3232results about "Weather condition prediction" patented technology

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention discloses a high-resolution radar echo extrapolation prediction method fused with satellite data, and the method specifically comprises the following steps: firstly, inputting historical radar echo sequence preprocessing at a previous T moment, including denoising, normalization processing and data set segmentation, and obtaining cleaned data; then, through a deterministic modeling method (SimVP), a fuzzy prediction sequence of a future T duration is obtained, then a variational auto-encoder (VAE) maps an original radar echo image and the fuzzy prediction sequence to a low-dimensional potential space, and two-stage diffusion modeling is carried out on the basis; in the first stage, a space-time converter (ST-Translator) is used to extract space-time evolution characteristics of radar echoes; in the second stage, satellite data at the corresponding time of the previous T moment is input, preprocessing including normalization processing, feature selection and data set segmentation is carried out, cleaned data is obtained, and the influence of the satellite data is dynamically adjusted in the diffusion process by adopting a multi-source fusion denoising network Fsrform so as to make full use of satellite information; and finally, inversely transforming output results of the two stages into a pixel space to obtain a high-resolution radar echo extrapolation prediction result of the future T duration. According to the invention, computing resource consumption can be effectively reduced, and the precision and detail fidelity of short temporary rainfall prediction are improved.

The invention provides a mesoscale convection parameter optimization method and system based on a genetic algorithm, and relates to the technical field of weather forecast, and the method comprises the steps: modeling a rainfall evolution state through a Sheng differential equation, inferring and recognizing power system parameters in combination with variation, and extracting features through a space-time heterogeneous graph neural network and a diffusion probability model; the parameter threshold is corrected by adopting the physically guided neural network, and the optimization objective function is constructed through the deep neural network to realize parameter optimization, so that the accuracy of rainfall forecasting can be improved, the forecasting error can be reduced, and the method has relatively strong adaptability and generalization ability.

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a Tm value of a target point based on GPT3 model, substituting the Tm value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.

The invention provides a meteorological element three-dimensional analysis method combining remote sensing and ground observation, and relates to the technical field of meteorological monitoring, and the method comprises the steps: obtaining remote sensing and ground observation data, and obtaining meteorological element data of a unified space-time reference; dividing the data into space-time grid units and performing quality evaluation to obtain a quality evaluation index; calculating a fusion weight coefficient according to the quality evaluation index based on an adaptive fusion algorithm of a dynamic weight, and performing weighted fusion on the meteorological element data in the grid units to generate an initial three-dimensional meteorological field; performing scale decomposition and reconstruction by adopting a spectrum analysis method to obtain an optimal three-dimensional meteorological field, constructing a self-adaptive tree-shaped composite analysis grid, analyzing evolution characteristics of a weather system in a multi-layer progressive mode, determining topological evolution parameters, and outputting a three-dimensional analysis result of meteorological elements.

The invention relates to the technical field of agricultural meteorological prediction, in particular to an agricultural meteorological disaster time sequence prediction system and method based on multi-modal data fusion, and the method comprises the steps: collecting and preprocessing agricultural meteorological disaster related data, constructing a dynamic semantic association graph, carrying out the multi-layer feature abstraction processing, and generating a semantic enhancement feature vector; the dual-branch prediction network processes time sequence dependence and local mode features, multi-granularity attention processing identifies key feature information, multi-scale feature fusion extracts different time scale feature information, and cascade fusion is carried out; the multi-target optimization module carries out model training based on the comprehensive feature representation and optimizes a plurality of targets; the multi-time scale prediction output module generates short-term accurate prediction, medium-term trend prediction and long-term risk assessment results, and provides prediction confidence, error range and risk level information; a dynamic semantic association graph and a multi-layer feature mapping mechanism are constructed, and deep fusion of multi-modal data on the semantic level is achieved.

