260 results about "Satellite observation" patented technology

The invention provides an ocean live big data fusion method based on deep learning, and relates to the technical field of ocean big data fusion, and the method comprises the steps: obtaining meteorological driving variables, ocean historical reanalysis data, satellite observation data and ocean field observation data, and performing unified format conversion processing on the basis of the acquired meteorological driving variables, the ocean historical reanalysis data, the satellite observation data and the ocean field observation data, and configuring a unified ocean environment feature tensor set. A unified marine environment feature tensor structure is configured, meteorological driving variables, marine historical reanalysis data, satellite remote sensing data and on-site buoy observation data are subjected to space resampling, time interpolation and missing complementation and are unified into a space-time grid under a geographic reference coordinate system, a four-dimensional tensor set is configured, and the four-dimensional tensor set is obtained. The problem of fusion obstacles caused by non-uniform data formats and inconsistent temporal-spatial resolutions in the prior art is effectively solved.

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

The invention provides a cloud condition adaptive geostationary satellite sea surface temperature inversion method based on deep learning, and relates to the technical field of sea surface temperature inversion, and the method specifically comprises the steps: obtaining geostationary satellite observation data, geographic information data, polar orbit satellite sea surface temperature and cloud products, and analyzing the sea surface temperature and atmospheric background data; performing standardization preprocessing on the obtained stationary satellite observation data; constructing a cloud detection data set; a cloud detection model of deep learning is constructed and trained; constructing a clear sky data set; a clear sky inversion model of deep learning is constructed and trained; constructing a data set for under-cloud inversion model training; constructing an under-cloud inversion model of deep learning and training the under-cloud inversion model; and integrating the cloud detection model, the clear sky inversion model and the under-cloud inversion model, adaptively calling the corresponding model according to a real-time cloud detection result, and generating a final sea surface temperature product. According to the technical scheme, the problem that in the prior art, the accuracy of a reconstructed product on the spatial structure cannot be comprehensively reflected is solved.

The invention discloses a troposphere delay model construction method based on a numerical meteorological model and an unmanned aerial vehicle, and the method comprises the steps: obtaining meteorological parameter values in the numerical meteorological model for a target region; a troposphere observation value obtained by the target unmanned aerial vehicle through the airborne GNSS receiver and the airborne multi-source meteorological equipment in the process of moving along the preset path in the target area is obtained; determining first troposphere delay data according to the meteorological parameter values and a corresponding preset oblique troposphere delay model; determining second troposphere delay data by adopting a preset ambiguity fixing algorithm based on the satellite observation value; and according to the first troposphere delay data and the second troposphere delay data, determining troposphere delay data of the target area. According to the method, accurate estimation of troposphere delay in a complex region and a meteorological condition rapid change region is realized, and a reliable troposphere delay model is provided for realizing high-precision monitoring of InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring.

The invention provides an improved robust Kalman filtering integrated navigation method and system based on chi-square detection, and belongs to the technical field of navigation, and the method comprises the following steps: constructing a GNSS / INS loose integrated navigation Kalman filtering basic framework; before combined navigation Kalman filtering measurement updating, calculating an innovation chi-square test amount, recording the number of available satellites of each epoch, and calculating a satellite observation quality index according to the number of the available satellites; sage-Husa robust Kalman filtering based on IAE windowing is improved, a weighting factor is determined through an innovation chi-square test amount and is used for adjusting the weight of current epoch innovation in a window, and the window length is dynamically adjusted according to the change of a satellite observation quality index; a robust factor obtained through calculation of improved Sage-Husa robust Kalman filtering based on IAE windowing acts on an observation noise covariance matrix, so that the observation noise covariance matrix is expanded, and the weight of observation information with gross error in data fusion is reduced.

