301 results about "Meteorological disasters" patented technology

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 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 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 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 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 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.

The invention discloses a sand and dust weather satellite-ground cooperative monitoring method, and belongs to the technical field of atmospheric environment and meteorological disaster monitoring, and the method comprises the steps: calculating a particulate matter concentration change abnormal coefficient of each monitoring station according to a ground environment air quality automatic monitoring network; calculating visible light, intermediate infrared and thermal infrared indexes; extracting a satellite remote sensing monitoring index near each ground monitoring station, and comprehensively comparing ground monitoring to confirm an optimal threshold value of sand and dust discrimination of each index; and extracting a sand and dust weather distribution area, and calculating a sand and dust intensity index. According to the invention, by fusing ground environment air quality monitoring point data and a large-range satellite remote sensing monitoring result, a sand-dust weather integrated monitoring technology method model is comprehensively constructed, dynamic monitoring of regional large-range sand-dust weather distribution is realized, full-coverage sand-dust weather distribution judgment can be carried out on a large-range region, and real-time monitoring of the large-range region is realized. And the sand and dust weather transmission path and the influence thereof can be dynamically reflected.

The invention relates to the technical field of orchard meteorological disaster prevention and reduction frost prevention, and provides a surface layer disturbance temperature inversion frost prevention method. According to the method, the frost rate of the orchard can be effectively reduced and the frost loss can be reduced through the processes of constructing the orchard air temperature prediction model, determining the frost disaster threshold value, installing fan pipe tower equipment in the orchard, collecting temperature data in real time based on a sensor network of an Internet of Things monitoring system and optimizing fan parameters through the heat balance model.

The invention discloses a lightning approaching trend forecasting method and system. The method comprises the steps of obtaining cloud cover space distribution and a time sequence, extracting a first cloud cover texture feature vector, and generating a cloud cover hierarchical feature matrix; performing dimensionality reduction on the first cloud cover texture feature vector to generate a second cloud cover texture feature vector, and generating a regionalized cloud cover distribution feature set through clustering in combination with the cloud cover hierarchical feature matrix; extracting humidity, temperature and wind field data to generate a lightning trigger factor data set, and analyzing a regionalized cloud cover distribution feature set by adopting a time sequence analysis method and an optical flow method to form a cloud cover dynamic feature set; and according to the cloud cover dynamic feature set and the cloud cover hierarchical feature matrix, in combination with a pre-established lightning association cloud type label, generating a lightning intensity grade classification feature set and the like. According to the invention, through deep fusion and dynamic optimization of spatial-temporal characteristics, the precision and timeliness of lightning prediction are significantly improved, and efficient technical support is provided for meteorological disaster early warning.

The invention discloses a storm surge nearshore disaster identification method, device and equipment fused with a typhoon dynamic influence domain, a medium and a product, and relates to the technical field of meteorological disaster monitoring. The method comprises the following steps: acquiring a meteorological ocean reanalysis data set in a historical set age limit of a target area, and preprocessing multi-source data; then background field elimination processing is carried out; determining a dynamic influence radius based on the typhoon intensity grade and the storm surge abnormal data; determining the center position of the typhoon according to the dynamic influence radius, and generating a typhoon dynamic influence domain space mask by adopting a spherical distance calculation method; a near-shore buffer area is determined according to the typhoon dynamic influence domain space mask, and a storm surge disaster area is determined based on a near-shore buffer area constraint and water increase threshold value judgment method; and carrying out storm surge near-shore disaster identification on the basis of the storm surge disaster area by adopting a geographic space visualization method to obtain an identification result. According to the invention, accurate storm surge disaster discrimination can be realized.

The invention relates to the technical field of meteorological disaster early warning, and provides an urban waterlogging early warning method and system based on grid meteorological prediction and an intelligent terminal, and the method comprises a data collection and local prediction step, a cloud fusion and regional prediction step, and a waterlogging evaluation and early warning response step. The lightweight AI model is deployed at an edge sensing terminal and an early warning terminal through a cloud edge-end collaborative architecture, local real-time prediction of meteorological data and dynamic fusion evaluation of waterlogging risks are realized, and the accuracy and efficiency of urban waterlogging early warning can be significantly improved.

