656 results about "Wave height" patented technology

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses a seawall overtopping amount and overtopping risk calculation method considering a mangrove forest wave dissipation effect, and relates to the technical field of coast engineering. The method comprises the following steps: acquiring typhoon path data, inputting the typhoon path data into a Holland typhoon empirical model, and accurately generating a typhoon wind field; calculating coastal site tide level data in combination with a coastal storm surge numerical model; obtaining wave data during a typhoon by using a storm surge typhoon wave forecasting model based on the SWAN; historical typhoon data and relevant information of the seawall and the mangrove forest are collected, a water level-wave-vegetation-seawall coupling model is constructed, the wave dissipation effect of the mangrove forest is accurately simulated, and wave characteristic calculation is optimized; constructing a database, training the database through a CNN neural network model, and improving the prediction precision of seawall front wave height and wave force parameters; the wave overtopping amount is calculated based on the prediction parameters, a scientific basis is provided for wave overtopping risk assessment, it is ensured that real-time assessment of the wave overtopping risk is more accurate and efficient, and the disaster prevention and reduction capacity is effectively improved.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

The invention, which relates to the technical field of offshore wave simulation, discloses a wave hydrodynamic numerical simulation method for a sea area with a breakwater, comprising the following steps: S1, determining a target sea area, and collecting sea chart data and water depth topographic data of the target sea area; s2, determining a shoreline position of the target sea area, and establishing a water depth terrain database of the target sea area; s3, setting grid precision, and constructing a wave hydrodynamic two-dimensional model of the target sea area; s4, setting boundary conditions for driving the wave hydrodynamic two-dimensional model, and setting initial parameters; and S5, based on the wave hydrodynamic two-dimensional model constructed in the S3, performing numerical simulation on the target sea area by using a sub-grid method to obtain the characteristic wave height and periodic data of the target sea area. According to the method provided by the invention, the calculation efficiency can be ensured, the wave transmission characteristics of the open type oyster reef are considered, and the influence of open type oyster reef construction on the wave hydrodynamic environment field can be accurately simulated.

The invention discloses a two-dimensional wave spectrum intelligent forecasting method based on a physical information neural network, and belongs to the technical field of ocean information prediction.The two-dimensional wave spectrum intelligent forecasting method comprises the steps that basic data are collected, and the basic data comprise high-temporal-spatial-resolution sea surface wind field data and sea wave height data within the research range; historical sea wave data are obtained, ocean and meteorological data closely related to sea wave changes are integrated and processed, and a sea wave forecasting database is constructed; physical constraints are determined according to the sea wave forecasting database; an ANN neural network model is adopted as a basic model, physical constraints are added, the neural network meets the control equation of the mode, and a PINN physical information neural network is constructed; and carrying out interpretability analysis on the model forecasting process and result. According to the method, the sea wave dynamic process and the deep learning method are fused, interpretability is added for deep learning, and the black box problem of the deep learning method is solved.

A whole ship strain field rapid reconstruction method using ship motion and discrete strain information can rapidly reconstruct a whole ship structure strain field through fusion analysis of ship motion information and finite discrete strain monitoring data, and provides a more reliable reference basis for ship structure safety assessment and auxiliary decision making. The method comprises the following steps: firstly, acquiring ship motion monitoring data, and establishing a wave time-frequency parameter inversion Kalman filtering model based on ship motion information; secondly, according to the wave height obtained through inversion, the wave pressure load on the surface of the ship body is solved by combining phase parameters and relative positions of the wave surface and the ship body; thirdly, establishing a whole ship structure strain field dynamic reconstruction artificial intelligence model fused with a physical information neural network-finite element method, and formulating a layout strategy of measuring points; and finally, performing network parameter optimization on the PINN-FEM strain field reconstruction model by using strain monitoring data of finite discrete measurement points, and improving the overall calculation precision of the model.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship are improved.

