189 results about "Meteorological satellite" patented technology

A 100-meter gridded spatialization method for carbon emissions of different land use types based on multi-source heterogeneous data is provided, including the following steps: S1, fitting Luojia-1A Satellite nighttime light data year by year with defense meteorological satellite program-operational linescan system (DMSP-OLS) and national polar-orbiting partnership-visible infrared imaging radiometer (NPP-VIIRS) fused 1-kilometer gridded nighttime light data, performing additive fusion on previous light data and current light data, introducing a time inertia weight factor to improve particle swarm optimization-back propagation (BP) neural network algorithm, and forming 100-meter nighttime light data correctable on a long time series; and S2, simulating a complex nonlinear dynamic changing relationship between multilevel 100-meter gridded data of different land use types in different industries and energy carbon emissions in different industries based on the 100-meter nighttime light data, and establishing a 100-meter spatialization inversion model for energy carbon emissions of different land use types in different industries.

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 discloses a satellite remote sensing image cloud classification method based on space-time coding guidance, and particularly relates to the technical field of satellite image processing and deep learning, and the method comprises the following steps: carrying out the preprocessing of the original data of a geostationary meteorological satellite and a Cloud Sat satellite; preprocessing operation such as cutting, projection conversion and feature selection is carried out on the geostationary meteorological satellite data, a top cloud type is selected for the Cloud Sat data to serve as a label, and space-time matching is carried out on the processed geostationary meteorological satellite data and the Cloud Sat data to obtain a training and testing data set; and establishing a GTCCT model, performing time and geocoding on the input data, inputting the coded feature data into a network for training, and finally obtaining the GTCCT model for cloud classification.

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 belongs to the field of remote sensor calibration, and provides an ROLO model improvement method combining foundation and meteorological satellite lunar observation data, which comprises the following steps of: acquiring a meteorological satellite lunar observation image data set and a foundation lunar observation image data set, and respectively correcting the meteorological satellite lunar observation image data set and the foundation lunar observation image data set; obtaining standard meteorological satellite observation moon radiance and foundation observation moon radiance, and calculating observation moon irradiance; calculating geometric parameters of observation points, the earth, the moon and the sun at the observation moments of the foundation and the meteorological satellite, and calculating the moon irradiance of the ROLO model; polynomial fitting is adopted for the relative deviation between the observed moon irradiance of different wave bands and different moon phase angles and the moon irradiance of the ROLO model, and the ROLO model is improved. The method is not affected by the atmosphere, and errors caused by atmospheric correction of ROLO model foundation observation data are directly avoided; and the phase angle dependence of the ROLO model can be corrected, and the prediction precision of the ROLO model can be improved.

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 an ADS-B (Automatic Dependent Surveillance-Broadcast)-based civil aviation flight pre-arrival data calculation method, which integrates ADS-B data, meteorological satellite data, airport runway state monitoring data and air traffic control real-time instruction data through multi-source data fusion, and dynamically adjusts the weight of each data source according to the flight stage of a flight. A dynamic adjustment time synchronization interval mechanism is adopted, a short synchronization interval is set in the take-off stage and the landing stage so as to capture rapidly-changed data, and the synchronization interval is prolonged in the cruise stage so as to optimize computing resources. Meanwhile, the deep learning model based on the Transform architecture utilizes a multi-head self-attention mechanism and position coding to enhance the feature capture capability, and reduces the calculation burden through a sparse activation strategy. The system is provided with a real-time monitoring layer, log records and visual analysis tools are integrated, and interpretability and traceability of the model are ensured. An efficient aviation management decision is supported, and the efficiency and safety of overall aviation traffic management are 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 belongs to the technical field of computer vision, and particularly relates to a time sequence sea fog detection method based on stationary meteorological satellite multichannel brightness temperature difference driving, which comprises the following steps: acquiring meteorological satellite remote sensing data, and marking sea fog; preprocessing meteorological satellite remote sensing data according to the data source; extracting motion features by using the preprocessed meteorological satellite remote sensing data, and constructing a sea fog motion feature data set; inputting the model training set into a sea fog segmentation neural network model based on time sequence characteristics for training to obtain a training model; and preprocessing data of the test set, inputting the data into the training model for testing, and outputting a prediction result after segmentation, namely a final sea fog detection result. According to the method, the difficulty of motion feature extraction is reduced, the accuracy of sea fog monitoring tasks is improved, the consistency of continuous moment segmentation results is enhanced by comparing learning loss, and better generalization and performance are achieved.

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 relates to the technical field of travel trend prediction, and discloses a South Pole travel trend prediction system and method based on ship position big data, and the system comprises a ship position data collection module, an environment data collection module and the like. Ship navigation data are collected through a satellite AIS system, multi-source data are obtained in combination with a meteorological satellite, an ocean buoy, a ship registration system and the like, the data are fused through a dynamic space-time grid coding technology, and space-time features are extracted to construct a dynamic feature matrix. And capturing a space-time coupling relationship by adopting a space-time diagram neural network, optimizing a parameter weight according to a historical error, and analyzing an abnormal event to generate an influence factor. And a prediction result output module superposes trend prediction and event influence factors and outputs a travel hotspot area probability distribution diagram. And the visual interaction module renders the result into a dynamic thermodynamic diagram and superposes the dynamic thermodynamic diagram to an electronic chart, and supports multi-layer display and time axis interaction. The Antarctic tourism trend can be accurately predicted, and powerful support is provided for tourism decision making.

