A water power environment prediction method and system for green navigation of a coastal ship

Abstract

The present application provides a kind of water power environment prediction method and system for green navigation of coastal ship, it is related to navigation technical field.For the problem of low accuracy of prediction result in prior art, the present application obtains calculation boundary condition;Monitoring beacon is arranged at key position, and measured water power environment data and measured wind wave data are obtained;According to the calculation boundary condition, a tidal wave coupling numerical model is established, and the model is calibrated according to the measured data;After calibration, the initial environmental prediction data is output by the tidal wave coupling numerical model according to the calculation boundary condition;The prediction deviation is obtained by comparing the measured real-time data with the initial environmental prediction data;The calibrated tidal wave coupling numerical model is dynamically corrected according to the prediction deviation, and the final environmental prediction data is output by the tidal wave coupling numerical model according to the calculation boundary condition.The periodical dynamic correction mode accurately adapts to the change characteristics of tidal current and wave, and timely eliminates the slight deviation of each period, so as to improve the accuracy of prediction result.

Description

Technical Field

[0001] This invention relates to the field of navigation technology, and in particular to a method and system for forecasting the hydrodynamic environment for green navigation of coastal vessels. Background Technology

[0002] Coastal hydrodynamic environment is a major factor affecting ship energy consumption and emissions. The hydrodynamic environment mainly includes tidal currents and waves. Influenced by multiple factors such as tides, ocean currents, and monsoons, it exhibits strong spatiotemporal variability. When sailing against waves, the frequency of wave impact increases, significantly increasing drag; when sailing against currents, ships require additional power to maintain speed, resulting in increased fuel consumption. Accurate forecasting of the coastal hydrodynamic environment, guiding ships to select favorable spatiotemporal hydrodynamic conditions, can achieve significant energy conservation and emission reduction effects, contributing to green navigation.

[0003] Currently, the field of coastal hydrodynamic environment forecasting has gradually formed a composite technology system with numerical simulation as its core, combined with data assimilation technology to improve data accuracy, and supplemented by intelligent algorithms to optimize the forecasting process. This system can integrate multi-source observation data and numerical calculation results, basically meeting the current basic requirements for the accuracy and real-time performance of coastal hydrodynamic environment forecasting. However, the data assimilation in existing forecasting methods is a static optimization of numerical simulation results, without adjusting the core parameters of the forecasting model. Forecasting models with fixed parameters have poor adaptability to dynamic changes in the coastal hydrodynamic environment. Small deviations accumulate over time, eventually forming significant cumulative errors. Due to the lack of dynamic real-time correction, the accuracy of the forecast results is low, failing to meet the core requirements of high-precision, long-term hydrodynamic environment forecasting for green navigation of ships.

[0004] Therefore, developing a hydrodynamic environment forecasting method and system for green navigation of coastal vessels is of great significance for improving the accuracy of forecast results. Summary of the Invention

[0005] To address the problem of low accuracy in forecasts due to the lack of dynamic real-time correction in existing technologies, this invention proposes a hydrodynamic environment forecasting method for green navigation of coastal vessels, which specifically includes the following steps:

[0006] S1. Obtain the computational boundary conditions of the target coastal waters;

[0007] S2. Deploy monitoring stations at key locations in the target coastal waters to obtain measured hydrodynamic environmental data through the monitoring stations; and obtain measured wind and wave data through marine meteorological observation stations.

[0008] S3. Establish a coupled numerical model of tidal current and wave based on the calculated boundary conditions. The coupled numerical model of tidal current and wave includes a tidal current sub-model and a wave sub-model.

[0009] S4. The tidal current sub-model is calibrated based on historical data of measured hydrodynamic environment data, and the wave sub-model is calibrated based on measured wind and wave data. The calibrated tidal current sub-model and the calibrated wave sub-model are coupled according to preset rules to obtain the calibrated tidal current and wave coupled numerical model. The calibrated tidal current and wave coupled numerical model outputs the initial environmental forecast data according to the calculated boundary conditions.

[0010] S5. Compare the real-time data of the measured hydrodynamic environment data with the initial environmental forecast data to obtain the forecast deviation; perform periodic dynamic correction on the calibrated tidal wave coupled numerical model based on the forecast deviation to obtain the corrected tidal wave coupled numerical model.

[0011] The periodic dynamic correction includes: controlling the tidal current sub-model and wave sub-model in the calibrated tidal current-wave coupled numerical model to output initial environmental forecast data according to different periods; obtaining the first period forecast deviation based on the initial environmental forecast data output in the first period and the first period real-time data of the measured hydrodynamic environment data; correcting the calibrated tidal current-wave coupled numerical model based on the first period forecast deviation, and forecasting the initial environmental forecast data for the second period based on the first period forecast deviation corrected tidal current-wave coupled numerical model; correcting the tidal current-wave coupled numerical model based on the second period real-time data of the measured hydrodynamic environment data and the second period initial environmental forecast data, and performing model correction in each period to achieve dynamic model correction;

[0012] S6. The modified tidal wave coupled numerical model outputs the final environmental forecast data based on the calculated boundary conditions.

[0013] Furthermore, in step S5, the calibrated tidal wave coupled numerical model is dynamically corrected periodically according to the forecast deviation to obtain a corrected tidal wave coupled numerical model. This includes: dynamically correcting the tidal sub-model in the calibrated tidal wave coupled numerical model according to the forecast deviation of a first set period to obtain a corrected tidal sub-model; dynamically correcting the wave sub-model in the calibrated tidal wave coupled numerical model according to the forecast deviation of a second set period to obtain a corrected wave sub-model; and obtaining the corrected tidal sub-model and the corrected wave sub-model to obtain the corrected tidal wave coupled numerical model.

