161 results about "Sea surface wind" patented technology

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 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.

The invention relates to the technical field of ship control, in particular to a fishing boat control system based on data analysis, which comprises a traction load analysis module, a fluctuation condition division module, an attitude disturbance estimation module, a propulsion power regulation module and a rudder angle correction output module. According to the method, by analyzing the traction force load sequence value and the moment arm length of the fishing net traction point, calculating the left and right side offset torque and marking the action direction, the attitude adjustment precision of the ship is optimized, the operation sea condition level is adjusted according to data, and the ship can be subjected to more reasonable attitude estimation and adjustment under the dynamic working condition; the wind pressure change rate and the pitch angle rate are combined to judge the direction resultant force trend, and the navigation attitude is adjusted according to the sea condition grade, so that the ship can more accurately resist the influence of sea surface storm waves and maintain the course stability, and the rudder angle is corrected by adjusting the propulsion power in real time, limiting the adjustment amplitude of the propulsion power and correcting the rudder angle according to the real-time data of the navigation attitude. And the propulsive efficiency and the navigation precision are optimized.

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 present application discloses a typhoon forecasting method based on domestic satellite data, which includes: a data fusion process: used to fuse GNSS-R sea surface wind data and IFS forecast data from the European center to generate a two-dimensional gridded sea surface vector wind field; a meteorological forecasting process combined with artificial intelligence, including a large meteorological model forecasting step and a numerical model forecasting step; a multi-typhoon path discrimination and typhoon intensity forecasting process, which is used to extract key meteorological element variables from regional forecast data files at different times, and judge the typhoon center based on the key meteorological element variables. The present application combines domestic satellite data and artificial intelligence technology, and through the deep combination of data fusion and numerical forecasting technology, improves the accuracy and refinement of typhoon path and intensity forecasts. In addition, through the application of large meteorological models, nonlinear and massive data can be processed more effectively, improving the forecast accuracy.

The invention provides a satellite scatterometer Ku wave band observation value rainfall influence correction method, and relates to the field of sea surface wind inversion methods, and the method comprises the steps: obtaining Ku wave band observation data, C wave band data and rainfall rate data of a satellite scatterometer, carrying out wind field matching and wind speed correction under a rain-free condition, so as to obtain an NRCS reference value corresponding to a sea surface real wind speed, and carrying out the calculation of the NRCS reference value; constructing to obtain a scatterometer rainfall matching data set; according to the scatterometer rainfall matching data set, the influence of rainfall on the scatterometer observation value is analyzed, and a Ku waveband rainfall influence conceptualization model is constructed; and calculating rainfall influence conceptualization model parameters and generating a deviation lookup table. According to the invention, on the basis of a classic scatterometer wind field inversion process, the problem of large inversion wind field deviation caused by the influence of rainfall on the observation value of the Ku wave band scatterometer in a medium-low rainfall rate range is solved.

The invention discloses a synthetic aperture radar sea surface wind field inversion method considering Doppler centroid anomaly, and the method comprises the following steps: obtaining vertical polarization SAR image data, and extracting a radar backscattering coefficient NRCS and an original Doppler centroid anomaly DCA related image; based on Doppler frequency data of the VV polarized SAR image, through quintic polynomial fitting and land area correction processing, DCA information caused by geophysical factors is extracted; acquiring actually measured wind speed data of the buoy, matching the actually measured wind speed data with an external background wind field, and performing nonlinear fitting correction by adopting an exponential function to obtain a corrected background wind field speed; constructing a variational analysis model containing an NRCS and DCA observation field, taking the corrected external wind field as a background field, and obtaining a wind speed field and a wind direction field through minimum cost function inversion; performing verification analysis on an inversion result by adopting buoy observation data subjected to space-time matching; the precision and reliability of the vector parameters of the sea surface wind field can be improved.

The invention relates to a floating platform-based wind measurement laser radar wind field inversion optimization method, system and device, and a medium, and the method comprises the steps: carrying out the PPI scanning through a laser radar, and obtaining the radial wind speed before correction; correcting the radial velocity component caused by the floating platform in the radial wind speed before correction for the first time; acquiring the real wind speed and wind direction at the preset position by using the mobile wind meter, and calculating the first radial wind speed corresponding to the azimuth angle of the mobile wind meter before laser radar correction according to the real wind speed and the real wind direction; performing RHI scanning at a preset position, and processing according to an RHI scanning result to obtain a second radial wind speed; based on the first radial wind speed and the second radial wind speed, carrying out second correction on the radial wind speed after the first correction; and based on the second correction result, performing inversion optimization on the sea surface wind field by adopting a VAP algorithm to obtain wind speed and wind direction data. The method can be widely applied to the technical field of remote sensing marine environment monitoring.

