73 results about "Sea surface temperature" patented technology

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

The invention discloses a sea surface temperature image diffusion completion method and system based on dual-stage fusion constraint, and the method comprises the steps: firstly constructing sea area multi-source priori knowledge features to guide the reconstruction of sea surface temperature through integrating three types of heterogeneous data of geography, space-time and power; deep fusion is carried out on the sea area multi-source priori knowledge features through a cross-modal feature fusion module to obtain fused multi-source priori knowledge, and the fused multi-source priori knowledge is fused with structural knowledge to form comprehensive condition features used for guiding the generation process of a conditional diffusion model; and finally, realizing refined reconstruction of the missing region based on a conditional diffusion model, and outputting a completion result. And obtaining a final complete image according to the complementation result, the sea surface temperature image to be complemented and the missing mask. According to the invention, the accuracy of sea surface temperature image completion is improved.

The invention relates to the technical field of ocean remote sensing monitoring, in particular to a remote sensing image-based warm discharge water reference temperature self-adaptive extraction method, which specifically comprises the steps of S01, data acquisition and preprocessing, S02, cumulative histogram generation, S03, curve fitting and smoothing, S04, second derivative calculation and S05, reference temperature determination. According to the method, the reference temperature is determined by analyzing the statistical distribution characteristics of the sea surface temperature data field, namely the form of the accumulative histogram, the physical demarcation point of the background water body and the warm drainage area on the temperature distribution is automatically positioned by using the second derivative, the process is completely driven by data, the interference of subjective factors is eliminated, and the accuracy of temperature distribution is improved. The method can automatically adapt to water areas with different geographical forms and different warm drainage diffusion modes, and objectivity and universality of reference temperature extraction are achieved.

The invention provides a Bohai sea temperature prediction model based on a fusion model, the previous research mainly carries out prediction through single models, and the single models have corresponding limitations more or less. In order to solve the problem, a Bohai sea temperature prediction model based on a fusion model is provided. According to the method, through fusion of an Informer model and an LSTM model, and through feature optimization and XGBoost correction, the model obtains better prediction performance. According to the model, the prediction precision of the sea surface temperature of the semi-closed sea area is improved, and high-reliability data support is provided for ocean decisions such as storm surge early warning, fishing ground scheduling and ecological management.

The invention provides an early warning method for sea surface temperature anomaly detection, which belongs to the technical field of ocean forecasting, and comprises the following steps of: constructing a multi-source sea temperature data fusion system and a multi-scale adaptive grid, generating a high-resolution temperature field by adopting ensemble Kalman filtering data assimilation, extracting an abnormal component by utilizing ensemble empirical mode decomposition, and carrying out early warning on the abnormal component. Multi-level anomaly discrimination is performed based on a sparse coding recognition model and fractal dimension mutation detection, anomaly types are distinguished in combination with an atmospheric compulsive event feature library and a random forest classifier, and partial differential equation inverse problem reverse deduction is performed on ocean endogenous anomaly to reconstruct a three-dimensional anomaly structure. And finally, the early warning level is determined through the early warning decision function and the multi-dimensional indexes, and the technical problem that the real-time performance and the accuracy of sea surface temperature anomaly detection are difficult to guarantee at the same time is solved.

A method includes converting, via a wavelet transform, (i) data associated with a prior ocean state forecast to wavelet space prior ocean state data and (ii) ocean observations to wavelet space observation data, and then filtering the wavelet space observation data. The method includes generating a correction value based on a difference between the wavelet space prior ocean state data and the filtered observation data, and determining a wavelet space increment value based on (i) the generated correction value, (ii) an error covariance associated with the prior ocean state forecast, and (iii) an error covariance associated with the ocean observations. The method includes converting, via an inverse of the wavelet transform, the wavelet space increment value to a physical space increment value, and generating a current ocean state forecast based on (i) the converted physical space increment value and (ii) a background state associated with the prior ocean state forecast.

