521 results about "Sky" patented technology

The invention provides a small and medium-sized reservoir operation safety evaluation system based on sky-ground work, and belongs to the technical field of hydrological monitoring. The device comprises a space-time alignment layer, a feature weighting layer and a dynamic coupling layer which are connected in sequence, the space-time alignment layer is used for performing time alignment and space alignment on the input data of the sky-land water conservancy project; the feature weighting layer is used for quantifying contribution degrees of different monitoring features to safety assessment according to the data after time-space alignment; and the dynamic coupling layer adopts a dynamic weight distribution strategy for different modules through a multi-module coordination engine, fuses feature weighting to obtain a fusion result, and outputs a small and medium-sized reservoir operation safety index according to the fusion result. The evaluation result is more accurate, and the timeliness is better.

The invention discloses a land-sea-air-space holographic sensing and collaborative decision-making system based on multi-mode edge intelligence. The system comprises a sensing layer which is used for carrying out three-dimensional monitoring on a ship driving process, the sea, the sky, a shore base and a space formed by the ship driving process, the sea, the sky, the shore base and the sea, the sky and the shore base; the network layer is used for constructing a global communication basis, preprocessing monitoring data, judging whether the ship has an abnormal condition or not, and realizing data transmission of the ocean, the sky, a shore base and a space formed by the ocean, the sky and the shore base; and the application layer is used for judging whether an abnormal condition exists in the ship driving process or not based on the data of the ocean, the sky, the shore base and the space formed by the ocean, the sky and the shore base transmitted by the network layer, performing collision early warning decision based on the abnormal condition, and performing dynamic channel capacity prediction. And navigation guidance and route tracking in extreme weather are carried out based on environmental risk early warning, underwater obstacle collision is avoided, and early warning information and decision information are fed back to a sensing layer.

The invention discloses a photovoltaic ultra-short-term power prediction method and system based on multi-modal information fusion, and belongs to the technical field of photovoltaic power generation prediction. The method comprises the steps: obtaining an all-sky image sequence, segmenting a cloud layer and a sky region, and extracting cloud layer boundary features; thickness features are analyzed by calculating cloud pixel point brightness indexes, overall motion features of a cloud layer are determined based on adjacent frame displacement vectors, a time sequence sub-image covering a sun area in the future is reversely captured in combination with sun position coordinates, and then time sequence features and image features are extracted by adopting a dual-channel fusion mechanism. And finally, introducing a weather-dependent decoder: identifying weather types through a lightweight classifier, dynamically weighting the fused features based on the types, and outputting a photovoltaic power prediction result. According to the invention, through a multi-scale feature cooperation and dynamic weighting mechanism, the prediction robustness under a complex meteorological condition is significantly improved.

The invention discloses a multi-modal photovoltaic power prediction method based on double-layer Transform, and the method comprises the steps: firstly carrying out the preprocessing of a sky image and historical meteorological data, and carrying out the similar day selection based on dynamic time warping and k-means clustering, so as to construct a historical data set similar to the current meteorological condition; inputting the sky image into a double-layer Transform structure, extracting spatial features and time features in a layered manner, and extracting meteorological features from historical meteorological data by using expansion convolution; and performing interactive fusion on the multi-source features through a cross-modal attention mechanism, and enhancing a global dependency relationship in combination with self-attention to obtain a multi-modal prediction result. And on the other hand, a photovoltaic power sequence is separated from historical meteorological data, a single-factor prediction result is generated by using a long short-term memory (LSTM) network, dynamic weighted fusion is performed on the two types of prediction results through adaptive gating, and a final photovoltaic power prediction value is output.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

The invention discloses a global enhancement method for a wide-area astronomical image. The method comprises the following steps: carrying out high-frame-frequency image acquisition on a starry sky area; converting the reference star from a star catalogue position to an observation position, and resolving a linear negative film model; geometric distortion correction is carried out based on the resolving result; based on the image after geometric distortion correction, matching and identifying a reference star in a full view field, and obtaining celestial coordinate information of all fixed star images in the view field; on the basis of celestial coordinate information, correcting a poorer astronomical effect of the astronomical image, establishing an ideal coordinate system taking the center of a view field as a tangency point, and projecting a time sequence observation image to the ideal coordinate system; translating the processed graph, accumulating all translated images, and averaging all accumulated pixel positions to realize image superposition enhancement, thereby effectively improving the dynamic range of a detector and the detection capability of a wide-area astronomical observation system; and meanwhile, the signal-to-noise ratio of the astronomical image can be effectively improved, and the astronomical image centering and light measuring precision can be further improved.

