442 results about "Satellite observation" patented technology

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

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 a positioning method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of positioning. According to the method, satellite observation data, SLAM positioning data and satellite shielding data are fused, the high-precision absolute positioning capability of an RTK algorithm and the high-precision relative positioning advantage of an SLAM algorithm are fully utilized, and meanwhile, the influence of satellite shielding on RTK positioning precision is considered, so that more accurate and reliable positioning is realized in a complex environment. According to the method, the positioning precision can be maintained by means of the relative positioning information of the SLAM algorithm under the condition that satellite signals are shielded or interfered, the SLAM result is corrected by means of the absolute positioning data of the RTK algorithm, and accumulated errors are effectively reduced. Besides, by considering satellite shielding data, the accuracy and reliability of positioning are further improved, so that the positioning system can stably output high-precision positioning results in various complex scenes.

The invention provides a polar region multi-source remote sensing sea wave significant wave height optimization interpolation fusion method and device, and belongs to the field of remote sensing, and the method comprises the steps: carrying out the preprocessing of collected sea ice marginal region and open sea surface data; north-pole geometric adaptation correction is carried out, system deviation of multiple satellite sensors is eliminated, and consistency and accuracy of data are ensured. Real-time wave height forecast is used as a background field, a weighting function is constructed in combination with a satellite observation error covariance matrix, dynamic background field fusion is carried out, and the precision and reliability of a data sparse region are improved; according to different characteristics of a sea ice marginal region and an open sea surface, different weights are set, a transition weight function is introduced, partition fusion is carried out, boundary discontinuity is avoided, and therefore the final fusion wave height is obtained; the robustness of the fusion method is verified through K-fold cross validation, and meanwhile precision verification is conducted in combination with multi-dimensional data. According to the method, the problem of insufficient accuracy verification spatial representativeness caused by rare north pole in-situ observation quantity and concentrated distribution is solved.

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 provides a dike group deformation monitoring method and system based on Beidou multi-source fusion, and the method comprises the steps: receiving a satellite observation signal and sensing data of a dike group monitoring region, obtaining multi-source input data, inputting the multi-source input data into a preset dike group digital twin model, carrying out the physical behavior simulation of a dike group structure, and generating a global deformation field. Inputting the initial boundary condition into a deformation conduction dynamical model module in the digital twinborn model of the reservoir dam group, identifying a deformation key conduction path in the reservoir dam group, and executing stress-deformation forward calculation and reverse traceability analysis in the digital twinborn model of the reservoir dam group on the basis of the deformation key conduction path; and calculating the influence weight of the core risk source on the deformation of the reservoir dam group, constructing simulation scenes of various regulation and control strategies in the digital twin model of the reservoir dam group, and performing simulation deduction on the deformation trend of the reservoir dam group in each simulation scene to obtain deformation early warning information. According to the invention, the reliability of early warning response is effectively improved.

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 discloses a single-satellite target sight atmospheric refraction correction method and system based on multi-spectral range finding, and relates to the field of atmospheric refraction processing, and the method comprises the steps: determining a target radiation intensity ratio based on two radiation intensities on different wavebands at the same moment; obtaining observation sight lines of the satellite on the measured target on different wave bands at the same moment, and carrying out light trace tracking to obtain a light ray tracking trajectory; determining a target grid through which each ray tracing trajectory passes from the atmosphere gridding model, determining an extinction coefficient of each target grid, and constructing a target function of a radiation intensity ratio with respect to the height of the target from the ground in combination with a radiation intensity calculation formula; determining a target height from the measured target to the ground based on a target function and a target radiation intensity ratio, determining the position of the measured target according to a track point matched with the target height in each light ray tracing track, and determining a sight line vector after atmospheric refraction correction by combining the position of a satellite, therefore, high-precision atmospheric refraction correction under a single-star observation condition is realized.

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 discloses a ground and satellite observation constraint combined GPP multi-task learning estimation method, system, medium and equipment, and belongs to the technical field of remote sensing, the method comprises the steps of obtaining OCO-2 SIF data and TROPOMI SIF data, outputting a global GPP product prediction value through an expert network, and obtaining a global GPP product prediction result; the processing flow comprises the following steps: extracting initial features based on an OCO-2 SIF data set and a TROPOMI SIF data set, inputting the initial features into a shared encoder module, learning cross-sensor spatial and temporal features through a feature alignment and fusion mechanism, outputting OCO-2 SIF time features and TROPOMI SIF spatial features, carrying out independent decoding and outputting predicted values, and outputting the predicted values. According to the global GPP high-precision estimation method, the problem of an error mode of high-value underestimation and low-value overestimation caused by insufficient space-time generalization due to single task model estimation is solved.

