137 results about "Sea surface temperature" patented technology

The invention belongs to the technical field of sea temperature complementation, and discloses a sea temperature complementation method and system based on an asynchronous diffusion Schrodinger bridge, and the method comprises the steps: firstly, generating a weight anomal which reflects the abnormal degree of a pixel through a preprocessing step S1, so as to guide a subsequent diffusion process; s2, establishing a bidirectional diffusion path between the initial complementation image and the real image based on a diffusion Schrodinger bridge theory, and dynamically adjusting a diffusion coefficient according to anomay to generate an intermediate state xt; predicting a score function pred through a U-Net network S3, and reconstructing a current image x0 for updating a state or calculating loss; model parameters are optimized through iteration during training, multi-round denoising reconstruction is carried out during inference, and finally a complete high-quality sea surface temperature image SSTrecon is output. According to the invention, local details are fully reserved, and the accuracy of image completion is improved.

The invention belongs to the technical field of image processing, and particularly relates to a time sequence frequency domain feature enhanced diffusion-based sea temperature image completion method and system, and the method comprises the following steps: inputting a damaged sea temperature image, an initialized cloud mask, a weekly average sea temperature image and a historical sequence sea temperature image into a time sequence frequency domain feature extraction module for processing; and mapping into fused frequency domain features, and outputting a complex frequency domain condition vector. A real SST image on the current day is coded into an initial latent variable through a latent space enhancement diffusion module, a complete noisy latent variable is generated through forward diffusion sampling, a denoised latent variable is obtained through backward stable diffusion sampling, after the denoised latent variable is decoded into a pixel field through an output reconstruction module, constraint post-processing is carried out in combination with a mask and a damaged sea temperature image, and a real SST image is obtained. And finally outputting a reconstructed image. And the damaged sea temperature image completion precision and the time sequence continuity are improved.

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 belongs to the technical field of ocean observation, and discloses an underwater sound velocity distribution super-resolution construction method based on multi-modal data fusion. According to the method, a sound velocity distribution forecasting model is constructed, the model carries out temperature preliminary reconstruction on original space area temperature data through a Unet neural network, and sea surface temperature features are extracted through a Mama neural network; correcting the preliminarily reconstructed temperature data through sea surface temperature features to obtain high-spatial-resolution temperature data; and finally, obtaining high-spatial-resolution sound velocity distribution data through a sound velocity distribution calculation module. According to the method, the underwater temperature and the sea surface temperature are subjected to data fusion, so that the sound velocity distribution of the whole space area is quickly and accurately forecasted under the condition of original low-spatial-resolution data, and the problem of insufficient spatial resolution of ocean sound velocity distribution estimation is solved.

The invention discloses a seawater parameter correlation analysis method and device, electronic equipment and a storage medium, and relates to the technical field of ocean monitoring. The method comprises the following steps: acquiring satellite remote sensing data of a target sea area and performing space-time quality screening; preprocessing the satellite remote sensing data to generate correction data for temperature inversion; analyzing tidal dynamic characteristics of the target sea area; inverting a sea surface temperature field based on the pre-processed data, and performing enhancement analysis by fusing tidal dynamic characteristics; establishing a coupling strength quantitative index of the tidal power and the temperature field; collecting and complementing historical observation data of the power plant; the relevance between the temperature and the liquid level parameters is counted and analyzed; and the correlation of the seawater parameters is judged by integrating the coupling strength quantitative index and the data statistical result. The invention provides a two-channel decision-making mechanism with cooperation of a physical mechanism and operation data statistics, and the mechanism not only improves the operation efficiency of the cooling system of the power plant, but also reduces the misjudgment rate of abnormal working conditions, and provides an enforceable optimization strategy for warm drainage management.

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 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 relates to a three-dimensional sea temperature monitoring method, and the method comprises the steps: predicting a residual error through a sea surface temperature reconstruction model based on low-resolution sea temperature background data, meteorological data and geographic feature data of a to-be-monitored sea area, superposing the residual error with the low-resolution sea temperature background data, and obtaining a high-resolution sea temperature parameter of each region of a sea surface; and the precision and the physical consistency of the sea surface temperature parameters are improved. In deep sea temperature inversion, based on biochemical environment data and high-resolution surface sea temperature parameters, sea temperature parameters of all depth areas of a sea area to be monitored are predicted through a deep sea temperature prediction model, and a heat exchange rule is reflected more accurately. According to the method, surface reconstruction and deep inversion are combined, an association connection mechanism is established, information collaborative integration is achieved, complete and coherent three-dimensional sea temperature data are output, and reliable support is provided for applications such as ocean heat transfer calculation.