The invention relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The invention discloses a data assimilation method for infrared hyperspectral data of a clear sky channel. The data assimilation method comprises the following steps: acquiring the infrared hyperspectral data of the clear sky channel; a linear or non-linear relationship to convert variables from a mode space to an observation space using a fast radiation transfer mode; satellite infrared hyperspectral atmospheric vertical detection data channel screening is carried out in combination with a principal component method; a channel which is not influenced by cloud pollution is searched by observing the difference between the brightness temperature observed by the infrared and the brightness temperature simulated by the background field; eliminating the statistical deviation between the radiation value observed by the satellite instrument and the radiation value simulated and calculated according to the background field profile; eliminating observation data with errors exceeding a preset threshold value on the mixed earth surface type; and outputting a data assimilation result of the infrared hyperspectral data of the clear sky channel. Analysis fields of all layers of the global atmosphere are improved, the numerical forecasting level is effectively improved, typhoon forecasting intensity errors are remarkably improved, and path errors before landing are superior to conventional observation data tests.

The invention discloses a large-range high-frequency all-sky earth surface solar irradiance machine learning estimation method and system suitable for a cloudy area, and relates to the technical field of irradiance estimation, and the method comprises the steps: obtaining earth surface radiation observation data, generating a station position table, and constructing a training data set; constructing a ground solar radiation degree model, and dividing the training data set into a training set and a test set according to a proportion of 80% to 20%; determining hyper-parameters of the Xgboost algorithm through parameter search, training the model on the training set, and checking the model by using the test set; new satellite reflectivity is used for observing data and meteorological elements, data cleaning is carried out, and the model is input to estimate the space-time distribution of the total solar radiation irradiance of the earth surface. According to the method, the space and time precision of radiation inversion is improved, the complex nonlinear characteristics and data interaction relation are accurately captured, efficient inversion of the earth surface solar radiation degree is achieved, and the prediction performance and generalization ability of the model are remarkably improved.

The invention discloses a sky-ground integrated sunlight-induced vegetation chlorophyll fluorescence monitoring system, and belongs to the technical field of remote sensing monitoring. The system provides functions, parameter constraints and cooperation relations of space-based, empty-based and foundation chlorophyll fluorescence information acquisition and data analysis processing and application modules. Spatial scale matching, spectral scale matching, observation angle matching and atmospheric element matching are performed on the acquired sky-ground multi-element data with different scales, so that reflection characteristics, heat dissipation, photochemical indexes, fluorescence emission and atmospheric transmission characteristics during vegetation photosynthesis can be completely acquired; the invention provides a parameter relation and a method for extracting a fluorescence full-spectrum radiation information curve based on oxygen absorption and Fraunhofer dark line. On the basis of sky and landmark calibration and model verification, chlorophyll fluorescence detection of different fineness degrees of global large-range, key areas and ground crown planting scales can be carried out, various stress effects of vegetation can be monitored in real time, and the primary total productivity of the vegetation can be accurately estimated.

The present invention discloses an apparatus for quickly finding a planet by using a mobile phone, including: a digital telescope, an electronic compass, a pan / tilt configured to install the digital telescope, a mobile phone, a PC terminal, and a data converter. The digital telescope includes a telescope housing. A main camera configured to photograph target starry sky and a planet-finding camera configured to search for and photograph a target planet are disposed at one end that is of the telescope housing and that faces starry sky. The present invention is used more easily when a user's professional level is reduced.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

The invention belongs to the technical field of sea fog monitoring, and particularly relates to a sea fog monitoring method and system based on multi-source satellite remote sensing data, and the method comprises the steps: collecting satellite radiation and microwave observation data, discriminating the cloud top phase state in combination with infrared ground microwave channel response differences, dividing clear sky and low cloud shielding scenes, and outputting identification data. And sea fog is detected in combination with the single-time infrared brightness temperature difference and the empty pixel parameters, optical parameters are inverted, and a sub-scene sea fog physical parameter set is formed. Carrying out normalized coupling reconstruction on the parameter set, constructing a sea fog exclusive feature space, separating a fog region from a non-fog region through phase state constraint to obtain an initial judgment feature field, correcting the edge of the fog region through neighborhood correlation and brightness temperature gradient constraint, strengthening spatial continuity to generate a spatial distribution field, locking a credible region, and outputting a final sea fog detection result. According to the invention, through layer-by-layer progressive design of scene division, feature optimization and multi-source verification, the precision and integrity of sea fog identification are significantly improved.

