed based on geospatial data, hydrological and meteorological data, dredging engineering parameters, and dredged material characteristics data of the port and waterway area. Based on the condition parameter database and the basic database, the finite volume method is used to construct a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff to obtain hydrodynamic field parameters. Based on dredged material characteristic data and hydrodynamic field parameters, a dredged material transport-deposition model was constructed, which includes diffusion, settlement, scour-deposition processes. Based on real-time collected hydrodynamic parameters and dredged material concentration data, the parameters of the hydrodynamic model and the dredged material transport-siltation model are corrected using the Kalman filter algorithm. Input the dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material. The simulation prediction results are verified. If the error is less than the preset threshold, a dredged material diffusion range map and a siltation statistics report are generated. Otherwise, the parameters of the hydrodynamic model and the dredged material transport-siltation model are revised. Visualization technology is used to display the dredged material diffusion range map and siltation volume statistics report in the form of graphics and charts.

[0008] Preferred, Geospatial data includes channel topography data, shoreline distribution data, and seabed sediment type data; Hydrometeorological data includes historical and real-time tidal data, wave data, runoff data, and wind speed and direction data; Dredging project parameters include the coordinates of the dredging operation point, dredging intensity, operation time, and type of dredging equipment; The dredged material characteristics data include particle size distribution, density, settling velocity, and critical initiation velocity.

[0009] Preferably, based on a condition parameter database and a basic database, a coupled atmospheric-tidal-wave-runoff hydrodynamic model is constructed using the finite volume method to obtain hydrodynamic field parameters. The methods include: Based on the flow field control equations of the non-hydrostatic model, wave radiation stress term, runoff supply term, Coriolis force, and friction force are introduced to construct the basic coupled control equations; The port channel area is divided into grids based on geospatial data in the basic database. Unstructured grids are used to densify the shallow water area and operation area of ​​the channel. The coupled control equations are solved discretically to obtain a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff. Using data from the condition parameter database as initial conditions and hydrological and meteorological data from the basic database as boundary conditions, the hydrodynamic model is solved to obtain hydrodynamic field parameters, including flow velocity, flow direction, water level, wave height, and wave period.

[0010] Preferred governing equations for the non-hydrostatic model flow field include: Momentum equation: x direction: ; y direction: ; z direction: ; In the formula, u , v , w They are respectively x , y , z directional flow velocity; For seawater reference density, The perturbation density; p The total pressure, including both static and non-static pressure; The turbulent eddy viscosity coefficient; f Coriolis parameters; g It is the acceleration due to gravity. t For time; Continuity equation: ; Free surface equation: ; In the formula, This is the rise in free surface height relative to the still water surface; h The still water depth is represented by the integral term, which represents the volumetric flux in the horizontal direction.

[0011] Preferably, a method for constructing a dredged material transport-deposition model that includes diffusion, settling, scour-deposition processes, based on dredged material characteristic data and hydrodynamic field parameters includes: Based on the particle size distribution in the dredged material characteristic data, the dredged material is divided into several particle groups. Settling velocity and critical starting flow velocity parameters are set for different particle groups. The Lagrange particle tracking method or Euler-Lagrange method is used to track the movement trajectory of the dredged material particles. A dredged material diffusion equation is constructed, and the horizontal and vertical diffusion range of the dredged material is calculated based on hydrodynamic field parameters and the turbulent diffusion coefficient in the hydrodynamic model. A sedimentation-scour discrimination equation was constructed. When the shear force of the water flow is less than the shear force corresponding to the critical starting velocity parameter, the amount of dredged material sedimentation was calculated. When the shear force of the water flow is greater than the shear force corresponding to the critical starting velocity parameter, the amount of bottom sediment scour and resuspension was calculated. Based on the trajectory tracking of dredged material particles, the dredged material diffusion equation, and the sedimentation-scour discrimination equation, a dredged material transport-sludge model that includes diffusion, settling, scour-sludge processes is obtained.

[0012] Preferably, the method for correcting the parameters of the hydrodynamic model and the dredged material transport-deposition model using the Kalman filter algorithm based on real-time acquired hydrodynamic parameters and dredged material concentration data includes: ADCP current meter, wave meter and turbidity sensor are deployed around the dredging operation site and key sections of the waterway to collect hydrodynamic parameters and dredged material concentration data in real time. The real-time collected hydrodynamic parameters and dredged material concentration data are compared with the simulation results of the hydrodynamic model and the dredged material transport-siltation model, and the error value is calculated. Based on the error values, the turbulence diffusion coefficient in the hydrodynamic model and the settling velocity parameters in the dredged material transport-siltation model are corrected using the Kalman filter algorithm.

[0013] Preferably, the method for simulating and predicting the diffusion range and sedimentation volume of dredged material by inputting dredging project parameters into the modified model includes: The coordinates of the dredging operation point and the dredging intensity are used as the source terms of the model, and the operation time is set as the simulation time scale. By inputting dredging parameters into the modified model, the concentration distribution data of dredged material in the port channel area at different times can be obtained to determine the diffusion range. Based on the concentration distribution data and the area and thickness of the grid cells, the siltation volume of each grid cell is calculated, and the total siltation volume and siltation distribution of the port channel area are obtained by summarizing the data.

