69 results about "Significant wave height" patented technology

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 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 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 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 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 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 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.

The invention discloses a wave impact resistance structure optimization method for a near-shore high-piled wharf of a port. The method comprises the following steps: firstly, based on historical observation and reanalysis data, carrying out conditional sampling according to a significant wave height, a peak period, an incidence direction, storm surge, a long-period energy ratio and a water depth, and obtaining a sea condition sample set covering a design quantile; secondly, building a high-pile wharf parameterized geometry and working condition model; and finally, performing high-fidelity full-sample regression verification on the candidate solution to form a design result document including design variables, sea condition coverage domains, robustness indexes and key response statistics. According to the method, high reliability and high efficiency are unified, and the method is suitable for wave impact resistance design and engineering implementation of the near-shore long-piled wharf.

This invention relates to the field of marine resource assessment technology, and particularly to a method and apparatus for refined assessment of marine wave energy resources. The method includes: acquiring wind field data, water depth and topography data, and boundary wave data of the target sea area to be analyzed; constructing a nested grid wave numerical model; based on the wave numerical model, obtaining wave elements for each moment within at least one complete climatic year, including significant wave height and wave period; based on the wave elements of the inner and outer grids, obtaining the target wave energy flux density corresponding to each inner grid; and based on the target wave energy flux density, obtaining the resource utilization rate, average wave energy flux density, significant wave occurrence frequency, and resource stability of each inner grid in the target sea area, thereby conducting a refined assessment of the wave energy resources of each inner grid in the target sea area. The above technical solution enables a refined assessment of wave energy resources in the target sea area, providing technical support for wave energy development.

The present application relates to the technical field of ocean observation, and discloses a GNSS wave inversion method for compensating dynamic draft deviation of a buoy, comprising the following steps: S1, GNSS high-frequency observation data acquisition and preprocessing stage; S2, buoy vertical dynamics modeling stage; S3, dynamic draft deviation characteristic analysis stage; S4, frequency domain response amplitude operator correction stage; S5, wave condition adaptive correction stage; S6, wave parameter inversion and quality evaluation stage; the present application establishes a buoy vertical inertia-buoyancy-damping dynamics model, quantitatively describes the relationship between the buoy dynamic draft deviation and the wave motion for the first time from the physical mechanism level, calculates the dynamic draft deviation amount by epoch, and compensates it into the GNSS measurement results, eliminates the systematic deviation caused by the buoy inertia effect, and reduces the inversion error of the significant wave height from 8% to 15% before correction to within 3%.

The present application belongs to the technical field of sea wave height prediction, and discloses a wave height prediction method considering the evolution characteristics of marine environment. The method comprises the following steps: (1) collecting the historical data of effective wave height, average period and wind speed of a target sea area, and processing the data; (2) performing seasonal trend decomposition on the effective wave height sequence to obtain a trend item, a seasonal item and a residual item; (3) calculating a wind wave response index according to the wind speed variation, the wave average period variation and the effective wave height variation; (4) generating a wind wave response state weight, and adjusting the action intensity of different historical time steps, different wave height components and meteorological marine elements in the prediction process by using the wind wave response state weight; (5) performing weight correction on the prediction contribution of the trend item, the seasonal item and the residual item, and reconstructing to obtain a future effective wave height prediction value. The present application can improve the stability of sea wave effective wave height prediction in the stage of obvious wind speed change or sea state conversion.

This invention belongs to the field of wave height prediction technology and discloses a wave prediction method that considers the evolution characteristics of the marine environment. The method includes the following steps: (1) collecting historical data on significant wave height, average period, and wind speed of waves in the target sea area and processing the data; (2) performing seasonal trend decomposition on the significant wave height sequence to obtain trend terms, seasonal terms, and residual terms; (3) calculating wind and wave response indices based on wind speed changes, wave average period changes, and significant wave height changes; (4) generating wind and wave response state weights and using these weights to adjust the influence of different historical time steps, different wave height components, and meteorological and oceanographic elements in the prediction process; (5) correcting the prediction contributions of the trend terms, seasonal terms, and residual terms with weights, and reconstructing the predicted future significant wave height. This invention can improve the stability of significant wave height prediction during periods of significant wind speed changes or sea state transitions.

