158 results about "Meteorological satellite" patented technology

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention relates to an emergency management disaster dynamic early-warning scheduling system based on multi-source data fusion, which comprises a multi-source data acquisition module, a physical sensor-containing data acquisition sub-module, a network data acquisition sub-module, a multi-source data integration module, a multi-source data management module and a multi-source data management module, the system comprises a preprocessing sub-module, an annotation sub-module, a fusion processing sub-module, a dynamic early warning generation module and a precise resource scheduling module. Data are collected through a meteorological satellite, a ground monitoring station, an Internet of Things sensor and a network channel, and disaster situation sensing information is formed through cleaning, labeling and fusion processing. Based on a disaster evolution model of a physical and machine learning algorithm, multi-level time period early warning information is generated in real time in combination with historical data. According to the GIS and the emergency resource database, an improved algorithm is used for planning a resource allocation scheme, efficient early warning of disasters and accurate resource scheduling can be achieved, and the emergency management level is improved.

The invention discloses a method and system for assimilating meteorological satellite hyperspectral error non-Gaussian distribution data based on quantum computing, and the method carries out the generalized quality control of meteorological satellite hyperspectral data in consideration of an outlier value on the observation brightness temperature and equivalent data of a satellite hyperspectral infrared detector channel. Comprising the steps of channel optimal selection considering quantum information entropy, deviation correction based on artificial intelligence generalized integration generation type deep learning and cloud detection based on a minimum residual method to remove cloud view field point data, so that quality control data is obtained. And performing quantum space solution on the quantum non-Gaussian increment four-dimensional variational assimilation model compatible with error Gaussian distribution and non-Gaussian distribution based on quality control data to obtain analysis field data, and solving the problem that the brightness temperature assimilation of the hyperspectral infrared channel with error non-Gaussian distribution (or obvious non-Gaussian characteristics) is limited in the prior art. Meanwhile, the method is high in solving speed, and the obtained analysis field data has good precision and calculation timeliness.

The invention discloses a wind power generation power prediction method and system based on deep learning, and the method comprises the steps: obtaining multi-source data related to wind power, carrying out the standardization processing and missing value filling of the multi-source data, and obtaining a wind power multi-source data set; extracting spatial features of meteorological satellite images in the wind power multi-source data set through a CNN (convolutional neural network), extracting features of time sequence meteorological data and wind power data based on an LSTM (long short-term memory) network, and extracting spatial layout features of a wind power plant by using a GCN graph convolutional network to obtain wind power feature data; the importance of different features in the wind power feature data is dynamically weighted through a space-time attention mechanism to obtain fused feature data; and inputting the fused feature data into a model for prediction, and outputting wind power generation power. A comprehensive feature system is constructed, and the wind power prediction precision is remarkably improved.

The invention relates to the technical field of satellite remote sensing monitoring, in particular to a method for monitoring a resident-free island by adopting satellite remote sensing, which comprises the following steps of: acquiring optical images and cloud layer data of a target island through optical and meteorological satellites, predicting a cloud shielding probability, and scheduling a radar satellite when the cloud shielding probability exceeds a threshold value; comparing the effective area of the optical image with a reference demand, triggering radar imaging when a difference value reaches a preset proportion, extracting a coastline feature point set from a historical image, calculating a spatial distribution entropy screening subset, constructing an optimized affine transformation matrix to compensate a radar image, and obtaining a new optical image when the cloud cover is low. Coastline feature point errors of the calibration radar image and the new optical image are calculated, secondary compensation is carried out, global time sequence monitoring data are generated, monitoring continuity and data consistency in a cloud shielding scene are improved, and the method is suitable for resident-free island dynamic monitoring.

