1040 results about "Typhoon" patented technology

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous marine data intelligent fusion and marine disaster prediction method and platform, and the method comprises the steps: carrying out the hierarchical deployment and network topology construction of marine environment monitoring equipment, communication relay equipment and data processing equipment, and obtaining a sea-air-shore integrated monitoring network; collecting marine environment data with a space-time identifier; carrying out ocean element modeling and dynamic feature extraction to obtain an ocean environment digital model; performing feature fusion and correlation analysis on the multi-source heterogeneous ocean data to obtain spatio-temporal feature data; and training and multi-model integration are carried out on the typhoon path prediction model, the storm surge prediction model and the red tide prediction model by adopting a transfer learning method to obtain a marine disaster prediction model set, so that the prediction precision and timeliness are improved, the allocation and scheduling of computing resources are optimized, and the real-time requirement of mass data processing is met.

The invention discloses a tidal river reach storm surge rapid forecasting method based on deep learning and an AI large model. The method comprises a data acquisition and preprocessing step, a meteorological element forecasting step, a typhoon path and flow forecasting step and a storm surge forecasting step. Multi-source data fusion is realized through a four-dimensional variational assimilation method and an adaptive weight fusion algorithm; a multi-model integrated system such as FourCatNet and the like is used for forecasting meteorological elements; an improved XLSTM-Transformer model is adopted to predict the flow and the typhoon path; and carrying out storm surge forecasting based on a three-layer nested model system and a multi-process coupling technology. According to the method, full-process automation from data acquisition to forecasting is realized, the forecasting timeliness is improved by 60%, the average absolute error is reduced by 45%, and a reliable basis is provided for disaster prevention and reduction decisions.

The present invention relates to the technical field of power distribution networks. Disclosed are a disaster prevention early warning and production decision support method and system for a power distribution network. According to the present invention, multiple types of data from a data center can be integrated, and classification, analysis and feature extraction are performed on multi-source data of a power distribution network, and then without the need to install additional meteorological environment monitoring devices, by means of calculating a maximum wind load, a lightning trip-out rate, a maximum current carrying capacity at a highest allowable operating temperature, and an average failure frequency and an average failure time which consider environmental factors, a power outage risk of the power distribution network is evaluated, and risk grading is performed. After an emergency occurs, by means of analyzing a power grid topology structure after device shutdown, a device shutdown probability can be further calculated, and a load power outage probability and the power outage risk can be obtained. According to the present invention, pre-disaster early warning, in-disaster monitoring, and post-disaster emergency repair auxiliary decision-making for the power distribution network under disasters such as typhoons, thunder and lightning, floods and icing are implemented, providing data and service support for safety production, planning and management, and commanding and decision-making of the power distribution network.

The invention provides a fan system multi-physics field coupling simulation and modeling method and device, and the method comprises the steps: building a coupling simulation frame of an electromagnetic field, a temperature field, a flow field and a structural mechanical field through multi-physics field collaborative modeling, employing a finite volume method FVM and multi-body dynamics MBS for collaborative solving, and precisely simulating the dynamic response of a fan under a complex working condition. And real-time simulation and verification: combining an S CADA system and a machine learning algorithm to realize online calibration and dynamic verification of a multi-physics field model, and supporting fan structure stability prediction under extreme conditions of typhoon, turbulence and the like. A multi-physics field coupling simulation software environment is developed, the functions of wake effect analysis, fan array layout optimization and the like are provided, the construction cost of an offshore wind field is reduced, and the power generation efficiency is improved.

The invention discloses a power distribution network operation safety risk assessment method considering typhoon and rainstorm composite disasters, and the method comprises the following steps: generating different typhoon paths as main scenes based on the typhoon and rainstorm disaster forecast data, and endowing each main scene with a weight according to the occurrence probability; the influence of microtopography and rainfall intensity on the wind speed V (r) in the main scene is considered, the corrected wind speed is calculated, the total failure probability of the distribution line between the node i and the node j is calculated according to the fault probability of the distribution line between the node i and the node j and the failure probability caused by foreign matter hanging, and the power distribution equipment failure probability is calculated according to the water accumulation amount of the power distribution network area; simulating and generating N fault scenes s through a Monte Carlo method to form a fault scene set S, and endowing each fault scene with a weight; and taking the minimum expected total load loss amount in all fault scenes as an objective function, constructing a risk assessment model, and solving the risk assessment model in combination with constraint conditions.