This invention relates to the field of marine information and pattern recognition technology, and discloses a method for automatic tracking and feature parameter extraction of ocean temperature fronts. The method includes: acquiring and preprocessing continuous sea surface temperature remote sensing data; constructing an adaptive multi-scale temperature gradient tensor field; automatically identifying initial seed points using a joint criterion of local extremum response and global structural saliency; generating a preliminary front through bidirectional chain growth along the main gradient direction; optimizing the front trajectory by fusing ocean dynamics priors and spatiotemporal continuity constraints; and finally extracting feature parameters such as position, intensity, direction, curvature, and lifespan. Through the above technical solution, this invention achieves fully automatic and highly robust temperature front tracking and accurate parameter extraction, significantly improving cross-scenario generalization capabilities and operational application value.

This invention provides a method and apparatus for correcting sea surface temperature (SST) predictions based on spatiotemporal axial attention. The method includes inputting target SST data into a target SST prediction correction model, comprising a convolutional input layer, an encoder, and a decoder. The convolutional input layer performs feature extraction, temporal encoding, and positional encoding on the target SST data to obtain a target feature vector. The encoder performs attention calculations on the target feature vector in three dimensions to obtain a first target output vector. The decoder outputs the target SST prediction result based on the first target output vector and historical prediction values ​​output by the decoder. Temporal and positional encoding are performed during the model input stage to enhance the representation of temporal and positional information contained in the original data. By performing attention calculations on the target feature vector in the spatiotemporal, longitude, and latitude dimensions respectively by the encoder, effective fusion of features in different dimensions is achieved, further improving feature representation capabilities and increasing the accuracy of SST prediction.

This invention relates to the field of satellite remote sensing image processing and meteorological big data analysis technology, specifically a satellite sea surface temperature (SST) reconstruction method based on a spatiotemporally constrained diffusion model. First, it acquires missing Himawari L3 satellite data and OSTIA L4 global data covering a global sea area. Then, it preprocesses the missing Himawari L3 and OSTIA L4 data to obtain several slice data points. Finally, it inputs the preprocessed slice data into a trained spatiotemporally constrained diffusion model, which reconstructs the slice data using a weight file. The reconstructed slice data undergoes texture enhancement and large-scale reconstruction optimization to obtain complete reconstructed data covering the entire sea area. This invention significantly improves the texture restoration accuracy and physical consistency of satellite SST under continuous, large-scale cloud cover.

The application provides a kind of spatio-temporal intelligent marine satellite internet of things sensing information screening method and device, belong to data processing technical field, this method is based on convolution bidirectional long short-term memory neural network realizes marine environment time series data repair, based on block time series transformer carries out marine environment data space-time prediction and based on mean shift density clustering algorithm carries out marine hotspot area mining, the marine environment data includes sea surface temperature, salinity, wave height and other sensing data, based on BP neural network carries out marine environment risk assessment and multi-agent reinforcement learning carries out information screening feedback control, meet the complex marine environment detection scene efficient information processing demand, realize low redundancy, high-precision data acquisition transmission adaptive feedback control effect.The present application can be directly applied to marine internet of things in marine observation detection facility networking system, can provide strong support in military application and civil application, has wide and important application prospect and value.

The invention is suitable for the field of marine meteorology, and provides a three-dimensional sea temperature space-time prediction method, which comprises the following steps: obtaining reanalysis data and numerical mode prediction data; and inputting the reanalysis data and the numerical mode prediction data into a pre-trained three-dimensional sea temperature intelligent prediction model to obtain a three-dimensional sea temperature space-time prediction result. According to the method, the spatial-temporal characteristics of the sea temperature change can be effectively captured, so that the prediction precision is improved, a high-precision sea temperature prediction result can be provided, good interpretability is achieved, and more powerful support is provided for scientific research and climate change response.

