690 results about "Satellite observation" patented technology

The invention discloses a multimode GNSS / inertia tight combination factor graph optimization method based on variance component estimation, and belongs to the technical field of satellite / inertia combination navigation. The implementation method comprises the following steps: fusing observed quantities of a multimode GNSS and an inertial navigation system on the basis of a factor graph optimization method under the condition of distributing prior weights among multiple GNSS systems; on the basis of a posteriori state estimation result obtained after factor graph optimization, combining a multi-mode GNSS observation equation, calculating a posteriori residual error, and utilizing a variance component estimation method to adaptively adjust weights among the multiple GNSS systems in real time according to the signal quality of satellites of the satellite navigation systems; on the basis of gross error detection and a weight reduction algorithm, weight reduction processing is carried out on the satellite observed quantity containing gross errors before the weight between the systems is adjusted in a self-adaptive mode. According to the invention, the robust variance component estimation is used to determine the weight among multiple GNSS systems, the reliability of satellite observation information of different satellite navigation systems is adjusted in real time, and the precision and robustness of the multimode GNSS / inertial tight integration navigation system are improved.

The invention relates to the technical field of satellite positioning navigation enhancement, in particular to a global ionosphere inversion method based on BP neural network fusion multi-source data, and the method comprises the steps: inverting STEC through ground-based GNSS observation data, then employing a projection function to project the STEC to a vertical direction of an ionosphere puncture point, and obtaining VTEC; a BP neural network and a Kriging interpolation method are utilized to fuse foundation observation data into an IRI-2016 ionosphere model to solve the problem that GNSS tracking stations are distributed unevenly in the world, so that the inversion precision of the ionosphere is improved, a spherical harmonic function is adopted to express VTEC space distribution, and then the ocean altimetry satellite observation data is utilized to achieve the purpose of solving the problem that the GNSS tracking stations are distributed unevenly in the world. According to the method, the problem of deficiency of ground-based GNSS observation data in an ocean area is solved by using ground-based GNSS observation data, DORIS observation data and COSMIC occultation data, a global ionosphere observation equation is established, a normal equation is constructed, and finally global ionosphere grid data VTEC is generated through Helmert variance estimation precise weighting calculation, so that the global ionosphere observation precision is improved.

The invention relates to the field of landfill methane emission remote sensing monitoring, and discloses a landfill methane emission flux measuring and calculating method and device and a medium, and the method comprises the following steps: based on regional satellite TROPOMI observation data, carrying out qualitative analysis on annual average methane emission signals of a landfill, and determining a target landfill according to a qualitative analysis result; based on data of a satellite-borne hyperspectral imager, the methane emission flux of the target landfill is quantitatively measured and calculated, and the position of a methane emission hot spot area in the target landfill is determined; and positioning the position of an emission source in the target landfill through an active methane laser detection lens carried by an unmanned aerial vehicle platform, and independently calculating the methane emission flux of the target landfill by adopting an upper and lower section method. Based on the method, the annual average methane emission condition of the landfill can be rapidly and accurately evaluated, the methane emission flux of the whole landfill is obtained, and the internal emission hot spot area of the landfill is positioned.

The invention belongs to the technical field of greenhouse gas emission monitoring, and discloses an observation and simulation integrated city CO2 emission plume monitoring method and system. The method comprises the following steps: collecting and preprocessing multi-source data; building an atmospheric CO2 column concentration (XCO2) prediction model based on machine learning and multi-source satellite observation; performing artificial CO2 concentration long-term simulation by using an atmospheric transmission model; artificial CO2 concentration data per hour is simulated; and verifying an artificial CO2 emission estimation model based on optimal estimation and a result. The system is high in automation degree, can automatically recognize the emission plume on the basis of multi-source data fusion and model simulation, reduces the demand of manual intervention, improves the monitoring efficiency, and is suitable for monitoring and management of large-scale urban emission sources.

The invention discloses a variational deviation correction method and device, and relates to the field of weather forecast, and the method comprises the steps: obtaining the observation data of a current time window and an updated deviation correction coefficient of a previous time window; calculating a satellite observation deviation correction amount based on the deviation correction coefficient, the observation information and the mode background field, calculating a deviation between simulation observation and actual observation based on an observation operator, and carrying out correction and quality control on the deviation; according to the satellite observation data after quality control, on the basis of the improved target functional and the gradient thereof, in the minimization iteration process, under the joint constraint of the observation data and a numerical weather forecast mode, a deviation correction coefficient is updated online; wherein the improvement process of the target functional comprises the steps that a deviation correction coefficient is used as a control variable to be combined with a mode state variable to construct a new target functional, and variable transformation is carried out in an increment form. The assimilation analysis quality of observation data is improved, and the numerical weather forecast level is improved.