[0014] Furthermore, the first set period is one quarter, and the second set period is several days or one week.

[0015] Furthermore, in S1, obtaining the calculation boundary conditions of the target coastal waters includes: obtaining the topographic boundary conditions of the target coastal waters based on topographic data; obtaining the shore boundary conditions of the target coastal waters based on shoreline data; obtaining the tidal level driving conditions of the outer sea boundary of the target coastal waters based on tidal data; obtaining the wind field driving boundary conditions of the target coastal waters based on wind speed data; and obtaining the current velocity boundary conditions of the target coastal waters based on vertical current velocity data.

[0016] Furthermore, in S2, the deployment of monitoring stations at key locations in the target coastal waters includes: selecting existing monitoring stations within the target coastal waters where measured hydrodynamic environmental data can be obtained; if existing monitoring stations where measured hydrodynamic environmental data can be obtained are missing, deploying monitoring stations within the target coastal waters; wherein the locations of the deployed monitoring stations at least cover the nearshore, offshore, estuary, and channel areas of the target coastal waters, as well as the area near the tidal wave coupling boundary.

[0017] Furthermore, the monitoring stations include shore markers and buoys.

[0018] Furthermore, in step S3, establishing a coupled numerical model of tidal current and wave based on computational boundary conditions includes: dividing the target coastal waters into unstructured triangular grid cells; setting the grid scale and computation time step according to computational accuracy requirements; determining the tidal current sub-model and wave sub-model respectively according to the computational boundary conditions; coupling the tidal current sub-model and wave sub-model according to preset rules; discretizing and solving the tidal current sub-model and wave sub-model in each grid cell to obtain hydrodynamic parameter results; and integrating the hydrodynamic parameter results of each grid cell to obtain the coupled numerical model of tidal current and wave in the target coastal waters.

[0019] Furthermore, after the modified tidal wave coupled numerical model outputs the final environmental forecast data, the method further includes: performing vector decomposition on the final environmental forecast data; and constructing annual coastal hydrodynamic environmental forecast data based on the vector decomposed data.

[0020] Furthermore, the final environmental forecast data is subjected to vector decomposition, including: obtaining the physical parameters of the main channel of the target coastal waters, the main channel physical parameters including the latitude and longitude coordinates of the channel edge and the latitude and longitude coordinates of the navigation mark; determining the course of the ship along the channel based on the main channel physical parameters, the course being consistent with the tangent of the channel direction; and decomposing the final environmental forecast data according to the course into tidal current data in the ship's forward direction, tidal current data in the ship's normal direction, wave data in the ship's forward direction, and wave data in the ship's normal direction.

[0021] The present invention also provides a hydrodynamic environment forecasting system for green navigation of coastal vessels, the system being used to execute the hydrodynamic environment forecasting method for green navigation of coastal vessels as described in any of the above claims, the system comprising:

[0022] The first acquisition module is used to acquire the computational boundary conditions of the target coastal waters;

[0023] The second acquisition module is used to deploy monitoring stations at key locations in the target coastal waters to acquire measured hydrodynamic environmental data through the monitoring stations; and to acquire measured wind and wave data through marine meteorological observation stations.

[0024] The model building module, connected to the first acquisition module, is used to establish a tidal current and wave coupled numerical model based on the calculated boundary conditions. The tidal current and wave coupled numerical model includes a tidal current sub-model and a wave sub-model.

[0025] The calibration output module, connected to the first acquisition module, the second acquisition module, and the model construction module, is used to calibrate the tidal current sub-model based on historical data of measured hydrodynamic environment data, and to calibrate the wave sub-model based on measured wind and wave data. The calibrated tidal current sub-model and the calibrated wave sub-model are then combined according to preset rules to obtain a calibrated tidal current-wave coupled numerical model. The calibrated tidal current-wave coupled numerical model is then controlled to output initial environmental forecast data based on calculated boundary conditions.

[0026] A correction module, connected to the second acquisition module and the calibration output module, is used to compare the real-time data of the measured hydrodynamic environment data with the initial environmental forecast data to obtain the forecast deviation; and to perform periodic dynamic correction on the calibrated tidal wave coupled numerical model based on the forecast deviation to obtain a corrected tidal wave coupled numerical model. The periodic dynamic correction includes: controlling the tidal sub-model and wave sub-model in the calibrated tidal wave coupled numerical model to output initial environmental forecast data according to different periods; obtaining the first-period forecast deviation based on the initial environmental forecast data output in the first period and the first-period real-time data of the measured hydrodynamic environment data; correcting the calibrated tidal wave coupled numerical model based on the first-period forecast deviation, and the tidal wave coupled numerical model with the corrected first-period forecast deviation forecasts the initial environmental forecast data for the second period; and correcting the tidal wave coupled numerical model with the corrected first-period forecast deviation based on the second-period real-time data of the measured hydrodynamic environment data and the second-period initial environmental forecast data, and performing model correction sequentially for each period to achieve dynamic model correction.

[0027] The final output module, connected to the first acquisition module and the correction module, is used to control the correction tidal wave coupled numerical model to output the final environmental forecast data according to the calculated boundary conditions.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] This invention acquires computational boundary conditions, measured hydrodynamic environment data, and measured wind and wave data for a target coastal water area; and establishes a tidal current-wave coupled numerical model based on the computational boundary conditions. The tidal current sub-model is calibrated using historical data from the measured hydrodynamic environment, and the wave sub-model is calibrated using the measured wind and wave data, resulting in a calibrated tidal current-wave coupled numerical model. The computational boundary conditions are input into the calibrated tidal current-wave coupled numerical model, which outputs initial environmental forecast data. The real-time data from the measured hydrodynamic environment is compared with the initial environmental forecast data to obtain the forecast deviation. Based on the forecast deviation, the calibrated tidal current-wave coupled numerical model is dynamically corrected periodically to obtain a corrected tidal current-wave coupled numerical model. The corrected tidal current-wave coupled numerical model outputs final environmental forecast data based on the computational boundary conditions.