The application discloses a data-driven large-scale regional sea wave rapid prediction method and device, wherein the method comprises the following steps: acquiring multi-source marine physical environment data of a target sea area, and generating a standard physical field data stream with unified space-time resolution; constructing a multi-channel space-time input tensor containing wind field driving information, topographic boundary information and historical wave state information; minimizing the difference between the predicted wave height and the real wave height through a back propagation mechanism, thereby obtaining a trained wave height prediction model; receiving the latest wind speed field data observed in real time or output by a numerical prediction model, and outputting a sea wave significant wave height prediction field at a future target time; and the device is used for realizing the above method. The method technical scheme provided by the application accurately captures the time delay of wind energy transmission to wave energy through a physical lag alignment mechanism, and improves the modeling accuracy; meanwhile, the deep learning is used to realize the rapid deduction of a large-scale sea wave field, and high accuracy and high timeliness are achieved.

This invention discloses a wave height prediction system and method integrating dynamic features and physical gating, belonging to the field of artificial intelligence and machine learning technology, for marine environmental monitoring. The system includes a data acquisition and preprocessing module, a feature selection module, and a model prediction module. The method includes acquiring and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient booster regression model to select the core variable set, constructing a physically guided gating feature-aware Transformer prediction model, constructing a hybrid loss function-constrained prediction model, and outputting significant wave height prediction values ​​after the prediction model is trained. This invention significantly improves the utilization efficiency and scenario adaptability of multi-source marine meteorological features through feature selection and dynamic weight allocation mechanisms. It can be flexibly extended to wave prediction tasks at different sea areas and spatiotemporal scales, providing high-precision and highly reliable technical support for marine engineering, shipping safety, and disaster early warning.

The invention relates to a method for estimating wave design parameters in different recurrence periods. The method is suitable for the technical field of offshore engineering design and construction. The technical scheme comprises the steps of obtaining a range of a concerned sea area, and determining a numerical model calculation region range including the concerned sea area; obtaining meteorological element historical data and ocean element historical data of the numerical model calculation area in the first time period, and typhoon characteristic data influencing the concerned sea area, and correcting the meteorological element historical data; constructing a wind wave flow coupling numerical model to obtain a wind wave time sequence on each grid point in the concerned sea area; training a typhoon period wave parameter estimation machine learning model, and inputting the ocean element historical data and the corrected meteorological element historical data in the first time period into the trained typhoon period wave parameter estimation machine learning model to obtain the maximum significant wave height of each year in the first time period; and fitting to obtain an effective wave height extreme value distribution curve, and further obtaining effective wave height design values corresponding to different return periods.

The invention specifically relates to a significant wave height estimation method based on machine learning. The method comprises the following steps: obtaining a radar echo speed space-time sequence by using a coherent X-band radar; extracting statistical characteristics from the speed space-time sequence; performing Spearman correlation analysis on the statistical characteristic value and the actual significant wave height; screening the characteristic values by using a Spearman correlation coefficient, and constructing a Gaussian process regression and stepwise regression integrated model in combination with significant wave height data; and estimating the significant wave height of the echo to be measured through the integrated model. According to the significant wave height estimation method based on machine learning, key information can be effectively obtained from radar echoes by analyzing the speed space-time sequence of the radar echoes, extracting the statistical characteristics and energy information of the radar echoes and combining with the machine learning technology, and the estimation accuracy of the significant wave height is improved. And a physical correlation model of speed characteristics and wave parameters is constructed by using a machine learning method, so that the accuracy and reliability of inversion are remarkably improved.

The invention relates to an SAR sea surface wind speed inversion method based on deep learning, and the method comprises the steps: carrying out the space-time matching of a preprocessed SAR image and reanalysis data ERA5, and extracting the sea surface wind speed data and significant wave height data of the reanalysis data ERA5; constructing a double-input wind speed inversion model based on a convolutional neural network CNN and a channel attention mechanism; the wind speed inversion model comprises an SAR image input branch and a significant wave height input branch which are parallel to each other; training the model and outputting a sea surface wind speed inversion result; the performance of the wind speed inversion model is evaluated by adopting the actually measured wind speed data observed by the NDBC buoy and comparing the error between the predicted wind speed and the actually measured wind speed of the buoy. According to the method, sea surface wind speed inversion can be realized through the SAR image and the corresponding ERA5 effective wave height, so that errors caused by inaccurate wind direction information are avoided, the operation process of wind speed inversion is simplified, the degree of dependence on external data is reduced, and the practicability and flexibility of the method are improved.

