90 results about "Metocean" patented technology

The invention relates to the field of weather forecasting, and discloses a marine meteorological coupling refined forecasting method and system, which are used for improving the precision and reliability of marine meteorological coupling forecasting. Comprising the following steps: based on a non-exchangeable geometric vortex equation and an external differential form, constructing a conservation manifold dynamic field in combination with submarine topography data, and realizing strict conservation of mass flux under a complex terrain; an additional pressure item caused by vacuum fluctuation is quantified, and an interface energy field of quantum correction is generated; the recognition precision of the mesoscale vortex and the frontal surface structure is improved; a spectral element method and lattice Boltzmann method mixed solver is combined, and high-precision coupling of ocean vertical layering and the atmospheric process is achieved. According to the method, the defects of conservation, interface energy transmission, initial field non-physics and grid adaptive capacity of a traditional method are overcome, refined forecasting of 72-hour typhoon paths, ocean frontal surfaces and energy flux is supported, and key technical support is provided for extreme weather early warning and climate change research.

The invention discloses a two-dimensional wave spectrum intelligent forecasting method based on a physical information neural network, and belongs to the technical field of ocean information prediction.The two-dimensional wave spectrum intelligent forecasting method comprises the steps that basic data are collected, and the basic data comprise high-temporal-spatial-resolution sea surface wind field data and sea wave height data within the research range; historical sea wave data are obtained, ocean and meteorological data closely related to sea wave changes are integrated and processed, and a sea wave forecasting database is constructed; physical constraints are determined according to the sea wave forecasting database; an ANN neural network model is adopted as a basic model, physical constraints are added, the neural network meets the control equation of the mode, and a PINN physical information neural network is constructed; and carrying out interpretability analysis on the model forecasting process and result. According to the method, the sea wave dynamic process and the deep learning method are fused, interpretability is added for deep learning, and the black box problem of the deep learning method is solved.

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention belongs to the cross technical field of artificial intelligence and marine meteorological prediction, and discloses a sea wave probability prediction method and system, and the method comprises the steps: obtaining historical wind field data and sea wave spectrum data of a target sea area, carrying out the preprocessing and organization of the data, and constructing a training data set; constructing a hybrid expert probability model, wherein the model comprises a gating network and a plurality of expert networks; the training data set is used for training the hybrid expert probability model, the trained model receives input wind field data, and hybrid probability distribution is output through the synergistic effect of the gating network and the expert network; and sampling is carried out from the mixed probability distribution to obtain a predicted sea wave spectrum set, and sea wave risk probability prediction is realized. Complete distribution information can be obtained through one-time forward calculation, a numerical mode set is not needed, the computing power and energy consumption expenditure are remarkably reduced, and high-frequency updating and quasi-real-time business application can be conveniently achieved in resource-limited environments such as shipborne, buoys and offshore stations.

The invention provides an HY-2A ocean water vapor inversion method based on machine learning, and the method comprises the steps: constructing a high-quality HY-2A scanning microwave radiometer data set through the data preprocessing, matching, feature extraction and normalization processing of HY-2A satellite scanning microwave radiometer data and ERA5 reanalysis data. A plurality of machine learning models are used for training and testing, Bayesian optimization is used for realizing hyper-parameter automatic adjustment, an optimal solution in finite time is obtained, an SHAP method is used for explaining the models, contribution of each characteristic variable is clear, interpretability of the used machine learning models is improved, and finally high-precision inversion of the water vapor over the sea is realized. The method has efficient calculation performance, can realize rapid processing of large-scale marine meteorological data, and improves the interpretability of a machine learning model. The method has important application value in numerical weather forecast, ocean extreme weather forecast, climate monitoring and other geophysical and meteorological fields.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention discloses a tropical region offshore marine meteorological fusion analysis method. The method comprises the following steps: step 1, data collection and preprocessing: collecting offshore multi-platform collaborative networking observation data of a tropical region; 2, data quality control: checking the data, and analyzing error characteristics of marine meteorological observation data; 3, data fusion processing: establishing a tropical region offshore multi-source data fusion analysis system, performing intelligent fusion on the multi-source observation data subjected to quality control, and constructing a marine meteorological fusion analysis data set; 4, constructing a coupling analysis model: performing coupling analysis on marine meteorological elements by combining a physical model and a numerical simulation technology, and discovering an evolution rule and influence factors of a marine meteorological system by simulating and predicting an interaction process between the ocean and the atmosphere; and 5, dynamic application and evaluation: through real-time analysis of key meteorological elements, potential marine meteorological disaster risk affairs are discovered and early warned.