[0014] The present invention also provides a simulation and prediction system for dredged material diffusion and siltation in port and waterway areas. The system is used to implement the aforementioned method and includes: a first acquisition module, a second acquisition module, a first construction module, a second construction module, a correction module, a simulation and prediction module, a verification module, and a visualization module. The first acquisition module is used to acquire overall hydrodynamic and sediment characteristic data of the sea area based on historical data of the port and waterway area and multi-source satellite data of sea surface sediment concentration, and to build a condition parameter database. The second data acquisition module is used to build a basic database based on geospatial data, hydrological and meteorological data, dredging engineering parameters and dredged material characteristics data of the port and waterway area. The first building module is used to construct a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff based on the condition parameter database and the basic database, and to obtain hydrodynamic field parameters. The second construction module is used to construct a dredged material transport-deposition model that includes diffusion, settling, scour-deposition processes, based on dredged material characteristic data and hydrodynamic field parameters. The correction module is used to correct the parameters of the hydrodynamic model and the dredged material transport-deposition model based on real-time collected hydrodynamic parameters and dredged material concentration data, using the Kalman filter algorithm. The simulation and prediction module is used to input dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material. The verification module is used to verify the simulation prediction results. If the result error is less than the preset threshold, a dredged material diffusion range map and a siltation statistics report are generated; otherwise, the parameters of the hydrodynamic model and the dredged material transport-siltation model are revised. The visualization module is used to display the dredged material diffusion range map and siltation volume statistics report in the form of graphics and charts through visualization technology.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention constructs a multi-field coupled hydrodynamic model of atmosphere-tidal current-wave-runoff, which can more accurately reflect the complex hydrodynamic conditions of port channels compared with traditional single hydrodynamic models, and provides a reliable dynamic basis for dredged material transport simulation; through unstructured grid densification processing, the simulation accuracy of the operation area and shallow water area of ​​the channel is improved.

[0016] (2) To address the differences in particle size distribution of dredged materials, a group simulation method was adopted, with the sedimentation velocity and critical start-up flow velocity parameters set separately. This solved the problem of insufficient consideration of dredged material characteristics in traditional models and improved the accuracy of simulating the transport process of dredged materials with different particle sizes.

[0017] (3) By introducing real-time monitoring data and Kalman filtering algorithm, the model parameters can be dynamically corrected, which can respond to changes in working conditions and environmental factors in real time, reduce simulation errors, and improve prediction accuracy. At the same time, it avoids the problem of poor adaptability caused by simply relying on historical data.

[0018] (4) The present invention can output a dredged material diffusion range map and siltation statistics for each area, providing an intuitive and scientific basis for decision-making in optimizing dredging operation plans (such as adjusting operation points and controlling dredging intensity), selecting dumping areas, and formulating waterway maintenance plans, thereby reducing dredging costs and minimizing the impact on the port's ecological environment.

[0019] (5) The results are displayed using visualization technology, which enables port and waterway management personnel to understand the diffusion and siltation of dredged materials intuitively and clearly, making it easier to adjust dredging operation plans and waterway management strategies in a timely manner, and helping to improve the operational efficiency of ports and waterways and the level of ecological environmental protection. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the 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.

[0021] Figure 1This is a schematic diagram of the method for obtaining overall hydrodynamic and sediment characteristic data of a sea area according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the method for obtaining hydrodynamic field parameters according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the method for constructing a dredged material transport-siltation model according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the method for simulating and predicting the diffusion range and siltation volume of dredged materials according to an embodiment of the present invention. Detailed Implementation

[0022] 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.

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Example 1 This invention provides a method for simulating and predicting the diffusion of dredged materials and the amount of siltation in port and waterway areas, the method comprising: Based on historical data of port and waterway areas and multi-source satellite data of sea surface sediment concentration, we obtained overall hydrodynamic and sediment characteristics data of the sea area and constructed a condition parameter database. A basic database is constructed based on geospatial data, hydrological and meteorological data, dredging engineering parameters, and dredged material characteristics data of the port and waterway area. Based on the condition parameter database and the basic database, the finite volume method is used to construct a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff to obtain hydrodynamic field parameters. Based on dredged material characteristic data and hydrodynamic field parameters, a dredged material transport-deposition model was constructed, which includes diffusion, settlement, scour-deposition processes. Based on real-time collected hydrodynamic parameters and dredged material concentration data, the parameters of the hydrodynamic model and the dredged material transport-siltation model are corrected using the Kalman filter algorithm. Input the dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material. The simulation prediction results are verified. If the error is less than the preset threshold, a dredged material diffusion range map and a siltation statistics report are generated. Otherwise, the parameters of the hydrodynamic model and the dredged material transport-siltation model are revised. Visualization technology is used to display the dredged material diffusion range map and siltation volume statistics report in the form of graphics and charts.

[0025] The specific implementation process of this invention is as follows: S1. Based on historical data of the port and waterway area and multi-source satellite data of sea surface sediment concentration, obtain overall hydrodynamic and sediment characteristic data of the sea area, and construct a condition parameter database for the initial sediment concentration parameter input for simulation and prediction, such as... Figure 1 As shown, it specifically includes: Based on historical data on seabed sediment, hydrodynamics, and topography, as well as satellite observation data, this study analyzes the spatial distribution, grain size distribution, and seabed topographic evolution characteristics of different seabed sediment types in a large-scale marine area where the port channel is located. Coupled with the combined effects of monsoon climate, extreme weather, runoff input, and tidal waves, and integrating machine learning algorithms with high-precision numerical simulation results, along with multi-source satellite data on sea surface sediment concentration, a multi-scale dynamic inversion model is constructed using intelligent algorithms such as backpropagation neural network (BPNN) and extreme gradient boosting (XGBoost). This model is used to rapidly predict the three-dimensional sediment concentration distribution in the ocean, obtain the overall near-bottom sediment transport patterns and the coupling mechanisms of various factors, serving as the overall background environmental conditions and initial conditions for S3 operations to analyze and predict dredged material transport within the port channel.