The invention discloses a coastal power plant warm discharge water background temperature calculation method, an electronic device and a storage medium, the background temperature is accurately calculated, and the method comprises the following steps: 1, if remote sensing image data exists before a power plant operates, taking a power plant sea area as a target sea area; and if not, selecting adjacent areas which are similar in shoreline characteristics and are not influenced by warm drainage. 2, selecting a remote sensing image similar to the field observation time for inversion to obtain a sea surface temperature data matrix; and 3, establishing a water temperature gradient observation strip consistent with the resolution interval of the remote sensing image in the target sea area from the shore to the offshore direction, and calculating a temperature mean value. And 4, fitting a curve representing the natural gradient change of the offshore water temperature by using an exponential function model based on the observation strip temperature data. And 5, introducing the water temperature of the reference point. And 6, in combination with the fitting curve and the datum point water temperature, background temperatures of different tidal hours and offshore distances are calculated to form a space-time continuous dynamic background temperature field. And 7, performing difference operation on the actually measured sea surface temperature field and the dynamic background temperature field to obtain an actual temperature rise field of the thermal discharge of the power plant.

The invention discloses a sea surface temperature space-time sequence acquisition method and system based on sea surface temperature inversion, and the method comprises the steps: obtaining a GF-5A satellite image and an MODIS remote sensing image, and obtaining a sea surface temperature (SST) inversion image of the corresponding MODIS remote sensing image and the brightness temperature of a thermal infrared band 3 and a thermal infrared band 4 of the GF-5A satellite image; obtaining a sea surface temperature inversion image of the GF-5A satellite image; establishing an SRCNN super-resolution reconstruction model, taking the sea surface temperature inversion image of the MODIS remote sensing image as input, training to obtain a sea surface temperature super-resolution reconstruction image of the MODIS remote sensing image, and performing verification by using the sea surface temperature inversion image of the GF-5A satellite image; and obtaining a sea surface temperature space-time sequence based on the super-resolution reconstruction result of the sea surface temperature inversion image of the MODIS remote sensing image. According to the invention, high-resolution sea surface temperature inversion and space-time sequence acquisition are realized.

The application discloses a kind of calculation method of coastal power plant warm discharge background temperature, electronic equipment and storage medium, accurately calculate background temperature, comprising:1.If there is power plant before running remote sensing image data, with power plant sea area as target sea area;If not, select adjacent, similar and not affected by warm discharge region of shoreline feature.2.Select and reverse the remote sensing image of the similar observation time, obtain the sea surface temperature data matrix.3.In the target sea area, the temperature mean value is calculated by establishing the water temperature gradient observation strip consistent with the resolution interval of remote sensing image along the coast to offshore direction.4.Based on the temperature data of observation strip, the curve representing the natural gradient change of offshore water temperature is fitted using the exponential function model.5.Introduce the reference point water temperature.6.Combined with the fitting curve and reference point water temperature, the background temperature of different tidal time and offshore distance is calculated to form a spatiotemporal continuous dynamic background temperature field.7.The actual temperature rise field of power plant warm discharge is obtained by difference operation between measured sea surface temperature field and dynamic background temperature field.

The invention relates to a physical constraint-based sea surface temperature prediction method and device, and the method comprises the steps: introducing a seawater density difference as a physical constraint term to construct a loss function, and forcing a prediction result to accord with an ocean dynamics law in a model training process; the method solves the problem of prediction result distortion caused by neglecting physical constraints in a traditional data driving method, and has the advantage of improving the physical consistency and accuracy of the sea surface temperature prediction result.

The invention discloses a typhoon frequency prediction method, system and device, a storage medium and a program product, and belongs to the technical field of weather prediction, and the method comprises the steps: obtaining historical typhoon frequency data and historical sea temperature data; on the basis of the historical sea temperature data, an ENSO representation index related to the typhoon frequency is determined to serve as a forecast factor; training a random forest model by using the forecast factor and the corresponding historical typhoon frequency data; and inputting the forecast factor of a year to be predicted into the trained random forest model for prediction, and outputting a typhoon frequency prediction result of the year. According to the invention, the occurrence trend of the typhoon frequency in the future can be predicted.