The invention relates to the technical field of signal denoising, in particular to a Beidou navigation satellite observation data purification method under a complex electromagnetic background. The method comprises the following steps: acquiring satellite observation station position data and satellite position data; satellite signal acquisition is carried out on the satellite observation stations according to the satellite observation station position data and the satellite position data, and satellite observation station receiving signals are obtained; multi-angle initial signal discretization is carried out on satellite observation station receiving signals, and an initial satellite observation comprehensive signal discrete diagram is generated. According to the invention, through systematic interference identification, signal purification, multi-angle processing and automatic precision optimization, the identification capability and filtering efficiency of interference signals are improved, so that the problem of electromagnetic interference to Beidou navigation satellite signals under a complex electromagnetic background is weakened, the flexibility of site selection of the Beidou navigation satellite continuous observation station is improved, and the accuracy of site selection of the Beidou navigation satellite continuous observation station is improved. And the precision and stability of positioning, navigation and time service are improved.

The embodiment of the invention discloses an agricultural vehicle positioning method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the posture, position and speed of an agricultural vehicle under a geographic coordinate system according to inertial navigation data obtained by an inertial navigation system; establishing an initial observation equation according to a satellite observation equation corresponding to a global satellite positioning system and a visual observation equation corresponding to a visual sensor; when it is detected that satellite data collected by a global satellite positioning system is valid, the initial observation equation serves as a target observation equation; when it is detected that the satellite data collected by the global satellite positioning system is invalid, the initial observation equation with the satellite observation equations removed serves as a target observation equation; the current error state quantity of the inertial navigation system is corrected according to the target observation equation, and the attitude, position and speed of the agricultural vehicle are corrected according to the updated error state quantity, so that the actual positioning information of the agricultural vehicle is obtained, and the robustness of the positioning result of the agricultural vehicle is improved.

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 in-ocean solitary wave amplitude inversion method, an electronic device and a readable storage medium, SWOT satellite observation data is combined with other remote sensing means observation data of close time to form a double-time-phase remote sensing image, and then the observed in-ocean solitary wave amplitude of a target sea area is obtained. According to the method, large-area two-dimensional sea surface height observation data with high resolution and high precision can be provided based on radar interference measurement, and under the condition that sea surface height abnormity caused by the marine internal solitary waves on the sea surface can be captured, the amplitude of the internal solitary waves is obtained in combination with other remote sensing observation data, so that the calculation process is simplified, and the calculation efficiency is improved. And the inversion precision is improved.

The invention discloses a satellite-based enhancement information generation method and system based on a low-orbit constellation, and relates to a satellite navigation enhancement technology, and the method comprises the steps: decoding and processing Beidou PPP-B2b message information, so as to obtain a Beidou satellite high-precision orbit and a clock error; smoothing the pseudo-range observation value by using the low-orbit satellite carrier phase observation value; constructing an ionosphere-free combined observation value by using the observation data of the low-orbit satellite, and resolving the real-time position of the low-orbit satellite based on the obtained high-precision orbit and clock correction of the Beidou satellite to obtain the position information and clock correction information of the low-orbit satellite; performing satellite-based enhanced differential correction calculation by using the obtained low-orbit satellite position information, low-orbit satellite observation data and GNSS basic navigation messages to obtain a satellite-based enhanced orbit and clock differential correction; and carrying out pseudo-range residual error correction according to the obtained satellite-based enhancement difference correction number, and carrying out satellite-based enhancement integrity parameter calculation based on a correction result. According to the method, the position of the low-orbit satellite can be determined synchronously without depending on an external high-precision GNSS satellite orbit and clock error data, and the state of the low-orbit satellite is monitored.

