33 results about "Surface ocean" patented technology

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 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 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 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.

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.

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.

The invention relates to the technical field of marine environment monitoring, in particular to a high-stability low-power-consumption marine surface layer fixed-point observation buoy system which comprises a buoy platform composed of a split columnar buoy body and a buoy anchoring system. The buoy platform attitude monitoring system is used for monitoring the attitude of the buoy platform; the attitude correction system is used for correcting the attitude of the buoy platform; the intelligent data acquisition and transmission system is used for acquiring observation data of the air-sea flux observation system and sending the data to the shore-based data receiving station; and an intelligent power management and power consumption control module. The attitude correction system comprises a coordinate system rotation module, a shake correction module and a tilt correction module. The device can stably run in an extreme environment for a long time, and long-term continuous accurate observation of sea waves, surface ocean currents, atmospheric water vapor content, air-sea flux and the like is realized.

This invention provides a GNSS-R receiver-based marine oil spill monitoring system, comprising an unmanned aerial vehicle (UAV) platform, a GNSS-R receiver mounted on the UAV platform and connected in sequence, a signal processing module, an oil spill feature extraction module, a data storage and transmission module, and a ground station data processing module. In this system, the GNSS-R receiver uses reduced polarization to acquire GNSS scattered signals from the ocean surface, effectively addressing the need for independent inversion and application of typical marine disaster parameter inversion. The GNSS-R receiver supports multi-band and multi-polarization GNSS scattered signal reception, improving the accuracy and reliability of oil spill detection and enabling high-precision oil spill detection under complex meteorological conditions.

A method and device for detecting high-resolution ocean surface physical state based on satellite-borne GNSS-R complex waveform data, the method comprising: acquiring complex waveform data; preprocessing the complex waveform data to obtain a complex waveform data sequence for detecting the ocean surface physical state, dividing the detection complex waveform data sequence into multiple complex waveform groups in chronological order, and determining the coherence coefficient between two consecutive complex waveforms in each complex waveform group; taking the complex waveform data sequence as a test set, selecting 20% of the data in the test set as a training set, determining the threshold value of the average coherence coefficient feature quantity according to the validation data and the training set using the histogram method, taking the threshold value of the average coherence coefficient feature quantity as the demarcation line for judging sea ice and seawater, and obtaining the surface physical state of the detection area determined by the multiple sampling points corresponding to each complex waveform group in the test set according to the threshold value and the average coherence coefficient of each complex waveform group in the test set.

The invention provides a pull-type sea surface phytoplankton cleaning and collecting device and a working method thereof, and relates to the technical field of sea surface phytoplankton cleaning. The device comprises a ship body, a cleaning mechanism, a conveying mechanism, a crushing mechanism, a compression dewatering mechanism and a collecting mechanism, and the cleaning mechanism comprises a roller and is used for salvaging and collecting sea surface phytoplankton; the conveying mechanism comprises a first conveying mechanism and a second conveying mechanism, the first conveying mechanism is used for conveying the sea surface phytoplankton collected by the roller to the second conveying mechanism, the second conveying mechanism is used for conveying the sea surface phytoplankton into the crushing mechanism, and the crushing mechanism is used for crushing the sea surface phytoplankton; conveying the crushed sea surface phytoplankton into a compression dehydration mechanism; and the compression dehydration mechanism is used for dehydrating, compressing and collecting the crushed sea surface phytoplankton. The sea surface phytoplankton collecting device is convenient to assemble, adopts modular design, and can realize relatively perfect cleaning, crushing, dewatering and collecting of sea surface phytoplankton.

The invention discloses a physical constraint enhanced and multi-factor driven satellite-borne GNSS-R sea surface ocean current velocity estimation combination model construction method. The method comprises the following steps: acquiring satellite-borne GNSS-R observation data and multi-source ocean environment parameter variable data; data quality control and variable parameter extraction and processing; the invention discloses a normalized bistatic radar scattering cross section physical empirical model construction method for spaceborne GNSS-R sea surface ocean current velocity estimation. A developed model simultaneously considers the comprehensive influence of multiple factors such as wind speed, ocean current, surge, rainfall, sea surface temperature and salinity on a normalized bistatic radar scattering cross section. Constructing a satellite-borne GNSS-R sea surface ocean current velocity estimation combination model, and selecting a Transform modulation diffusion algorithm to develop the ocean current velocity estimation combination model; and the performance of the combined model is verified by adopting OSCAR data. The problem of high-precision estimation of the multi-factor-driven satellite-borne GNSS-R sea surface ocean current speed is solved.

