1458 results about "Sea waves" 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 provides an unmanned ship charging cable control method based on visual identification, and relates to the technical field of data processing, and the method comprises the steps: collecting continuous image frames of an unmanned ship charging area, identifying the spatial displacement and inclination angle change of an unmanned ship, analyzing the attitude offset of the unmanned ship caused by sea waves, calculating a water surface disturbance factor, and calculating the water surface disturbance factor. The method comprises the following steps: identifying the position of a charging interface, obtaining an initial positioning coordinate, carrying out prediction analysis on a disturbance trend in a preset time window, predicting a position change range of the charging interface, obtaining prediction coordinate data, planning a butt joint path of a cable and the charging interface, obtaining a compensation path sequence, and generating a cable propulsion instruction. And collecting an area image of the charging interface to obtain path tracking image data, judging whether the butt joint of the charging interface is successful or not, and if not, dynamically correcting the compensation path sequence. According to the invention, the cable is controlled through visual identification to charge the unmanned ship.

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.

The invention discloses a regional refined sea wave coupling numerical forecasting method based on a non-static mode, and aims to improve forecasting accuracy. The method comprises the following steps: firstly, constructing a regional refined sea wave coupling numerical forecasting system consisting of a data collection subsystem, a preprocessing subsystem, a data assimilation subsystem, a coupling mode calculation subsystem and a post-processing subsystem; the data collection subsystem obtains related data of atmosphere, ocean and sea waves; the preprocessing subsystem generates atmosphere, ocean and sea wave mode grid files and interpolates atmosphere, ocean and sea wave forecast fields to obtain preprocessed data, namely an atmosphere initial field, an atmosphere boundary field, an atmosphere forced field, an ocean initial field, an ocean boundary field and a sea wave boundary field. The data assimilation subsystem assimilates the preprocessed data to obtain assimilated data; and the coupling mode calculation subsystem performs integration on assimilated data to obtain forecast fields of atmosphere, ocean and sea waves. And the post-processing subsystem generates a forecast product according to each forecast field. The method is small in forecasting error and high in forecasting accuracy.

The invention relates to the technical field of virtual-solid twinning, in particular to a ship ladder cable-following coupling virtual-solid twinning model construction method under the action of sea wave interference, and the method comprises the steps: obtaining attitude images of a ship ladder at each moment when the ship ladder moves along with a cable, and obtaining stress data of each cable section on the ship ladder along with the cable at each moment; determining the bending degree of each cable section at each moment; calculating the potential bending weight of each cable section at each moment; obtaining a coupling characteristic value of each cable section at each moment; obtaining a fault risk value of the ship ladder along with the cable at each moment; constructing a fault evaluation model; and constructing a physical virtual model and a knowledge model, and constructing a virtual-solid twinborn model. The fault sensing capability of the ship ladder following cable can be improved, the fault of the ship ladder following cable can be monitored more accurately, the authenticity and reliability of a virtual and real twin model are improved, the problem that the potential fault of the following cable cannot be found under forced random disturbance is solved, and the operation safety of the ship ladder is ensured.

The invention belongs to the cross technical field of artificial intelligence and marine meteorological prediction, and discloses a sea wave probability prediction method and system, and the method comprises the steps: obtaining historical wind field data and sea wave spectrum data of a target sea area, carrying out the preprocessing and organization of the data, and constructing a training data set; constructing a hybrid expert probability model, wherein the model comprises a gating network and a plurality of expert networks; the training data set is used for training the hybrid expert probability model, the trained model receives input wind field data, and hybrid probability distribution is output through the synergistic effect of the gating network and the expert network; and sampling is carried out from the mixed probability distribution to obtain a predicted sea wave spectrum set, and sea wave risk probability prediction is realized. Complete distribution information can be obtained through one-time forward calculation, a numerical mode set is not needed, the computing power and energy consumption expenditure are remarkably reduced, and high-frequency updating and quasi-real-time business application can be conveniently achieved in resource-limited environments such as shipborne, buoys and offshore stations.

