734 results about "Sea waves" patented technology

The invention provides a method and a device for predicting influence of sea wave fluctuation on signal intensity based on ship data. The method provided by the invention comprises the following steps: acquiring multi-attitude angle data of a ship based on a nine-axis inertial measurement unit; the multi-attitude angle data comprises a rolling angle, a pitch angle and a yawing angle; establishing a Cartesian coordinate system taking the gravity center of the ship as an original point; based on the multi-attitude angle data, calculating spatial transformation of the antenna position in the Cartesian coordinate system through an automatic spatial transformation algorithm to obtain the transformed antenna position; determining the direction of the transformed antenna through vector operation by combining the initial orientation of the antenna and the position of the transformed antenna; and determining a corresponding angle through an angle resolving algorithm based on the initial antenna direction and the transformed antenna direction, matching a corresponding antenna gain in an antenna gain database based on the angle, and determining a signal strength change prediction value based on the antenna gain.

The invention provides a multi-source data fused SAR sea surface significant wave height deep learning inversion method, and belongs to the technical field of remote sensing ocean, and the method specifically comprises the steps: preprocessing buoy data, and obtaining sea wave significant wave height data observed by a buoy; acquiring dual-polarization single-view complex SAR data collected in an interference wide-width mode; preprocessing the SAR data, and taking significant wave height data obtained by buoy observation as a label of the SAR data; acquiring auxiliary data corresponding to the SAR data, wherein the auxiliary data comprises wind speed, wind direction data, rainfall data and OCN data; and constructing a multi-source data-fused SAR significant wave high-depth learning inversion model, training the multi-source data-fused SAR significant wave high-depth learning inversion model, and testing and verifying the trained model by using the test set and the verification set to obtain a final significant wave height inversion result. According to the method, the SAR data inversion capability of the model is improved.

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 provides an ocean vortex region one-dimensional wave spectrum inversion method based on a synthetic aperture radar, and the method comprises the steps: recognizing an ocean vortex through HY-2 fusion data, collecting a Sentinel-1 dual-polarized SAR image covering a vortex region, and obtaining WAVEWATCH-III wave mode data; a hydrodynamic modulation transfer function is improved, and a vortex shear flow influence factor is introduced; an XGBoost machine learning model is adopted, and an ocean vortex region SAR one-dimensional sea wave spectrum inversion algorithm is established; and verifying the accuracy of the SAR one-dimensional sea wave spectrum inversion algorithm. According to the method, the precision limitation of traditional wave theory inversion in a vortex region is broken through, and the one-dimensional wave spectrum inversion precision is improved.

The invention relates to the technical field of ocean new energy technology utilization, and discloses an oscillating water column type wave energy power generation system with an energy gathering structure at the bottom, which comprises a jacket, an air chamber, a turbine power generation system, a radar altimeter, an infrared camera, an airflow velocity sensor, a control host and a bottom energy gathering assembly, the bottom energy-gathering assembly is installed at the lower end of the air chamber and provided with a transmission mechanism, reflection and diversion of seawater at the bottom of the air chamber are achieved through the arranged horizontal bottom plate and the two symmetrically-arranged energy-gathering plates, energy gathering beyond the width of the air chamber is completed, and opening and closing of the energy-gathering plates are synchronously controlled through the transmission mechanism. And the amount of seawater flowing into the air chamber from the water inlet is changed. According to the wave energy power generation system, the oscillation intensity of the water column in the air chamber can be adjusted according to changes of sea surface waves, the speed of the generated air flow can be matched with the optimal working frequency of the turbine power generation system, the power generation efficiency of the device is guaranteed, and the oscillation water column type wave energy power generation device can be protected and prevented from being impacted by sea waves.

The invention provides a ship path tracking and rolling comprehensive control method and device based on a straight-wing propeller, belongs to the technical field of ship motion control, and solves the technical problem that path tracking and rolling control precision and efficiency of a ship are insufficient due to environmental disturbance. Constructing a four-degree-of-freedom ship motion equation, constructing a four-degree-of-freedom ship motion model with interference and measurement noise, reconstructing a ship motion state by adopting a Kalman filter, calculating a path tracking error by utilizing a self-adaptive sight guidance algorithm, and generating an expected course angle; determining a longitudinal thrust, a rolling stabilizing moment and a course control thrust; and the revolution speed and the rotation swing angle of the straight wing propeller are optimized, and the rotation swing angle is recalculated. The method is suitable for the ship provided with the straight-wing propeller, and high-precision path tracking and efficient rolling stabilization are achieved by controlling the revolution speed and the swing angle of the propeller.