The invention discloses a seawall overtopping amount and overtopping risk calculation method considering a mangrove forest wave dissipation effect, and relates to the technical field of coast engineering. The method comprises the following steps: acquiring typhoon path data, inputting the typhoon path data into a Holland typhoon empirical model, and accurately generating a typhoon wind field; calculating coastal site tide level data in combination with a coastal storm surge numerical model; obtaining wave data during a typhoon by using a storm surge typhoon wave forecasting model based on the SWAN; historical typhoon data and relevant information of the seawall and the mangrove forest are collected, a water level-wave-vegetation-seawall coupling model is constructed, the wave dissipation effect of the mangrove forest is accurately simulated, and wave characteristic calculation is optimized; constructing a database, training the database through a CNN neural network model, and improving the prediction precision of seawall front wave height and wave force parameters; the wave overtopping amount is calculated based on the prediction parameters, a scientific basis is provided for wave overtopping risk assessment, it is ensured that real-time assessment of the wave overtopping risk is more accurate and efficient, and the disaster prevention and reduction capacity is effectively improved.

The invention discloses a typhoon wave forecasting method based on integrated machine learning. The method comprises the following steps: firstly, integrating historical typhoon wave data, meteorological data and marine environment data; preprocessing the data, including integration, cleaning, vacancy filling and standardization, and performing multi-source data completion by adopting a K-nearest neighbor algorithm and a spline interpolation method; secondly, screening key characteristic parameters through a Pearson's correlation coefficient, and reinforcing nonlinear correlation representation in combination with a mutual information method; then, constructing an integrated prediction model containing an LSTM (Long Short Term Memory), an XGBoost (X Goose Boost) and a Transform; and finally, dividing a training set and a verification set by adopting a dynamic time sequence division strategy, optimizing model hyper-parameters, and completing training and testing of the typhoon wave height prediction model. According to the method, the data sparsity problem is solved through multi-source data fusion and feature selection optimization, the generalization ability is improved through an integrated model architecture, and compared with a traditional single model, the training period is remarkably shortened, and the forecasting precision and timeliness are improved.

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 near-shore wind wave prediction method and system based on data driving, and the method comprises the steps: obtaining multi-source data of long-time series data of each meteorological data variable of a research region, carrying out the preprocessing, selecting a target variable and a feature variable, and carrying out the region grid point division of the research region; taking the characteristic variables of the open sea lattice points as a reference sequence, taking the target variables of the inner sea or near-shore lattice points as a target sequence, and analyzing a time delay effect and a spatial propagation mode between the reference sequence and the target sequence; introducing the analysis result as priori knowledge into a deep learning model, and training the deep learning model to obtain a typhoon prediction model; and acquiring real-time data of each meteorological data variable in the research area, preprocessing the real-time data, and inputting the characteristic variable in the real-time data into the typhoon prediction model to obtain a prediction result of the target variable. According to the method, the time lag effect and the spatial relation of all variables of the open sea on the offshore area information are comprehensively considered, fusion and integration of multi-source data are achieved, and efficient and accurate real-time prediction is carried out.

The invention relates to a data and model fused tower crane displacement response prediction method under the typhoon action, which comprises the following steps: constructing a monitoring system, acquiring actual measurement data of a construction site and a tower crane, and constructing a tower crane finite element model by utilizing a design parameter set of the tower crane; the extreme wind speed under the most unfavorable wind direction of a construction site is obtained based on typhoon full-path simulation and wind field simulation, and the tower crane finite element model is calibrated by analyzing a parameter object needing to be calibrated in the tower crane finite element model through sensitivity, so that the calibrated tower crane finite element model can accurately reflect the response condition of a tower crane structure; a calibrated tower crane finite element model is used for simulating a data set formed by displacement data under the typhoon working condition to serve as a training set, the defect of an extreme weather sample in an actually-measured training sample is overcome, then response prediction of tower crane displacement under the typhoon working condition is achieved by training a CNN-BiLSTM-Adaboost displacement response prediction model, and the method is simple and convenient to implement. The precision and robustness of tower crane displacement prediction under the extreme typhoon working condition are remarkably improved, and sufficient emergency processing time is reserved for a construction site.

