2217results about "Weather condition prediction" patented technology

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

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

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

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

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

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

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

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

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

The invention belongs to the technical field of rainfall prediction, and discloses a short and temporary rainfall prediction method based on multi-model random scheduling integration, which develops a robust training and pushing framework based on a continuous rolling prediction strategy, and decomposes long-sequence prediction into manageable stages. According to the method, training is carried out through teacher forcing and planned sampling, error propagation is relieved, and the training process is stabilized. The invention further designs asymmetric encoder-decoders (DSE and AFD) that achieve lower FLOPs than competitive baselines under standardized assessment, where DSE selectively compresses significant features and AFD stepwise reconstructs details to mitigate excessive smoothing problems. Finally, an intensity weighted Gaussian KL divergence loss function is designed, and the key problem of data balance is solved by modeling and predicting on a distribution level and endowing a large weight to a meteorological important heavy rainfall event.

The embodiment of the invention provides a radar echo extrapolation prediction method and system based on a test time training strategy. According to the embodiment of the invention, the method comprises the steps: carrying out the time axis calibration and space grid alignment processing of a radar echo historical collection sequence collected by an atmosphere detection system, and generating radar echo sequence data with time-space consistency; constructing an echo evolution relevance representation model based on the data, and generating an echo evolution dependency graph and a multi-step extrapolation initial condition set; then introducing a test time training strategy to carry out adaptive prediction processing, and adjusting edge weight parameters in real time to obtain a radar echo multi-step extrapolation prediction sequence with time dynamic characteristics; and finally, according to the sequence, extracting an echo intensity change characteristic and a space movement track rule, and combining historical climate cycle evolution data to generate a climate evolution trend description set containing an echo intensity trend curve and a path offset vector so as to realize more accurate climate evolution prediction and weather forecast.

The invention provides a meteorological-power collaborative prediction method and device, and belongs to the field of new energy power systems. The method provided by the invention comprises the steps of determining condition parameters of a prediction task and a prediction site, wherein the condition parameters at least comprise multi-source meteorological observation data and new energy station operation data; according to the multi-source meteorological observation data and a pre-trained meteorological prediction large model, generating a future time sequence meteorological prediction result of the prediction site in a future time period; according to the time scale of the prediction task and historical weather conditions, adaptively matching a corresponding parameter set of the pre-trained power prediction large model; and inputting the future time sequence weather prediction result into the large power prediction model to generate a new energy power prediction result of the new energy station. According to the meteorological-power collaborative prediction method and device provided by the invention, the technical problems that the collaboration of meteorological prediction and power prediction in a new energy power system is insufficient, the precision is limited, and a complex scene is difficult to adapt can be solved.

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.

A method and apparatus for constructing a new-energy meteorological large model, and a power predicting method are provided by the present application. The method includes, based on ERAS data, constructing datasets for training the new-energy meteorological large model; constructing a graph-neural-network large model that has a wind-solar node attention mechanism and a dynamic graph structure; applying the constructed input dataset and output dataset into the graph-neural-network large model, and performing training on the graph-neural-network large model to obtain the new-energy meteorological large model; and by using the new-energy meteorological large model, obtaining new-energy meteorological-element-prediction data, and inputting the station new-energy meteorological-prediction data into the meteorology-power post-correction model to obtain a predicted power. In the present application, the accuracy of prediction on wind-solar meteorological variables by meteorological large models is increased, and it is of great significance for the improvement of the power prediction service.

The invention discloses a refractive index structure constant adaptive forecasting method and system based on atmospheric turbulence multi-scale characteristics. The method comprises the following steps: 1, measuring and preprocessing an atmospheric refractive index structure constant; 2, constructing a spatial-temporal feature extraction module of an atmospheric turbulence refractive index structure constant; 3, learning a dependency relationship representing a long-time sequence by using a gating mechanism, and constructing a long-time correlation feature extraction module; 4, capturing dependency relationships of different times through convolution kernels of different time steps, and constructing a short-time correlation feature extraction module; 5, designing a self-adaptive turbulence multi-scale feature fusion module which comprises three groups of bidirectional cross attention modules to realize alignment and fusion among different turbulence feature extraction modules; and introducing a gating mechanism, performing dynamic control and feature selection on output information streams of the three groups of bidirectional cross attention modules, and simulating the change of importance of different scale features of turbulence along with conditions to obtain a predicted value.

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 relates to a weather situation intelligent analysis method based on artificial intelligence multi-modal causal reasoning, and the method comprises the following steps: obtaining multi-modal meteorological data, and carrying out the preprocessing of the multi-modal meteorological data, and obtaining the processed data; processing the processed data through a pre-constructed multi-modal causal reasoning neural network model to obtain a weather situation intelligent interpretation result; performing feedback correction on the weather situation intelligent interpretation result through an automatic feedback correction mechanism based on a large model so as to update the weather situation intelligent interpretation result; and generating a weather situation analysis result based on the updated weather situation intelligent interpretation result, and visually displaying the weather situation analysis result. Through the cooperative work, the weather system can be automatically identified, the causal relationship between the systems can be analyzed, a professional weather situation analysis report can be generated, and multi-mode forecast comparative analysis is supported. According to the invention, the efficiency and accuracy of weather situation analysis can be improved, and reliable technical support is provided for weather forecast service.

