13 results about "Internal wave" patented technology

The application belongs to the technical field of marine environment monitoring and early warning, and specifically relates to a multi-channel internal wave flow early warning system and an early warning measurement method, which comprises the following steps: a buoy body is fixedly arranged through a fixed anchor, and a marine profile comprehensive detection module synchronously collects multi-dimensional profile data such as flow velocity, temperature and salinity. A main control unit is embedded with a mixed driving early warning model, a phase velocity of an internal wave is solved based on a physical model, a nonlinear correction is performed through a machine learning residual correction module, a high-precision internal wave phase velocity is obtained, and then early warning time, shear flow and displacement amplitude are calculated and an alarm level is determined. Alarm information is redundantly sent to a shore station receiving system through a ground mobile communication link and a satellite communication link of a multi-channel communication module, and the shore station receiving system regularly updates the machine learning model by taking measured data as incremental samples. The application realizes high-precision and low-delay early warning of internal wave flow, has high communication reliability, and is suitable for marine engineering, underwater vehicles and safety guarantee of offshore operations.

This invention relates to the field of marine observation data processing technology, specifically a method for quality control and evaluation of real-time communication mooring data, comprising the following steps: data preprocessing to obtain preprocessed data; multi-dimensional anomaly identification of the preprocessed data, removing anomalies to obtain data to be verified; multi-node spatiotemporal synchronization verification of the data to be verified to obtain synchronized data; adaptive consistency verification of the synchronized data using an internal wave mode; checking the presence of internal wave data observation signals on the synchronized data after consistency verification; and data quality level evaluation. This invention solves the data deviation problem caused by resetting the coefficients of inductively coupled sensors, achieving dual quality control of real-time data and post-corrected data; it constructs a data quality evaluation system adapted to deep-sea real-time communication moorings, quantifies data quality levels, and provides a basis for data reliability in marine scientific research.

This invention discloses an intelligent identification and positioning method for ocean internal waves based on multi-source remote sensing imagery, belonging to the field of marine remote sensing technology. The key technical solutions include the following steps: determining the geographical location and time range of the target sea area; collecting multi-temporal high-resolution remote sensing image data from synthetic aperture radar (SAR) and optical satellite imagery; labeling the internal wave features of the preprocessed multi-temporal high-resolution remote sensing image data to generate ground truth images; using a trained deep convolutional neural network model to perform pixel-level analysis on the input remote sensing images to extract ocean internal wave features and generate binarized images of the internal wave features; and converting the extracted binarized images of the internal wave features into corresponding latitude and longitude locations based on the geographical location information of the original remote sensing images. This achieves high-precision geographic positioning of internal waves, is suitable for marine research and high-precision environmental monitoring, and has broad application prospects.

This invention provides a porous wave-damping plate for aquaculture cages, specifically belonging to the field of wave-damping technology for aquaculture cages. It comprises a porous plate, wave-damping buoys, and a hollow cavity structure, forming a highly efficient energy dissipation system. The alternating openings on the surface of the porous plate allow waves to pass through. When waves impact the plate, seawater enters the hollow cavity and drives the internal wave-damping buoys to generate minute movements and collisions, thereby significantly reducing wave height and impact force. This directly protects the fish in the cage from the impact of violent water currents. The invention includes a plate body, which is detachably and fixedly installed on the outside of the cage. The plate body is a hollow plate-like structure with through holes on both the front and back sides, and the cavity of the hollow plate-like structure is filled with several wave-damping buoys for dissipating wave energy. It includes multiple plate bodies, which are spliced ​​together to form a wave-damping plate assembly. The upper end of the plate body is provided with a tenon, and the lower end of the plate body is provided with a mortise. Adjacent plates are interlocked and positioned by the tenon and mortise.

This invention discloses a semantic segmentation method for ocean internal wave stripes in SAR images, relating to the field of semantic segmentation of remote sensing images. The method model consists of an encoder and a decoder. The encoder comprises four Transformer modules, each containing a self-attention layer, a feedforward neural network, and an overlap patch merging module. Within each module, the input image is processed N times through a multi-head self-attention mechanism, and then the merging module generates feature maps at four scales. The decoder consists of three modules: a serpentine convolution, an EVC module, and an expectation-maximization attention network. The advantages of this invention are: the model fully utilizes the multi-scale fusion module, improving performance and robustness; the use of serpentine convolution can better extract features of linear shapes; and the use of the expectation-maximization attention network improves model accuracy while reducing computational complexity.

The invention relates to an ocean internal wave three-dimensional visualization method based on a high-temporal-spatial-resolution unstable flow field, and belongs to the technical field of computer vision and ocean fluid mechanics. According to the method, a GPU texture visualization technology is utilized, an internal wave data processing and feature extraction method is provided for vertical flow characteristics of ocean internal waves, and complex dynamic behaviors of the ocean internal waves in an unstable flow field are accurately displayed by highlighting a vertical velocity component W. Compared with a traditional method, the method has the advantages that the spatial and temporal change of high-resolution internal wave data can be reflected more truly, and the vertical flowing characteristics of ocean internal waves in a three-dimensional space can be clearly visualized. The technology is suitable for influence analysis of marine ecological and physical processes and artificial object motion trails, and helps researchers to deeply understand the influence of internal waves on marine fluid motion.

