119 results about "Significant wave height" patented technology

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

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 typhoon wave height prediction method based on a deep learning MOE-Transform model, and the method comprises the following specific steps: collecting historical typhoon data of a research region, and constructing a virtual typhoon data set in combination with a central pressure difference formula; processing the historical and virtual typhoon data sets through a Holland typhoon empirical model and a storm growth relation to obtain a wind field, an air pressure field and a significant wave height field; correcting the field data by using an error model on the basis of the ERA5 data set to form a typhoon space-time fusion database containing meteorological data, the significant wave height field and typhoon data; and training and testing the MOE-Transform model to obtain a typhoon wave significant wave height prediction model for typhoon wave height prediction. According to the method, the multi-task adaptability and generalization ability are improved, and the precision and timeliness of typhoon wave height prediction are improved.

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

The invention discloses a 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 discloses a significant wave height time sequence intelligent prediction method based on deep learning, and the method comprises the steps: S1, obtaining a wave data set, and carrying out the preprocessing of the wave data set; s2, performing modal decomposition on a time sequence in the processed wave data set by adopting an empirical mode decomposition algorithm, and splicing a modal decomposition result with the processed wave data set to obtain an enhanced time sequence feature; s3, constructing a significant wave height time sequence prediction model, and performing focused learning on frequency components, which have significant influence on the significant wave height, in the enhanced time sequence features through an adaptive fusion mechanism to obtain a trained significant wave height time sequence prediction model; and S4, predicting the significant wave height of the time point needing to be predicted. According to the method, the significant wave height time sequence prediction model can simultaneously capture local details (such as wind speed abrupt change) and a global trend (such as tidal cycle) of wave generation by obtaining enhanced time sequence characteristics; by learning the frequency components which have obvious influence on the significant wave height in the enhanced time sequence characteristics, the complex dynamic state of the marine environment can be modeled more accurately, so that the prediction precision of the significant wave height is improved.

The invention provides an effective wave height real-time prediction method and device based on dynamic residual correction, and relates to the field of ocean engineering. The method comprises the following specific steps: acquiring wave height data and performing multi-dimensional feature screening; constructing an integrated filter fusing L1 trend filtering and variational mode decomposition, optimizing parameters by using a sea image optimization algorithm, introducing a causal sliding window to extract features so as to construct a time sequence input tensor, and inputting the time sequence input tensor into a stacked bidirectional long-short-term memory network based on an attention mechanism after noise addition standardization so as to obtain a basic predicted value; calculating a manifold coherent structure, PID dynamics and physical statistical characteristics, and cascading with the basic prediction characteristics to construct a comprehensive element characteristic vector; a LightGBM architecture is constructed, and a prediction residual error is fitted after optimization is carried out through a sea image optimization algorithm; and finally, executing linear reconstruction based on the dynamic safety threshold constraint, and outputting a real-time correction result. According to the method, the error evolution rule is deeply mined by using manifold geometric features, and the real-time precision and robustness of significant wave height prediction are remarkably improved.

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

The invention provides 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 sea wave height prediction, and discloses a significant wave height prediction method and system based on spatio-temporal evolution multi-scale feature extraction. The method comprises the following steps: performing significant wave height prediction on input significant wave height grid data of a sea area to be predicted by applying a trained hybrid heterogeneous parallel double-convolution dynamic network; the method further comprises the steps of obtaining the effective wave height grid data, inputting the effective wave height grid data into the hybrid heterogeneous parallel double-convolution dynamic network for effective wave height prediction, and then outputting an effective wave height prediction sequence of a future time step. According to the method, multiple features of SWH spatio-temporal dynamic evolution can be extracted, and hierarchical spatio-temporal features from a local scale to a global scale and local spatio-temporal dynamic crossing from a coarse scale to a fine scale can be mined at the same time; by constructing a heterogeneous parallel double-convolution framework, the internal irregularity of the SWH field and the irregular relation between SWH field data are overcome, and meanwhile the capacity of capturing the invariant relation in the SWH field is kept.

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

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

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 discloses a three-dimensional wave liquid level visual intelligent reconstruction evaluation system, and belongs to the field of ocean engineering monitoring. The system comprises an image acquisition module, an image stereoscopic vision processing module, an attention-enhanced reconstruction neural network module, a camera attitude evaluation module, a visualization and output module and a hydrodynamic parameter analysis module. An image is collected through a fixed baseline binocular camera system, after preprocessing, a neural network fused with a multi-scale attention mechanism is utilized to reconstruct a three-dimensional wave structure, coordinate system conversion is achieved in combination with self-supervised attitude evaluation, and finally hydrodynamic parameters such as significant wave height and a three-dimensional velocity field are extracted and visualized. The system does not need explicit calibration and large-scale data, has high automation, real-time performance and strong environmental adaptability, can be deployed on various platforms, and significantly improves the precision and engineering applicability of non-contact wave observation.