The invention discloses a multi-source rainfall forecast fusion method based on typhoon similarity, and relates to the field of rainfall forecast, and the method comprises the steps: selecting key factors affecting the formation and development of typhoons, obtaining a multi-dimensional vector corresponding to each typhoon path point for a current typhoon and each historical typhoon, and guaranteeing the comprehensive description of typhoon features; according to the method, a historical typhoon data set is constructed, and a comprehensive correction coefficient of each forecasting mechanism is obtained based on the set and a one-dimensional vector corresponding to each forecasting mechanism, so that the system error of each forecasting mechanism is effectively corrected; meanwhile, the optimal weight coefficient and the standard deviation are calculated through the comprehensive correction coefficient, the latest future rainfall forecast numerical value of each forecasting mechanism is obtained, the comprehensive correction coefficient is used for correction, the forecast correction value corresponding to each forecasting mechanism is obtained, and the forecasting mechanism is obtained by combining the optimal weight coefficient, the standard deviation and the forecast correction value of each forecasting mechanism. The prediction fusion value and the corresponding variance estimation value are obtained, and the prediction precision is greatly improved.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

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 discloses a typhoon wind speed prediction method based on Mamb-ASPP and R-TCN. The method comprises the following steps: obtaining a satellite cloud picture data set and carrying out preprocessing; a Mama-ASPP module is constructed for spatial feature extraction, an R-TCN module is constructed for time feature extraction, a global spatial feature map and a time feature map of an input image are obtained and spliced, the spliced image is put into an ITSAM module for comprehensive feature processing, and a predicted value of the typhoon wind speed is obtained; integrating the three modules into an initial wind speed prediction model, and optimizing and training hyper-parameters of the initial wind speed prediction model by using a seagull algorithm optimized by an adaptive genetic algorithm to obtain a target wind speed prediction model; and inputting the image data of the previous hour into the target wind speed prediction model, and predicting the typhoon wind speed of the next hour. According to the method, by integrating different time and space scale feature information, the complexity of typhoon wind speed change is dealt with, and the typhoon prediction efficiency and accuracy are effectively improved.

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 provides a source network collaborative planning method and system considering typhoon and wind power capacity credibility, and the method comprises the steps: carrying out the simulation of a typhoon wind field, calculating the theoretical fault rate of a line through employing a line vulnerability curve calculation method under a typhoon disaster, and obtaining the state of each power transmission line of the system; calculating the output power of the wind power plant unit by using the basic information of the power grid, and predicting the output sequence of the wind power plant unit in the typhoon wind field based on the simulated typhoon wind field; obtaining the fault probability of the power transmission line at each moment, generating a typical fault scene, and calculating the wind power credible capacity according to the state of each power transmission line of the system; and constructing a source-network collaborative planning model based on the output sequence of the wind power plant unit and the wind power credible capacity. According to the method, the problems of wind power plant unit output, wind power spinning reserve and line reinforcement planning in typhoon-free weather are solved, and the purposes of considering the load importance level and energy storage access and coordinating resources in the aspects of source-network-load-storage and the like during planning are achieved.

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 discloses a typhoon-rainstorm flood composite disaster monitoring and evaluating method and system, and belongs to the field of intelligent disaster monitoring, and the method comprises the following steps: building a disaster recognition model through a machine learning algorithm based on multi-factor data, and obtaining a composite disaster judgment result; based on the composite disaster judgment result, constructing a disaster evaluation model for the area forming the disaster; dynamically updating parameters of the disaster assessment model based on real-time monitoring data to obtain a real-time composite disaster assessment result; based on the real-time composite disaster assessment result, dynamically adjusting loss probability distribution to obtain a comprehensive disaster damage assessment result; and carrying out disaster situation space-time evolution based on the comprehensive disaster damage assessment result to obtain a disaster situation development trend.