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention discloses a multispectral satellite cloud picture prediction method based on motion stripe decoupling. The method comprises the following steps: carrying out normalization preprocessing on multi-channel satellite observation data; utilizing a motion branch model to extract motion features based on a displacement field, iteratively updating a prediction frame in an autoregressive distortion-correction pipeline, and keeping physical consistency in combination with atmospheric dynamics and smoothness constraint; a texture branch model is utilized to sequentially pass through a high-fidelity encoder, long memory state space modeling and a high-fidelity decoder, time sequence texture features are extracted, and cloud picture details are kept; the motion features output by the motion branches and the texture features output by the texture branches are input into a gating fusion module, adaptive weighting of the features is achieved through convolution and a gating mechanism, and fusion features are output; and carrying out reverse normalization processing on the fusion features to obtain satellite cloud picture prediction results at a plurality of moments in the future. According to the method, the spatial texture fidelity of cloud picture prediction can be improved while the physical interpretability is ensured, and high-precision satellite cloud picture prediction is realized.

The invention discloses a satellite navigation positioning method and device based on scene recognition, electronic equipment and a storage medium, and belongs to the technical field of satellite navigation positioning, and the method comprises the steps: determining the geometric space characteristics of each satellite according to the observation time, the satellite number of each satellite and preset satellite ephemeris information; according to the observation time, the satellite pseudo-range of each satellite and the satellite distance change rate of each satellite, determining the signal quality characteristics of each satellite; determining a satellite observation feature set based on the geometric space features of the satellites and the signal quality features of the satellites; inputting the satellite observation feature set and the satellite signal-to-noise ratio of each satellite into a preset scene recognition model to generate a scene category; and determining the positioning coordinate of the satellite navigation receiver according to the scene category in combination with the satellite signal-to-noise ratio of each satellite and the satellite pseudo-range of each satellite. According to the invention, the problem that satellite navigation positioning is not accurate enough in the prior art can be solved.

The invention relates to the technical field of hydrological monitoring, and discloses a regional groundwater reserve change dynamic monitoring method based on gravity satellite time series data analysis. According to the method, the multi-source data scale effect and observation noise are effectively eliminated, the physical reliability and resolution of groundwater signal extraction are improved, and the recognition precision and early warning perspectiveness of the abnormal loss trend are enhanced. A full-chain monitoring closed loop of multi-source satellite observation, high-precision physical inversion and dynamic statistical benchmark is constructed, multi-dimensional environment data is aligned, a component separation strategy based on multi-element physical mechanism constraint is established based on a water balance principle, and anomaly diagnosis is performed in combination with a self-adaptively updated long-time-sequence historical database.

The invention provides an inertia / satellite / vision adaptive integrated navigation method and system. The method comprises the following steps: carrying out inertia measurement and navigation calculation to obtain inertia data; calculating satellite navigation observed quantity according to the satellite navigation data and the inertial data, and constructing a satellite observation model; calculating visual navigation observed quantity according to the visual navigation data and the inertial data, and constructing a visual observation model; aiming at a satellite and a visual observation model, respectively adopting an innovation-based adaptive covariance estimation method to obtain satellite and visual observation noise covariance updated values; constructing a satellite and visual navigation health degree, and calculating a satellite and visual fusion weight according to the satellite and visual navigation health degree; controlling updating of satellite and visual navigation observed quantity according to the satellite and visual fusion weight; and calculating a weighted equivalent observation matrix and a noise covariance, and carrying out filtering estimation. According to the method, an adaptive noise estimation and sensor health degree evaluation mechanism is introduced, dynamic weighted fusion of multi-sensor data is realized, and the robustness and precision of a navigation system in complex environments such as satellite signal lock losing and visual feature missing are improved.

The invention relates to the technical field of atmospheric science and remote sensing, in particular to a volcanic sulfur dioxide vertical section inversion method based on a Lagrange diffusion model, and the method comprises the steps: obtaining volcanic eruption total mass constraint, satellite sulfur dioxide column concentration and effective pixels, three-dimensional meteorological driving data and chemical clearing parameters; calculating a vertical weighting function by utilizing a radiation transmission mode, and generating initial vertical distribution by combining with a column concentration layering ratio; constructing a diffusion model to simulate an air mass backward track and match observation pixels to obtain attribution air mass distribution; optimizing a volcanic sulfur dioxide vertical emission profile time sequence based on attribution distribution and total mass constraint; and inputting the sequence into a diffusion model for forward simulation, comparing satellite observation data to calculate a verification index, correcting a diffusion coefficient according to a threshold value, and outputting a target vertical emission profile time sequence. According to the method, through backward trajectory attribution and forward verification closed-loop optimization, the volcanic sulfur dioxide vertical distribution inversion precision is remarkably improved.

