54 results about "Space weather" patented technology

The invention discloses a global ionosphere modeling method and device based on Beidou single-frequency ionosphere weighted PPP, and belongs to the technical field of GNSS navigation positioning and atmosphere modeling. The method comprises the following steps: acquiring real-time precise orbit and clock error information of a Beidou satellite and an initial ionospheric delay correction value; a single-frequency ionosphere weighted precision single point positioning (SF-IW-PPP) observation equation is constructed; establishing a space-time correlation constraint model of the ionized layer delay error and determining a prior variance; carrying out combined calculation by taking the previous variance as a constraint to obtain an ionosphere delay residual error, and further obtaining the total delay of the high-precision inclined ionosphere; and finally generating a global ionosphere grid model. According to the invention, high-precision and real-time global ionosphere modeling is realized by only needing a low-cost single-frequency receiver and combining Beidou real-time enhancement service, the hardware threshold and data processing delay are remarkably reduced, and the method is suitable for the fields of public navigation, intelligent transportation, space weather monitoring and the like.

The invention discloses an ionospheric scintillation monitoring method based on a geodetic measurement receiver, which belongs to the technical field of satellite navigation, and comprises the following steps of: accurately eliminating non-scintillation errors by constructing an inter-station single-difference function model and an inter-station single-difference random model, extracting carrier phase residual errors by using a single epoch ambiguity fixing technology, and finally obtaining an ionospheric scintillation monitoring result. Establishing a reliable ionospheric scintillation monitoring index; according to the ionospheric scintillation monitoring method based on the geodetic measurement type receiver, high-precision real-time monitoring of ionospheric scintillation can be realized through a single-epoch ambiguity fixing technology under the condition of not depending on an external precision product, and reliable technical support is provided for space weather monitoring.

The invention discloses an ionosphere TEC forecasting method, system, medium and equipment fused with a geomagnetic field, and belongs to the field of ionosphere TEC forecasting, the method comprises the following steps: obtaining current ionosphere TEC grid data, solar activity index data and global geomagnetic field observation data, and constructing to obtain multi-channel fusion data; based on a preset ConvLSTM model, processing the multi-channel fusion data, and outputting an ionized layer TEC forecast result; wherein the ConvLSTM model is obtained by training an input sequence in historical multi-channel fusion data and a corresponding target sequence; the input sequence is composed of multi-channel fusion data of a plurality of consecutive time steps, and the target sequence is composed of ionized layer TEC grid data immediately after the input sequence. By implementing the method, the forecasting precision of the ionized layer TEC under complex space weather conditions such as magnetic storm can be improved.

The invention discloses a grid point correction method and device for space meteorological data and a medium, and relates to the technical field of meteorological artificial intelligence, and the method comprises the steps: collecting original space meteorological data, and carrying out the preprocessing; extracting grid point geographic coordinates, observation stations and satellite pixels from the preprocessed original space meteorological data, defining the grid point geographic coordinates, the observation stations and the satellite pixels as nodes, and constructing a multi-modal meteorological space-time heterograph; performing deep feature extraction and fusion processing on the multi-modal meteorological space-time heterogeneous graph through a graph neural network to obtain a high-dimensional feature fusion vector, and calculating a preliminary residual error estimation value according to the high-dimensional feature fusion vector to obtain preliminary residual error field data; according to the method, the multi-modal meteorological space-time heterogeneous graph is constructed, and deep feature extraction and fusion processing are performed by using the graph neural network, so that space-time correlation modeling and nonlinear feature learning of multi-source heterogeneous data are realized, the data representation capability is enhanced, and the accuracy of preliminary residual estimation is improved.

The application provides a satellite communication power adaptive regulation method, system and device, which divides the space where satellite communication belongs into multiple space grid areas according to a preset space division scale; acquires satellite sensing data, space weather data and near-earth weather data in each space grid area to determine communication link loss evaluation data corresponding to each space grid area; generates space interference summary information according to the loss evaluation data corresponding to each space grid area and location data thereof; and adjusts the transmission power of satellite communication in each space grid area according to the space interference summary information. The method and system of the application collect weather data in the space area and the near-earth area thereof to determine loss evaluation data, and realize dynamic adaptive adjustment of satellite transmission power in each space grid area based on the loss evaluation data, thereby improving the reliability of satellite communication in a complex space environment.