The invention discloses an atmosphere and ocean integrated atmosphere temperature and humidity profile inversion method based on multiple sources and multiple frequency bands. The method comprises the following steps: S1, acquiring multi-source collaborative observation data, and preprocessing the acquired data; s2, constructing an integrated state vector and coupling forward model, and obtaining an integrated state vector X; s3, performing optimal estimation based on the preprocessed data and the integrated state vector X, and performing joint inversion to realize integrated inversion solution; and S4, outputting an inversion result and theoretical uncertainty output. According to the method, integrated cooperative inversion is carried out through a physical model and an optimal estimation theory, and the inversion precision of the atmosphere temperature and humidity profile in the clear sky and in the presence of clouds, especially in the air above the ocean, is remarkably improved.

The invention discloses a method and a system for measuring gravitational potential by a satellite-air-ground optical clock time-frequency comparison system. According to the method for measuring the gravitational potential, an optical clock superior to 110-18 orders of magnitude serves as the basis, a free space time-frequency comparison link based on an optical frequency comb serves as a bridge, the optical clocks among the satellite, the air and the ground are connected, high-precision time-frequency transmission is carried out, a gravity frequency shift signal is accurately extracted through time-frequency comparison of a remote clock, and the gravitational potential is measured. And then the gravity potential difference of the position where the remote clock is located is obtained by using a gravity frequency shift equation, and the gravity potential difference between air and ground or between any ground stations is determined.

The invention relates to a method for verifying a Mars sky radiance simulation result based on a Mars real-shot photo, and belongs to the technical field of Mars sky background radiance. Comprises: image acquisition; performing radiation calibration; correcting dark current; performing flat field correction; converting a radiation value; and geometric correction. The Mars sky radiance simulation method breaks through the dependence of an existing model on idealized hypothesis, performs dynamic verification by using actual shot image data, remarkably improves the authenticity and reliability of a Mars sky radiance simulation result, and effectively solves the problem of systematic deviation under a specific wave band; by constructing a complete process from image acquisition, radiation calibration, geometric modeling to brightness inversion and comparison verification, dynamic feedback and closed-loop correction of a theoretical radiation model are realized, model adaptability is improved, and fine modeling under multi-temporal and multi-meteorological conditions is supported; the method can be applied to an existing Mars rover navigation camera and an image processing link of the Mars rover navigation camera, does not need additional modification, has good engineering practicability and task compatibility, and is convenient to popularize and apply.

The invention discloses a background spectrum accurate acquisition method for infrared remote measurement in a complex scene, and the method comprises the steps: carrying out the division of sky and ground object backgrounds for an input background image, and selecting different radiation spectrum generation modes based on a division result; if yes, sky radiation spectrum extraction is carried out based on an MODTRAN radiation transfer model; if the background is judged to be a single ground object background, performing background spectrum extraction by adopting ELM-EMD (Extreme Learning Mode-Empirical Mode Decomposition) which is extracted in real time based on a measurement spectrum and changes slowly at low frequency; and if the background is not single, fusion of the two spectrum extraction modes is adopted. The method has the advantages that the background classification method with the visible image as the reference is adopted, the difference of infrared radiation characteristics of the sky background and the ground feature background is analyzed, different spectrum extraction modes are selected according to the difference, and therefore accurate obtaining of the target smoke plume transmittance spectrum is achieved.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

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.

We present a ground-based laser system for searching, ranging, and tracking that actively illuminates small resident space objects (RSOs) in low Earth orbit (LEO) and observes position and transit-time of reflections, enabling orbital custody of most one-to-few centimeter-sized objects. A diode-pumped alkali laser (DPAL) augmented with mode-locking and Q-switching serves as an illuminator and offers exceptionally high spectral brightness due to its output beam's sub-nanosecond pulse duration and picometer-level spectral linewidth. Kilohertz fast-rastering enables interrogations of distinct few-meter-sized sky-patches at megahertz rates. Paired on the observation side with one or more telescopes outfitted with Faraday anomalous dispersion optical filters (FADOFs), image intensifiers, multi-pixel arrays, and nanosecond timing, the combined system could offer nearly background-free discoveries of RSOs every hour, even during daytime. Periodically re-flashing previously discovered objects to maintain orbit custody expends only a small fraction of total observing time.