The invention discloses a geometric constraint fitting point cloud filtering method for sea surface three-dimensional reconstruction, and belongs to the technical field of computer vision and three-dimensional reconstruction. The objective of the invention is to solve the problem of insufficient subsequent three-dimensional reconstruction precision caused by interference of reflection noise, mismatching points and the like in sea surface point cloud. The method specifically comprises the following seven steps: firstly, acquiring sea surface original point cloud through three-dimensional data acquisition equipment; a filtering technology is adopted to obtain a to-be-fitted point cloud; fitting a quadric surface through an improved RANSAC (Random Sample Consensus) algorithm to solve an initial parameter; constructing a comprehensive error function, and optimizing the model through gradient descent; effective inner points are screened through quadratic term coefficient constraint and a distance threshold value; and finally, iterating until a termination condition is met, and outputting an optimal effective point cloud. The method is high in noise rejection rate, the point cloud fits the sea surface form, and high-quality data support can be provided for sea surface fitting, sea wave simulation and unmanned ship control.

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 significant wave height two-stage space-time prediction method and system based on diffusion residual correction, and relates to the technical field of sea wave prediction, historical significant wave height data and corresponding historical wind field data are input into a significant wave height space-time prediction model for processing, and a preliminary significant wave height prediction result is obtained; and inputting the initial prediction error, historical significant wave height data and synchronous forecast wind field data into a residual error correction module based on a diffusion model, and compensating the initial prediction error to obtain a final significant wave height prediction result. A diffusion model based on an EDM framework is innovatively introduced to carry out refined reconstruction on a prediction residual error, and a basic prediction field, a synchronous wind field dynamic factor and a prediction aging code are used as multi-dimensional physical strong conditions to be injected into a diffusion generation process. According to the method, high-frequency texture details and non-linear fluctuation components missed by the first-stage model can be certainly recovered, long-term deviation accumulation is relieved to a certain extent, the high-wave region prediction quality is improved, and the prediction stability is improved.

The invention discloses a dynamic feature and physical gating fused wave height prediction system and method, belongs to the technical field of artificial intelligence and machine learning, is used for marine environment monitoring, and comprises a data acquisition and preprocessing module, a feature screening module and a model prediction module. The method comprises the steps of obtaining and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient elevator regression model to screen a core variable set, constructing a physical guidance gating feature perception Transform prediction model, constructing a mixed loss function constraint prediction model, and outputting a sea wave significant wave height prediction value after prediction model training is completed. According to the method, through a feature screening and dynamic weight distribution mechanism, the utilization efficiency and scene adaptability of multi-source marine meteorological features are remarkably improved, the method can be flexibly expanded to sea wave prediction tasks of different sea areas and spatial-temporal scales, and high-precision and high-reliability technical support is provided for ocean engineering, shipping safety and disaster early warning.

The invention belongs to the technical field related to energy storage safety, and particularly relates to a simulation method, a simulation device and a simulation system for container shipping vibration. A sea wave spectrum model and a cargo ship model are obtained, and a time domain signal is obtained according to the sea wave spectrum model; defining the time domain signal as fluid excitation, and obtaining a first response signal of a preset acquisition point of the cargo ship model according to the fluid excitation; acquiring a frequency domain sequence according to the first response signal, and calculating and acquiring power spectral density according to the frequency domain sequence; obtaining a container model, and calculating and obtaining a second response signal of the container model according to the power spectral density; whether the container of the current cargo ship meets the safety requirement or not is judged according to the second response signal. Safety simulation is carried out on the energy storage container body during marine transportation, so that mechanical damage caused by design problems during marine transportation of the designed energy storage container is avoided.