The invention relates to the technical field of marine survey and big data processing, and provides a rolling sea wave forecast correction method and device, electronic equipment and a storage medium. Comprising the steps of training a sea wave rolling forecast correction model based on artificial intelligence and real-time observation; obtaining to-be-corrected numerical mode sea wave factor forecast data and real-time observation data; the numerical mode sea wave element forecast data comprises sea wave significant wave height data, and the real-time observation data comprises sea surface wind vectors and sea wave significant wave height real-time observation data; correcting the numerical mode sea wave element forecast data according to a sea wave rolling forecast correction model obtained through training, obtaining a group of new sea wave forecast data after new observation data are input, and obtaining numerical mode sea wave significant wave height data capable of being updated and corrected in a rolling manner; the method can effectively reduce the negative influence of the error on the subsequent application in the fields of marine meteorology and the like, and effectively improves the application effect of the sea wave forecast data.

The invention provides a multi-modal information fusion sea surface wind speed inversion method, device and system, and the method comprises the steps: obtaining satellite navigation data, wind speed reference data and significant wave height data, performing matching and quality control on the wind speed reference data and the significant wave height data with the satellite navigation data to obtain an initial data set; recombining the initial data set according to a track to obtain a target data set, and dividing the target data set into a training set, a verification set and a test set; and constructing a wind speed inversion model by using the spatial-temporal correlation of continuous time delay-Doppler diagram observation values in the same trajectory and combining with auxiliary feature information. According to the invention, the extracted information can be fused with various auxiliary feature information, the inversion precision is improved, and the influence characteristics of the ocean state on the wind speed inversion can be quantitatively analyzed.

The invention belongs to the field of ocean wave feature extraction, and particularly relates to a sea wave feature inversion method based on reverse echo measurement, which comprises the following steps of: acquiring an echo signal from a seabed to an ocean surface, and performing adaptive time window smoothing processing on the echo signal to obtain an echo signal; carrying out abnormal value identification and missing data filling on the smoothed data; removing abnormal values by adopting an improved Tukey method, and filling missing data by adopting a segmented cubic spline interpolation method; through high-pass filtering and detrending processing, sea wave echo signals with low-frequency trends removed are obtained; and selecting a proper time window to calculate the dispersion of echo time, establishing a mathematical relationship between the dispersion and the significant wave height through a regression algorithm, and substituting observation data into an equation to obtain an inversion result of the significant wave height. According to the method, the sea waves are subjected to feature inversion based on the reverse echo measurement technology, the precision of sea wave feature extraction is effectively improved, the field measurement cost is reduced, and the stable and low-cost sea wave measurement method is provided.

The invention relates to a sea surface wind wave field parameter integrated inversion method combining HFR and SAR, and the method mainly comprises the steps: extracting the positive and negative first-order echo spectrum peak values of HFR, carrying out the inversion of a sea surface wind direction, and carrying out the interpolation of an HFR wind direction result to an SAR data grid through bilinear interpolation; based on the registered HFR wind direction result, utilizing a CMOD5. N model and an SAR backscattering coefficient to invert the wind speed; constructing a wind wave spectrum based on the inverted wind speed and wind direction results; calculating a surge spectrum peak wave number, a surge spectrum peak amplitude and a surge propagation direction by using the SAR image information, and constructing a surge spectrum based on the surge spectrum peak wave number, the surge spectrum peak amplitude and the surge propagation direction; and superposing the obtained wind wave spectrum and the surge spectrum to obtain a sea wave spectrum result, and further inverting the significant wave height. According to the method, the defect of sea state parameter observation through a single means is overcome, integrated high-precision inversion of the wind wave parameters is achieved, and the method has important significance on marine environment monitoring, disaster forecasting, shipping safety and the like.

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

The invention provides a sea clutter scattering coefficient estimation method and device based on wave height guided sea conditions, and relates to the technical field of radar detection performance evaluation. The method comprises the following steps: determining a true value of a sea clutter scattering coefficient according to radar system parameters; according to the target corresponding relation, the Douglas sea condition, the effective wave height and the multiple coefficients, cubic fitting of the Douglas sea condition and the effective wave height is determined; according to the cubic fitting polynomial, the radar grazing angle, the radar wavelength, the radar sight angle and the wind direction angle, a target sea clutter scattering coefficient estimation model is constructed, and the target sea clutter scattering coefficient estimation model is used for estimating sea clutter scattering coefficient estimation values in the HH polarization mode and the VV polarization mode; according to the true value and the sea clutter scattering coefficient estimation values in the HH polarization mode and the VV polarization mode, the average relative error of the sea clutter scattering coefficients is determined, and the average relative error is used for evaluating radar performance. Therefore, the estimation precision of the sea clutter scattering coefficient is high.