[0030] The periodic dynamic correction involves controlling the calibrated tidal current and wave coupled numerical model to output initial environmental forecast data for each sub-model according to different periods. Based on the initial environmental forecast data output in the first period and the real-time data of the measured hydrodynamic environment in the first period, the forecast deviation for the first period is obtained. The calibrated tidal current and wave coupled numerical model is then corrected based on the first period forecast deviation, and the corrected model forecasts the initial environmental forecast data for the second period. Finally, based on the real-time data of the measured hydrodynamic environment in the second period and the initial environmental forecast data for the second period, the model is corrected again, and this process is repeated for each period to achieve dynamic model correction. Periodic dynamic correction can adapt to different changing characteristics of tidal currents and waves, continuously feeding the measured deviation back to the model during the forecasting process and iteratively adjusting core parameters, forming a dynamic closed loop of forecasting-verifying deviation-correcting parameters-re-forecasting. This periodic dynamic correction mode can accurately adapt to the natural changes of currents and waves, promptly eliminate minor deviations generated in each round of forecasting, and fundamentally prevent errors from accumulating as the forecast period extends, thus significantly improving the accuracy of hydrodynamic environment forecasting results.

[0031] Furthermore, the final environmental forecast data accurately reflects the true distribution characteristics of tidal currents and waves in the target coastal waters, guiding ships to avoid high-energy-consuming navigation environments such as headwinds and headwinds, and to select favorable temporal and spatial navigation conditions. This solves the key problem of accumulated forecast errors and insufficient accuracy in existing technologies, significantly improving the accuracy and practicality of coastal hydrodynamic environmental forecasts. Attached Figure Description

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a flowchart of a hydrodynamic environment forecasting method for green navigation of coastal vessels provided in an embodiment of the present invention;

[0034] Figure 2 This is a comparison curve of tidal level and measured tidal level in the calibrated tidal sub-model provided in this embodiment of the invention;

[0035] Figure 3 This is a schematic diagram of the hydrodynamic environment forecasting system for green navigation of coastal vessels provided in an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0037] The specific embodiments of the present invention will be described below.

[0038] To address the issue of low accuracy in forecasts in existing technologies, this invention obtains computational boundary conditions; deploys monitoring stations at key locations to acquire measured hydrodynamic environmental data and measured wind and wave data; establishes a tidal current-wave coupled numerical model based on the computational boundary conditions, and calibrates the model using measured data; after calibration, the tidal current-wave coupled numerical model outputs initial environmental forecast data based on the computational boundary conditions; compares the measured real-time data with the initial environmental forecast data to obtain the forecast deviation; and dynamically corrects the calibrated tidal current-wave coupled numerical model in stages based on the forecast deviation, ultimately outputting the final environmental forecast data based on the computational boundary conditions. This staged dynamic correction mode accurately adapts to the changing characteristics of tidal currents and waves, promptly mitigating minor deviations in each cycle and improving the accuracy of forecast results.

[0039] Example 1

[0040] This invention provides a method for forecasting the hydrodynamic environment for green navigation of coastal vessels. Figure 1 This is a flowchart of a hydrodynamic environment forecasting method for green navigation of coastal vessels provided by an embodiment of the present invention, such as... Figure 1 As shown, the specific steps include the following:

[0041] S1. Obtain the calculation boundary conditions of the target coastal waters.

[0042] The target coastal waters refer to the specific coastal sea areas where hydrodynamic environment forecasting is to be carried out, and are the core service areas for green navigation of ships. The calculated boundary conditions are the core set of fundamental parameters supporting the construction and solution of the coupled tidal wave numerical model.

[0043] Specifically, the calculation boundary conditions for the target coastal waters are obtained, including topographic data, shoreline data, tidal data, wind speed data, and vertical current velocity data, based on hydrological and meteorological data, topographic survey results, and tide tables of the surrounding sea area. Topographic boundary conditions are obtained based on topographic data; shoreline boundary conditions are obtained based on shoreline data; tidal level driving conditions are obtained for the offshore boundary of the target coastal waters based on tidal data; wind field driving boundary conditions are obtained based on wind speed data; and current velocity boundary conditions are obtained based on vertical current velocity data.

[0044] The topographic boundary conditions are obtained from topographic data and include the topographic feature boundary parameters of the target coastal waters, determining the topographic constraints on water flow and waves. The shoreline boundary conditions are obtained from shoreline data and include the shoreline's constraint boundary parameters on water flow and waves. The offshore tidal level driving condition is obtained from tidal data and includes the boundary parameters of the offshore tidal level's driving effect on the tidal currents of the target coastal waters. The wind field driving boundary condition is obtained from wind speed data and includes the boundary parameters of the wind field driving wave generation and propagation. The current velocity boundary strip is derived from vertical current velocity data and includes the boundary parameters of the initial current velocity distribution in the target coastal waters.

[0045] By obtaining the computational boundary conditions of the target coastal waters, accurate input is provided for the subsequent model construction, ensuring that the model conforms to the actual geographical, hydrological, and meteorological characteristics of the target sea area.