The invention relates to a basalt reinforced high-density polyethylene composite material as well as a preparation method and application thereof. The basalt reinforced high-density polyethylene composite material comprises high-density polyethylene, a modified basalt reinforced filler and a compatibilizer, the basalt reinforced filler comprises modified basalt fibers and modified basalt scales. The modified basalt fibers and the modified basalt scales are added into the high-density polyethylene, the modified basalt fibers construct a three-dimensional supporting network to bear main loads, and the sheet structures of the modified basalt scales can prevent the material from deforming in the two-dimensional direction; the basalt reinforced high-density polyethylene composite material and the preparation method thereof have the advantages that the mechanical strength of the basalt reinforced high-density polyethylene composite material is enhanced synergistically, the prepared basalt reinforced high-density polyethylene composite material has the advantages of excellent mechanical strength, good wear resistance and corrosion resistance and the like, and the long-term use requirement of the material in a sea wave high-frequency friction environment is met.

The invention discloses an electromagnetic field simulation method under the coupling action of tidal waves and sea waves. The method comprises the steps that input parameters of the tidal waves and the sea waves are set; configuring environmental parameters and geomagnetic conditions; superposing the tidal wave velocity field and the sea wave disturbance velocity field to form a total velocity field; the tidal wave part and the sea wave part are linearly coupled in time and space to obtain a tidal wave-sea wave coupling field evolved along with a tidal phase; in the tidal wave-sea wave coupling field, electromagnetic response calculation is executed on each frequency-direction sampling point; general field synthesis and modulation effect analysis: introducing a tidal wave modulation factor to clearly reflect amplitude periodic change and spectrum reconstruction characteristics of an electromagnetic field signal along with a tidal phase; and outputting a time sequence, an amplitude spectrum and a spatial distribution result of the comprehensive electromagnetic field. According to the method, the authenticity and applicability of sea wave induction electromagnetic field simulation can be improved, and a new technical means is provided for electromagnetic noise recognition, sea wave parameter inversion and complex ocean dynamics process research.

The invention discloses a Transform-based high-precision sea wave intelligent prediction method, and relates to the technical field of marine environment prediction.The method comprises the steps that geographic information data and wind field data of a prediction area are collected, the collected data are preprocessed, and a numerical model computational domain is constructed; generating a high-resolution wave data set based on the SWAN numerical model, and dividing the data set into a training set and a test set; constructing a Transform model containing a multi-head attention mechanism, and training by taking a historical wave sequence as input to obtain a wave forecasting model; evaluating the model prediction performance by using the test set, and analyzing the influence weight of each wave parameter on the prediction result through the attention weight matrix; by fusing the numerical model and the deep learning technology, the precision and efficiency of wave forecasting are remarkably improved, and meanwhile, the method has model interpretability.

The invention relates to the technical field of sea wave height prediction, and discloses a multi-scale physical enhancement type irregular sea wave prediction method which comprises the following steps: (1) collecting historical data of sea waves, preprocessing the data, and dividing a training set, a verification set and a test set; (2) decomposing the wave data into a high-frequency component, a medium-frequency component and a low-frequency component through wavelet decomposition; (3) constructing a WaveFormer model; (4) the WaveFormer model is trained, and (5) the test set is sent to the WaveFormer model to be trained, and a wave height prediction value is obtained. According to the method, an original wave sequence is decomposed into a high-frequency component, an intermediate-frequency component and a low-frequency component by adopting two-layer stationary wavelet transformation, the model captures multi-scale characteristics of irregular waves explicitly, the reasonability of wave prediction is ensured, sequence-level prediction is realized on the premise of ensuring the prediction precision, the prediction efficiency is improved, and the prediction cost is reduced. And a data driving item is constructed based on a wave prediction result, so that the model prediction accuracy is further improved.

The invention relates to an underwater electromagnetic detection noise suppression method based on an NARX neural network, and the method comprises the steps: collecting first magnetic field data through a first vector magnetic sensor, and collecting second magnetic field data through a second vector magnetic sensor; training a sea wave noise prediction model based on the first magnetic field data and the second magnetic field data, and repeatedly and synchronously intercepting the first magnetic field data and the second magnetic field data in a preset time range through a sliding window, inputting the intercepted first magnetic field data and the intercepted second magnetic field data into the sea wave noise prediction model for prediction to obtain a corresponding sea wave noise prediction value; and performing differential processing on the first magnetic field data in each sliding window and the sea wave noise prediction value in the corresponding preset time range, and splicing differential results to obtain secondary field information in the first magnetic field data. According to the invention, the accuracy of noise estimation is improved, and the background interference in the signal can be better stripped.