[0026] The basic principle of the BPNN model is as follows: For neurons in the network, u This is the input to the neuron. v The activated output after threshold adjustment, such as u j (J) and v j (J) Hidden layers J Middle neuron j Inputs and outputs v j (I) For hidden layer I Middle neuron i The outputs are calculated as follows: in, w ij For connection weights, f is the activation function for the input layer.

[0027] The basic principle of the XGBoost model is as follows: Dataset M = {( x i , yi )} indicates inclusion n One sample and m A set of features x i It is the first i The feature vector of each sample y i It is the first i The true label / response value corresponding to each sample, where the predictor variable is a combination of... K A composite model consisting of several basic models. The final prediction result of this model can be expressed as: in, For the final prediction result, variables K This indicates the total number of regression trees used in the improvement process. This represents the shrinkage factor (also known as the learning rate), which helps alleviate the overfitting problem.

[0028] S2. Based on geospatial data, hydrological and meteorological data, dredging engineering parameters, and dredged material characteristic data of the port and waterway area, a basic database is constructed, specifically including: Geospatial data includes channel topography data, shoreline distribution data, and seabed sediment type data; Hydrometeorological data includes historical and real-time tidal data, wave data, runoff data, and wind speed and direction data; Dredging project parameters include the coordinates of the dredging operation point, dredging intensity, operation time, and type of dredging equipment; The dredged material characteristics data include particle size distribution, density, settling velocity, and critical initiation velocity.

[0029] S3. Based on the condition parameter database and the basic database, a coupled atmospheric-tidal-wave-runoff hydrodynamic model is constructed using the finite volume method to obtain hydrodynamic field parameters, such as... Figure 2 As shown, it specifically includes: S31. Based on the flow field control equations of the non-hydrostatic model, wave radiation stress term, runoff supply term, Coriolis force, and friction force are introduced to construct the basic coupled control equations. S32. Based on the geospatial data in the basic database, the port channel area is divided into grids. Unstructured grids are used to densify the shallow water area and operation area of ​​the channel. The coupled control equations are solved discretically (unstructured grids have better fit with seabed topography and land boundaries, and the grid division is efficient and easy to operate). A coupled hydrodynamic model of atmosphere-tidal current-wave-runoff is obtained. The hydrodynamic model is calibrated and verified (sensitivity analysis → iterative calibration → accuracy verification) to enable it to accurately simulate the actual hydrodynamic conditions of the port channel area. S33. Using the data in the condition parameter database as the initial conditions and the hydrological and meteorological data in the basic database as the boundary conditions, solve the hydrodynamic model to obtain the hydrodynamic field parameters, which include flow velocity, flow direction, water level, wave height, and wave period.

[0030] Furthermore, the governing equations for the non-hydrostatic model flow field in S31 include: Momentum equation: x direction: ; y direction: ; z direction: ; In the formula, u , v , w They are respectively x , y , z directional flow velocity; For seawater reference density, The perturbation density; p The total pressure, including both static and non-static pressure; The turbulent eddy viscosity coefficient; f Coriolis parameters; g It is the acceleration due to gravity. t For time.

[0031] The momentum equation describes the state of seawater in... x , y (Horizontal direction) and z The changes in motion (in the vertical direction) need to take into account the effects of inertial force, pressure, viscosity, gravity, etc., and the pressure includes both static and non-static components, which is the core equation that reflects the non-static characteristics.

[0032] Continuity equation: ; The continuity equation is based on the assumption that seawater is incompressible and describes the conservation of mass, i.e., the rate of change of fluid volume is zero.

[0033] Free surface equation: ; In the formula, This is the rise in free surface height relative to the still water surface; h The still water depth is represented by the integral term, which represents the volumetric flux in the horizontal direction.

[0034] The free surface equation is derived from the continuity equation along the water depth integral. It is used to characterize the dynamic changes in sea level rise and fall and reflects the coupling relationship between fluid motion and free surface evolution.

[0035] S4. Based on dredged material characteristic data and hydrodynamic field parameters, construct a dredged material transport-deposition model that includes diffusion, settlement, and scour-deposition processes, such as... Figure 3 As shown, it specifically includes: S41. Based on the particle size distribution in the dredged material characteristic data, the dredged material is divided into several particle groups. Settling velocity and critical starting flow velocity parameters are set for different particle groups. The Lagrange particle tracking method or Euler-Lagrange method is used to track the movement trajectory of the dredged material particles. S42. Construct the dredged material diffusion equation, and calculate the horizontal and vertical diffusion range of the dredged material based on the hydrodynamic field parameters and the turbulent diffusion coefficient in the hydrodynamic model. S43. When the shear force of the water flow is less than the shear force corresponding to the critical starting velocity parameter, calculate the dredged material accumulation volume. Use the unbalanced sediment transport theory and construct a sediment transport rate equation to calculate the dredged material accumulation volume in different areas. When the shear force of the water flow is greater than the shear force corresponding to the critical starting velocity parameter, calculate the bottom sediment flushing flux and resuspension volume. S44. Based on S41, S42, and S43, a dredged material transport-deposition model incorporating diffusion, settling, and scour-deposition processes is obtained: The meanings of the physical quantities in the formulas for the dredged material transport-siltation model are shown in Table 1: Table 1 Equation for the evolution of scour and sedimentation in the subsoil (including scour-sedimentation): Characterizing the dynamic changes in bed elevation with erosion and deposition, strongly coupled with the suspended sediment equation: in, For the subgrade elevation ( ); The dry density of the substrate; Particle density; Water density ( ); The sedimentation efficiency coefficient (dimensionless) ).