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 discloses a diffusion model iteration correction precipitation prediction method based on space-time conditions, which improves the precision and timeliness of precipitation prediction, and is especially suitable for prediction of extreme weather events. The method comprises the following steps: firstly, receiving multi-modal input data including a radar echo image, a time identifier, earth surface remote sensing data and multichannel data of a meteorological satellite, and extracting characteristics of various input data by utilizing a plurality of encoders; in order to solve the problem that radar image rainfall distribution does not conform to a probability model, after an encoder is adopted to extract features, M frames of radar images are classified into different categories through a k-means clustering algorithm, so that corresponding prediction models are selected according to the categories, and multi-task prediction is carried out. A diffusion model predicts precipitation in a hidden space in a step-by-step generation mode, and iterative correction is introduced in each time period of a prediction result so as to reduce error accumulation. And finally, outputting a high-precision long-time sequence rainfall prediction result by the model. According to the method, multi-modal information, time conditions and an iterative correction structure are combined, the rainfall prediction capacity under complex terrains and extreme weather is remarkably improved, and the method has high practical value and application prospects.

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 belongs to the technical field of data processing, and particularly relates to a digital emergency plan generation method based on multi-source heterogeneous data fusion. The method comprises the following steps: firstly collecting Internet of Things sensor data, historical disaster text data and meteorological satellite remote sensing data, processing various types of data by adopting different fusion rules and generating log data, and performing secondary fusion through a fourth fusion rule to form final fusion data. Threshold logic division is carried out by calculating the correlation degree of the fused data and the initial data, and the data quality is guaranteed. And finally, constructing a digital plan model by using an improved hierarchical reinforcement learning framework, and inputting final fusion data to generate an emergency plan. Compared with a traditional method, multi-source data are fused, the data quality and reliability are improved, the generated emergency plan is more scientific and accurate, the emergency response capability of the power grid is effectively improved, fault loss is reduced, and stable operation of the power grid is guaranteed.

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 discloses a channel dynamic cloud detection method and system based on a meteorological satellite hyperspectral infrared detector, and the method firstly proposes nonlinear channel optimal selection based on Gaussian mixture model probability approximation quantization relative entropy, and can reduce the error distribution of temperature and humidity. Secondly, based on layer-by-layer cloud simulation and clear sky brightness temperature simulation, a cloud profile inversion method of a weighted L1 norm constraint inverse problem of Gaussian disturbance + numerical approximation signal degree-of-freedom optimization channel weight is provided, cloud amount information of multiple layers from bottom to top is obtained, and the actual condition of actual cloud vertical distribution is met. And finally, a cloud detection result is obtained by customizing each channel of each field point by adopting an adaptive dynamic cloud detection method, which is beneficial for reasonably using the brightness temperature data of the hyperspectral infrared channel and realizing the application of partial cloud region data, so that the data quantity of the brightness temperature of the variational assimilation channel is increased, and the precision of numerical weather forecast is further improved.

The invention provides a large mountain photovoltaic environment fire danger identification method based on a meteorological satellite, and the method comprises the steps: carrying out the radiometric calibration processing based on the Himawari-8 data of a detection region, obtaining the spectral reflectivity or spectral radiation brightness of each wave band of the detection region, and obtaining a cloud shielding pixel through a cloud detection method; a normalized brightness temperature time sequence change function is established based on the time sequence change of the background brightness temperature, a brightness temperature estimation value is estimated, the maximum assumed threshold value of each stage is determined according to the brightness temperature time sequence change function, and then a time sequence method is used for distinguishing fire point pixels. And finally, carrying out credibility grading on the extracted fire point pixels to obtain a fire point grading graph, and sending out corresponding early warning according to fire point grades.

The invention discloses a winter rainfall phase state diagnosis method and system based on a geostationary satellite temperature and humidity profile, and the method comprises the steps: obtaining the atmosphere temperature and humidity profile data of a geostationary meteorological satellite, carrying out the preprocessing, and generating a basic data set; obtaining winter rainfall phase state data of the ground station, and performing classification preprocessing to generate a winter rainfall phase state data set; performing matching and partitioning processing on the basis of the basic data set and the winter rainfall phase state data to generate a matched and partitioned data set; constructing a diagnosis model based on a preset key meteorological characteristic quantity and the data set after matching and partitioning; and acquiring real-time atmosphere temperature and humidity profile data of the stationary meteorological satellite, inputting the real-time atmosphere temperature and humidity profile data into the diagnosis model, performing precipitation phase state diagnosis, and generating a precipitation phase state diagnosis result. Therefore, according to the winter rainfall phase state diagnosis method based on the stationary satellite temperature and humidity profile, the timeliness and accuracy of winter rainfall phase state monitoring are improved.

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.