The invention discloses a C-SAR satellite sea surface wind speed inversion method combining spatial features, and relates to the technical field of sea surface wind speed inversion, and the method comprises the steps: selecting C-SAR data and ERA-Interim wind field data; pre-processing the C-SAR data, and then carrying out time and space matching on the pre-processed C-SAR data and ERA-Interim wind field data; and inputting the matched C-SAR data and ERA-Interim wind field data into a double-branch feature fusion network model based on attention mechanism guidance, inverting the wind speed of the central pixel position of the sub-image, and then comparing the wind speed with the ASCAT wind field data and the ERA-Interim wind field data for verification. According to the method, a double-branch neural network structure is adopted, Branch1 is utilized to construct nonlinear features among a backscattering coefficient, an incident angle, a viewing angle, a sea surface wind speed and a wind direction, and Branch2 is utilized to excavate spatial features of C-SAR data and ERA-Interim wind field data; carrying out feature fusion on the nonlinear features and the spatial features by utilizing a channel attention mechanism module; the dynamic characteristics of C-SAR inversion of the sea surface wind speed are guaranteed, and the inversion precision is improved.

The invention discloses a sea surface wind speed inversion method and device based on marine environment noise, and a storage medium. The method comprises the following steps: S1, obtaining marine noise data; s2, acquiring wind speed data according to time and latitude and longitude information in the ocean noise data; s3, audio data in the ocean noise data are processed, and center frequency point noise spectrum level data are obtained; s4, summarizing the noise spectrum level data of the center frequency point and the corresponding wind speed data into a neural network data set through time and latitude and longitude information; step S5, constructing an initial Elman network; s6, performing iterative optimization on the network weight and bias of the Elman network by adopting a cuckoo search algorithm to generate optimized network parameters; step S7, initializing an Elman network by using the optimized network parameters, and generating a CS-Elman model; training a CS-Elman model by using the neural network data set; and step S8, carrying out quantitative evaluation on the CS-Elman model. The method can achieve the high-precision prediction of the sea surface wind speed.

The invention discloses an SAR sea surface flow field inversion method and system based on Doppler frequency shift correction, and belongs to the technical field of synthetic aperture radars. The method comprises the following steps: dividing an SAR single-view complex image into # imgabs0 # sub-image blocks; estimating the Doppler center frequency shift of each sub-image block; estimating distance deviation according to the polarization mode and the beam center visual angle; calculating a direct current offset according to the adjacent land area data; removing a non-geophysical item Doppler frequency shift error by combining the distance direction deviation and the direct current offset; doppler frequency shift caused by sea surface wind waves is calculated and removed, and a sea surface flow field is obtained. According to the method, the estimation precision of the Doppler frequency shift error of the non-geophysical item is improved, and the error of SAR sea surface flow field inversion is reduced. And meanwhile, the requirement of fitting a non-geophysical item Doppler frequency shift error on land area data is reduced, and the application scene of SAR sea surface flow field inversion is expanded.

The invention discloses a typhoon center positioning method, device and equipment based on an SAR (Synthetic Aperture Radar) satellite, relates to the technical field of ocean microwave remote sensing, and is used for solving the problems that a typhoon positioning method in the prior art cannot accurately extract the position of a typhoon center and the positioning accuracy of the typhoon center is low. Comprising the following steps: acquiring basic data of an SAR satellite according to typhoon path information; performing sea surface wind speed and wind direction inversion based on the basic data to obtain inversion data; according to the inversion data, a gradient wind equation, a wind profile equation and the maximum wind speed are adopted to determine a high-wind-speed area of the typhoon; according to a cyclonic vortex structure of the typhoon, based on the vertical vortex field of the high-wind-speed area, the horizontal divergence field of the high-wind-speed area and the composite field of the high-wind-speed area, determining the position range of the center point of the typhoon; and positioning a typhoon center meeting preset precision according to the typhoon center point position range. According to the technical scheme, the positioning precision of the typhoon center can be improved.

