376 results about "Meteorological disasters" patented technology

The present application provides a method and system for intelligent monitoring and early warning of meteorological disasters in power grids based on GIS, the method comprising: obtaining historical multi-source data including power grid equipment data, meteorological data and equipment operation data; standardizing the historical multi-source data to obtain a four-dimensional data model including longitude, latitude, altitude and time dimensions; constructing a disaster risk assessment model based on the four-dimensional data model using a deep learning method; inputting real-time multi-source data into the disaster risk assessment model to obtain disaster risk assessment results; making early warning decisions based on the disaster risk assessment results to generate early warning information; and generating emergency response plans based on the early warning information. The system comprises a data processing module, a model building module, a risk assessment module, an early warning decision module and an emergency response module. The technical solution of the present application realizes the effective fusion of multi-source heterogeneous data, improves the accuracy and real-time performance of early warnings, and improves the efficiency of emergency response.

The invention relates to the technical field of data analysis, provides an urban meteorological disaster data identification method and system based on deep reinforcement learning, and is used for effectively improving the model performance so as to enhance the accuracy and real-time performance of meteorological disaster monitoring. The method comprises the steps of obtaining a meteorological monitoring data set of a target city area, executing meteorological data preprocessing operation on the meteorological monitoring data set to obtain a preprocessed meteorological spatial-temporal feature set, calling a trained deep reinforcement learning recognition model, and performing dynamic disaster mode matching processing on the meteorological spatial-temporal feature set to obtain a dynamic disaster mode recognition model. And generating a meteorological disaster recognition result set of the target city region, generating a disaster coping strategy set according to the meteorological disaster recognition result set, and performing dynamic strategy optimization processing on the deep reinforcement learning recognition model based on the disaster coping strategy set to obtain an optimized deep reinforcement learning recognition model. And deploying the optimized deep reinforcement learning recognition model to a meteorological disaster monitoring system.

The invention relates to the technical field of agricultural meteorological disaster monitoring, in particular to a construction method of a composite agricultural meteorological disaster monitoring index system. The method comprises the following steps: acquiring regional agricultural component data, carrying out spatial distribution analysis to identify an agricultural distribution boundary, delimiting an agricultural overlapping region, then collecting historical meteorological disaster data, generating a meteorological disaster time-space sequence, identifying a correlation mode of meteorological and agricultural disasters, and determining the agricultural overlapping region according to the correlation mode of the meteorological and agricultural disasters. According to the agricultural overlapping area, composite agricultural boundary development data is predicted, available resources are identified, heterogeneous component interaction simulation is performed, an agricultural interaction effect field is constructed, meteorological disaster absorption capability is deduced, finally, composite agricultural disaster prediction is performed based on a historical association mode and the absorption capability, and a monitoring index system is constructed. And systematic monitoring and management of agrometeorological disasters are realized. According to the invention, systematic monitoring and evaluation of agrometeorological disasters are realized, and intelligence and scientization of agricultural management are promoted.

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 hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

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 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 data analysis, and discloses a precise targeted early warning method and device for meteorological disasters of kiwifruit canker and a medium. The method comprises the following steps: performing standardization processing on historical occurrence time, morbidity degree and distribution range data of kiwifruit canker as well as meteorological observation data and cultivation management data of corresponding time periods to obtain a historical disease database; performing multivariate statistical analysis processing on the three-dimensional meteorological monitoring network data and the historical disease database to obtain a disease risk assessment index; performing feature extraction processing on the disease risk assessment indexes through a deep learning algorithm to obtain disease early warning parameters; performing gridding partition processing on the disease early warning parameters to obtain partition early warning data; and performing dynamic optimization processing on the accuracy, the advance and the coverage rate of the partition early warning data to obtain accurate targeted early warning information of the meteorological disaster of the kiwifruit canker. According to the application, the accuracy and timeliness of the existing kiwi fruit canker meteorological disaster early warning method are improved.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention relates to the technical field of marine disaster early warning, and discloses a digital bay hydrological condition early warning method and system, and the method comprises the steps: collecting historical meteorological data and real-time meteorological data of a to-be-predicted bay region; according to the historical meteorological data, establishing a wind and wave relation model by using a deep reinforcement learning and genetic algorithm, and generating wave prediction data based on the relation model and the real-time wind data of the to-be-predicted bay area; based on the theoretical model wind field and the background wind field, synthesizing a synthesized wind field of the to-be-predicted bay area; performing storm surge simulation by using a hydrodynamic mathematical model and a tidal current mathematical model according to the synthesized wind field and the real-time meteorological data; and on the basis of the wave prediction data and the storm surge simulation result, hydrological condition early warning information is generated in combination with an early warning index threshold value, the prediction precision and early warning timeliness of waves and storm surge in the bay area are improved, and accurate grading response of complex meteorological disasters is realized.