The invention discloses a method for performing quality control on hydrometeorological data by using relevance, and relates to the field of marine meteorology. According to the rule of hydro meteorological element data, data quality control is carried out by using the relevance between elements, namely the relevance quality control method. The method comprises the following steps: firstly, sorting data to enable the data to contain time, place, instrument state and other information, performing conventional quality control inspection and identifying abnormal values; the element relevance is analyzed and divided into natural relevance and algorithm relevance. According to natural association, data exception is judged by checking reasonability and contradiction of element values; according to algorithm association, after abnormity is detected in conventional quality control, it is judged that the associated derivative elements are abnormal. The correlation quality control covers a plurality of elements such as time, place, salinity, vector, relative humidity, wind-wave-flow and the like, and emphasizes that correlation is judged according to element observation sources and modes, so that excessive quality control is prevented. The method is clear in process and high in expansibility, suspicious or abnormal data can be effectively detected, and a reliable quality control method is provided for quality control of hydro meteorological observation data.

The invention provides a data transmission method and device for ocean perception data and electronic equipment, and relates to the technical field of communication networks. In the application, through the at least one sensing node, the intermediate node and the computing node included in the node set corresponding to the target sea area, a multi-source target ocean sensing network can be constructed, higher requirements of ocean meteorological prediction on data transmission and processing are met, and a future intelligent ocean meteorological prediction scene is enabled. Moreover, at least one data transmission link meeting the preset data transmission link condition is selected from the target ocean sensing network, so that the optimization of link resources in the target ocean sensing network is realized, the link configuration in the target ocean sensing network is dynamically adjusted, the transmission delay of the ocean sensing data is reduced, and the transmission efficiency of the ocean sensing network is improved. Therefore, the overall transmission efficiency of the target ocean sensing network is improved. The rationality and timeliness of ocean sensing data transmission can be improved, and the performance and real-time forecasting capability of an ocean sensing network are further improved.

The invention belongs to the technical field of marine meteorology, and relates to a marine low-altitude atmospheric waveguide forecasting method based on artificial intelligence set error correction. According to the method, by establishing a forecast error correction algorithm set and dynamically adjusting weights of set members, error set correction of forecast results is achieved, and the problems that a single correction algorithm is high in space-time uncertainty and unstable in correction precision are solved. Compared with a pure data-driven low-altitude atmospheric waveguide forecasting method, the low-altitude atmospheric waveguide forecasting method has the advantages that the result reasonability and stability are higher, and the effective forecasting time efficiency is longer.

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 provides an ocean state variable forecasting method based on thermocline adaptive loss, and belongs to the technical field of ocean meteorology. The self-adaptive thermocline loss function designed by the invention can dynamically adjust the weight of the loss function according to the intensity and complexity of the temperature change of the thermocline region, and the self-adaptive mechanism enables the model to pay more attention to the temperature gradient change characteristics of the thermocline region in the training process; therefore, the model is guided to learn and simulate the complex evolution process of the thermocline more accurately. Compared with the prior art, the traditional loss function gives the same weight to the temperature change of all regions, and the importance of the thermocline region cannot be highlighted, so that the model has a poor effect when learning and simulating the complex evolution process of the thermocline; according to actual verification, the accuracy and stability of thermocline forecasting are remarkably improved, meanwhile, the model training process is simplified, and a more efficient and reliable thermocline forecasting tool is provided for marine scientific research and practical application.

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 present application relates to a floating marine meteorological and hydrological monitoring device, comprising a floating body assembly; a central buoy assembly arranged in the floating body assembly; and a counterweight arranged below the central buoy assembly, wherein the counterweight is connected to the central buoy assembly by means of a plurality of columns, and side walls are arranged between the plurality of columns.

