51 results about "Surface ocean" patented technology

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

A computer-implemented method for downscaling an ocean surface wind (OSW). The method comprises: obtaining an input image with a set of wind data, constructing an OSW downscaling model based on a TransUNet architecture; using the OSW downscaling model to generate an output image with a set of resulting wind data according to the input image, and before the OSW downscaling model is used to generate the output image, training the OSW downscaling model according to a plurality of sets of training samples, wherein the OSW downscaling model is trained by optimizing model parameters of the OSW downscaling model in a sense of minimizing a loss function for improving resolution.

The invention belongs to the technical field of ocean surface flow velocity remote sensing measurement, and particularly relates to a Luoga No.2 SAR (Synthetic Aperture Radar) flow velocity correction method based on Bragg orbit velocity, which comprises the following steps of: verifying the physical correctness of an orbit velocity mechanism relative to a phase velocity mechanism; under a DopRIM framework, a physically consistent sea wave error correction model is established; calibrating parameters of the sea wave error correction model; applying the calibrated sea wave error correction model to along-orbit interference SAR observation data of the Luoma No. 2 satellite, and calculating the radial surface flow velocity and sea wave error of each resolution unit; comparing an output result with a matched reference truth value to evaluate the effectiveness of the sea wave error correction model; comparing the sea wave error correction model with a KaDOP model, and respectively counting and analyzing simulated sea wave errors under different methods; the sea wave error simulation result is visually displayed, and the improvement effect is quantitatively evaluated through in-situ observation data and a KaDOP simulation result.

The invention belongs to the field of ocean remote sensing detection, and particularly relates to an ocean surface salinity inversion method, system and device and a storage medium, and the method comprises the steps: obtaining training day brightness temperature data and training month salinity data of an ocean surface; preprocessing the brightness temperature data of the training day and then carrying out regional screening to obtain regional brightness temperature data; performing interpolation processing on the training month salinity data to obtain interpolation salinity data; performing time alignment on the regional brightness data and the interpolation salinity data to obtain a brightness temperature data set and a salinity data set; extracting features according to the brightness temperature data set to obtain brightness temperature features, inputting the brightness temperature features into the salinity intelligent inversion network model to obtain an output salinity value, calculating the output salinity value and an output loss value of the salinity data set through a loss function, and adjusting model parameters of the salinity intelligent inversion network model according to the output loss value to obtain an optimization model; and inputting the actual brightness temperature data into the optimization model to obtain a salinity value. The method has the effect of improving the inversion precision.

The invention belongs to the technical field of ocean surface flow velocity remote sensing observation, and particularly relates to a wave deviation correction method based on wave orbit motion and scattering characteristic analysis, which comprises the following steps of: establishing a wave deviation correction method driven by a physical mechanism in combination with wave orbit motion and Bragg wave scattering intensity change; generating simulation data through the M4S model to train the correction model; applying the trained model to actually measured data of an airborne Ka-band along-track interferometric synthetic aperture radar of the Jones Zhou straits of the South China Sea, and verifying the effectiveness of the model by comparing and analyzing the actually measured data with real data provided by a GNSS drifting buoy; the reduction amplitude of the radial surface flow velocity measurement error is calculated, and the experimental result shows that the error is averagely reduced by about 83.5%. The invention provides an efficient and innovative technical scheme for improving the remote sensing measurement accuracy of the ocean surface flow velocity, can obviously reduce the remote sensing measurement error of the ocean surface flow velocity, and is suitable for the application fields of ocean environment monitoring, climate change research and ocean engineering.