The invention relates to an ecological landscape type disaster reduction seawall, the section form of the seawall is a gentle slope into-sea multi-stage composite type, and the seawall structurally comprises a seawall foot on the seaside, a first-stage planting platform, a gentle slope, a second-stage planting platform, a waterside platform, a wave wall and a road which are sequentially arranged from sea to land; on the basis of ensuring safety, the seawall is subjected to ecological landscape design, so that the seawall can play roles in intercepting silt and purifying water; the effects of weakening wave energy and reducing marine disaster threats can be achieved; and meanwhile, the hydrophilicity of the seawall can be improved, and a leisure activity space is provided for people. The invention further relates to a design method of the ecological landscape type disaster reduction seawall and a method for determining the crest elevation of the ecological landscape type disaster reduction seawall. The section form of the seawall can be determined according to the wave height and the height difference between the sea level and the road surface, the crest elevation of the seawall can be effectively reduced, the engineering cost is reduced, and the seawall ecological function is increased.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention discloses an intelligent optimization control method for a wave power generation device based on an improved MPC, and the method comprises the steps: collecting the wave height, period, flow velocity and device motion posture data in real time through a multi-source sensing module, constructing an exciting force prediction model based on a bidirectional long-short-term memory network, and achieving the precise prediction of the wave exciting force of n control periods in the future; prediction data and real-time feedback signals are input into an improved jellyfish search optimization algorithm, through a two-stage chaotic mapping perturbation mechanism, the convergence speed and the global optimization ability of the algorithm are remarkably improved, and finally optimal electric damping control parameters are output. Compared with a traditional MPC method, the method has the advantages that a feed-forward-feedback composite control framework is formed by fusing deep learning prediction and an intelligent optimization algorithm, so that the energy efficiency conversion efficiency of the power generation system is improved.

The invention discloses a near-shore wave-flow field-sediment coupling simulation method considering a wave breaking nonlinear effect, and belongs to the field of near-shore hydrodynamics, and the method comprises the steps: inputting WRF high-resolution wind field, tide boundary conditions and water depth information, and initializing a wave module, a flow field module and a sediment module; calculating local wave breaking dissipated energy by adopting an improved wave breaking energy dissipation formula to obtain a wave energy density spectrum and a breaking probability; a near-shore flow field is calculated under radiation stress nonlinear correction, and hydrodynamic boundary conditions are updated; calculating sediment suspension and deposition by using a coupling diffusion coefficient, and updating sediment concentration distribution; waves, flow fields and sediment are mutually coupled through loop iteration, and coastal wave-flow field-sediment simulation is achieved under extreme conditions such as typhoons, storm surge and strong storm waves. The method can improve the prediction precision of the wave height, the flow velocity and the sediment concentration under the condition of offshore strong storm waves, and is used for typhoon storm surge prediction, harbor basin deposition evaluation and offshore ecological environment management.

The invention discloses a risk monitoring and control method for offshore construction, and particularly relates to the technical field of collision control for offshore construction, and the method comprises the steps: obtaining the real-time position, the navigational speed, the course, the tonnage, the wind speed, the wind direction, the wave height, the wave direction, the flow speed, the ship type, the ship historical track and the ship behavior mode of a ship, and carrying out the preprocessing; and inputting the processed multi-source data into the constructed ship trajectory prediction model, outputting a plurality of possible trajectories and trajectory probabilities of a future time window, and then calculating a collision probability and a dynamic safety distance, thereby improving the degree of closeness between the collision probability and a real collision probability, improving the accuracy of collision probability analysis, and improving the accuracy of ship trajectory prediction. The safety boundary of the ship and the construction area is quantified in real time, the failure of a fixed threshold value in a complex environment is avoided, a dynamic judgment reference is provided for risk level classification, the risk identification precision is improved, a response instruction is matched more accurately, and the collision harm is further reduced.

The invention provides a sea wave significant wave height prediction method based on a space-time sequence deep learning model, and relates to the technical field of sea wave significant wave height prediction, and the method specifically comprises the steps: obtaining historical wind speed data and historical sea wave significant wave height data of a test area; performing quality screening on the acquired data, and then performing time-space registration to enable historical wind speed data and historical sea wave significant wave height data to be staggered for 1 hour and aligned in a time dimension; generating a space-time sequence pair by using a sliding window algorithm, and constructing a training set; constructing a space-time sequence deep learning model, and inputting the training set into the space-time sequence deep learning model for iterative training to obtain a sea wave significant wave height prediction model based on the space-time sequence deep learning model; and forecasting the wind field according to a numerical value provided externally, and inputting the acquired future wind speed data into the prediction model to obtain a forecasting result. According to the technical scheme, the problem that in the prior art, sea wave forecasting is low in precision and reliability is solved.