The invention relates to the technical field of ocean monitoring and data processing, in particular to a sea condition estimation method based on multi-source information fusion. The method comprises the following steps: emitting electromagnetic waves to a target sea area by using a radar, receiving a sea clutter reflection signal and converting the sea clutter reflection signal into a digital image signal to obtain a sea clutter image, and after preprocessing and sea wave edge feature extraction, calculating to obtain a first meaningful wave height parameter of sea waves in the sea area through a preset formula based on extracted sea wave edge discrete points; collecting the surging data of the ship in the target sea area in real time through RTK GPS equipment, performing preprocessing and spectral analysis to extract frequency components related to the sea waves, and calculating a second meaningful wave height parameter of the sea waves in the sea area based on the frequency components; and effectively fusing the two types of parameters, performing deviation correction on the first meaningful wave height parameter by using a fusion result, and outputting a sea condition estimation result. The method can improve the accuracy and reliability of sea condition estimation, can adapt to different marine environments and weather conditions, and is wide in application prospect.

A method of training a machine learning model to predict seafloor ripple geometry that includes receiving one or more input values, each input value based on an observation associated with ocean wave and seafloor conditions, and preprocessing the one or more input values. The method includes generating a training data set based on the preprocessed data set, splitting the training data set into a plurality of folds, and training via stacked generalization the machine learning model by performing a cross validation of each fold of training data based on at least one deterministic equilibrium ripple predictor model and on at least one machine learning algorithm. The method may include generating via the trained machine learning model, a set of one or more seafloor ripple geometry predictions, and performing Bayesian regression on the set of one or more seafloor ripple predictions to generate a probabilistic distribution of predicted seafloor ripple geometry.

The application discloses a polymer repair mortar for marine concrete and a preparation method thereof. The polymer repair mortar comprises dry powder mortar and a composite modified polymer. The dry powder mortar comprises 525 cement, an expanding agent, high-activity metakaolin, sand, a water reducing agent, a defoaming agent, latex powder and modified polypropylene fibers. The composite modified polymer comprises water-based epoxy resin, graphene, sodium hydroxymethyl cellulose and amide modified hydrogenated castor oil. The dry powder mortar and the composite modified polymer are mixed uniformly to obtain the polymer repair mortar. The polymer repair mortar improves the impermeability and impact resistance of the repaired marine concrete. The composite modified polymer can form a dense waterproof protective film on the surface of the mortar, effectively reducing the influence of the dry-wet cycle of the sea, chloride ion corrosion and the impact of sea waves. In addition, the adhesion between the repair mortar and the marine concrete matrix is further improved.

The invention relates to a pressure sensing inverse echo measurement LSTM short-time sea wave significant wave height prediction method, and belongs to the technical field of marine environment parameter inversion and marine dynamics observation. The method comprises the following steps: firstly, preprocessing multi-source observation data, and matching and integrating data from different sources; establishing a model, and improving the performance of the model by adjusting the structure and parameters of the model; and finally, evaluating the precision of the model, and verifying the precision and stability of the model in a complex environment. According to the method provided by the invention, a plurality of long and short-term memory network layers are combined with a plurality of full-connection layers to realize hierarchical fusion of time features, so that the prediction precision of the significant wave height of the short-term sea wave is improved, and important data support is provided for the directions of ocean dynamics, environmental evaluation and the like.

The invention relates to an experimental device for simulating dry-wet alternation and sea wave erosion corrosion and a using method, the experimental device comprises a box body, a wave making plate, a sample clamp and a central control system, the wave making plate is arranged in the box body, and the wave making plate is driven by a driving assembly to reciprocate; the sample clamp is used for clamping a metal sample, and the height of the sample clamp in the box body is adjusted through a lifting assembly. The central control system comprises a driver, an industrial control board, a computer and a wave meter, the driver, the computer and the wave meter are all connected with the industrial control board, and the wave meter is arranged in the ocean and the box body and used for collecting ocean wave parameters in the ocean and wave parameters generated by the wave generation board in real time and sending the collected parameters to the industrial control board; and the driver is used for controlling the driving assembly to act. The problem that an experimental device cannot accurately simulate the complex corrosion working condition of a steel structure in a marine environment can be solved.