[0036] The four key processes are specifically described by the following equations: (1) Diffusion process Lateral diffusion is determined by the turbulent diffusion coefficient. Dominant, Calculation Method: in, Kármán's constant, For frictional flow velocity, It is a Prandtl number.

[0037] Vertical diffusion adopts a vertical mixing model (such as...) - (Model), adapted for stratified flow and strong shear flow.

[0038] (2) Settling process Considering the flocculation effect, the settling velocity of fine particles (clay / silt) is corrected as follows: in, w s0 The initial settling velocity is SSC, the background sediment concentration is salinity, and the temperature is temp.

[0039] For coarse particles (sand), use the Stokes formula: in, g It is the acceleration due to gravity. For particle size, This refers to hydrodynamic viscosity.

[0040] (3) Flushing process Critical shear stress : in, τ 0 represents the initial value of shear stress. For moisture content, This is an empirical coefficient.

[0041] Flux flux: in, The scouring coefficient is... For index, This represents the shear stress on the bed surface.

[0042] (4) Sedimentation process Without considering flocculation, sedimentation flux: ; When considering flocculation and bed cover, the probability of sedimentation is introduced. Revised to: .

[0043] S5. Based on real-time acquired hydrodynamic parameters and dredged material concentration data, the parameters of the hydrodynamic model and the dredged material transport-deposition model are corrected using the Kalman filter algorithm, specifically including: S51. ADCP flow meters, wave meters and turbidity sensors are deployed around the dredging operation site and at key sections such as the entrance, middle section and end of the channel to collect hydrodynamic parameters and dredged material concentration data in real time. S52. Compare the real-time collected hydrodynamic parameters and dredged material concentration data with the simulation results of the hydrodynamic model and the dredged material transport-siltation model, and calculate the error value. S53. Based on the error value, the turbulence diffusion coefficient in the hydrodynamic model and the settling velocity parameter in the dredged material transport-siltation model are corrected using the Kalman filter algorithm.

[0044] Furthermore, the state equation of the Kalman filter algorithm is: Simplified single-parameter correction special case (with settling velocity) (For example) If only the settlement velocity (single parameter) is corrected, the state vector simplifies to: The core equation simplifies to: 1. Equation of State: ; 2. Observation equation: ; 3. Recursive formula: predict: , ; renew: , , .

[0045] in, and These are the values ​​of the settlement velocity at different times; This is the process noise driving matrix; It is a nonlinear term. For variance; The elevation of the seabed surface; and These are the observed and predicted values ​​of bed surface changes; for k Predicted settlement velocity at any given time; For effective deposition rate; The viscosity coefficient of the water. for k The initial value of the predicted settlement velocity at time t; for k The initial value of the predicted settlement velocity at time -1; for k The initial values ​​of the prediction matrix at time 1; for k The initial values ​​of the prediction matrix at time -1; This is the observation matrix; This is an error correction term; for k Correction value for the predicted settlement velocity at time; for k The correction value of the prediction matrix at time 1.

[0046] S6. Input the dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material, such as... Figure 4 As shown, it specifically includes: S61. Use the coordinates of the dredging operation point and the dredging intensity as the model source terms, and set the operation time as the simulation time scale. S62. Input the dredging project parameters into the corrected model to obtain the concentration distribution data of dredged material in the port channel area at different times, and determine the diffusion range. S63. Based on the concentration distribution data and the area and thickness of the grid cells, calculate the siltation volume of each grid cell, and summarize the total siltation volume of the port channel area and the siltation volume distribution of key areas (such as the channel axis and anchorage).

[0047] S7. Verify the simulation prediction results. Use monitoring data from historical dredging projects to verify the simulation prediction results. If the result error is less than the preset threshold, generate a dredged material diffusion range map and a siltation statistics report. Otherwise, return to S5 to correct the parameters of the hydrodynamic model and the dredged material transport-siltation model.

[0048] S8. Through visualization technology, the dredged material diffusion range map and siltation statistics report are displayed in the form of graphics and charts, including the dynamic changes of the dredged material diffusion range and the distribution of siltation in different areas, providing port and waterway management personnel with clear and easy-to-understand decision-making basis.

[0049] In summary, this invention constructs a multi-field coupled hydrodynamic model of atmosphere, tidal current, wave, and runoff, which can more accurately reflect the complex hydrodynamic conditions of ports and waterways compared with traditional single hydrodynamic models, providing a reliable dynamic basis for dredged material transport simulation; through unstructured mesh densification, the simulation accuracy of the operating area and shallow water area of ​​the waterway is improved.

[0050] This invention addresses the differences in particle size distribution of dredged materials by employing a grouped simulation approach. It sets the settling velocity and critical initiation flow velocity parameters separately, thus solving the problem of insufficient consideration of dredged material characteristics in traditional models and improving the accuracy of simulating the transport process of dredged materials with different particle sizes.