The invention discloses a short-time rainfall prediction method based on radar image and reanalysis data fusion, and the method comprises the following steps: collecting radar images and reanalysis data at continuous times, and generating input data in a unified grid format through spatial interpolation, time alignment and standardization processing; respectively extracting spatial and temporal features of the radar image and the reanalysis data by using a dual-channel encoder, and carrying out weighted fusion through a channel attention mechanism to generate a fusion feature tensor; inputting the fusion features into a ConvLSTM (Convolutional Long Short-Term Memory Neural Network), modeling a spatio-temporal evolution process of a rainfall system, and outputting a preliminary rainfall prediction image in 0-3 hours in the future; constructing a residual learning network, and performing deviation correction on the preliminary prediction result based on historical residual and observation information; when the radar image input is missing, the completeness of the input structure is maintained through the replacement feature generation module; generating a rainfall intensity image or a probability graph in 0-3 hours in the future; the method supports visual output.

Systems and methods for monitoring and evaluating time-series real-time environment data to create a high-resolution, high-fidelity actual (e.g., nowcast) and predicted (e.g., forecast) representation of an environment of interest. In some aspects, the system comprises instructions to obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period, identify one or more precursory signals within the set of real-time environment measurements, determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold, generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements, and display, at a user interface, the generated time-series representation of the actual environment state.

The invention provides a power equipment meteorological monitoring and early warning system based on artificial intelligence. The power equipment meteorological monitoring and early warning system based on artificial intelligence comprises a data acquisition module, a data preprocessing module, a spatio-temporal feature fusion module and a meteorological disaster prediction model, the meteorological disaster prediction model adopts a deep reinforcement learning framework, inputs a multi-dimensional spatio-temporal feature matrix, and carries out meteorological disaster prediction on the multi-dimensional spatio-temporal feature matrix. And outputting meteorological disaster risk levels and key parameter predicted values in a future preset time period, including a wind speed, precipitation, temperature anomaly and tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The power equipment meteorological monitoring and early warning system based on artificial intelligence provided by the invention has the advantages that the data interpolation precision of a complex terrain region can be improved, high-precision prediction of a typhoon path, short-time strong wind and an icing risk can be realized, and the early warning accuracy and defense response efficiency of a power system to meteorological disasters can be comprehensively improved.

The invention relates to the technical field of new energy power generation prediction, in particular to a new energy power prediction method fusing typhoon meteorological information and a micrometeorological prediction result, which comprises the following steps: collecting multi-source observations such as a satellite scatterometer, a radar wind profile and laser wind measurement, and unifying coordinates; constructing a mesoscale wind field by adopting four-dimensional variational assimilation, inferring a micrometeorological field by utilizing a spectrum embedding diffusion network, and generating a multi-scale meteorological field by frequency domain phase consistency fusion; probabilistic wind speed is sampled in the countercurrent model meeting the condition of mass and momentum conservation, an energy conservation graph neural network is input, the wake effect is coupled, and a unit power quantile value is obtained; the power probability is sent to a risk weighting model, the weight is adjusted in real time according to the peak load, the reserve capacity and the climbing rate risk, a dispatching power curve and uncertainty are output, and grid-connected power errors are used for periodically updating the weight and the parameters of the last layer of the reversible model. According to the invention, the safety acceptance margin of the power grid to new energy is obviously improved.

The invention discloses a multi-mode cooperative local intelligent thunder and lightning grading early warning method and system and a storage medium, and relates to the technical field of meteorological disaster early warning, the multi-mode cooperative local intelligent thunder and lightning grading early warning method comprises the following steps: preprocessing three types of heterogeneous data sources of lightning positioning data, radar cloud picture data and atmospheric electric field data; a unified input reference of space-time alignment is constructed, and then the lightning movement state, the thundercloud movement direction and the lightning occurrence probability are obtained through a lightning movement track prediction module, a thundercloud movement direction obtaining module and a lightning occurrence probability calculation module; fusing the lightning moving state, the thundercloud moving direction and the lightning occurrence probability through a multi-mode dynamic fusion module to obtain a lightning comprehensive risk probability and lightning predicted arrival time, and analyzing the lightning comprehensive risk probability Prisk and the lightning predicted arrival time Tarrival through a grading early warning module to obtain a lightning grading early warning result. The system overcomes the limitation of a single data source, is high in adaptability, and achieves the precise protection and early warning of local thunder and lightning.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention relates to the technical field of aviation, in particular to a low-altitude flight micrometeorological intelligent decision-making system based on multi-source sensing and AI fusion, and aims at solving the problems of difficulty in local disturbance identification, insufficient meteorological data granularity, weak flight task adaptation and the like in urban and complex terrain low-altitude flight scenes. According to the system, multi-dimensional meteorological data such as wind speed, wind direction, temperature and humidity are acquired through cooperation of fixed nodes and an air-based unmanned aerial vehicle, a risk area is dynamically identified in combination with a disturbance index model, and meteorological perception with minute-level and hectometer-level resolution is realized. The system supports intelligent perception scheduling, edge AI processing and multi-link communication transmission based on task characteristics, has task adaptability evaluation, flight path optimization, risk early warning and closed-loop learning capabilities, is suitable for scenes such as low-altitude airspace management, flight examination and approval, urban air traffic and the like, and remarkably improves the safety and intelligent level of low-altitude operation.