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.

The invention discloses a factor graph optimization method for aircraft cluster positioning and navigation, which comprises the following steps: for each aircraft in an aircraft cluster, establishing a corresponding local factor graph based on a kinematic model and observation data of the aircraft; constructing a local optimization objective function of each aircraft based on the local factor graph; the method comprises the following steps: solving a local optimization objective function of each aircraft based on a distributed optimization algorithm through interaction and cooperation information among aircrafts in an aircraft cluster, and enabling position estimation of each aircraft to be converged to a global consistent solution; according to the method, the factor graph is constructed for the aircrafts in the cluster, the optimization problem is disassembled and distributed to each aircraft by introducing the idea of distributed calculation, the position of the cluster is solved through mutual communication cooperation among the aircrafts, the positioning precision is optimized, the problems of low calculation efficiency, insufficient calculation resources and the like existing in traditional centralized optimization are solved, and the method is suitable for large-scale popularization and application. And the robustness of the system under the conditions of communication limitation and satellite observation data deviation is enhanced.

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 discloses an ocean current extraction method and system, and relates to the technical field of data processing, and the method comprises the steps: building causal priori from SWOT satellite observation data based on a set variation assimilation system and a structural equation model; wherein the causal priori comprises a causal path and strength between wind stress forcing and ground rotation adjustment; carrying out iterative generation through a physical constraint diffusion model under the conditions of causal priori and SWOT satellite observation data, and outputting a sea surface flow field; performing multi-level interpretability verification on the causal path and the step of performing iteration generation through the physical constraint diffusion model to obtain a verification result; a verification result is fed back to the structural equation model and the physical constraint diffusion model to form closed-loop optimization, and then the optimized sea surface flow field is obtained. According to the method, iteration is carried out through the physical constraint diffusion model to generate the sea surface flow field, multi-level interpretability verification is carried out, the sea surface flow field can be optimized by utilizing the verification result, and high-resolution ocean current acquisition is realized.

The invention relates to the technical field of early warning, in particular to a sand and dust early warning method, device and system based on satellite remote sensing, and the method comprises the steps: constructing a historical sand and dust event library which is formed based on the association and fusion of historical sand and dust events with multi-source spatio-temporal data; and carrying out sand source ground sand raising feature analysis based on the historical sand and dust event library, and extracting underlying surface feature parameters and meteorological driving parameters to form a structured sand raising event sample data set. According to the sand and dust early warning method based on satellite remote sensing provided by the invention, a historical event library is constructed based on multi-source satellite observation, underlying surface and meteorological characteristics are fused through machine learning, a high-precision sand rising wind speed estimation model is established, large-range continuous dynamic monitoring is performed on a sand source land, and the sand and dust early warning method based on satellite remote sensing is realized through fusion of a physical mechanism and a deep learning technology. The collaborative prediction of the sand and dust conveying path and the strength evolution is realized, and the accurate and probabilistic prediction of the sand and dust event evolution process is realized.

The invention provides an inertial navigation aided navigation positioning method and system, and the method comprises the steps: calculating the coordinate of each navigation satellite in an ECEF coordinate system according to the satellite ephemeris of a satellite navigation system; according to the navigation satellite coordinate and the inertial navigation system positioning result, the azimuth angle and the pitch angle of the navigation satellite relative to the optical detection equipment under the ENU coordinate system are solved, and the navigation satellite is tracked; acquiring star maps collected by the optical detection equipment, matching the star maps through a star map recognition algorithm, resolving a coordinate transformation matrix between two continuous frames of star maps, and recognizing fixed stars and satellites in the star maps based on the transformation matrix; converting the identified fixed star in the field of view into an ECEF coordinate system, calculating an observation angular distance between the fixed star and the satellite, and calculating a satellite observation vector in the ECEF coordinate system according to the observation angular distance; and performing error correction on the positioning result of the inertial navigation system based on the satellite observation vector. Through the scheme, the positioning precision of the inertial navigation system can be improved, and the influence of interference deception signals is reduced.

