4313results about "Weather condition prediction" patented technology

The invention relates to a multi-model space-time combination flood peak prediction method fusing physical constraints, which comprises the following steps of: acquiring static space data, dynamic time sequence data and boundary data of a research drainage basin, converting the static space data of the digital elevation model into a grid matrix, and calculating the dynamic time sequence data of the research drainage basin according to the grid matrix and the dynamic time sequence data of the research drainage basin; representing an elevation value of each geographic position, extracting gradient features by using gradient calculation according to the elevation values, and performing normalization processing on the gradient features to obtain normalized gradient features; a CNN-Bi-LSTM-Transform prediction model is constructed, the prediction model comprises a spatio-temporal data alignment module, a CNN convolutional network, a spatio-temporal feature splicing module, a bidirectional long and short term memory network and a Transform module, the prediction model is trained, and a loss function during training adopts a physical constraint loss function composed of mean square error loss and water conservation constraint terms and is used for flood peak prediction.

The invention discloses a multi-source heterogeneous meteorological data assimilation fusion method, which comprises the following steps that meteorological data of a plurality of data sources are synchronously acquired through a distributed data interface and are preprocessed, and the data sources comprise satellite remote sensing data, ground meteorological station data and meteorological radar data; after finishing preprocessing the meteorological data of the plurality of data sources, fusing the data of the plurality of data sources into a meteorological element value of each meteorological element based on a weighted least square method; and assimilating the fused meteorological element values and a numerical weather forecast model. Compared with the prior art, the method has the following advantages and effects that abnormal data can be accurately obtained and specifically repaired, the weight of a data source is dynamically adjusted through weighting, interference of low-quality data on a fusion result is effectively avoided, and the final prediction accuracy is improved by improving the assimilation step of the numerical weather prediction model.

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

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

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

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

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

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

The invention relates to a rainfall forecast-based landslide surface scene deformation intelligent monitoring method and system. The method comprises the steps of determining a landslide rainfall risk level through multi-index coupling calculation; selecting and implementing a monitoring scheme based on the landslide rainfall risk level; the rainfall intensity is calculated according to the intelligent grid forecast data, a risk grade-rainfall intensity two-dimensional regulation and control matrix is established in combination with the landslide rainfall risk grade, and the risk grade and the rainfall intensity are mapped into regulation and control parameters in a cross mode, so that tracking dynamic adjustment of the monitoring frequency of deformation and rainfall is achieved; and constructing a rainfall-landslide meteorological risk index model, and when the rainfall-landslide meteorological risk index model exceeds a preset risk threshold, generating and issuing an early warning message. According to the invention, through combination of the multi-dimensional risk assessment model and the dynamic regulation and control matrix and integration of time-space coupling analysis, monitoring frequency adaptive optimization and multi-dimensional early warning linkage are realized, a landslide risk accurate perception-intelligent regulation and control-closed loop response full-chain prevention and control system is formed, and the active disaster prevention and control capability is significantly improved.

The invention relates to the technical field of meteorological detection, in particular to a tornado collaborative observation method based on detection equipment. The method comprises the following steps: carrying out real-time monitoring on regional micro-pressure and meteorological echoes of a suspicious target region through a micromanometer and an S-band new-generation weather radar, and carrying out severe convection occurrence matching prediction to obtain a severe convection occurrence prediction target region; performing solar radio calibration processing on the corresponding radar detection equipment and triggering a tornado detection strategy cooperation instruction to perform severe convection target three-dimensional cooperation observation and target strong correlation area fusion division so as to generate a strong convection target and other strong correlation area radar field; performing equal-intensity convection characteristic analysis on the radar field of the equal-intensity correlation area of the severe convection target to generate a tornado equal-intensity convection characteristic data set; and obtaining a preset tornado feature library and carrying out tornado identification analysis to generate a tornado occurrence susceptible lattice point region. According to the invention, the monitoring precision and real-time performance of the tornado detection process can be improved.

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

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

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

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 provides a weather forecast learning system based on an artificial intelligence algorithm, and the system comprises a data access collection module which collects a multi-source data set; the data fusion and processing module is used for carrying out data fusion and processing to obtain a multi-source fusion data set; the AI model design module is used for constructing a multi-model collaborative architecture and carrying out multi-model parallel training and optimization; the model fusion module is used for carrying out multi-model weighted fusion to obtain an AI model; the real-time prediction module is used for updating a prediction result according to the real-time data; the visualization and interpretability module is used for designing a visualization interface and carrying out interpretability verification; and the evaluation and iteration module is used for carrying out comprehensive evaluation and continuous improvement on a prediction result in combination with evaluation indexes. According to the method, accurate and reliable observation data can be obtained, massive meteorological data are efficiently processed by combining an artificial intelligence algorithm, trend analysis and prediction are automatically carried out, and a reliable prediction result is generated.