The application relates to the technical field of marine environment prediction, and discloses a two-branch two-stage sea surface temperature prediction method based on multi-element input. The method acquires and pre-processes multi-element data such as sea surface temperature, 2-meter temperature and atmospheric top incident solar radiation; a two-branch collaborative optimization deep learning model is constructed, and the model is trained; data of continuous days before the time to be predicted is input into the model to generate a future sea surface temperature prediction result. Among them, a short-term prediction branch extracts space-time features through ConvGRU and multi-scale convolution to predict a short-term result, and a medium and long-term prediction branch models long-range dependence through adaptive weighting and a Transformer encoder to predict a medium and long-term result; a future multi-day prediction is generated through self-recurrence rolling. Through two-branch collaboration, the application suppresses error accumulation, significantly improves the precision and stability of medium and long-term sea surface temperature prediction, and can provide efficient and accurate technical support for marine resource development.

The application discloses a sea surface height medium and long term prediction method and device, system and storage medium, comprising: obtaining spatial modes MEOFs, time series PCs and explained variances according to sea surface height anomaly SLA data and sea surface temperature SST data; selecting time series PCs corresponding to n spatial modes MEOFs with total explained variances greater than 95% for VMD decomposition to obtain VMD sub-modes corresponding to the PCs; using iTransformer training according to the VMD sub-modes corresponding to the PCs to obtain a sea surface height medium and long term prediction model; inputting a test set into the sea surface height medium and long term prediction model to obtain predicted sub-modes; synthesizing the predicted sub-modes into a new time series, and meanwhile, reconstructing the new time series together with the MEOFs to obtain SLA prediction values. The technical scheme of the application can realize higher-accuracy sea surface height medium and long term prediction in a selected region.

The application relates to a three-dimensional sea temperature monitoring method, which is based on low-resolution sea temperature background data, meteorological data and geographical feature data of a sea area to be monitored, predicts residual errors through a sea surface temperature reconstruction model, superimposes the residual errors on the low-resolution sea temperature background data, obtains high-resolution sea temperature parameters of each region of the sea surface, and improves the accuracy and physical consistency of the sea surface temperature parameters. In deep sea temperature inversion, based on biochemical environmental data and high-resolution surface sea temperature parameters, the sea temperature parameters of each depth region of the sea area to be monitored are predicted through a deep sea temperature prediction model, and the heat exchange law is more accurately reflected. The surface reconstruction and the deep inversion are combined, a connection mechanism is established, information is collaboratively integrated, complete and coherent three-dimensional sea temperature data are output, and reliable support is provided for marine heat transport calculation and other applications.

The invention belongs to the technical field of ocean monitoring and sensors, and discloses a measuring method and device for correcting the actual ocean shallow profile temperature. The method comprises the following steps: obtaining the real emissivity of a calm water surface; inputting the wind speed wave height of the seawater in the actual sea condition, the wind speed of the air and the obtained actual emissivity of the calm water surface into a Monte Carlo emissivity correction model, outputting the corrected actual sea condition seawater emissivity, and taking the corrected actual sea condition seawater emissivity as a parameter for measuring the actual water surface temperature by a second IR infrared thermometer; and based on the obtained modified parameters of the real water surface temperature measured by the second IR infrared thermometer, carrying out actual sea surface temperature measurement by arranging thermosensitive chains with different scales. The method is obviously superior to the prior art (the satellite is 0.3-1K, the shipborne infrared is 0.05-0.1 K, and the anchor system CTD is 0.002 K and is larger than or equal to 5cm on average).

The present disclosure provides a sea surface temperature prediction method and device, electronic equipment and storage medium, relating to the technical field of ocean science and technology, which comprises: obtaining historical sea surface temperature data of a target sea area; performing image block embedding processing on the historical sea surface temperature data to obtain an embedding vector sequence; performing bidirectional time sequence scanning on the embedding vector sequence to obtain bidirectional time sequence fusion features; extracting memory from the bidirectional time sequence fusion features to obtain time sequence memory features and spatial memory features; fusing the time sequence memory features and the spatial memory features to obtain sea surface temperature spatiotemporal features; and reconstructing the sea surface temperature spatiotemporal features to obtain predicted sea surface temperature data of the target sea area. The present scheme can achieve efficient and high-precision sea surface temperature prediction.

