39 results about "Ocean wave height" patented technology

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 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 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 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 embodiment of the invention provides a sea wave height determination method and device, a storage medium and electronic equipment, and the method comprises the steps: segmenting target point cloud data into a plurality of time sequence frames, and determining a wave crest region and a wave trough region of sea waves according to a local extreme point in the target point cloud data, each time sequence frame is used for indicating the sea surface shape at each moment; according to the wave crest area, the wave trough area and the multiple time sequence frames, a target sea wave contour model is fitted, and the target sea wave contour model is used for indicating the sea wave height and the three-dimensional shape of the sea wave at each time point; and determining the sea wave height at any time point based on the target sea wave contour model. According to the method, the problem that high-precision sea wave height data cannot be provided under complex sea conditions such as high storm waves and changeable tides in the prior art can be solved.

The invention discloses a sea wave height inversion algorithm based on C-band SAR, and relates to the field of sea wave height inversion, and the algorithm comprises the steps: selecting mode data in offshore satellite data; eRA5 wind field data and wave height data are selected, mode data are processed after time matching, SAR image sub-images are selected, and then space matching is carried out on the SAR image sub-images and the ERA5 wind field data and the wave height data; obtaining training data by using the SAR image sub-image after space matching, the SAR data auxiliary information file, the ERA5 wind field data and the wave height data to construct a training data set; and constructing an inversion model, inputting the training data into the inversion model, and finally obtaining the SAR sea wave height. Ocean wave height inversion is carried out by adopting complete polarization data, the precision is high, the application threshold is high, and the application range is small; meanwhile, the problems of inversion precision and applicability are comprehensively considered, more SAR information is used for sea wave inversion, and meanwhile the application range of an inversion model is guaranteed.

The embodiment of the invention provides a sea wave height prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: fitting an autoregressive integrated moving average (ARIMA) model according to first height observation values of sea waves of a target region at a plurality of first time points of a first time period, predicting first height prediction values of the sea waves at a plurality of second time points in a second time period according to the ARIMA model; under the condition of determining that a nonlinear relationship exists in residual distribution corresponding to the ARIMA model, training a random forest model according to a residual corresponding to the ARIMA model and an environment variable of the target area in the first time period, and predicting second height prediction values of sea waves at a plurality of second time points in the second time period according to the trained random forest model; and according to the first height predicted value and the second height predicted value, determining a target height predicted value of the sea wave at a plurality of second time points of the second time period.

This invention discloses a method for predicting the wave height of irregular ocean waves based on numerical water tank simulation and a deep learning model. The method includes using a numerical water tank simulation combined with extreme weather conditions to generate a relatively complete dataset of irregular ocean waves; and constructing a deep learning model, TA-DCLSTM, for end-to-end training and prediction, effectively improving the prediction accuracy of wave height. This invention employs numerical water tank simulation to accurately reproduce wave dynamics, while incorporating extreme weather conditions to generate an irregular wave dataset. After preprocessing, the dataset is input into a deep learning model, proposing a time-series prediction model, TA-DCLSTM, based on a time attention mechanism and dilated convolution. Dilated convolution expands the receptive field, capturing multi-scale temporal features in the wave data, while the time attention mechanism assigns different weights to each time step, focusing on key features to achieve accurate prediction of the wave height of irregular ocean waves.

The present application relates to the technical field of ocean engineering, in particular to a nearshore sea wave prediction correction method based on physical factor coupling ChebNet, comprising the following steps: S1, constructing a dynamic graph model based on nearshore sea area sensing data, taking physical factors such as sea wave height, wind speed, wind direction and terrain elevation as graph nodes, using graph convolution to extract the space-time dependence between nodes, and generating a preliminary sea wave prediction; S2, introducing a space-time adaptive mechanism to dynamically adjust the coupling strength of physical factors in the graph and optimize the connection relationship between nodes; S3, using multi-order ChebNet to model the space-time variation of physical factors, iteratively correcting the prediction results, and outputting the final optimized sea wave prediction; the present application realizes accurate prediction, dynamic correction and strong robustness correction of nearshore sea waves by constructing a physical factor driven dynamic graph structure, introducing a space-time adaptive coupling mechanism and multi-order ChebNet high-order modeling, and improves the prediction accuracy, adaptability and space-time consistency.