[0051] This invention introduces real-time monitoring data and a Kalman filter algorithm to achieve dynamic correction of model parameters. It can respond in real time to changes in operating conditions and environmental factors, reduce simulation errors, and improve prediction accuracy. At the same time, it avoids the problem of poor adaptability caused by relying solely on historical data.

[0052] This invention can output a map of the dredged material diffusion range and statistical data on siltation in each area, providing an intuitive and scientific basis for decision-making in optimizing dredging operation plans (such as adjusting operation points and controlling dredging intensity), selecting dumping areas, and formulating waterway maintenance plans, thereby reducing dredging costs and minimizing the impact on the port's ecological environment.

[0053] This invention uses visualization technology to display the results, enabling port and waterway managers to intuitively and clearly understand the diffusion and siltation of dredged materials. This facilitates timely adjustments to dredging operation plans and waterway management strategies, thereby improving the operational efficiency of ports and waterways and the level of ecological environmental protection.

[0054] Example 2 The present invention will be further described in detail below with reference to specific embodiments.

[0055] Taking a coastal port channel dredging project as the research object, the method described in the aforementioned embodiments is used to simulate and predict the diffusion of dredged materials and the amount of siltation. The specific steps are as follows: Step S1: Construct a condition parameter database and a basic database. Deploy Acoustic Doppler Current Profilers (ADCPs) in the port channel area to measure current velocity and direction, and wave buoys to monitor wave height and period. Simultaneously, install tide gauges to monitor tidal changes with an accuracy of ±1 cm. In areas prone to extreme weather such as storm surges, additional wave buoys will be installed to monitor wave height, period, and direction parameters with an accuracy of ±0.1 m for wave height, ±0.2 s for period, and ±5° for direction. The data collected by these devices will be processed through a data acquisition system.

[0056] The multibeam echo sounder system acquires high-precision topographic data, enabling rapid and accurate acquisition of water depth data over large areas with centimeter-level accuracy. Simultaneously, by combining satellite remote sensing imagery and UAV aerial photography, it obtains land topography and shoreline information for the port and waterway area. Geographic Information System (GIS) technology is used to integrate and process this data, constructing a detailed three-dimensional topographic model. The topographic data is updated regularly, such as quarterly or in a timely manner based on major engineering construction progress, to ensure its timeliness and accurately reflect the dynamic changes in the port and waterway area's topography.

[0057] Based on historical data and satellite observations of seabed sediment, hydrodynamics, and topography, this study analyzes the spatial distribution, grain size distribution, and seabed topographic evolution characteristics of different seabed sediment types in a large-scale marine area where ports and channels are located. Coupled with the combined effects of monsoon climate, extreme weather, runoff input, and tidal waves, and integrating machine learning algorithms with high-precision numerical simulation results, along with multi-source satellite data on sea surface sediment concentration, a multi-scale dynamic inversion model is constructed using intelligent algorithms such as backpropagation artificial neural networks and extreme gradient boosting. This model is used to rapidly predict the three-dimensional distribution of marine sediment concentration, obtain overall hydrodynamic and sediment characteristic data of the marine area, and construct a conditional parameter database.

[0058] Simultaneously, during dredging operations, dredged material samples were collected, and particle size distribution was determined using a laser particle size analyzer with a measurement range of 0.01 - 2000 μm and an accuracy of ±0.5%. The specific gravity of the sediment was determined using the hydrostatic bottle method with an accuracy of ±0.001 g / cm³. 3 The collected data were processed using a median filter to remove outliers, and a Kalman filter was used for data fusion. The sedimentation velocity was calculated using the sedimentation method combined with Stokes' theorem, while also considering flocculation to correct for the sedimentation velocity. X-ray diffraction was used to analyze the mineral composition of the sediment to understand its chemical properties.

[0059] A data-sharing mechanism was established with the local meteorological department to obtain meteorological data such as wind speed, wind direction, air pressure, and precipitation in the port and waterway area. The temporal resolution of the meteorological data is 1 hour, and the spatial resolution depends on the distribution of meteorological stations, generally ranging from 1 to 10 kilometers. For wind speed and direction data, on-site monitoring and calibration were conducted using meteorological towers or marine meteorological buoys to ensure data accuracy. The correlation between meteorological conditions and water flow and sediment transport was analyzed. For example, strong winds may cause increased waves, which in turn affect water flow and sediment transport. These related factors were incorporated into subsequent model considerations.

[0060] Basic data obtained: 1:5000 topographic data of the port and waterway, shoreline distribution vector map, and data on seabed sediment type (silty clay); tidal current measurement data for the past 5 years (maximum flow velocity at high tide 1.2 m / s, maximum flow velocity at low tide 1.5 m / s), wave data with a prevailing southeast direction (significant wave height 0.8-1.5 m), and runoff data of surrounding rivers (average annual runoff 50 m³ / h). 3 / s) and real-time wind speed and direction data; dredging project parameters are: work point coordinates (120.5°E, 38.2°N), dredging intensity 800m 3 / h, operation time 15 days, using a cutter suction dredger; dredged material characteristics: particle size distribution 25% clay, 60% silt, 15% sand, density 1.8g / cm³ 3The settling velocity of clay particles is 0.02 cm / s, the settling velocity of powder particles is 0.1 cm / s, the settling velocity of sand particles is 1.2 cm / s, and the critical starting velocity is 0.6 m / s.