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 provides a short-time rainfall prediction method based on an MSPTS-Net network model. The short-time rainfall prediction method comprises the following steps: acquiring a radar echo data set formed by radar echo images; a network model is constructed based on MSPTS-Net, a spatial-temporal feature extraction and fusion mechanism is embedded in a framework of the MSPTS-Net, and an initial short-term rainfall prediction model is generated; training the initial short-time rainfall prediction model by using the radar echo data set, and carrying out reverse adjustment and optimization on network parameters through a combined loss function to obtain a final short-time rainfall prediction model; and inputting a radar echo image to be measured into the final short-time rainfall prediction model to obtain a short-time rainfall prediction result. Through the MSPTS-Net model, spatio-temporal change information in radar echo data can be more comprehensively captured, multi-scale feature extraction and a self-attention fusion mechanism are utilized, data changes of different time scales and spatial scales are processed, a combined loss function is combined, the model training process is more efficient, convergence to an optimal solution can be faster, and the method is suitable for being applied to the field of radar imaging. And the precision of short-time rainfall prediction is improved.

The present invention is a rainfall prediction method, system, device and medium based on machine learning, which relates to the field of meteorological prediction technology. It uses atmospheric precipitable water volume (PWV) data, rainfall data and related meteorological parameters to input into a trained rainfall prediction network, and realizes accurate prediction of rainfall through an improved Transformer model. The model includes an encoder, a decoder and a final output layer, wherein feature extraction is performed inside the encoder through a multi-head probabilistic sparse self-attention module and a distillation module, and the encoder output containing feature information is used as the input of the decoder. The decoder passes through the decoder mask multi-head probabilistic sparse self-attention layer, and performs a multi-head self-attention operation with the intermediate result output by the encoder, and finally adjusts the data output dimension through a fully connected layer to generate a prediction result.

The invention relates to the technical field of risk early warning, and discloses a meteorological risk intelligent early warning method and system based on multi-source data analysis, and the method comprises the following steps: obtaining multi-source meteorological data, historical meteorological risk event data and environment change related data; carrying out normalization, feature extraction and coding processing on the data; fusing the processed multi-source data by adopting a deep neural network; constructing a deep reinforcement learning model; network parameters of the deep reinforcement learning model are updated through minimizing the loss function, and an optimized early warning strategy is obtained; according to the method, feature extraction and fusion are carried out by adopting a deep learning method after multi-source heterogeneous data are fused, meteorological risk influence factors can be comprehensively analyzed, the early warning accuracy is remarkably improved, and an early warning strategy can be dynamically adjusted based on a deep reinforcement learning model according to real-time data and historical experience, so that early warning better meets actual requirements.

The present invention provides a distributed hydrological forecasting method, device, computer equipment, and medium that simulates runoff data by comprehensively taking into account the climate data and atmospheric boundary surface data of a target area, and corrects the runoff data obtained by the simulation with human activity data, thereby improving the accuracy of the simulated runoff data. [Solution] The method includes steps of acquiring multiple types of human activity data within a target area and first climate data and atmospheric boundary surface data for the target area in a climate scene mode; inputting the first climate data and atmospheric boundary surface data into a pre-constructed hydrological model to obtain first simulated runoff data corresponding to the climate scene mode; constructing a correction model corresponding to each human activity data based on each human activity data; and correcting the first simulated runoff data by each correction model to obtain second simulated runoff data corresponding to each human activity data.

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

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

The invention relates to the technical field of meteorological data processing and geographic information, in particular to a near-surface meteorological field downscaling method based on a topographic constraint Transform model, which comprises the following steps of: extracting multi-modal fusion features from reanalysis meteorological data and digital elevation model data; mapping to a target resolution by means of a topographic constraint Transform network to obtain initial downscaling meteorological data; latent variable modeling is carried out on the initial data through a variational auto-encoder to describe multi-scale uncertainty, physical constraints such as energy balance and mass conservation are incorporated in combination with an iterative updating mechanism based on meta-learning, and core downscaling meteorological data are generated; according to the invention, detail capture and dynamic adaptation of local meteorology are realized through self-supervised pre-training and online updating.

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

The invention discloses a regional numerical forecasting method and system based on multi-scale background field error covariance. The method comprises the following steps: firstly, constructing a multi-scale background field error covariance model containing different spatial scale error structures, then obtaining an initial atmospheric state and a boundary condition of a forecast area, and generating a background field set sample by using an ensemble forecast algorithm; thirdly, performing multi-scale decomposition on a background field set sample based on the model to obtain error components of different spatial scales, and determining a corresponding error covariance matrix; and combining the matrixes to form a complete multi-scale background field error covariance. Then, meteorological observation data are obtained, a variational assimilation algorithm is applied, optimal combination of the observation data and a background field set sample is achieved through multi-scale background field error covariance, and an optimal initial field is obtained; and finally, performing regional numerical forecasting based on the optimal initial field to obtain a future weather condition forecasting result of the forecast region. According to the method, the forecasting accuracy and reliability are improved.

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

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

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

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

The invention provides a multi-dimensional visual analysis method for meteorological observation and detection data, and relates to the field of visualization. According to the method, an unmanned aerial vehicle platform carries a meteorological sensor to collect data, and primary processing, abnormal value detection and intelligent data compression are carried out; transmitting the processed data to a ground measurement and control terminal; the ground terminal fuses the multi-source data to generate a three-dimensional meteorological data set; constructing an interactive three-dimensional visual scene by utilizing self-adaptive interaction logic and a dynamic data display technology; a user can check and perform multi-view linkage operation through the interactive operation interface; a severe weather and weather raster data set can be constructed based on the three-dimensional weather data set and historical severe weather records, and simulation and display are carried out; and finally exporting the data, verifying the precision and integrity, and generating a visual analysis report.