The invention relates to the field of marine meteorological monitoring, and discloses a GNSS-based offshore troposphere atmospheric parameter profile inversion method, which comprises the following steps of: obtaining actual observation values of total troposphere zenith delay, dry delay and wet delay; obtaining troposphere atmospheric parameter profile statistical characteristics through clustering analysis; data points related to temperature, water vapor pressure and air pressure are constructed by utilizing troposphere atmospheric parameter profile statistical characteristics, and theoretical values of zenith total delay, dry delay and wet delay corresponding to the data points are calculated through forward modeling; and selecting one or more of total troposphere zenith delay, dry delay and wet delay, utilizing an optimization algorithm to invert a troposphere atmospheric parameter profile, performing iteration to obtain an optimal data point, performing data quality control on the temperature, the water vapor pressure, the air pressure and the atmospheric refractive index corresponding to the optimal data point, and outputting the temperature, the water vapor pressure, the air pressure and the atmospheric refractive index. The method disclosed by the invention is low in equipment installation and maintenance cost, is suitable for an offshore observation platform, is reliable in inversion result, and can provide support for offshore troposphere atmosphere research.

The invention provides a black tide intelligent forecasting method and system based on ocean three-dimensional environment information, and relates to the technical field of artificial intelligence and ocean meteorological service industries, and the method comprises the following steps: obtaining an ocean three-dimensional environment field through multiple ways, and carrying out the preprocessing of data; making an ocean large model training data set; a large black tide forecasting model is constructed, and parameters of an optimal forecasting model are obtained; generating a future marine environment forecasting field and a black tide forecasting result; and pushing through a website and a WeChat official account. According to the method, accurate forecasting of the black tide by combining the ocean three-dimensional environment information and the artificial intelligence model is proposed for the first time, the limitation of a traditional black tide forecasting method is broken through, and higher forecasting accuracy and broadness are achieved. Through the system, more detailed black tide flow and path forecast can be provided, rapid response and real-time early warning of dynamic changes of the black tide can be realized, and the system has extremely high practical application value.

The invention provides a wind power prediction method based on marine meteorological characteristics and wake flow interference, and aims to solve the problem that the existing wind power prediction does not fully consider the thermal and dynamic characteristics of a marine boundary layer, the wake flow interference effect of a fan group and the attenuation of a salt mist environment. According to the method, power prediction is realized by constructing a multi-source marine meteorological characteristic field, a wake flow interference field and an environment attenuation compensation mechanism. Specifically, a meteorological characteristic matrix is generated by combining an integral norm of wave height change, a position and wind direction characteristic matrix is constructed based on a fan position and a prevailing wind direction, wake flow interference intensity is quantified through a Gaussian kernel function, and meanwhile, an attenuation compensation item containing an error function is designed to process a salt spray corrosion effect. Establishing a bimodal wind speed probability distribution model, and combining a space-time adaptive quantile regression output power prediction interval; the method effectively solves the problems of marine meteorological dynamic characterization, wake flow interference quantization and environmental attenuation compensation, and significantly improves the precision and reliability of offshore wind power prediction.

The invention discloses a sea temperature prediction method and system based on long-time modeling and cross-branch fusion, and belongs to the technical field of marine meteorological intelligent prediction and deep learning modeling. The method comprises the following steps: carrying out spatial feature coding on a standardized sea temperature time sequence image sequence by using a deep convolution encoder to obtain multi-level spatial features; performing two-dimensional discrete wavelet decomposition on the multilevel spatial features to obtain low-frequency approximate representation, horizontal singular features, vertical singular features and diagonal edge features, and inputting the features into a wavelet attention module to obtain short-term time sequence enhancement features; inputting the multi-level spatial features and the low-frequency approximate representation into a ConvLSTM module to obtain high-frequency dynamic enhancement features; and inputting the short-term time sequence enhancement feature and the high-frequency dynamic enhancement feature into a cross-branch attention fusion module to obtain a fusion feature, and sequentially inputting the fusion feature into an Informer time sequence encoder and a decoder to obtain a prediction result.

