99 results about "Meteorological satellite" patented technology

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 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 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 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 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 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 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 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.

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.

The invention relates to the technical field of intelligent control, in particular to a static meteorological satellite dynamic task intelligent control method, which enables a static meteorological satellite ground system to realize automatic execution and quick response of a dynamic task through an intelligent control algorithm and flow when facing the dynamic task. And the requirements in the aspects of emergency response requirements, meteorological service guarantee, satellite safety management and the like are effectively met. According to the intelligent control strategy, the intelligent level and the operation efficiency of the system are improved, and more accurate and efficient services are provided for meteorological observation and satellite operation; meanwhile, diversified meteorological service requirements can be better met, the efficiency and reliability of meteorological observation and disaster early warning are improved, and the risk of manual operation is reduced.

The invention provides a two-dimensional tracking control method and system for a sun observation load of a stationary meteorological satellite. The two-dimensional tracking control method comprises the steps that a satellite service management unit receives the attitude and orbit data of a current information control computer, the measurement data of a sensor and the rotation angle data of a driving mechanism; the east-west solar angle and the south-north solar angle are calculated, and a driving mechanism is driven to complete initial sun capture in the east-west direction and the south-north direction of the optical axis of the EUV imager; when initial sun capture is completed, the sun enters the digital too sensitive field of view; and when the sun enters the field of view of the guide mirror, the data of the guide mirror in the measured data is used as input, and the south-north dimension driving mechanism and the solar array driving mechanism are fed back and controlled in real time, so that two-dimensional coarse alignment and tracking to the sun are realized. The problem of east-west, south-north two-dimensional tracking of the sun observation load is solved, a two-dimensional tracking control method and a control strategy are provided, the sun observation load can accurately point to the sun in real time, and the method can be applied to the research and development process of a stationary meteorological satellite.

The invention belongs to the technical field of meteorological satellite data assimilation, and discloses an overlarge deviation observation assimilation method, system and device for all-sky infrared satellite data and a medium, so as to overcome the defect that a traditional method is exposed when processing overlarge deviation caused by a cloud physical process. The method comprises the following steps: in a quality control link of data assimilation, reserving overlarge deviation observation in all-sky infrared satellite data; an assimilation strategy is constructed to assimilate the reserved overlarge deviation observation, and the assimilation strategy comprises the steps that deviation and variable dependent observation error expansion is carried out on the reserved overlarge deviation observation; for the reserved overlarge deviation observation, horizontal localization scale attenuation dependent on deviation is introduced; setting the set expansion coefficient as a preset value; and on the basis of an assimilation strategy, assimilation analysis is carried out on the reserved overlarge deviation observation. According to the method, the infrared satellite data with assimilation value under the all-sky condition are effectively reserved and utilized, and the coverage rate and the information integrity of the observation data are improved.

This invention relates to a planar superlens for spaceborne terahertz detectors. The planar superlens comprises an independent aluminum foil, on which an array of resonant units arranged in concentric rings is disposed. The resonant unit array includes various C-shaped resonant units with different structural parameters, each C-shaped resonant unit being a C-shaped hole structure etched into the aluminum foil. C-shaped resonant units with the same structural parameters are arranged at equal intervals on a ring band with the same arrangement radius, forming, from the inside out, a paraxial fine control module, a mid-ring band phase compensation module, a mid-to-far ring band phase modulation module, and an edge region phase coverage module. Compared with existing technologies, the superlens proposed in this invention has outstanding features such as large depth of focal length, tight focusing, lightweight, low cost, and resistance to space environments. It is particularly suitable for integration into terahertz detection payloads of meteorological satellites, which are extremely sensitive to weight and volume, and can be used for remote sensing missions such as ice cloud detection and atmospheric composition spectral line observation.