This invention discloses a marine disaster early warning method and system based on an AI model specifically designed for surveying. The invention embeds the KdV equation for internal solitary waves as a constraint into the model using a physical information neural network, giving it physical interpretability and enabling accurate reconstruction of the flow field based solely on sparse observations. The method transforms water acoustic echo data and shipborne inertial measurement unit attitude data into effective inputs, achieving perception of internal wave structure and sea surface wave spectrum without increasing hardware costs. The system utilizes Transformer-based multimodal fusion technology to correct satellite cloud image features using meteorological forecast data, significantly improving the reliability of typhoon and storm surge predictions. Ultimately, high-precision environmental perception is translated into customized operational decisions, filling the gap in general meteorological navigation for engineering protection and effectively ensuring the safety of surveying equipment and the project schedule.

This invention specifically relates to a time-varying finite element modeling method for extremely low frequency (UHF) sound fields under dynamic internal wave processes, belonging to the field of underwater acoustic physics. The method first simulates the internal wave propagation process using the modified Korteweg-de Vries equation based on background field environmental information, and then obtains the vertical modes of the internal waves using the Taylor-Goldstein equation to construct a dynamic sound velocity field. Secondly, in a fluid-structure interaction model containing multiple layers of seabed media, the wave equation is solved using the finite element method to achieve time-varying calculation of the UHF sound field. This invention can precisely characterize the spatiotemporal evolution of the sound velocity field under complex terrain and dynamic internal wave disturbances, accurately simulate the distribution characteristics of the UHF sound field throughout the entire internal wave propagation process, and is suitable for sound field prediction and analysis in sea areas with high internal wave incidence, such as the South China Sea.

The application discloses a data-physical double-driven ocean internal wave prediction model training method and device, and relates to the technical field of ocean observation. The method is used to solve the problem of low prediction accuracy caused by the fact that the existing ocean internal wave prediction method cannot accurately capture the edge structure or local strength mutation of isolated internal waves and other detailed features. The method comprises the following steps: obtaining target ocean data; determining a symbolic partial differential equation representing the propagation of ocean internal waves of the target ocean data by using a symbolic regression method; constructing an initial ocean internal wave prediction model based on the symbolic partial differential equation; and training the initial ocean internal wave prediction model based on the target ocean data to obtain a trained ocean internal wave prediction model. The data-physical double-driven ocean internal wave prediction model training method provided by the application is used to construct an ocean internal wave prediction model with accuracy and physical consistency.

The application discloses a kind of marine mesoscale vortex and near-inertial internal wave energy exchange analysis method, belong to the technical field of ocean dynamics, including the following steps: S1, determining target mesoscale vortex area, obtaining high-resolution three-dimensional flow field data and preprocessing to obtain standardized three-dimensional flow field data;S2, by frequency domain filtering separation processing extracts near-inertial flow component and background flow component;S3, by sliding time average processing, obtain Reynolds stress component;S4, by spatial derivative calculation and strain rate derivation processing, obtain horizontal direction strain rate component;S5, substitute energy exchange equation, obtain energy exchange rate;S6, combined with seawater density, complete depth and time integration processing, obtain net energy exchange and realize energy transfer direction discrimination.The above method is used, realize the high-precision quantitative analysis of marine mesoscale vortex and near-inertial internal wave energy exchange direction and intensity and accurate discrimination of transfer direction.

This invention belongs to the field of marine remote sensing technology, specifically relating to an automatic SAR ocean internal wave detection method based on a position-adaptive convolutional neural network. The method includes collecting and preprocessing SAR image data within a target area over a given time period; drawing internal wave mask images as ground truth labels based on the internal wave characteristics of the images; constructing a position-adaptive convolutional neural network; training the model using remote sensing images and ground truth labels to obtain an internal wave extraction model; and inputting SAR images from other time periods into the internal wave extraction model to extract internal wave information, thereby achieving accurate identification and mapping of the morphology and location of ocean internal waves. This invention is based on deep learning for ocean internal wave recognition, overcoming the difficulty of traditional image detection methods being susceptible to speckle noise and improving the fragmented recognition results caused by the inability of conventional convolutional methods to adapt to internal wave morphology.

The application discloses a tail trace detection model construction method based on frequency domain multi-scale perception, relates to the technical field of ocean observation, and is used for capturing weak tail trace features to perform sea surface internal wave tail trace detection.The tail trace image dataset is constructed, an RT-DETR model is taken as a baseline model to construct a tail trace detection model, a multi-scale texture information selection network is constructed to replace a backbone network of the baseline model, a frequency domain multi-scale attention perception module is constructed to replace an AIFI module of the baseline model, a gold-cane-rod convolution downsampling module is adopted to replace a standard convolution downsampling module of the baseline model, the tail trace detection model is trained and optimized based on the tail trace image dataset, and a target detection result is acquired.The tail trace detection model AUVRT-DETR constructed by the application keeps the lightweight advantage, improves the detection precision, and achieves the best balance between the detection precision and the calculation efficiency, and while realizing the highest detection precision, the model parameter quantity and the calculation cost are significantly reduced.

This application provides a method, apparatus, device, and storage medium for forecasting internal ocean waves, applied in the field of marine phenomenon forecasting technology. The method includes acquiring historical marine environmental data at multiple spatiotemporal locations within a target area; generating an internal wave label dataset that spatiotemporally matches the historical marine environmental data based on historical satellite internal wave observation data, where each internal wave label indicates the presence of internal waves at a corresponding spatiotemporal location; determining feature variables for model training from the historical marine environmental data, including salinity and temperature; and training a machine learning model based on the historical data of the feature variables and their corresponding internal wave labels to obtain an internal wave prediction model for predicting the probability of internal wave occurrence. This application can reduce computational costs and improve forecast timeliness; furthermore, the obtained forecast results can be used to send risk warnings and avoidance suggestions to ships, offshore platforms, etc.