The invention discloses a global offshore air temperature inversion method based on a lightweight gradient elevator and application. The method comprises the following steps: step (1), data acquisition: collecting multi-source data; step (2), data preprocessing: performing data cleaning and quality control on the acquired multi-source data, and performing feature extraction and space-time matching on the multi-source data to obtain a feature data set; (3) model training: constructing a global offshore air temperature inversion model based on the lightweight gradient elevator and completing model training; step (4), model evaluation: taking offshore platform air temperature observation data as a test source, and performing quantitative evaluation on an output result of the global offshore air temperature inversion model; and step (5), global offshore air temperature inversion: outputting a global offshore air temperature inversion result by using the constructed global offshore air temperature inversion model. By using the method, a global offshore air temperature inversion live analysis product with the temporal-spatial resolution of 10km / h can be developed, and the product has relatively high accuracy.

The application relates to the technical field of data processing, and discloses a fishing situation prediction method and system based on multi-source heterogeneous data. The method comprises the following steps: acquiring heterogeneous data such as sea surface temperature, salinity, fishing boat trajectory and sea current, and converting the data into a space-time data matrix; extracting environmental parameters to calculate a water body layering index and a nutrient salt enrichment index, and combining the indexes to form a fishery feature vector; inputting the four-layer BP neural network to train and establish a prediction model; calculating a space-time distance to distribute weights and generate a correlation prediction result; adding real-time data to form a dynamic training set, detecting environmental changes, updating model parameters and outputting a prediction result. The application solves the problem that multi-source heterogeneous marine data cannot be effectively fused and intelligently processed, and improves the accuracy and real-time adaptability of fishing situation prediction.

The invention discloses an intelligent ocean front detection method based on a U-Net network structure, and belongs to the technical field of ocean remote sensing image processing and computer vision. According to the method, sea surface temperature data obtained by satellite remote sensing are preprocessed, and a fusion algorithm is utilized to make a corresponding ocean front label to construct a data set; the core of the method is that an improved U-Nct model is adopted for training and detection; according to the model, a residual learning unit is introduced into a classic encoder-decoder architecture, so that the problems of network performance degradation and feature loss are effectively solved. The encoder extracts deep features through three-level residual connection and down-sampling, the decoder fuses shallow space information through up-sampling and jump connection, and pixel-level classification is achieved through a convolution layer and an activation function. According to the method, the defects of low efficiency and poor generalization ability of a traditional method are overcome, high-precision and high-efficiency automatic identification of the ocean front is realized, and reliable technical support is provided for business applications such as ocean scientific research and navigation safety guarantee.

The invention relates to the technical field of marine environment forecasting, and discloses a double-branch two-stage sea surface temperature forecasting method based on multi-element input. The method comprises the following steps of: acquiring multi-element data such as sea surface temperature, 2-meter temperature and atmospheric top incident solar radiation and preprocessing the multi-element data; constructing a double-branch collaborative optimization deep learning model, and training the model; and inputting data of continuous days before a to-be-forecasted moment into the model, and generating a future sea surface temperature forecasting result. Wherein the short-term forecasting branch extracts spatial-temporal characteristics through ConvGRU and multi-scale convolution to forecast a short-term result, and the medium and long-term forecasting branch carries out modeling long-range dependence through adaptive weighting and a Transform encoder to forecast a medium and long-term result; and a future multi-day forecast is generated through autoregression rolling. According to the method, error accumulation is inhibited through double-branch cooperation, the precision and stability of medium-and-long-term forecasting of the sea surface temperature are remarkably improved, and efficient and accurate technical support can be provided for ocean resource development.