The invention discloses a numerical weather forecast cold start error suppression method and device, and relates to the technical field of offshore wind power, and the suppression method comprises the steps: S1, collecting initial data through a marine meteorological sensor array, including sea wave height, period, wave direction and corresponding wind field initial observation data, and constructing a multi-dimensional data set after preprocessing; s2, on the basis of the data set and historical sea wave-wind field time series data, a physical mechanism and data driving combination method is adopted to construct a sea wave-wind field coupling model, and the nonlinear coupling relation between sea waves and the wind speed and the wind direction is quantified. According to the method, a physical mechanism and data driving fusion modeling is adopted, so that the meteorological rule conformity is guaranteed, the nonlinear coupling relationship is accurately captured, the limitation of a pure physical and pure data driving model is avoided, and the prediction core support is tamped; based on neural network mining time sequence and mode characteristics, a lattice point relaxation algorithm parameter strategy is dynamically adjusted, and the problem that traditional fixed parameters are poor in adaptability is solved.

The invention discloses a sea wave prediction method based on weather forecast field data fusion and a space-time attention mechanism, and mainly relates to the technical field of sea wave prediction. The method is used for solving the problems that meteorological element information generated by driving sea waves cannot be effectively fused, a multi-scale spatial-temporal feature interaction mechanism is lacked, and a physical driving mechanism of wind wave coupling is ignored in an existing scheme. Comprising the following steps: performing space attention calculation on a historical sea wave data patch sequence to obtain a hidden state sequence; inputting the hidden state sequence into the LSTM network to obtain a hidden state at the current moment; taking a historical weather forecast data patch sequence as Query, taking the hidden state at the current moment as Key and Value, and performing cross attention calculation; recursively generating a hidden state of a preset future moment in combination with cross attention output and a time attention mechanism; and performing linear layer and reconstruction operation on the hidden state at the preset future moment to obtain final sea wave height prediction.

The invention provides a sea wave observation method based on a binocular camera, and belongs to the technical field of image processing, and the method comprises the steps: S1, enabling the binocular camera to shoot a sea wave image, and generating sea wave point cloud data at a current moment t; s2, converting the sea wave point cloud data in the S1 into sea wave point cloud data in a geodetic coordinate system; s3, performing quality screening and optimization on the sea wave data in the S2; the method comprises the steps of selecting a region of interest, performing quality screening, converting noise data points into null values, performing null value filling and performing Gaussian filtering. The quality of the sea wave data is divided into excellent, good and poor through quality screening; s4, calculating the wave height, wavelength and period of the sea wave at the moment based on the sea wave point cloud data with high quality and good quality; and S5, repeating the steps S1 to S4 to obtain multi-frame sea wave point cloud data at different moments, and calculating effective sea wave height, wavelength, period and sea wave spectrum. According to the method, the null value can be filled, so that the overall data is more reasonable, and the obtained elevation data is more accurate.

The invention belongs to the technical field of downscaling, and relates to an effective sea wave height downscaling method based on a generative adversarial network, which comprises the following steps: acquiring input data, and preprocessing the input data, the input data comprising sea wave height data and wind speed data; introducing an attention coordinating mechanism, and capturing characteristics of wind speed data in longitude and latitude directions; a multi-resolution feature fusion generator is constructed, and sea wave height data is up-sampled to a high-resolution grid; a spectral normalization Markov discriminator SN-PatchGAN is adopted, and local features of the image are captured; constructing a loss function according to the training target of the discriminator; and carrying out training and parameter updating on the model by utilizing an optimizer, and carrying out iterative training on the model in combination with an early stop strategy to generate high-resolution sea wave height data. According to the method, more real and higher-quality high-resolution sea wave height data can be generated, and the method is suitable for the fields of marine weather forecast, climate research and the like.

The invention discloses a sea wave height prediction method and device, a storage medium and an electronic device, and relates to the field of ocean engineering.The sea wave height prediction method comprises the steps that real-time wave height data of sea waves in a target area and environmental data of the target area are obtained, and the real-time wave height data are used for indicating the sea wave heights of the sea waves at different moments; processing the real-time wave height data through a short-term prediction model to obtain a first prediction result of the height of the sea wave in the first time period, and determining residual distribution corresponding to the first prediction result, the residual distribution being used for indicating a difference value between the first prediction result and an actual observation result of the sea wave in the first time period; the environmental data and the residual error distribution are processed through a long-term prediction model, a second prediction result of the sea wave height in a second time period is obtained, and the second time period comprises the first time period; and determining a final prediction result of the sea wave height according to the first prediction result and the second prediction result.