[0061] Step S2: Construct a multi-field coupled hydrodynamic model. The coupled control equations are based on the non-hydrostatic model flow field control equations constructed using the finite volume method. Wave radiation stress terms (calculated based on the SWAN wave model) and runoff recharge terms are introduced. The channel area is divided into unstructured grids with a grid size of 2m×2m in the channel axis area and 10m×10m in the offshore area. The hydrodynamic field parameters are obtained by numerical solution using tidal current, wave, and runoff data as boundary conditions: flood current velocity 0.9-1.1m / s, ebb current velocity 1.2-1.4m / s, and significant wave height 1.0-1.3m in the operating area.

[0062] Key parameters in the hydrodynamic model, such as the Manning roughness coefficient and eddy viscosity coefficient, are determined using field measurement data combined with empirical formulas. The Manning roughness coefficient is assigned a value based on different substrate types (e.g., sandy, muddy), generally ranging from 0.015 to 0.035. The eddy viscosity coefficient is calculated using the Smagorinsky model, and its value is related to the grid scale and flow characteristics. The hydrodynamic model is validated using historical measured flow data, and the simulation results are compared and analyzed with flow velocity and direction data monitored by equipment such as ADCP. Statistical indicators, such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R), are used to evaluate the accuracy of the hydrodynamic model. If RMSE is less than 0.1 m / s, MAE is less than 0.05 m / s, and R is greater than 0.9, the simulation results of the hydrodynamic model are considered to be in good agreement with the measured data and can accurately reflect the flow characteristics of the port channel area.

[0063] Step S3: Setting initial and boundary conditions. For the hydrodynamic mathematical model of flow, the initial conditions are set as the water level and velocity distribution at the initial moment, which can be set based on previous monitoring data or historical averages. Boundary conditions include open and closed boundaries. The water level and velocity data at the open boundary are given based on measured tidal data or the results of the ocean numerical model; the closed boundary uses a no-slip boundary condition, i.e., the velocity is zero at the boundary. For the hydrodynamic mathematical model of sediment transport, the initial conditions are set as the sediment concentration distribution at the initial moment, which can generally be set as the background sediment concentration in the port channel area. In the dredging operation area, the initial discharge rate of the sediment source term is determined according to the dredging plan and construction intensity. Regarding boundary conditions, at the open boundary, the sediment concentration is given based on the offshore input situation or relevant research results; at the closed boundary, it is assumed that there is no net flux of sediment. Step S4: Numerical Solution Method and Calculation Process. The mathematical models of water flow and sediment transport are discretized. The computational domain is divided into multiple control volumes, and the equations within each control volume are integrated to obtain a discretized set of equations. For time progression, explicit or implicit time integration schemes, such as the second-order Runge-Kutta method or the Crank-Nicolson method, are used to ensure computational stability and accuracy. The calculation process is as follows: First, based on the initial and boundary conditions, the water flow mathematical model is solved to obtain the distribution of parameters such as water velocity and water level. Then, the water flow calculation results are used as input and substituted into the sediment transport mathematical model to solve for the sediment concentration distribution. Within each time step, the states of water flow and sediment are continuously updated until the simulation reaches the set time length. During the calculation process, the stability and convergence of the calculation results are monitored in real time. If abnormal situations such as calculation divergence occur, the calculation parameters or grid settings are adjusted promptly.

[0064] Step S5: Construct a dredged material transport-deposition model, dividing the dredged material into three groups: clay, silt, and sand, and setting the settling velocity and critical initiation velocity for each group; construct the dredged material diffusion equation, with an initial value of 0.5m for the turbulent diffusion coefficient. 2 / s; Calculated using the sedimentation-scour discrimination equation: when the shear force of the water flow in the working area is 0.8Pa (corresponding to a flow velocity of 1.0m / s), which is greater than the shear force of 0.4Pa corresponding to the critical starting flow velocity, there is no scour of the bottom sediment, and the dredged material mainly settles and diffuses.

[0065] The sediment diffusion coefficient is determined by empirical formulas or experimental data, and its value generally ranges from 0.1 to 10 m. 2 The diffusion coefficient is relatively large in near-shore and estuarine areas due to stronger water turbulence, with values ​​between 0.5 and 0.5 seconds. The sediment settling velocity is determined based on previously measured sediment particle size, specific gravity, and other characteristics, combined with a calculation formula considering sediment flocculation. Model calibration also utilizes historical measured sediment concentration data, adjusting model parameters (such as diffusion coefficient and settling velocity) to ensure the simulated sediment concentration distribution closely matches the measured data. During calibration, optimization algorithms, such as genetic algorithms and particle swarm optimization, are employed to automatically search for the optimal combination of model parameters, improving calibration efficiency and accuracy.

[0066] Step S6: Real-time monitoring and parameter correction. Three ADCP flow meters, two wave meters, and five turbidity sensors were deployed around the work site to collect flow velocity, wave height, and dredged material concentration data in real time. The turbulent diffusion coefficient and settling velocity were corrected using the Kalman filter algorithm: after correction, the turbulent diffusion coefficient was 0.42 m² / s, the clay particle settling velocity was 0.022 cm / s, the silt particle settling velocity was 0.11 cm / s, and the sand particle settling velocity was 1.18 cm / s. The simulation error was reduced from 18% before correction to 7%.

[0067] Step S7: Simulation Prediction and Result Output. Input dredging project parameters for simulation to obtain the dredged material diffusion range during the 15-day operation period: the maximum diffusion distance along the ebb tide direction is 1.2km, and along the rise tide direction is 0.8km; siltation statistics: the siltation volume in the channel axis area is 1200m³. 3 The siltation volume in the anchorage area is 800m³. 3 Total siltation volume: 2800 m³ 3 .