The invention relates to the technical field of time-space sequence prediction, in particular to a downscaling prediction method for a sea wave significant wave height time sequence, which can simulate a downscaling process from low-resolution sea wave data to local high-resolution sea wave data. The method comprises the following steps of 1, generating large-range low-resolution effective wave height data based on the SWAN; step 2, generating local high-resolution effective wave height data based on SWAN-ADCIRC; and step 3, performing downscaling prediction on the significant wave height based on the time sequence network model. On one hand, the problem of low resolution of numerical model prediction data can be efficiently solved, and on the other hand, the prediction precision of the significant wave height value can be improved. The method is especially suitable for scenes with higher requirements for spatial resolution and significant wave high prediction precision, and significant wave high-precision prediction is carried out on coastal sea areas with complex geometrical shapes.

The invention discloses a wind speed forecasting system based on deep learning, and belongs to the technical field of weather forecasting. The system comprises a coupled mode framework which integrates a triple nested architecture of an atmospheric mode WRF, an ocean mode ROMS and a sea wave mode SWAN, and transmits variables through a model coupling tool MCT; the deep learning roughness parameterization module is integrated in an MYNN boundary layer scheme of the atmospheric mode WRF and calculates the sea surface roughness in real time; the full-wave spectrum integration module is embedded into the ocean mode ROMS, receives wave spectrum data provided by the ocean mode SWAN and calculates a Stokes drift profile; and the intelligent correction module is used for carrying out spatial interpolation on the wind speed data output by the mode, correcting errors by combining a LightGBM model, and outputting a final forecast product. According to the method, the deep neural network is embedded into the WRF boundary layer model kernel, the sea surface roughness parameter is updated in real time, the wave evolution process is dynamically responded, and the space-time adaptive capacity of wind field forecasting under the complex sea condition is remarkably improved.

The invention discloses a surface topography modeling method and particularly provides a roughness calculation formula based on Fourier series and polynomial combination, Fourier series can effectively describe periodic roughness features of a surface, a polynomial function can describe an overall deformation trend of the surface, the advantages of the Fourier series and the polynomial function are combined, and the surface topography modeling method has the advantages that the surface topography modeling efficiency is improved. The method can accurately simulate the random roughness and overall deformation characteristics of material corrosion, terrain, sea wave plane and other phenomena, and avoids the problem that the traditional surface topography modeling method is difficult to accurately describe the randomness and nonlinear characteristics of a complex surface based on simple statistical model or experimental data fitting. According to the method, the morphological characteristics of phenomena such as material corrosion, topography and sea wave planes can be simulated more accurately, and technical support is provided for analysis and prediction in related fields.

The invention belongs to the technical field of ocean surface flow velocity remote sensing measurement, and particularly relates to a Luoga No.2 SAR (Synthetic Aperture Radar) flow velocity correction method based on Bragg orbit velocity, which comprises the following steps of: verifying the physical correctness of an orbit velocity mechanism relative to a phase velocity mechanism; under a DopRIM framework, a physically consistent sea wave error correction model is established; calibrating parameters of the sea wave error correction model; applying the calibrated sea wave error correction model to along-orbit interference SAR observation data of the Luoma No. 2 satellite, and calculating the radial surface flow velocity and sea wave error of each resolution unit; comparing an output result with a matched reference truth value to evaluate the effectiveness of the sea wave error correction model; comparing the sea wave error correction model with a KaDOP model, and respectively counting and analyzing simulated sea wave errors under different methods; the sea wave error simulation result is visually displayed, and the improvement effect is quantitatively evaluated through in-situ observation data and a KaDOP simulation result.

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 belongs to the technical field of marine environment monitoring, and discloses a multi-mode deep learning fused marine remote sensing sea wave parameter inversion method and system, and the method comprises the steps: obtaining three-degree-of-freedom motion time calendar data of a ship in a target region and spaceborne synthetic aperture radar (SAR) image data; preprocessing ship motion time calendar data and the SAR image data, and constructing time-space aligned multi-modal input data; and inputting the preprocessed multi-modal input data into a pre-trained sea wave parameter inversion model, and calculating to obtain an inversion value of the sea wave parameter. According to the method, an attention mechanism is innovatively used to realize adaptive alignment and complementation of heterogeneous information in two dimensions of space and time, and an attention module automatically allocates weights according to task correlation; through time-space collaborative feature enhancement, the model can stably extract characterization strongly related to target parameters even under complex sea conditions, so that the inversion precision and robustness of sea wave parameters are remarkably improved.