The present application relates to the technical field of sea wave height prediction, and discloses a multiscale physical enhancement type irregular sea wave prediction method, comprising the following steps: (1) collecting marine wave historical data, preprocessing the data, and dividing the data into a training set, a verification set and a test set; (2) decomposing the wave data into three components of high, medium and low frequencies through wavelet decomposition; (3) constructing a WaveFormer model; (4) training the WaveFormer model; and (5) inputting the test set into the trained WaveFormer model to obtain a wave height prediction value. The present application decomposes the original wave sequence into three components of high, medium and low frequencies through two-layer stationary wavelet transformation, explicitly allows the model to capture the multiscale characteristics of irregular waves, ensures the rationality of wave prediction, realizes sequence-level prediction on the premise of ensuring prediction accuracy, improves the prediction efficiency, and constructs a data-driven item based on the wave prediction result, so that the model prediction accuracy is further improved.

The present invention belongs to the technical field of ocean wave height prediction and discloses a multi-scale ocean wave height prediction method and system based on a large language model. The method comprises: S1, based on amplitude-phase decoupling interaction, obtaining enhanced ocean wave height data; S2, based on multi-scale semantic information extraction, constructing a feature pyramid structure on the enhanced ocean wave height data to output ocean wave autoencoding features of different scales, performing autoencoding on the vocabulary of the large language model, outputting word vector features, and utilizing a cross-modal attention mechanism and a dynamic fusion mechanism to obtain multi-scale semantic information; and S3, based on ocean wave height domain prompt word-driven large language model prediction, inputting multi-scale semantic information and domain-specific semantic features into the large language model, and outputting prediction features. The present invention effectively mines complex temporal and spatial variation information of ocean waves, achieving accurate ocean wave height prediction.

The invention provides a sea wave height prediction method and device based on a physical guidance dynamic graph Mama network, belongs to the technical field of sea wave height prediction, and adopts a selective scanning mechanism of a state space model to reconstruct a calculation normal form of time sequence modeling. Therefore, the state space model can adaptively determine which historical information is reserved and which noise is discarded according to the current marine environment characteristics, and time sequence modeling with linear calculation complexity is realized. The discretization parameter delta value is increased under the rapidly changing weather condition, and the model pays more attention to recent information; under a stable condition, the delta value is reduced, and the model retains more historical information, so that the purposes of ensuring the calculation efficiency and improving the prediction precision are achieved, the technical problem that the long sequence modeling efficiency and precision are difficult to consider at the same time is solved, the technical inertia that serial calculation or secondary complexity calculation must be used in the traditional technology is broken through, and the prediction precision is improved. A physical perception graph learner is adopted as a solution mechanism, so that a spatial relationship modeling method is reconstructed.

The invention relates to the technical field of offshore wind power jackets, in particular to a multi-buoy anti-tilting offshore wind power jacket structure which comprises a suction barrel, reinforced chord members fixedly connected to the top of the suction barrel and reinforced supporting rods fixedly connected between every two adjacent reinforced chord members. The outer sides of the multiple sets of reinforced chord members are provided with buoy anti-tilting mechanisms used for conducting multi-direction self-adaption sea wave impact resistance on the jacket, and reinforcing mechanisms used for strengthening stable installation of the suction barrels are arranged in the suction barrels. By means of the buoy anti-tilting mechanism, anti-tilting type descending can be achieved when the jacket is installed, and it is guaranteed that the jacket is accurately and vertically installed on an uneven seabed; the height of sea waves coming from all directions can be reduced in a self-adaptive mode, and the impact force of the sea waves on the jacket is obviously reduced; and a reinforcing mechanism is combined to conveniently and stably mount the suction barrel and the seabed, so that the jacket can be prevented from inclining, and the safety and stability of the jacket under complex sea conditions are improved.

The invention discloses a sea wave height determination method and device, a storage medium and an electronic device.The method comprises the steps that sea wave video data collected by image collection equipment deployed at a preset position in real time are obtained, the original video frame rate of the sea wave video data is a preset value, and the original video frame rate of the sea wave video data is the preset value; the preset position comprises at least one of an offshore wind power tower, an ocean platform and a ship; inputting the sea wave video data and a first spectrogram corresponding to each frame of image in the sea wave video data into a trained first convolutional neural network to obtain an output result; and performing moving average processing on the continuous output results to determine the sea wave height corresponding to the sea wave video data. The problems of high cost and limited coverage range of traditional sea wave height monitoring methods such as buoys and radars are solved.