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 discloses a typhoon long-distance rainstorm rapid identification method based on an AI meteorological model, a storage medium and a computer program product, and the method comprises the steps: obtaining historical weather data and / or forecast weather data, selecting an AI weather forecast model which is based on a deep learning technology and carries out the training through a large amount of historical weather data, representative and typical rainfall examples are screened, a control experiment and a sensitivity experiment are designed, and typhoon core vortexes are removed from an initial field. By comparing the difference between the results of the control experiment and the sensitivity experiment, the influence of the typhoon on the long-distance rainfall is quantified based on the whole-layer water vapor divergence change and rainfall change analysis, and the long-distance rainstorm influence range and intensity of the typhoon are identified and determined. According to the method, the precision of medium-term weather forecast and extreme weather event recognition is remarkably improved, efficient calculation and wide practical application value are achieved, a scientific basis is provided for disaster early warning and disaster reduction management, and a new method is provided for meteorological scientific research and teaching.

The embodiment of the invention provides an estuary region storm surge disaster assessment method and device. The assessment method comprises the steps of obtaining basic geographic data, meteorological data, tide and wave data and disaster-bearing body data of a preset region of an estuary region; constructing a discrete grid according to the basic geographic data; according to the tide and wave data, constructing a storm surge and wave numerical simulation model in a discrete grid; simulating different typhoon scenes in a numerical simulation model of the storm surge and the waves according to the meteorological data to obtain simulation results; evaluating according to the simulation result to obtain the danger level of each area; evaluating according to the disaster-bearing body data to obtain the vulnerability level of each region; and determining the comprehensive risk level of each area according to the danger level and the vulnerability level. According to the embodiment of the invention, the evolution process of storm surge disasters can be simulated more accurately, and then the comprehensive risk level of each area of the estuary region is determined.

The invention discloses a dual-polarized SAR sea surface typhoon wind speed joint inversion algorithm based on transfer learning and a residual network, and relates to the technical field of marine environment information monitoring. The method comprises the following steps: constructing a sea surface typhoon wind speed joint inversion algorithm model; the model structure comprises a pre-training model constructed by a ResNet network model, a medium-low wind speed inversion model and a high wind speed inversion model. The model structure further comprises a feature splicing model and a feature fusion model which are used for splicing and fusing features extracted from the medium-low wind speed inversion model and the high wind speed inversion model, and an inversion model for typhoon wind speed inversion. The invention designs a dual-polarized SAR sea surface typhoon wind speed joint inversion algorithm based on transfer learning and a residual network, and the inversion algorithm can effectively alleviate the small sample learning problem of a high wind speed region, solves the feature mismatching problem in cross-wind-speed-section transfer, improves typhoon wind speed inversion, and improves the typhoon wind speed inversion efficiency. Especially, the stability and the accuracy of wind speed inversion in a high-wind-speed area are improved.

An estimation method and system for quasi-real-time monitoring of a moving velocity vector of a typhoon based on a synthetic aperture radar includes: (1) determining, using a threshold method, a position of a typhoon eye wall of a synthetic aperture radar (SAR) sea surface wind field of a typhoon; (2) extracting a final typhoon eye region using a maximum gradient method according to the position of the typhoon eye wall, and estimating an approximate ellipse of the typhoon eye wall and determining a center of the approximate ellipse as a typhoon center; (3) estimating large-scale background wind vector information using the estimated typhoon center and approximate ellipse; and (4) determining a moving velocity vector of the typhoon using the estimated background wind vector information and typhoon center.

The invention discloses an inland typhoon long-distance extreme rainfall prediction method based on deep learning and integrated with an IDII index, a medium and a program product, and the method comprises the steps: constructing a whole-layer dry intrusion index, and comprehensively considering the overall effect of dry and cold air at different heights; historical TRP rainfall events, typhoons and reanalysis meteorological data are obtained, preprocessing is carried out, IDII indexes are fused, and a training data set is constructed; constructing a prediction model comprising a feature extraction layer, an IDII feature fusion layer and a TRP prediction output layer by adopting a deep learning technology; training, verifying and testing the model by using a TRP sample data set; and predicting a future TRP event by using the trained model, and outputting prediction results such as TRP precipitation, occurrence time, place and duration. According to the method, the dry intrusion physical quantity IDII and the deep learning technology are fused, the prediction precision of the TRP event is improved, and technical support is provided for forecasting of inland extreme rainfall and disaster prevention and reduction.