The invention provides a multi-source multi-attribute photovoltaic power prediction time series data set construction method. The method comprises the following steps: acquiring urban meteorological data, wind cloud static meteorological satellite secondary cloud picture data and photovoltaic power station micro-meteorological data; traversing a wind cloud satellite secondary cloud picture with high photovoltaic power generation association degree, and realizing space registration by unifying longitude and latitude; extracting each attribute parameter of a pixel subjected to spatial registration at each corresponding time point in each secondary cloud picture file, and exporting corresponding wind cloud satellite secondary cloud picture time sequence data; carrying out abnormal value detection and processing on the multi-dimensional meteorological attribute time series data; performing time registration operation on the meteorological time sequence data to realize alignment and synchronization of multi-dimensional heterogeneous data; meteorological characteristic attribute parameters having great influence on photovoltaic power generation are screened, and photovoltaic power prediction multi-dimensional characteristic vectors under each timestamp are formed; and constructing a time sequence meteorological data set according to the target multi-dimensional feature vector, and completing normalization processing, thereby constructing a multi-source multi-attribute photovoltaic power prediction time sequence data set.

The invention discloses a data fusion processing method based on a meteorological satellite and electronic equipment. The method comprises the following steps: receiving load data for an observation target obtained by MERSI scanning on the meteorological satellite; calculating to obtain a visual vector of the load in the system; converting the visual vector under the load body system into the visual vector under the satellite body system; converting a visual vector under a satellite orbit system into a visual vector under a geocentric rotation system; calculating an intersection point of the visual vector under the geocentric rotation system and a preset earth ellipsoid model; the coordinates of the intersection points are converted into latitude and longitude information to serve as latitude and longitude information of an observation target; based on the brightness value of the MERSI in the load data, calculating to obtain the radiance value of the observation target; and calculating the latitude and longitude information and the radiance value of the observation target to obtain the information of the observation target corresponding to the target service. According to the method and the system, the meteorological satellite data can be quickly and accurately processed, and the processed data can be effectively applied to specific businesses.

The application provides a potential fire point identification method and device based on a stationary meteorological satellite, relates to the technical field of fire point identification, and comprises the following steps: acquiring stationary meteorological satellite remote sensing image data of a region to be identified within a preset time period, and determining attribute data of each pixel in the stationary meteorological satellite remote sensing image data; determining a target pixel in the stationary meteorological satellite remote sensing image data based on the attribute data, wherein the target pixel is a pixel other than a cloud pixel, a water body pixel and an absolute fire point pixel; calculating a background brightness temperature threshold value and a background brightness temperature difference threshold value of the target pixel based on a dynamic window algorithm and the attribute data; and determining a final pixel in the target pixel based on a preset condition, the background brightness temperature threshold value and the background brightness temperature difference threshold value of the target pixel, determining the final pixel and the absolute fire point pixel as potential fire points, and solving the technical problem of low identification accuracy of the existing potential fire point identification method.

The invention discloses a polar orbit satellite cloud phase state product quality inspection method, device and equipment and a medium. The method comprises the steps that to-be-inspected satellite data and inspection source satellite data are obtained, the to-be-inspected satellite data are polar orbit meteorological satellite FY-3E, FY-3D and FY-3F satellite data, and the inspection source satellite data comprise MODIS data and CALIPSO data of other similar meteorological satellites. And respectively processing the to-be-inspected satellite data and the inspection source satellite data to obtain target to-be-inspected data and target inspection source data. And determining a quality inspection index corresponding to the to-be-inspected satellite data according to the target to-be-inspected data and the target inspection source data, and determining a quality inspection result corresponding to the to-be-inspected satellite data according to the quality inspection index. According to the technical scheme of the embodiment of the invention, the business real-time inspection system of the satellite cloud phase product is constructed, the inspection efficiency and the inspection accuracy of the data quality are improved, and a correction basis is provided for error adjustment of the satellite cloud phase product.