The invention discloses a satellite orbit forecasting method based on in-orbit calibration of model parameters. Belongs to the technical field of remote sensing satellite measurement, operation and control and particularly relates to the technical field of remote sensing satellite orbit forecasting. According to the method, the satellite windward side-to-mass ratio in an atmospheric resistance model is calibrated regularly according to actually measured orbit data, the resistance coefficient in the atmospheric resistance model is calibrated in real time, and more accurate model parameters are used for modeling a perturbative force model in the satellite orbit forecasting process, so that the error of the atmospheric resistance model is reduced, and the orbit forecasting precision is improved. In the time period that the space weather changes gently, the atmospheric resistance modeling precision is improved to 90% or above from about 60%-80% of a traditional method through the method.

This invention discloses an automatic method for extracting ionospheric TEC anomalies based on global seismic events. The method acquires a global seismic event catalog, global ionospheric TEC data, and space weather indices, performs data preprocessing, and for each seismic event, selects those occurring during quiescent periods based on space weather conditions. Ionospheric TEC data within fixed observation windows and space windows are extracted and differiated with contemporaneous background TEC data to obtain ionospheric TEC difference values. Normalized statistical analysis is then performed on the quiescent period TEC difference values ​​and their frequency corresponding to global seismic events to obtain statistical characteristics of ionospheric TEC anomaly rates and frequencies. Random seismic events are generated globally for comparison. This method is comprehensive, programmable, and suitable for automated processing of large global samples, possessing good scientific rigor, scalability, and engineering application value.

The invention provides a satellite communication power adaptive regulation and control method, system and device, and the method comprises the steps: dividing a space to which satellite communication belongs into a plurality of space grid regions according to a preset space division scale; acquiring satellite sensing data, space weather data and near-earth weather data in each space grid region to determine communication link loss evaluation data corresponding to each space grid region; generating spatial interference summary information according to the loss evaluation data and the location data corresponding to each spatial grid region; and adjusting the transmitting power of satellite communication in each space grid area according to the space interference summary information. According to the method and the system provided by the invention, the loss evaluation data is determined by collecting the weather data in the space region and the near-earth region, and the dynamic self-adaptive adjustment of the satellite transmitting power in each space grid region is realized based on the loss evaluation data, so that the reliability of satellite communication in a complex space environment is improved.

An electronic device and method for adaptively storing data in bundle form. An aspect of the present disclosure provides an electronic device comprising: a processor; a memory storing instructions; a first storage storing data; and a second storage having a radiation tolerance higher than that of the first storage, wherein, when executed by the processor, the instructions cause the electronic device to transfer data stored in the first storage to the second storage when a deterioration of a space weather environment of a region of space in which the electronic device is located occurs, and to transfer data stored in the second storage to the first storage when the deterioration of the space weather environment is resolved, and wherein the data stored in the first storage or the data stored in the second storage is data that is transmitted to another electronic device via space communications in bundle form.

The invention relates to the technical field of magnetic sheath pressure balance analysis, in particular to a magnetic sheath pressure balance analysis method based on an IMF clock angle box. According to the technical scheme, the method comprises the following steps: data acquisition: acquiring plasma data, magnetic field data and interplanetary magnetic field IMF parameters of an earth magnetic sheath region in a target time period; and the IMF clock angle box is used for calculating an IMF clock angle based on the IMF parameters. The system analysis method based on the IMF clock angle box is established, and the problems of rough classification and information loss caused by the fact that traditional research only depends on an IMF single component are solved; according to the method, quantitative statistics of the dynamic balance characteristics of the hot pressure and the magnetic pressure is realized, and the independence and coupling influence of each component of the IMF on the pressure fluctuation can be determined through the regression model, so that a direct and reliable observation analysis tool is provided for deeply understanding the physical process of the magnetic sheath and constructing an accurate space weather model.

