105 results about "Sea-surface height" patented technology

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention discloses a marine environment forecasting method based on combination of machine learning and numerical forecasting, and particularly relates to the field of marine environment forecasting, which comprises the following steps: extracting marginal region features based on real-time observation data, constructing a forecasting field through a double-loss function, extracting extreme event features through space-time decoupling, and constructing a forecasting field through a double-loss function; and outputting a forecasting result of the target sea area. According to the marine environment forecasting method based on the combination of machine learning and numerical forecasting, the problem of initial field optimization failure caused by covariance matrix estimation deviation when marginal sea area data are sparse is relieved by embedding vorticity conservation equation residual calculation in a hidden layer; the dependence on the number of extreme event samples is reduced by realizing the targeted extraction of the extreme ocean event features; through a physical hard constraint layer technology of a residual error correction network, an initial field error of a data sparse region is corrected in combination with sea surface height and a flow velocity field constraint residual error feature map, and the problem of a large prediction error of an edge sea area is relieved.

The invention provides a sea surface height abnormal data downscaling method based on deep learning, and relates to the technical field of data processing, and the method specifically comprises the following steps: preprocessing data including AVISO data, SWOT data and key auxiliary variables; an enhanced super-resolution generative adversarial network ESRGAN is constructed, a high-resolution feature map is generated, and the ESRGAN comprises a generative network and a discrimination network; using a plurality of loss function combinations to optimize the performance of the generative network and the discriminant network, including adversarial loss, perception loss and pixel loss; and evaluating the pixel-level error and the spatial detail consistency output by the ESRGAN by adopting a root-mean-square error and a structural similarity index respectively. According to the technical scheme, the problems that in the prior art, a data downscaling method is limited in applicability and generalization ability, and is difficult to adapt to fusion requirements of different regions and multi-source data are solved.

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

The invention provides an ocean mesoscale vortex identification method and device, and the method comprises the steps: obtaining the input data of an ocean environment numerical forecasting product, and the input data at least comprise the sea surface height abnormality, the horizontal flow velocity field, the temperature and the salinity; preprocessing the input data to obtain processed marine environment data; recognizing vortexes in the processed marine environment data according to a plurality of preset vortex recognition algorithms to obtain a plurality of vortexes; wherein the preset multiple vortex recognition algorithms at least comprise an Okubo-Wei ss parameter method, a vector geometry method, a rotation invariance method and an SSH isoline method; performing clustering analysis according to the vortex centers of the plurality of vortexes to obtain a target vortex corresponding to the input data; performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex; and the feature parameters are displayed according to a preset display mode, so that the recognition accuracy and comprehensiveness are improved.

The invention relates to the technical field of satellite altimetry, in particular to a height measurement method based on a feature-enhanced multi-base integrated re-tracking model, which is characterized in that the feature-enhanced multi-base integrated re-tracking model FMERM is constructed to optimize the position of a reflection waveform re-tracking point, and the FMERM model executes the following processing processes: 1, extracting input variable features by using PCA, and extracting the input variable features by using PCA; combining with an original input variable to form an enhanced feature set; secondly, optimizing the hyper-parameters of the XGBoost model by using a grid search method, so that the hyper-parameters of the XGBoost model are optimal; thirdly, utilizing the trained model to accurately calculate a re-tracking point normalization power value of the reflected signal waveform; fourthly, the sea surface height is inverted through the geometrical relationship and direct reflection signal time delay and atmospheric correction; an effective means is provided for solving the problem that a reflection waveform re-tracking method is inaccurate, and powerful support is provided for high-precision satellite-borne GNSS-R sea surface height measurement.