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 belongs to the technical field of marine environment prediction, and discloses a neural network-based sea wave spectrum intelligent correction method and system. The method comprises the following steps: setting a target sea area and acquiring environmental data; performing numerical calculation on the sea wave spectrum in the target sea area through a sea wave numerical mode; a sea wave spectrum correction model comprising a numerical calculation module, an actual measurement processing module and an intelligent correction module is constructed through a deep learning method; and carrying out precision verification and adaptability evaluation on the constructed sea wave spectrum correction model. According to the model, the problem of errors caused by physical process simplification in the simulation process of a traditional method is solved. Meanwhile, the problem that an existing data assimilation method is limited by observation data is solved. According to the method, the simulation precision of the sea wave mode can be effectively improved, and the method has good sea area adaptability.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

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 biodegradable plastic and a preparation method thereof, belongs to the field of bioplastic preparation, and aims to solve the problem that the existing plastic is slow in degradation rate in a marine environment. The biodegradable plastic is prepared from the following components in percentage by weight: 50-80% of a biomass-based high polymer material; 10 to 35% of a degradable toughening agent; 1-5% of a photosensitizer; 2-10% of a microbial inducer; 1-5% of a processing aid; after the biomass degradable plastic is subjected to ultraviolet radiation for 7-15 days in a marine environment, initial cracks with the density being larger than or equal to 20 pieces / cm < 2 > appear on the surface, the initial cracks are broken into degradation units with the particle size being 5-20 mm after being subjected to mechanical impact of sea waves for 30-60 days, and the biological decomposition rate of the degradation units is larger than or equal to 90% within 6-12 months under the action of marine microorganisms; and no micro-plastic particles with the particle size of less than or equal to 1mm exist in the residual product. Through the synergistic effect of the photosensitizer and the microbial inducer, the degradation period in the marine environment is shortened, the degradation efficiency is improved, and the method is remarkably superior to the prior art.

The invention belongs to the technical field of sea wave height prediction, and discloses a multi-scale sea wave height prediction method and system based on a large language model, and the method comprises the steps: S1, carrying out the decoupling interaction based on an amplitude phase, and obtaining enhanced sea wave height data; s2, extracting based on multi-scale semantic information: constructing a feature pyramid structure for the enhanced sea wave height data, outputting sea wave self-encoding features of different scales, performing self-encoding on a lexicon of a large language model, outputting word vector features, and obtaining the multi-scale semantic information by using a cross-modal attention mechanism and a dynamic fusion mechanism; and S3, a step of large-scale language model prediction based on sea wave height domain cue word driving: inputting the multi-scale semantic information and the domain specific semantic features into a large-scale language model, and outputting prediction features. According to the method, sea wave complex spatial-temporal change information is effectively mined, and accurate prediction of the sea wave height is realized.

The invention relates to an unsteady-state sea wave direction spectrum estimation method. The method comprises the steps of S1, signal decomposition; s2, direction distribution calculation; s3, adaptively adjusting the time domain analysis step length; s4, performing nonparametric kernel estimation on the transient wavelet distribution probability; and S5, direction spectrum time domain adaptive reconstruction is carried out. In the time domain analysis process of wavelet analysis, a two-dimensional plug-in non-uniform bandwidth algorithm is adopted to determine a data-adaptive two-dimensional non-uniform and non-parametric kernel function, the probability of different wavelets in a wave number-direction space is analyzed, the lateral propagation sea wave energy in the non-main wave direction is estimated, and the wave energy in the non-main wave direction is obtained. Therefore, the precision and the anti-noise capability of the direction spectrum estimation method are improved. In the time domain analysis process of wavelet analysis, a sea wave signal fluctuation intensity detection algorithm based on the frequency-direction-wave number three-dimensional change rate of wave energy distribution is introduced, the time duration of sea wave signal sections is divided according to the fluctuation intensity, the time domain precision is adjusted, the calculation efficiency is improved, and meanwhile, the sea wave signals which rapidly change in the time domain are accurately captured. Measurement is avoided.

The invention discloses a sea surface channel prediction method based on equipment pitching motion characteristics, which comprises the following steps of: in a modeling stage, setting coordinates according to a general application scene, simulating heaving and pitching motion of a transceiver according to system parameters and sea surface environment parameters, establishing SW-GBSM according to a motion track to model a channel, and obtaining channel impulse response; calculating the antenna position offset of the transceiver at the same time according to the motion trail, and obtaining the sea wave characteristics of the transceiver; and in the prediction stage, frequency domain conversion is performed on the channel impulse response of the time domain, and the sea wave characteristics SWF-TA of the transmitter and the receiver and the frequency domain channel response are spliced and then input into the SWF-GRU channel prediction network. In the SWF-GRU, the cascaded GRU updates the hidden state of itself according to an input sequence, and then the full connection layer maps the final hidden state of the GRU into a channel prediction result. According to the method, the channel modeling effect of a real sea surface environment is met, and the sea surface channel prediction performance is greatly improved on the premise of increasing relatively low calculation complexity.