The invention relates to the field of construction temperature regulation and control, in particular to an intelligent temperature control method and device for fan foundation construction in an alpine region. The method comprises the following steps: collecting real-time concrete temperature monitoring parameters based on a distributed temperature sensor, carrying out time sequence temperature discrete trend analysis and spatial temperature fluctuation distribution evolution, and constructing a temperature fluctuation distribution evolution diagram; concrete surface environment parameters are extracted, heat exchange simulation processing is carried out on the temperature fluctuation distribution evolution diagram, multivariate coupling correlation analysis is carried out, and a temperature-environment quantitative relation model is constructed; and obtaining historical meteorological logs and meteorological environment data of the construction area, carrying out similar meteorological condition matching calculation, carrying out short-term meteorological trend prediction, and generating short-term meteorological trend prediction features. According to the dynamic temperature regulation and control requirements, safe construction of the fan foundation concrete in the high and cold environment is guaranteed, and the construction quality is improved.

The application discloses a thunderstorm monitoring device and path planning method of unmanned aerial vehicle based on electric field and magnetic field detection. The device comprises a quadrature magnetic antenna array, a vertical differential electric field sensor, a signal processing module, a positioning algorithm module, a distance estimation module, an autonomous decision module, a GNSS module and an inertial measurement module. The quadrature magnetic antenna array is composed of two orthogonally arranged magnetic antennas, which are at a 45° angle with the flight direction of the unmanned aerial vehicle, and is used for receiving the horizontal magnetic field signal of lightning radiation. The vertical differential electric field sensor comprises two groups of upper and lower electrode plates and measures the differential value of the vertical electric field intensity. Through the cooperative measurement of the magnetic antenna array and the electric field sensor, the lightning azimuth (θ) is calculated by combining the magnetic field intensity ratio, and the quadrant is corrected by using the electric field polarity. Based on the magnetic field amplitude attenuation model and the waveform rise time correction factor, a distance estimation model is constructed. The autonomous decision module generates an approach path or an obstacle avoidance path according to the lightning azimuth and distance, and drives the flight control system to adjust the heading.

The invention relates to a temperature forecast correction method based on a PSD-Net and a PSD-Net, and belongs to the technical field of weather forecast, and the method comprises the steps: obtaining a dynamic meteorological variable based on the PSD-Net, and loading topographic data and a report starting time observation truth value at the same time; preprocessing the dynamic meteorological variable, the topographic data and the report starting time observation truth value; inputting the preprocessed data into a temperature forecast correction model to obtain temperature correction field data; wherein the temperature forecast correction model is obtained by training a PSD-Net model based on a training data set, and the training data set comprises historical dynamic meteorological variables, topographic data and a report starting time observation true value. Compared with the traditional numerical mode and the original output of the archaeological model, the temperature forecast MAE corrected by the method is reduced by more than 37.6%, the accuracy rate within 2 DEG C is improved by 14%-19%, and the precision advantage is more prominent especially in complex terrain areas and extreme weather events.

The invention belongs to the technical field of extreme weather emergency management, and particularly relates to an extreme weather event prediction and emergency response system. The problems of multi-source data splitting, disaster chain prediction missing, static plan stiffness and lack of closed-loop evolution in the prior art are solved. A global disaster map is constructed through real-time fusion of multi-source data, a physical mechanism and machine learning are coupled to realize refined deduction of extreme weather and secondary disasters, a self-adaptive emergency scheme is generated based on a dynamic resource library and an optimization algorithm, and disaster feedback data is utilized to drive model and strategy iteration, so that the disaster situation feedback data can be used for driving the model and strategy iteration. And a'prediction-decision-feedback-evolution 'intelligent closed loop is formed. The method has the advantages that information islands are eliminated, the disaster chain modeling bottleneck is broken through, resource scheduling is dynamically optimized, a self-evolution mechanism is established, and the extreme weather response efficiency is improved.

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 relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The invention relates to the technical field of meteorological prediction, and discloses a meteorological element short-term prediction method and system based on spatio-temporal feature adaptive extraction, and the method comprises the steps: obtaining multi-source meteorological data, and carrying out the preprocessing of the multi-source meteorological data, and generating a spatio-temporal grid tensor; in the short-term prediction model of the meteorological elements of the space-time grid tensor, the model dynamically updates parameters of the model according to currently input data and outputs a meteorological element prediction value, and the method comprises the following steps: extracting multi-scale spatial features of meteorological data through a multilayer convolutional neural network based on the space-time grid tensor; generating enhanced spatio-temporal features based on an attention enhancement mechanism; and performing regression prediction on the enhanced spatial-temporal characteristics through a full connection layer, and outputting a meteorological element prediction value of a future time step. According to the method, the stable prediction precision is kept in different meteorological scenes, the calculation efficiency is improved by virtue of the convolution parallelism and the lightweight attention design, the hour-level real-time prediction requirement can be met, and timely and reliable meteorological prediction support can be conveniently provided for deployment in edge equipment or a service system.