The invention relates to the technical field of meteorological satellite observation, and discloses a mode rainfall forecast correction method based on deep learning and a wind cloud satellite, and the method comprises the steps: constructing a rainfall correction main model, collecting multi-source data, and carrying out the primary processing of the collected multi-source data, and obtaining a fusion input data set A; carrying out feature extraction and generating a satellite feature tensor B, generating a mode physical feature tensor C, generating a fusion feature tensor D based on inter-modal correlation, generating a corrected rainfall prediction tensor E, and generating a radar reflectivity prediction tensor F and an uncertainty estimation tensor G; and deploying the trained model into the system, and inputting a fusion data set A at the current time. By introducing the physical consistency loss and combining with the physical constraint to optimize the rainfall prediction model, strict conformity between the rainfall prediction result and the meteorological physical law is realized, the rainfall field better conforming to the physical reality is obtained, unreasonable fluctuations and errors in the prediction result are effectively reduced, and the physical consistency of rainfall prediction is improved.

The invention discloses a thunder approaching prediction method and device based on multi-source meteorological data, and particularly relates to the technical field of thunder disaster prediction.The method comprises the steps that standardization processing of temporal-spatial resolution unification is conducted on meteorological satellite data, radar data and lightning positioning data, and standardized temporal-spatial input data is generated; then spatial-temporal features are extracted through a depth separable 3D convolution module, and a compressed spatial-temporal feature graph is generated; then, a channel-space double attention mechanism (CPCA) is applied to the compressed feature map for feature optimization; and finally, processing the optimized feature map through a coding-decoding structure, and outputting a thunder and lightning probability distribution map. According to the method, the problem of spatial-temporal resolution difference and physical feature mismatching in multi-source data fusion is innovatively solved, the calculation efficiency is remarkably improved through a lightweight network architecture, the feature expression ability is enhanced by using an attention mechanism, and high-precision prediction of sudden thunder and lightning events is realized.

The invention is suitable for the technical field of forest fire disaster early warning, and provides a forest fire disaster risk early warning method considering multi-source satellite information fusion, and the method comprises the steps: obtaining multi-source satellite information data which comprise stationary meteorological satellite data, polar orbit meteorological satellite data, high-resolution satellite data and auxiliary data; performing data preprocessing on the obtained multi-source satellite information data, and then performing multi-scale space-time fusion to obtain a fused target feature vector; performing fire point identification on the target feature vector by using a eucalyptus forest fire point enhancement detection algorithm, and judging whether a risk fire point exists or not; if yes, performing multi-dimensional coupling analysis on the risk fire point; according to the multi-dimensional coupling analysis result, a grading early warning graph is generated, decision data including fire point coordinates, spreading speed and emergency paths are output, an accurate and timely data basis is provided for disaster prevention and reduction, and power grid and economic losses caused by mountain fire disasters are effectively reduced.

The invention provides a farmland soil multi-parameter real-time monitoring system and method based on the Internet of Things. The system comprises a sensor module, a sensor collaborative scheduling module, a data transmission module, a data processing and storage module, an intelligent decision module, a user interaction module and a power supply module. Soil monitoring data, meteorological satellite information and historical yield records are fused, deep learning, a decision tree and a nutrient balance algorithm are combined, an accurate fertilization and irrigation scheme is generated, maintenance decision optimization is achieved, the water and fertilizer utilization efficiency is improved, and resource waste caused by extensive management is reduced; through dynamic sampling, data cross validation, sensor fault self-diagnosis and multi-source data fusion, the system can make a decision, effectively solves the defects of an existing system in the aspects of monitoring precision, data reliability, equipment endurance and decision refinement, and provides an efficient and reliable monitoring and management scheme for precision agriculture.

The invention relates to the field of wind power generation, and discloses a wind power generation control system and method. The data acquisition module is used for acquiring front three-dimensional wind field information detected by the laser radar, near-field wind speed and direction information measured by the ultrasonic sensor, regional wind regime data issued by the meteorological satellite and operation parameters of the wind generating set. According to the invention, through multi-source information fusion, wind regime accurate perception is realized, power generation stability is improved from the source, through multi-source fusion of the laser radar, the ultrasonic sensor and meteorological satellite data, a three-dimensional wind regime perception system is constructed, wind speed and wind direction changes can be predicted in advance, wind regime measurement errors are reduced, and power generation efficiency is improved. The problem of power generation fluctuation caused by wind energy intermittency is solved from the source, and accurate data support is provided for dynamic adjustment of the pitch angle.