The invention discloses a satellite-based enhanced integrity monitoring method and system combined with satellite-earth observation, and relates to a satellite navigation enhancement technology, and the method comprises the steps: obtaining current epoch observation data; constructing an ionosphere-free combined observation value for the low-orbit star observation data and the ground station observation data; determining a low-orbit star orbit and a clock error according to the low-orbit star and ground station observation data; according to the determined orbit of the low-orbit star and the clock error, calculating a pseudo-range residual error of pseudo-range observation data of the low-orbit star and the ground station; according to the calculated pseudo-range residual error and satellite-earth observation data, calculating a satellite-based enhanced differential correction number; and calculating a satellite-based enhancement integrity parameter according to the calculated satellite-based enhancement differential correction and the satellite-earth observation data. According to the invention, the GNSS monitoring receiver is carried on the low-orbit satellite, and the GNSS observation data of the ground monitoring station and the satellite-borne GNSS observation data of the low-orbit satellite are jointly processed, so that the continuous satellite-based enhanced integrity monitoring of the full flight segmental arc of the GNSS satellite is realized.

The invention discloses a forest grass vegetation coverage refined inversion method and system based on multi-scale remote sensing, and the method comprises the steps: firstly providing end member types and end member abundance information through the field observation data of an unmanned plane, inputting the prior knowledge into a linear mixed pixel decomposition model, and carrying out the back calculation to obtain end member spectrum information of the corresponding end member types; by taking unmanned aerial vehicle over-resolution observation data as priori knowledge, the end member spectrum extraction precision is improved, and a technical support is provided for improving the inversion precision of the total coverage of forest and grass vegetation; the textural features of high-resolution satellite observation data are fully utilized, the spectral features of medium resolution and the textural features of high resolution are combined, and the optimal response relation between different remote sensing feature combinations and woody vegetation coverage measured data is constructed by utilizing a random forest model. Therefore, high-precision inversion of the woody vegetation coverage in a large-area range is realized, and data support is provided for further estimation of the herbaceous vegetation coverage.

The invention discloses an earth surface CO2 concentration space-time prediction method fused with satellite-earth observation data, and is suitable for the field of atmosphere and space data monitoring. Preprocessing the acquired XCO2 observation data of the multi-source satellite, the CO2 observation data of the ground station and the auxiliary variable data, and fusing the XCO2 data observed by the multi-source satellite based on an uncertainty weighting method; quantifying the correlation between each auxiliary variable data and the CO2 concentration of the ground station based on a Pearson's correlation coefficient, carrying out significance test, and screening auxiliary variable data with significant correlation; a CNN-GRU-Attention deep fusion model is constructed, spatial-temporal correlation modeling is carried out on the CO2 concentration of a ground station, multi-source satellite observation XCO2 fusion data around the ground station and remarkably related auxiliary variables, and a spatial-temporal characteristic relation is mined, so that spatial-temporal prediction is carried out on the CO2 concentration of the earth surface. The method is strong in generalization ability and high in prediction precision, and can be suitable for space-time prediction of the surface CO2 concentration in different regions.

The present application relates to an Informer-based autonomous satellite positioning accuracy prediction method and apparatus. The method includes: collecting satellite observation data of a train within a given operational section, there being an area where satellite signals are blocked within the given operational section, where the satellite observation data is satellite observation data collected by the train on the given operational section via a satellite positioning receiver; according to the satellite observation data on the given operational section, obtaining a prediction data set based on an effective value range of a preset three-dimensional position accuracy factor; and based on a preset accuracy prediction model, outputting an autonomous satellite positioning accuracy predictive value by taking the prediction data set as an input.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