The utility model discloses an aquaculture net cage truss main structure which comprises a base platform structure and a truss structure, and the base platform structure is provided with a stand column structure. The base platform structure is provided with a buoyancy tank assembly and a support reinforcing structure; the base platform structure and the buoyancy tank assembly form a rectangular frame structure. The base platform structure and the buoyancy tank assemblies are arranged to form the rectangular frame structure to guarantee the strength of the main structure of the aquaculture net cage truss, the supporting reinforcing frames are arranged in a matched mode to form various supporting structures, the impact resistance of external force such as wind and sea waves is improved, and the overall stability is improved; the buoyancy tank assembly composed of the first buoyancy tanks and the edge buoyancy tanks is used for improving the adjustable efficiency of lifting, preventing the situation of deviation and toppling and improving the stability of lifting, when one of the buoyancy tanks breaks down, only the faulted buoyancy tank needs to be replaced, and the maintenance efficiency and cost are reduced.

The invention relates to the technical field of trestle construction platforms, in particular to an offshore lamplight trestle construction platform which comprises a supporting column, a bridge floor is detachably installed at the upper end of the supporting column, limiting seats are fixedly connected to the upper end and the lower end of the supporting column, the outer surface of the supporting column is sleeved with a movable barrel, the outer surface of the supporting column is further sleeved with a balance weight ring, and the balance weight ring is located below the movable barrel. The outer surface of the movable cylinder is sleeved with an auxiliary ring, the outer surface of the auxiliary ring is rotationally provided with a plurality of extension rods, the outer surfaces of the extension rods are sleeved with floating belts, the floating belts can float above the sea level, the movable cylinder automatically ascends and descends along with the sea level under the buoyancy action of the floating belts, and the communication cylinder is kept in isobaric communication with an external water body. The water column pressure formed in the rising process of sea waves and tides can automatically drive the turbofan in the driving barrel to rotate, so that continuous mechanical energy output can be achieved without additional power, and then the cleaning ring is driven to circularly clean the outer surface of the supporting column.

The invention relates to a stabilization self-adaptive control method and device based on double gyroscopes, and the self-adaptive control method comprises the steps: constructing a real-time updated'sea wave-ship-double gyroscope 'coupling dynamic model, and introducing the online identification of ship rolling parameters; physical limit, damping, maximum torque and other constraints of the double-gyroscope executing mechanism are explicitly written into a control law; the two gyroscopes are symmetrically arranged and opposite in precession direction, so that yawing torques are mutually counteracted and rolling torques are superposed, and the stabilization efficiency is improved; adaptively selecting PID according to the rolling amplitude, the sea condition uncertainty and the working state of the actuator, and adaptively switching the fuzzy adaptive PID and the model prediction control strategy; calculating a control instruction based on the selected strategy and the updating model, and performing physical constraint processing such as torque saturation and angle amplitude limiting; and outputting a safety instruction to drive the gyroscope to generate stabilization torque. The method can adapt to different working conditions, and the efficient and stable active stabilization performance and stabilization effect are kept under the multiple working conditions.

The invention provides a WaveWatch III and U-Net-based global wave numerical forecasting system and a significant wave height intelligent correction network construction method. The method comprises the steps of mode input, WaveWatch III source code downloading, model output, significant wave height intelligent correction network construction and the like. The method has the beneficial effects that the forecasting precision of the WaveWatch III model in all sea areas in the world is remarkably improved, particularly the forecasting capability under extreme sea wave conditions is improved, and more accurate and stable scientific basis and technical support can be provided for global marine environment monitoring, disaster early warning and coping strategy formulation.