The invention discloses a power distribution network satellite meteorological data analysis method, system and device and a medium, and the method comprises the following steps: obtaining and preprocessing multi-source satellite meteorological data, and generating a time-space continuous meteorological element distribution diagram; extracting disaster associated features through the meteorological element distribution map; constructing a first calculation model to train the disaster associated features, and outputting a disaster distribution confidence interval; carrying out risk space mapping on the disaster distribution confidence interval and the topological structure of the power distribution network to generate a tower overturning risk assessment result; and establishing a multi-level early warning mechanism according to a tower overturning risk assessment result, generating a three-dimensional risk map and deploying a mobile terminal application. Through a multi-source satellite data fusion preprocessing technology, the problem of a space-time blind area caused by a traditional single data source is effectively solved, collaborative acquisition and space-time registration of data of a static meteorological satellite, a polar orbit meteorological satellite and a radar are achieved, and the problems of resolution difference and cloud noise interference of different satellite systems are solved.

The invention discloses a meteorological satellite multispectral imaging observation-based subtropical high voltage parameter extraction method, which comprises the following steps of: S1, projecting target data into a static meteorological satellite observation view field to calculate a subtropical high voltage influence area, and performing space-time matching on the subtropical high voltage influence area, historical observation data and sea-land template data; s2, randomly sampling the space-time matching data set, selecting time or space discrete points, and dividing a plurality of sampling areas according to different sky states; s3, sample sets of the multiple sampling areas are divided into a training set and a test set, an XGBoost classification decision tree is adopted to establish an identification inversion model, and Bayesian iterative optimization is adopted to establish an identification optimal model; s4, inputting real-time static observation data, and identifying the subtropical high-voltage influence area by using the model to obtain the influence probability of each point; s5, dividing according to a probability threshold value; s6, performing projection conversion on a division result, and applying Canny edge detection on a specified range to extract a subtropical high-voltage outer boundary; and S7, calculating the area index or the strength index of the subtropical high pressure.

The invention belongs to the technical field of artificial intelligence, and particularly provides an isolated mature convective cloud cluster dynamic identification system and method fusing satellites and radars, and the system comprises a data processing module which is used for carrying out the preprocessing of obtained wind cloud static meteorological satellite data and radar data; the convective cloud cluster sample extraction module is used for identifying convective cloud cluster samples; the isolated convective cloud cluster sample identification module is used for determining an isolated convective cloud cluster sample; the isolated mature convective cloud cluster sample screening module is used for carrying out mature convective and immature convective discrimination on the isolated convective cloud cluster samples and screening out the isolated mature convective cloud cluster samples; the sample dynamic inspection module is used for obtaining a final isolated mature convective cloud cluster sample; the identification model construction module is used for fusing and verifying satellite features and radar features of the final isolated mature convective cloud cluster sample and establishing an intelligent identification model; according to the technical scheme, full-life-cycle tracking of the isolated mature convection system can be realized.

The invention relates to the technical field of irradiance estimation, in particular to an irradiance estimation method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining ground site historical data of a research region, reanalysis data of the research region and an aerosol public data set; dividing weather types into clear sky samples and cloudy sky samples according to the obtained historical ground station data of the research area; inputting the clear sky sample, the reanalysis data and the aerosol public data set into a solar irradiance model to calculate clear sky expected irradiance GHIcs; obtaining observation data of a geostationary orbit meteorological satellite, and estimating a cloud cover coefficient CI by using satellite reflectivity; inputting the multi-cloud sky sample and the cloud cover coefficient CI into a cloud index-based semi-empirical model, and calculating a clear sky coefficient Kc; and multiplying the clear sky expected irradiance GHIcs by the clear sky coefficient Kc to obtain an irradiance estimation value under the all-sky condition. Through the improvement of the method, the regional adaptability and the estimation accuracy of irradiance estimation are improved.