The paper presents a high-latitude ionospheric scintillation intensity prediction method, belonging to the field of space weather monitoring and satellite navigation technology. The low-frequency trend item, high-frequency non-stationary item and residual item are generated by using the time series of high-latitude ionospheric phase scintillation factor. The high-latitude ionospheric scintillation intensity prediction model is constructed by using the ionospheric phase scintillation factor and the target physical characteristics. The prediction value is generated by using the low-frequency trend item to train the multivariate CNN-BiLSTM-Attention. The prediction value is generated by using the high-frequency non-stationary item and the residual item to train the single variable CNN-BiLSTM-Attention. The ionospheric scintillation intensity can be predicted by combining the trained high-latitude ionospheric scintillation intensity prediction model. The method can effectively improve the accuracy, stability and cross-station migration ability of multi-step prediction of scintillation in complex ionospheric environment, and provides a means for studying ionospheric disturbance law and improving the reliability of high-precision application of global navigation satellite system.

The invention discloses a system and a method for measuring celestial body radiation noise based on an idle channel of a GNSS (Global Navigation Satellite System) receiver, relates to the crossing field of satellite navigation and radio astronomy detection, fully utilizes the idle channel in the receiver, realizes function expansion through firmware and algorithm upgrading, and does not need to add radio frequency hardware. This means that the idle channel and the navigation channel share the same radio frequency link in the links of the antenna, the feeder line, the low noise amplifier, the down-conversion and the like, so that the noise measurement result is consistent with the real observation condition. The deep fusion of navigation and scientific detection is realized, the baseband output is converted into a real-time radio radiation detector through statistical analysis, and the time sequence data of the equivalent noise temperature of the antenna can be continuously acquired. The data can be directly used for tasks such as sun storm monitoring, moon brightness temperature measurement, space background radiation change analysis and the like, and a real-time data source is provided for radio astronomy, space weather monitoring and planetary scientific detection.

This invention discloses a method for extracting ionospheric scintillation factors based on DORIS dual-frequency observations, belonging to the field of space weather monitoring and satellite navigation technology. By constructing a cycle slip detection and low elevation angle control method suitable for the DORIS system, the RODTI (Rate of Change in Total Electron Content) factor of the ionosphere is extracted. A two-dimensional grid background field of ROTI is constructed using GPS observation data from IGS stations, achieving spatiotemporal matching between DORIS and GPS scintillation factors. Furthermore, a support vector regression method considering latitudinal constraints is introduced to normalize and correct the RODTI amplitude, solving the problem of inconsistent scintillation factor amplitudes between different observation systems. This yields a more accurate RODTI with amplitudes consistent with ROTI, thus forming a complete method for extracting ionospheric scintillation factors based on DORIS dual-frequency observations. This method can reliably reflect the spatiotemporal distribution characteristics of ionospheric scintillation on a global scale, providing a new data source for constructing a multi-source ionospheric scintillation dense monitoring network covering oceans, polar regions, and other areas, and has broad applicability.

A method for dynamic adaptive threshold orbit maintenance of low-Earth orbit satellites based on space environment parameter sensing includes the following steps: S1, Offline mapping model construction: With the goal of optimizing global fuel efficiency, a mapping model between space environment parameters and the optimal orbit maintenance threshold is generated through simulation and optimization algorithms; S2, Online sensing and control: Space environment parameters are sensed in real time, and the adaptive threshold under the current space environment is dynamically determined based on the mapping model. The satellite orbit decay is monitored, and when the orbit decay reaches or exceeds the adaptive threshold, an orbit maintenance maneuver is triggered. By constructing an intelligent mapping model of "space environment - optimal threshold", the trigger threshold for orbit maintenance is upgraded from a fixed value to a variable that dynamically adapts to space weather, thereby achieving optimization of fuel consumption at the global level, while effectively balancing control efficiency during calm space weather periods and orbit safety under extreme events such as geomagnetic storms.

The invention belongs to the technical field of ionosphere total electron content prediction, and particularly discloses an ionosphere total electron content prediction method based on physical parameter enhancement. The data set comprises ionosphere TEC observation data, space environment data and two-dimensional physical parameter data output by a thermal layer-ionosphere electrodynamic circulation model, and the thermal layer-ionosphere electrodynamic circulation model is based on a first principle; the evolution of a thermal layer and an ionized layer is calculated by adopting thermodynamics, momentum conservation, electromagnetic field coupling and plasma kinetic equations; the data set is preprocessed, so that the TEC observation data and the physical parameter data are matched on the spatial scale; inputting the preprocessed data set into a pre-established ConvLSTM deep learning model for training; and using the trained ConvLSTM model to predict the future ionosphere TEC distribution. According to the invention, the prediction precision of the ionized layer TEC in an extreme space weather state can be effectively improved.