The invention provides a wave height prediction method for multi-strategy snow ablation optimization, and belongs to the field of ocean engineering. The wave height prediction method comprises the following five steps: preprocessing sea condition data, performing feature extraction on the sea condition data by using a CNN model, training the model, optimizing parameters and evaluating the model. According to the method, the multi-strategy snow ablation optimization algorithm, the convolutional neural network, the bidirectional gating circulation unit and the multi-head attention mechanism are comprehensively utilized, and efficient processing and accurate prediction of the sea condition data are achieved; the introduction of the multi-strategy snow ablation optimization algorithm increases the diversity of the population, prevents the model from falling into local optimum, and improves the robustness of the algorithm. The CNN and the bidirectional GRU are combined, so that the model has excellent performance in the aspect of processing high-dimensional, nonlinear and time sequence data; the application of the attention mechanism can highlight important information, perform weighting processing on features, and further improve the prediction performance of the model.

The invention discloses a large-scale regional sea wave rapid forecasting method and device based on data driving, and the method comprises the steps: obtaining multi-source marine physical environment data of a target sea area, and generating a standard physical field data flow with unified temporal-spatial resolution; constructing a multi-channel space-time input tensor containing wind field driving information, terrain boundary information and historical wave state information; the difference between the predicted wave height and the real wave height is minimized through a back propagation mechanism, so that a trained wave height prediction model is obtained; receiving latest wind speed field data output by a real-time observation or numerical forecasting mode, and outputting a sea wave significant wave height prediction field at a future target moment; the device is used for implementing the method. According to the technical scheme of the method provided by the invention, through a physical lag alignment mechanism, the time delay of transmitting wind energy to wave energy is accurately captured, and the modeling precision is improved; and meanwhile, rapid deduction of a large-scale sea wave field is realized based on deep learning, and high accuracy and high timeliness are achieved.

The invention discloses a spectral analysis-based sea surface wave model construction method and system, and the method comprises the steps: carrying out the grid division of a sea surface, calculating parameters for each grid of the sea surface, and building a gravity wave model, a surge model and a shockwave model for the whole sea surface; obtaining a gravity waveform height amplitude according to the gravity wave model, obtaining a surge waveform height amplitude according to the surge model, and obtaining a shock wave height amplitude according to the established shock wave model; superposing the gravity waveform height amplitude, the surge waveform height amplitude and the shockwave height amplitude, and carrying out inverse fast Fourier transform on the superposed amplitudes to calculate a sea surface waveform height spectrum which changes along with time in an airspace within a certain range; and constructing a sea surface wave geometric model in an STL format according to the sea surface waveform height spectrum changing along with time in the airspace in a certain range. According to the invention, the authenticity and accuracy of simulation are improved.

The invention provides a polar region multi-source remote sensing sea wave significant wave height optimization interpolation fusion method and device, and belongs to the field of remote sensing, and the method comprises the steps: carrying out the preprocessing of collected sea ice marginal region and open sea surface data; north-pole geometric adaptation correction is carried out, system deviation of multiple satellite sensors is eliminated, and consistency and accuracy of data are ensured. Real-time wave height forecast is used as a background field, a weighting function is constructed in combination with a satellite observation error covariance matrix, dynamic background field fusion is carried out, and the precision and reliability of a data sparse region are improved; according to different characteristics of a sea ice marginal region and an open sea surface, different weights are set, a transition weight function is introduced, partition fusion is carried out, boundary discontinuity is avoided, and therefore the final fusion wave height is obtained; the robustness of the fusion method is verified through K-fold cross validation, and meanwhile precision verification is conducted in combination with multi-dimensional data. According to the method, the problem of insufficient accuracy verification spatial representativeness caused by rare north pole in-situ observation quantity and concentrated distribution is solved.

The invention belongs to the technical field of ocean engineering and water electrolysis hydrogen production. In order to solve the problem that a traditional model is difficult to deal with dynamic sea conditions, a platform-environment coupling digital twin model is constructed, multi-source monitoring data is fused, and platform motion response is predicted through deep reinforcement learning. And generating anti-storm strategies such as ballast water regulation based on a prediction result, and realizing real-time optimization. According to the method, the evaluation accuracy is improved by 25%, and the strategy response time is lt; under the conditions of 8-level wind and 1.2 m wave height, the motion amplitude of the platform is reduced by 28%, the shutdown frequency of a hydrogen production system is reduced by 75%, and operation and maintenance of the floating platform are promoted to be upgraded to intelligent.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

The invention provides a significant wave height prediction method and device and electronic equipment, and relates to the technical field of sea wave data processing. The method comprises the following steps: acquiring an effective wave height data set for wave height prediction; determining model input data based on the correlation; carrying out two-layer decomposition on the effective wave height; wherein the primary decomposition comprises empirical mode decomposition based on a self-adaptive noise complete set and K-means clustering of mode components; the secondary decomposition comprises the step of optimizing variational mode decomposition of parameters based on a crown porcupine optimization algorithm; inputting model input data into a bidirectional long-short-term memory network based on an attention mechanism for training; the method comprises the steps of firstly screening data with relatively high correlation with the significant wave height as input data to reduce the calculation amount, then performing two-layer decomposition on the significant wave height to reduce the data complexity, and finally fully extracting data features through a bidirectional long-short-term memory network based on an attention mechanism to comprehensively predict the data so as to improve the prediction accuracy of the significant wave height. And the accuracy of predicting the significant wave height is improved.