The invention provides a sea surface oil spill monitoring system based on a GNSS-R receiver. The sea surface oil spill monitoring system comprises an unmanned aerial vehicle platform, the GNSS-R receiver, a signal processing module, an oil spill feature extraction module, a data storage and transmission module and a ground station data processing module, wherein the GNSS-R receiver, the signal processing module, the oil spill feature extraction module and the data storage and transmission module are installed on the unmanned aerial vehicle platform and connected in sequence. According to the system, the GNSS-R receiver acquires the GNSS scattering signals on the surface of the ocean by acquiring the reduced polarization, the requirements of independent inversion and application of parameter inversion of typical disasters of the ocean can be effectively met, the GNSS-R receiver supports multi-band and multi-polarization GNSS scattering signal receiving, the oil spill detection precision and reliability are improved, and the reliability of the system is improved. And high-precision oil spill detection can be realized under complex meteorological conditions.

The invention belongs to the field of ocean wave feature extraction, and particularly relates to a sea wave feature inversion method based on reverse echo measurement, which comprises the following steps of: acquiring an echo signal from a seabed to an ocean surface, and performing adaptive time window smoothing processing on the echo signal to obtain an echo signal; carrying out abnormal value identification and missing data filling on the smoothed data; removing abnormal values by adopting an improved Tukey method, and filling missing data by adopting a segmented cubic spline interpolation method; through high-pass filtering and detrending processing, sea wave echo signals with low-frequency trends removed are obtained; and selecting a proper time window to calculate the dispersion of echo time, establishing a mathematical relationship between the dispersion and the significant wave height through a regression algorithm, and substituting observation data into an equation to obtain an inversion result of the significant wave height. According to the method, the sea waves are subjected to feature inversion based on the reverse echo measurement technology, the precision of sea wave feature extraction is effectively improved, the field measurement cost is reduced, and the stable and low-cost sea wave measurement method is provided.

The invention discloses a marine pollution traceability big data analysis method based on a space-time diagram neural network, which is applied to the field of marine monitoring, and comprises the following steps: extracting seawater pollution diffusion characteristics according to periodic water quality data of a target sea area and a marine flow field spectrum, and processing the seawater pollution diffusion characteristics based on the diagram neural network, and reconstructing the pollution diffusion network through ocean surface remote sensing data, and adjusting nodes and edges of the pollution diffusion network according to a bidirectional propagation analysis result to obtain a target pollution diffusion network. According to the method, the diffusion path of pollutants can be simulated more accurately, so that the positioning precision of the pollution source is improved, the method better adapts to the actual marine pollution monitoring condition, and the reliability of the traceability result is improved.

The invention discloses a long-period prediction and forecast method for a sea surface marine environment. The method comprises the following steps: acquiring GLORYS reanalysis data and satellite observation data; data preprocessing including data latitude expansion, data resolution alignment, mask marking, exception processing and data normalization is carried out on the collected data; 3, constructing a training and testing sample by adopting a sliding window strategy; constructing a spatio-temporal data set; through ConvLSTM layer stacking design, space-time dependency of data is extracted, and redundant information is reduced through feature fusion compression; constructing a hybrid model special for a sea surface downscaling prediction task through feature up-sampling, a convolution refining network and residual connection; a hybrid model of a sea surface downscaling prediction task is trained, long-period prediction and forecast of a sea surface marine environment are carried out, feature level fusion of GLORYS and satellite data is realized, prediction and downscaling tasks share a feature extraction network, error accumulation of staged training is avoided, and calculation efficiency is improved.

The present invention discloses a method for analyzing ocean pollution source tracing big data based on a spatiotemporal graph neural network. The method is applied to the field of ocean monitoring. The method involves extracting seawater pollution diffusion characteristics based on periodic water quality data and ocean flow field maps of a target sea area, processing these seawater pollution diffusion characteristics based on a graph neural network, obtaining a pollution diffusion network for pollutants in the target sea area, and reconstructing the pollution diffusion network using ocean surface remote sensing data. The method then adjusts the nodes and edges of the pollution diffusion network based on the results of a two-way propagation analysis to obtain a target pollution diffusion network. The method can more accurately simulate the diffusion path of pollutants, thereby improving the accuracy of pollution source location, better adapting to actual marine pollution monitoring conditions, and enhancing the reliability of tracing results.