The invention provides a low-orbit satellite planning method which can be applied to the technical field of satellite observation task planning. The method comprises the following steps: predicting a potential activity area of a target in a planning time interval based on multi-satellite collaborative observation information; mesh generation is carried out on the potential activity area, and a target motion potential area mesh set is constructed; calculating the existence probability of the target in each grid; selecting satellite resources meeting conditions, and determining available satellites in combination with the observation time interval and the potential activity area; determining an intersection part of a satellite observable area and a potential activity area, and constructing a satellite candidate observation area set; calculating a profit value of each candidate observation area based on the existence probability of the target in each grid and the coverage condition of the satellite candidate observation area; selecting the satellite observation area strip with the maximum profit value through iterative calculation, and obtaining the optimal solution of the multi-satellite observation profit; and generating a multi-satellite collaborative observation planning scheme according to all the determined satellite observation areas.

The invention discloses a satellite task element completion method fusing semantic and graph structure information, and belongs to the technical field of satellite task planning and artificial intelligence. The method comprises the following steps: constructing a training set; based on a Word2Vec model, generating an initial semantic embedding vector by utilizing triple data in a training set, and constructing an initial embedding matrix; constructing a graph attention network based on global perception; training the graph attention network by using the training set and the initial embedding matrix; and generating a to-be-complemented satellite observation task knowledge graph based on to-be-complemented satellite observation task data, establishing triples formed by head entities, relationships and tail entities, inputting all the triples into the trained graph attention network, and complementing the missing tail entities. The satellite observation task element identification and learning ability of the model is improved, the complementation accuracy is high, the adaptability is high, and the method can be widely applied to scenes such as satellite task element identification and demand complementation reasoning.

The invention provides an integrated navigation method and system based on deep learning assistance, and the method comprises the steps: based on a preset reference coordinate system, obtaining a satellite observation position and a satellite observation speed through a navigation satellite system, obtaining an inertial navigation observation position, an inertial navigation observation speed and an inertial navigation observation attitude through an inertial navigation system, acquiring a first combined navigation solution according to the data; obtaining target point cloud data and solving depth information based on a deep learning auxiliary algorithm, point cloud data collected by a laser radar and a visual image collected by a visual camera, and obtaining an estimated position and an estimated attitude of a motion carrier according to the depth information; obtaining a second combined navigation solution based on the estimated position, the estimated attitude and the first combined navigation solution; and inputting the second combined navigation solution into the inertial navigation system to generate an error to compensate and correct the navigation deviation of the motion carrier so as to complete the combined navigation. According to the invention, the depth information is solved, various navigation results are fused for navigation compensation correction, and the navigation reliability is improved.

The invention relates to the technical field of satellite observation task scheduling, in particular to a low-orbit satellite large-scale earth observation task self-adaptive scheduling method and system. The method comprises the following steps: generating a task allocation result according to a task candidate resource mapping table, namely generating an initial allocation scheme by using a greedy strategy of double factors of time and load, and establishing an allocation frequency memory matrix; constructing an inverse correlation selection probability model by utilizing the distribution frequency memory matrix, and outputting a task distribution result considering global revenue and load balance; task scheduling optimization is carried out based on a task allocation result, wherein conflict resolution fine adjustment is carried out by utilizing a time elastic adjustment mechanism of conflict perception; inter-satellite link collaborative rescheduling based on task characteristics; and global optimization and real-time response capability are effectively considered. According to the method, a complex joint scheduling problem is decoupled into two sub-problems with clear logic and low coupling degree, and the calculation complexity of single scheduling is greatly reduced.

