816 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 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 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 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 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 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 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.

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.

The invention discloses a near-shore wave-flow field-sediment coupling simulation method considering a wave breaking nonlinear effect, and belongs to the field of near-shore hydrodynamics, and the method comprises the steps: inputting WRF high-resolution wind field, tide boundary conditions and water depth information, and initializing a wave module, a flow field module and a sediment module; calculating local wave breaking dissipated energy by adopting an improved wave breaking energy dissipation formula to obtain a wave energy density spectrum and a breaking probability; a near-shore flow field is calculated under radiation stress nonlinear correction, and hydrodynamic boundary conditions are updated; calculating sediment suspension and deposition by using a coupling diffusion coefficient, and updating sediment concentration distribution; waves, flow fields and sediment are mutually coupled through loop iteration, and coastal wave-flow field-sediment simulation is achieved under extreme conditions such as typhoons, storm surge and strong storm waves. The method can improve the prediction precision of the wave height, the flow velocity and the sediment concentration under the condition of offshore strong storm waves, and is used for typhoon storm surge prediction, harbor basin deposition evaluation and offshore ecological environment management.

The invention relates to a load quantification method for a floating fan in a typhoon scene, and the method comprises the steps: constructing an environment data simulation frame which comprises an external nesting and an internal nesting, the internal nesting is used for achieving wave flow coupling and generating wave flow scene data, and the external nesting is used for carrying out wave flow coupling and obtaining external environment data of the floating fan; the method comprises the following steps: constructing a multi-time-scale load prediction framework, inputting environmental data into the multi-time-scale load prediction framework, and outputting load distribution of typhoon to a floating fan under a long time scale, the multi-time-scale load prediction framework comprising a short-time-scale dynamic coupling analysis model and a long-time-scale load prediction model, and the long-time-scale dynamic coupling analysis model comprises a short-time-scale dynamic coupling analysis model and a long-time-scale dynamic coupling analysis model. The short-time-scale dynamic coupling analysis model is used for obtaining a load prediction sample, and the long-time-scale load prediction model is used for predicting the load prediction sample and outputting load distribution under a long time scale. Compared with the prior art, the method has the advantages of being more practical in environment simulation, accurate and efficient in load prediction and the like.

The invention discloses a typhoon wave height prediction method based on a deep learning MOE-Transform model, and the method comprises the following specific steps: collecting historical typhoon data of a research region, and constructing a virtual typhoon data set in combination with a central pressure difference formula; processing the historical and virtual typhoon data sets through a Holland typhoon empirical model and a storm growth relation to obtain a wind field, an air pressure field and a significant wave height field; correcting the field data by using an error model on the basis of the ERA5 data set to form a typhoon space-time fusion database containing meteorological data, the significant wave height field and typhoon data; and training and testing the MOE-Transform model to obtain a typhoon wave significant wave height prediction model for typhoon wave height prediction. According to the method, the multi-task adaptability and generalization ability are improved, and the precision and timeliness of typhoon wave height prediction are improved.

The invention provides a deep sea temporary platform large-diameter steel casing dynamic response analysis method, and relates to the technical field of deep sea engineering, and the method comprises the steps: building a three-dimensional explicit dynamic coupling model according to the typhoon frequent generation characteristics of a target sea area and steel casing structure parameters; according to the three-dimensional explicit dynamic coupling model, the wave load is decomposed, the vibration hammer load effect, the ocean current load effect and the internal wave current load effect are integrated, and a comprehensive dynamic load set of the steel casing is determined. By establishing a precise model, integrating multi-source loads, identifying a risk area, monitoring stress in real time and optimizing design parameters based on data, self-adaptive matching of steel casing design and a complex marine environment is achieved, the structural safety of the deep sea temporary platform steel casing is effectively improved, and the cost is reduced.

The invention discloses a data assimilation method for infrared hyperspectral data of a clear sky channel. The data assimilation method comprises the following steps: acquiring the infrared hyperspectral data of the clear sky channel; a linear or non-linear relationship to convert variables from a mode space to an observation space using a fast radiation transfer mode; satellite infrared hyperspectral atmospheric vertical detection data channel screening is carried out in combination with a principal component method; a channel which is not influenced by cloud pollution is searched by observing the difference between the brightness temperature observed by the infrared and the brightness temperature simulated by the background field; eliminating the statistical deviation between the radiation value observed by the satellite instrument and the radiation value simulated and calculated according to the background field profile; eliminating observation data with errors exceeding a preset threshold value on the mixed earth surface type; and outputting a data assimilation result of the infrared hyperspectral data of the clear sky channel. Analysis fields of all layers of the global atmosphere are improved, the numerical forecasting level is effectively improved, typhoon forecasting intensity errors are remarkably improved, and path errors before landing are superior to conventional observation data tests.