The invention discloses an oil spill rapid simulation method applied to a submarine pipeline oil leakage accident. The method comprises the following steps: firstly, adding a reading module for three-dimensional flow field data on the basis of a conventional ocean surface two-dimensional oil spill model; secondly, designing a stress driving algorithm of the sea surface wind field to oil particles at different water depth positions below the sea surface; thirdly, designing a floating speed algorithm of oil particles below the sea surface; fourthly, designing a sea surface vertical mixing layer height and vertical diffusion coefficient algorithm; and finally, driving a three-dimensional oil spill drifting and diffusing model to calculate in combination with flow field forecast data and wind field forecast data. According to the method, on the basis of a conventional two-dimensional oil spill model, a vertical physical change process of oil particles is added, and three-dimensional flow field data are loaded; simulation calculation of related physical processes such as floating, drifting and diffusion of oil particles in a three-dimensional water body space from the seabed to the sea surface is achieved, and reference values are provided for rapid simulation and emergency deduction decision making of petroleum pollutant leakage in marine pollution.

The invention belongs to the field of marine environment monitoring, and particularly relates to a method for synchronously measuring the drifting speed of floating objects on the sea surface and the flow speed of ocean currents on the peripheral surface layer, which comprises the following steps: manufacturing a drifting buoy, and enabling the drifting buoy to vertically float on the sea surface and be completely submerged in water through a steel bar counterweight; releasing the drifting buoy to a target position through the unmanned aerial vehicle; tracking and shooting drifting buoys and sea surface floating objects, and acquiring and storing image data containing positions of the drifting buoys and the sea surface floating objects; converting the relative positions of the pixels of the image data acquired by the unmanned aerial vehicle into latitude and longitude coordinates, and generating a TIFF file; after the unmanned aerial vehicle image of the TIFF file is subjected to geometric correction, the corrected TIFF file is transmitted to QGIS software, the drifting buoy position and the sea surface floating object position are marked, and the imaging moment is synchronously recorded; and calculating the sea surface floater drift speed and the peripheral surface ocean current flow velocity based on the position change at the adjacent moments. According to the scheme, the sea surface flow velocity of the near area of the sea surface floating object can be accurately measured, and the depth of the measured surface flow velocity is determined.

The present invention belongs to the field of ocean wave feature extraction, and specifically relates to a method for inverting sea wave features based on reverse echo measurement, which includes the following steps: obtaining the echo signal from the seabed to the ocean surface, performing adaptive time window smoothing processing on the echo signal, and performing outlier identification and missing data filling on the data after smoothing processing; using the improved Tukey method to remove outliers and using the piecewise cubic spline interpolation method to fill in the missing data; obtaining the sea wave echo signal with the low-frequency trend removed through high-pass filtering and detrending processing; selecting an appropriate time window to calculate the dispersion of the echo time, establishing a mathematical relationship between the dispersion and the significant wave height through a regression algorithm, and substituting the observed data into the equation to obtain the inversion result of the significant wave height. The present invention performs feature inversion on sea waves based on reverse echo measurement technology, effectively improving the accuracy of sea wave feature extraction, reducing the on-site measurement cost, and providing a stable and low-cost sea wave measurement method.

Disclosed in the present invention is a method for studying the interannual variability of marine heatwaves in the Southern Indian Ocean. The method includes: on the basis of acquired data, exploring the spatial distribution characteristics and linear variation trend characteristics of marine heatwaves in the Southern Indian Ocean; analyzing the relationship between the intensity of marine heatwaves in the Southern Indian Ocean and an El Niño-Southern Oscillation (ENSO) process; analyzing the spatial evolution of marine heatwaves and sea surface temperature anomalies in the Southern Indian Ocean during El Niño events; analyzing the energy contributions of the ocean and atmosphere to marine heatwave events during El Niño events, and exploring the physical mechanism of marine heatwaves in the Southern Indian Ocean; and determining whether marine heatwaves in the Southern Indian Ocean are affected by shortwave radiation and latent heat modulated by the ENSO process. The present invention discovers that during El Niño events, within a study area, low cloud cover decreases while high cloud cover increases, the mixed-layer depth reduces, and shortwave radiation penetrates the sea surface; moreover, northwesterly winds anomalously intensify while climatological southeasterly winds are suppressed, thereby reducing evaporation, decreasing latent heat energy loss, and thus promoting warming.