The invention relates to the technical field of meteorological disaster early warning, in particular to a lightning disaster early warning system and method based on multi-source data, and the system comprises an atmospheric electric field monitoring module, an early warning strategy customization module, a risk area recognition module and a real-time early warning release module. According to the invention, through multi-source data integration and analysis and in combination with the regional electric field intensity and lightning frequency, the lightning activity intensity is accurately evaluated, the monitoring sensitivity and accuracy are significantly improved, and detailed data support is provided for intensity change, so that a more targeted early warning strategy is formulated; the safety level and the response requirement are optimized through frequency and intensity analysis, resource allocation is more reasonable, response time is more efficient, potential risk areas are further identified through path analysis, thunder and lightning conduction paths are refined, the risk areas are accurately calibrated, the early warning level is dynamically adjusted, it is ensured that early warning information has timeliness and space adaptability, and the early warning efficiency is improved. Powerful support is provided for power grid safety and lightning protection management, and the intelligent disaster prevention level is comprehensively improved.

The invention provides a meteorological disaster dynamic monitoring method and system applied to real-time meteorological data, and the method comprises the steps: firstly obtaining a real-time meteorological data set of a target region, and generating time change features and spatial distribution features of each meteorological data unit through spatial-temporal feature extraction processing; the method comprises the following steps: firstly, acquiring time change characteristics of a meteorological disaster, generating regional correlation characteristics according to the correlation between the time change characteristics and spatial distribution characteristics, then inputting the regional correlation characteristics into a pre-trained meteorological disaster prediction model for disaster risk assessment to generate a disaster prediction result, and finally, outputting the disaster prediction result. And generating a dynamic monitoring strategy including a monitoring equipment deployment scheme and an early warning information pushing rule according to the disaster prediction result, and feeding back the dynamic monitoring strategy to the meteorological monitoring platform to trigger a monitoring resource allocation operation, so that the meteorological disaster can be dynamically monitored by fully utilizing the real-time meteorological data, and the accuracy and timeliness of disaster early warning are improved.

The invention discloses a radar echo extrapolation method and system based on frequency domain enhancement, and the method mainly comprises the following steps: obtaining and preprocessing a historical radar echo grayscale image sequence, generating a sequence sample through a sliding window, and dividing the sequence sample into a training set, a verification set and a test set; the method comprises the following steps: constructing a frequency domain enhanced U-Net network comprising an encoder-decoder structure, introducing a multi-scale deep convolution structure into an encoder and a decoder, and enhancing frequency domain features by using a frequency domain dynamic attention mechanism in jump connection; inputting the training set into the model for training by adopting a composite loss function comprising intensity weighted loss, frequency domain consistency loss and structural similarity loss; and inputting the test set into the trained model, and outputting a radar echo prediction result at a future moment. The method can be effectively applied to the fields of short-term and temporary weather forecast, severe convection monitoring and the like, and provides more accurate and reliable radar echo prediction support for meteorological disaster early warning.