The invention belongs to the technical field of sea wave height prediction, and relates to a sea wave height multi-site joint prediction method based on a deep learning model, which comprises the following steps: acquiring sea wave height observation data of a plurality of sites in the same sea area, and decomposing the data of each site through a seasonal trend decomposition method; constructing a joint model of a fusion graph convolutional neural network and a long short-term memory network for the trend component, and constructing a long short-term memory network model for the season component and the residual component respectively; the extracted spatial-temporal features are transformed through a full connection layer, and prediction sequences of trend components, seasonal components and residual components of multiple time steps in the future are output respectively; and according to the prediction sequences of the trend component, the seasonal component and the residual component in each station, adding to obtain a prediction result of the multi-station sea wave height. According to the method, spatial structure information between observation stations and time evolution characteristics of each station are fully fused, and the accuracy and stability of multi-station sea wave height joint prediction are effectively improved.

This application discloses a dynamic adaptive amphibious vehicle path and energy consumption optimization method, relating to the field of path and energy consumption optimization. The method includes: acquiring information data; the information data includes: environmental data and amphibious vehicle-related data; the environmental data includes: wind speed, wind direction, ocean current speed, ocean current direction, wave height, and wave frequency; the amphibious vehicle-related data includes: position, speed, and acceleration; the information data contains timestamps; inputting the information data into a path and energy consumption optimization model to obtain optimized planned path information; the optimized planned path information is the lowest energy-consuming planned path determined based on a heuristic function; the path and energy consumption optimization model employs reinforcement learning algorithms and A / B algorithms. * The algorithm is derived from an amphibious vehicle surface energy consumption model and a heuristic function, after updates and optimizations. This application enables path and energy consumption optimization, improving the amphibious vehicle's endurance and mission completion efficiency.

The application provides a kind of ocean dynamic target detection method based on AI and radar signal fusion, comprising: obtaining real-time sea wave height data and target echo sequence, carrying out low wave, middle wave, high wave three-level classification processing to sea wave height, and carrying out echo interruption prediction in combination with the relationship between signal attenuation gradient and noise peak value;According to the stable information of large ship echo, the cluster moving track features of ship group are extracted, the echo mode difference degree of different target types is identified, and the echo mode cluster of multi-target classification is obtained;From the echo mode cluster of multi-target classification, the sea wave shielding response features are extracted, the echo sequence dominated by intermittent echo is identified, and the target type distinguishing features under severe sea conditions are extracted;According to the target type distinguishing features, the mode difference degree of continuous echo and intermittent echo is evaluated, and the sea multi-type target classification result is obtained.

The invention provides an ocean dynamic target detection method based on AI and radar signal fusion, and the method comprises the steps: obtaining real-time sea wave height data and a target echo sequence, carrying out the low-wave, medium-wave and high-wave three-stage classification processing of the sea wave height, and carrying out the echo interruption prediction through combining the relation between a signal attenuation gradient and a noise peak value; extracting cluster movement track characteristics of a ship group according to stable information of large ship echoes, identifying echo mode difference degrees of different target types, and obtaining echo mode clusters of multi-target classification; sea wave shielding response features are extracted from the multi-target classified echo mode cluster, an intermittent echo dominant echo sequence is identified, and target type distinguishing features under the severe sea condition are extracted; and according to the target type distinguishing features, evaluating the mode difference degree of continuous echoes and intermittent echoes to obtain a marine multi-type target classification result.

The invention discloses a wind power and wave energy combined power generation adaptation method based on marine environment changes. Wind speed, wind direction, sea wave height and sea wave period sensors are deployed at selected positions in a sea area to collect environmental parameters, denoising is performed through a data processing and analysis module, and environmental state categories are divided. The power generation adaptation strategy module calculates the optimal power distribution proportion accordingly, and the regulation and control execution module adjusts parameters such as the blade angle, the rotating speed and the power output of the wind driven generator and the working mode of the wave energy power generation equipment according to the optimal power distribution proportion. The method can adapt to ocean environment changes in real time, improve the efficiency of the combined power generation system, enhance the power generation stability and prolong the service life of equipment, and is of great significance in promoting ocean energy development and utilization and relieving energy shortage.