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

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

The invention relates to a multi-channel multi-mode space-time 3D convolution and Transform fused satellite-borne GNSS-R wave height inversion model, and aims to solve the problems that a traditional empirical model is poor in adaptability to complex oceans and existing deep learning cannot fully excavate space-time correlation. According to the method, three modules are innovatively fused for cooperative processing: firstly, a 3D CNN-ConvLSTM is utilized to extract local spatial-temporal characteristics of multichannel GNSS-R data and capture a dynamic time sequence; secondly, performing deep coding on the sea surface environment parameters through a multi-head attention mechanism of Transform, and establishing global dependence between features; and finally, realizing cross-modal fusion by adopting a weighted summation and feature splicing strategy. And through training of an Adam optimizer and control of an early stop strategy, optimization is carried out by taking a mean square error as a loss function. Experiments show that compared with a traditional model and a machine learning model, the method has the advantages that the significant wave height estimation error is reduced by 40-53%, the correlation coefficient reaches 0.84-0.91, the precision and generalization ability under the complex sea condition are remarkably improved, and reliable support is provided for ocean remote sensing monitoring and disaster early warning.

The invention discloses a method and a device for detecting marginal sea significant wave height by a spaceborne altimeter, relates to the technical field of ocean remote sensing, and aims to solve the problem of poor marginal sea significant wave height detection precision of the spaceborne altimeter in the prior art. The method comprises the following steps: acquiring sub-satellite point echo waveform data acquired by a spaceborne altimeter; the method comprises the following steps: determining the waveform category of each echo waveform in sub-satellite point echo waveform data collected by a spaceborne altimeter by using an echo waveform classification model constructed based on the sub-satellite point echo waveform data collected by a SWIM spectrometer, and obtaining a first echo waveform classification library; obtaining a first edge sea echo waveform from a first echo waveform classification library, determining a target characteristic parameter of the first edge sea echo waveform, and determining an effective wave height of the first edge sea echo waveform by using an effective wave height inversion model constructed based on sub-satellite point echo waveform data collected by an SWIM spectrometer and wave height data in ERA5; therefore, the edge sea significant wave detection precision is improved.

The application discloses a kind of short-term wave height distribution characterization methods based on spectral width, it is related to marine engineering technical field, and the method comprises: based on the statistical parameter of given measured short-term sea state calculation spectral width factor;Wherein, statistical parameter includes spectral frequency, spectral peak frequency, energy density spectrum and energy density spectrum maximum;According to local measured data situation determines characteristic parameter;Wherein, local measured data includes the significant wave height under multiple groups of short-term sea state, three-one wave height, spectral frequency, spectral peak frequency, energy density spectrum and energy density spectrum maximum;Distribution parameter is calculated according to characteristic parameter;Based on spectral width factor and distribution parameter determine the exceeding probability of short-term wave height distribution, provide more accurate input parameter for the design and construction of related structure of nearshore engineering, ocean engineering, near island reef engineering, make preparation for the characterization of short-term wave height distribution and short-term wave height prediction.

The present invention belongs to the field of ocean wave feature extraction, and specifically relates to a method for inverting sea wave features based on reverse echo measurement, which includes the following steps: obtaining the echo signal from the seabed to the ocean surface, performing adaptive time window smoothing processing on the echo signal, and performing outlier identification and missing data filling on the data after smoothing processing; using the improved Tukey method to remove outliers and using the piecewise cubic spline interpolation method to fill in the missing data; obtaining the sea wave echo signal with the low-frequency trend removed through high-pass filtering and detrending processing; selecting an appropriate time window to calculate the dispersion of the echo time, establishing a mathematical relationship between the dispersion and the significant wave height through a regression algorithm, and substituting the observed data into the equation to obtain the inversion result of the significant wave height. The present invention performs feature inversion on sea waves based on reverse echo measurement technology, effectively improving the accuracy of sea wave feature extraction, reducing the on-site measurement cost, and providing a stable and low-cost sea wave measurement method.

The application discloses an effective wave height double-stage space-time prediction method and system based on diffusion residual correction, relates to the technical field of sea wave prediction, and inputs historical effective wave height data and corresponding historical wind field data into an effective wave height space-time prediction model for processing to obtain a preliminary effective wave height prediction result, and then inputs the preliminary effective wave height prediction result, the historical effective wave height data and synchronous forecast wind field data into a residual correction module based on a diffusion model to compensate for the preliminary prediction error and obtain a final effective wave height prediction result. The diffusion model based on the EDM architecture is innovatively introduced to finely reconstruct the prediction residual, the basic prediction field, the synchronous wind field dynamic factor and the prediction time limit are taken as multi-dimensional physical strong conditions to inject the diffusion generation process, the application can deterministically restore the high-frequency texture details and nonlinear wave components missed by the first-stage model, to a certain extent, the application can relieve the long-term deviation accumulation, improve the prediction quality in high wave areas and improve the prediction stability.