[0068] A multi-dimensional analysis was conducted on the simulated dredged material diffusion and sedimentation results. In the temporal dimension, the changing trends of dredged material diffusion range and sedimentation volume in different seasons and years were analyzed, exploring their relationship with factors such as seasonal water flow variations and meteorological conditions. In the spatial dimension, contour maps of the dredged material diffusion range and spatial distribution maps of sedimentation volume were drawn to identify severely sedimentated areas and diffusion-sensitive areas. By comparing the simulation results under different dredging schemes (such as different dredging times, locations, and intensities), the impact of each scheme on dredged material diffusion and sedimentation was evaluated, providing a basis for optimizing dredging schemes.

[0069] Step S8: Model validation is performed using on-site monitoring data from the port's historical dredging projects (measured siltation volume of 2950 m³). The prediction error is 5.1%, less than the preset threshold of 10%. A diffusion range map and a siltation volume statistical report are output. In this embodiment, the prediction error of the method of the present invention is only 5.1%, significantly improving accuracy compared to the 16.3% error of the traditional MIKE21 model, providing a reliable basis for optimizing the port's channel dredging scheme.

[0070] Step S9: Visualization and Application: Utilizing GIS technology and 3D modeling software, the simulation and prediction results are presented in an intuitive and visual manner. A 3D scene of the port channel area is generated, displaying the real-time diffusion dynamics of dredged materials and the distribution of siltation. Different colors and transparency are used to distinguish areas with different concentrations of dredged materials and different degrees of siltation. A user interface is developed, allowing users to select different time points, dredging schemes, and other parameters to view the corresponding simulation results. The simulation and prediction results are applied to the planning and management of port and waterway engineering. For example, in new port construction or waterway widening projects, the potential environmental impacts of dredging operations can be predicted in advance to optimize engineering design schemes. During waterway maintenance, dredging time and equipment can be rationally arranged based on the predicted siltation volume, improving waterway maintenance efficiency and reducing maintenance costs.

[0071] Example 3 Based on the same inventive concept, the present invention also provides a simulation and prediction system for dredged material diffusion and siltation in port and waterway areas, for implementing the aforementioned method. The system includes: a first acquisition module, a second acquisition module, a first construction module, a second construction module, a correction module, a simulation and prediction module, a verification module, and a visualization module. The first acquisition module is used to acquire overall hydrodynamic and sediment characteristic data of the sea area based on historical data of the port and waterway area and multi-source satellite data of sea surface sediment concentration, and to build a condition parameter database. The second data acquisition module is used to build a basic database based on geospatial data, hydrological and meteorological data, dredging engineering parameters and dredged material characteristics data of the port and waterway area. The first building module is used to construct a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff based on the condition parameter database and the basic database, and to obtain hydrodynamic field parameters. The second construction module is used to construct a dredged material transport-deposition model that includes diffusion, settling, scour-deposition processes, based on dredged material characteristic data and hydrodynamic field parameters. The correction module is used to correct the parameters of the hydrodynamic model and the dredged material transport-deposition model based on real-time collected hydrodynamic parameters and dredged material concentration data, using the Kalman filter algorithm. The simulation and prediction module is used to input dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material. The verification module is used to verify the simulation prediction results. If the result error is less than the preset threshold, a dredged material diffusion range map and a siltation statistics report are generated; otherwise, the parameters of the hydrodynamic model and the dredged material transport-siltation model are revised. The visualization module is used to display the dredged material diffusion range map and siltation volume statistics report in the form of graphics and charts through visualization technology.

[0072] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for simulating and predicting the diffusion and siltation of dredged materials in a port channel area, characterized in that, The method includes: Based on historical data of port and waterway areas and multi-source satellite data of sea surface sediment concentration, we obtained overall hydrodynamic and sediment characteristics data of the sea area and constructed a condition parameter database. A basic database is constructed based on geospatial data, hydrological and meteorological data, dredging engineering parameters, and dredged material characteristics data of the port and waterway area. Based on the condition parameter database and the basic database, the finite volume method is used to construct a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff to obtain hydrodynamic field parameters. Based on dredged material characteristic data and hydrodynamic field parameters, a dredged material transport-deposition model was constructed, which includes diffusion, settlement, scour-deposition processes. Based on real-time collected hydrodynamic parameters and dredged material concentration data, the parameters of the hydrodynamic model and the dredged material transport-siltation model are corrected using the Kalman filter algorithm. Input the dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material. The simulation prediction results are verified. If the error is less than the preset threshold, a dredged material diffusion range map and a siltation statistics report are generated. Otherwise, the parameters of the hydrodynamic model and the dredged material transport-siltation model are revised. Visualization technology is used to display the dredged material diffusion range map and siltation volume statistics report in the form of graphics and charts.

2. The method according to claim 1, characterized in that, Geospatial data includes channel topography data, shoreline distribution data, and seabed sediment type data; Hydrometeorological data includes historical and real-time tidal data, wave data, runoff data, and wind speed and direction data; Dredging project parameters include the coordinates of the dredging operation point, dredging intensity, operation time, and type of dredging equipment; The dredged material characteristics data include particle size distribution, density, settling velocity, and critical initiation velocity.