[0046] S2. Deploy monitoring stations at key locations in the target coastal waters to obtain measured hydrodynamic environmental data; and obtain measured wind and wave data through marine meteorological observation stations. This includes: selecting existing monitoring stations in the target coastal waters where measured hydrodynamic environmental data can be obtained; if existing monitoring stations where measured hydrodynamic environmental data can be obtained are missing, deploy monitoring stations in the target coastal waters; wherein the deployed monitoring stations shall at least cover the nearshore, offshore, estuary, and channel areas of the target coastal waters, as well as the area near the tidal wave coupling boundary.

[0047] The key locations in the target coastal waters are the core areas within the target coastal waters that reflect different water depths, topography, and wave propagation characteristics. These specifically include nearshore, offshore, estuary, channel areas, and areas near the tidal wave coupling boundary. Monitoring stations are sites or equipment used to observe coastal hydrodynamic environmental data; monitoring stations include shore markers and buoys. Marine meteorological observation stations are specialized sites used to monitor marine meteorological elements and are the carriers of measured wind and wave data. Measured hydrodynamic environmental data refers to the actual hydrodynamic data of the target sea area obtained through monitoring stations, while measured wind and wave data refers to the actual wind and wave data of the target sea area obtained through marine meteorological observation stations.

[0048] By deploying monitoring stations in key locations, covering different typical areas of the target sea area, it is ensured that the measured data can reflect the true distribution characteristics of hydrodynamics and wind waves, avoiding model calibration deviations due to data bias. Acquiring measured hydrodynamic environment data and measured wind and wave data provides authentic historical verification basis for the initial calibration of subsequent tidal current sub-models and wave sub-models, which is beneficial for accurate model construction.

[0049] S3. Establish a tidal current and wave coupled numerical model based on the calculated boundary conditions. The tidal current and wave coupled numerical model includes a tidal current sub-model and a wave sub-model.

[0050] The tidal current and wave coupled numerical model is a computer numerical model that integrates a tidal current sub-model and a wave sub-model, enabling data interaction between the two to simulate the coupling effect of tidal currents and waves. It is used for simulating and forecasting the hydrodynamic environment of target coastal waters. The tidal current sub-model is a sub-model built based on the fluid dynamics continuity equation and momentum equation, used to simulate the tidal motion characteristics of target coastal waters. The wave sub-model is a sub-model built based on the wave action conservation equation, driven by numerical meteorological wind data from the Cross-Calibrated Multi-Platform (CCMP) and the Climate Forecast System Reanalysis (CFSR), used to simulate the wave motion characteristics of target coastal waters.

[0051] Specifically, a coupled numerical model of tidal current and wave is established based on computational boundary conditions, including: dividing the target coastal waters into unstructured triangular grid cells; setting the grid scale and computation time step according to the computational accuracy requirements; determining the tidal current sub-model and wave sub-model respectively according to the computational boundary conditions; coupling the tidal current sub-model and wave sub-model according to preset rules; discretizing and solving the tidal current sub-model and wave sub-model in each grid cell to obtain hydrodynamic parameter results; and integrating the hydrodynamic parameter results of each grid cell to obtain the coupled numerical model of tidal current and wave in the target coastal waters.

[0052] To address the topographical and shoreline characteristics of the target coastal waters, unstructured triangular mesh units are used, with localized densification in key areas such as waterways and estuaries. The mesh scale and computation time step are set according to the required accuracy of the hydrodynamic environment forecast results for the target coastal waters. The computation time step refers to the time interval for iterative solutions of the coupled tidal wave numerical model, and is a core parameter for the model's time-dimensional computational accuracy. By using unstructured triangular meshes, the model adapts to the complex topography and shoreline characteristics of the coastal waters, while localized densification ensures computational accuracy in key areas such as waterways and estuaries.

[0053] By obtaining parameters such as topography, shoreline, and flow velocity from the computational boundary conditions, a tidal current sub-model is constructed using the continuity equation and momentum equation from fluid mechanics. The continuity equation is shown below:

[0054] ;

[0055] in, This indicates the water level (m) relative to a certain datum surface. t Indicates time (s). h This indicates the water depth (m) relative to a certain datum surface. u It represents the average vertical velocity (m / s) in the x-direction (i.e., east-west direction). v This represents the average vertical velocity (m / s) in the y-direction (i.e., north-south direction). x Represents the east-west coordinate (m). y Represents the north-south coordinate (m).

[0056] The momentum equation is as follows:

[0057] The momentum equation in the x-direction is:

[0058] ;

[0059] y-momentum equation:

[0060] ;

[0061] in, g This represents the acceleration due to gravity (9.8 m / s²). f Coriolis coefficient, f =2 ω sin φ , φ Latitude ω This is the Earth's rotation speed. f b This indicates the coefficient of friction at the bottom. f b = g / C 2 , C This is the Xie Cai coefficient. The turbulent viscosity coefficient of the water flow in the x-direction (m) 2 / s), The turbulent viscosity coefficient of the water flow in the y direction (m) 2 / s).

[0062] By acquiring parameters such as wind speed under calculated boundary conditions, the driving wind field is constructed using CCMP / CFSR numerical meteorological wind data. A wave sub-model is built using the wave action conservation equation, which is shown below:

[0063] ;

[0064] Where N represents the wave action. Let be the divergence operator, characterizing the propagation and variation of wave action in geographic space; S be the source function in the energy balance equation expressed as spectral density; and σ be the relative frequency. For wave group velocity, , Let be the wave group velocity component propagating in the x-direction of geographic space. This represents the wave group velocity component representing the wave propagating in the y-direction of geographic space. This indicates the wave group velocity component corresponding to the change in the relative frequency of waves caused by changes in water depth and current. It essentially quantifies the degree of influence of water depth and current on the relative frequency of waves and is a key correction data reflecting frequency changes. It represents the wave group velocity component corresponding to wave refraction caused by water depth and current. It is the core data reflecting the degree of correction of wave direction by water depth and current.

