17 results about "Meteorological satellite" patented technology

The application provides a state monitoring method and system for offshore wind farms, which is based on meteorological satellite monitoring data of the sea area where the offshore wind farm is located to determine the corresponding environmental state information of the sea area, so as to determine all target power generation wind turbines that may have fault events inside the offshore wind farm; based on the distribution position information of all target power generation wind turbines, directional radar scanning is performed on the corresponding area inside the offshore wind farm to obtain radar echo data of the corresponding area, so as to identify abnormal power generation wind turbines that stop power generation operation inside the offshore wind farm; and based on real-time power generation state data of all power generation wind turbines inside the offshore wind farm that maintain power generation operation, power generation wind turbines that are not in a full load state are identified, and the power generation wind turbines that are not in a full load state are adjusted in working state in combination with power adjustable capacity information and environmental state information of the corresponding power generation wind turbines, so that the offshore wind farm can maintain stable and continuous power transmission to the outside in the case of power generation wind turbine failure.

This invention relates to the field of AI weather forecasting technology and discloses a weather forecasting method based on an AI weather forecasting model and a WRF model. The method includes: inputting ERA5 global atmospheric reanalysis gridded data into the AI ​​weather forecasting model to obtain an assimilated background field; constructing an effective multi-source observation vector based on observation data from ground automatic weather stations, radiosonde observation data, geostationary meteorological satellite radiance data, and Doppler radar VAD wind profile data; solving for the optimal atmospheric analysis field; extracting atmospheric state variables from the optimal atmospheric analysis field and writing them into the WRF model's side boundary driving file, driving the WRF model to complete the integration operation, and obtaining a gridded forecast field of meteorological elements. This method enables the WRF boundary condition update frequency to be higher than the traditional 6-hour static scheme, effectively characterizing the rapid evolution of the atmospheric state in the boundary region at the sub-hourly scale, thereby improving the WRF model's forecasting capability for high-impact weather events such as severe convection.

The present application relates to the technical field of irradiance prediction, and discloses a short-term irradiance AI prediction system and method combined with meteorological satellite data. The method collects a historical meteorological satellite remote sensing data sequence and a ground irradiance monitoring data sequence of a target area to form a training sample set. Multi-scale spatio-temporal feature extraction is performed on the historical meteorological satellite remote sensing data sequence to generate a three-dimensional meteorological feature tensor. The ground irradiance monitoring data sequence and the three-dimensional meteorological feature tensor are spatio-temporally aligned and matched to establish a meteorological-irradiance correlation feature matrix. A deep spatio-temporal fusion network model is then constructed, and the meteorological-irradiance correlation feature matrix is input into the model for end-to-end training to obtain a trained irradiance prediction model. Real-time meteorological satellite remote sensing data flow is acquired, real-time three-dimensional meteorological feature tensors are extracted and input into the trained model, and an irradiance prediction value sequence within a preset time window in the future is output.

This invention provides a method, apparatus, and device for identifying water bodies on a single orbit of a polar-orbiting meteorological satellite, comprising: acquiring first-band data, first-positioning information data, and second-band data, second-positioning information data, and product data at a first spatial resolution from a polar-orbiting meteorological satellite; sampling the data at the second spatial resolution to obtain second-band data, second-positioning information data, and product data at the first spatial resolution; acquiring high-quality binary classification data; training a decision tree model based on the first-band data, second-band data, normalized index data, band difference data, and band ratio data at the first spatial resolution corresponding to high-quality water body pixels and high-quality non-water body pixels within the target area, to obtain a target decision tree model; and obtaining the single-orbit water body identification area based on the target decision tree model. The solution of this invention accurately identifies water bodies in complex background conditions, improving monitoring accuracy and applicability.

This invention discloses a real-time precipitation retrieval method for meteorological satellites based on a deep learning geographic attention mechanism. The method includes: constructing a training dataset of FY-4B AGRI infrared cloud images and IMERG Final Run labels, preprocessing it, and inputting it into a U-shaped encoder-decoder network; achieving deep fusion of multi-scale features by embedding a GeoAB geographic attention module; using a dual-objective loss function to complete rain area classification and precipitation intensity regression training; and outputting precipitation data with 0.05° spatial resolution and 15-minute temporal resolution through T-transform decomposition of the cloud image, independent inference, T-inverse transform fusion, and average pooling to eliminate grid effects. Compared with existing technologies, this invention solves the technical problems of balancing real-time performance and accuracy in satellite precipitation retrieval, and the blurred distinction between rain areas and clear skies. This invention achieves a CSI of 0.693, a 3.3% improvement over IMERG Final Run, with a total retrieval time of less than 20 seconds. It balances high accuracy and real-time performance, providing data support for disaster early warning, agricultural management, and other applications, and has promising application prospects.