The application discloses a method and system for assimilating meteorological satellite hyperspectral error non-Gaussian distribution data based on quantum computing, which performs generalized quality control of meteorological satellite hyperspectral data considering outliers on the observed brightness temperature of a satellite hyperspectral infrared detector channel and equivalent data thereof, including optimal selection of the channel considering quantum information entropy, deviation correction based on artificial intelligence generalized integrated generative deep learning, and cloud detection and removal of cloud view point data based on the minimum residual method, thereby obtaining quality control data. Quantum non-Gaussian increment four-dimensional variation assimilation models compatible with error Gaussian distribution and non-Gaussian distribution are solved based on the quality control data, and analysis field data are obtained. The method solves the problem that the existing technology is limited in the assimilation of hyperspectral infrared channel brightness temperature with error non-Gaussian distribution (or non-Gaussian characteristics are obvious), and meanwhile, the solving speed of the method is fast, and the obtained analysis field data have good precision and calculation timeliness.

The invention discloses a land surface vegetation parameter product production method and system based on a vegetation index reference library, a terminal and a medium, and the method comprises the steps: constructing the vegetation index reference library through ground observation data and a remote sensing product; preprocessing the obtained wind cloud meteorological satellite data, and establishing a vegetation parameter inversion training data set in combination with a vegetation index reference library; inverting the training data set based on the vegetation parameters, performing model training by using a random forest machine learning algorithm, determining an optimal inversion model, and determining a direct inversion result corresponding to the wind cloud satellite remote sensing orbit division data based on the optimal inversion model; and performing space-time filtering processing on the direct inversion result to obtain a land surface vegetation parameter product. According to the method, the defect that a land surface vegetation parameter product depends on a high-precision atmospheric correction result is overcome, the existing high-quality remote sensing inversion and ground observation data accumulation are fully utilized, and the complexity of a data product algorithm model is effectively reduced.

The invention discloses a canopy combustible moisture content inversion method cooperating with multi-source new generation geostationary meteorological satellite data. According to the method, high-timeliness remote sensing data provided by a plurality of stationary satellites are utilized, space and time characteristics of remote sensing factors such as vegetation indexes and surface temperature are combined, and the inversion precision of the combustible moisture content is improved by utilizing a radiation transmission model and a numerical optimization algorithm. The method comprises the following steps: firstly, acquiring reflectivity data of a multi-source stationary satellite and preprocessing the reflectivity data; then, different standard satellites are selected based on different areas and different vegetation types, and different BRDF semi-empirical models are adopted for data angle unification; then, a vegetation radiation transmission model is constructed based on the vegetation index and the surface temperature, and sensitive parameters are analyzed through an SCOPE model; finally, through a strict initial value setting strategy, a GRG optimization algorithm is used for jointly optimizing sensitive parameters of the model, and accurate inversion is carried out to obtain the water content of the combustible matter. The method has important application value in wildfire risk monitoring and early warning.

The invention discloses an unmanned aerial vehicle flight control method for meteorological detection, and relates to the technical field of unmanned aerial vehicle scheduling. The method comprises the following steps: acquiring meteorological satellite remote sensing data, identifying a cloud cluster boundary through deep learning and inverting macroscopic parameters, and screening out a preliminary detection area; constructing a three-dimensional prior potential map of each region based on satellite data, wherein each grid point in the map is endowed with a prior value representing the catalytic potential; planning a customized detection flight path of the unmanned aerial vehicle by taking the prior potential map as guidance, and triggering local encryption sampling according to real-time data in a detection process; constructing a three-dimensional cloud field dynamic grid model according to the detection data, and dividing a target area into a core area and a potential area by adopting a dynamic adaptive threshold value; and finally, a precise sowing path is planned for the core area, a self-adaptive coverage sowing path is planned for the potential area, and the unmanned aerial vehicle is controlled to cooperatively execute operation, so that a whole-process closed loop from satellite preliminary exploration, intelligent detection to dynamic partition sowing is realized.