679 results about "Typhoon" patented technology

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 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 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 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 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 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 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 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.

The invention provides an intelligent pressure fluctuation detection and regulation system for a building curtain wall, relates to the technical field of intelligent control and wind pressure regulation, and integrates five modules of data acquisition, credibility evaluation, dynamic wind pressure prediction, cooperative regulation and fault-tolerant guarantee to construct a high-precision wind pressure sensing and closed-loop control system. Rejecting failure nodes by evaluating the credibility of real-time data; a wind pressure trend is predicted based on an LSTM model in combination with a typhoon path, a current prediction error parameter Epr is calculated, and an error threshold E is dynamically compared to guarantee prediction reliability; a multi-objective optimization function is adopted to balance related parameters, a regulation and control instruction set is generated, and millisecond-level response and emergency switching are supported; the reconstruction module utilizes Dk1 (t), Dk2 (t) and Drec (t) to repair missing data, and a standby mechanism is started to guarantee continuous operation when necessary; meanwhile, a regulation and control instruction is dynamically and finely adjusted and closed-loop optimization is And the robustness, the response precision and the regulation and control stability of the system in high-humidity and strong-typhoon environments are remarkably improved.

The invention discloses a virtual electronic channel design method and system based on an AIS (automatic identification system) technology, and the method comprises the steps: carrying out the precise correction of the position of a ship through multi-source data fusion in combination with AIS and GNSS (global navigation satellite system) data, generating a continuous and smooth virtual channel, and achieving the real-time dynamic correction of the channel through an environment adaptation correction term and a Kalman filtering algorithm. The influence of ocean current, wind speed, tide and other factors on the channel is dealt with. The system transmits updated virtual navigation channel data to a ship terminal and a port management center in real time in a low-delay manner through a VHF data link and a 5G edge computing technology, so that safe navigation of the ship under the condition of no physical navigation mark is ensured. In addition, the system can dynamically adjust the channel path, improves the emergency response capability of the port, and keeps the efficient operation of the port. The method is suitable for ship navigation and safety guarantee in extreme environments such as freezing and typhoon, and has a wide application prospect in sea areas which are susceptible to typhoon, such as frozen ports in northern China, East China Sea and the like.

Disclosed in the present invention are an optimization-based regulation and control method and apparatus for distributed energy storage, a device, and a storage medium. The method comprises: on the basis of current weather parameters, constructing an extreme weather model; determining a plurality of faulted and out-of-service elements in which faults will occur in a power distribution network; then on the basis of the faulted and out-of-service elements, system parameters of a distributed energy storage system, and an operation parameter of the power distribution network, constructing a dual-objective optimization model taking load loss minimization and power failure time minimization as objectives; and finally, solving for the dual-objective optimization model to generate an optimal regulation and control scheme applied to the distributed energy storage system. Therefore, when a fault has occurred in the power distribution network, the distributed energy storage system is regulated and controlled by means of the optimal regulation and control scheme, such that power supply of the power distribution network is restored as soon as possible in extreme typhoon weather, and the transient power deficit of the power distribution network is made up promptly. Therefore, the present invention can effectively guarantee the safe operation of power distribution networks under the condition of extreme typhoon weather.

According to the typhoon disaster dynamic risk estimation method and system based on intelligent grid forecasting, meteorological data of an intelligent grid forecasting system with high refinement degree and good accuracy are adopted, intelligent grid forecasting data are corrected by fusing observation data, and data deviation is effectively reduced. The typhoon wind and rain comprehensive index can objectively reflect the typhoon composite disaster-inducing effect, and the typhoon wind and rain comprehensive index is based on the nature of a disaster-inducing mechanism, and the limitation of single-element evaluation is solved by dynamically fusing wind speed and rainfall data. The disaster-pregnant environment influence coefficient considering the dynamic influence of the terrain factors is constructed, the limitation of traditional fixed geographic parameters is broken through, and the risk assessment error caused by neglecting the dynamic nature of the disaster-pregnant environment in a traditional model is solved. A typhoon disaster risk assessment model based on index weight is designed, the risk assessment model focuses on disaster-causing risk, exposure degree and vulnerability key factors, the model structure is simplified, and the calculation efficiency is improved.

The invention provides a typhoon disaster risk traceability and prediction method based on disaster grade clustering and an interpretable model. Comprising the following steps: constructing an index system containing multi-dimensional typhoon disaster influence variables, carrying out clustering analysis on disaster consequences by utilizing a Gaussian mixture model based on historical typhoon disaster event data, and dividing severity levels of typhoon disasters; a Borderline-SMOTE algorithm is adopted to process the problem of class imbalance, and an XGBoost model is utilized to construct a nonlinear mapping relation between disaster influence variables and disaster grades; an SHAP method is introduced, the contribution degree of each input variable to disaster grade prediction is clarified, key disaster-inducing factors and disaster-inducing paths thereof are disclosed, and the interpretability of a model result is realized. The method also supports typhoon information input before a disaster, realizes disaster grade prediction and risk tracing, proposes targeted disaster prevention and reduction suggestions based on an SHAP analysis result, provides real-time and accurate typhoon disaster early warning and risk intervention basis for coastal cities, and has relatively strong practical guidance significance and popularization and application values.

