113 results about "Sea 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 sea temperature complementation method and system based on recursive double-flow Mama, and the method comprises the following steps: splicing a damaged SST image and a weekly average SST image as input, and outputting a predicted weekly average SST image and a predicted abnormal SST image through N times of recursive iteration of N same recursive hierarchical Mama blocks, and the two output images are added to obtain a final complemented SST image. According to the method, a recursive hierarchical Mama block for a sea surface temperature completion task is set, two parallel layers, namely a stable information representation module and an abnormal information representation module, are integrated in each block, features related to stability and features related to anomalies are extracted respectively, long-range dependency relationship modeling under large-area deficiency is achieved, and completion accuracy is improved.

The invention relates to an ocean internal temperature inversion method fusing PIES and sea surface multi-source parameters, and belongs to the technical field of ocean environment parameter inversion and ocean dynamics observation. Comprising the following steps: constructing a nonlinear empirical model (Multi-RF) of seawater temperature inversion by using a random forest algorithm and fusing data such as a historical observation temperature-salinity profile observed by a temperature-salinity-depth meter (CTD) and sea surface multi-source parameters; and inverting the seawater temperature through a Multi-RF model by using the sound propagation time and multi-source sea surface data calculated by the PIES. Compared with a traditional empirical mode mapping (GEM) model, the Mull-RF model provided by the invention has the advantages that on one hand, the Mull-RF model utilizes fewer samples to establish a mapping relation between the seawater temperature and multiple parameters, and the problem of sample shortage in construction of a seawater temperature inversion model fusing sea surface multiple parameters and PIES observation sound propagation time is solved; on the other hand, compared with polynomial fitting in a GEM model, the random forest adopted by the Multi-RF model can achieve nonlinear fitting from multiple parameters to the seawater temperature, and therefore the temperature inversion precision is remarkably improved.

The invention discloses a method for researching the cross-seasonal relation of rainfall abnormity in spring and summer in the middle and America, and the method comprises the steps: downloading a data set, and processing the data set to retain interannual signals; calculating Chinese American rainfall indexes in spring and summer by using the data; calculating a precipitation level phase transition index to obtain a transition type event and a persistent type event; further dividing the transition event and the persistent event into a positive event and a negative event; the physical mechanism of rainfall anomaly in the middle and American regions in spring and summer during the occurrence period of transformation and persistent events is studied; identifying and exploring the physical process of the sea temperature key area influencing rainfall anomaly in spring and summer; and exploring precursor factors influencing the cross-seasonal relationship of rainfall anomalies in spring and summer. The method solves the problem that in the prior art, cross-season continuous rainfall abnormity in the middle and American regions is not involved. According to the method, the precursor factors of the persistent events and the transformed events are identified, and the method has the potential to be applied to rainfall forecast in the middle and American regions, and provides reference for disaster prevention and reduction and agricultural production.

The invention discloses a seawater temperature, salinity and sound velocity inversion method based on a Brillouin laser radar, and belongs to the technical field of marine environment monitoring. According to the method, the temperature, salinity and sound velocity of seawater are inverted by measuring the Brillouin frequency shift and the Brillouin line width and combining the MLP neural network; the method specifically comprises the steps of collecting seawater parameter data of different sea areas, different seasons and different depths and corresponding frequency shifts and line widths; carrying out data preprocessing and standardization on the collected data; and establishing an inversion model by using an MLP neural network, and outputting temperature, salinity and sound velocity. The multi-layer perceptron MLP neural network processes frequency shift and line width data measured by the Brillouin laser radar, high-precision and real-time inversion of seawater temperature, salinity and sound velocity is achieved, the parameter range is remarkably expanded, the inversion precision is improved, and efficient and reliable marine environment monitoring is provided.