The application provides a cloud condition adaptive static satellite sea surface temperature retrieval method based on deep learning, relates to the technical field of sea surface temperature retrieval, and specifically comprises the following steps: obtaining static satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, reanalysis sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained static satellite observation data; constructing a cloud detection dataset; constructing a deep learning cloud detection model and training the same; constructing a clear sky dataset; constructing a deep learning clear sky retrieval model and training the same; constructing a dataset for cloud retrieval model training; constructing a deep learning cloud retrieval model and training the same; integrating the cloud detection model, the clear sky retrieval model and the cloud retrieval model, adaptively calling corresponding models according to real-time cloud detection results, and generating a final sea surface temperature product. The technical scheme of the application overcomes the problem in the prior art that the accuracy of the reconstructed product in the spatial structure cannot be comprehensively reflected.

The invention discloses an offshore photovoltaic system in the technical field of offshore renewable energy sources, which comprises a floating platform formed by connecting a plurality of floating body units, a photovoltaic assembly is arranged on any floating body unit, a ballast tank is arranged below the floating platform, one end, far away from the floating platform, of the ballast tank is fixedly connected to the seabed through an anchor point assembly, and the other end of the ballast tank is fixedly connected to the bottom of the sea. An adjusting mechanism is arranged between every two adjacent floating body units and comprises a first connecting piece, a memory alloy assembly and a second connecting piece which are fixedly connected in sequence, the first connecting piece and the second connecting piece are connected with the two adjacent floating body units respectively, and the phase change temperature of the memory alloy assembly corresponds to the sea surface temperature. The adjusting mechanism of the photovoltaic system can automatically adjust the rigidity according to the wave frequency, the phenomenon of local stress concentration of the floating platform is reduced, the photovoltaic system can better adapt to changeable sea waves on the sea surface, and the sea wave resisting capacity of the photovoltaic system is improved.

The application discloses a method and system for decomposing a dynamic process of a marine eddy sea surface temperature anomaly, and the method comprises the following steps: for an eddy region of the sea surface temperature anomaly, a process simulation of a meridional transport is carried out by considering diffusion and damping effects, and a process simulation of vertical motion is carried out by considering the upward and downward flow of the eddy center; a total simulation model of the eddy sea surface temperature anomaly is established according to the process simulation of the meridional transport and the process simulation of the vertical motion, and parameter inversion is carried out to obtain optimal inversion parameters; and the eddy sea surface temperature anomaly caused by the meridional transport and the vertical motion is determined respectively according to the optimal inversion parameters. The application can clearly reflect the physical source of the temperature anomaly by decomposing the sea surface temperature anomaly into the process of the meridional transport and the process of the vertical motion from the perspective of the dynamic process, and has physical interpretability.

The invention discloses a sea surface driving type marine chlorophyll and light field vertical profile reconstruction method, which belongs to the technical field of marine remote sensing and marine monitoring, is used for vertical profile reconstruction, and comprises the following steps: screening an Argo profile, extracting surface photosynthetically active radiation, surface chlorophyll concentration and sea surface temperature; calculating a three-dimensional unit sphere coordinate and an observation time periodic code of an observation point, and constructing an input vector; and constructing a deep learning model of double-branch joint inversion and performing training, inputting an Argo profile to be predicted into the trained deep learning model of double-branch joint inversion, and outputting a final chlorophyll concentration profile prediction result. According to the method, the photosynthetically active radiation information of the sea surface and the profile is explicitly introduced in the chlorophyll profile inversion process, so that the model can more truly reflect the influence of the illumination condition in the water body on the vertical distribution of the chlorophyll, the inversion uncertainty is effectively reduced, and the overall inversion effect is improved.

The invention relates to a regional offshore sea surface temperature inversion method based on LGBM-MLP. The method comprises the following steps: constructing a data set; wherein the data set comprises the microwave radiation data of the earth surface and the atmosphere, the sea surface temperature data and the related data of the reanalyzed ocean atmosphere; the data set is preprocessed; based on the preprocessed data set, training the LGBM-MLP hybrid model to obtain a sea surface temperature inversion model; and carrying out regional offshore sea surface temperature inversion based on the sea surface temperature inversion model. According to the LGBM-MLP-based regional offshore sea surface temperature inversion method, sea air environment information is fully fused, and environment characteristics of brightness temperature data can be better captured, so that the regional offshore sea surface temperature inversion precision is improved.