The invention relates to the technical field of atmospheric environment monitoring, and discloses a sand and dust path monitoring system, which is characterized by comprising a data acquisition module, an image processing module, a feature extraction module, a path fitting module, a path fusion module and a geographic mapping module which are sequentially cascaded, the data acquisition module is used for receiving remote sensing data of a stationary meteorological satellite and generating a multi-time sand and dust monitoring binary image; the image processing module performs edge detection and morphological operation on the sand and dust monitoring binary image to realize accurate segmentation of a sand and dust region; the feature extraction module comprises a minimum enclosing rectangle calculation unit and an edge analysis unit and is used for extracting four types of feature points of the sand and dust area, and the four types of feature points comprise the lower right corner, the upper right corner, the right side line and the lower side line. According to the invention, by fusing edge detection, morphological operation and multi-feature endpoint identification, precise segmentation of a complex dust region and stable capture of motion features are realized, and the problem that feature points are easy to drift in a traditional method is effectively solved.

The invention discloses a data transmission method for a meteorological satellite communication system, and relates to the technical field of data transmission. Receiving original meteorological data, performing security level analysis on the original meteorological data, determining a security level, and processing the key change parameter by using the master key based on a preset key derivation function to obtain an encrypted session key; performing encryption processing on the original meteorological data based on the encrypted session key to obtain first ciphertext data; combining the key change parameter with the communication identification information to obtain first associated data; performing message authentication code calculation on the first associated data and the first ciphertext data to obtain a message authentication code; assembling the security level, the first associated data, the first ciphertext data and the message authentication code based on a preset format to obtain a target data frame; and sending the target data frame to a meteorological data center so as to decrypt the target data frame to obtain original meteorological data. By implementing the technical scheme provided by the invention, the security in the transmission process is ensured.

The invention discloses a quantitative rainfall estimation method based on a Hurdle-IMDL framework. The method comprises the following steps: acquiring historical rainfall measurement data and meteorological satellite observation data; constructing a biased quantitative rainfall estimation probability model based on a Hurdle model, correcting a biased rainfall estimation probability sub-model according to an IMDL method, substituting into empirical distribution to construct an empirical quantitative rainfall estimation probability model, and deriving a negative logarithm likelihood function of the empirical quantitative rainfall estimation probability model; constructing an AI model, and optimizing the AI model by taking a negative log-likelihood function as a loss function; and inputting meteorological satellite observation data of a to-be-inverted region into the trained AI model to obtain an estimated value of a parameter of the empirical quantitative precipitation estimation probability model, and estimating the precipitation according to conditional expectation. According to the invention, the Hurdle model is used to solve the zero expansion problem, the IMDL learning method is used to cope with the long tail problem, and the inversion accuracy of extreme rainfall is improved.

The invention provides an on-satellite processing method and system for a low-orbit meteorological satellite, and the method comprises the steps: a data exchange module is responsible for the data management of the system, and achieves the on-demand exchange of external and internal data; the preprocessing module is used for unpacking, checking, positioning and calibrating meteorological load data; the resampling module realizes unification of data of different loads and different channels in spatial dimensions; the product generation module realizes inversion of various application products; the dynamic backup module realizes a redundancy function, can replace the functions of the preprocessing module, the resampling module and the product generation module by uploading different software, and is used under the condition that the corresponding module fails; and the power supply module converts primary power supply of the satellite into secondary power supply and supplies power to each module. According to the invention, on-orbit real-time processing and product generation of low-orbit meteorological satellite data are realized, and the problems of bandwidth progress and insufficient timeliness of current satellite-to-ground transmission are effectively relieved.