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 circulating water system optimization control, in particular to a circulating water system energy-saving optimization control method based on pipeline characteristic identification, and the method comprises the steps: building a lumped parameter model of a pipeline according to a momentum equation of fluid in the pipeline; constructing an agent model of the thermodynamic system mechanism model; identifying at least one pipeline characteristic of the circulating water system; predicting the seawater temperature and the unit load in the target period; establishing a pipeline characteristic model for energy-saving optimization control of the circulating water system; and on the basis of a closed-loop adaptive online correction and model iteration mechanism, optimizing the weights of the long and short-term memory neural network model and the regression model of the circulating water system by using the new operation data. Based on mechanism model and data model hybrid modeling and measuring point data verification, high-precision pipeline characteristic identification is realized, and by predicting future working conditions such as seawater temperature and the like and combining real-time pipeline characteristics, prospective optimization is carried out, and closed-loop frequency conversion optimization control is realized.

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 relates to the technical field of sea water desalination impurity treatment, and particularly discloses a sea water desalination impurity pretreatment system and technology.The pretreatment technology comprises rough filtration, flocculating settling, filter filtration and ultrafiltration, and the flocculating settling comprises the following steps that S100, the temperature of sea water obtained after rough filtration is detected, and when the temperature is lower than a set temperature interval beneficial to a flocculation reaction, the flocculation reaction is stopped; heating the roughly filtered seawater by using waste heat recovered by evaporating the seawater; s200, conveying the heated seawater to a mixing tank, detecting the temperature of the seawater, and adding a flocculating agent according to the dosage corresponding to the temperature; s300, conveying the seawater mixed with the flocculating agent to a flocculation reaction tank; and S400, conveying the seawater subjected to the flocculation reaction to a flocculation sedimentation tank, measuring concentration data of the flocculation sedimentation tank at different depths, and correcting front-end reaction regulation and control parameters according to the measured data. According to the invention, the temperature influence is regulated in a targeted manner, and meanwhile, a dynamic feedback regulation mechanism based on the flocculating settling effect is established.

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.

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 a three-dimensional sea temperature space downscaling method and device and electronic equipment, and relates to the field of image processing, and the method comprises the steps: obtaining a target data set which comprises wind field data, ocean current data, first sea temperature data and second sea temperature data, and inputting the wind field data, the ocean current data, the first sea temperature data and the second sea temperature data in the target data set into a three-dimensional sea temperature generation model for spatial downscaling processing, and outputting three-dimensional sea temperature data. A target data set is obtained, wind field data, ocean current data and second sea temperature data are further introduced in the target data set except first sea temperature data, and the wind field data and the ocean current data contain information capable of representing the sea temperature data; and the first sea temperature data, the wind field data, the ocean current data and the second sea temperature data are sent to the three-dimensional sea temperature generation model for downscaling processing, so that the finally output sea temperature data is more accurate.

The invention relates to an East India Ocean sea level height positive anomaly next year ENSO event forecasting method, and the method comprises the steps: employing the mark parameters of the sea abnormally descending Kelvin wave of the tropical East India Ocean during the India Ocean negative dipole event, such as the sea level height anomaly or the subsurface sea temperature anomaly; comprising the following steps: using near equatorial sea level height or subsurface sea temperature abnormity of Indonesian sea or Western Pacific Ocean, or Kelvin wave of East India Ocean obtained through India Ocean wind field, using a regression forecasting model, and using path change of old flow of Pacific Ocean cotton lancea in winter and spring or Western Pacific Ocean west wind outbreak index as a logic switch, and the starting process and the explosion intensity of the ENSO event can be forecasted. According to the method, spring obstacles can be broken through, and the outbreak and the intensity of the El Nino and La Nina events can be successfully forecasted.

The invention discloses a sea temperature prediction method and system based on long-time modeling and cross-branch fusion, and belongs to the technical field of marine meteorological intelligent prediction and deep learning modeling. The method comprises the following steps: carrying out spatial feature coding on a standardized sea temperature time sequence image sequence by using a deep convolution encoder to obtain multi-level spatial features; performing two-dimensional discrete wavelet decomposition on the multilevel spatial features to obtain low-frequency approximate representation, horizontal singular features, vertical singular features and diagonal edge features, and inputting the features into a wavelet attention module to obtain short-term time sequence enhancement features; inputting the multi-level spatial features and the low-frequency approximate representation into a ConvLSTM module to obtain high-frequency dynamic enhancement features; and inputting the short-term time sequence enhancement feature and the high-frequency dynamic enhancement feature into a cross-branch attention fusion module to obtain a fusion feature, and sequentially inputting the fusion feature into an Informer time sequence encoder and a decoder to obtain a prediction result.