The invention discloses an AI-NWP three-dimensional closed-loop bidirectional dynamic feedback coupling method for extreme rainfall event area simulation and a program product, and belongs to the technical field of meteorological numerical simulation and artificial intelligence fusion. According to the method, a mid-term forecast field is generated based on an AI global weather prediction model, an analysis field is constructed by assimilating multi-source observation data, and a high-resolution NWP region mode is driven to perform rolling simulation. Furthermore, a space-time residual field is constructed by using the difference between an NWP region simulation result and live data, a residual learning model is trained, an AI model prediction structure is fed back and corrected, and dynamic weight adjustment updating of the AI prediction model is realized. A closed-loop two-way feedback system among AI output, NWP simulation and residual evaluation is integrally formed, the space structure reduction capability and the area positioning precision of a medium-term heavy rainfall event are effectively improved, the continuous predictability and the simulation credibility of an extreme weather process are remarkably enhanced, and the method has good stability, universality and engineering expansion value.

The invention belongs to the technical field of power transmission line monitoring, and discloses a power transmission tower power transmission line galloping monitoring system which comprises a data acquisition module, a data processing module, an intelligent prediction module, an early warning response module and a man-machine interaction module. The galloping prediction precision is improved through multi-source data fusion and physical constraint modeling, a mixed architecture of time sequence analysis and dynamic characteristic fusion is adopted, complex correlation characteristics of meteorological parameters and conductor dynamic behaviors are effectively captured, an intelligent prediction model is optimized in combination with physical equation constraints, and the galloping prediction accuracy is improved. The physical rationality and extreme scene adaptability of a prediction result are obviously enhanced; the line state change is adapted in real time based on a dynamic threshold adjustment mechanism, and the early warning sensitivity and reliability are optimized; through spatio-temporal feature alignment and a multi-mode galloping mode identification technology, a composite vibration form is accurately analyzed, medium and long term trend pre-judgment and short-time risk early warning are synchronously realized, and a multi-dimensional decision support is provided for line safety regulation and control.

The invention discloses a three-dimensional wind field spatial-temporal feature reconstruction and efficient prediction method, and relates to the field of spatial-temporal feature reconstruction and efficient prediction. Constructing a three-dimensional terrain computational domain based on the digital elevation model of the target mountain region, performing multi-scene numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, and constructing and training a wind field spatial feature mapping model; training a wind speed and wind direction short-time prediction model based on the actually measured data set; inputting the monitoring data obtained in real time into the wind speed and wind direction short-time prediction model to obtain a future wind speed and wind direction prediction value of each monitoring station; and inputting the wind speed and direction predicted values into the wind field spatial feature mapping model to obtain the mountain overall wind field distribution of the target mountain region at the future moment. By constructing an'actual measurement-simulation-modeling-prediction-reconstruction 'integrated technical framework, high-temporal-spatial-resolution short-time prediction from observation of local wind speed and wind direction to the overall three-dimensional wind field of the mountainous region is realized.

The invention relates to the technical field of intelligent weather forecast, and discloses a WRF wind speed simulation correction method, system and device and a storage medium, and the method comprises the steps: obtaining multi-source basic meteorological data, and carrying out the preprocessing; constructing a fan field feature matrix according to the fan field distribution data; inputting the preprocessed data and the wind field feature matrix into a multi-source space-time convolution fusion module, and extracting and fusing time sequence features and space correlation features; inputting the fused features into a normalized convolutional neural network introducing physical constraints, and extracting spatial-temporal features of the wind speed field; and performing channel splicing on the spatial-temporal characteristics of the wind speed field and to-be-corrected wind speed data, inputting the spliced data into a multi-scale convolutional wind speed correction network, and obtaining a correction result of the wind speed simulation data through encoding and decoding operations. Through a multi-source data dynamic fusion mechanism, a spatial-temporal feature collaborative optimization algorithm and a multi-scale correction network architecture, system errors of traditional numerical mode simulation are remarkably reduced, and the accuracy of wind speed forecasting is greatly improved.

