958 results about "Sea waves" patented technology

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 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 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 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 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 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 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.

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

The invention discloses a 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 floatable instantaneous generator comprises an outer layer in the form of a hemispherical floatable supporting bowl and an inner layer in the form of a liquid-solid triboelectric nano generator (ls TENG); the inner layer includes a fluorinated ethylene propylene (FEP) layer on an inner side of the bowl, a ring electrode on an upper surface of the FEP layer around a periphery thereof, a circular electrode on a bottom surface at a central portion of the FEP layer, and a droplet slidable on the upper surface of the FEP layer. When subjected to open sea waves, the bowl floats and tilts the ls TENG so as to slide droplets on the upper surface of the FEP, thereby creating and disconnecting contact between the ring electrode and the circular electrode and providing power to an electrical circuit connected between the ring electrode and the circular electrode.

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 discloses a port wave spectrum parametric modeling algorithm based on machine learning, and relates to the technical field of ocean engineering, and high-precision modeling is realized through three steps: step one, constructing a federated learning framework, training sub-models by data parties only sharing parameters, combining homomorphic encryption fusion features and dynamically adjusting weights, and constructing a model; privacy protection type data fusion is realized; a second step of deploying a generative adversarial network at each local node, extracting multi-scale wave features through a frequency domain hierarchical attention branch network, implanting a sea wave energy conservation constraint and optimizing a generator, generating an extreme sample conforming to a physical law, and forming a mixed training data set; and a third step of training a CNN-LSTM basic model, and realizing dynamic optimization of the model through a sliding window, a forgetting factor, Bayesian optimization and performance monitoring reconstruction. The method effectively improves the wave spectrum prediction precision and long-term stability, reduces the maintenance cost, and can be used for scenes such as port ship berthing and disaster prevention early warning.

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 provides a sea wave height prediction method and system based on hybrid quadratic decomposition, and relates to the technical field of sea wave height prediction, and the method comprises the steps: obtaining to-be-predicted original wave height data; performing primary decomposition on the original wave height data to obtain an intrinsic mode function and a residual term; calculating a sample entropy of the intrinsic mode function; according to the size of the sample entropy, classifying the intrinsic mode function to obtain a reconstructed mode component; performing secondary decomposition on the high-frequency components through a VMD algorithm to generate sub-sequence components; establishing a periodic sensing time deep learning model; and inputting the subsequence component, the intermediate-frequency component and the low-frequency component into a periodic sensing time deep learning model, and outputting a sea wave height prediction result.

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 the technical field of marine planting, and discloses a seaweed planting device for marine ecological restoration, which comprises a frame, a buoyancy device is arranged above the frame, an attachment plate is arranged in the frame through an adjusting mechanism, and the adjusting mechanism is used for adjusting the angle of the attachment plate. A distance measuring mechanism is arranged on the front side wall of the attachment plate, an anchoring device is arranged at the bottom end of the frame, the adjusting mechanism comprises a first pull rod connected into the frame in a sliding mode, a fluted disc is fixedly connected to the end, close to the attachment plate, of the first pull rod, and the fluted disc is connected into the frame in a sliding mode. Through the structural arrangement of the adjusting mechanism, the problems that the spatial position cannot be dynamically adjusted according to the illumination condition, the illumination of algae on the lower layer of the base plate is insufficient, the growth of the algae is influenced, meanwhile, the wave impact resistance of the attachment plate is poor, the disturbance of sea waves and strong water flow is difficult to resist, and algae seedlings are difficult to stably attach are solved.