The invention discloses a method and device for predicting a non-uniform temperature field in an urban three-dimensional street canyon, and the method comprises the steps: constructing an urban three-dimensional street canyon model, and dividing the urban three-dimensional street canyon model into a plurality of surface regions; calculating sky view parameters of each surface area; generating a shadow distribution field of each surface area; according to the sky view parameter and the shadow distribution field of each surface area, obtaining a solar short-wave radiation flux and a ground long-wave radiation flux of each surface area; calculating sensible heat flux and latent heat flux of each surface area; generating a non-uniform temperature field according to the sensible heat flux, the latent heat flux, the solar short-wave radiation flux and the ground long-wave radiation flux of each surface area; dividing an illumination area and a shadow area based on the shadow distribution field of each surface area; and calculating the average temperature of the illumination area and the shadow area based on the non-uniform temperature field. According to the invention, real-time and accurate prediction of the non-uniform temperature field in the urban three-dimensional street and valley is realized, and consumption of computing resources is reduced.

The invention discloses a meadow steppe satellite remote sensing intelligent monitoring system based on sky-ground integration, and relates to the technical field of remote sensing monitoring, and the system comprises an environment perception judgment module which is used for obtaining meteorological data and grassland health state data in real time, analyzing the influence of environment change on the monitoring precision, and sending the monitoring result to a satellite remote sensing module; and a data acquisition mode is intelligently adjusted according to an analysis result, and a monitoring mode suitable for the current environmental condition is preferentially selected. According to the meadow steppe satellite remote sensing intelligent monitoring system based on sky-ground integration, through intelligent fusion of multiple data sources, environment adaptive adjustment, dynamic resource optimization and an abnormal data restoration mechanism, the ecological monitoring precision and efficiency in a meadow steppe complex environment are significantly improved; particularly, the defects of the traditional remote sensing technology on the limitation of resolution and precision are overcome in the aspects of dealing with the monitoring requirements of grassland ecosystem detail analysis, local change monitoring and complex meteorological conditions.

The invention discloses an intelligent rainfall forecasting method and system based on real-time assimilation driving of sky-air-ground multi-mode sensing data, and relates to the field of machine learning, and the method comprises the steps: carrying out the coordinate alignment of an obtained standardized multi-source meteorological data set, distributing a weight according to the time-space credibility of a data source, and carrying out the calculation of the time-space credibility of the data source; generating an enhanced radar image feature set implying multi-modal information, and inputting the enhanced radar image feature set into the double-branch-UNet-GAN collaborative optimization model for training to obtain an optimal model adaptive to multi-modal data; based on severe convective weather characteristics and business requirements, precision correction and lightweight processing are performed on a forecast result after test set adversarial optimization through a regional attention mechanism, visual display is realized through an APP and a webpage terminal, positioning query and early warning push functions are synchronously provided, and a closed loop from data to service is completed. According to the method, full-chain innovation is formed from data fusion, model performance, aging precision, resource adaptation to service landing, and the scientificity and application value of intelligent rainfall forecasting are remarkably improved.

The invention discloses a star-sky dim and small target detection method fusing coordinate attention and a convolutional feedforward neural network, and belongs to the technical field of dim target detection. The method comprises the steps of obtaining a starry sky image data set, outputting an enhanced feature map by applying a coordinate attention mechanism, constructing a network model fusing the coordinate attention mechanism and a ConvFFN module through the ConvFFN module of a convolutional feedforward neural network, training a verification model based on simulation data, and finally outputting the centroid position and bounding box information of a target. According to the method, features are extracted in the transverse and longitudinal directions through a coordinate attention mechanism, the feature expression ability of a weak and small target in a low signal-to-noise ratio environment is effectively enhanced, the detection precision is remarkably improved, the omission ratio is reduced, the calculation efficiency is improved by combining the lightweight structure of ConvFFN and deep convolution optimization, and the method is suitable for large-scale popularization and application. A reliable and efficient detection solution is provided for astronomical observation and space debris monitoring.