[0065] The tidal current sub-model and the wave sub-model are coupled using pre-defined rules. These rules include: the tidal current sub-model provides water depth and velocity field data to the wave sub-model, serving as the core input for the wave sub-model to perform wave motion characteristic calculations based on the wave action conservation equation; the wave sub-model, in turn, feeds back the mass flux and radiated stress generated by wave action to the tidal current sub-model, thereby quantifying the driving force and influence of waves on tidal current motion and achieving accurate simulation of the wave-current coupling effect. Simultaneously, considering the wave energy change process represented by the energy balance source function S in the wave sub-model, and taking into account the actual physical phenomena of wave breakage and energy dissipation during propagation, the energy loss during this process is numerically simulated using two quantified parameters: the white crown dissipation term and the bottom friction term, thus reconstructing the energy attenuation law of waves caused by surface breakage and seabed friction. Furthermore, to ensure the stability of the coupled calculation, the coupling time steps of the tidal current sub-model and the wave sub-model must be matched, and the parallel computing technology of the Message Passing Interface (MPI) is used to improve the solution efficiency of the entire coupled numerical model.

[0066] The finite volume method is used to discretize and solve the continuity and momentum equations of the tidal current sub-model and the wave action conservation equations of the wave sub-model in each unstructured triangular mesh element. This transforms the continuous governing equations into discrete algebraic equations, yielding the hydrodynamic parameters for each mesh element. These hydrodynamic parameters include the water level in the tidal current sub-model. ζ The parameters include current velocity (u / v) and wave action (N), wave height, and wave direction in the wave sub-model. The tidal current and wave hydrodynamic parameters of all grid cells are summarized to form a parameter distribution covering the entire target coastal waters, ultimately constructing a complete numerical model that reflects the coupling effect of tidal current and waves.

[0067] S4. The tidal current sub-model is calibrated based on historical data of measured hydrodynamic environment data, and the wave sub-model is calibrated based on measured wind and wave data. The calibrated tidal current sub-model and the calibrated wave sub-model are coupled according to the preset coupling rules in the above embodiment to obtain the calibrated tidal current and wave coupled numerical model. The calibrated tidal current and wave coupled numerical model outputs initial environmental forecast data according to the calculated boundary conditions.

[0068] The tidal level, velocity, and direction calculated by the tidal sub-model were compared with historical measured hydrodynamic environment data. Parameters such as eddy viscosity and friction coefficient were adjusted according to the accuracy requirements in the standard technical specifications until the accuracy requirements were met. Driven by CCMP / CFSR numerical meteorological wind data, the wind speed, direction, and wave height calculated by the wave sub-model were compared with measured wind and wave data. If the deviation exceeded a reasonable range, the model parameters were adjusted until the calculated results closely matched the measured data.

[0069] For example, Figure 2 This is a comparison curve of tidal level and measured tidal level in the calibrated tidal sub-model provided in this embodiment of the invention. The measured value represents the historical data of the measured hydrodynamic environment, and the calculated value represents the data output by the calibrated tidal sub-model. Figure 2 It can be seen that the data output by the calibrated tidal current sub-model is close to the measured values, and the trend is consistent. Calibration using historical measured data corrects the model parameter deviations, improving the model's simulation accuracy of the hydrodynamic environment of the target sea area. The calibrated model can reduce the cumulative error caused by the extended forecast period, laying a core foundation for subsequent dynamic correction and high-precision forecasting.

[0070] The aforementioned calculated boundary conditions are used as core input parameters and imported into a tidal wave coupled numerical model calibrated with measured historical hydrodynamic environmental data and measured wind and wave data. The calibrated tidal wave coupled numerical model performs numerical simulation calculations on the hydrodynamic environment of the target coastal waters based on the continuity equation and momentum equation of the tidal sub-model, the wave action conservation equation of the wave sub-model, the coupling rules, and the finite volume method discretization solution rules, and outputs initial environmental forecast data covering the core elements of the target coastal waters such as tidal currents and waves.

[0071] S5. Compare the real-time data of the measured hydrodynamic environment with the initial environmental forecast data to obtain the forecast deviation; perform periodic dynamic correction on the calibrated tidal wave coupled numerical model based on the forecast deviation to obtain the corrected tidal wave coupled numerical model.

[0072] The periodic dynamic correction includes: controlling the tidal current sub-model and wave sub-model in the calibrated tidal current-wave coupled numerical model to output initial environmental forecast data according to different periods; obtaining the first period forecast deviation based on the initial environmental forecast data output in the first period and the first period real-time data of the measured hydrodynamic environment data; correcting the calibrated tidal current-wave coupled numerical model based on the first period forecast deviation, and forecasting the initial environmental forecast data for the second period based on the first period forecast deviation corrected tidal current-wave coupled numerical model; correcting the tidal current-wave coupled numerical model based on the second period real-time data of the measured hydrodynamic environment data and the second period initial environmental forecast data, and performing model correction in each period to achieve dynamic correction of the model.

[0073] Specifically, this includes: dynamically correcting the tidal current sub-model in the calibrated tidal current and wave coupled numerical model according to the forecast deviation of the first set period, to obtain the corrected tidal current sub-model; dynamically correcting the wave sub-model in the calibrated tidal current and wave coupled numerical model according to the forecast deviation of the second set period, to obtain the corrected wave sub-model; and obtaining the corrected tidal current and wave coupled numerical model based on the corrected tidal current sub-model and the corrected wave sub-model.