The invention relates to the technical field of satellite height measurement, and discloses a GNSS-R ocean plumb line deviation calculation method, device and equipment and a storage medium. The method comprises the following steps: acquiring a height measurement result, a geoid height and an average dynamic terrain of each GNSS-R mirror reflection point in an ocean area, and calculating a residual geoid height; dividing a global geographic area according to a longitude and latitude target interval to generate a target grid; for any lattice point in the target grid, screening a plurality of GNSS-R mirror reflection points around the lattice point as target points; and determining the weight of the residual geoid height of each target point based on the GNSS-R height measurement precision table, calculating the residual geoid height of the network point through weighted average, and further calculating the corresponding ocean plumb line deviation. By adopting the method, the global ocean plumb line deviation can be quickly calculated based on the GNSS-R sea surface height measurement result, and the accuracy of ocean plumb line deviation calculation is improved.

The invention relates to an internal solitary wave identification and amplitude inversion method in mode data based on machine learning, and belongs to the technical field of ocean observation. In combination with a Mask-RCNN and a variational auto-encoder, firstly, sea surface height data is preprocessed and imaged, texture features are extracted through a local binary pattern algorithm, a labeled data set is constructed, model training is performed, and internal solitary wave stripes and positions thereof are identified from the labeled data set; through extraction and processing of temperature field data, the temperature field at the water depth is inversed by using a variational auto-encoder; and extracting the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. According to the method, large-scale and refined ocean mode data can be efficiently and accurately processed, and the efficiency and accuracy of data processing are remarkably improved. The method has a wide application prospect, and especially in marine disaster early warning, marine scientific research and internal wave mechanics research, the automatic identification and amplitude extraction capability of internal waves can be greatly improved.

The invention provides a curly wave sea surface modeling method, device and equipment based on modified Lie transformation, and relates to the technical field of computer modeling. The method comprises the steps that when a curly wave sea surface model is constructed, sea spectrum information of a to-be-modeled target sea surface can be firstly acquired; based on the sea spectrum information, constructing a linear sea surface height model of a frequency domain dimension corresponding to the target sea surface; transforming the linear sea surface height model of the frequency domain dimension to obtain a linear sea surface height model of the time domain dimension; under a linear sea surface height model of a time domain dimension, correcting Lie transformation based on a wind condition of a target sea surface; and then based on the corrected Lie transformation, carrying out superposition on the linear sea surface height model of the frequency domain dimension so as to construct a curled wave sea surface model of the target sea surface. In this way, the Lie transformation is corrected in combination with the wind condition of the sea surface, so that the corrected Lie transformation can better simulate the curl wave under the high sea condition, and the modeling effect of the constructed curl wave sea surface model is effectively improved.

The invention relates to the technical field of satellite altimetry, in particular to an altimetry method based on a residual network multi-mode deep learning model, which can remarkably improve the robustness of SSH inversion under sea condition noise interference, and is characterized in that a fusion self-attention mechanism and a residual network multi-mode deep learning model ViTResNetMDL is established, and the model ViTResNetMDL is applied to improve the robustness of the SSH inversion under sea condition noise interference. A satellite-borne GNSS-R sea surface height inversion geometric physical model is established, a ViTResNetMDL model is composed of ResNet50, ViT and MHL-NN, the first part adopts a ResNet50 convolution structure to extract effective features from an original DDM, the second part adopts a ViT layer which captures global features in an effective scattering area DDM based on a self-attention mechanism in a Transform module, the third part is composed of linear layers, and the fourth part is composed of linear layers. And using the MHL-NN to retrieve the sea surface height.

The invention relates to the technical field of electronic signal processing and satellite height measurement, in particular to a GNSS-R (Global Navigation Satellite System-Receiver) sea surface height measurement method based on a novel stacked beam method, which can obviously improve the measurement precision, and is characterized in that a plurality of GNSS-R beam signals corresponding to a plurality of GNSS satellites are synthesized through the novel stacked beam method; the method is used for improving the signal-to-noise ratio of reflected signals received by GNSS-R satellites, and comprises the following steps: step 1, receiving and processing the reflected signals from a plurality of satellites; 2, synthesizing and superposing the received signals by using a beam forming technology; and step 3, analyzing the superposed signal to obtain sea surface height information.

