21 results about "Weather satellite" patented technology

The invention discloses a multi-modal neural network fusion system fusing an attention mechanism, and particularly relates to the technical field of environmental monitoring artificial intelligence. The defects of high meteorological / water quality monitoring false alarm rate and large response delay caused by static weighted fusion, insufficient domain feature extraction and edge deployment bottleneck of an existing multi-modal fusion system are overcome. According to the method, meteorological satellite / water quality sensor data are fused in a cross-modal manner by adopting a dynamic attention mechanism, domain features are extracted in combination with space-time convolution and graph convolution, and the domain features are deployed to edge equipment through hybrid quantization compression. The severe convection weather identification and pollutant traceability precision is improved, and the real-time early warning capability of a field terminal is guaranteed.

The invention discloses a power distribution network risk management method, system and device in extreme weather and a medium, and the method comprises the following steps: obtaining meteorological data through detecting weather conditions, and predicting a weather development trend; analyzing the fault risk of each node of the power distribution network according to the meteorological data and the weather development trend; and generating a decision scheme according to the analysis result of the fault risk, and coordinately executing power dispatching and emergency disposal by combining the decision scheme with an emergency communication network for power supply guarantee. According to the invention, the comprehensive management method integrating meteorological monitoring, risk assessment, emergency communication, power dispatching and power supply guarantee is constructed, so that the risk management capability of the power distribution network under the extreme weather condition is remarkably improved; and the precision and timeliness of weather prediction are improved.

The invention relates to the technical field of power distribution networks, in particular to a disaster weather power distribution network grid power failure prediction method, system and device and a medium, a panoramic disaster sensing system is constructed by fusing meteorological satellites, sensors and power grid operation multi-source heterogeneous data, and a topology-electrical coupling complex network model is established to quantify the node-level fault probability; a multi-level risk conduction rule from meteorological factors to a user level is analyzed based on machine learning and a knowledge graph technology, and accurate prediction of the grid power failure probability is realized; a multi-dimensional early warning information driving dynamic response mechanism is synchronously generated, flexible load regulation and control, energy storage cooperative scheduling and network reconstruction strategies are integrated, rapid fault suppression is realized through linkage of first-aid repair resources, prediction-defense-recovery closed-loop management is formed, and the fault handling efficiency and disaster prevention toughness of the power distribution network in extreme weather are remarkably 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 provides an accumulated snow monitoring method and device, a storage medium and electronic equipment, and the method comprises the steps: triggering a snowstorm emergency response task when a target condition is detected, carrying out the projection conversion of weather forecast data, target sensor data, accumulated snow data, a meteorological satellite map and a target text which belong to the same grid at a current time point according to latitude and longitude coordinates, and obtaining a snowstorm emergency response task; the feature vectors corresponding to the grids are obtained, the feature vectors corresponding to the grids are input into the graph neural network, multi-modal feature fusion is carried out according to the spatial adjacency relation between the grids, and the current accumulated snow fusion state corresponding to the target road section in each grid is obtained. The multi-modal feature fusion is carried out according to the spatial adjacency relationship between the grids, so that the final accumulated snow state representation of each grid is fused with the influence of the surrounding environment, the noise is effectively smoothed, the data blank area is filled, and the accuracy of the current accumulated snow fusion state acquisition result corresponding to the target road section in the grid is ensured.

The application relates to an ARMS-based satellite microwave humidity data all-weather assimilation method, which comprises the following steps: collecting YH4DVAR global background field data; calculating the effective particle radius of water condensate; establishing a symmetric error model for ARMS scattering characteristics according to the forecast deviation of cloud and precipitation intensity and spatial position, wherein the symmetric error model expands the observation error into a segmented function of cloud amount or precipitation amount in the mode and observation, and assigns an assimilation weight to the microwave observation data under all-weather conditions; and using the model to assimilate and forecast the all-weather satellite microwave humidity data. The application has good all-weather microwave radiation simulation capability, provides a new observation operator for YH4DVAR all-weather assimilation, and has a wide application prospect in the field of satellite microwave data assimilation.

The invention relates to an ARMS-based satellite microwave humidity data all-weather assimilation method. The method comprises the following steps: acquiring YH4DVAR global background field data; calculating the effective particle radius of the hydrogel; establishing a symmetric error model aiming at the scattering characteristics of the ARMS for the forecast deviation of the cloud and rainfall intensity and the spatial position, expanding the observation error into a piecewise function of the cloud amount or the rainfall amount in the mode and observation by the symmetric error model, and distributing assimilation weights for microwave observation data under the all-weather condition; and assimilating and forecasting all-weather satellite microwave humidity data by using the model. The method has a good all-weather microwave radiation simulation capability, provides a new observation operator for YH4DVAR all-weather assimilation, and has a wide application prospect in the field of satellite microwave data assimilation.