The invention provides a simulation method for a GNSS scattering signal of a sea surface wind field. The simulation method comprises the following steps: establishing a simulation model of a delay-Doppler domain mapping waveform; a compact polarization model based on a microwave scattering model is established, a polarization component of a complete polarization scattering coefficient matrix is calculated by using the complete polarization microwave scattering model, and a scattering coefficient of a compact polarization signal is obtained according to the polarization component of the complete polarization scattering coefficient matrix and a Stokes sub-vector of the compact polarization signal. The coefficient is used as a bistatic radar scattering coefficient in a simulation model of a delay-Doppler domain mapping waveform; establishing a sea wave spectrum model for obtaining a sea wave spectrum density function; and simulating by using the model to obtain a delay-Doppler domain mapping waveform. According to the method provided by the invention, the full-polarization DDM waveform of the GNSS scattering signal can be generated in the compact polarization mode, so that the hardware complexity is reduced, and sufficient and comprehensive sea surface wind field inversion information is reserved.

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 invention discloses a construction and inversion method of a sea surface wind speed inversion model based on a dual-frequency wind field radar, the sea surface wind speed inversion model comprises a sea surface wind speed inversion model and a geophysical model function, and scattering measurement data, microwave radiometer data and sea surface wind field data are acquired; taking latitude and longitude and time information of the scattering measurement data as a reference, and respectively carrying out quasi-synchronous matching on the microwave radiometer data and the sea surface wind field data with the scattering measurement data by adopting a space-time matching threshold method to obtain an active and passive data set; on the basis of sea surface radiation and scattering characteristics, a geophysical model function for wind direction inversion and a sea surface wind speed inversion model capable of inhibiting rainfall influence are constructed by utilizing the characteristic that different frequencies have different penetrating power and adopting an active and passive spectrum gradient ratio, and the sea surface wind speed and the sea surface wind direction of a high-wind-speed sea surface can be obtained at the same time; the technical problem that it is difficult to quantitatively measure the sea surface wind speed and the sea surface wind direction of the high-wind-speed sea surface wind field at the same time in the prior art is solved.

The invention provides a method and a device for correcting a backscattering coefficient of a wide scanning synthetic aperture radar, and the method comprises the steps: determining a backscattering coefficient of each pixel in a to-be-processed image, and then determining a backscattering coefficient correction coefficient of the pixel through employing the data of a mode sea surface wind field, a geophysical model function and the like; and correcting the backscattering coefficient of the pixel by using the backscattering coefficient correction coefficient to generate a target backscattering coefficient corresponding to the pixel. According to the method and the device, the backscattering coefficients of the pixels are corrected, so that the amplitude band inconsistency between the SAR image strips in the wide-width scanning mode can be eliminated, the more uniform and higher-precision SAR image in the wide-width scanning mode can be obtained, and better data support is provided for subsequent processing.

The invention provides a multi-modal information fusion sea surface wind speed inversion method, device and system, and the method comprises the steps: obtaining satellite navigation data, wind speed reference data and significant wave height data, performing matching and quality control on the wind speed reference data and the significant wave height data with the satellite navigation data to obtain an initial data set; recombining the initial data set according to a track to obtain a target data set, and dividing the target data set into a training set, a verification set and a test set; and constructing a wind speed inversion model by using the spatial-temporal correlation of continuous time delay-Doppler diagram observation values in the same trajectory and combining with auxiliary feature information. According to the invention, the extracted information can be fused with various auxiliary feature information, the inversion precision is improved, and the influence characteristics of the ocean state on the wind speed inversion can be quantitatively analyzed.

The invention discloses a shallow sea self-sinking and floating type section buoy based on a flexible solar material and a method thereof. The shallow sea self-sinking and floating type section buoy comprises a detection unit, and the detection unit comprises a shell assembly, a sensing assembly arranged at the top of the shell assembly, a hydraulic system arranged in the shell assembly and a control assembly arranged in the shell assembly; the hydraulic system comprises an inner oil bag, an outer oil bag, a first two-position two-way electromagnetic directional valve connected with the inner oil bag through a pipeline and a second two-position two-way electromagnetic directional valve connected with the inner oil bag through a pipeline, and the first two-position two-way electromagnetic directional valve is connected with a third two-position two-way electromagnetic directional valve. Efficient collection of solar energy is achieved, the gravity center of the buoy is adjusted by rotating the position of the mass block, so that the posture of the buoy is changed, the buoy body of the buoy rotates by 90 degrees and transversely floats on the sea surface, the influence of sea surface stormy waves on the buoy body is reduced, and the service life of the buoy is prolonged. And the daylighting area of the buoy body exposed out of the water surface is larger.