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 relates to the technical field of temperature forecasting and machine learning, and provides a sea surface temperature forecasting statistical correction method based on machine learning, and the method comprises the following steps: obtaining sea surface temperature forecasting data of a target area and sea surface temperature observation data of the target area; constructing a statistical correction model; a neural network used for calculating model parameters of a statistical correction model is constructed, features reflecting the position of a target area and the sea surface temperature of the target area are input into the neural network, and the neural network outputs the model parameters of the statistical correction model; the sea surface temperature forecast data, the sea surface temperature observation data and the model parameters are input into the statistical correction model, and the statistical correction model calculates a sea surface temperature forecast statistical correction result based on the model parameters, the sea surface temperature forecast data and the sea surface temperature observation data; by adopting the method, the sea surface temperature forecasting error can be reduced, and the forecasting precision is improved.

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.

Systems and methods for predicting decrease in average sea surface temperature (SST) are disclosed. The method includes setting a value of a first Boolean flag when at least one of an eclipse season start date and an eclipse season end date in a particular calendar year occurs in a period of thirty days preceding a timestamp for a particular partition in a plurality of partitions segmenting the particular calendar year. The method includes inputting to a trained machine learning model the first Boolean flag. The trained machine learning model is trained using a plurality of time series data using lunar orbit characteristics. The method includes predicting, using the trained machine learning model, a probability of decrease in the average SST for a predefined time interval in the particular calendar year. The method includes invoking a weather forecasting system to generate weather forecast in dependence on the predicted probability.

This invention relates to the field of meteorological services and provides a method and system for correcting subseasonal model precipitation based on climate zoning and sea surface temperature (SST) background constraints. The method includes: climate zoning, adding SST background constraints, reanalysis data modeling, model historical return data modeling, real-time prediction model matching, and prediction result correction. The system includes: a data preprocessing unit, a climate zoning unit, an SST background constraint unit, a reanalysis data modeling unit, a model historical return data modeling unit, and a real-time model matching and prediction result correction unit. This invention considers the importance of regional differences in precipitation and SST background constraints, solving the problems of significant regional differences in daily summer precipitation in my country, the tendency for overfitting in single-point modeling, and the lack of physical constraints that make correction methods unsuitable. It effectively utilizes physically meaningful predictability sources to modify the cumulative probability density correction method based on percentile mapping, thereby improving my country's summer subseasonal precipitation prediction capabilities.

The invention discloses an ocean nearshore dissolved oxygen estimation method, system and equipment based on remote sensing data. The method comprises the following steps: S1, acquiring measured data of a dissolved oxygen concentration site of a sea area to be measured, ocean remote sensing product data and ocean numerical mode HYCOM reanalysis data; s2, performing data reconstruction on missing data of four elements including chlorophyll, granular organic carbon, suspended solids and sea surface temperature in the marine remote sensing product data by adopting a DINCAE algorithm to obtain complete marine remote sensing product data; s3, performing correlation analysis and variance expansion factor analysis on the five ocean elements related to the dissolved oxygen, selecting characteristic variables, and constructing a remote sensing estimation model of the dissolved oxygen; and S4, inverting the sea surface dissolved oxygen concentration of the sea area to be measured by adopting the optimal dissolved oxygen remote sensing estimation model, and drawing the spatial distribution of the dissolved oxygen concentration. According to the invention, the problem that the dissolved oxygen as a non-optical active substance has no characteristic response on a remote sensing spectrum and cannot be inversed directly by satellite remote sensing is solved.

The invention discloses a sea surface temperature and sea level anomaly joint prediction method based on cross attention, and the method comprises the steps: constructing a deep neural network joint prediction model, taking collected sea surface temperature SST data and sea level anomaly height SLA data as input, and predicting future SST data and SLA data; the prediction process comprises the following steps: (1) training a prediction model by adopting a training data set; (2) after each training iteration is finished, verifying the prediction model by adopting a verification data set, and storing prediction model parameters with optimal performance; and (3) testing the trained prediction model by adopting the test data set. The prediction model adopts a double-branch structure to extract spatial-temporal characteristics of SST and SLA through a convolutional long-short-term memory network, and dynamic information interaction fusion between two variables is realized by using a cross attention mechanism, so that a coupling relationship between thermodynamics and dynamics is described. The model carries out end-to-end training through a unified loss function, and continuously outputs high-precision and high-consistency prediction results in a long period.

