221 results about "Meteorological disasters" patented technology

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 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 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 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 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 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 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 invention discloses a meteorological monitoring, forecasting and early warning service intelligent pushing method and system and a storage medium, and the method comprises the steps: carrying out the sorting and modeling of meteorological data, and setting a meteorological disaster early warning threshold value and a pushing channel selection instruction; carrying out anomaly detection on the meteorological live data, and pushing reminding information for correction when an anomaly is detected; and according to the meteorological disaster early warning threshold value, the real-time meteorological actual condition and the forecast result, the early warning issuing time is intelligently calculated, and reminding information is sent. According to the method, accurate acquisition, efficient processing and intelligent pushing of meteorological information are realized, the accuracy, timeliness and pertinence of meteorological monitoring, forecasting and early warning are improved, the individual requirements of users are met, and the meteorological service quality and the disaster prevention and reduction capability are improved.

The invention relates to the technical field of meteorological disaster monitoring and early warning and numerical forecasting fusion, in particular to an extreme gale weather forecasting method based on a weather forecasting large model, which comprises the following steps: acquiring a numerical weather forecasting three-dimensional physical field, a weather radar, a weather satellite and ground meteorological observation and assimilating the numerical weather forecasting three-dimensional physical field into a consensus field; time advances, sinking potential energy and cold pool diagnosis are obtained through a micro-downburst calculation program, coarse-resolution gust is formed through boundary layer similarity mapping and a hysteresis kernel, and a fine-scale base map is generated under cold pool frontal surface limitation through optimal transmission of advection constraint; the conditional diffusion probability generation model outputs pixel gust distribution and a threshold exceeding probability and extracts a wind damage polygon; radar, satellites, lightning and ground gust are fused, monotonous normalized flow calibration is used, parameters are updated through intersection-to-parallel ratio and shape-preserving coverage inspection and online amplitude limitation, and the credibility of spatial positioning, occurrence time and probability description is improved.

The invention discloses a meteorological disaster early warning response method and system based on artificial intelligence. According to the method, feature extraction processing is carried out on to-be-early-warned meteorological data of a target geographic area and meteorological source associated data of the to-be-early-warned meteorological data to obtain a meteorological multi-dimensional feature matrix; performing feature extraction on the at least two kinds of disaster associated data of the to-be-early-warned meteorological data to obtain at least one disaster representation vector of the to-be-early-warned meteorological data; performing fusion processing on the meteorological multi-dimensional feature matrix and the at least one disaster representation vector to obtain a fusion feature matrix; adopting a meteorological disaster model to predict the meteorological disaster type of the meteorological data to be pre-warned based on the fusion feature matrix; the corresponding early warning response service is triggered according to the predicted disaster grade and the scene type of the target geographic area, the meteorological disaster early warning response can be realized by adopting an artificial intelligence technology, the accuracy of the meteorological disaster early warning response is improved, and personalized early warning response services can be customized for different scenes.

The invention is suitable for the technical field of disaster monitoring and early warning, and provides a meteorological disaster monitoring and early warning method based on multi-source remote sensing data, and the method comprises the steps: obtaining the multi-source remote sensing image data of a target monitoring region; performing image registration and fusion processing on the multi-source remote sensing image data to obtain a registered multi-modal remote sensing image; identifying and extracting a potential meteorological disaster target area from the registered multi-modal remote sensing image based on a preset disaster target extraction model, and performing hierarchical locking identification; extracting disaster characteristic parameters of the potential meteorological disaster target area, and performing comprehensive assessment based on a preset disaster assessment model to generate a disaster risk level assessment result; meteorological disaster early warning information is generated according to the disaster risk level assessment result, and early warning is issued; the meteorological disaster monitoring accuracy is effectively improved, and the emergency response timeliness is greatly enhanced.

The invention discloses an extreme short and temporary rainfall forecasting method and system based on wavelet domain decoupling and multi-course learning, and belongs to the technical field of meteorological big data processing and artificial intelligence deep learning. According to the invention, a meteorological image sequence is decomposed into a low-frequency approximate component and a high-frequency detail component through frequency domain decoupling, a double-branch deep neural network is adopted to extract features and predict corresponding frequency domain coefficients, and a rainfall prediction map is output through fusion of a spectral domain reconstruction and refining module. In addition, a multi-task course learning strategy is introduced during model optimization, and the loss weight is dynamically adjusted. The method is characterized in that modeling is moved to a wavelet domain to explicitly protect high-frequency details, local severe convection is accurately captured in combination with a double-branch architecture and a feature pyramid, the problem of traditional prediction fuzziness is effectively solved, the prediction definition and the extreme rainfall early-warning capacity are remarkably improved, high-fidelity and high-precision minute-level short-temporary rainfall prediction is achieved, and the prediction efficiency is improved. And reliable technical support is provided for meteorological disaster prevention and reduction.