The invention relates to the technical field of meteorological simulation, in particular to a dynamic three-dimensional grid meteorological simulation model based on historical observation data. Based on abundant historical observation data, the meteorological element change rule can be accurately reflected, the accuracy of meteorological simulation is greatly improved, and a reliable basis is provided for meteorological research. A dynamic three-dimensional grid result shows complex evolution of weather in a three-dimensional space in all directions, limitation of a traditional model is overcome, weather change details in the vertical direction and the horizontal direction are clearly shown, and deep understanding of a weather mechanism is facilitated. Physical driving and data driving models are fused, the advantages of the physical driving and data driving models are brought into full play, the simulation and prediction capability of various meteorological phenomena is enhanced, and particularly for complex and extreme meteorological phenomena, meteorological disaster early warning is better served. Through visualization and interaction functions, a vivid three-dimensional meteorological scene can be generated, a user can conveniently observe and analyze visually, and development and decision making of the meteorological field and related industries such as aerospace and agriculture are powerfully promoted.

The invention provides an intelligent meteorological service method and system triggered by meteorological risk pre-assessment, and relates to the technical field of meteorological disaster early warning, the method constructs a description vector library of various historical meteorological disasters, provides rich and high-quality data support for the training of a general large model, and improves the early warning efficiency of the meteorological disasters. A disaster risk pre-assessment large model is obtained through fine tuning and retraining of a general large model, so that various meteorological disaster risks can be identified and assessed more accurately. After a user inputs the multi-modal prompt data, the multi-modal prompt data comprises at least one of the following data: position data, scene image data and scene video data, and the model can quickly obtain a meteorological disaster risk assessment result, so that the method gets rid of the technical problem that a meteorological risk estimation area is limited. In addition, under the condition that the assessment result is determined to have the meteorological disaster risk, the method can also trigger the intelligent meteorological service based on the assessment result, and the intelligent meteorological service can effectively improve the pertinence of meteorological disaster early warning.

The invention relates to a meteorological disaster prediction method and system based on an improved BERT-RG-TGA model. The method comprises the following steps: constructing a dynamic spatio-temporal knowledge graph joint feature vector; processing is carried out through a graph attention component, a dynamic time gating circulation component, a static semantic constraint component and a key node screening component in the improved BERT-RG-TGA model, and a trained BERT-RG-TGA model is obtained; and obtaining to-be-predicted time period data, performing convolution operation on the fused data features through the trained BERT-RG-TGA model, and capturing time sequence interaction information and a dependency relationship by using a self-attention mechanism to obtain a meteorological disaster prediction probability distribution result. By fusing meteorological text data and time-space sensor data and processing through a graph attention component, a dynamic time gating circulation component, a static semantic constraint component and a key node screening component in the improved BERT-RG-TGA model, the prediction accuracy can be improved.

The invention discloses a ship navigation meteorological disaster risk forecasting method and device, relates to the technical field of marine traffic meteorological influence forecasting, and aims to solve the technical problem of poor pertinence of ship navigation meteorological disaster risk forecasting. The method comprises the following steps: acquiring hydro-meteorological forecast data of a target area; the hydrometeorological forecast data is real-time dynamically updated meteorological element forecast for a future time period; obtaining a ship navigation comprehensive risk evaluation model which is constructed in advance based on target factors at least including hydro meteorology, channel natural environment and navigation capability; based on the hydrometeorological forecast data of the target area, utilizing the ship navigation comprehensive risk evaluation model to carry out rolling forecast on the meteorological disaster risk of ship navigation of the target area; the meteorological disaster risks at least comprise main meteorological disasters influencing ship navigation, risk levels and risk probabilities; and the pertinence of ship navigation meteorological disaster risk forecasting service is improved.

The invention discloses an agricultural meteorological disaster chain type early warning method and system based on cloud computing, and the method comprises the steps: collecting meteorological data in real time through meteorological monitoring equipment, carrying out the preprocessing of the received meteorological data on a cloud computing platform, carrying out the disaster chain recognition and analysis of the processed meteorological data, and carrying out the early warning of the disaster chain. According to the method, a possible agro-meteorological disaster chain is identified, an agro-meteorological disaster chain type early warning model is constructed based on historical meteorological data and disaster chain identification results, disaster chain early warning analysis is carried out, and publishing is carried out through a network. The system comprises a data acquisition module, a data processing module, a disaster chain identification module, an early warning model module, an early warning analysis module and an information issuing module. Therefore, the accuracy and timeliness of agricultural meteorological disaster early warning can be remarkably improved, and decision support is provided for agricultural disaster prevention and reduction by collecting meteorological data in real time, analyzing by using the cloud computing platform, constructing an accurate early warning model and issuing early warning information in real time.

