2022 results about "Sea waves" patented technology

The invention provides an ocean wave disaster defensive area demarcation optimization method based on ship AIS data, and belongs to the technical field of natural disaster prevention and control, and the ocean wave disaster defensive area demarcation optimization method based on ship AIS data comprises the steps: dividing a target sea area into regular grids, and building coordinate mapping; cleaning the data, removing abnormal values and segmenting the data; a distributed track segment pre-clustering method is adopted, and clustering is carried out through dimension reduction, feature extraction and an Euclidean distance algorithm; establishing a multi-dimensional feature tensor according to ship length classification; constructing a composite sea wave disaster danger index model, and comprehensively evaluating the risk; carrying out statistics on ship track space-time characteristics in each grid, and constructing a passing frequency tensor; fusing the trajectory frequency and the disaster risk model, and constructing a dynamic risk assessment hypergraph; based on multi-criterion threshold clustering, defensive areas are identified, boundaries are optimized, and key defensive areas are delimited; according to the invention, sea area risk conditions can be provided for marine mobile disaster-bearing bodies such as sailing ships and the like.

The invention discloses a coastal port pile foundation structure load calculation method based on a coupling mode, and the method comprises the steps: a hydrodynamic module which evaluates the load and mechanical properties of a port pile foundation structure under different environment conditions; the water power module at least comprises an atmospheric mode module, a sea wave mode module, an ocean mode module and a coupler module; wherein the coupler module exchanges and synchronizes data among the atmosphere mode module, the sea wave mode module and the ocean mode module; the data conversion module is used for converting the wind field data, the wave field data and the flow field data obtained by the hydrodynamic force module into aerodynamic force and hydrodynamic force input files of the pile foundation structure; the finite element analysis module is used for receiving the aerodynamic force and hydrodynamic force input files, establishing a port pile foundation structure model and calculating stress distribution and displacement response of a pile foundation structure, and simulation results output by the finite element analysis module include but not limited to a stress nephogram, displacement field distribution and a buckling phenomenon of a pile foundation.

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 a power generation performance evaluation method for an offshore floating photovoltaic power generation system, and the method comprises the steps: considering the influence of offshore wind wave fluctuation, researching the inclination angle change characteristics of a photovoltaic module installed on a floating body structure under different sea conditions, and building a single-sided and double-sided photovoltaic module dynamic and static irradiance model considering the influence of sea wave motion; the invention provides a method for generating a scene with uniform and non-uniform irradiance distribution of an offshore floating type photovoltaic array. The irradiance distribution model and the electrical model of the offshore floating photovoltaic array are combined to form a method for simulating the maximum output power Pmax change characteristic of the offshore floating photovoltaic array under the influence of sea wave operation; and evaluating the annual energy output in the target sea area by combining the wind wave data and irradiance distribution in the target sea area and the maximum output power Pmax change characteristics of the floating photovoltaic array. Compared with an existing photovoltaic power generation performance evaluation method, the influence of sea wave fluctuation on the power generation characteristics of the photovoltaic array is considered, and the method is suitable for an offshore floating type photovoltaic power generation scene.

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 provides a sea wave height detection method, medium and system based on semantic segmentation, and belongs to the technical field of sea wave height detection.The sea wave height detection method comprises the steps that a near-shore camera obtains multiple pieces of sea wave image data, and a training data set is formed after preprocessing; a GS-SegFormer semantic segmentation model is utilized, and the model adopts an improved self-attention mechanism, a Ghost module and a SimAM attention mechanism to segment a sea wave image to be detected to obtain a pixel level boundary of sea waves. Gaussian filtering smoothing processing is carried out on the segmented image, noise is reduced, pixel information is converted into an actual distance according to camera parameters, and finally the actual height of each sea wave pixel is calculated. According to the method, sea wave features are identified through a deep learning model, and the actual height is calculated by combining camera parameters. The invention solves the technical problem that the precision of subsequent height calculation is influenced because the real contour of the sea wave cannot be accurately segmented by the existing sea wave height detection method.

Disclosed is an autonomous vessel comprising a hydrodynamic pump that converts the energy of ocean waves into electrical power when the vessel is floating adjacent to an upper surface of an ocean, and utilizes a portion of the generated electrical power produced to charge an electrical energy storage device. The vessel may submerge itself, and then propel itself beneath the water's surface, after which it may return to the surface and resume its production of electrical energy and recharge its electrical energy storage device.

The invention provides a synthetic aperture radar sea surface flow velocity inversion method and device based on machine learning. The method comprises the following steps: acquiring an original synthetic aperture radar image, and OCN data, mixed coordinate ocean model data and NDBC buoy data corresponding to the original synthetic aperture radar image; calculating a sea wave spectrum of the synthetic aperture radar image by using a PFSM algorithm, and exploring a relationship between the flow velocity and synthetic aperture radar image parameters; a synthetic aperture radar sea surface flow velocity inversion algorithm based on machine learning is constructed; and verifying the accuracy of the inversion algorithm. The invention provides an effective method for SAR sea surface flow velocity inversion.

The invention provides a sea wave magnetic interference suppression method and device for ocean electromagnetic detection, and relates to the technical field of electromagnetic signal processing, and the method comprises the steps: synchronously collecting original electromagnetic signals through a plurality of annularly arranged magnetic sensors, generating interference parameters through employing the calculated interference signal amplitude and phase information, and carrying out the subsequent interference suppression processing. The method comprises the following steps of: calculating a covariance matrix, performing principal component analysis, extracting spatial characteristics of low-frequency magnetic interference, projecting a signal to an orthogonal subspace outside an interference subspace, removing a low-frequency magnetic interference component, retaining a target signal, performing Fourier transform to convert the signal to a frequency domain, analyzing a frequency spectrum by using a band elimination filter, and setting an interference threshold value. According to the identified interference frequency, an interference suppression signal is generated, the interference suppression signal is returned to the time domain through inverse Fourier transform and is superposed with the target signal to generate a standard electromagnetic signal, the magnetic interference in the ocean detection area can be effectively suppressed, and accurate acquisition and analysis of the standard electromagnetic signal are ensured.