The invention belongs to the field of meteorological disaster forecasting, and discloses a physical meteorological factor guidance-based generative typhoon prediction method, which comprises the following steps of: inputting typhoon historical time sequence multi-attribute data, typhoon external environment meteorological variable data and a typhoon inclination coefficient into a physical constraint coding module to obtain a physical constraint preliminary feature; inputting the physical constraint preliminary features into a multi-constraint fusion alignment coding module to obtain a historical multi-attribute information constraint, a typhoon external environment constraint and a typhoon internal structure constraint after fusion alignment coding; and inputting the standard noise, the historical multi-attribute information constraint, the typhoon external environment constraint and the typhoon internal structure constraint which are consistent with the predicted attribute in shape and have uncertainty higher than a threshold value into an uncertainty reduction module under the guidance of the physical constraint to obtain a predicted value of the typhoon target attribute after uncertainty reduction. According to the method, the uncertainty is reduced, and transferable and accurate global typhoon prediction with low computing resource requirements is realized.

The invention discloses an electric power multiple risk assessment identification and early warning method, system and device and a medium, and belongs to the technical field of electric power system safety operation and maintenance, and the method comprises the steps: obtaining meteorological events and fault events, and carrying out the statistics of the number of times of the meteorological events and the fault events; calculating association strength through the number of times of the meteorological events and the fault events, and predicting the probability of occurrence of the fault events according to the key meteorological data through a machine learning model; calculating a comprehensive risk value and determining a risk level by combining consequences caused by equipment faults; generating early warning information according to the comprehensive risk value and the corresponding fault event; according to the invention, weather forecast and meteorological disaster early warning technologies are combined, so that the advancement of risk early warning is realized; the risk level is adjusted and evaluated according to real-time monitoring data, it is ensured that early warning information and extreme weather evolution are synchronous, and the problems that traditional early warning is high in static performance, obvious in hysteresis and lack of pertinence are solved.

The invention discloses an electric power climate risk assessment method for the influence of high temperature and / or drought events on both sides of supply and demand, a medium and a program product, and belongs to the technical field of meteorological disaster risk assessment and energy system safety crossing. Then identifying a high-temperature or drought event based on a temperature percentile threshold value and an SPEI drought index, and constructing a bivariate distribution model by adopting a joint probability density function to identify a high-temperature drought composite event; respectively constructing a power supply side response model and a load side response model, and quantitatively evaluating output changes of different power supplies and load response characteristics of various users in a high-temperature and / or drought scene; key indexes such as a power gap and load risk exposure intensity are calculated through supply-demand coupling offset analysis; and finally, based on the climate mode prediction data, driving the risk model, and outputting a risk evolution trend in a future scene. The method can provide support for climate toughness improvement and scheduling decision making of the power system.

The invention discloses a meteorological disaster weather intelligent identification method based on AI image identification, and the method comprises the steps: unifying the sizes of a plurality of data sets, carrying out the fusion to obtain a comprehensive data set, enhancing the quality and diversity of the comprehensive data set through a GAN network, generating a weather identification data set, and carrying out the recognition of the weather through a Transform visual recognition model. And carrying out deep analysis and learning on the generated weather identification data set, completing weather identification and outputting an identification result. According to the method, the GAN and the Transform visual recognition model are adopted, and the global features and the long-distance dependency relationship in the meteorological image are captured through a self-attention mechanism, so that the problem of insufficient complex meteorological mode recognition caused by local sensing field limitation of a traditional model is solved, feature extraction of disaster weather is more comprehensive, and the recognition accuracy is remarkably improved.

The invention relates to the technical field of meteorological disaster monitoring, and particularly discloses a severe convective weather tracking and early warning method based on radar data and deep learning, and the method comprises the steps: collecting original radar data in real time through a laser radar and a spaceborne radar, obtaining the original radar data, and carrying out the cleaning and feature extraction processing, thereby obtaining real-time spatial-temporal feature data; real-time spatial-temporal characteristic data of radar data are automatically learned through a deep learning model, so that the deep learning model has the capability of automatically identifying and tracking a severe convection system, the boundary and strength of the severe convection system are directly output through the deep learning model, and a mask of the severe convection system is directly generated through one-time forward propagation. The calculation amount of radar data is effectively reduced, finally, the future influence area of the severe convection system is predicted based on the evolution characteristics of the severe convection system, early warning information is generated and output, the whole process from data processing to early warning release is free of manual intervention, and the automation degree and efficiency of early warning are improved.

The invention discloses a power grid operation inspection operation intelligent optimization system based on multi-source meteorological data fusion, and belongs to the technical field of power grid operation inspection. The data acquisition layer is used for realizing access and convergence of multi-source heterogeneous data and providing original data support for an upper layer; the fusion processing layer is used for carrying out preprocessing and multi-source fusion on the original data and outputting a standardized data set; the risk assessment layer is used for quantifying the influence of meteorological disasters on the power grid based on fusion data, dividing risk levels and triggering early warning; the intelligent optimization layer is used for generating an optimal operation and maintenance operation scheme based on a risk assessment result and supporting dynamic adjustment; the decision application layer is used for realizing risk and operation state visualization, man-machine interaction and closed-loop control; according to the invention, a closed loop of minute-level meteorological perception-second-level power grid state evaluation-millisecond-level decision response is realized, the disaster early warning accuracy is improved, the routing inspection path length is shortened, the fault power failure duration is reduced, and the annual operation and maintenance cost is reduced.