The invention discloses a meteorological disaster risk assessment method and system based on disaster risk grading, and belongs to the technical field of meteorological disaster assessment, and the method specifically comprises the steps: collecting the meteorological and disaster-bearing body data and geographic information of a disaster area, and carrying out the preprocessing through a spatial interpolation method; setting meteorological disaster-inducing factors, disaster-pregnant environments and disaster-bearing body vulnerability indexes, and constructing a judgment matrix and distributing index weights by using an analytic hierarchy process; constructing a disaster risk grading model according to the risk indexes and the corresponding weights, inputting data of the to-be-evaluated region, calculating a risk value, judging a risk grade, and generating a report; establishing a real-time monitoring system, updating a risk assessment result, and issuing early warning information when a risk value reaches an early warning threshold value; according to the invention, through comprehensive analysis and real-time monitoring, the accuracy and timeliness of meteorological disaster risk assessment are improved.

The invention discloses an agricultural meteorological disaster monitoring and early warning method and system based on a remote sensing technology, and the method comprises the steps: generating a multi-dimensional data set through obtaining and processing multi-source remote sensing data, and generating a comprehensive data set through weighted average fusion. Then, extracting land surface temperature data, comparing the land surface temperature data with a historical value, calculating a temperature deviation value, and generating temperature anomaly distribution data; and calculating a disaster intensity index by combining the soil humidity and vegetation index data change trend, and generating disaster intensity distribution data. The data meteorology is imported into a driving simulation system, disaster evolution is simulated, and the disaster influence range and duration are predicted. And if the prediction data exceeds an early warning threshold, generating early warning data including disaster categories, influence areas and prediction time, and generating disaster risk distribution data by using a spatial interpolation method for dynamic monitoring. According to the invention, the accuracy and timeliness of disaster monitoring and early warning are improved.

The invention relates to the technical field of intelligent early warning, and discloses a risk early warning method and system for low-temperature rain, snow and freezing meteorological disasters, and the method comprises the steps: collecting multi-source data, and constructing a heterogeneous graph; preprocessing the data to obtain a training and reasoning sample; a graph neural network is constructed on the heterogeneous graph, joint learning is carried out on node time sequence features and edge relations, and graph representation representing cold air invasion, rain and snow zone movement and risk propagation delay is obtained; outputting road surface temperature evolution, icing threshold arrival time and wire icing growth rate based on the physical guidance neural network; fusing the features obtained by the graph neural network and the features obtained by the physical guidance neural network, outputting an asset-level risk score and a confidence interval thereof, and obtaining a predicted arrival time; and establishing early warning according to the prediction result and the observation result. According to the invention, finer and more reliable grading risk early warning is provided, and the accuracy, interpretability and practicability of early warning are improved.

The invention discloses a tropical region offshore marine meteorological fusion analysis method. The method comprises the following steps: step 1, data collection and preprocessing: collecting offshore multi-platform collaborative networking observation data of a tropical region; 2, data quality control: checking the data, and analyzing error characteristics of marine meteorological observation data; 3, data fusion processing: establishing a tropical region offshore multi-source data fusion analysis system, performing intelligent fusion on the multi-source observation data subjected to quality control, and constructing a marine meteorological fusion analysis data set; 4, constructing a coupling analysis model: performing coupling analysis on marine meteorological elements by combining a physical model and a numerical simulation technology, and discovering an evolution rule and influence factors of a marine meteorological system by simulating and predicting an interaction process between the ocean and the atmosphere; and 5, dynamic application and evaluation: through real-time analysis of key meteorological elements, potential marine meteorological disaster risk affairs are discovered and early warned.