The invention relates to the technical field of data analysis, and discloses a parameterization analysis method and system based on a sea wave spectrum mode. The method comprises the following steps: carrying out abnormal value detection and missing data filling on multi-source ocean data by adopting a 3 sigma criterion and Kriging interpolation, and unifying to a 0.1-degree grid to obtain a standardized data set; decomposing a sea wave energy balance equation source item into three modules for numerical calculation; configuring ST2, ST4 and ST6 schemes for the numerical solutions, and carrying out statistical analysis to obtain performance evaluation indexes; based on the evaluation indexes, a genetic algorithm is combined with particle swarm optimization to carry out parameter optimization; dividing the offshore China into four sub-regions for differential adjustment according to the optimization parameters; and carrying out trend checking and parameter increment updating by adopting a sliding window technology based on region configuration. The technical problems that a sea wave spectrum mode parameterization scheme lacks a systematic comparison and optimization mechanism, parameter configuration cannot adapt to regional marine environment differences, and parameters lacks real-time monitoring and dynamic adjustment capabilities are solved.

The invention belongs to the technical field of microwave radar image processing, and relates to a wind wave significant wave height inversion method based on a wind wave period, which comprises the following steps: firstly, establishing a significant wave height calculation model taking the wind wave period as input; then, acquiring a sea surface microwave remote sensing image, and performing coordinate transformation, such as preprocessing of a shore-based sea observation radar image, a shipborne navigation radar image, noise suppression and the like; thirdly, inverting a wind wave period from the preprocessed image through a spectrum analysis technology; and finally, substituting the wind wave period obtained by inversion into a pre-established model, and calculating to obtain the significant wave height. According to the method, the wind wave period is introduced to serve as a key intermediate variable, the image features and the sea wave physical parameters are closely associated, the defect that an existing direct inversion method is insufficient in precision and stability is overcome, and an effective wave height obtaining means which is clear in physical significance, high in inversion precision and easy to popularize in a business mode is provided.

The invention discloses a sea wave spectrum prediction method and system based on machine learning, and relates to the technical field of marine disasters, and the system comprises a data acquisition module, a data reconstruction module, a data coding module and a prediction module. According to the method, a space unit constructs a space matrix according to longitude and latitude based on a comprehensive feature vector, the space matrix accords with sea wave space distribution characteristics, multi-layer convolution is carried out by using a convolutional neural network, space features are extracted from local to global, key information is reserved in a maximum pooling manner, sea wave space correlation is accurately captured, and a time unit depends on an LSTM gating mechanism. Dynamic evolution and long-time dependence of a sea wave spectrum along with time are mined, space-time fusion units are spliced and pooled, space-time features are fused, complete space-time representation is formed, sea wave space-time dimension internal relation is fully mined, high-quality input capable of reflecting sea wave dynamic changes is provided for a prediction model, the model accurately masters the change rule of the sea wave spectrum in the space-time, and the prediction accuracy is improved. The sea wave spectrum prediction precision is improved.

The embodiment of the invention provides a sea wave prediction method and device. The method comprises the steps that sea information parameters of a target sea area are acquired; determining a stability parameter of the corresponding sea area according to the ocean information parameter; according to the stability parameter, a corresponding wind stress correction coefficient is determined for the current sea area through the relation between the stability parameter and the wind stress correction coefficient; and correcting a wind stress parameter in a sea wave prediction model according to the wind stress correction coefficient, and outputting corresponding sea wave prediction information through the sea wave prediction model. According to the sea wave prediction method provided by the invention, the stability parameter is introduced to represent the influence of the atmospheric stratification stability on the sea wave growth, and compared with an existing sea wave prediction method, the sea wave prediction accuracy is improved.

The invention belongs to the technical field of ship electric propulsion, and particularly relates to a ship electric propulsion dual-mode control system with a multi-redundancy architecture, and the system comprises the steps: firstly, accurately obtaining the actual rotating speed and electromagnetic torque of a motor through a sensor group with redundancy configuration and two-out-of-three voting logic; secondly, calculating load disturbance intensity by utilizing time domain difference analysis, and calculating the deviation between the actual torque and the theoretical hydrodynamic torque in combination with a propeller characteristic coefficient, so as to generate a load anomaly index; generating a dynamic flexible factor with a damping characteristic based on the index by using a Sigmoid function and low-pass filtering logic; and finally, carrying out weighted fusion on output instructions of the rotating speed mode and the torque mode according to the factor. According to the method, normal acceleration and sea wave disturbance can be accurately distinguished, smooth seamless switching between a static water speed stabilization mode and a severe sea condition machine protection mode is achieved, and control oscillation and mechanical damage are effectively avoided.