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 deviation correction method and a deviation correction system for forecast data. The method comprises the following steps: acquiring initial forecast data of a target area; extracting sea-land boundary features of the target area from the initial forecast data of the target area to obtain a coast mask and a coastal mask of the target area; and inputting the initial forecast data and the coastal mask of the target area into the deviation correction model to obtain corrected forecast data output by the deviation correction model, so that the coastal mask can be efficiently extracted, and the effective guide information of the forecast data is reserved in combination with an incomplete noise addition strategy, and the prediction accuracy is improved. According to the method, initial forecast data is subjected to partial noise addition and feature optimization, so that the model can be trained only by analyzing the data, and the problem of error correction caused by large three-dimensional sea temperature forecast error, long-time-sequence error accumulation and land-sea interference in an offshore area is effectively solved by combining feature extraction and dynamic parameter optimization; and the adaptability of the model to complex small-scale environmental factors is remarkably improved.

The invention discloses an ENSO ensemble forecasting method and device, a medium and a product, and relates to the field of marine atmosphere prediction.The method comprises the steps that a sea temperature climate state and a corresponding climate state singular vector are determined according to a known service forecasting mode; performing mode dimensionality reduction by taking the climate state singular vector of an actual service forecast starting month as a base, and extracting flow dependent feature disturbance; the flow dependent characteristic disturbance comprises a nonlinear growth characteristic of a coupling mode forecast error; obtaining initial value disturbance according to the flow dependence characteristic disturbance; on the sea temperature initial field, according to initial value disturbance, an ENSO set is obtained; and performing ENSO service forecasting according to the ENSO set. According to the invention, the ENSO ensemble forecasting performance can be improved.

The invention discloses a method and system for optimizing an artificial habitat cold water supply control strategy, and relates to the technical field of coral cultivation. According to the scheme, through environment data, a temperature prediction model, a preset optimization algorithm and iterative optimization of a cold water supply control strategy, advanced prediction, simulation and closed-loop optimization control of the seawater temperature of an artificial habitat coral culture area in an open sea area are achieved. While the prevention accuracy and intervention efficiency of the coral whitening heat stress are improved, maximum energy conservation and consumption reduction are realized, and the optimal growth temperature of the coral is intelligently maintained.

The invention discloses a tidal mixing parameter determination method and device for an ocean numerical model and a product, and the method comprises the steps: building the ocean numerical model, and obtaining a corresponding state vector; determining an observation matrix corresponding to the state vector; obtaining a prediction error covariance matrix corresponding to the state vector based on the state vector; respectively calculating the Kalman gain of the tidal mixing parameter value and the Kalman gain of the seawater temperature value; respectively calculating to obtain a tidal mixing parameter analysis value and a seawater temperature analysis value; the tidal mixing parameter analysis value is a new tidal mixing parameter value in the ocean numerical model. According to the method, the tide mixing parameters and the seawater temperature value of the ocean numerical model are optimized and estimated through the combined state variable set Kalman filtering, the tide mixing parameters are estimated more accurately through data assimilation, the value error of the tide mixing parameters is reduced, and the simulation prediction precision of the ocean numerical model for the future time period is improved.

The present invention relates to the field of electronic digital data processing technology, and provides a WRF sea temperature noise removal method, system, device, and medium. The method comprises the following steps: obtaining initial field data and public meteorological files required for the WRF model; preprocessing the initial field data to obtain an initial field file; performing initial sea temperature correction on the initial field file to obtain an initial temperature field; using a multi-scale filtering model to perform physical simulation on the initial temperature field to obtain a simulation result; optimizing the multi-scale filtering model based on the comparison between the public meteorological file and the simulation result to obtain an optimized model; and inputting the data to be noise-removed into the optimized model to obtain a noise-removed result. The present invention achieves filtering of sea temperature by designing a multi-scale filtering model to smooth small-scale noise, retain large-scale structure, and avoid blurring important ocean features such as fronts and vortices.

