7 results about "Ocean wave height" patented technology

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 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 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 application provides a sea wave height prediction method and device based on a physical guidance dynamic graph Mamba network, belongs to the technical field of sea wave height prediction, adopts a selective scanning mechanism of a state space model, thereby reconstructing a calculation paradigm of time series modeling, enabling the state space model to adaptively determine which historical information to retain and which noise to discard according to current marine environment characteristics, and realizing time series modeling with linear calculation complexity. In the condition of rapidly changing weather, the discrete parameter Δ value increases, the model pays more attention to recent information; in the condition of stability, the Δ value decreases, the model retains more historical information, and thus the purpose of guaranteeing calculation efficiency and improving prediction accuracy is achieved, the technical problem that long sequence modeling efficiency and precision are difficult to consider is overcome, the technical inertia that serial calculation or quadratic complexity calculation must be used in the traditional technology is broken, and a physical perception graph learner is used as a solution mechanism, thereby reconstructing a space relationship modeling method.

The application relates to a sea wave height prediction and model training method, electronic equipment and a computer storage medium, which comprises the following steps: constructing a sample data set, wherein the sample data set comprises sea wave height space-time sequences in different regions; constructing a model to perform forward propagation processing on sample data in the sample data set and output a prediction result, wherein the prediction result comprises a sea wave height space-time prediction sequence in the next moment in different regions, and the model is constructed based on a ConvGRU encoder-decoder network structure; performing back propagation on the prediction result to update model parameters; and performing the propagation process multiple times until the model converges, thereby obtaining a trained sea wave height prediction model. By using the ConvGRU structure, combining the time information features and the space information features of regional sea waves, and performing multi-scale learning, the prediction accuracy of large-scale sea wave data prediction is improved.

This invention relates to a method for maritime target detection based on embodied cognition enhancement under harsh environmental conditions, comprising: acquiring relevant data and extracting interference feature maps; constructing a historical information map to generate an environment-target baseline; generating embodied feature vectors and dynamically compensating the fused data to obtain a stable image; correcting the stable image using baseline preprocessing parameters, and obtaining an enhanced image by combining the interference map with adaptive exposure, wavelet denoising, and polarized light filtering; generating an initial region of interest based on the high-frequency region of the baseline, determining the dynamic sensitive region by combining the heading priority direction and wave height, performing super-resolution reconstruction and contrast enhancement, and reducing the frame rate of non-sensitive regions; constructing a hybrid detection model, based on an improved target detection algorithm, and correcting the confidence level based on the historical accuracy matrix, deblurring strong wind swaying and fusing multiple frames obscured by heavy rain, and outputting the detection result. Compared with existing technologies, this invention has the advantages of improving the accuracy of maritime target detection under harsh marine environmental conditions.