The invention discloses a valley-type tailing pond flood runoff prediction method based on underlying surface parameter dynamic correction, which comprises the following steps: S1, acquiring valley-type tailing pond multi-source data for preprocessing, and constructing a basic database; s2, underlying surface parameters of the valley-type tailings pond are obtained, and the initial value range of each parameter is determined; s3, establishing an underlying surface parameter dynamic correction model, setting differentiated production and confluence parameters for different areas, and performing dynamic correction; s4, constructing a coupled hydrological-hydrodynamic model, simulating a runoff forming process and time-varying characteristics, outputting predicted values of runoff flow, flood peak time and flood peak water level, and comparing the predicted values with actual measured values of a historical flood area of a satellite remote sensing image for verification and calibration; and S5, inputting the real-time data into the hydrological-hydrodynamic model, and predicting the submerging range, submerging time and submerging degree of the flood runoff based on a geographic space analysis method. According to the method, the timeliness and the accuracy of runoff prediction of small watershed areas without runoff data such as valley type tailings ponds are improved.

The invention provides a new energy electric power meteorological prediction method and device based on a multi-modal large model, and belongs to the field of meteorological prediction. The method provided by the invention comprises the steps of obtaining multi-source meteorological observation data and new energy station operation data, and generating a multi-modal fusion feature; a large meteorological prediction model is constructed, the large meteorological prediction model adopts a dynamic graph neural network structure, nodes represent geographic space positions, edges represent spatial adjacent relations, and node features comprise numerical values of meteorological elements; using the multi-modal fusion features to train the meteorological prediction large model, and minimizing a meteorological element prediction error; and inputting real-time multi-source data of a to-be-predicted area into the trained meteorological prediction large model, and outputting meteorological types and meteorological element values of the to-be-predicted area within a preset duration. According to the method and the device provided by the invention, the problems of insufficient accuracy of new energy electric power weather prediction, weak combination of geographical and physical rules and difficulty in adaptation to different weather types can be solved.

The invention provides a meteorological disaster dynamic monitoring method and system applied to real-time meteorological data, and the method comprises the steps: firstly obtaining a real-time meteorological data set of a target region, and generating time change features and spatial distribution features of each meteorological data unit through spatial-temporal feature extraction processing; the method comprises the following steps: firstly, acquiring time change characteristics of a meteorological disaster, generating regional correlation characteristics according to the correlation between the time change characteristics and spatial distribution characteristics, then inputting the regional correlation characteristics into a pre-trained meteorological disaster prediction model for disaster risk assessment to generate a disaster prediction result, and finally, outputting the disaster prediction result. And generating a dynamic monitoring strategy including a monitoring equipment deployment scheme and an early warning information pushing rule according to the disaster prediction result, and feeding back the dynamic monitoring strategy to the meteorological monitoring platform to trigger a monitoring resource allocation operation, so that the meteorological disaster can be dynamically monitored by fully utilizing the real-time meteorological data, and the accuracy and timeliness of disaster early warning are improved.

The invention discloses a thunder and lightning early warning method based on atmospheric electric field networking monitoring. The method comprises the following steps: collecting multi-point electric field intensity data in a region in real time through a high-density electric field sensor network, and constructing an electric field intensity distribution matrix; for the constructed electric field intensity distribution matrix, space-time evolution characteristics of lightning activities are extracted, and lightning occurrence probability distribution is generated; acquiring temperature, humidity and air pressure parameters from multi-source meteorological data, performing data fusion with the generated thunder and lightning occurrence probability distribution, and constructing a comprehensive environment characteristic data set; and for the constructed comprehensive environment characteristic data set, calculating a comprehensive risk index, and if the index exceeds a preset thunder and lightning triggering threshold, etc. According to the lightning early warning method, the distribution matrix is constructed through the high-density electric field network, the spatial-temporal characteristics are extracted to generate the initial probability, and the physical rationality and the environment false alarm resistance of early risk identification are remarkably improved through fusion with the multi-source meteorological data.