The application discloses a seawater filter automatic deicing system utilizing ship waste heat, which comprises a seawater filter for leading outboard seawater from a ship sea chest to the ship for the whole ship use through a seawater main pipe, wherein the seawater filter communication end with the sea chest is taken as an import end, the seawater filter communication end with the ship system is taken as an export end, the seawater main pipe is provided with a seawater cooling system and a differential pressure sensor in parallel on both sides of the seawater filter, the seawater cooling system provides high-temperature cooling seawater for the automatic deicing system and is connected with a seawater cooling pipe discharge outboard pipe in series, and the differential pressure sensor monitors the pressure difference between both sides of the seawater filter in real time; temperature sensors are arranged on the seawater main pipe of the seawater filter export end and in the seawater cooling system to monitor seawater temperature in real time; and the automatic deicing system further comprises a remote control valve system, which realizes the control of seawater flow in each pipeline by controlling the opening of the remote control valve. The seawater filter is automatically deiced by utilizing the ship waste heat, the seawater filter is prevented from being blocked by ice blocks, and the navigation safety of the ship is ensured.

The utility model discloses a kind of compressed air energy storage systems suitable for marine environment, it is related to compressed air energy storage technical field.It includes including n group compressor, n group heat exchanger, n group cooler and closed cooling water system, it is sequentially connected through heat exchanger and cooler between adjacent compressor, nth group compressor is sequentially connected with gas storage through nth group heat exchanger and nth group cooler;Cooler one end is connected with seawater condenser closed water inlet through cooler closed water outlet pipeline, the other end is connected with seawater condenser closed water outlet through cooler closed water inlet pipeline.The utility model uses seawater as cooling water source, water source is stable, conducive to system stable operation;Adopt seawater as cold source, inexhaustible, and seawater temperature fluctuation is small, it can be regarded as constant temperature within 1 energy storage period, conducive to seawater condenser temperature control.

The invention discloses a ship radar comprehensive energy adjusting and cooling system, and relates to the technical field of cooling systems, the ship radar comprehensive energy adjusting and cooling system comprises a supporting bottom plate, and a controller is arranged on the outer surface of the supporting bottom plate; a heat exchange assembly used for cooling a cooling liquid in the ship radar comprehensive energy adjusting system is arranged at the position, close to the edge of one side, of the top of the supporting bottom plate. According to the system, in summer, when the temperature difference between the seawater temperature of the surface depth and the cooling liquid is small, any driving motor is started to drive the telescopic pipe to extend downwards, so that the bottommost end of the depth adjusting assembly moves towards the position below the seawater; when the temperature detector located at the lowest position detects that the temperature difference between the temperature of seawater at the depth and the temperature of cooling liquid in the liquid inlet pipe is within a reasonable heat exchange range, the driving motor can be closed, and through the effect of the depth adjusting assembly, when the temperature difference between the temperature of the seawater surface layer and the cooling liquid is small, the driving motor is started. And the temperature difference between the cooling liquid and the cooling water can be increased by automatically adjusting the water taking depth.

The invention relates to the technical field of meteorological short-term climate prediction, and discloses a temperature prediction method based on hybrid space-time neural network and multi-scale feature fusion. The method comprises the following steps: acquiring a historical temperature data sequence and a multi-source meteorological data sequence, wherein the multi-source meteorological data sequence comprises wind speed data, soil temperature data, snow cover data and sea temperature data; strict time sequence feature extraction is conducted on the historical temperature data sequence, a temperature short-term fluctuation feature set and a temperature long-term trend feature set are obtained, and it is ensured that only historical information is used without data leakage through strict time sequence feature extraction; and on the basis of the multi-source meteorological data sequence, the temperature short-term fluctuation feature group and the temperature long-term trend feature group, predicating through an improved LSTM neural network to obtain predicated temperature data of a preset number of days in the future. The accuracy of temperature prediction is improved by constructing a temperature short-term fluctuation feature group and a temperature long-term trend feature group during temperature prediction.