The invention discloses a method for acquiring ocean surface wave and wind-generated flow parameters in a high-wind-speed environment, which comprises the following steps of: constructing a wave spectrum model, and inputting wind speed information into the wave spectrum model to obtain a one-dimensional function of wave, namely a one-dimensional height spectrum of the wave; constructing a direction function model, and inputting the wind speed information and the wind direction information into the direction function model to obtain a wave two-dimensional direction spectrum; and constructing a Doppler spectrum model, and inputting the wave one-dimensional height spectrum and the wave two-dimensional direction spectrum into the Doppler spectrum model to obtain wind-generated flow velocity information caused by wind waves in a high-wind-speed environment. The application value in the high-wind-speed environment is particularly remarkable, a brand new solution is provided for kinetic research of extreme ocean phenomena such as typhoon, and an important scientific basis is provided for ocean resource development and weather forecast.

The invention discloses a wave information extraction method and system based on video monitoring, and relates to the technical field of data processing, and the method comprises the steps: collecting the video data of an ocean surface and the meteorological data of a corresponding space-time position; processing the video data and the meteorological data, and training a video feature encoder to enable the video feature encoder to learn a physical causal relationship between a wind field and a wave field; inputting video data into the trained video feature encoder, and extracting to obtain a video feature vector; processing the video feature vector to enhance space-time relevance, and injecting a physical prior embedded vector to obtain an enhanced feature vector; and reconstructing a continuous wave field based on the enhanced feature vector, and outputting physical quantities of wave height, wave direction and wave steep. According to the invention, large-range, high-temporal-spatial-resolution, low-cost, non-contact and long-term continuous wave monitoring can be realized.

The invention relates to the technical field of ocean observation, in particular to an ocean bottom layer heat wave prediction method and system based on a generative adversarial network and computer device.The method comprises the steps that a data set is established, satellite remote sensing data and reanalysis data of the ocean surface are preprocessed based on longitude and latitude grids, and a multi-dimensional data set is constructed; and a bottom layer heat wave prediction step: based on a pre-trained heat wave prediction model, feature extraction is performed on the multi-dimensional data set, the heat wave prediction model outputs a prediction result by using a generator, and the generator is obtained by training confrontation based on historical heat wave phenomenon data of the ocean bottom layer and a discriminator. The method can effectively predict ocean bottom layer heat waves, thus improving deep sea heat wave phenomenon perception capability and response efficiency.

This application relates to the field of computer graphics technology, and in particular to a marine visualization method and system for simulating ship motion. The method includes: generating an ocean height field based on a selected wave spectrum; acquiring viewpoint height and topographic information, and generating an ocean mesh model based on the viewpoint height, the topographic information, and the ocean height field; calculating ocean surface reflected light, ocean surface scattered light, and foam based on the ocean mesh model to obtain the ocean visualization result. This application can simulate ocean height fields and lighting effects under different sea conditions, and users can quickly adjust the simulation environment according to their needs. Visualizing marine elements can intuitively reflect changes in the marine environment, which is helpful for environmental monitoring and management. For example, visualization of data such as temperature and ocean currents can assist in scientific research and decision-making.

A computer-implemented method for ocean surface wind (OSW) downscaling. The method comprises the following steps: obtaining an input image with a group of wind data; an OSW downscaling model based on a TransUNet framework is constructed; generating an output image having a set of resulting wind data from the input image using the OSW downscaling model; and before generating the output image using the OSW downscaling model, training the OSW downscaling model according to a plurality of sets of training samples, where the OSW downscaling model is trained by optimizing model parameters of the OSW downscaling model in the sense of minimizing a loss function to improve resolution.