The invention relates to the technical field of data processing, and particularly discloses a Huanglongguo meteorological disaster monitoring method and system based on the Internet of Things, and the method comprises the steps: firstly, obtaining a target environment meteorological risk parameter, determining a risk level through model analysis, and synchronously monitoring the image attributes of Huanglongguo fruits, according to a preset threshold value, the planting areas are divided into a qualified type, a critical qualified type and an unqualified type; on the basis, fusing the meteorological risk and a region classification result, and implementing hierarchical adjustment; meanwhile, fruit burn characteristic parameters are continuously monitored, and whether secondary adjustment is needed or not is dynamically judged by means of real-time analysis of the Internet of Things; after adjustment, images are collected again for contrastive analysis, and once burn risks or quality decline trends are found, corresponding disaster early warning is triggered immediately; according to the scheme, the intelligent level of management of the growth environment of the Huanglongguo fruits can be effectively improved, early warning of meteorological disasters can be achieved, burn and disease losses are reduced, the fruit quality and yield are improved through accurate regulation and control, and scientific and efficient planting decision support is provided for farmers.

The invention provides a gradation-oriented meteorological disaster risk grading intelligent evaluation and pushing method, and relates to the technical field of meteorological disaster early warning, and the method comprises the steps: obtaining meteorological monitoring data of a plurality of monitoring points in a town, employing a space-time attention mechanism and a dynamic weight mechanism to extract and fuse space-time features and geographic element features, and obtaining a gradation-oriented meteorological disaster risk grading intelligent evaluation and pushing result; generating a meteorological disaster risk level through a bidirectional long-short-term memory network; meanwhile, a vulnerability evaluation model is constructed based on functional area distribution information, and risk levels and vulnerability features are dynamically fused to obtain a comprehensive risk index; and finally, constructing a distributed storage network through a hierarchical cascade block chain, and carrying out encryption and fragmentation pushing on the early warning information. According to the invention, accurate assessment and efficient early warning of the meteorological disaster risk of the calmweight can be realized, and the reliability and timeliness of early warning information are improved.

The invention discloses an inland area TRP feature recognition and evaluation method based on data driving and numerical simulation, a medium and a program product, and relates to the technical field of meteorological disaster monitoring and early warning and numerical simulation. And objectively identifying a TRP event which occurs outside the typhoon circulation and is connected with the strong water vapor conveying belt by combining the typhoon peripheral circulation radius, the distance constraint and the whole-layer water vapor conveying flux. Key physical fields such as whole-layer water vapor flux, water vapor income and expenditure, dry invasion and monsoon water vapor surge are diagnosed by utilizing a regional numerical mode and reanalysis data, and a characteristic index set reflecting typhoon, monsoon and dry and cold air coupling influence is formed; on the basis, a data-driven learning algorithm is introduced, a TRP occurrence and rainstorm intensity grading recognition model is obtained through training, a risk assessment product is generated through calibration and risk grading, and therefore the inland typhoon long-distance rainstorm recognition and assessment capacity is improved.

The invention relates to a meteorological disaster early warning system based on C-V2X and edge calculation, and belongs to the technical field of traffic meteorological monitoring. The system comprises an edge calculation module and a C-V2X communication module. The edge calculation module collects video image information and vehicle state information and carries out multi-mode perception fusion, meteorological disaster identification is carried out on fused data through an embedded artificial intelligence model, and then direct communication between vehicles is realized based on a C-V2X technology. Early warning information is accurately delivered to subsequent vehicles which are on the same route and run in the same direction through direct communication between the vehicles, a driver is reminded to drive carefully or take necessary measures in advance, and the road traffic safety is improved. The vehicle-mounted edge calculation and artificial intelligence technology is utilized, real-time monitoring and early warning of the road weather condition are achieved, the safety and passing efficiency of road traffic are improved, and the method has wide application prospects.