The invention discloses a deep learning CO2 near-real-time inversion method and system fused with multi-scale characteristics, and the method comprises the steps: inputting the observed spectral data into an inversion model, carrying out the in-orbit near-real-time calculation, and outputting a satellite data inversion result; the inversion model comprises a two-stage full connection projection, a Transform encoder, a learnable attention pooling and a regression decoder which are connected in sequence; wherein each layer of the encoder comprises two sequentially connected sub-layers, namely a multi-head self-attention layer and a feedforward network layer, a residual connection layer and a normalization layer are introduced behind each sub-layer, in the multi-head self-attention layer, rotation position coding is applied to Q and K before dot product attention is entered, and relative displacement is implicitly injected into attention weight calculation through phase rotation; according to the inversion method, satellite observation is taken as a characteristic, and ground in-situ observation is taken as a supervision CO2 data driving inversion thought, so that the calculation efficiency is maintained, and meanwhile, the modeling capability of a nonlinear relationship between characteristics is improved.

The application discloses a petrochemical field precise space-time information processing and service system based on Beidou. The application is based on a petrochemical Beidou ground enhancement network of the Beidou navigation satellite system, relies on a petrochemical special network to collect satellite observation data, deploys a set of precise space-time information processing and service system of China Petrochemical in a petrochemical intelligent cloud, and provides independent controllable real-time centimeter-level positioning navigation and geographic information collection services in a plurality of high-precision positioning and navigation work scene applications such as long-distance pipeline inspection, dangerous chemical vehicle monitoring, industrial control system time service, oil and gas pipeline safety monitoring, geographic information data collection and geological disaster prevention monitoring.

The invention provides a method and device for establishing a backscattering coefficient correction model, and the method comprises the steps: obtaining multi-source data comprising an observation value corresponding to a backscattering coefficient; performing space-time matching on the multi-source data, generating a data unit corresponding to each satellite observation pixel, and filtering the data units through a preset rule to obtain a reserved data set; outputting a simulation value through a physical model based on the reserved data set not including the observation value, constructing a training set based on the simulation value and the reserved data set, and using the observation value as a target variable of the training set; and training a machine learning model by using the training set, and performing grid search optimization on preset hyper-parameters in the machine learning model to obtain a final backscattering coefficient correction model. A random forest algorithm is adopted to effectively learn and fit a complex and high-order nonlinear relationship between a physical model simulation value and a satellite observation value.

The invention relates to a full-vision-field virtual observed quantity generation method based on a GNSS array receiver, and the method comprises the steps: projecting the original pseudo-range and carrier phase observed quantity of an array reference receiver to a target point, and obtaining a virtual pseudo-range and carrier phase virtual observed quantity; selecting a reference receiver to execute inter-station single-difference processing to obtain a single-difference carrier phase and a single-difference pseudo-range; constructing a deviation full-rank estimation model to obtain a receiver end deviation floating point solution and a fixed ambiguity, and obtaining a receiver end deviation sequence of each receiver at the k epoch moment; constructing a virtual receiver reference, updating a receiver end deviation sequence when a reference receiver is switched, performing receiver end deviation closed-loop feedback correction on the virtual pseudo-range and the carrier phase virtual observed quantity, and obtaining the virtual pseudo-range and the carrier phase virtual observed quantity of the array receiver with consistent deviation; and obtaining a weighted fusion coefficient of the satellite observed quantity, and carrying out weighted fusion on the virtual pseudo-range and the carrier phase virtual observed quantity to generate a full-vision-field virtual observed quantity.

The invention relates to the field of remote sensing satellite observation task planning, in particular to a remote sensing satellite hotspot target observation method and system based on meteorological elements, which are used for carrying out priority division and analysis on tasks according to the conditions of importance degree, emergency degree, observation time window and the like of hotspot targets, determining hotspot target observation tasks, and carrying out real-time observation on the hotspot targets. And obtaining a corresponding geographic information data set. On the basis, simulation calculation of the satellite transit area is carried out in sequence, and a spatio-temporal data set of the hotspot observation target in the satellite transit process is generated. And in combination with CMA-GFS data, carrying out space-time screening on observable targets, determining hotspot targets with clear sky observation conditions, and forming a selected space-time data set. And finally, the whole process operation from satellite observation task planning, platform attitude maneuver and instrument pointing calculation to observation guidance generation and instruction uploading execution, and observation state tracking and effect evaluation is completed, invalid observation caused by cloudy and foggy weather is reduced, and the satellite observation efficiency is improved.