The invention discloses a multi-source rainfall measurement product data fusion method, system and device and a medium, and belongs to the technical field of meteorological data processing, and the method comprises the steps: obtaining meteorological satellite rainfall measurement data, weather radar rainfall measurement data and ground rain gauge rainfall measurement data, and constructing a multi-source rainfall data set; performing preprocessing through the multi-source rainfall data set to obtain satellite rainfall data with a unified coordinate system and resolution, radar rainfall data after noise reduction and rainfall gauge rainfall data after interpolation completion; and in combination with the preprocessed multi-source rainfall data, constructing a time-space consistency data set. According to the method, meteorological satellite, radar and rain gauge data are integrated, a multi-source rainfall data set is constructed, a coordinate system and resolution are unified, noise reduction interpolation ensures time-space consistency, a Kriging interpolation method is adopted to fuse data, resolution and accuracy are improved, an evaluation model is subjected to cross validation, and accurate support is provided for weather forecast and disaster prevention and reduction.

The invention discloses an emergency method for extreme weather prediction of a power system. The method comprises the following steps: comprehensively analyzing data from a meteorological satellite, a ground meteorological station and a radar detection system by using a multi-source data fusion algorithm; developing a precise weather prediction model by integrating numerical weather prediction, machine learning optimization and data visualization technologies; carrying out vulnerability analysis on the key components of the power system to establish a risk assessment model, and assessing the operation risk of the power system under the extreme weather event; based on a risk assessment result, a real-time monitoring system and an automatic early warning mechanism are established, and an early warning signal is sent to an electric power system operator in time; strategy measures are formulated and implemented according to different weather situations, so that reliable power supply of the key area is ensured; and according to geographical and climate conditions of different regions, configuration and function optimization are carried out on the power management software. Through accurate meteorological data processing and model prediction, the weather prediction accuracy is improved, and early warning information is sent out in time.

The invention provides an intelligent monitoring method for on-orbit observation quality of a meteorological satellite remote sensing instrument, and relates to the technical field of intelligent monitoring, and the method comprises the steps: carrying out the preprocessing of a multi-dimensional observation data set according to a preset data processing strategy, and obtaining a standard multi-dimensional observation data set; performing defect identification on the standard multi-dimensional observation data set based on a preset defect identification channel, and determining a multi-dimensional defect identification feature which comprises a defect proportion; the defect proportion is judged according to a preset proportion threshold value, and a monitoring early warning signal is generated and comprises an early warning type and an early warning level; and carrying out visual display on the early warning type and the early warning level. The technical problems that potential instrument defects cannot be found and the on-orbit observation quality of the instrument is poor due to the fact that an existing on-orbit observation quality monitoring method for the meteorological satellite remote sensing instrument lacks monitoring on other state parameters and data quality characteristics of the instrument can be solved, the on-orbit observation quality of the instrument can be guaranteed, and the on-orbit observation quality of the instrument is improved. And guarantee is provided for data application.

The invention provides a cloud atmosphere high-fidelity vector radiation simulation method based on physical constraints. The method comprises the following steps: A1, generating cloud layer micro-physical parameters by a WRF model; a2, integrating simulation parameters generated in the step A1, and inputting the simulation parameters into an MYSTIC model for three-dimensional radiation transfer simulation; a3, constructing a PC-GAN (U-Net + + + differentiable radiation layer + double-branch discriminator) physical constraint generative adversarial correction network, and defining adversarial loss, physical loss and KL divergence loss; a4, simulation data are input into PC-GAN, CALIPSO, PLODER3 and other actual measurement data are compared with the simulation data to calculate loss, physical loss is fed back, and cloud micro physical parameters are iteratively optimized until RMSElt is radiated; 5%; a5, backfilling the cloud layer micro-physical parameters corrected in the step A4, carrying out MYSTIC model radiation transfer simulation again, and carrying out satellite / foundation verification; and A6, outputting high-fidelity radiation simulation data. The method can be widely applied to meteorological satellite data verification, climate model development, cloud characteristic inversion and remote sensor design, and has the technical advantages of high precision and high efficiency.