The invention relates to the technical field of marine surveying and mapping, and discloses a sea level change trend accurate calculation method based on multi-source data fusion, which comprises the following steps: acquiring multi-source satellite height measurement data, performing data grouping, establishing a plurality of average sea level models, establishing a time-varying average sea level model through the average sea level models, and calculating the sea level change trend according to the time-varying average sea level model. And performing linear regression fitting on the average sea surface height time sequence at each grid point, constructing a grid average sea level change model, and calculating a change trend at each grid point. The method can improve the separation capability of signals with different time scales in sea level changes, is not affected by extreme climate events, natural environment changes and physical environment correction, and can greatly improve the prediction accuracy of the sea level trend.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention belongs to the technical field of physical ocean and remote sensing application, and particularly relates to a sub-mesoscale signal extraction method based on Ku / Ka dual-frequency SAR (Synthetic Aperture Radar) height measurement, which comprises the following steps of: acquiring a Ku / Ka dual-frequency sea surface height observation original data set and IMU (Inertial Measurement Unit) attitude and trajectory data; registering the average sea surface and tide height to a space-time sampling position of Ku / Ka synchronous observation and deducting from the original height to obtain an initial sea surface height anomaly sequence of the two channels, and processing the initial sea surface height anomaly data of the two channels to obtain a fused height anomaly sequence; performing attitude coupling mode extraction on the fused height anomaly sequence to obtain a height anomaly sequence after platform error elimination; meanwhile, smoothing processing is carried out to obtain a smoothed sequence; and interpolating the smoothed sequence into a two-dimensional regular grid, performing small-scale secondary smoothing denoising on grid data, interpolating a mesoscale background field at the same time into the same grid, and deducting the mesoscale background field to obtain a sea surface height anomaly data system with sub-mesoscale as a main part.

The invention discloses an ocean vortex three-dimensional flow field reconstruction method based on physical constraints, and belongs to the technical field of physical oceanography and data modeling, and the method comprises the steps: calculating sea surface height abnormal data according to climate state data and sea surface observation data, recognizing vortex features, and dividing a vortex into grid points; according to the climate state data and the vortex vertical mode characteristic equation, constructing a vortex vertical mode function, and obtaining a vortex horizontal flow velocity vertical structure function; by taking the sea surface flow velocity abnormal condition and the deep layer flow velocity abnormal condition as constraints, determining a vortex vertical modal coefficient according to a vortex horizontal flow velocity vertical structure function, and reconstructing a vortex horizontal flow velocity vertical structure at each grid point; and superposing the vortex horizontal flow velocity vertical structure and the climate state horizontal flow velocity vertical structure to obtain a seawater horizontal flow velocity vertical structure to construct a vortex three-dimensional flow field structure. The problems that the prior art is limited to the sea surface, and the vertical structure of the vortex is difficult to accurately describe are solved.

The invention relates to the technical field of crossing of satellite altimetry, electronic signaling and the like, in particular to a CYGNSS satellite intermediate frequency signal processing and sea surface altimetry method capable of improving altimetry precision, which comprises the following steps of: processing direct intermediate frequency signals, calculating specular reflection points, calculating bistatic delay, establishing an open-loop tracking model to obtain reflected signal parameters; the reflected intermediate frequency is processed, and direct intermediate frequency signal processing is achieved through open source GNSS receiver software so as to achieve estimation of the position, the speed and the time; according to the satellite-borne GNSS-R reflected intermediate frequency signal processing method, the SGRISP one-dimensional time delay correlation result is compared with the processing result provided by the Spanish Space Science Institute ICE-CSIC, and it is indicated that the SGRISP has high reliability in processing the satellite-borne GNSS-R reflected intermediate frequency signal.

The invention belongs to the technical field of ocean measurement, and discloses a method and a system for measuring the sea surface height of deep and far sea based on Beidou PPP-B2b. The method comprises the following steps: calculating an antenna instantaneous geodetic height of a four-antenna GNSS receiver based on a WGS84 coordinate system; correcting the attitude of the buoy by using a buoy attitude correction algorithm to obtain the height of the buoy in the vertical direction; calculating the buoy draft according to a built-in instantaneous average water depth algorithm; the sea surface instantaneous height is calculated through a sea surface instantaneous height algorithm; carrying out noise reduction processing on the calculated instantaneous height of the sea surface through a filtering calculation unit; and the final result is sent to a monitoring service platform through a data transmission module. The method not only supports ship route planning and navigation safety, helps to avoid risks, but also provides long-term, stable and reliable data support for global climate change monitoring, marine environment protection and deep sea scientific research.