The invention relates to the technical field of power grid meteorological disaster prevention and reduction, in particular to a power transmission line gale influence assessment method, device and equipment and a medium. According to ground meteorological observation data of a preset distance near the target power transmission line, spatial interpolation is carried out, an initial height gridding wind field with the spatial resolution being the same as the geographic information data is obtained, and the initial height gridding wind field comprises a latitudinal wind field and a longitudinal wind field; according to the initial height gridding wind field and the wire height of the target power transmission line, performing vertical profile extrapolation to obtain a wire height gridding wind field; according to the electric wire height gridding wind field and the geographic position information of each tower pole in the target power transmission line, performing local wind load evaluation to obtain the maximum wind power corresponding to each tower pole; according to the maximum wind power and the safety threshold value corresponding to each tower pole, disaster judgment is carried out, a strong wind influence evaluation result is output, and the fine management and active defense capability of a power grid to cope with strong wind disasters is improved.

The invention provides a manual hail elimination operation method, device and equipment and a storage medium. Relates to the technical field of meteorological disaster defense. The method comprises the following steps: acquiring multi-source data including numerical forecasting and the like, and performing quality control; based on the data after quality control, building a short-term model based on numerical values and intelligent grid forecasting to obtain a first hail occurrence probability, calculating a second hail occurrence probability based on sounding data, calculating a third hail occurrence probability based on satellite data, and calculating a fourth hail occurrence probability based on radar and extrapolation forecasting; interpolating the four hail occurrence probabilities and then obtaining a comprehensive probability according to the weight; obtaining artificial influence weather resources of the operation areas, screening operation ranges, sorting and scheduling the areas meeting conditions, and issuing a scheme; and after operation, multi-source data is used for evaluating the effect, optimizing the weight and storing process data. The problems of single data source, low forecasting accuracy, unscientific operation sequence, unreasonable operation arrangement and the like are solved, and the effect of manually eliminating hail operation is improved.

The invention discloses a typhoon disaster assessment method based on WRF adaptive nesting and machine learning. The typhoon disaster assessment method comprises the steps that typical typhoon examples in a research area are selected, and weather, disaster loss and exposure data in the typhoon period are collected and preprocessed; evaluating a plurality of candidate thresholds under the constraint of the existing typhoon disaster wind speed threshold, and preferentially determining a disaster-causing threshold; constructing multi-layer WRF nesting to downscale a wind field, recognizing an over-threshold region along a typhoon path according to a determined disaster-causing threshold, performing high-resolution encryption solution in the over-threshold region, checking the over-threshold region, and obtaining the maximum wind speed of the region; matching the over-threshold region with an exposure data space to form an exposure degree, taking the maximum wind speed of the region and the exposure degree as input, and taking a historical loss rate as output, so as to establish a loss rate intelligent prediction model for vulnerability quantification; and calculating the typhoon disaster risk by integrating the disaster intensity, the exposure degree and the vulnerability. According to the invention, the accuracy of typhoon disaster risk assessment can be effectively improved, and a reliable basis is provided for graded early warning and emergency decision making.

The utility model discloses an agricultural meteorological disaster early warning device, relates to the technical field of agricultural meteorological monitoring and early warning, and is used for solving the problems related to crop harvesting caused by insufficient agricultural meteorological guidance. The agricultural meteorological disaster early warning device comprises meteorological monitoring equipment, meteorological early warning equipment and an upper computer. A plurality of meteorological monitoring devices are arranged, the plurality of meteorological monitoring devices are distributed in a preset area at intervals, and wireless communicators are arranged in the meteorological monitoring devices; the meteorological early warning equipment is arranged among a plurality of meteorological stations, the meteorological early warning equipment comprises a wireless communication module, a control module, a display screen and a loudspeaker, the wireless communication module is electrically connected with the control module, the control module is electrically connected with the display screen, and the wireless communication module is in signal connection with the wireless communicator; the upper computer wireless communicator is electrically connected with the upper computer, and the wireless communication module is electrically connected with the upper computer. According to the invention, the meteorological information of the small-range region is displayed by arranging the small-range meteorological early warning equipment.

The application discloses a lightning target recognition model and method facing image input modal imbalance and high false alarm rate, and belongs to the technical field of image recognition and meteorological disaster monitoring. Firstly, Gaussian mixture density estimation and time-weighted fusion are performed on ground-based lightning location data to convert the image label with spatial probability distribution characteristics; then, Himawari-8 satellite multi-channel brightness temperature images and their derived feature maps and radar echo images are used as multi-source heterogeneous inputs. In view of the information imbalance between different observation modalities, an MDE-UNet deep learning model is constructed, a multi-scale feature fusion module, a MLP enhanced decoding unit based on a weighted sliding window and a radar image information enhancement module are innovatively designed, the key image features are enhanced and irrelevant noise is suppressed, and the feature imbalance problem in heterogeneous data fusion is solved. The model adopts an asymmetric weighted BCE-DICE loss function to strengthen the attention to lightning target pixels.