The invention relates to the technical field of data analysis, in particular to an intelligent acquisition and analysis method and system for marine meteorological and hydrological data, and the method comprises the following steps: S1, communication node deployment and initial configuration; s2, dynamically adjusting the role of the communication node in the multi-hop transmission network based on the real-time energy state and the data traffic load of the communication node; s3, dynamic path optimization and load balancing: optimizing a data transmission path by using a dynamic path optimization algorithm based on reinforcement learning in combination with the channel quality parameters of the relay nodes; s4, when the energy collection rate is insufficient, switching to a standby battery for power supply; and S5, converging the data transmitted through the multi-hop transmission network to a central node, and analyzing the meteorological and hydrological data through a preset central processing unit. According to the invention, the reliability and load balance of a data transmission path are ensured, single node overload and network bottleneck are effectively avoided, and the overall network operation efficiency is improved.

The invention discloses a sea surface wind speed inversion method and system based on a CYGNSS and Willoughby model, and belongs to the technical field of ocean remote sensing. The method comprises the following steps: firstly, extracting a cyclone parameter from IBTrACS data through a Willoughby model, and encrypting the cyclone parameter to 1 second for sampling by utilizing segmented three times of Hermite interpolation; screening CYGNSS mirror reflection points and constructing a data set in combination with a space matching algorithm; processing CYGNSS L1-level data and extracting observation parameters; establishing a basic GMF (Gaussian Mixture Function) according to incidence angle grouping, and optimizing the weight through nonlinear fitting; the Willoughby model wind speed and the basic GMF are fused to generate an enhanced GMF, the DDM signal-to-noise ratio is dynamically adjusted in combination with minimum variance estimation, and the precision is improved. According to the method, the GNSS-R technology is utilized to overcome the defects of high cost, limited coverage and the like of a traditional method, the sea surface wind speed monitoring reliability under the extreme weather is enhanced through collaborative optimization of a physical model and actually measured data, and the method has the advantages of low cost, wide coverage and interference resistance and is suitable for refined marine meteorological monitoring.

This invention provides a method, apparatus, and device for generating marine meteorological data, relating to the field of data processing technology. The method for generating marine meteorological data includes: acquiring identification observation data and numerical simulation data of marine meteorological observation data; interpolating the numerical simulation data onto a preset target grid to generate interpolated wind field data; generating corrected wind field data based on the interpolated wind field data and the identification observation data; generating simulated ocean wave data based on the corrected wind field data; generating corrected ocean wave data based on the simulated ocean wave data and the identification observation data; and encoding the corrected wind field data and the corrected ocean wave data into target marine meteorological data. This invention achieves the fusion of autonomous observation data and numerical simulation data, and efficiently generates high-precision auxiliary meteorological data that meets the input requirements of marine satellites.

The invention belongs to the technical field of ship tracking and positioning, and discloses a ship dynamic tracking method and system based on Beidou positioning, and the method comprises the steps: employing a ship-borne Beidou satellite receiver to determine the position in real time, judging whether to trigger a ship dangerous case alarm, marking a ship in distress, and transmitting the dangerous case alarm information to a Beidou comprehensive application service platform; positioning a ship in distress on an electronic chart, marking a dangerous case range, querying surrounding ships or a global marine meteorological model to generate a vector marine field of the dangerous case range, predicting a drifting high-probability area of the ship in distress, searching a prepared rescue ship for the ship in distress, generating a minimum-cost path, and calculating predicted arrival time; the method comprises the following steps: acquiring the position and state of a ship in real time, generating a pre-rescue ship list, sending a rescue request to the pre-rescue ship, marking an actual rescue ship according to a reply, and carrying out parallel obstruction detection and obstruction coordination on the actual rescue ship. And full-process tracking rescue from dangerous case alarm to rescue execution is realized.