The application discloses an extraterrestrial body surface environment comprehensive simulation device, and relates to the field of extraterrestrial body surface environment simulation.The device comprises a vacuum cavity, which can simulate the vacuum environment or the atmospheric composition of the extraterrestrial body surface after being vacuumized or filled with a specific atmosphere.The vacuum cavity is provided with an extension interface, an observation window and a reserved interface on the side wall or other preferred positions, and a pulsed laser deposition system and other equipment are incident from the reserved interface or the observation window to simulate the space weathering process represented by micro-meteorite impact; a temperature control system is used to adjust the temperature of a loading platform to a temperature value required for simulating the high-temperature and low-temperature environment of the extraterrestrial body surface; the observation window is used to observe the test sample in the simulation process, and the reserved interface is connected with a characterization instrument to obtain the change of the related characteristics of the test sample in situ.The application can simulate and truly reflect the extraterrestrial body surface environment and the space weathering modification effect, and can be used for in-situ characterization, thereby providing reliable ground verification and prediction for the difficulties and challenges that may be faced by a deep space exploration task.

The invention provides a data processing method and system of an ionized layer Doppler monitoring system. The method comprises the following steps: acquiring to-be-processed output data of the ionized layer Doppler monitoring system; performing fast Fourier transform according to the to-be-processed output data to obtain power distribution data; performing parameter inversion according to the power distribution data to obtain Doppler frequency shift data; performing dynamic quality control on the Doppler frequency shift data, and outputting quality grading data; according to the invention, fast Fourier transform is carried out based on an original signal output by a traditional ionospheric Doppler monitoring system to realize conversion from time domain to frequency domain, which is beneficial for accurately capturing signal frequency characteristics; frequency domain features are converted into ionosphere physical parameters through parameter inversion, and dynamic quality control is combined, so that the reliability and availability of data are guaranteed; therefore, the method provided by the invention is beneficial to improving the ionosphere monitoring data processing efficiency and precision, thereby providing high-quality data support for ionosphere disturbance monitoring, space weather gas early warning and the like.

The invention relates to an ultra-low orbit 100-300km single-site atmospheric wind speed modeling method based on transfer learning, and the method comprises the steps: collecting multi-source wind field data at different heights, and carrying out the preprocessing of the multi-source wind field data; performing fusion processing on the preprocessed multi-source wind field data to obtain fused wind field data; performing wavelet multi-scale decomposition on the fused wind field data to obtain multi-scale different-height feature data; inputting the multi-scale different-height feature data into a pre-constructed transfer learning framework, and extracting field-independent features and target field generalization modeling; and inputting the extracted domain irrelevant features and a target domain generalization modeling result into a pre-constructed DeepONet model, obtaining a wind speed prediction value, and obtaining a gravity wave amplitude and a tide phase parameter at the same time. The problem of height expansion is effectively solved under the small sample condition, and the method is suitable for scenes such as ultra-low orbit aircraft orbit control, space weather disturbance early warning and atmospheric density modeling.

The invention discloses a coronal mass ejection arrival time prediction method based on deep learning, and belongs to the technical field of space weather forecast. The method comprises the following steps: S1, establishing a prediction platform, acquiring existing data, and constructing the existing data to obtain a feature matrix and a target vector; s2, classifying the data set, and obtaining a verification set and a training set through cross validation division; s3, preprocessing the feature matrix and the target vector; s4, constructing a prediction model, and carrying out early-stage training and evaluation; and S5, performing final training on the prediction model after the early-stage training to realize prediction of the coronal mass ejection arrival time. According to the method, machine learning and a deep learning model are utilized to predict the arrival time of coronal mass ejection CoronalMasEjection propagating from the solar surface to the vicinity of the earth, and the accurate prediction of the arrival time is of great significance to the safe operation of related infrastructures.