The invention discloses a low-to-low multi-satellite observation task planning and designing method, and the method comprises the following specific steps: 1) determining target constellation orbit deployment related parameters in a low-to-low multi-satellite observation task system, the target constellation orbit deployment related parameters specifically comprising a target constellation orbit height H < Tar >, a target constellation orbit inclination angle i < Tar >, a target constellation orbit height i < Tar >, a target constellation orbit inclination angle i < Tar > and a target constellation orbit inclination angle i < Tar >; the target constellation orbital plane number PTar and the target constellation same orbital plane satellite number NSar are calculated; 2) determining the orbit height of the monitoring satellite by referring to the target constellation orbit height distribution characteristics; 3) determining the number of orbital planes needing to be deployed by the monitoring satellite system and the number of monitoring satellites on each plane by combining target constellation orbit deployment related parameters and task time requirements; 4) analyzing task requirements according to given specific tasks, and proposing a task planning strategy of a single satellite to multiple satellites; and 5) completing multi-target simulation analysis of multiple satellites, and giving a low-to-low multi-satellite observation task result. According to the method, two technologies of maneuvering orbital transfer and natural intersection are combined, the problem of narrow imaging window of a single-to-single target different-plane grazing autonomous task planning algorithm in actual engineering use is successfully solved, and low-consumption and high-efficiency multi-target observation is finally 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 relates to an optical satellite absolute radiometric calibration method, device and equipment under ocean low brightness based on an offshore pseudo-invariant field and a medium, and belongs to the technical field of in-orbit absolute radiometric calibration. According to the method, a marine pseudo-invariant field with stable water and atmospheric optical characteristics is determined according to marine observation data of a calibrated optical satellite, the off-water radiance of the marine pseudo-invariant field is calculated according to the chlorophyll a concentration of the marine pseudo-invariant field, the zenith radiance is theoretically calculated, a data source is provided for absolute radiometric calibration of a satellite to be calibrated, and calibration is completed. The method explores the potential of the marine pseudo-invariant field in supplementing the traditional radiometric calibration method, gets rid of the limitation of a fixed and single marine optical buoy MOBY site data source, improves the data volume, the coverage area and the acquisition frequency of effective calibration data, provides guarantee for the continuity of an optical satellite observation task, and has a wide application prospect. And a plurality of calibration sources can be utilized to realize rapid and accurate calibration of the optical satellite, so that the calibration efficiency of the offshore optical satellite is remarkably improved.

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 provides a wind cloud satellite observation data assimilation method, a wind cloud satellite observation data assimilation device, electronic equipment and a storage medium, and relates to the technical field of meteorological data processing. Real-time land surface temperature and space lattice points of a background field are subjected to temporal-spatial resolution alignment; if so, replacing the land surface temperature in the background field at the corresponding moment with the real-time land surface temperature corresponding to the non-missing measurement area; if the actual measurement area is a missing measurement area, correcting the land surface temperature at the corresponding moment in the background field based on a land surface temperature correction model to obtain an optimized background field; combining the observed radiance with the optimized background field, constructing an objective function, and calculating to obtain an analysis field; and carrying out iterative optimization on the analysis field to minimize the target function, and outputting an optimal analysis field. According to the method, background field errors can be reduced, so that the system has better satellite data assimilation capability, and meanwhile, the assimilation effect of the satellite data under the high-altitude complex terrain is improved.

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 multi-satellite autonomous cooperation method for wide-area searching and tracking of a maritime moving target, and the method comprises the steps: S1, carrying out the preliminary observation of a set maritime target region through a high-orbit satellite, obtaining rough target information, and transmitting the rough target information to a ground station in real time; s2, the ground station forms an initial marine target situation list; s3, the ground station generates a high and low orbit satellite observation demand list according to the marine target situation list; s4, the ground station plans a task sequence of the satellite at the current moment according to the high and low orbit satellite observation demand information and sends the task sequence to the corresponding satellite, and the corresponding satellite executes the task sequence and feeds back an observation result to the ground station; and S5, the ground station updates the sea target situation list according to the observation results of the corresponding satellites, and the step S3 is returned to realize wide-area search and continuous tracking of the sea target. According to the invention, situation updating and prediction can be carried out according to dynamic information, dynamic observation requirements are automatically generated, and a satellite can rapidly plan task response requirements.

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 belongs to the field of ionosphere satellite magnetic field seismic anomaly extraction, and particularly relates to a satellite magnetic field seismic anomaly extraction method based on complex non-negative matrix factorization, which comprises the steps of reading Swarm A satellite magnetic field data, and removing a main magnetic field of a ground core and a lithosphere magnetic field of an earth crust to obtain a residual magnetic field; performing short-time Fourier transform on the preprocessed data to obtain a complex time-frequency matrix in a rectangular coordinate representation form of a complex domain; decomposing the complex time-frequency matrix by using complex non-negative matrix decomposition to obtain R feature components; performing inverse short-time Fourier transform on each feature component to obtain a time domain feature component; a mean square root of a time domain seismic component of each orbit is calculated, a threshold value is set by using an over-limit rate method, and abnormal points are extracted from the seismic components of the orbits. The amplitude and phase information contained in the time-frequency domain of the satellite observation data is fully utilized, so that the component related to the earthquake is identified, and the earthquake anomaly extraction is more accurate and reliable.