The present invention belongs to the technical field of marine meteorology and relates to a method for forecasting low-altitude atmospheric ducts at sea based on artificial intelligence ensemble error correction. This method establishes a set of forecast error correction algorithms and dynamically adjusts the weights of the ensemble members to achieve ensemble error correction of the forecast results, overcoming the high spatiotemporal uncertainty and unstable correction accuracy of a single correction algorithm. The method of the present invention incorporates numerical forecasting methods using empirical statistical post-processing techniques. Compared with purely data-driven low-altitude atmospheric duct forecasting methods, its results are more reasonable and stable, and its effective forecast time is longer.

The invention is suitable for the field of marine meteorology, and provides a three-dimensional sea temperature space-time prediction method, which comprises the following steps: obtaining reanalysis data and numerical mode prediction data; and inputting the reanalysis data and the numerical mode prediction data into a pre-trained three-dimensional sea temperature intelligent prediction model to obtain a three-dimensional sea temperature space-time prediction result. According to the method, the spatial-temporal characteristics of the sea temperature change can be effectively captured, so that the prediction precision is improved, a high-precision sea temperature prediction result can be provided, good interpretability is achieved, and more powerful support is provided for scientific research and climate change response.

A method of designing a floatable foundation 100 for a wind turbine generator, the method comprising generating a three-dimensional (3D) computer model of the foundation 100; providing a set of metocean data representative of environmental conditions at a proposed operational site for the foundation 100; simulating an operation of the foundation 100 at the proposed operational site; and determining a parameter indicative of stress and / or fatigue loads on the floatable foundation 100 under the environmental conditions at the proposed operational site. There is also provided a floatable foundation 100 for a wind turbine generator, the floatable foundation 100 comprising three outer column members 20,21,22 disposed about a tubular central column 10, three horizontally extending pontoon members 30,31,32, three horizontally extending beam members 40,41,42, and a plurality of reinforcement members (50, Fig.26) each extending between at least two pontoon members 30,31,32.

The invention relates to a ship meteorological course and speed synchronous planning method, device, equipment and medium, and belongs to the technical field of ship operation management.The method comprises the steps that ship navigation data and marine meteorological historical data are obtained, and a marine meteorological discrete grid is established based on the ship navigation data and the marine meteorological historical data; constructing a double-layer mapping swarm intelligent optimization algorithm based on the marine meteorological discrete grid; and mapping and optimizing in a decision layer and an information layer of the double-layer mapping swarm intelligence optimization algorithm according to the target function and the constraint condition, and adjusting the course angle and the navigational speed. According to the ship meteorological course and speed synchronous planning method designed in the invention, fuel consumption and pollution emission can be reduced, and the ship navigation efficiency can be improved.

The invention relates to the technical field of deep learning and ocean engineering meteorology, and particularly provides an industrial ocean meteorological disaster forecasting method based on deep learning. The method comprises the following steps: acquiring real-time meteorological data, and constructing a dynamic text prompt set; performing semantic-physical feature joint modeling on the dynamic text prompt set by using a multi-modal fusion expert module to obtain a real disaster sample; constructing a dream learning framework, entering a dream stage, and obtaining a virtual disaster dream sample; in an awakening stage, high-dimensional potential representation is obtained through a real disaster sample and a virtual disaster dream sample; according to the high-dimensional potential representation, a forecast result is output, a full-closed-loop industrial meteorological disaster prediction system is formed, and the method improves the accuracy, robustness and interpretability of meteorological disaster prediction in a marine industrial scene.

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.

This application provides a method, device, and medium for assisting decision-making in the search of distressed targets at sea, relating to the field of maritime technology. The method includes: acquiring marine meteorological and environmental forecast information for the distressed area and surrounding waters to determine the search area; calculating the sea area risk level and the urgency of the search operation in the search area; acquiring search resources; establishing a search resource decision-making model, combining the urgency of the search operation, the sea area risk level, and the search resources to obtain the optimal search resource planning scheme; dividing the search area into sub-regions according to the sea area risk level, and combining the optimal search resource planning scheme to obtain the working sea area of ​​the search forces and determine the optimal global path planning for the search forces. This scheme achieves a balance between search speed, search cost, and search safety in search operations, improving the efficiency of maritime search operations.

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.