This application provides a millimeter-wave cloud radar observation method and related apparatus based on multi-source data. A fused dataset is formed by real-time acquisition and preprocessing of at least one of the following: weather radar mosaic data, meteorological satellite data, ground visibility observation data, and co-located or adjacent aerosol lidar data. A preset rule set is used to determine that the current weather scene at the cloud radar station includes at least weather echo approaching, weather echo receding, local weather processes, fog, and non-precipitating clouds. Based on the identified weather scene type, the corresponding combined scanning strategy (composed of a basic scanning mode consisting of vertical top-view scanning, distance-height scanning, and three-dimensional volume scanning, and a fine scanning mode consisting of local planar horizontal scanning and layered planar horizontal scanning, combined according to preset logic) is called from a preset strategy mapping table and executed. During execution, the parameters of the fine scanning mode are dynamically adjusted based on the position and intensity information of the real-time radar echo or changes in the detection mode.

The invention discloses a multi-source data fused vertical plane hourly total radiation calculation method and related equipment, and the method comprises the steps: firstly obtaining multi-source data of a target area, including horizontal plane solar radiation, ground meteorology, satellite inversion data and astronomical parameters; judging data conditions of a target area, wherein the data conditions are divided into two types with and without horizontal plane solar radiation data; selecting a corresponding input parameter combination from the multi-source data according to the data condition, selecting the horizontal plane solar radiation data and the ground meteorological data when the horizontal plane solar radiation data exists, and selecting satellite inversion and astronomical parameters when the horizontal plane solar radiation data does not exist; then training a predefined machine learning model by using the selected parameter combination to obtain a trained vertical plane hourly total radiation calculation model; and finally, inputting current multi-source data into the model to obtain a vertical plane hourly total radiation calculation value of the target area. The method aims at solving the problem that an existing method depends on experience judgment on input variable selection, so that the adaptability and generalization ability of a model are insufficient in a data condition change scene.

The application relates to the technical field of photovoltaic power station evaluation, and particularly discloses a photovoltaic power station total solar radiation data correction method and system. The method uses high-precision ground surface radiation observation sequence data of a solar radiation in a certain range of the power station, corrects a weather scale radiation component, namely a low-frequency component, of meteorological satellite radiation sequence data at a position of the power station according to spatial similarity and randomness of total solar radiation through a wavelet analysis method, reduces deviation caused by an optical thickness related parameter model inversion, and improves evaluation accuracy of satellite radiation data.

The invention relates to a rainfall prediction method, device and equipment based on satellite positioning service signals. The method comprises the following steps: acquiring satellite positioning service signals at different moments in a current time period in a target area, reflectivity factor data of a meteorological radar, infrared cloud picture data of a meteorological satellite and rainfall related data, and calculating the rainfall of the target area according to the acquired satellite positioning service signals, reflectivity factor data, infrared cloud picture data and rainfall related data. A multi-source fusion feature vector is constructed, a time sequence convolutional network model is adopted, the rainfall prediction probability is determined according to the multi-source fusion feature vector, rainfall early warning information is generated according to the rainfall prediction probability, and rainfall related data comprises temperature data, relative humidity data and air pressure data. By adopting the method, the reliability of rainfall prediction can be improved.

The invention provides a domestic passive meteorological satellite cloud base height high-precision inversion method and system, and belongs to the field of atmospheric cloud parameter remote sensing inversion and quantitative remote sensing mechanisms. According to the method, re-analysis data such as satellite multi-channel atmospheric top reflectivity observation, brightness temperature observation and atmospheric temperature profile are fused, a complex nonlinear feature extraction and modeling method is developed, and the nonlinear mapping capability among the atmospheric top reflectivity, the brightness temperature and the cloud base height is improved; the night and all-day inversion capability is enhanced by combining all-time infrared channel characteristics and layer junction information; through a multi-source sample expansion and stack learning strategy, the generalization adaptability of the model is improved; and a sample expansion and auxiliary training mechanism is designed, so that the problem of insufficient high-quality training data is effectively relieved, and finally, high-precision inversion of the all-day cloud base height of a domestic static or polar orbit meteorological satellite is realized.