The invention discloses an exercise visual command method, device and equipment based on Beidou positioning and wireless transmission and a medium, and relates to the technical field of exercise visual command, and the method comprises the steps that an individual soldier terminal obtains original longitude and latitude height data through Beidou satellite observation, a host base station forwards a message to a main control computer, and red and blue adversarial software sends the message to the main control computer according to a preset exercise scheme; a remote instruction is issued to an individual soldier controller through a host base station, when an individual soldier is judged to be hit, a tactical helmet immediately triggers electronic smoke, a report is generated after a drill is finished, the report is synchronously displayed through a large screen, and the system converts field real coordinates into large screen pixel coordinates in real time through deep fusion of Beidou positioning and dynamic proportional mapping. A commander can directly see the weapon force movement and fire crossing situations of the two parties on a satellite base map without experience estimation, real-time commanding is achieved, multiple requirements are met at the same time through a set of lightweight architecture, data support is provided for follow-up replay and tactical analysis, and the training effect and the construction cost are both considered.

The application discloses a kind of surface solar radiation prediction method and equipment based on satellite observation data, belong to solar radiation remote sensing prediction technical field.The method includes obtaining target observation data and prediction model;Difference is obtained by the target observation data of the adjacent observation time of each spectral channel of the top reflectivity of atmosphere layer, and cloud movement sensing feature is obtained;The input sequence that target observation data and cloud movement sensing feature jointly constitute is input into the prediction model, and the surface solar radiation prediction result is obtained.The application can realize more rapid, more continuous surface solar radiation short-term prediction, and reduce the dependence of intermediate inversion product and multi-source auxiliary data while maintaining good prediction accuracy in actual prediction stage, and has practical application value for solar energy resource monitoring, photovoltaic power generation power estimation and short-time power dispatching.

The invention provides a differential enhancement method, device and equipment based on a low earth orbit satellite, a medium and a product. The method comprises the following steps: acquiring satellite observation data of a global navigation satellite system, satellite observation data of the low earth orbit satellite and a satellite mixed ephemeris of the global navigation satellite system and the low earth orbit satellite which are received by a continuous operation reference station network in real time; forming a base line by every two stations according to the second data to obtain a triangulation network base line; according to the triangulation network base line and the first data, sequentially constructing double-difference equations of a satellite and a low-orbit satellite of a global navigation satellite system; performing ambiguity calculation according to the double-difference equation to obtain ambiguity fixed solutions of the global navigation satellite system and the low-orbit satellite; substituting the ambiguity fixed solution into the observation equation to obtain a baseline atmosphere; a differentially enhanced user location is generated based on the baseline atmosphere and the user request. According to the method, high-precision calculation is enhanced through ground-based difference combined with data of a low-orbit satellite and a global navigation satellite system, and the calculation efficiency is improved.

The invention discloses a multi-source-driven CO2 concentration high-temporal-spatial-resolution prediction method based on a dynamic diffusion model, and the method integrates the satellite observation of OCO-2, GOSAT and the like, ERA5 meteorological data, vegetation index NDVI, population activity and other characteristics on the basis of DCRNN, and achieves high-precision prediction through physical prior and a dynamic graph structure. The method comprises the following steps: unifying multi-source data to a regular grid; an adjacent matrix is dynamically generated based on the geographic position and the wind field to serve as the input of the improved DCRNN, and the diffusion relation of COs in the wind direction is described; multi-source node features are input into the improved DCRNN, and physical constraints such as mass conservation and convection consistency are introduced to improve the reliability of a result; a graph-to-grid decoding and super-resolution technology is adopted to reconstruct prediction to a higher resolution level by level, and pixel-level uncertainty can be output. According to the method, multi-source observation and meteorological driving information are fully utilized, the satellite observation blank is effectively supplemented, the fineness and continuity of a COconcentration product are improved, and the method is suitable for carbon emission accounting, carbon source and sink evaluation and environmental decision support.