The invention relates to the technical field of ocean remote sensing and intelligent ocean monitoring, in particular to a sea surface ocean current inversion method and system fusing ground wave radar and GNSS reflection, and the method comprises the steps: obtaining ground wave radar radial flow and GNSS-R original data; aI-driven space-time interpolation is carried out on the ground wave radar data so as to restore missing; constructing a wind-wave-current coupled GNSS-R forward physical model to extract weak ocean current signals; taking a regional ocean model as a framework, dynamically distributing two types of observation weights according to a shore distance, and carrying out partition self-adaptive data assimilation by adopting ensemble Kalman filtering; and finally, a high-resolution seamlessly-connected two-dimensional ocean current field product is output. According to the method, the problems of discontinuous flow field, high data missing rate and difficulty in GNSS-R ocean current signal extraction in a coastal-ocean transition zone of a traditional method are solved, and physically consistent and high-precision business ocean current monitoring from a coastal area to an open sea area is realized.

The present invention provides a system and method for enhancing adhesion between microplastics and air bubbles. More specifically, the present invention relates to a system and method for enhancing adhesion between microplastics and air bubbles so that the maximum number of microplastics can be collected from aquatic environments. The pressure difference between the stern section (from the buttock stern to the bottom baseline) and the outflow and inflow of air bubbles maximizes collection efficiency. The collected phytoplankton are reintroduced to the ocean surface to enhance the conversion of carbon dioxide to oxygen as a means of mitigating global warming.

The invention discloses an ocean surface wave monitoring system based on a synthetic aperture radar, which relates to the technical field of ocean surface wave monitoring, and comprises the following steps: carrying out azimuth split view on single-view complex data, and generating a split view cross spectrum and an azimuth self spectrum; determining a frequency domain selection area according to the cross spectrum energy, and calculating azimuth antisymmetric phase skewness in the area; determining azimuth cut-off by the azimuth auto-spectrum, constructing an odd phase compensation operator, and performing phase correction on the split-view cross-spectrum; performing direction projection on the correction spectrum to obtain a dominant propagation direction, and determining a peak wave number according to a direction correlation weight; and the peak wavelength and the peak period are obtained by using linear dispersion relation conversion. According to the invention, the monitoring reliability and consistency under complex sea conditions can be improved.

The present application belongs to the technical field of remote sensing measurement of ocean surface current velocity, and in particular to a Lujia-2 SAR current velocity correction method based on Bragg orbital velocity, comprising the following steps: verifying the physical correctness of the orbital velocity mechanism relative to the phase velocity mechanism; and under the DopRIM framework, establishing a physically consistent sea wave error correction model; calibrating the parameters of the sea wave error correction model; applying the calibrated sea wave error correction model to Lujia-2 satellite along-track interferometric SAR observation data to calculate the radial surface current velocity and sea wave error of each resolution unit; comparing the output results with the matched reference true value to evaluate the effectiveness of the sea wave error correction model; comparing the sea wave error correction model with the KaDOP model, respectively, to statistically analyze the simulated sea wave error under different methods; visualizing the sea wave error simulation results, and quantitatively evaluating the improvement effect through in-situ observation data and KaDOP simulation results.

The invention relates to a parameter adjustment method for large-scale mooring buoy seaborne comparative measurement, which comprises the following steps: establishing connection with a data acquisition system of a target buoy in a wireless communication mode, acquiring an observation element type and a sampling frequency parameter of the target buoy, and adjusting the observation element type and the sampling frequency parameter of the target buoy based on the acquired parameter; performing observation parameter configuration on each sensor arranged in the comparison measurement device, synchronizing the comparison measurement device with a system clock of the target buoy, monitoring the drifting direction and speed of a water sail at the bottom of the comparison measurement device, and determining the drifting speed of the target buoy according to the collected wave observation data and the height-corrected wind speed and wind direction data. And calculating the flow velocity and the flow direction of the surface ocean current through a preset algorithm model.