Provided are a method and a system for constructing an ocean significant wave height retrieval model of spaceborne GNSS-R under high sea conditions, acquiring modeling data, auxiliary data and verification data; extracting characteristic variable parameters and auxiliary variable parameter of modeling data, and matching that extracted variable parameter in time and space; carrying out data quality control and data set division on the variable parameters after time-space matching; using the divided data set to construct and train the WaveMambaFormer model, and using the SHAP explainer to calculate the feature importance and global interpretation, so as to enhance the explainability of the WaveMambaFormer model; the significant wave height data of ERA5, WaveWatch III and Jason-3 are used as reference data to evaluate the retrieval performance of WaveMambaFormer model.

The application discloses a wave field reconstruction method, which comprises the following steps: firstly, obtaining a standardized space-time data set, the data set containing effective wave height, average wave period and wave direction; then, constructing a low-rank joint wave feature subspace according to the data set; after obtaining sparse observation data and constructing an observation matrix and an observation vector based on the observation data, performing sparse regression on each variable of the data set based on the feature subspace, the matrix and the vector, obtaining a preliminary spatial distribution result of each variable, then performing Gaussian weighted fusion smoothing according to the distribution result, obtaining a preliminary reconstruction result, then determining the dynamic weight of each variable based on the reconstruction result, and optimizing the preliminary reconstruction result according to the weight, obtaining a target reconstruction result. The embodiment of the application can effectively reconstruct a high-resolution wave field under limited observation conditions, improve the reconstruction accuracy, simplify the calculation process, adapt to complex data scenarios, and quickly output high-quality results.

The application discloses a wave energy resource quantity accounting method and device, medium and product, relates to the field of wave energy development, and comprises the following steps: constructing a wave numerical model based on measured wave data and environmental data; performing numerical simulation analysis by using the wave numerical model; calculating the wave energy flow density of each grid node in the accounting sea area according to the effective wave height and the average wave period of each grid node in the accounting sea area; determining the boundary grid unit where the boundary is located and a group of boundary grid nodes around the boundary according to the boundary of the accounting sea area; determining the wave energy flow density actually transmitted into the accounting sea area of the boundary grid nodes according to the wave energy flow density at the boundary grid nodes and the included angle between the wave propagation direction and the normal vector of the midpoint of the corresponding grid boundary line segment pointing to the inside of the accounting sea area; determining the total wave energy power of the accounting sea area according to the wave energy flow density, and accounting for the wave energy resource quantity. The application can improve the accuracy and scientificity of the wave energy resource quantity accounting of the sea area.

The invention discloses a rapid calculation method and system for an effective wave height field of a tropical cyclone in an open sea area, and the method comprises the following steps: embedding a dynamic wind field structure into to-be-measured tropical cyclone event information, constructing a space-time distribution field, extracting a cyclone key parameter and a relative position coordinate in a preset time period containing the current moment, and calculating the effective wave height field of the tropical cyclone in the space-time distribution field; forming a time sequence matrix; and inputting the time sequence matrix into a space-time fusion neural network model, and quickly calculating the effective wave height space-time distribution of the open sea area at the target moment. According to the method, the neural network is adopted to replace a traditional numerical value wave model for effective wave height prediction, transformation from low-precision and high-time-consumption traditional numerical value simulation to high-precision and low-time-consumption intelligent modeling is achieved, and the tropical cyclone wave field prediction and modeling quality and efficiency are greatly improved.

The present disclosure provides a sea surface height measurement correction method, device, computer equipment and program product, wherein the method comprises: obtaining each time window; for any time window, performing adaptive spectral decomposition and wave system signal extraction on the vertical displacement in each three-way displacement to obtain the time window in the band-pass frequency band corresponding to the wind wave component and the swell component and the significant wave height; determining the wind wave component direction and the swell component direction according to the mutual covariance between the three-way displacements, and determining the main frequency of the wind wave component and the main frequency of the swell component according to the band-pass frequency band; determining the wave number of the wind wave component and the wave number of the swell component through the wave number iteration mode according to the projection component of the background flow field in the wind wave component direction and the swell component direction, and the main frequency of the wind wave component and the main frequency of the swell component; determining the vertical offset according to the wave number of the wind wave component, the wave number of the swell component and the significant wave height, and determining the height sequence of the sea surface by using the vertical offset and each vertical displacement.