The invention discloses a crop yield prediction method and device based on meteorological disaster influence, and relates to the technical field of crop management.The method comprises the steps that a yield prediction model database is constructed, and the yield prediction model database is composed of crop production areas of different disaster risk levels and corresponding crop yield prediction models; obtaining the meteorological disaster risk, the exposure of disaster-bearing bodies, the environmental vulnerability and the disaster prevention and reduction capability in the to-be-predicted crop production area, so as to determine the disaster risk level of the to-be-predicted crop production area; and searching a crop yield prediction model corresponding to the to-be-predicted crop production area in a preset database according to the disaster risk level of the to-be-predicted crop production area, and obtaining the final yield of the to-be-predicted crop production area by using the crop yield prediction model. According to the invention, the problem of low accuracy during crop yield prediction in the prior art is solved.

The invention discloses a near space detection method, device and equipment based on cooperation of multiple unmanned aerial vehicles, and a storage medium, and the method comprises the steps: carrying out the region division of a near space according to a meteorological detection task, generating a plurality of detection regions, and determining the number of unmanned aerial vehicles needed by each detection region; distributing each unmanned aerial vehicle in the unmanned aerial vehicle network to a corresponding detection area according to the performance parameters of the unmanned aerial vehicles and the number of the unmanned aerial vehicles; the unmanned aerial vehicles carry out cooperative detection in the corresponding detection areas to obtain meteorological detection data; carrying out fusion processing on the meteorological detection data to generate meteorological data of a near space; predicting through a weather prediction model according to the weather data to obtain a weather change trend; according to the invention, meteorological early warning is carried out according to the meteorological change trend, efficient and accurate detection of near space meteorological data is realized through cooperative operation of the unmanned aerial vehicle, meteorological prediction is accurately carried out through the meteorological prediction model, meteorological early warning is timely carried out, and risks caused by meteorological disasters are effectively reduced.

The invention discloses a power grid meteorological disaster damage grade prediction method and system based on a LightGBM model, and belongs to the field of power system disaster early warning. The method comprises the following steps: preprocessing meteorological elements, power grid distribution transformer faults, equipment and regional data; calculating the exposure degree and the vulnerability to obtain a grid-level disaster-bearing body index, aggregating data, and screening key features through correlation analysis; defining disaster damage labels according to the number of power failure users, establishing a two-stage classification data set, configuring and training LightGBM two-stage and three-stage classification models, and optimizing a two-stage classification threshold value; and deploying the model, processing real-time meteorological data, and sequentially reasoning and outputting disaster damage levels. The method solves the problems of low recall, scene separation and poor precision of traditional prediction of serious disaster damage, improves the recognition and prediction precision, fits the operation and maintenance of the power grid, supports emergency decision, and improves the early warning and emergency efficiency of power grid disasters.

The embodiment of the invention provides a cold air and strong wind identification method and system based on multi-source data and deep learning. The method is applied to the technical field of data analysis and comprises the steps of creating a historical meteorological data set; constructing a meteorological disaster positive sample and a meteorological disaster negative sample; training a cold air and strong wind identification model based on Res-Unet according to the meteorological disaster positive sample and the meteorological disaster negative sample; and acquiring real-time meteorological element data and real-time three-dimensional radar detection data, and inputting the real-time meteorological element data and the real-time three-dimensional radar detection data into the trained cold air and strong wind identification model based on Res-Unet to obtain a strong wind weather prediction result. According to the scheme, the recognition precision of the model in a complex meteorological phenomenon can be remarkably improved, and a strong wind weather prediction result can be obtained in real time. Therefore, early warning can be given out before cold air and strong wind events occur, precautionary measures are taken in advance, and losses caused by disasters are reduced.