A detection method for determining the communication availability of a satellite-to-ground laser link based on sky infrared remote sensing images comprises the following steps: 1) obtaining meteorological information and sky infrared remote sensing images above a satellite-to-ground laser communication ground station; 2) calculating the corresponding elevation angle and position relationship between the satellite and the ground station at each moment, and calculating the cloud base height and cloud optical depth of the spatial region through which the satellite-to-ground link passes based on the meteorological information and the sky infrared remote sensing images; 3) using a Monte Carlo laser vector transmission model to model the cloud laser channel based on the corresponding elevation angle and position relationship, cloud base height, and cloud optical depth results between the satellite and the ground station at each moment, and normalizing the residual to serve as a reference value for the link communication availability; 4) judging the communication availability of the laser link between the satellite and the ground station based on the atmospheric turbulence obtained in step 1; and 5) predicting future sky infrared remote sensing image change information of a specified satellite-to-ground laser ground station using machine learning technology.

The present invention discloses a real-time high-precision PNT service method based on joint observation resources of the earth and the sky, and belongs to the field of navigation and positioning. The method estimates the precise orbit and clock error products of GNSS satellites based on a small number of regionally distributed GNSS ground monitoring stations and a small number of low-orbit satellite-borne GNSS observation data; carries out GNSS satellite orbit and clock error prediction processing, fits into GNSS satellite ephemeris products, annotates to visible low-orbit satellites, and distributes to the entire low-orbit constellation; low-orbit satellites solve precise orbits and clock errors in real time, calculate predicted orbits, calculate predicted clock errors, fit predicted orbits and clock error products into low-orbit navigation messages and broadcast them to users; users carry out GNSS / low-orbit satellite joint precise single-point positioning processing, and solve user terminal positions and clock error information in real time. The present invention can effectively reduce the dependence on the global deployment of GNSS ground monitoring stations, and can realize real-time high-precision PNT services on a global scale based on a small number of regionally distributed ground monitoring networks.

The invention relates to the technical field of building thermotechnical testing, solves the technical problem that in the prior art, due to the fact that an experimental environment lacks the sky background radiation cold effect, the accuracy of enclosure structure performance testing is low, and provides an enclosure structure performance testing method and system coupled with the sky cold and hot radiation effect. The method comprises the following steps: establishing an equivalent sky effective temperature control model according to a meteorological database; acquiring test environment parameters through a test environment parameter control module according to the equivalent sky effective temperature control model; acquiring real-time test data of the enclosure structure test piece under the control of the test environment parameters through a test data acquisition module according to the test environment parameters; and analyzing the real-time test data through a test data analysis module to obtain a test result. The controllable equivalent sky effective temperature model is introduced, and the problems that in the prior art, the sky background radiation cold effect is lacked, and the deviation between the test result and the actual building service environment is large are effectively solved.

The invention discloses an air-space-ground landslide monitoring method and system based on deep learning, and relates to the technical field of landslide monitoring of mines. The system divides a mine area into a plurality of monitoring sub-areas by establishing a visual man-machine interaction platform and combining a digital geological disaster model; the multi-source data collection covers various sensing data of air-based, space-based and ground-based data, and the consistency and reliability of the data are ensured through standardization, time synchronization and space registration processing; a dynamic landslide monitoring model is constructed by adopting a convolutional neural network, multi-scale spatial-temporal characteristics of a landslide mass are extracted in real time, and accurate identification of landslide physical effect risks is realized; in combination with a geomechanical strain response index, a micro-seismic excitation response coefficient and a fluctuation disturbance abnormal index, a landslide risk is evaluated in multiple dimensions through a threshold judgment mechanism, a risk state and an early warning level are dynamically output, an early warning strategy is generated, mine safety management and emergency response are assisted, and life safety of mining personnel and environmental stability are guaranteed.

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 provides a satellite multi-load moon forecasting method and device and electronic equipment. The method comprises the following steps: acquiring coordinates of a satellite moon vector connecting line under preset time precision in an instrument coordinate system; performing cold air view field constraint, scanning view field constraint, earth night side observation constraint and lunar phase angle constraint on each instrument to obtain a rough time window of the moon entering the view field corresponding to each instrument; and carrying out dynamic adaptive golden section extremum search at a preset time interval, calculating the visibility of each instrument view field to the moon, and obtaining the optimal observation time of the moon. According to the satellite multi-load lunar forecasting method and device and the electronic equipment provided by the embodiment of the invention, the FY-3 F satellite multi-load lunar observation rapid forecasting method based on the combination of rough time search and adaptive golden section extremum search is constructed, and by optimizing the calculation of the cold air view field and the scanning view field angle, the accuracy of lunar observation is improved. The moon forecasting method for the cross-rail circular scanning instrument is provided, and limitation of a traditional rectangular view field is avoided.