This invention discloses a method and system for orbit determination of BeiDou satellites by compensating for solar radiation pressure using a deep learning network. Addressing the problem of limited orbit determination accuracy due to inaccurate estimation of solar radiation pressure parameters under regional observation networks, this invention acquires raw satellite observation data and space weather data to construct a dataset containing features such as satellite position, velocity, acceleration, attitude, solar altitude angle, and geomagnetic index. A gradient boosting tree algorithm is used to filter key features; a Transformer network containing only the encoder is used to predict radiation pressure acceleration errors; a virtual observation equation is constructed based on the prediction error and jointly solved with the GNSS observation equation to constrain ECOM radiation pressure parameter estimation; and residual detection is introduced to eliminate outlier predicted values. This invention significantly improves the orbit determination accuracy of BeiDou satellites under regional station networks without requiring additional hardware, and exhibits stable performance across different regions and with different numbers of stations.

This disclosure pertains to a space weather forecast system (1), comprising a space weather forecast apparatus (100), a plurality of the LEO satellites (202), and one or more ground stations (204) connected to the space weather forecast apparatus (100), the one or more ground stations (204) being further configured to send first electromagnetic signals to the LEO satellites (202), to receive second electromagnetic signals from the LEO satellites (202) and to transmit the received second electromagnetic signals to the space weather forecast apparatus (100).

The invention discloses an ionosphere VTEC prediction method and device based on KAN and step-by-step learning, and belongs to the technical field of crossing of geophysics and artificial intelligence. The method comprises the following steps: firstly, constructing a multi-source fusion data set containing solar geomagnetic parameters and spatio-temporal characteristics, and fitting a nonlinear relationship between VTEC and input characteristics through a B-spline basis function by using a KAN network as a prediction model to realize high-precision prediction and extract an interpretable mathematical expression; an incremental learning mechanism is further introduced, initial training, symbolic distillation, symbolic guidance retraining and period-by-period incremental adaptation are carried out on the model in stages, data distribution drifting is effectively dealt with, and catastrophic forgetting is avoided. According to the method, the interpretability and the time sequence adaptability of the model are remarkably enhanced while the VTEC prediction precision is improved, and the method is suitable for space weather monitoring and navigation positioning error correction.

The invention discloses a method for grading geomagnetic storm disasters caused by CME, and relates to the technical field of space weather disaster monitoring, and the method specifically comprises the steps of data collaborative collection, data standardization preprocessing, parameter fusion inversion, trigger source positioning and grade pre-judgment and correction. According to the method, multi-source satellite data are synchronously acquired through a solar dynamics observatory, a sun-earth relation observatory and a Parker solar detector, and through a standardized preprocessing process, a CME comprehensive parameter inversion formula is fused with the multi-source satellite data; comprehensive inversion of multi-dimensional key parameters such as CME explosion time, source region position, three-dimensional propagation direction, initial speed, acceleration, mass and magnetic flux is realized, the innovation point directly aims at the defects that a traditional method is single in data source and incomplete in inversion parameter, and through a multi-source data fusion and mutual verification mechanism, the multi-dimensional key parameters of the CME are obtained. The precision and reliability of CME parameter inversion are improved, and data support is provided for subsequent geomagnetic storm trigger source positioning and disaster grading.

The application discloses a kind of robust spline decomposition and extreme value extraction method of solar wind speed time series, comprising, after obtaining solar wind speed time series, according to predetermined spline node number n, utilize quantile position method in generated time index selects n node positions, constructs cubic B spline base function, forms spline base function;Then, realize robust fitting using Huber loss function, by iterative optimization dynamically reduce the weight of abnormal point, extract stable trend item, and utilize the absolute median deviation of residual to set extreme value discrimination threshold, accurately identify abnormal fluctuation;The final output decomposition result includes smooth trend item, extreme fluctuation item and weight vector;The above decomposition result can effectively distinguish long-term trend and extreme event in solar wind speed time series, improve solar background analysis and the identification ability of space extreme weather, applicable to space weather forecast and disaster risk assessment.

The application belongs to the technical field of artificial intelligence, and particularly relates to a TEC prediction method with double-branch frequency domain filtering and weight self-adaptation, taking the ionospheric TEC prediction task in the global range as an example, firstly, the historical TEC grid data is preprocessed, and the input sequence is processed by using reversible instance normalization (RevIN). The model extracts features through parallel double branches: the space-frequency branch transforms the grid data at each time point to the frequency domain through Fourier transform, and uses a learnable filter to capture spatial correlation; the time-frequency branch performs Fourier transform on the time sequence of each grid point, and extracts time period features in the frequency domain. The outputs of the two branches are adaptively fused through a learnable weight module, and finally the future 24-hour global TEC prediction result is obtained through RevIN reverse normalization. The model can effectively predict the change of ionospheric TEC, and has important theoretical reference and application value in the fields of space weather, navigation and earthquake detection.