[0074] The first set period is the time period set for the dynamic correction of the tidal current sub-model. For example, the first set period is one quarter. This first set period is the core time node for verifying tidal current forecast deviations and carrying out dynamic corrections. The corrected tidal current sub-model is the tidal current sub-model with optimized accuracy after correcting forecast deviations over a one-quarter period. The second set period is the time period set for the dynamic correction of the wave sub-model, specifically several days or one week. The second set period can adapt to the correction time nodes of the short-period wave variation characteristics. The corrected wave sub-model is the wave sub-model with optimized accuracy after correcting forecast deviations over a several-day or one-week period.

[0075] At the end of the first set period, the forecast data output by the calibrated tidal current sub-model is compared with the measured tidal current data from buoys and shore markers to calculate the forecast deviation. At the end of the second set period, the forecast data output by the calibrated wave sub-model is compared with the measured wave data to calculate the forecast deviation. Based on the forecast deviation of the first set period, parameters such as the eddy viscosity coefficient and friction coefficient of the tidal current sub-model are adjusted to complete dynamic correction, resulting in the corrected tidal current sub-model. Based on the forecast deviation of the second set period, relevant parameters of the wave sub-model are adjusted to complete dynamic correction, resulting in the corrected wave sub-model. The corrected tidal current sub-model and the corrected wave sub-model are integrated according to preset coupling rules to obtain the corrected tidal current-wave coupled numerical model.

[0076] Differentiated correction cycles are set for the different spatiotemporal variations of tides and waves. Periodic dynamic corrections can adapt to the different changing characteristics of tides and waves. During the forecasting process, measured deviations are continuously fed back to the model, and core parameters are iteratively adjusted, forming a dynamic closed loop of forecasting-verifying deviations-correcting parameters-forecasting again. This dynamic correction mode can accurately adapt to the natural changing patterns of tides and waves, promptly eliminate minor deviations generated in each round of forecasting, and effectively reduce forecast errors.

[0077] S6. The modified tidal wave coupled numerical model outputs the final environmental forecast data based on the calculated boundary conditions.

[0078] After correcting the final environmental forecast data output by the coupled tidal wave numerical model, the process also includes: performing vector decomposition on the final environmental forecast data; and constructing annual coastal hydrodynamic environmental forecast data based on the vector decomposed data.

[0079] Specifically, the final environmental forecast data is decomposed into vectors, including: obtaining the physical parameters of the main channel in the target coastal waters, which include the latitude and longitude coordinates of the channel edge and the navigation mark; determining the course of the ship along the channel based on the main channel physical parameters, with the course consistent with the tangent of the channel direction; and decomposing the final environmental forecast data according to the course into tidal current data in the ship's forward direction, tidal current data in the ship's normal direction, wave data in the ship's forward direction, and wave data in the ship's normal direction.

[0080] By performing vector decomposition on the final environmental forecast data, general hydrodynamic environmental forecast data is transformed into forward and normal data that closely match the actual navigation scenarios of ships. This accurately quantifies the direct impact of tidal currents and waves on ship navigation, solving the problem that general hydrodynamic environmental forecast data cannot directly guide green navigation. By constructing an annual coastal hydrodynamic environmental forecast database and integrating hydrodynamic environmental forecast data from the entire cycle, it can provide ships with directly applicable forward and normal hydrodynamic references to guide them in avoiding high-energy-consuming navigation conditions and achieving energy-saving effects. It can also provide data support for shipping companies' long-term route planning and energy consumption management, meeting the development needs of green shipping.

[0081] This embodiment acquires the computational boundary conditions, measured hydrodynamic environment data, and measured wind and wave data of the target coastal waters; and establishes a tidal current-wave coupled numerical model based on the computational boundary conditions. The tidal current sub-model is calibrated using historical data from the measured hydrodynamic environment, and the wave sub-model is calibrated using the measured wind and wave data, resulting in a calibrated tidal current-wave coupled numerical model. The computational boundary conditions are input into the calibrated tidal current-wave coupled numerical model, which outputs initial environmental forecast data. The real-time data from the measured hydrodynamic environment is compared with the initial environmental forecast data to obtain the forecast deviation. Based on the forecast deviation, the calibrated tidal current-wave coupled numerical model is dynamically corrected periodically to obtain a corrected tidal current-wave coupled numerical model. The corrected tidal current-wave coupled numerical model outputs final environmental forecast data based on the computational boundary conditions.

[0082] The periodic dynamic correction involves controlling the calibrated tidal current and wave coupled numerical model to output initial environmental forecast data for each sub-model according to different periods. Based on the initial environmental forecast data output in the first period and the real-time data of the measured hydrodynamic environment in the first period, the forecast deviation for the first period is obtained. The calibrated tidal current and wave coupled numerical model is then corrected based on the first period forecast deviation, and the corrected model forecasts the initial environmental forecast data for the second period. Finally, based on the real-time data of the measured hydrodynamic environment in the second period and the initial environmental forecast data for the second period, the model is corrected again, and this process is repeated for each period to achieve dynamic model correction. Periodic dynamic correction can adapt to different changing characteristics of tidal currents and waves, continuously feeding the measured deviation back to the model during the forecasting process and iteratively adjusting core parameters, forming a dynamic closed loop of forecasting-verifying deviation-correcting parameters-re-forecasting. This periodic dynamic correction mode can accurately adapt to the natural changes of currents and waves, promptly eliminate minor deviations generated in each round of forecasting, and fundamentally prevent errors from accumulating as the forecast period extends, thus significantly improving the accuracy of hydrodynamic environment forecasting results.