The invention provides a method, a medium and a system for calculating ocean wave spectrum parameters of ocean digital twinning, and belongs to the technical field of electrical digital data processing. The method comprises the following steps: acquiring and preprocessing original sea wave spectrum data, selecting a Jonswap frequency sea wave spectrum as a theoretical model, establishing a multi-index fitting criterion, optimizing and solving a peak rise factor parameter, performing statistical analysis according to a significant wave high period level, and distinguishing sea wave spectrum characteristics of typhoon and non-typhoon working conditions. And calculating the wave amplitude wave number and the random phase to construct a wave surface height model, and finally forming a sea wave spectrum parameter database. The technical problem that sea wave spectrum parameters in the ocean digital twinning system are difficult to accurately describe and calculate is solved, the extreme sea condition simulation capability is particularly optimized, and high-precision sea wave field simulation technical support is provided for the ocean digital twinning system.

The invention discloses a typhoon influence distinguishing method and system based on seawater CO2 partial pressure data, and the method comprises the steps: obtaining sea area situation monitoring data in a target sea area typhoon transit period through a multi-source observation platform, and constructing a standardized data set and a climate state background field; a DBSCAN clustering algorithm is adopted to perform water mass division, and the typhoon characteristic water mass distribution is determined in combination with a Manchester-HuitiniU test and correlation analysis. And calculating a seawater carbon dioxide partial pressure abnormal field based on the climate state background field, constructing a typhoon influence index sequential sequence, identifying a core influence area, and generating a typhoon influence trace path. And then seawater carbon dioxide partial pressure grid point reconstruction is carried out, and a typhoon influence space map is constructed. And finally, analyzing space-time change of carbon dioxide flux of air-sea, evaluating carbon sink stability, and generating a typhoon-carbon sink sensitivity zoning map. Seawater carbon dioxide partial pressure is used as a core index, limitation of a traditional method is overcome, and reliable technical support is provided for typhoon marine environment influence assessment.

The invention relates to the technical field of artificial intelligence, is applied to financial or medical insurance claim settlement scenes, and particularly relates to a typhoon claim settlement disaster damage assessment method, device and equipment and a medium, and the method comprises the steps: obtaining a multi-source typhoon data set, carrying out the feature data recognition of the multi-source typhoon data set, and constructing a target claim settlement knowledge base according to the feature data; and inputting the obtained real-time typhoon data and early warning city information into the target claim settlement evaluation model based on the target claim settlement knowledge base to obtain a claim settlement evaluation result. Visibly, according to the method, feature data identification is performed on the multi-source typhoon data set, the target claim knowledge base is constructed according to the feature data, and then the acquired real-time typhoon data and early warning city information are input into the target claim evaluation model according to the target claim knowledge base, so that accurate evaluation of typhoon claim disaster damage is realized; and the insurance claim settlement efficiency is improved, so that an insurance claim settlement reference for typhoon disasters is provided for an insurance company.

The invention relates to a complex terrain area typhoon wind field downscaling method and system based on deep learning. A data acquisition and standardization preprocessing module acquires wind field and terrain data and performs standardization preprocessing; a first typhoon wind field grid prediction module inputs the standardized wind field and topographic data into a multi-scale wind-topographic spatial correlation extraction network to generate a first typhoon wind field grid prediction value; a second typhoon wind field grid prediction module establishes a site space location map and a site feature correlation map according to meteorological observation site data, inputs standardized data into meteorological observation site space features to extract a meteorological observation site space linkage network, and generates a second typhoon wind field grid prediction value; a final typhoon wind field grid fusion prediction module performs weighted fusion on the generated first typhoon wind field grid prediction value and the second typhoon wind field grid prediction value to obtain a final typhoon wind field grid prediction value; according to the typhoon wind field downscaling method, the accuracy and efficiency of typhoon wind field downscaling in a complex terrain area are improved.

The invention belongs to the technical field of intelligent monitoring, and discloses a typhoon safety evaluation system and method for an ocean engineering structure. The method comprises the steps of collecting original data, preprocessing the original data to obtain standard data, and performing time-frequency analysis on the standard data to obtain time-frequency characteristics; performing analysis based on the time-frequency characteristics to obtain modal parameters; analyzing according to the modal parameters to obtain a non-linear state judgment result; carrying out damage positioning according to a non-linear state judgment result; carrying out residual safety life prediction according to a damage positioning result; the safety guarantee level of the ocean engineering structure in the typhoon period is remarkably improved.