The invention relates to the technical field of typhoon path prediction, and discloses a typhoon path prediction method, system, terminal and storage medium based on multi-modal data of sounding equipment and a sky big model, and the method comprises the steps: obtaining multi-source heterogeneous data collected by detection equipment related to typhoon prediction, carrying out fusion and adaptive enhancement on the multi-source heterogeneous data on the basis of a big Pantou meteorological model, and generating a high-resolution three-dimensional field; constructing a cascaded AI downscaling network, introducing a physical constraint layer, carrying out local refined modeling on a typhoon eye region according to the high-resolution three-dimensional field, and outputting a high-resolution three-dimensional meteorological field; and designing a typhoon-environment field interaction model based on a graph neural network, performing typhoon prediction according to the high-resolution three-dimensional meteorological field, and outputting a typhoon path ensemble forecast containing a confidence interval and a typhoon thermodynamic structure analysis report. The typhoon path prediction speed and precision are improved, the prediction time is shortened, the prediction error is reduced, and calculation resources are saved.

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The present application discloses a typhoon forecasting method based on domestic satellite data, which includes: a data fusion process: used to fuse GNSS-R sea surface wind data and IFS forecast data from the European center to generate a two-dimensional gridded sea surface vector wind field; a meteorological forecasting process combined with artificial intelligence, including a large meteorological model forecasting step and a numerical model forecasting step; a multi-typhoon path discrimination and typhoon intensity forecasting process, which is used to extract key meteorological element variables from regional forecast data files at different times, and judge the typhoon center based on the key meteorological element variables. The present application combines domestic satellite data and artificial intelligence technology, and through the deep combination of data fusion and numerical forecasting technology, improves the accuracy and refinement of typhoon path and intensity forecasts. In addition, through the application of large meteorological models, nonlinear and massive data can be processed more effectively, improving the forecast accuracy.

The invention discloses a typhoon intensity prediction method based on an AI initial field driven NWP area mode and fused with vortex power initialization, a medium and a computer program, belongs to the technical field of artificial intelligence and area numerical simulation, and aims to improve the intensity prediction precision and the structure reduction capability of a fast enhanced typhoon. The method comprises the following steps: firstly, constructing an AI weather model based on deep learning, generating a continuous initial field and a boundary field covering multiple meteorological elements, and driving an area NWP mode; secondly, a vortex power initialization process is introduced before report starting, and the consistency and strength accuracy of a typhoon axial symmetry structure in a simulation initial field are enhanced; then, integral simulation is carried out based on an atmosphere-ocean bidirectional coupling mode, and dynamic optimization of a local structure is realized in combination with high-frequency multi-source observation data assimilation; and finally, an AI-NWP residual feedback mechanism is constructed, and closed-loop correction and model fine tuning of prediction errors are realized. The method is suitable for high-precision prediction of typhoon intensity and has good engineering practicability.

The invention relates to an offshore wind power operation environment scene construction method considering typhoon forecast errors. The method comprises the steps of selecting similar historical typhoon forecast data according to current typhoon forecast information; based on wind speeds of similar historical typhoon forecast and current typhoon forecast, combining marine environment element information to construct a marine environment scene information space; a kernel density estimation model of historical typhoon wind speed forecast errors is constructed, corresponding kernel density bandwidths are calculated and updated according to different typhoon forecast timeliness, and historical forecast wind speed error distribution is obtained; and inputting the marine environment scene information space and the historical forecast wind speed error distribution into the improved generative adversarial network, and generating a marine environment scene of which the wind speed interval meets the historical typhoon wind speed forecast error distribution through the wind speed interval constraint loss. Compared with the prior art, the marine environment scene is updated according to the wind speed error distribution under different forecast time periods of the typhoon, and the scene generation quality is improved.