The invention provides a north pole atmosphere coupling forecasting method capable of automatically fusing sea ice concentration and thickness, which belongs to the technical field of meteorology, and comprises the following steps: firstly, acquiring GFS background field data and correcting by applying an ice-sea heat exchange correction equation; and then sea ice concentration and thickness information is extracted, and physical consistency verification is carried out based on a thermodynamic equilibrium equation. Performing multi-element fusion through an Arctic ice sea assimilation model, calculating the sea ice surface temperature by applying a polar region sea ice heat flux equation, and establishing a sea ice concentration thickness boundary layer interaction equation set to describe the modulation effect of sea ice on a boundary layer; the method comprises the core steps of performing deep fusion on various sea ice features by applying a feature fusion model, adaptively adjusting the weight based on an ice-gas interface balance index, generating an optimized mode initial field and boundary conditions, and finally setting a parameter scheme to execute an integral program to obtain a forecasting result. The technical problem of low forecasting accuracy caused by insufficient physical consistency of sea ice information is solved.

The invention discloses a numerical simulation prediction method and system for typhoon binocular wall structure reconstruction judgment and maintenance duration after terrain interference and a medium, and relates to the technical field of typhoon fine structure evolution description and numerical simulation. According to the method, the binocular wall typhoon is identified through multi-source data fusion, a high-resolution numerical simulation system is constructed, and a typhoon structure is divided into four quadrants for differential diagnosis according to the wind shear direction based on vertical wind shear phase limit analysis. And the distribution characteristics of the rapid vortex silking area are captured by calculating vortex silking time parameters. And establishing a ditch sinking airflow detection mechanism, and quantitatively evaluating the contribution of radial advection, tangential advection, vertical advection and friction force items to tangential wind evolution by using tangential wind income and expenditure diagnostic analysis. And calculating an energy growth rate, and predicting a double-eye wall maintaining time length. According to the method, the outer eye wall re-formation judgment is output, the outer eye wall radius zone, the maintenance duration interval and the uncertainty are predicted, and the method is suitable for real-time business and research.

The invention relates to the field of weather forecasting, and discloses a marine meteorological coupling refined forecasting method and system, which are used for improving the precision and reliability of marine meteorological coupling forecasting. Comprising the following steps: based on a non-exchangeable geometric vortex equation and an external differential form, constructing a conservation manifold dynamic field in combination with submarine topography data, and realizing strict conservation of mass flux under a complex terrain; an additional pressure item caused by vacuum fluctuation is quantified, and an interface energy field of quantum correction is generated; the recognition precision of the mesoscale vortex and the frontal surface structure is improved; a spectral element method and lattice Boltzmann method mixed solver is combined, and high-precision coupling of ocean vertical layering and the atmospheric process is achieved. According to the method, the defects of conservation, interface energy transmission, initial field non-physics and grid adaptive capacity of a traditional method are overcome, refined forecasting of 72-hour typhoon paths, ocean frontal surfaces and energy flux is supported, and key technical support is provided for extreme weather early warning and climate change research.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention provides an intelligent storm surge prediction method and device based on deep learning and a medium, and relates to the field of storm surge disaster risk assessment, and the method comprises the steps: obtaining multi-source heterogeneous data of a typhoon storm surge disaster, and carrying out the preprocessing; the multi-source heterogeneous data comprises meteorological and marine observation data and high-precision geographic information data; constructing a typhoon and storm surge prediction network; the typhoon and storm surge prediction network comprises a multi-scale spatial-temporal feature module, a physical mechanism enhancement module and a spatial-temporal sequence prediction model based on a fusion attention mechanism and a graph convolutional network; training a typhoon and storm surge prediction network through multi-source heterogeneous data; acquiring meteorological and tide level live data; and inputting the meteorological and tide level actual data into the trained typhoon and storm surge prediction network to obtain a typhoon and storm surge state prediction result. A physical mechanism is introduced into the neural network to predict the states of the typhoon and the storm surge, and the prediction precision and efficiency are remarkably improved.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.