[0083] Furthermore, the final environmental forecast data accurately reflects the true distribution characteristics of tidal currents and waves in the target coastal waters, guiding ships to avoid high-energy-consuming navigation environments such as headwinds and headwinds, and to select favorable temporal and spatial navigation conditions. This solves the key problem of accumulated forecast errors and insufficient accuracy in existing technologies, significantly improving the accuracy and practicality of coastal hydrodynamic environmental forecasts.

[0084] Example 2

[0085] This invention also provides a hydrodynamic environment forecasting system for green navigation of coastal vessels. Figure 3 This is a schematic diagram of the structure of a hydrodynamic environment forecasting system for green navigation of coastal vessels provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the system includes:

[0086] The first acquisition module is used to acquire the computational boundary conditions of the target coastal waters;

[0087] The second acquisition module is used to deploy monitoring stations at key locations in the target coastal waters to acquire measured hydrodynamic environmental data through the monitoring stations; and to acquire measured wind and wave data through marine meteorological observation stations.

[0088] The model building module, connected to the first acquisition module, is used to establish a tidal current and wave coupled numerical model based on the calculated boundary conditions. The tidal current and wave coupled numerical model includes a tidal current sub-model and a wave sub-model.

[0089] The calibration output module, connected to the first acquisition module, the second acquisition module, and the model construction module, is used to calibrate the tidal current sub-model based on historical data of measured hydrodynamic environment data, and to calibrate the wave sub-model based on measured wind and wave data. The calibrated tidal current sub-model and the calibrated wave sub-model are then combined according to preset rules to obtain a calibrated tidal current-wave coupled numerical model. The calibrated tidal current-wave coupled numerical model is then controlled to output initial environmental forecast data based on calculated boundary conditions.

[0090] A correction module, connected to the second acquisition module and the calibration output module, is used to compare the real-time data of the measured hydrodynamic environment data with the initial environmental forecast data to obtain the forecast deviation; and to perform periodic dynamic correction on the calibrated tidal wave coupled numerical model based on the forecast deviation to obtain a corrected tidal wave coupled numerical model. The periodic dynamic correction includes: controlling the tidal sub-model and wave sub-model in the calibrated tidal wave coupled numerical model to output initial environmental forecast data according to different periods; obtaining the first-period forecast deviation based on the initial environmental forecast data output in the first period and the first-period real-time data of the measured hydrodynamic environment data; correcting the calibrated tidal wave coupled numerical model based on the first-period forecast deviation, and the tidal wave coupled numerical model with the corrected first-period forecast deviation forecasts the initial environmental forecast data for the second period; and correcting the tidal wave coupled numerical model with the corrected first-period forecast deviation based on the second-period real-time data of the measured hydrodynamic environment data and the second-period initial environmental forecast data, and performing model correction sequentially for each period to achieve dynamic model correction.

[0091] The final output module, connected to the first acquisition module and the correction module, is used to control the correction tidal wave coupled numerical model to output the final environmental forecast data according to the calculated boundary conditions.

[0092] The hydrodynamic environment forecasting system for green navigation of coastal vessels provided in this embodiment executes the hydrodynamic environment forecasting method for green navigation of coastal vessels described in any of the above embodiments, and has the beneficial effects of any of the above embodiments, which will not be repeated here.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A hydrodynamic environmental prediction method for green sailing of a coastal vessel, characterized by, The method comprises the following steps: S1, obtaining a calculation boundary condition of a target coastal water area; S2, arranging monitoring stations at key positions of the target coastal water area, obtaining measured hydrodynamic environment data through the monitoring stations, and obtaining measured wind wave data through a marine weather observation station; S3, establishing a tidal wave coupling numerical model according to the calculation boundary condition, wherein the tidal wave coupling numerical model comprises a tidal sub-model and a wave sub-model; S4, calibrating the tidal sub-model according to historical data of the measured hydrodynamic environment data, calibrating the wave sub-model according to the measured wind wave data, coupling the calibrated tidal sub-model and the calibrated wave sub-model according to a preset rule, and obtaining a calibrated tidal wave coupling numerical model; the calibrated tidal wave coupling numerical model outputs initial environment prediction data according to the calculation boundary condition; S5, comparing real-time data of the measured hydrodynamic environment data with the initial environment prediction data to obtain a prediction deviation; performing periodical dynamic correction on the calibrated tidal wave coupling numerical model according to the prediction deviation, and obtaining a corrected tidal wave coupling numerical model; The periodical dynamic correction comprises: controlling the tidal sub-model and the wave sub-model in the calibrated tidal wave coupling numerical model to output initial environment prediction data according to different periods respectively; obtaining a first period prediction deviation according to the initial environment prediction data output by the first period and the first period real-time data of the measured hydrodynamic environment data; correcting the calibrated tidal wave coupling numerical model according to the first period prediction deviation; the tidal wave coupling numerical model corrected according to the first period prediction deviation predicts initial environment prediction data of a second period; correcting the tidal wave coupling numerical model corrected according to the first period prediction deviation according to the second period real-time data of the measured hydrodynamic environment data and the initial environment prediction data of the second period, and sequentially performing model correction according to each period to realize dynamic correction of the model; S6, the corrected tidal wave coupling numerical model outputs final environment prediction data according to the calculation boundary condition.