A method, system, medium, and product for controlling a meteorological satellite communication terminal are disclosed, relating to the field of satellite communication. The method includes: acquiring data packet transmission records of a fusion communication unit in real time during a preset statistical period via a main control unit; calculating the real-time data transmission density during the preset statistical period based on the data packet transmission records; generating a first target instruction when the real-time data transmission density meets preset sleep conditions; controlling the meteorological satellite communication terminal to enter a target sleep state via a power management unit upon receiving the first target instruction and a second target instruction; and controlling the meteorological satellite communication terminal to enter a fully operational state from the target sleep state via the power management unit upon receiving a target wake-up instruction. This application can improve the robustness of meteorological satellite communication terminals.

The application discloses a meteorological satellite cloud map extrapolation method based on a geographical attention mechanism, characterized by a deep learning framework constructed by GeoAttX, the introduction of a geographical attention mechanism to enhance the modeling ability of spatial features, and the synergistic effect of geographical semantic modeling, prediction path optimization and distribution correction mechanism, using the observation data of meteorological satellites to realize high-precision and low-error accumulation cloud prediction. The method uses a reverse greedy algorithm to realize flexible forecast time by reducing the autoregressive step, and proposes a second-order standardization method to normalize the pixel distribution, to maintain the consistency of brightness statistics and reduce error propagation. Compared with the prior art, the application has the advantages of using real-time stationary meteorological satellite observation data, having error suppression capability, geographical structure modeling capability, long-term stable prediction capability, high precision and stability, and can effectively improve the cloud motion extrapolation capability, and has important application value for weather forecasting and disaster warning.

The application provides a meteorological satellite data set generation method, device and terminal equipment, which can improve the cloud block recognition accuracy and help simulate the clear sky low temperature condition after night radiation cooling. The meteorological satellite data set generation method comprises the following steps: calculating the cloud cover of each historical meteorological satellite data set through cloud grid points; the historical meteorological satellite data set is historical infrared data, historical visible light data and historical vertical temperature distribution data in the same observation time period of the same observation region; the cloud cover is evenly divided into a plurality of cloud cover intervals, and cloud cover distribution data is obtained according to the cloud cover intervals; the same number of data samples are extracted from each cloud cover interval to generate a meteorological satellite data set; and the daytime clear sky area brightness temperature in the meteorological satellite data set is adjusted downward to simulate the clear sky low temperature condition after night radiation cooling.

The present application relates to a kind of solar field cloud shelter monitoring and control method, system based on multisensor data fusion.The method includes: the low-density scattered solar radiation sensor network and high-density overall solar radiation sensor network are cooperatively collected basic data in deployment, combined with adaptive filtering technique to filter noise interference, then based on the inverse deduction direct solar radiation intensity of radiation balance equation;While aiming at shelter area, non-shelter area uses differentiated interpolation and extrapolation method;Through convolutional neural network, cloud layer features are extracted, cloud movement trajectory and shelter range are predicted;Introduce geographic information system data, according to the projection length and shape of topographic relief correction cloud shadow;While fusing meteorological satellite data and cloud height data to construct cloud layer three-dimensional model;Based on direct solar radiation intensity distribution map and cloud shelter trajectory prediction result, the working state of heliostat in shelter area is dynamically adjusted, to avoid invalid operation and energy waste.

This application discloses a lightweight real-time geolocation method and system for meteorological satellites. The method includes acquiring onboard remote sensing data and orbital and attitude auxiliary data, unifying the time of the auxiliary data, and constructing a unified line-of-sight vector across payloads; performing a lightweight coordinate system transformation, sequentially transforming the instrument coordinate system to the satellite body coordinate system, orbital coordinate system, J2000 inertial coordinate system, and WGS84 geodetic coordinate system to obtain initial geographic coordinates; performing lightweight DEM iterative elevation correction to obtain corrected geographic coordinates; dividing the image corresponding to the onboard remote sensing data into multiple non-overlapping sub-blocks, and using a CPU / GPU collaborative heterogeneous parallel architecture to perform rigorous imaging model calculations on the corner pixels of each sub-block; based on the corrected geographic coordinates of the corner pixels of the sub-blocks, performing geographic coordinate interpolation on the pixels within the sub-blocks and outputting the geographic positioning result of the image, thereby achieving high-precision, low-latency, and highly stable onboard real-time geolocation of multi-sensor, wide-swath meteorological satellite data.