The invention discloses an island shoreline identification method and system based on artificial intelligence, and relates to the technical field of shoreline change monitoring. According to the invention, through fusion modeling of the optical image, the SAR image and the topographic data, the limitation of a single data source is broken through; a vegetation coverage area and a non-vegetation shoreline can be accurately distinguished through the multispectral characteristics of the optical image, and the problem of monitoring blind areas in scenes such as cloud and mist and nighttime is solved through the all-weather penetrating power of the SAR image; a random forest classifier can effectively distinguish bedrock steep cliff, sandy sand beach and the like in combination with superpixel unit feature vectors constructed by topographic data, and accurate partitioning of a target island shoreline is completed; moreover, a dynamic global correction mechanism further eliminates space-time error accumulation, a standard deviation mean value is calculated through cross-season historical data, accidental errors such as typhoon and rainstorm are filtered, a future global correction coefficient is predicted in combination with a time sequence model, and the reliability of long-term monitoring is remarkably improved.

The invention discloses a dynamic typhoon similarity matching method based on meteorological structure similarity, and the method comprises the steps: obtaining the feature data of a current typhoon and a historical typhoon, and the feature data comprise typhoon path data and typhoon meteorological data; the method comprises the following steps: selecting three continuous real-time points of a current typhoon and three continuous typhoon point data in historical typhoons, respectively calculating the similarity of gas field element fields on the three real-time points and the typhoon points, and sequentially screening a plurality of historical typhoons which are most similar to the current typhoon; according to the screened historical typhoon paths most similar to the current typhoon, the minimum value of the accumulated distance between the historical typhoon and the current typhoon path is calculated, the historical typhoon corresponding to the minimum value of the accumulated distance is the historical typhoon most similar to the current typhoon, and the historical typhoon is successfully matched with the current typhoon. According to the method, the similarity of typhoon paths is not simply considered, comprehensive consideration is carried out in combination with the typhoon meteorological field, historical typhoons with multiple similar elements are matched, and formulating of typhoon coping strategies can be better helped.

The invention discloses a typhoon nascent identification method based on a double-AI model architecture and typhoon asymmetry characteristics, a medium and a program product, and belongs to the technical field of meteorological monitoring and artificial intelligence, and the method comprises the steps: firstly, obtaining multi-channel satellite image data, and carrying out the preprocessing; then, the cloud cluster center is positioned through a first AI model; taking the center of the cloud cluster as a reference, and extracting symmetry features and asymmetry features of the cloud cluster; analyzing the extracted features through a second AI model, and judging whether the target cloud cluster is in a typhoon primary stage or not; and finally, outputting identification results including the typhoon center position, the birth time, the development probability and the like. According to the method, the accuracy and the reliability of typhoon nascent identification are effectively improved through staged identification of the double-AI model architecture in combination with the unique asymmetry characteristic of the cloud cluster, and the method has a relatively high practical value.

The invention provides a typhoon intensity evaluation method and system, electronic equipment and a storage medium. The typhoon intensity evaluation method comprises the steps that a satellite data set is acquired; the satellite data set comprises brightness temperature data and latitude and longitude data of a plurality of data grid points; determining a plurality of data grid points in the typhoon calculation area to obtain position information of a typhoon center point; related data grid points are intercepted in the satellite data set, and the typhoon calculation area is divided into a plurality of concentric circular ring areas based on the maximum value of the distance between the typhoon center point and the related data grid points; calculating typhoon intensity evaluation parameters of each concentric annulus region; and taking the brightness temperature data of the related data grid points, the position information of the typhoon center point and the typhoon intensity evaluation parameters as current analysis data to obtain an evaluation result of the typhoon intensity. According to the method, the typhoon intensity can be continuously evaluated in real time without being limited by terrains, traffic and human activities, remote evaluation of the typhoon intensity is achieved, and higher safety is achieved.

The invention discloses an aquaculture capacity prediction method and system based on multi-source data, and relates to the technical field of aquaculture management. An aquaculture capacity prediction system based on multi-source data comprises a data acquisition module, a capacity prediction module, a density adjustment analysis module, a typhoon correction module, an elastic capacity generation module and a space mapping module. According to the method, the typhoon influence correction model is constructed, and the basic capacity is dynamically corrected by using the capacity correction weight, so that automatic adjustment of the prediction capacity in the typhoon season is realized, and the robustness of a prediction result facing extreme meteorological conditions is enhanced; on the basis of recommended capacity, a historical disaster damage data fitting model and risk preference setting, three capacity strategy intervals of conservative, neutral and aggressive are generated, hierarchical selection is provided for breeding decisions of different risk bearing capacities, and decision elasticity and risk control capacity are improved.