The invention discloses a multi-interference-source synchronous positioning method based on a single LEO satellite, and relates to the technical field of single-satellite multi-GNSS interference source positioning. The method comprises the following steps: firstly, establishing a scene in which a single satellite observes Doppler signals of multiple interference sources, modeling an interference source problem into an integer programming optimization problem, grouping Doppler observed quantities through equally-spaced sampling, and resolving frequency deviations and positions of the multiple interference sources by using a least square method; and further separating the homologous Doppler observed quantity by using a clustering method, and finally obtaining the accurate positions of a plurality of interference sources. According to the multi-GNSS interference source positioning method used in the invention, high-precision synchronous positioning of multiple ground interference sources by using a single low earth orbit satellite is realized, and the calculation complexity is reduced.

This invention relates to the field of short-term meteorological forecasting technology, specifically to a forecasting model architecture based on point cloud data and a spatiotemporal frequency domain joint attention mechanism. This forecasting model architecture includes a Z-coordinate system three-dimensional point cloud visualization data model and a P-coordinate system four-dimensional spatiotemporal frequency domain joint attention model. The three-dimensional point cloud visualization data model directly represents the three-dimensional distribution of meteorological elements. Combined with the DEM trust propagation completion algorithm, the observation coverage of a 1km resolution area is increased by 40%, significantly improving the forecast accuracy in complex terrain areas (such as mountainous regions). Then, a decision tree model and satellite observation data are used to fuse precipitation data, filling in the gaps in precipitation observation data for areas without monitoring stations. Finally, based on the Earth's rotation period and P-coordinate system four-dimensional spatiotemporal time domain data, a Fourier transform + phase modulation module enhances the frequency domain capture capability of precipitation cycle signals, significantly improving the TS score for severe convective weather.

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 Beidou GNSS-R-based strong wind disaster monitoring and early warning method, and the method comprises the steps: 1, enabling an unmanned plane to carry GNSS-R receiving equipment to be located at the periphery of a strong precipitation region, carrying out the wind speed detection of an internal water surface, and carrying out the data calculation of an obtained satellite observation quantity; step 2, constructing an LSTM wind speed inversion model fused with two-factor decoupling, performing inversion prediction on the water surface wind speed of a reflection point area by inputting the received satellite observed quantity and a resolving result and performing error correction, and obtaining future time step wind speed prediction based on a past time step wind speed inversion result; step 3, establishing a boundary adaptive grid and a dynamically updated Kriging interpolation model, obtaining wind speed data of a specific signal reflection point missing area, and assisting wind speed data supplement of the reflection point missing area; and 4, constructing a wind speed time sequence extrapolation and inversion stability coordinated double-effect strong wind early warning strategy, judging whether strong wind is about to appear or not, and performing early warning.

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 discloses a multi-track joint Doppler positioning method based on two-step solution, which aims at a scene of a multi-track Doppler positioning system, a receiver receives ground interference source information through a satellite signal, and processes data through the multi-track joint Doppler positioning method so as to realize more accurate positioning. The method comprises the following steps of: respectively solving Doppler frequency difference on each orbit, calculating and correcting frequency deviation caused by clock deviation, then carrying out joint solution on multi-orbit data, establishing a unified observation equation model, estimating the position of a target interference source by utilizing a least square method, gradually adjusting and optimizing estimated values of target parameters by adopting an iterative optimization method, and finally calculating and correcting the frequency deviation caused by clock deviation. And the convergence condition is reached. According to the method, the clock skew problem in multi-orbit combined positioning is effectively solved, the positioning precision and the convergence speed are improved, information can be more effectively integrated by combining two-orbit data, the influence of single satellite observation errors is reduced, and therefore higher positioning precision is achieved.