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 belongs to the technical field of model fusion in a marine environment, and provides a reinforcement learning-based sea wave multi-model fusion method, which comprises the following steps of: S1, defining a state, an action and a reward function of a reinforcement learning model; s2, designing and initializing a reinforcement learning agent; s3, collecting data by using the experience playback buffer area; s4, sampling data from the experience playback buffer area to update the network; and S5, dynamically adjusting the fusion weight of the model to realize adaptive weighting. Based on a reinforcement learning algorithm, the weighting coefficient of each basic model is dynamically adjusted through interaction and feedback with the environment, so that the optimal model fusion effect is realized in different marine environments. According to the method, reasonable states, actions and reward functions are defined, so that the model can adaptively learn how to optimally combine with the output of the basic model, and relatively high prediction precision and reliability are always kept in a complex and dynamic marine environment.

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 provides a stabilization control method and system for a fuel filling ship at a zero navigational speed. The method provided by the invention comprises the following steps: acquiring rolling data and wind wave data of a refueling ship under a target sea condition in a fuel refueling process in real time; the rolling data and the wind wave data are processed based on a radial basis function neural network, and the precession angular velocity of the stabilization gyro device under the target sea condition is obtained through calculation; the stabilization gyro device is controlled to rotate at the precession angular speed, the stabilization gyro device generates a gyroscopic effect and generates rolling moment, the rolling moment is converted into stabilizing moment for resisting rolling through precession motion, and the rolling angle of the filling ship is reduced; and the stabilizing moment is opposite to the vibration direction of the rolling. According to the method and system, rolling control of the filling ship at the low speed or the zero navigational speed is achieved, the stabilization torque can be adjusted in real time according to sea wave changes, and the stability and safety of the filling ship in the fuel filling operation process are improved.

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 discloses an electromagnetic field observation data-based sea wave characteristic analysis method, which comprises the following steps of: processing electromagnetic field data by adopting a time-frequency spectrum analysis method, identifying distribution characteristics of electromagnetic field signals caused by sea waves at different time and frequencies through time-frequency decomposition, and extracting periods and time-varying characteristics of the sea waves; preprocessing the electromagnetic field data by using dominant frequency analysis and band-pass filtering technologies to obtain sea wave induced magnetic field time domain data; time domain and frequency domain induction electromagnetic field polarization direction analysis technologies are combined, and the movement direction of sea waves and the change characteristics of the sea waves along with time are inferred by measuring the change of the electromagnetic field polarization direction; the quantitative relation between the sea wave induction electromagnetic field intensity and the sea wave height is established, and the sea wave height change of the place is effectively estimated through the sea wave induction electromagnetic field intensity. By comprehensively analyzing the electromagnetic field data, the sea wave period, the time-varying characteristics, the motion direction and the sea wave height are evaluated, and a new way is provided for marine environment monitoring.

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 belongs to the technical field of sea wave height prediction, and discloses a sea wave height prediction method and system based on a large language model and a medium, and the method comprises the steps: S1, geographic position coding: carrying out geographic position coding on sea wave height data, and generating sea wave height data fused with geographic position information; s2, a step of ocean wave height semantic feature coding based on an attention mechanism: carrying out self-coding on the ocean wave height data output in the step S1, outputting ocean wave self-coding features, carrying out self-coding on ocean wave physical field features, outputting word vector features, and realizing interaction between the ocean wave self-coding features and the word vector features by utilizing the cross-modal attention mechanism; outputting domain-specific semantic features; and S3, a step of large-scale language model prediction based on sea wave height domain cue word driving: inputting 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 discloses a distributed sound wave sensing monitoring device and method for sea wave impact force. The device comprises a vibration sensing optical fiber, an anchoring unit, a distributed optical fiber sound wave sensing demodulator, a vibration signal processing unit and a sea wave impact force inversion unit. The method comprises the following steps: fixing the vibration sensing optical fiber on a coast impacted by sea waves through the anchoring unit; the distributed optical fiber sound wave sensing demodulator transmits a monitored sea wave vibration signal to the vibration signal processing unit for preprocessing; the vibration signal processing unit transmits the processed optical fiber vibration amplitude signal to the sea wave impact force inversion unit; obtaining an optical fiber vibration amplitude signal and sea wave impact force training data set through a calibration experiment, and constructing an attention convolutional neural network model for training; and the sea wave impact force inversion unit establishes a corresponding relation between the optical fiber vibration amplitude signal and the sea wave impact force by using an attention convolutional neural network model to obtain the real impact force of the coast sea wave. According to the invention, accurate monitoring of the coastal wave impact force is realized.

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.

A system and method by which energy from ocean waves is converted into hydrogen, and that hydrogen is used to manifest electrical and mechanical energies by an energy consuming device. A portion of the generated electrical power is communicated to water electrolyzers which produce oxygen and hydrogen from water as gases. At least a portion of the generated hydrogen gas is transferred to a transportation ship via a hose-carrying, remotely operated (or otherwise unmanned) vehicle, and subsequently transferred to an energy-consuming module or infrastructure, where a portion of the hydrogen is consumed in order to manifest a generation of electrical energy, a mechanical motion, and / or a chemical reaction.

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.