The embodiment of the invention provides a sea surface wind wave detection method and system based on Beidou reflected signals. The method is applied to the technical field of sea surface wind wave detection, and comprises the following steps: acquiring and processing Beidou direct reflection signals through a double-antenna system to obtain feature and orbit data; calculating reflection point positions and tide correction according to the characteristic orbit data to obtain reflection area characteristics; evaluating sea surface roughness based on the regional features and identifying interference to obtain scattering features; modeling and inverting wind wave parameters according to the scattering characteristics to obtain original data; fusing and screening multi-source observation according to the original data to obtain quality control data; and evaluating and optimizing system parameters by using the quality control data to obtain a storm detection result. According to the invention, interference sources such as land reefs and the like can be effectively identified and processed, and Beidou reflected signal sea surface wind wave detection based on high-precision wind wave parameter inversion is realized.

The invention discloses a novel GNSS-R typhoon area sea surface high wind speed inversion method, and relates to the technical field of wind speed inversion, and the method comprises the steps: introducing a multi-source feature fusion structure, achieving the combined modeling of CYGNSS parameters and environment field data, enhancing the response capability of a model to high wind speed disturbance and a complex wind field structure, and improving the stability of the model; a KL divergence optimization strategy is adopted to carry out explicit alignment on feature representation of a CNN layer and a Transform layer in a training stage, so that the problem of inversion deviation of a traditional single-path model in a wind speed rapid change area is relieved, and the wind speed inversion precision and stability of a contrast learning model in extreme weather are remarkably improved; a physical constraint channel of a medium-low wind speed interval is constructed through a GMF model, so that the model is guided to output an inversion result more conforming to a physical mechanism in a non-high wind speed scene, and over-fitting of a high wind speed sample is avoided; meanwhile, an adaptive fusion layer is designed to dynamically adjust the fusion weight according to the output of each module so as to enhance the robustness of the model under the wind speed multi-scale distribution.

The invention relates to a sea surface wind wave field parameter integrated inversion method combining HFR and SAR, and the method mainly comprises the steps: extracting the positive and negative first-order echo spectrum peak values of HFR, carrying out the inversion of a sea surface wind direction, and carrying out the interpolation of an HFR wind direction result to an SAR data grid through bilinear interpolation; based on the registered HFR wind direction result, utilizing a CMOD5. N model and an SAR backscattering coefficient to invert the wind speed; constructing a wind wave spectrum based on the inverted wind speed and wind direction results; calculating a surge spectrum peak wave number, a surge spectrum peak amplitude and a surge propagation direction by using the SAR image information, and constructing a surge spectrum based on the surge spectrum peak wave number, the surge spectrum peak amplitude and the surge propagation direction; and superposing the obtained wind wave spectrum and the surge spectrum to obtain a sea wave spectrum result, and further inverting the significant wave height. According to the method, the defect of sea state parameter observation through a single means is overcome, integrated high-precision inversion of the wind wave parameters is achieved, and the method has important significance on marine environment monitoring, disaster forecasting, shipping safety and the like.

The invention relates to the technical field of global navigation satellite system reflection, in particular to a GNSS-R sea surface wind speed inversion method, which comprises the following steps of: firstly, comprehensively considering multiple constraint conditions such as inversion error distribution characteristics, interval quantity, sample distribution uniformity and interval minimum sample requirements by utilizing the global optimal search characteristic of an SA algorithm; a multi-objective optimization function is constructed to preliminarily divide a wind speed interval, and then a GD algorithm is adopted to accurately adjust the boundary of the preliminarily divided interval. And constructing a special XGBoost prediction model for each wind speed interval, and finally, fusing wind speed prediction results of a plurality of sub-interval models through a stacked integrated learning architecture to obtain a wind speed inversion value. According to the method provided by the invention, the problem that the inversion error of a traditional single model in different wind speed ranges is extremely unstable can be effectively relieved, the inversion precision of the whole wind speed range is improved, and meanwhile, the problem that a real-time observation sample cannot correspond to a sub-model under an interval model in practical application is solved; and the application conversion of the GNSS-R sea surface wind speed inversion technology is promoted.

The invention discloses a sea surface wind field inversion method and device based on small incidence angle synthetic aperture radar data, and relates to the field of ocean remote sensing and application. Calculating a C-band Bragg scattering backscattering coefficient based on a Bragg scattering geophysical model, and establishing a two-dimensional variational cost function; the two-dimensional variational cost function comprises a background item and an observation item; and carrying out iterative solution on the two-dimensional variational cost function to obtain an optimal sea surface wind field. The problems that the Bragg scattering factor is not fully considered and the existing small incident angle scattering model is not applicable in the interval of 11-20 degrees are solved.