The invention relates to the technical field of ocean dynamic environment intelligent forecasting, and provides a three-dimensional ocean dynamic environment intelligent forecasting method and device and electronic equipment, and the forecasting method comprises the steps: S1000, preparing 1 / 30-degree high-resolution ocean dynamic environment element data (three-dimensional temperature and salt flow and two-dimensional water level), and carrying out the normalization; s2000, selecting a corresponding model input sequence from the training set according to a forecasting strategy of the model; s3000, training a hierarchical feature projection network; and S4000: performing reverse normalization and generating future prediction data: performing reverse normalization on the feature-enhanced output data according to the maximum value and the minimum value of the ocean data, and finally generating a day-by-day prediction product of the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity with 1 / 30-degree high resolution in the future day, namely, the product is obtained by predicting the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity.

The invention discloses a real-time sea clutter simulation method based on a physical neural network, belongs to the field of sea clutter simulation, and particularly relates to a real-time sea clutter simulation method. The invention aims to solve the problems of poor real-time performance and long time consumption of the existing sea clutter simulation method. The method comprises the following steps: S1, initializing parameters and simulation; s2, numerical simulation of sea surface dynamics is realized according to a shallow water equation, and the obtained sea surface height field changing along with time, the flow velocity of the sea surface height field in the axial direction and the flow velocity of the sea surface height field in the axial direction are periodically stored; s3, constructing a physical neural network; s4, obtaining a trained physical neural network; s5, inputting the space coordinate and the time coordinate of the real-time sea clutter to be measured into the trained physical neural network, and outputting the sum; generating a real-time file in an STL format; and S6, obtaining sea clutter data by using the open source library.

The invention discloses a physical constraint-based ocean vortex three-dimensional temperature-salinity density reconstruction method and system, and the method comprises the steps: constructing a vertical distribution model of vortex density through integrating climate state temperature-salinity data, sea surface height, temperature, salinity observation data and other multi-source information, and employing a physical constraint condition and a vertical modal structure of vortex, thereby achieving the reconstruction of the ocean vortex three-dimensional temperature-salinity density. And further calculating the temperature-salt vertical structure of the vortex, and correcting a reconstruction result in combination with sea surface observation data, thereby realizing reconstruction of the vortex three-dimensional temperature-salt density structure. According to the method, vortex feature recognition and vortex high-precision three-dimensional temperature-salinity density reconstruction can be quickly realized by only observing sea surface height, temperature and salinity data in combination with historical data.

The invention relates to the technical field of ocean remote sensing image processing, and discloses an internal solitary wave amplitude inversion method based on SWOT observation data, which comprises the following steps of: acquiring sea surface height abnormal data of an SWOT satellite Ka-band radar interferometer L3, performing preprocessing, and performing adaptive noise filtering, maximum between-class variance method enhancement and morphological edge detection respectively; overlapping analysis is carried out, and pixel points which are all judged to be effective are reserved to serve as sea surface height abnormal data of internal solitary wave disturbance; inverting the first-order amplitude of the internal solitary wave; introducing a non-linear term in the extended KdV equation, and carrying out non-linear correction on the vertical modal function; and performing inversion to obtain the high-order amplitude of the internal solitary wave. According to the method disclosed by the invention, real and effective internal solitary wave disturbance information can be extracted as an input source of amplitude inversion, so that accurate inversion of the large-amplitude internal solitary wave is realized, and the technical potential of an SWOT satellite in quantitative research and practical application of the internal solitary wave is expanded.