2. The method of claim 1, wherein, In S5, the calibrated tidal wave coupling numerical model is corrected according to the prediction deviation, and a corrected tidal wave coupling numerical model is obtained, which comprises: performing dynamic correction on the tidal sub-model in the calibrated tidal wave coupling numerical model according to a first set period prediction deviation, and obtaining a corrected tidal sub-model; performing dynamic correction on the wave sub-model in the calibrated tidal wave coupling numerical model according to a second set period prediction deviation, and obtaining a corrected wave sub-model; obtaining the corrected tidal wave coupling numerical model according to the corrected tidal sub-model and the corrected wave sub-model.

3. The method of claim 2, wherein, The first set period is a quarter, and the second set period is several days or a week.

4. The method of claim 1, wherein, In S1, the calculation boundary condition of the target coastal water area is obtained, which comprises: obtaining a topographic boundary condition of the target coastal water area based on topographic data; obtaining a shore boundary condition of the target coastal water area based on shore line data; obtaining an offshore boundary tide level driving condition of the target coastal water area based on tide data; obtaining a wind field driving boundary condition of the target coastal water area based on wind speed data; The flow velocity boundary condition of the target coastal water area is obtained based on the vertical flow velocity data.

5. The method of claim 1, wherein, In the S2, monitoring stations are arranged at key positions of the target coastal water area, including: Selecting monitoring stations in the target coastal water area that have existing and obtainable measured hydrodynamic environment data; If the monitoring stations that have existing and obtainable measured hydrodynamic environment data are missing, monitoring stations are arranged in the target coastal water area; wherein the point positions of the arranged monitoring stations cover at least the near-shore, off-shore, estuary, channel region and the region near the tidal wave coupling boundary of the target coastal water area.

6. The method of claim 5, wherein, The monitoring stations include shore stations and buoys.

7. The method of claim 1, wherein, In the S3, a tidal wave coupling numerical model is established according to the calculated boundary condition, including: The target coastal water area is divided into unstructured triangular grid cells, and the grid size and calculation time step are set according to the calculation accuracy requirement; The tidal flow sub-model and the wave sub-model are determined according to the calculated boundary condition, and the tidal flow sub-model and the wave sub-model are coupled according to a preset rule; The tidal flow sub-model and the wave sub-model are discretely solved in each grid cell to obtain hydrodynamic parameter results; The hydrodynamic parameter results of each grid cell are integrated to obtain the tidal wave coupling numerical model of the target coastal water area.

8. The method of claim 1, wherein, After the final environmental prediction data is output by the corrected tidal wave coupling numerical model, the method further includes: Vector decomposition is performed on the final environmental prediction data; Annual data of the coastal hydrodynamic environment prediction is constructed according to the data after vector decomposition.

9. The hydrodynamic environmental forecasting method for green sailing of a coastal vessel according to claim 8, characterized in that, The vector decomposition of the final environmental prediction data includes: Obtaining the main channel physical parameters of the target coastal water area, including the channel edge latitude and longitude coordinates and the beacon latitude and longitude coordinates; Determining the heading of the ship sailing along the channel based on the main channel physical parameters, wherein the heading is consistent with the tangent of the channel direction; The final environmental prediction data is decomposed into ship forward tidal flow data, ship normal tidal flow data, ship forward wave data and ship normal wave data according to the heading.

10. A hydrodynamic environmental forecasting system for green sailing of a coastal vessel, characterized by The system is used to perform the method of claim 1-9 for predicting the hydrodynamic environment of the coastal ship green navigation, and the system includes: A first obtaining module for obtaining the calculated boundary condition of the target coastal water area; A second obtaining module for arranging monitoring stations at key positions of the target coastal water area, obtaining measured hydrodynamic environment data through the monitoring stations, and obtaining measured wind wave data through the marine weather observation station; A model construction module connected with the first obtaining module, for establishing a tidal wave coupling numerical model according to the calculated boundary condition, wherein the tidal wave coupling numerical model includes a tidal flow sub-model and a wave sub-model; The calibration output module is connected with the first acquisition module, the second acquisition module and the model construction module, configured to calibrate the tidal flow sub-model according to historical data of the measured hydrodynamic environment data, calibrate the wave sub-model according to the measured wind wave data, and obtain the calibrated tidal flow wave coupling numerical model according to the calibrated tidal flow sub-model and the calibrated wave sub-model according to a preset rule; and control the calibrated tidal flow wave coupling numerical model to output initial environment prediction data according to the calculation boundary condition. The correction module is connected with the second acquisition module and the calibration output module, configured to compare real-time data of the measured hydrodynamic environment data with the initial environment prediction data to obtain a prediction deviation; and correct the calibrated tidal flow wave coupling numerical model according to the prediction deviation in a period to obtain a corrected tidal flow wave coupling numerical model; wherein the periodical dynamic correction includes: controlling the tidal flow sub-model and the wave sub-model in the calibrated tidal flow wave coupling numerical model to output the initial environment prediction data according to different periods respectively; obtaining a first period prediction deviation according to the initial environment prediction data output in the first period and first period real-time data of the measured hydrodynamic environment data; correcting the calibrated tidal flow wave coupling numerical model according to the first period prediction deviation, and the tidal flow wave coupling numerical model corrected according to the first period prediction deviation predicts initial environment prediction data in a second period; correcting the tidal flow wave coupling numerical model corrected according to the first period prediction deviation according to second period real-time data of the measured hydrodynamic environment data and the initial environment prediction data in the second period, and sequentially correcting the model according to each period to realize dynamic correction of the model. The final output module is connected with the first acquisition module and the correction module, configured to control the corrected tidal flow wave coupling numerical model to output final environment prediction data according to the calculation boundary condition.

Images