The application belongs to the technical field of artificial intelligence, and specifically provides a dynamic identification system and method for isolated mature convective clouds combining satellites and radars, which comprises: a data processing module, which is used for pre-processing wind-weather stationary meteorological satellite data and radar data obtained; a convective cloud sample extraction module, which is used for identifying convective cloud samples; an isolated convective cloud sample identification module, which is used for determining isolated convective cloud samples; an isolated mature convective cloud sample screening module, which is used for distinguishing mature convective clouds from non-mature convective clouds for the isolated convective cloud samples, and screening out isolated mature convective cloud samples; a sample dynamic inspection module, which is used for obtaining final isolated mature convective cloud samples; and an identification model construction module, which is used for combining and verifying satellite features and radar features of the final isolated mature convective cloud samples, and establishing an intelligent identification model; the technical scheme of the application can realize tracking of the whole life cycle of isolated mature convective systems.

The application relates to the technical field of AI meteorological prediction, and discloses a meteorological prediction method based on an AI meteorological prediction model and a WRF model, wherein the method comprises the following steps: inputting ERA5 global atmospheric reanalysis grid data into the AI meteorological prediction model to obtain a assimilation background field; constructing an effective multi-source observation vector based on ground automatic meteorological station observation data, sounding observation data, stationary meteorological satellite radiance data and Doppler radar VAD wind profile data; solving an optimal atmospheric analysis field; extracting an atmospheric state variable from the optimal atmospheric analysis field and writing the atmospheric state variable into a WRF model side boundary driving file to drive the WRF model to complete integral operation and obtain a meteorological element grid prediction field; and the method makes the WRF boundary condition update frequency higher than that of a traditional 6-hour static scheme, and effectively characterizes the sub-hour scale rapid evolution of the atmospheric state in the boundary region, thereby improving the prediction capability of the WRF model for high-impact weather events such as strong convection.

The application provides a domestic passive meteorological satellite cloud bottom height high-precision inversion method and system, and belongs to the field of atmospheric cloud parameter remote sensing inversion and quantitative remote sensing mechanism. The application fuses satellite multi-channel atmospheric top reflectivity observation, brightness temperature observation and atmospheric temperature profile reanalysis data, develops a complex nonlinear feature extraction and modeling method, improves the nonlinear mapping capability among the atmospheric top reflectivity, the brightness temperature and the cloud bottom height, combines the whole period infrared channel characteristics and the stratification information, enhances the night and all-time inversion capability, improves the generalization adaptability of the model through the multi-source sample expansion and the stacking learning strategy, designs the sample expansion and the auxiliary training mechanism, effectively alleviates the problem of insufficient high-quality training data, and finally realizes the high-precision inversion of the all-time cloud bottom height suitable for the domestic stationary or polar orbit meteorological satellite.

The invention provides an intelligent satellite communication sentinel vehicle, and relates to the technical field of road safety early warning, the intelligent satellite communication sentinel vehicle comprises a driving assembly and a control module for controlling the driving assembly to operate, the driving assembly comprises a roller and a driving part for driving the roller to rotate, and the intelligent satellite communication sentinel vehicle further comprises a fender fixed at the top of the roller; the control module comprises a compartment shell, an energy supply assembly, a monitoring assembly, a control assembly and a communication assembly, the energy supply assembly, the monitoring assembly, the control assembly and the communication assembly are fixed in the compartment shell and sequentially connected, the positioning assembly is fixed to the periphery of the compartment shell, and the positioning assembly comprises a plurality of lifting seats arranged in a lifting mode and adjusting rods for controlling the lifting seats to ascend and descend. In addition, the control module further comprises a guide assembly for controlling the moving direction of the driving assembly, the guide assembly is fixed to the front end of the compartment shell, the guide assembly comprises a guide wheel fixed to the front end of the compartment shell and a rotating piece for driving the guide wheel to rotate, and the control module further comprises a camera fixed to the rotating piece. Therefore, a plurality of places and road sections are monitored, and the monitoring effect is better.