The invention belongs to the technical field of ocean surface flow velocity remote sensing measurement and satellite load design, and particularly relates to a load design optimization method for SAR measurement of ocean surface flow velocity, and the method comprises the following steps: constructing a representative sea condition set of a target sea area based on multi-year storm reanalysis data; calculating wave induction deviations under different observation geometries by using a Doppler wave deviation model; searching the optimal observation geometry with the minimum time average deviation of each space lattice point and the distribution thereof; constructing a deviation lower limit, a coverage rate and a geometric consistency task index based on the optimal result; selecting a typical observation corridor, and comparing and evaluating the deviation improvement effect of the optimal geometry and the reference geometry; and according to the indexes and the improvement effect, determining an SAR load incident angle band and a sight line pointing range which meet task requirements, and forming an optimization scheme and performing visual output. According to the method, the wave induction deviation is used as design preposed constraint, a quantitative basis is provided for load parameter selection, the ocean current inversion deviation is effectively reduced, and the observation precision is improved.

A portable oil skimmer includes a collection unit configured to collect spilled oil while floating on the surface of the ocean. The collection unit includes a main body configured to allow a first oil-skimming member to be rotatably mounted on the front end thereof, a second oil-skimming member to be rotatably mounted on the rear end thereof, and a pipe to be connected to the upper portion thereof so as to communicate with the interior thereof, a pair of buoyant bodies mounted on two side surfaces of the main body, with the main body interposed therebetween, and an oil pump disposed in the main body and configured to convey oil collected by the first oil-skimming member and the second oil-skimming member to the outside of the main body through the pipe.

The application discloses a sea surface wind speed prediction method, comprising the following steps: receiving GNSS-R data of a sea surface, and setting the GNSS-R data as first data; receiving measurement data of the sea surface by a spaceborne microwave scatterometer, and setting the measurement data as second data; inputting the first data and the second data into a sea surface wind speed prediction model to obtain a predicted sea surface wind speed value. The sea surface wind speed prediction model comprises a machine learning (ML) regression model, and the first data and the second data are subjected to inversion calculation and fusion through the ML regression model to obtain the predicted sea surface wind speed value.

The application discloses a kind of high-precision two-dimensional flow field measurement methods based on marine surface multi-parameter.The along-track baseline in interferometric measurement can provide along-track interference for SAR imaging, and the along-track interference SAR complex image can be used to inverse the radial velocity of sea surface flow field;The cross-track baseline can provide cross-track interference for SAR and altimeter imaging, and the cross-track interference complex image can generate sea surface elevation information, and the height difference between the ocean current region and other sea areas can assist in judging the flow direction and position of the ocean current, and the geostrophic flow field of the ocean can be inverted;Mixed baseline can provide along-track + cross-track baseline multi-angle observation degree of freedom for system imaging, and the system can obtain the multi-angle scattering characteristics of sea surface, which can help to avoid image saturation under complex sea conditions, and the two-dimensional sea surface wind field, sea wave can be inverted by multi-angle interference complex image, and the two-dimensional sea surface flow field can be removed from the ocean background field (sea surface wind, sea wave, sea surface height) to further improve the measurement accuracy of two-dimensional flow field.

The application belongs to the technical field of microwave remote sensing and detection, and particularly relates to a method and system for long-time sequence marine surface salinity prediction, which obtains input values and forgetting values by inputting monthly salinity data into input gates and forgetting gates, generates a multi-scale periodic coding vector according to a time step, inputs the multi-scale periodic coding vector into a full connection neural network, and performs dimension expansion to obtain a gating tensor; trend features are extracted from a previous cell state; a current hidden state corresponding to the current cell state is restored according to the trend features and the gating tensor to obtain current salinity data; the current salinity data is input into a prediction model for training, the prediction model is modulated according to a loss function, prediction model parameters are obtained when the loss function is minimum, the prediction model is adjusted according to the prediction model parameters, and an optimized prediction model is obtained; and the monthly salinity data set is input into the optimized prediction model to obtain predicted salinity values. The application can improve the accuracy of marine surface salinity prediction without a large number of parameters.