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 provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention discloses a knowledge graph retrieval intelligent power storage system based on a feedforward neural network, and relates to the field of power equipment accessory management. The method comprises the following steps: constructing a power equipment resource classification model and a meteorological disaster risk periodic evaluation model in a coverage storage range; obtaining static data and dynamic data of different power devices; predicting the replacement demand level of the equipment through the feedforward neural network model; a model prediction evaluation result is used as a data source of the knowledge graph warehousing system, and terminal real-time fault prediction evaluation query, periodic equipment purchase guidance and backlog warning are realized. According to the invention, the storage demand can be intelligently predicted according to the actual state of the power equipment, the accuracy and response speed of storage management are improved, and an efficient spare part support scheme is provided for power network maintenance and equipment storage.

The invention provides a meteorological disaster early warning service data generation method and apparatus, and an electronic device, relates to the technical field of meteorological early warning, and solves the technical problem of low meteorological disaster early warning service data generation efficiency. The method comprises the following steps: acquiring early warning data through an early warning interface, and identifying an early warning release object, early warning time, an influence space range, an early warning type and an early warning level in the early warning data; based on an early warning release object, early warning time, an influence space range, an early warning type and an early warning level, through a multi-class data source automatic matching algorithm, target meteorological data matched with the early warning data are called from weather data, and the target meteorological data and the early warning data are fused according to the early warning level and the early warning type; obtaining fused meteorological disaster early warning data; and establishing a modularized rendering pipeline based on the fused meteorological disaster early warning data, and generating visual meteorological disaster early warning service data through a specified image visual production tool based on the modularized rendering pipeline.

The invention discloses a radar echo extrapolation method based on a space-time attention mechanism, and the method comprises the steps: firstly obtaining the radar echo data of a target region, carrying out the preprocessing of the obtained radar echo data, carrying out the sliding grouping of the preprocessed radar echo data, carrying out the data augmentation, and obtaining a radar echo sequence data set, dividing the data into a training set and a test set; a radar echo prediction network model based on a SimVP architecture is constructed, training and testing are carried out, and a multi-target loss function is adopted to supervise model training; and a future radar echo image is obtained through real-time prediction. Through the method, the dynamic evolution of complex weather phenomena such as storm can be more accurately captured, the precision and interpretability of short-time approaching weather forecast can be remarkably improved, more reliable technical support is provided for timely and accurate early warning of meteorological disasters, and the method has important practical application value.

The invention discloses a meteorological disaster early warning response method and system based on a meteorological early warning aircraft, and the system comprises a data collection module which is used for constructing a database containing the real-time meteorological data, the terrain data, the historical disaster data and the early warning aircraft operation data; the multi-disaster terrain adaptation analysis module is used for analyzing waterlogging and gale comprehensive risks by using a synchronous superposition model in plains and analyzing landslide and thunder-gale chain risks by using a time sequence chain model in mountainous areas through a double-branch fusion neural network; the early warning response strategy generation module is used for generating differentiation strategies according to regional risks and terrains; and the terminal interaction module pushes information and receives feedback. The system dynamically generates protection guidance for different geographic features such as a plain high-risk area and a mountainous area high-risk area through multi-source meteorological data integration and analysis, and improves the timeliness and coverage of early warning information.

The invention relates to the technical field of meteorological disaster monitoring and early warning, in particular to a rainstorm early warning method and system based on a wireless network, a terminal and a storage medium, and the method comprises the steps: collecting multi-source data through distributed sensor nodes; preprocessing the multi-source data to obtain real-time data; constructing a spatial-temporal feature extraction model; generating a dynamic threshold function, performing weighted fusion on the dynamic threshold function and a preset static threshold, and outputting a graded early warning threshold; judging whether the current accumulated water depth acceleration exceeds a gradient critical value of a graded early warning threshold value or not; and if so, outputting a rainstorm early warning level. The method has the advantages that the problem that a static threshold mechanism cannot dynamically adapt to rainstorm disaster risks in a complex environment is solved, and the accuracy and practicability of the rainstorm early warning system are improved.