This invention provides a solar panel assembly and its deployment method, belonging to the field of meteorological satellite technology. The solar panel assembly of this invention includes a solar panel composed of a left panel, a middle panel, and a right panel, a timing mechanism, a clamping and releasing mechanism, and a deployment locking mechanism. The timing mechanism is installed on the deployment shaft of the first deployed solar panel. Through the cooperation of a rotating pin and a semi-circular pin, the left and right panels are deployed step by step in a set sequence. The deployment locking mechanism drives a locking pin to slide along a guide slide and lock into a locking groove via a locking spring, triggering a microswitch to provide a feedback signal. After the clamping and releasing mechanism unlocks, the solar panel is deployed under the drive of the deployment locking mechanism. This invention achieves orderly deployment of the two panels through purely mechanical timing control, avoiding collisions and interference during the deployment process, and improving system reliability and safety.

The application discloses a Gaussian transformation and mixed four-dimensional variation assimilation method for instantaneous precipitation rate of a stationary satellite, constructs a FY-4 stationary meteorological satellite precipitation rate observation operator and a mixed four-dimensional variation assimilation scheme, establishes a Gaussian transformation and error optimization statistical method meeting different precipitation rate data requirements, introduces high-temporal and high-spatial resolution stationary satellite precipitation rate data into a regional model mixed four-dimensional variation assimilation system, and improves analysis and prediction effects of the model on a typhoon. Meanwhile, two selectable Gaussian transformation schemes meeting different requirements and a precipitation rate observation error scheme which changes adaptively with rain intensity are established in the assimilation system, and the problems that the assimilation effect of precipitation physics in the model is weakened due to the strong non-Gaussian distribution characteristics of the precipitation physics are effectively improved.

The application discloses a kind of unmanned aerial vehicle flight control methods for meteorological detection, it is related to unmanned aerial vehicle scheduling technical field.The method includes: obtaining meteorological satellite remote sensing data, cloud boundary is identified by deep learning and macroscopic parameter is reversed, and preliminary detection area is screened out;Based on satellite data, the three-dimensional prior potential diagram of each region is constructed, and each grid point in the diagram is assigned with the prior value representing catalytic potential;With prior potential diagram as guide, the customized detection flight path of unmanned aerial vehicle is planned, and local encryption sampling is triggered according to real-time data in the detection process;According to the detection data, a three-dimensional cloud field dynamic grid model is constructed, and the target area is divided into core area and potential area using dynamic adaptive threshold;Finally, accurate broadcast path is planned for the core area, adaptive coverage broadcast path is planned for the potential area, and unmanned aerial vehicle is controlled to execute operation cooperatively, and the application realizes the whole process closed loop from satellite preliminary exploration, intelligent detection to dynamic zoning broadcast.

This invention discloses a method for intraday dynamic prediction of cyanobacterial blooms based on meteorological and water quality synergy, relating to the field of water environment monitoring. This method, based on satellite data, identifies hourly cyanobacterial bloom area results images using a multi-index consensus method and calculates the actual coverage area of ​​the cyanobacterial bloom. Using joint data of meteorological factors and water quality parameters as input and the actual coverage area of ​​the cyanobacterial bloom as output, a BP neural network model is constructed and optimized. The optimized model is then used to predict the estimated coverage area of ​​the cyanobacterial bloom in the future hourly, and the efficiency coefficient between the estimated and actual coverage areas of the cyanobacterial bloom is calculated. This invention fully utilizes the high temporal resolution advantage of geostationary meteorological satellites to identify cyanobacterial blooms and establish a prediction model, achieving intraday prediction of the cyanobacterial bloom area.

This invention discloses a collaborative estimation method for lightning location and frequency based on attention mechanisms and bias calibration, belonging to the fields of meteorological disaster monitoring and deep learning technology. The method includes: constructing a spatiotemporally aligned multi-source mapping dataset based on multi-channel data from geostationary meteorological satellites and ground-based lightning location network data; constructing a feature-enhanced U-shaped network FE UNet, which integrates a channel feature enhancement module, a dual-attention residual module, and an attention gating module, and is trained using a mask loss function based on Gaussian priors to enhance the spatial perception capability of sparse lightning features; and performing post-processing statistical bias calibration on the lightning frequency map output by the model, recovering the physical magnitude through binning statistics and linear fitting. This invention solves the problems of intensity underestimation and inaccurate spatial positioning in traditional methods for sparse lightning estimation, significantly improving the accuracy and reliability of lightning monitoring, and can be widely applied in the field of wide-area all-weather meteorological disaster early warning.