The application discloses a kind of based on Fengyun polar orbit meteorological satellite South China Sea summer monsoon double-index monitoring method, this method obtains 850hPa atmospheric temperature and specific humidity by polar orbit meteorological satellite FY-3D vertical detection instrument group VASS, 850hPa false equivalent potential temperature index is calculated;Ocean surface wind speed and wind direction are obtained by ocean surface wind field data of the wind field measurement radar WindRAD inversion of polar orbit meteorological satellite FY-3E, average zonal wind index is calculated;By evaluating the accuracy of FY-3D / VASS temperature and specific humidity relative to ERA5, and the accuracy of ocean surface wind of FY-3E / WindRAD relative to MetOp-C / ASCAT is evaluated index, the accuracy of the double-index monitoring South China Sea summer monsoon outbreak process is verified from the evaluation result.The application uses FY-3D to calculate false equivalent potential temperature and FY-3E ocean surface wind double-index monitoring South China Sea summer monsoon, double-index is well monitored 2022 South China Sea summer monsoon outbreak process temperature and humidity field and wind field conversion, so as to verify the application ability of two kinds of satellite data in South China Sea summer monsoon climate monitoring.

The present application relates to the technical field of meteorological disaster monitoring and early warning and numerical prediction, and more particularly to an extreme gale weather prediction method based on a meteorological prediction large model, which comprises the following steps: obtaining a numerical weather prediction three-dimensional physical field and weather radar, meteorological satellite and ground meteorological observation and assimilating them into a consistent field; time advancing and obtaining sinking potential energy and cold pool diagnosis through a micro-downburst calculation program, forming a coarse resolution gust through boundary layer similar mapping and hysteresis kernel, generating a fine scale base map under the restriction of a cold pool front by using an optimal transport of advection constraint, outputting an image element gust distribution and an over-threshold probability by a conditional diffusion probability generation model and extracting a wind damage polygon; fusing radar, satellite, lightning and ground gust, using monotonic normalization flow calibration and intersection and union ratio and conformal coverage verification, updating parameters with limited online amplitude, and improving the reliability of spatial positioning, occurrence time and probability description.

The present application relates to the technical field of satellite autonomous monitoring, and relates to a GPU-based all-weather real-time satellite autonomous monitoring system for sea and air space, which solves the problem of efficient monitoring in the absence of a real-time monitoring scheme for sea and air space; specifically comprising a target identification system for infrared and visible light image fusion on the satellite, a VGG16 network-based super-resolution reconstruction of the fused infrared and visible light images, and simultaneously obtaining a super-resolution target image; a target identification module for target identification according to the generated super-resolution image, cutting the target, packing the target with auxiliary data, and returning the target to the ground for analysis; a ground target classification and early warning system for analyzing the returned data packet, classifying the target image (military ship, aircraft carrier, airbus, fighter jet, etc.), identifying the target danger level, and determining whether to trigger an alarm. The system can achieve all-weather real-time autonomous monitoring, and the satellite data transmission data volume can be reduced by more than 80%, and the equipment maintenance cost is low.

The invention discloses a mountain torrent meteorological risk early warning method and system based on big data analysis and a storage medium. The method comprises the following steps: collecting meteorological and hydrological data and preprocessing the data; processing the data of each data source by using a Kalman filtering algorithm, and fusing the multi-source data subjected to Kalman filtering processing by using a weighted average fusion algorithm to generate fused data; constructing an efficient space-time big data storage platform, clustering regions and time periods with similar meteorological conditions and mountain torrent generation characteristics, analyzing the clustered data, and mining a potential association mode between meteorological elements and mountain torrent generation; and constructing a mountain torrent risk refined evaluation model based on a spatio-temporal data analysis result. According to the method, multi-source data of a meteorological satellite, a radar, a ground observation station, a hydrological monitoring station and the like are creatively integrated, the limitation of a traditional single data source is broken through, and a more comprehensive and abundant data basis is provided for mountain torrent early warning.

The application discloses a photovoltaic power ultra-short-term prediction method based on cloud field dynamic perception and related devices, and relates to the technical field of meteorology and new energy power prediction. Cloud field initial data output by a numerical weather prediction model, an infrared brightness temperature sequence of a stationary meteorological satellite, and reflectivity factor data of an X-band weather radar are acquired; the reflectivity factor data is assimilated into a cloud water mixing ratio correction amount by using a physical perception fusion bridging model, so that the cloud field initial data is corrected and a fused cloud field is obtained; a three-dimensional dynamic cloud motion vector field and a cloud microphysical structure field are constructed based on the fused cloud field; the three-dimensional dynamic cloud motion vector field and the cloud microphysical structure field are input into a radiation transmission model, and surface irradiance data is calculated; theoretical power generation is determined according to the surface irradiance data, and online rolling correction is carried out in combination with measured data of a photovoltaic power station, and an ultra-short-term photovoltaic power prediction result is output. The accuracy of the photovoltaic power ultra-short-term prediction under a sudden cloud blocking working condition can be improved.