The invention discloses a sea surface small target tracking method fused with sea wave dynamic prediction. The sea surface small target tracking method comprises the following steps: selecting an Elfouhail sea wave spectrum model to construct a motion model of a sea surface small target; parameters of the motion model are set, and sea surface small target track data sets of the motion model in different sea conditions are obtained through simulation; preprocessing the data; inputting the preprocessed data into the constructed DA-RNN time sequence prediction network, and predicting the sea wave height at the next moment; and inputting the predicted sea wave height into an EKF filtering algorithm module to estimate the target position. The sea wave height is predicted by using a deep learning network, and the predicted sea wave height is input into a traditional filtering algorithm, so that sea surface small target tracking fused with sea wave dynamic prediction is realized.

The invention discloses a sea wave height prediction method and device, a storage medium and a computer program product, and relates to the field of ocean engineering, and the sea wave height prediction method comprises the steps: carrying out the feature extraction of a first sea wave image in a target time period through an image processing technology, and obtaining a first sea wave feature, the target time period is a period of time before the current time; performing waveform analysis on the first sea wave feature through Fourier transform, and determining a second sea wave feature of the first sea wave feature in a frequency domain; the second sea wave features are input into a prediction model to be processed so as to predict the sea wave height at target time, and the target time is time after the current time. By adopting the technical scheme, the problem of low prediction precision easily caused by a traditional sea wave height monitoring method in the related technology is solved.

The application discloses a kind of adaptive floating breakwater suitable for complex sea conditions;Belong to the technical field of ocean engineering facilities, including wave system, floating capsule, anchoring system, control center, draft adjusting mechanism and floating body posture adjusting mechanism;The top of the wave system is fixedly installed with wind direction sensor, sea wave height sensor and control center, and the side of the wave system is fixedly installed with flow rate sensor.The adaptive floating breakwater suitable for complex sea conditions, by integrating multiple sensors, such as radar type sea wave height sensor, wind cup type wind direction sensor and electromagnetic type flow rate sensor, can accurately obtain key sea condition information such as sea wave height, wind direction and water flow speed, and accurately calculate with the help of a preset formula.The draft adjusting mechanism and the floating body posture adjusting mechanism can quickly and accurately adjust the draft and the floating body posture of the breakwater according to the sensor data and the control center instructions.

The invention discloses a sea wave height prediction method and device, a storage medium and an electronic device.The method comprises the steps that a real-time sea wave video collected through a high-resolution camera is obtained, and the deployment position of the high-resolution camera at least comprises an offshore wind power tower; extracting a feature vector of each frame of sea wave image in the real-time sea wave video through a pre-trained target encoder; and inputting the feature vector into a dynamic prediction model connected with the target encoder so as to predict the sea wave height corresponding to the real-time sea wave video through the dynamic prediction model, the dynamic prediction model comprises a sliding window connected with an output interface of the target encoder, and a long and short term memory network connected with the sliding window. The problem that a traditional buoy monitoring method for monitoring the sea wave height is high in precision but high in cost is solved.

The embodiment of the invention provides a sea wave height prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining the long-distance height data of sea waves detected based on a microwave radar, based on close-range height data of sea waves detected by a laser range finder and attitude change data of a monitoring platform monitored by an inertial measurement unit, the monitoring platform is a platform integrated with a microwave radar and the laser range finder; performing real-time correction on the long-distance height data and the short-distance height data through the attitude change data, and performing data fusion on the corrected short-distance height data and the corrected long-distance height data based on a Bayesian estimation framework; and predicting the height change trend of the sea waves according to the fused data. According to the method, the problems that a microwave radar is suitable for long-distance measurement of the sea wave height but not high in precision, and a laser range finder is suitable for short-distance accurate measurement of the sea wave height but limited in coverage range in the prior art can be solved.

The invention discloses a prediction distribution obtaining method and device, a storage medium and an electronic device.The method comprises the steps that first data of a target ocean area is obtained, and the first data is data influencing the sea wave height of the ocean; discretizing the first data to obtain second data, the second data being the first data divided into a plurality of levels; and inputting the second data into a preset Bayesian network to obtain predicted distribution of the sea wave height of the target sea area. The problem that the sea wave height of a dynamic complex marine environment is difficult to accurately predict is solved.