The invention discloses an ocean mesoscale vortex intelligent identification method and system based on flow field geometric features and a satellite altimeter, and the method comprises the steps: obtaining the synchronous SSHA data of at least three satellites, and carrying out the calibration according to the actual measurement data of a buoy; on the basis of the calibrated SSHA data, utilizing a ground-to-balance equation to calculate weft-direction and warp-direction ground-to-balance flow velocity components; generating a flow field geometric feature VG vortex center criterion; dynamically adjusting VG vortex core criterion parameters according to the ocean dynamic environment; converting the SSHA and geostationary flow field data into a grayscale image, marking vortex polarity and a bounding box, and adding a salt and pepper noise expansion sample through random rotation; resNet-50 is adopted as a backbone network, and a bidirectional feature pyramid network BiFPN is added to optimize multi-scale feature fusion; and inputting the VG preliminary identification result into the trained ResNet-BiFPN model, and outputting the vortex position, radius and polarity. The technical defects of a traditional method in weak vortex recognition, background noise interference and calculation timeliness are overcome.

The invention discloses a satellite height measurement data optimal interpolation fusion method based on cross calibration and spectrum analysis, and the method comprises the steps: carrying out the sea surface height anomaly calculation and power spectrum analysis, and calculating the sea surface height anomaly of a satellite altimeter according to the precise orbit determination coordinates of a satellite and the distance measurement value of the altimeter; the related scale of adjacent observation points of the to-be-fused satellite altimeter is obtained through the power spectrum analysis of the sea surface height anomaly of the to-be-fused satellite altimeter; cross calibration: performing cross calibration on sea surface height anomaly of the fusion satellite altimeters by using the sea surface height anomaly of the reference satellite altimeter to obtain system deviation in the to-be-fused satellite altimeters; performing self-crossing calibration by using sea surface height anomaly of the satellite altimeter to be fused to obtain a diagonal element of an observation error covariance matrix; constructing a covariance matrix, wherein a non-diagonal element of the observation error covariance matrix is a product of a standard deviation at the two measurement points and a correlation function of the two measurement points; and fusing the fused satellite altimeter and the background field data by using an optimal interpolation method.

The present application relates to the technical field of satellite altimetry and ocean surveying and mapping, and in particular to a GNSS-R sea surface height measurement method based on variable step line segment intermediate frequency data processing, which can effectively improve the measurement accuracy and efficiency, comprising obtaining satellite-borne GNSS-R original intermediate frequency data, calculating mirror reflection points through a new variable step line segment (NVSLSM), generating a delay Doppler map (DDM), and performing sea surface height inversion. Compared with the prior art, the NVSLSM is used to process the mirror reflection points, the calculation time is increased by about 2.04 times and 1.47 times compared with the line segment bisection method and the QH method, and on the premise of ensuring the convergence accuracy, the stable solution can be reached through fewer iteration steps, thereby reducing the calculation cost per sample; in the delay Doppler map generation link, the overall calculation efficiency is increased by more than about 90%.

The invention relates to the technical field of sea surface height measurement, and particularly discloses a novel troposphere refraction fusion correction method, which comprises the following steps of GPT2W data calculation, ERA5 data set calculation and data fusion and correction. According to the method, the GPT2W model and the ERA5 data are combined, the temperature and the pressure are subjected to horizontal space and time sequence encryption, and the elevation angle change caused by troposphere refraction is corrected by using the temperature and pressure data with high temporal-spatial resolution, so that the sea surface height measurement precision is improved.

The application provides a method for vortex-uplift flow coordinated regulation chlorophyll-a concentration variability, relates to the field of ocean remote sensing and environmental monitoring technology, obtains multi-source satellite and reanalysis data set, constructs smooth time series; identifies dominant variability period in chlorophyll-a concentration, sea surface temperature and sea surface height anomaly, extracts monthly anomaly value sequence; applies complex empirical orthogonal function analysis to sea surface height anomaly, extracts its dominant spatial variability mode, identifies vortex distribution position and shape; according to vortex type and season, classifies and filters target vortex, carries out normalization processing to chlorophyll-a concentration and aligns to vortex center, analyzes spatial response relationship between vortex and chlorophyll-a concentration; in combination with surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, density time-depth profile graph and CHL-SLA composite graph are drawn, and the influence mechanism of vortex-uplift flow on chlorophyll-a horizontal distribution is analyzed.