The invention discloses a wave height short-term prediction system and method based on variational mode decomposition and wind and wave time correlation, and belongs to the technical field of ocean engineering.The method comprises the steps that firstly, original storm time series data are decomposed into IMF components with different center frequencies through variational mode decomposition; secondly, performing data cleaning on the decomposed IMF component based on a Pearson's correlation coefficient; then combining two wind speed IMF components with the strongest correlation with the wave height IMF component based on correlation analysis to form a wind speed wave height component combination, and determining an input time length through an autocorrelation coefficient ACF of wave height data and a cross correlation coefficient CCF of the wind speed IMF components and the wave height IMF components; and finally, according to the formed wind speed wave height component combination and the determined input time length, carrying out short-term prediction on the wave height based on a long short-term memory neural network. According to the system and the method provided by the invention, the influence of noise on prediction is reduced, and the prediction precision is improved while the prediction efficiency is improved.

The invention belongs to the field of ship navigation safety evaluation, and discloses a second-generation complete stability forecasting system and method for real sea navigation of a ship. The method comprises the following steps: acquiring actually measured marine environment parameters for sea wave forecasting, and acquiring ship navigation parameters for second-generation complete stability forecasting of a ship; sea wave forecasting is carried out, wherein regional meaningful wave height and average zero-crossing period forecasting of sea wave data is carried out based on an HP-VIT model; a pure stability loss sensitivity index forecasting model is constructed, and pure stability loss sensitivity index forecasting is completed; and a parameter rolling sensitive index forecasting model is constructed, and regional parameter rolling forecasting is completed. According to the method, rapid forecasting of the second-generation stability of the ship in the real sea environment can be achieved, the research result can provide auxiliary decision-making information for sailing safety guarantee, route planning correction and the like of the container ship, and the method has great significance in guaranteeing the safety of the ship and goods.

The invention discloses a wind wave and surge extraction method and system based on a GNSS and an anemometer. The method comprises the following steps: respectively obtaining water level height data and wind data detected by a buoy for multiple times according to a reference time sequence and a detection time sequence; the buoy water level height data and the wind data are subjected to data preprocessing, the water level height and the wind data are preprocessed, and the preprocessed water level height and the preprocessed wind data are obtained; calculating the water level height and the wind data in unit time by using the preprocessed water level height and the wind data according to a segmented interpolation function, and calculating wave height data under a corresponding detection time sequence according to the water level height and the wind data in the unit time; feature set classification is carried out according to wind speed grades, IFFT is carried out after screening, and final wind wave and surge data are obtained, the water surface wind wave and surge data extraction precision is effectively improved, and low-cost and long-time-efficiency water surface wind wave and surge detection can be achieved.

The invention belongs to the technical field of wave observation, and proposes an adaptive window fusion method for capturing wave height in a large wave process, the method comprising: given a target area, collecting effective wave height data of multi-source satellite altimeters in the target area; determining the initial spatial resolution and initial time resolution of the wave height field in the target area; based on the time-space inverse distance weighted fusion method, generating a wave height sequence of target grid points in the target area at the initial time resolution; performing large wave process identification on the wave height sequence, capturing peak information in the large wave process; refining the fusion process of the preset time period threshold before and after the peak moment, adaptively adjusting the time resolution, and recording the dynamic change of the wave height sequence and the corresponding peak information in the large wave process at the adjusted time resolution. The method realizes the fusion of multi-source satellite data under different sea conditions, takes into account the fusion efficiency and fusion accuracy, and autonomously judges and identifies the large wave process.