The invention discloses a Beidou-based electric power tower attitude change on-line monitoring method, which comprises the following steps: A, setting a tower attitude acquisition system, and respectively acquiring multi-source data of a tower in real time by using the tower attitude acquisition system; b, constructing a meteorological database; c, constructing a tower attitude analysis model; the tower attitude analysis model comprises a data acquisition and preprocessing module, a multi-source data fusion module, a comprehensive analysis module, a situation diagnosis and risk assessment module, an intelligent prediction module, an early warning decision module and a visualization and feedback module; and D, on the basis of the trained constructed tower attitude analysis model, acquiring multi-source data of the tower in real time by using a tower attitude acquisition system, and performing online monitoring on the attitude change of the power tower in combination with a meteorological database. According to the invention, the posture change of the power tower can be obtained more accurately, and further prediction and early warning are carried out on the posture change of the power tower in combination with historical and predicted environmental data.

The invention discloses a water vapor chromatography modeling method and device based on GNSS-MET, and belongs to the technical field of atmospheric water vapor information inversion. According to the method, satellite signals and meteorological parameters are obtained through a GNSS-MET receiver, and the oblique path water vapor content (SWV) is calculated to serve as chromatography input; densely and uniformly distributed observation stations are selected to construct a chromatography area, and voxel grids are divided; a horizontal constraint is constructed by using Gaussian distance weighting, a vertical constraint is constructed in combination with a water vapor vertical index distribution characteristic, and ERA5 reanalysis data is introduced as a prior constraint, so that the limitation of a traditional sounding data constraint is solved; and resolving the tomographic equation through a singular value decomposition (SVD) method to obtain three-dimensional water vapor density distribution. According to the method, the high temporal-spatial resolution characteristic of ERA5 data is utilized, the precision and reliability of water vapor chromatography are remarkably improved, and the method is suitable for the fields of extreme weather prediction, meteorological monitoring and the like.

The invention provides a weather forecasting method and system for thunderstorm and gale. The method comprises the following steps: acquiring multi-source weather observation data in a past preset time period; inputting the multi-source meteorological observation data into a pre-trained progressive wind speed forecasting model to extract and fuse multi-scale spatial-temporal characteristics in the multi-source meteorological observation data, and sequentially generating average wind speed forecasting results covering a plurality of future time periods; inputting the average wind speed forecast result into a pre-trained gust mapping model, and performing nonlinear mapping to obtain gust wind speed forecast results corresponding to a plurality of future time periods; and based on the gust speed forecast result, whether thunderstorm and gale risks exist in the future time period is judged. In the mode, the multi-source meteorological observation data is acquired and prediction is performed in combination with the progressive wind speed prediction model and the gust mapping model, so that the accuracy of wind speed and gust prediction can be improved, the thunderstorm and gale risk can be identified in advance, and the disaster early warning and disaster prevention and reduction capabilities are further improved.

The invention relates to the technical field of meteorological disaster early warning and emergency management, and discloses a meteorological disaster risk assessment and prevention method based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data, carrying out the cleaning, alignment and standardization processing of the multi-source heterogeneous data, and constructing a multi-dimensional feature data set; based on the multi-dimensional feature data set, outputting predicted meteorological elements of the target area in a future preset time period through a meteorological prediction model, extracting interaction features of the predicted meteorological elements and non-meteorological factors from the multi-dimensional feature data set, and inputting the predicted meteorological elements and the interaction features into a long and short term memory-convolutional neural network hybrid model to obtain a long and short term memory-convolutional neural network hybrid model; outputting the meteorological disaster risk probability and risk level of each grid unit in the target area; based on the meteorological disaster risk probability and the risk level, differential prevention instructions for different risk level areas are generated, and the technical problems that in an existing meteorological disaster risk assessment and prevention method, multi-source data integration is difficult, and meteorological prediction precision is insufficient are solved.

The invention discloses a thunder approaching prediction method and device based on multi-source meteorological data, and particularly relates to the technical field of thunder disaster prediction.The method comprises the steps that standardization processing of temporal-spatial resolution unification is conducted on meteorological satellite data, radar data and lightning positioning data, and standardized temporal-spatial input data is generated; then spatial-temporal features are extracted through a depth separable 3D convolution module, and a compressed spatial-temporal feature graph is generated; then, a channel-space double attention mechanism (CPCA) is applied to the compressed feature map for feature optimization; and finally, processing the optimized feature map through a coding-decoding structure, and outputting a thunder and lightning probability distribution map. According to the method, the problem of spatial-temporal resolution difference and physical feature mismatching in multi-source data fusion is innovatively solved, the calculation efficiency is remarkably improved through a lightweight network architecture, the feature expression ability is enhanced by using an attention mechanism, and high-precision prediction of sudden thunder and lightning events is realized.