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 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 relates to the technical field of ocean engineering, and discloses a method for training a sea wave significant wave height prediction model and predicting the sea wave significant wave height, and the method for training the sea wave significant wave height prediction model comprises the following steps: according to the correlation between historical meteorological characteristic time series data and historical sea wave height time series data, calculating the sea wave significant wave height prediction model; selecting a target meteorological feature from the plurality of meteorological features; based on the decomposition number and penalty factor of the variational mode decomposition, optimizing to obtain a target decomposition number and a target penalty factor, and performing variational mode decomposition on the historical sea wave height time sequence data to obtain an intrinsic mode sub-sequence; the sea wave significant wave height prediction model is trained according to the target historical meteorological characteristic time series data and the intrinsic mode subsequences, the correlation between the meteorological data and the historical sea wave height data is analyzed, and the variational mode decomposition parameters are optimized, so that the input accuracy of the sea wave significant wave height prediction model is improved, and the prediction accuracy of the sea wave significant wave height prediction model is improved. Therefore, the precision of the sea wave significant wave height prediction model is improved.

The invention provides a method, a medium and a system for calculating ocean wave spectrum parameters of ocean digital twinning, and belongs to the technical field of electrical digital data processing. The method comprises the following steps: acquiring and preprocessing original sea wave spectrum data, selecting a Jonswap frequency sea wave spectrum as a theoretical model, establishing a multi-index fitting criterion, optimizing and solving a peak rise factor parameter, performing statistical analysis according to a significant wave high period level, and distinguishing sea wave spectrum characteristics of typhoon and non-typhoon working conditions. And calculating the wave amplitude wave number and the random phase to construct a wave surface height model, and finally forming a sea wave spectrum parameter database. The technical problem that sea wave spectrum parameters in the ocean digital twinning system are difficult to accurately describe and calculate is solved, the extreme sea condition simulation capability is particularly optimized, and high-precision sea wave field simulation technical support is provided for the ocean digital twinning system.

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 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 marine oil spill path planning system and method based on multi-source data fusion, and belongs to the technical field of marine environment protection. According to the marine oil spill path planning method, real-time image data of an oil spill area is obtained through a multispectral camera carried by an unmanned aerial vehicle, oil spill thickness, flow velocity and oil contamination component data are collected through multiple sensors carried by a ship, meteorological data such as wind speed, wind direction and sea wave height are obtained, multi-source data are input into a reinforcement learning model, and a marine oil spill path is planned. And data fusion is carried out through dynamic weight distribution, a global optimal path is generated based on fused data, and ship-unmanned aerial vehicle cooperative operation is realized to execute an oil spill recovery task. The system comprises an unmanned aerial vehicle module, a ship module and a ground control module. Experiments show that compared with a ship oil cleaning method which does not adopt multi-source data fusion, the system and the method have the advantages that the path planning efficiency can be improved by 25%, the oil spill diffusion area can be reduced by more than 20%, the recovery efficiency can be improved by 30%, and the response time is less than or equal to 10 minutes.

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 wave power generation device for an island, and relates to the technical field of power generation for the island, the wave power generation device comprises a floating body and a fixed support, the floating body floats along with sea waves, an energy generation mechanism and an energy conversion mechanism are arranged on the inner side of the fixed support, the fixed support does not float along with the sea waves, and the energy generation mechanism is arranged on the top surface of the floating body; the fixed support controls the buoyancy difference value of the floating body based on the moving state of the energy generation mechanism, the energy conversion mechanism is connected with a generator through a transmission mechanism and drives the generator through the transmission mechanism, the generator is connected with a frequency-stabilized voltage-stabilized power supply through a wire, and the frequency-stabilized voltage-stabilized power supply outputs stabilized voltage electric energy. The wave power generation device has the advantages of being high in wave energy conversion efficiency, simple and compact in structure and reliable in operation.