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 application discloses a red tide polarization database acquisition device and method based on a polarization bidirectional reflection model, belongs to the technical field of data acquisition devices, selects a polarization bidirectional reflection model to perform modeling, and performs data acquisition on related data such as different solar zenith angles, different solar azimuth angles, different observation zenith angles, different observation azimuth angles, different wind speeds, different wind directions, different seawater temperatures and different seawater salinities in different regions of the sea through the red tide polarization database acquisition device. In actual testing, the theoretical formula derivation is compared with the actual acquisition result, if the confidence degree is more than 60%, the model is confirmed to be accurate, otherwise, the model is optimized, secondary modeling is performed to increase the fitting degree of the model, and the purpose of accurately identifying red tide dominant species is achieved, and the prediction accuracy of red tide is improved. The application solves the problems that the existing polarization database lacks data of red tide dominant species, a large amount of manpower and material resources are required to detect and analyze collected red tide water sample, and the efficiency is low and the cost is high.

The invention relates to an influence analysis method and system for pressure response in a containment after an accident, a medium and equipment. The method comprises the following steps: determining an initial predicted temperature of seawater and a design benchmark accident; performing grid division to obtain a grid model; determining initial condition parameters and performing assignment to obtain initial calculation conditions; inputting a calculation input condition; based on the seawater initial predicted temperature, the design benchmark accident, the grid model, the calculation initial condition and the input condition, calculation is carried out, and a calculation result is obtained; judging whether an acceptance criterion is met based on a calculation result; if yes, determining a target predicted temperature and verifying the target predicted temperature; and if not, adjusting the initial predicted temperature of the seawater, and carrying out pressure and temperature response calculation again and verifying the acceptance criterion until the acceptance criterion is met. According to the method, the influence of the seawater temperature rise on the pressure and temperature response in the containment is analyzed to determine the seawater target predicted temperature, so that a reference is provided for retreat of the unit, and the safety of the unit is guaranteed.

The application discloses a kind of umbilical cable complex environment simulation test devices for deep-sea mining, the device includes test box, ocean current simulation mechanism, vibration mechanism, guide mechanism, test piece installation mechanism, wind load simulation mechanism, monitoring component and temperature control mechanism.Test box inner cavity fills sea water solution to simulate seawater environment;Ocean current simulation mechanism is communicated with test box inner cavity, for driving sea water solution to produce controlled circulation flow to simulate deep-sea ocean current load;Vibration mechanism is set at the top of test box, for producing multi-degree-of-freedom vibration to simulate ship body movement load;Guide mechanism is arranged on the bearing surface of vibration mechanism, and the direction of test piece is guided by rolling element;Test piece installation mechanism is used to lock the two ends of test piece and apply controllable tension;Wind load simulation mechanism is towards test piece to simulate wind load;Monitoring component real-time acquisition environmental parameter and test piece state parameter;Temperature control mechanism is immersed in sea water solution to adjust temperature to simulate deep-sea temperature environment.

The present application belongs to the technical field of sea surface temperature prediction, and particularly relates to a multi-scale physical information fusion large language model sea surface temperature prediction method and system; the method comprises the following steps: inputting historical sea surface temperature data, preprocessing, generating sea temperature input data fused with spatial geographic features, and performing multi-level and multi-scale spatial and temporal feature extraction to obtain physical features; obtaining a pre-constructed marine field knowledge library, obtaining word vectors from a pre-trained language model, linearly mapping text semantic features, fusing the physical features and the text semantic features in the same feature space to obtain joint semantic features; constructing prompt word vectors related to the prediction task, inputting the joint semantic features into a large language model, guiding the model to perform deep feature interpretation and prediction through the prompt word vectors, and outputting the prediction result of the future sea temperature field; thereby the complex spatio-temporal change information of the sea temperature can be mined, and high-precision spatio-temporal prediction of the sea temperature field can be realized.