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.

The invention discloses a regional evaporation waveguide prediction method based on path loss inversion, and relates to the technical field of maritime communication. Actual measurement path losses corresponding to the multiple propagation signals are obtained through a point-to-point microwave propagation link, inversion is carried out on the actual measurement path losses corresponding to the multiple propagation signals, and the long-time-sequence evaporation waveguide height is obtained; taking the long-time-sequence evaporation waveguide height and the corresponding long-time-sequence meteorological and hydrological characteristics as a training sample set, training a recurrent neural network, and obtaining an evaporation waveguide height prediction model; the meteorological and hydrological characteristics comprise wind speed, air temperature, humidity, sea surface temperature, evaporation capacity, atmospheric pressure and rainfall capacity; predicting an evaporation waveguide height prediction value of each grid point in the target area through an evaporation waveguide height prediction model; and generating continuous regional evaporation waveguide height distribution through inverse distance weighted interpolation according to the evaporation waveguide height prediction value of each grid point in the target region. The method improves the prediction accuracy of the regional evaporation waveguide.

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.

The invention provides an underwater vehicle one-way heat storage type ocean temperature difference energy power generation system which comprises an underwater vehicle body, a one-way heat storage type evaporator power generation system and a diving system. The one-way heat storage type evaporator power generation system and the rising and diving system are arranged in the underwater vehicle body. The power generation system comprises a turbine generator set, a condenser, a working medium pump and a heat storage type evaporator. An outlet of the heat storage type evaporator is connected with an inlet of the turbine generator set, and the turbine generator set is sequentially connected with the turbine generator set, the condenser, the working medium pump and an inlet of the heat storage type evaporator. Sea surface temperature seawater heat is stored through the energy storage phase change material, heat is not released to the environment in the submerging process of the underwater vehicle, when the underwater vehicle reaches the seabed, the stored heat and the cold energy of environment cold seawater are used for constructing temperature difference, and Rankine cycle is formed to achieve power generation. Therefore, the underwater vehicle has the capability of autonomously generating power by using the ocean temperature difference in the navigation process, and the endurance mileage and the sensor carrying capability of the underwater vehicle are improved.

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 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.

A detection apparatus 1 for detecting an upwelling region includes: a simulation unit 11 for simulating sea surface temperature data, chlorophyll-a concentration data, and ocean current velocity data of regions in the ocean; a preprocessing unit 12 for creating a ground truth label of an upwelling region on the basis of a vertical velocity included in the ocean current velocity data; a classification unit 14 for training a learning model for outputting a classification result of an upwelling region in response to the input of sea surface temperature data and chlorophyll-a concentration data, wherein the training is performed by inputting the simulated sea surface temperature data and chlorophyll-a concentration data to the learning model, and further inputting the ground truth label of the upwelling region to the learning model; and a detection unit 17 for acquiring a classification result of an upwelling region in the ocean from the learning model by inputting sea surface temperature data and chlorophyll-a concentration data of the ocean acquired from a satellite to the learning model.

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.

Systems and methods for predicting increase in average sea surface temperature (SST) are disclosed. The method includes setting a value of a first Boolean flag when at least one of an eclipse season start date and an eclipse season end date in a particular calendar year occurs in a period of thirty days preceding a timestamp for a particular partition in a plurality of partitions segmenting the particular calendar year. The method includes inputting to a trained machine learning model the first Boolean flag. The trained machine learning model is trained using a plurality of time series data using lunar orbit characteristics. The method includes predicting, using the trained machine learning model, a probability of increase in the average SST for a predefined time interval in the particular calendar year. The method includes invoking a weather forecasting system to generate weather forecast in dependence on the predicted probability.

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 chlorophyll a prediction method based on spatial heterogeneity perception graph timing confrontation network relates to the technical field of chlorophyll a prediction, and is used for solving the problems that the subjectivity is strong in response to spatial heterogeneity by artificial partition, the partition boundary is not fine enough, and the statistical characteristic difference in the region is large, etc.The reconstructed daily scale chlorophyll a concentration remote sensing data and numerical simulation sea surface temperature data SST are taken as inputs, through spatial heterogeneity partition based on the time evolution behavior of chlorophyll a, graph convolution network GCN spatial coding, time convolution network TCN time coding and regional discriminator constraint, short-term prediction of the spatial distribution of chlorophyll a concentration in the future several days is realized, so that the representation ability of the model to the inhomogeneous change process of chlorophyll a in the complex offshore sea area is improved.