The application discloses a beacon machine working method and a storage medium, and relates to the technical field of beacon machines.The method comprises the following steps: a beacon machine is arranged on a submersible device; a rechargeable battery continuously supplies power to a main control module through a power conversion module; when the submersible device is submerged into the sea, two detection probes are conducted through seawater, the main control module controls the power control module to be disconnected, and the communication board is in a power-off state; when the submersible device is floated to the surface of the sea, the two detection probes are disconnected, and the main control module controls the power control module to be conducted; the main control module controls the power conversion module to supply power to a positioning module and a communication module at a preset frequency, the positioning module is used for positioning the beacon machine, the position information of the beacon machine is obtained, and the position information is sent through the communication module; and the main control module updates the preset frequency according to the change of the position information multiple times.The beacon machine working method can reduce the power consumption of the beacon machine and improve the endurance.

The present invention discloses a method for rapid simulation of oil spills applied to submarine pipeline oil leakage accidents. First, the present invention adds a module for reading three-dimensional flow field data on the basis of a conventional two-dimensional oil spill model on the ocean surface; secondly, designs a stress-driven algorithm for the sea surface wind field on oil particles at different water depths below the sea surface; thirdly, designs an algorithm for the floating velocity of oil particles below the sea surface; fourthly, designs an algorithm for the vertical mixing layer height and vertical diffusion coefficient of the sea surface; finally, combines flow field forecast data and wind field forecast data to drive the calculation of a three-dimensional oil spill drift and diffusion model. On the basis of a conventional two-dimensional oil spill model, the present invention adds the vertical physical change process of oil particles, and by loading three-dimensional flow field data, realizes the simulation calculation of related physical processes such as floating, drifting and diffusion of oil particles in the three-dimensional water space from the seabed to the sea surface, which provides reference value for rapid simulation and emergency deduction decision-making of oil pollutant leakage in marine pollution.

The application provides a kind of ocean dynamic monitoring method and system based on fusion image, comprising: collecting the RGB image and short wave infrared SWIR image of target marine area, image registration is carried out to RGB image and SWIR image, and multivariate data set is constructed;Improved BEMD is used to carry out spatial decomposition to two-dimensional image in multivariate data set, improved MEMD is used to carry out joint decomposition to multichannel image in multivariate data set, and a plurality of eigenmode function images are obtained;A plurality of eigenmode function images are calculated, and the wave direction, wave height and ocean current velocity of sea wave are obtained.The application combines the principle of ocean dynamics, uses improved BEMD method and MEMD to decompose ocean image and infrared image, accurately calculates the wave and ocean current characteristics of ocean surface, and provides reliable data support for marine environment monitoring, shipping safety, ocean resource development and other fields.

The present invention belongs to the field of ocean remote sensing and specifically relates to a method, system, device, and storage medium for inverting ocean surface salinity. The method comprises obtaining daily training brightness temperature data and monthly training salinity data on the ocean surface; preprocessing the daily training brightness temperature data and then performing regional screening to obtain regional brightness temperature data; interpolating the monthly training salinity data to obtain interpolated salinity data; temporally aligning the regional brightness data and the interpolated salinity data to obtain a brightness temperature dataset and a salinity dataset; extracting features from the brightness temperature dataset to obtain brightness temperature features, inputting the brightness temperature features into a salinity intelligent inversion network model to obtain an output salinity value, calculating the output salinity value and the output loss value of the salinity dataset using a loss function, adjusting the model parameters of the salinity intelligent inversion network model based on the output loss value to obtain an optimized model; and inputting actual brightness temperature data into the optimized model to obtain a salinity value. The present invention has the effect of improving inversion accuracy.

A sea going instrument is used to measure the 234Th activity on settling particles in the ocean as an indicator of carbon sequestration as export from the surface ocean. The instrumentation can be adapted to multiple sampling platforms with the goal of providing high temporal resolution 234Th flux measurements at multiple locations and depths in the ocean. This proxy of mass flux is used to complement other in situ sensors to provide high resolution data for features such as Chlorophyll-a concentration (Chla), accessory pigments, and PIC normalized to mass flux.