The invention discloses a receiving system for fusion of multiple meteorological satellites. The receiving system comprises a data receiving subsystem, a data processing and fusion subsystem, a data storage and management subsystem, a data presentation subsystem, a communication and interface subsystem and a satellite signal monitoring and calibration subsystem, the data receiving subsystem comprises an antenna unit, a satellite signal tracking module, a satellite signal demodulation module and a satellite signal decoding module. By integrating a plurality of advanced subsystems, efficient receiving, fusion processing, storage management, presentation display and communication sharing of the multi-source meteorological satellite data are realized, and the requirements of simultaneous receiving and efficient processing of the multi-source heterogeneous meteorological satellite data are met; the problems of poor compatibility and limited fusion processing capability in the prior art are effectively solved, more powerful and comprehensive technical support is provided for development of meteorological services, and the method has remarkable innovativeness and practical value.

The invention provides a change-based satellite remote sensing tracking evaluation method for a hail disaster affected area. A change-based hail disaster affected area satellite remote sensing tracking evaluation method comprises the following steps: S1, based on hectometer-level spatial resolution observation data of a FY-4 stationary meteorological satellite, continuously acquiring multispectral images of a target area at a minute-level time resolution, monitoring the initial occurrence position of a severe convective cloud cluster in real time, and determining the initial occurrence position of the severe convective cloud cluster according to the initial occurrence position of the severe convective cloud cluster; the space expansion path and the intensity evolution process are tracked in a time-phase-by-time manner through a continuous tracking technology; s2, aiming at the full life cycle of the severe convection process, synchronously extracting a time-space sequence data set of visible light wave band reflectivity, infrared brightness temperature, an NDVI vegetation index and an NDWI water body index; the hail disaster affected area satellite remote sensing tracking evaluation method based on changes has the advantages of being accurate in recognition, capable of conducting high-frequency tracking on severe convective cloud clusters and high in evaluation result reliability.

The present application relates to the technical field of satellite remote sensing monitoring, in particular, the present application relates to a kind of method for monitoring uninhabited islands by satellite remote sensing, the present application obtains target island optical image and cloud data by optical and meteorological satellite, predicts cloud cover probability, dispatches radar satellite when threshold value, compares the effective area of optical image with benchmark demand, difference reaches preset proportion when triggering radar imaging, extract the coastline feature point set from historical image, calculate spatial distribution entropy to filter subset, build optimized affine transformation matrix to compensate radar image, obtain new optical image when cloud cover is low, calculate the coastline feature point error of calibration radar image and new optical image, carry out secondary compensation, generate global time series monitoring data, improve the monitoring continuity and data consistency under cloud cover scene, applicable to the dynamic monitoring of uninhabited islands.

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.

The application discloses a kind of static meteorological satellite prairie fire high-frequency dynamic remote sensing monitoring method and system, based on static meteorological satellite, wherein static meteorological satellite prairie fire high-frequency dynamic remote sensing monitoring method includes: obtaining static meteorological satellite data, and data preprocessing is generated monitoring area grid data.Based on monitoring area grid data, target pixel in monitoring area grid data is determined.Based on target pixel, fire point pixel is generated.Based on fire point pixel, open fire area is generated.Based on open fire area, output fire monitoring result.By this, the static meteorological satellite prairie fire high-frequency dynamic remote sensing monitoring method of the application can discover and monitor the state of prairie fire with high timeliness and high accuracy rate, and output fire monitoring result.

The invention discloses a precise evaluation method for negative shear of a mountain wind power plant, and the method comprises the steps: high-resolution mountain topographic feature parameterization extraction: extracting microscopic topographic parameters in an evaluation region through a topographic feature recognition algorithm based on digital elevation model data; space-time fusion correction of multi-source observation data: integrating anemometer tower data, unmanned aerial vehicle aerial survey data and meteorological satellite data, and constructing a space-time continuous wind speed data set through a space-time interpolation algorithm; negative shear characteristic simulation based on improved CFD: adopting a double-layer calculation strategy of'macroscopic simulation + local encryption ', and performing airflow motion simulation in combination with terrain parameters and fused wind speed data; quantitative evaluation and visual output of negative shear characteristics: calculating negative shear characteristic parameters based on the three-dimensional wind speed field obtained through simulation, and outputting an evaluation result through a GIS platform; the technical problem that in the prior art, precision is insufficient due to neglecting of terrain complexity, or engineering application is difficult due to too high cost and low efficiency is solved.