3. The method according to claim 1, characterized in that, Based on condition parameter databases and basic databases, a coupled atmospheric-tidal-wave-runoff hydrodynamic model is constructed using the finite volume method. Methods for obtaining hydrodynamic field parameters include: Based on the flow field control equations of the non-hydrostatic model, wave radiation stress term, runoff supply term, Coriolis force, and friction force are introduced to construct the basic coupled control equations; The port channel area is divided into grids based on geospatial data in the basic database. Unstructured grids are used to densify the shallow water area and operation area of ​​the channel. The coupled control equations are solved discretically to obtain a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff. Using data from the condition parameter database as initial conditions and hydrological and meteorological data from the basic database as boundary conditions, the hydrodynamic model is solved to obtain hydrodynamic field parameters, including flow velocity, flow direction, water level, wave height, and wave period.

4. The method according to claim 3, characterized in that, The governing equations for the non-hydrostatic model flow field include: Momentum equation: x direction: ; y direction: ; z direction: ; In the formula, u , v , w They are respectively x , y , z directional flow velocity; For seawater reference density, The perturbation density; p The total pressure, including both static and non-static pressure; The turbulent eddy viscosity coefficient; f Coriolis parameters; g It is the acceleration due to gravity. t For time; Continuity equation: ； Free surface equation: ； In the formula, This is the rise in free surface height relative to the still water surface; h The still water depth is represented by the integral term, which represents the volumetric flux in the horizontal direction.

5. The method according to claim 1, characterized in that, Methods for constructing dredged material transport-deposition models that include diffusion, settling, scour-deposition processes, based on dredged material characteristic data and hydrodynamic field parameters include: Based on the particle size distribution in the dredged material characteristic data, the dredged material is divided into several particle groups. Settling velocity and critical starting flow velocity parameters are set for different particle groups. The Lagrange particle tracking method or Euler-Lagrange method is used to track the movement trajectory of the dredged material particles. A dredged material diffusion equation is constructed, and the horizontal and vertical diffusion range of the dredged material is calculated based on hydrodynamic field parameters and the turbulent diffusion coefficient in the hydrodynamic model. A sedimentation-scour discrimination equation was constructed. When the shear force of the water flow is less than the shear force corresponding to the critical starting velocity parameter, the amount of dredged material sedimentation was calculated. When the shear force of the water flow is greater than the shear force corresponding to the critical starting velocity parameter, the amount of bottom sediment scour and resuspension was calculated. Based on the trajectory tracking of dredged material particles, the dredged material diffusion equation, and the sedimentation-scour discrimination equation, a dredged material transport-sludge model that includes diffusion, settling, scour-sludge processes is obtained.

6. The method according to claim 1, characterized in that, The methods for correcting the parameters of the hydrodynamic model and the dredged material transport-deposition model based on real-time acquired hydrodynamic parameters and dredged material concentration data, using the Kalman filter algorithm, include: ADCP current meter, wave meter and turbidity sensor are deployed around the dredging operation site and key sections of the waterway to collect hydrodynamic parameters and dredged material concentration data in real time. The real-time collected hydrodynamic parameters and dredged material concentration data are compared with the simulation results of the hydrodynamic model and the dredged material transport-siltation model, and the error value is calculated. Based on the error values, the turbulence diffusion coefficient in the hydrodynamic model and the settling velocity parameters in the dredged material transport-siltation model are corrected using the Kalman filter algorithm.

7. The method according to claim 1, characterized in that, Methods for simulating and predicting the diffusion range and sedimentation volume of dredged materials by inputting dredging project parameters into a modified model include: The coordinates of the dredging operation point and the dredging intensity are used as the source terms of the model, and the operation time is set as the simulation time scale. By inputting dredging project parameters into the modified model, the concentration distribution data of dredged material in the port channel area at different times are obtained, and the diffusion range is determined. Based on the concentration distribution data and the area and thickness of the grid cells, the siltation volume of each grid cell is calculated, and the total siltation volume and siltation distribution of the port channel area are obtained by summarizing the data.

8. A simulation and prediction system for dredged material diffusion and siltation in a port channel area, the system being used to implement the method described in any one of claims 1-7, characterized in that, The system includes: a first acquisition module, a second acquisition module, a first construction module, a second construction module, a correction module, a simulation prediction module, a verification module, and a visualization module; The first acquisition module is used to acquire overall hydrodynamic and sediment characteristic data of the sea area based on historical data of the port and waterway area and multi-source satellite data of sea surface sediment concentration, and to build a condition parameter database. The second data acquisition module is used to build a basic database based on geospatial data, hydrological and meteorological data, dredging engineering parameters and dredged material characteristics data of the port and waterway area. The first building module is used to construct a coupled hydrodynamic model of atmosphere-tidal current-wave-runoff based on the condition parameter database and the basic database, and to obtain hydrodynamic field parameters. The second construction module is used to construct a dredged material transport-deposition model that includes diffusion, settling, scour-deposition processes, based on dredged material characteristic data and hydrodynamic field parameters. The correction module is used to correct the parameters of the hydrodynamic model and the dredged material transport-deposition model based on real-time collected hydrodynamic parameters and dredged material concentration data, using the Kalman filter algorithm. The simulation and prediction module is used to input dredging project parameters into the corrected model to simulate and predict the diffusion range and siltation volume of dredged material. The verification module is used to verify the simulation prediction results. If the result error is less than the preset threshold, a dredged material diffusion range map and a siltation statistics report are generated; otherwise, the parameters of the hydrodynamic model and the dredged material transport-siltation model are revised. The visualization module is used to display the dredged material diffusion range map and siltation volume statistics report in the form of graphics and charts through visualization technology.

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