The invention discloses an anti-storm liftable floating type photovoltaic system which comprises a floating body structure, a photovoltaic panel is installed above the floating body structure, and the lower portion of the floating body structure is connected with a gravity anchor located on the seabed through an anchor chain; the winch is connected with the floating body structure through a steel wire rope and can drive the floating body structure to descend to the position below the sea level. The device can adapt to the deep sea environment and can well cope with severe environments such as strong wind, and the winch can be used for driving the floating body structure to sink to the seabed so as to avoid sea surface storm waves; a traditional floating photovoltaic device depends on a weighted base or rigid anchoring, the wind resistance is limited, and the application sea area range (such as a typhoon high-incidence area) is remarkably expanded by dynamically adjusting the position of a floating body; by sinking to a safe depth, the floating body avoids a typhoon strong wind area, the wind load is reduced, and the photovoltaic panel is prevented from being turned over, torn or collided with the fan tower drum.

The invention belongs to the technical field of satellite-borne GNSS-R marine environment monitoring, and discloses a satellite-borne GNSS-R multi-system multi-polarization sea surface wind speed estimation method and system, and the method comprises the steps: extracting a satellite-borne GNSS-R feature value and ERA5 environment factor data; performing quality control on the multi-GNSS system and the multi-polarization mode data set, and performing grid matching interpolation with the ERA5 data; dividing a training set and a test set, and constructing a machine learning wind speed estimation model based on Optuna optimization; increasing the proportion of the high-wind-speed data set to determine an optimal high-wind-speed proportion and an optimal model; and performing multi-GNSS system fusion and multi-polarization mode fusion, comparing with pure data fusion to determine an optimal model, and finally outputting a sea surface wind speed estimation value of a track point. According to the method, accurate inversion under different wind speed conditions is realized, the industrial problem of wind speed underestimation under extreme weather is effectively solved, and the sea surface wind speed estimation capability and reliability of satellite-borne GNSS-R are improved.

The invention relates to a sea surface wind speed inversion method and system based on a GNSS-R forward model, and belongs to the technical field of ocean remote sensing and environment monitoring. The method comprises the following steps: firstly, inputting four types of CYGNSS data to obtain a modeling DDM and a Jacobian matrix, secondly, carrying out feature extraction and data quality screening to obtain a plurality of features related to sea surface wind speed, then carrying out space-time matching on the features and the wind speed, inputting the features into an inversion model, and finally obtaining that the distribution of the DDM generated by a DDM forward model and the distribution of the observation DDM are relatively concentrated, and the overall simulation effect is good. According to the method, the problems that the neural network lacks physical interpretability and the inversion process is complex are solved, and a new thought is provided for later inversion of soil humidity and other indexes.

The invention relates to a sea surface wind speed inversion method, system, equipment and medium in combination with advantages of a simulation model and satellite data, a simulated time delay Doppler (DDM) image is generated by inputting parameters such as positions and speeds of a satellite and a receiver, gain of a receiving antenna and the like, satellite actual measurement data is expanded in combination with the simulated image, an actual measurement-simulation combined data set is constructed, and a sea surface wind speed inversion result is obtained. Extracting a leading edge slope and a trailing edge slope, and calculating to obtain a leading edge-trailing edge composite characteristic value to invert a sea surface wind speed value; the system, the equipment and the medium are used for implementing the method. According to the invention, the simulation of the time delay Doppler diagram is carried out through the position and speed of the satellite and the receiver, the wind speed, the GNSS signal frequency and the receiving antenna gain parameter, and the data sample can be expanded under the condition that the actually measured data sample of a specific sea area or sea condition is scarce. The method has the advantages that the applicability is wide, and sea surface wind speed inversion can be accurately achieved under the condition that samples are scarce.

The invention discloses an enteromorpha green tide full life cycle wind action coefficient determination method, and belongs to the field of enteromorpha green tide drift prediction. The method comprises the following steps: S1, acquiring satellite remote sensing images of enteromorpha green tide in different development stages, and interpreting the satellite remote sensing images to obtain enteromorpha green tide distribution data; and / or acquiring sea drift tracking observation data of enteromorpha green tide at different development stages; s2, constructing an enteromorpha green tide short-term drift numerical model; s3, acquiring surface ocean current and sea surface wind data of the research sea area; s4, determining optimal wind action coefficients of the enteromorpha green tide in different development stages; and S5, performing quadratic polynomial fitting, establishing a wind action coefficient dynamic model suitable for the enteromorpha green tide full life cycle, and further determining an enteromorpha green tide full life cycle wind action coefficient value. According to the method, the prediction precision of the enteromorpha green tide drift path can be 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.

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.