The invention discloses a global ionosphere modeling method based on a spherical radial basis function, and the method comprises the steps: laying Reuter grid points at an interval of 10 degrees on an ionosphere thin layer, and converting the Reuter grid points into a daily fixed geomagnetic coordinate system for modeling. Cycle slip monitoring and abnormal value identification and elimination are carried out on obtained satellite observation data (O file), then a precise satellite orbit position is obtained through interpolation in combination with a precise ephemeris file (SP3), hardware deviation is corrected through differential code deviation (DCB file), an AbBPoisson spherical surface radial basis function is adopted and converted into a 15-order truncated AbBPoisson spherical surface radial basis function, and finally, a precise satellite orbit position is obtained. The bandwidth is set to be 0.25, L2 regularization optimization is used, parameters of a radial basis function are solved through a least square method, and therefore global ionosphere modeling is carried out. Different from a traditional spherical harmonic function method, the abbeposon spherical radial basis function controls the control range of a radial base point by adjusting the bandwidth, higher precision can be provided in a data-intensive area, and the adaptability is higher.

The invention belongs to the field of global navigation satellite system (GNSS) data processing, and particularly relates to a real-time cycle slip detection method based on ionosphere variation trend constraint, which comprises the following steps of: firstly, fitting an ionosphere variation trend by using all observation satellite data at the current moment, and selecting a non-geometric combination observation value according to a sliding window; then ionosphere variation trend constraint is applied on the basis of a polynomial function, geomagnetic latitude is used as an independent variable to fit a polynomial coefficient, the ionosphere variation of each satellite observed value at the current moment is forecasted, and then the ionosphere variation and the non-geometric combination are differentiated to obtain ambiguity variation; and finally, a detection threshold is determined through the adaptive expansion factor changing along with latitude, and the ambiguity variation is detected to judge whether cycle slip occurs or not. The method is convenient to calculate, fully considers the change trend of the ionosphere, is suitable for real-time cycle slip detection application, and is more suitable for cycle slip detection application in the ionosphere disturbance period.

The invention provides an atmospheric humidity profile inversion method based on a Unet3 + neural network. The atmospheric humidity profile inversion method comprises the following steps: constructing an atmospheric humidity profile inversion model based on Unet3 + MHS microwave data; performing radiometric calibration, projection conversion and quality control on the multi-channel brightness temperature observed by the original satellite; the method comprises the following steps: selecting long-time scale radiation brightness temperature data in a Chinese area range and specific humidity data of ERA-5 corresponding to time and space to make a data set; and performing humidity profile inversion according to the humidity profile inversion model of the brightness temperature data constructed in the step S1. The method has the advantages that the Unet3 + model in deep learning and microwave sensor data which are not affected by time and weather are combined, and the problems that the space distribution of the atmospheric humidity profile is small, and the time resolution is low can be further solved; the method provides atmospheric condition data with higher temporal and spatial resolution for numerical weather forecast research, and has good application value.

The invention provides a low-orbit navigation enhanced satellite orbit and clock error integrated determination method, which comprises the following steps that: a low-orbit navigation enhanced satellite carries a GNSS (Global Navigation Satellite System) receiver, can receive a multi-system GNSS satellite navigation signal and can issue a navigation enhanced signal at the same time, a downlink signal and a satellite-borne GNSS receiver clock share the same source, and a deviation exists between the clock error and the clock error of the satellite-borne receiver; the satellite-borne observation data of the low-orbit satellite, the GNSS of the ground station and the observation data of the low-orbit satellite are fused, an observation model for fusion processing is given, and the orbit of the low-orbit navigation enhanced satellite, the receiver clock error and the downlink signal time delay deviation are integrally determined. Unified adjustment and parameter joint calculation of satellite-borne and downlink observation values of the low earth orbit satellite are realized.

The invention provides a receiver autonomous integrity monitoring method, device and equipment and a storage medium, and the method comprises the steps: obtaining satellite observation data and 5G base station observation data, and carrying out the preprocessing; performing weighted nonlinear least square method solution based on the satellite observation data and the 5G base station observation data, and determining an observation value residual error; performing fault detection and fault identification on the satellite system according to the observation value residual error; and performing autonomous integrity monitoring availability judgment based on a fault-free satellite in the satellite system to obtain a judgment result. According to the invention, the positioning precision of the low-altitude aircraft is greatly improved, the integrity monitoring level can be remarkably improved, and the problem of poor monitoring precision in the prior art is solved.