The application relates to a terminal GNSS and LEO multi-mode multi-frequency ionospheric disturbance monitoring method. The method comprises the following steps: collecting GNSS and LEO satellite full-system and full-frequency point observation data, respectively calculating ionospheric amplitude scintillation indexes, phase indexes and absolute / relative TEC indexes, and outputting high-frequency adjustable original data; integrating four types of indexes and space weather data to construct a training set, extracting feature factors after removing abnormal values through an isolation forest algorithm, inputting the feature factors into an LSTM model to train and optimize hyperparameters; and finally inputting real-time index data into the trained model to realize real-time monitoring of ionospheric disturbance. The method can realize real-time monitoring of ionospheric scintillation.

The application discloses a method for analyzing the influence of global ionospheric scintillation on GPS positioning performance during geomagnetic storm, which belongs to the technical field of satellite navigation and space weather. Based on the obtained data of GPS stations and moderate geomagnetic storm events in the solar activity cycle, the global ionospheric scintillation intensity is calculated by using the elevation angle weighted average ROTI, and the PPP-AR solution and positioning performance index are extracted. The correlation between the global average ROTI and the minimum value of the geomagnetic index during the main phase of the geomagnetic storm event is statistically analyzed, and the linear correlation between the global average ROTI and the total number of cycle slips is statistically analyzed. The correlation between the elevation angle weighted average ROTI and the three-dimensional PPP relative residual and the fixed satellite ratio is analyzed, and the latitude-dependent response law and time delay characteristics of ionospheric scintillation and positioning accuracy reduction during the geomagnetic storm are analyzed. The method is accurate in analysis, effectively reveals the differential response law of different latitude regions, and provides statistical basis for the space weather warning of dynamic high-precision GNSS positioning.

The application provides a solar energetic particle (SEP) source region injection profile inversion fitting method, a fitting device, a storage medium and a terminal. The inversion fitting method provided by the application comprises constructing an inversion fitting equation. The inversion fitting equation uniformly describes the symmetric and asymmetric source region injection profile by introducing a continuously adjustable logarithmic Gaussian function with a shape parameter. The function is embedded in a physical framework that comprehensively considers the energy-dependent propagation delay and the instrument limited energy channel integration. The inversion fitting method provided by the application can improve the inversion accuracy and universality of the source region injection profile of various SEP events, and provides a key and reliable source parameter input for space weather physical prediction.

The invention discloses a solar dynamics observation data pre-training method based on multi-scale contrast learning, and belongs to the technical field of intelligent image processing. The method comprises the following steps: preprocessing SDO observation data, and constructing a pre-training framework combining an auto-encoder and contrast learning, including reconstruction loss and three levels of contrast targets: class mark alignment of global time indexes, cross-modal patch alignment of consistent positions, and spatial discrimination constraints in images at the same moment; signature logarithm intensity transformation and synchronous geometric enhancement are adopted, CNN / ViT backbones are compatible, and multi-granularity characterization with an invariable output mode and physical perception is achieved. The model trained by the method can be used as a unified visual backbone of solar image analysis, supports channel translation and missing measurement interpolation, event classification and forecast, segmentation and retrieval and other applications, reduces the labeling and computing power requirements, improves the precision and robustness, and is suitable for multi-task migration and rapid landing deployment in solar physical research and space weather services.

The invention provides a plasma detection data calibration method and device, a medium and electronic equipment. The method comprises the following steps: acquiring a downlink plasma data sequence of a satellite; performing noise separation processing on the plasma data sequence; based on a preset correlation model of plasma data and space environment parameters, according to the space environment parameters when the plasma data sequence is collected, determining target reference values corresponding to the plasma data at all moments of the plasma data sequence; and based on the plasma data of the plasma data sequence at each moment and the target reference value, performing calibration processing on the plasma data to obtain calibrated plasma data. According to the invention, high-quality conversion from original, noisy and environment-interfered observation data to calibration data is realized, and a data foundation is laid for subsequent establishment of an accurate plasma environment model and reliable space weather forecast.