The invention provides a distributed InSAR sea surface two-dimensional flow velocity and elevation measurement method and device, and belongs to the technical field of ocean microwave remote sensing. According to the method, a dual-beam BiDi mode is introduced on a distributed InSAR platform, sea surface echoes are obtained at the same time through forward and backward squint angle beams, a multi-squint angle interference pair is constructed through beam separation and sub-aperture processing, and the sea surface two-dimensional flow velocity and elevation measurement is achieved. Image registration, interference, flat ground effect removal, phase unwrapping, interference suppression and Bragg wave phase velocity removal are sequentially completed, a multi-baseline phase equation set is finally established, and decoupling and synchronous inversion of the two-dimensional flow velocity and the elevation phase are achieved. According to the method, an additional antenna or a cooperative target is not needed, the sea surface flow velocity vector and the sea surface elevation can be obtained with high precision in one-time flight, and the ocean observation efficiency and the measurement precision of the distributed InSAR are remarkably improved.

The present invention discloses a fishery sea area monitoring system based on satellite remote sensing, which belongs to the field of fishery monitoring technology and specifically comprises: a remote sensing data acquisition module that acquires and pre-processes abnormal data on sea surface temperature, chlorophyll concentration and sea level height; a buoy data acquisition module that synchronously acquires temperature and salinity profile data of an Argo buoy array, and whose deployment density is dynamically adjusted according to historical fish catch and ocean front strength; a spatiotemporal alignment module that divides a time window by orbital period, corrects spatial offset in combination with buoy drift trajectory, sets a time buffer interval based on geostrophic velocity and satellite image resolution, and generates a spatiotemporally unified three-dimensional ocean environment data set; a fishery suitability calculation module that constructs a biophysical coupling model, associates surface chlorophyll with subsurface temperature and salinity structure, and calculates the suitability index of each water layer; a fishing ground division module that intelligently demarcates core areas and edge areas based on the three-dimensional distribution characteristics of the index; the present invention realizes accurate identification of fishing grounds based on multi-source data fusion.

The present invention belongs to the field of marine engineering technology and discloses a method, equipment, medium, and terminal for synchronous observation of waves, tides, and currents over the entire ocean area. The method comprises collecting GNSS phase, pseudorange, Doppler shift observations, and broadcast ephemeris using a GNSS receiver or board and antenna carried by a wave buoy on the ocean surface. The three-dimensional velocity of the buoy is obtained by epoch-by-epoch differential analysis of the phase observations, thereby obtaining the directional spectrum and frequency spectrum of the waves, as well as information on wave height, period, and direction. The sampling elements recorded by a shore-based station are sent to a GNSS receiver on the ocean surface via the Beidou short message service (BDS). Low-frequency sea surface height fluctuations are calculated in real time using the PPP method. Combined with the low-frequency velocity obtained by filtering, high-frequency tidal fluctuations are interpolated. The present invention expands the operating range of GNSS-based ocean wave and tide measurements and has practical application value.

The present application relates to the technical field of satellite altimetry and ocean surveying and mapping, and in particular to a GNSS-R sea surface height measurement method based on variable step line segment intermediate frequency data processing, which can effectively improve the measurement accuracy and efficiency, comprising obtaining satellite-borne GNSS-R original intermediate frequency data, calculating mirror reflection points through a new variable step line segment (NVSLSM), generating a delay Doppler map (DDM), and performing sea surface height inversion. Compared with the prior art, the NVSLSM is used to process the mirror reflection points, the calculation time is increased by about 2.04 times and 1.47 times compared with the line segment bisection method and the QH method, and on the premise of ensuring the convergence accuracy, the stable solution can be reached through fewer iteration steps, thereby reducing the calculation cost per sample; in the delay Doppler map generation link, the overall calculation efficiency is increased by more than about 90%.