12 results about "Significant wave height" patented technology

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.

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.

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.

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 provides an X-band radar significant wave height inversion method based on Bayesian optimization and an adaptive threshold, comprising the following steps: acquiring an X-band radar image and preprocessing; defining a parameter space of a local window size and a false alarm probability, constructing a multi-dimensional shadow quality score function as an optimization objective function, and performing Bayesian iteration optimization to select an optimal parameter combination; calculating a threshold value of each pixel point through a local dynamic threshold value formula, generating a binary shadow mask based on the threshold value of each pixel point, and calculating an optimal shadow rate of the whole image; performing wave number spectrum analysis on the preprocessed X-band radar image, screening effective wave numbers through a wave number spectrum mask, calculating a sea wave root mean square wave steepness based on the optimal shadow rate, and combining the zero-crossing period to calculate the significant wave height. The method realizes high-precision and adaptive significant wave height inversion without the support of buoys.

This invention provides an intelligent prediction method, apparatus, and device for two-dimensional typhoon wave significant height. The method includes: acquiring forecast wind field data and historical wave field data for a target sea area; preprocessing the forecast wind field data and historical wave field data to obtain normalized wind field data and normalized wave field data; inputting the normalized wind field data and normalized wave field data into a trained prediction model to obtain the predicted two-dimensional typhoon wave significant height; wherein the prediction model is trained using a training dataset and validated using a validation dataset, and the training dataset and the validation dataset include independent grade-level forecast wind field data and historical wave field data. This solution, by optimizing the training dataset and providing a prediction model that balances speed and effectiveness, can reduce computation time, meet the real-time forecasting requirements of operations, and achieve higher accuracy in two-dimensional significant wave height forecasts under extreme weather conditions such as typhoons.

This invention relates to an adaptive fault early warning method for offshore wind turbines, belonging to the field of offshore wind turbine fault monitoring and early warning technology. Addressing the technical problem that traditional fixed-threshold early warning methods struggle to adapt to dynamic changes in wind speed and wave height in the marine environment, leading to high false alarm and false alarm rates, this invention utilizes vibration sensors installed in the nacelle and tower, temperature sensors in the generator bearing housing, an ultrasonic anemometer on the top of the wind turbine, and a wave radar on the foundation structure to collect real-time effective values ​​of vibration velocity, bearing temperature, ten-minute average wind speed, and significant wave height. The average wind speed and significant wave height are input into an adaptive threshold calculation module with built-in vibration and temperature threshold response functions to obtain corresponding adaptive early warning thresholds. The real-time collected effective values ​​of vibration velocity and bearing temperature are then compared with the corresponding thresholds, enabling accurate and timely early warning of offshore wind turbine faults and ensuring the safe and reliable operation of the turbine.

This invention belongs to the field of time series forecasting technology, specifically a method and system for effective wave height prediction based on dynamic constraints and interactive learning. The method includes: acquiring wave time series data, segmenting it according to the parity of the time index to obtain odd and even sequences; determining the boundary frequency based on the spectral distribution, and performing frequency domain decomposition on the two sub-sequences to obtain swell components and wind wave components; performing evolutionary prediction using a linear Koopman predictor and a nonlinear Koopman predictor respectively; and then performing recursive interactive fusion and time series reorganization based on the prediction results to obtain fused features and outputting an effective wave height prediction sequence for a preset future time period. This invention can improve the accuracy, stability, and generalization ability of medium- and long-term predictions.

This invention discloses a coherent radar method for inverting ocean waves and currents, considering shadowing and curvature effects. First, initial wave parameters are calculated based on the Pierson-Moskowitz wave spectrum. Then, the spatial distribution of effective wave height is solved by combining shallowing effects and frictional dissipation, and the orbital velocity of sea surface wave particles is calculated. A shadowing function and a curvature correction function are established to correct the orbital velocity. Finally, a Doppler frequency shift model incorporating wave and current elements is constructed. Combined with the range-Doppler spectrum observed by coherent radar, the current velocity is inverted using the least squares method. This invention overcomes the deficiency of traditional models that neglect non-Bragg scattering, achieving simultaneous inversion of wave and current parameters, improving inversion accuracy, and meeting the high spatiotemporal resolution observation requirements of nearshore waters. It is applicable to fields such as marine engineering, shipping, and aquaculture.

This invention belongs to the field of microwave radar image processing technology, and relates to a method for inverting significant wave height based on wave period. First, a significant wave height calculation model is established with the wave period as input. Then, microwave remote sensing images of the sea surface are acquired and preprocessed, such as coordinate transformation (e.g., images from shore-based sea observation radar and shipborne navigation radar) and noise suppression. Next, the wave period is inverted from the preprocessed images using spectral analysis techniques. Finally, the inverted wave period is substituted into the pre-established model to calculate the significant wave height. This invention, by introducing the wave period as a key intermediate variable, closely correlates image features with ocean wave physical parameters, overcoming the shortcomings of insufficient accuracy and stability in existing direct inversion methods. It provides a physically meaningful, highly accurate, and easily operational method for obtaining significant wave height.

This invention belongs to the field of offshore wind turbine fatigue assessment technology, specifically involving a method, system, equipment, and storage medium for offshore wind turbine fatigue assessment. The method includes: (1) establishing an average wind speed sub-box; (2) determining significant wave height and peak spectrum period; (3) constructing a wind-wave consistent coupling environment input; (4) applying the wind-wave consistent coupling environment input to the offshore wind turbine finite element model to obtain the time history response of key parts; (5) identifying fatigue hotspot components; (6) assessing long-term fatigue damage; and (7) outputting the distribution of fatigue hotspot components, hotspot migration results, and long-term fatigue assessment results. The system, equipment, and storage medium are all designed to implement the above method, and corresponding modules or software are provided. This invention can more realistically reflect the consistency relationship of the wind-wave environment, improving the rationality and stability of offshore wind turbine fatigue analysis, hotspot identification, and life assessment.

The application belongs to the field of resource allocation, and discloses a planning method and device for an offshore integrated energy island, comprising the following steps: collecting multi-source heterogeneous operation data and marine environment data of the offshore integrated energy island, and calculating the credible weight of each data source by constructing a cross-source consistency entropy model; establishing a physical mechanism model, and performing parameter inversion based on a weighted least deviation criterion to determine a device parameter set reflecting the actual operation environment; constructing a multi-modal energy flow network covering electric energy, chemical energy and thermal energy based on the device parameter set, introducing wind speed and significant wave height as exogenous disturbances into the multi-modal energy flow network, and establishing an uncertain performance flow propagation model; taking the capacity of wind power, energy storage, electrolytic hydrogen production, ammonia production and a sending channel as decision variables, constructing a comprehensive objective function considering construction cost, operation cost and risk loss, and solving to obtain an optimal capacity configuration scheme, so as to plan the offshore integrated energy island according to the optimal capacity configuration scheme.