The invention discloses a delta-beach bar composite sand body prediction method, which comprises the following steps of S1, acquiring geological background data of a target work area, and dividing delta and beach bar deposition to obtain a beach bar deposition thickness; s2, carrying out source and sink system analysis, establishing a sediment transport channel path, and obtaining a sand body transport path plane distribution diagram; s3, the ancient landform, ancient wind field features and ancient wave field features of the target work area in the deposition period are restored, the ancient landform height and the distance to the source area of the underwater ancient protrusion are obtained according to the ancient landform, and the features include the ancient wind direction, the ancient wind path, the ancient water depth and the ancient wave height; s4, determining a favorable development zone, a favorable development layer section and a favorable development target zone of the delta-beach bar composite sand body; and S5, obtaining distribution characteristics of the delta-beach bar composite sand body of the target work area according to the favorable development zone, the favorable development layer section and the favorable development target area. According to the method, the delta-beach bar composite sand body can be accurately predicted, and technical support is provided for oil exploration and development.

The invention relates to the technical field of sea wave height prediction, and discloses a significant wave height prediction method and system based on spatio-temporal evolution multi-scale feature extraction. The method comprises the following steps: performing significant wave height prediction on input significant wave height grid data of a sea area to be predicted by applying a trained hybrid heterogeneous parallel double-convolution dynamic network; the method further comprises the steps of obtaining the effective wave height grid data, inputting the effective wave height grid data into the hybrid heterogeneous parallel double-convolution dynamic network for effective wave height prediction, and then outputting an effective wave height prediction sequence of a future time step. According to the method, multiple features of SWH spatio-temporal dynamic evolution can be extracted, and hierarchical spatio-temporal features from a local scale to a global scale and local spatio-temporal dynamic crossing from a coarse scale to a fine scale can be mined at the same time; by constructing a heterogeneous parallel double-convolution framework, the internal irregularity of the SWH field and the irregular relation between SWH field data are overcome, and meanwhile the capacity of capturing the invariant relation in the SWH field is kept.

The invention discloses a ship navigational speed prediction and optimization method and device and a readable storage medium, and the method comprises the steps: building a ship navigational speed prediction model by taking the host power, the ship course, the flow velocity, the relative flow direction, the wind speed, the relative wind direction, the effective wave height, the relative wave direction and the combined period of stormy waves and surges as the input, and taking the ship navigational speed as the output; acquiring ship route planning information and marine weather forecast information; dividing the planned ship route into a plurality of planned route segments; acquiring related information of each planned leg in combination with marine weather forecast information, and predicting real ship navigational speeds of each planned leg of the ship under corresponding weather sea conditions and different host powers in combination with a ship navigational speed prediction model; and establishing a cost function according to the host power and the corresponding real ship navigational speed, constructing a ship navigational speed optimization model in combination with the constraint condition, and finally obtaining the optimized navigational speed of each planned leg. According to the invention, the ship speed can be predicted and optimized.

The invention belongs to the technical field of multi-constellation satellite-borne GNSS-R data ocean significant wave height inversion, and discloses a satellite-borne GNSS-R ocean significant wave height inversion model construction method and system under a high sea condition, and the method comprises the steps: obtaining modeling data, auxiliary data and verification data; feature variable parameters and auxiliary variable parameters of the modeling data are extracted, and space-time matching is carried out on the extracted variable parameters; performing data quality control and data set division on the variable parameters after space-time matching; constructing and training a WaveMambaFormer model by using the divided data set, and calculating feature importance and global interpretation by using an SHAP interpreter, so as to enhance the interpretability of the WaveMambaFormer model; and the effective wave height data of the ERA5, the WaveWatch III and the Jason-3 are respectively used as reference data to evaluate the inversion performance of the WaveMambaFormer model.

The invention relates to a super-large L-shaped high-sea-condition high-precision absorption type wave maker system and a wave simulation method.The wave maker system comprises a central control layer, an equipment control layer and an execution mechanism layer which are sequentially connected, and the central control layer is characterized in that a main control computer operates wave making control software; a target wave time calendar curve is generated, theoretical motion instructions needing to be executed by the wave making plates are synchronously deduced, the main PLC conducts format conversion and time sequence synchronization processing on the theoretical motion instructions, and wave making plate displacement curve instructions are generated and issued; the equipment control layer is used for calculating active absorption compensation from the PLC and generating a motor motion instruction; the executing mechanism layer comprises a super-large L-shaped wave making unit, a fan-shaped driving arm, a plurality of motors and other assemblies to execute wave making motion and feed back wave height information, and the motors drive the fan-shaped driving arm to drive a wave making plate to swing around a hinge assembly and drive the wave making plate to execute compensation action opposite to the phase of incident waves; high-sea-condition wave simulation and high-precision wave active absorption are realized, and a full-wave-direction test with navigational speed is supported.