87 results about "Space weathering" patented technology

The invention provides an ionosphere inversion method and system based on space-ground multi-source data fusion, and belongs to the technical field of ionosphere inversion. The problems that an existing ionosphere inversion method is poor in precision, low in timeliness and poor in adaptability to ionosphere change characteristics in different regions and under different conditions are solved. According to the method, after a proper ionized layer model is selected, sky-ground data are acquired and processed, and the critical frequency of an ionized layer, the vertical height at which the electron density reaches the maximum value and the total electron content of the ionized layer are reconstructed by using an improved Kriging interpolation method; and based on the reconstruction result, analyzing the change rule of the ionized layer characteristics along with time and space distribution and under different disturbance conditions. The method can improve the precision and reliability of ionosphere inversion, so that key parameters such as electron density distribution, critical frequency, total electron content and the like of the ionosphere are described more accurately, and more accurate data support and theoretical guidance are provided for the fields of navigation positioning, communication system optimization and space weather monitoring.

The invention discloses a marine meteorological observation data millisecond query system based on time sequence block indexing. The marine meteorological observation data millisecond query system comprises a data input module, a data preprocessing module, a data hierarchical modeling module, a data storage cluster, a data analysis engine, a data query engine and a data operation interface module. Based on data modeling, data thinning and data partitioning technologies, data preprocessing is carried out for different meteorological ocean space weather data, and distributed storage and access of mass data are achieved; the method comprises the following steps: inputting ocean actual observation data, forecast data or historical data, carrying out model construction and index design according to meteorological ocean space weather data characteristics, completing distributed storage, and applying a data query engine and a data analysis engine to realize rapid aggregation query of the meteorological ocean space weather data; storage and query of lattice point data are realized by using a distributed database, and the problems of scale and query performance of meteorological ocean space weather data are effectively solved.

The invention provides a loop current prediction method based on a CNN and a BiLSTM neural network. The loop current prediction method is used for improving the prediction precision of proton flux data in space weather. The method comprises the following implementation steps of: acquiring and preprocessing data; a CNN neural network model and a BiLSTM neural network model are constructed; training the model; and evaluating and visualizing the model. According to the method, the advantages of the CNN in the aspect of spatial feature extraction and the powerful time sequence processing capability of the BiLSTM neural network are utilized, and finally, the outputs of the CNN and the BiLSTM neural network are fused, so that the spatial-temporal dynamic distribution of the ring current proton flux can be more effectively captured. Experimental results show that the method has excellent performance in a loop current prediction task, is suitable for real-time detection and early warning of space weather, and has relatively high prediction precision and stability.

The invention discloses a global ionosphere TEC prediction method based on a coding and decoding space-time Transform model, and belongs to the technical field of ionosphere total electron content prediction, and the method comprises the steps: S1, obtaining and preprocessing TEC, Dst, Kp, SSN and F10.7 data, making a sample, and dividing the data into a training set, a verification set and a test set; s2, a space-time Transform model based on encoding and decoding is constructed, an encoder is composed of a multi-branch convolution layer and a space-time Transform layer, and a decoder is composed of a convolution decoding layer; and S3, training the model by using a training set, optimizing parameters through a loss function, adjusting and optimizing by using a verification set, and evaluating performance and generalization ability by using a test set. According to the method, the global TEC prediction precision is remarkably improved, relatively high prediction performance and robustness can be kept under a severe space weather condition, and efficient extraction of space-time local features and global features is realized.

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 belongs to the technical field of aerospace, and relates to a TLE parameter prediction method and system based on TFT multi-source data fusion. The method comprises the following steps: generating a feature data set according to a historical TLE data set of an LEO uncontrolled space target, target static feature data and space weather data; adopting a # imgabs0 # model to obtain an angle parameter at the prediction moment according to the TLE data at the moment before the prediction moment; adopting a # imgabs1 # model to obtain core orbit parameters at a prediction moment according to the feature data set; acquiring a temporary TLE orbit parameter at the prediction moment according to the angle parameter at the prediction moment and the core orbit parameter at the prediction moment; updating a mean anomaly prediction value according to the current prediction step number and the temporary TLE orbit parameter at the previous moment; and replacing the mean anomaly preliminary prediction value in the temporary TLE orbit parameters at the prediction moment with the mean anomaly prediction value to obtain the TLE orbit parameters at the prediction moment. And the precision, real-time performance and efficiency of long-term orbit parameter prediction are improved.

The invention provides an aviation ADS-B anomaly detection method and system based on semi-supervised joint modeling, and belongs to the technical field of aviation safety monitoring. The method comprises the following steps: receiving and screening ADS-B data, and extracting NACp, position, speed and time features; denoising and screening low-confidence samples through Hampel filtering and a Gaussian mixture model; using trajectory clustering and NACp precision clustering to construct a dual anomaly labeling set; fusing point-level, trajectory-level and space grid features to form a multi-dimensional feature set; generating a high-confidence pseudo label based on multi-model voting, and dynamically training and inferring in combination with a LightGBM semi-supervised model; and outputting an abnormal level and visualizing the abnormal level through a thermodynamic diagram. Through semi-supervised learning and multi-modal clustering, the problems of label scarcity and category imbalance are solved, and the detection precision is improved; in combination with heterogeneous calculation and dynamic iterative optimization, efficient real-time processing is realized, and support is provided for aviation safety monitoring in complex space weather.

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 an LEO track single event effect inversion method and device, a medium and a product, and relates to the field of space environment monitoring and evaluation, and the method comprises the steps: carrying out the training and optimization of a first LSTM neural network and a second LSTM neural network at the same time through a training data set, and obtaining a classification model and a regression model; the current position and the current time are pushed forwards for a set number of hours, the average value of the space weather parameters of each hour is input into a classification model and a regression model at the same time, and a numerical value output by the classification model and a particle flux predicted value at the current position output by the regression model are multiplied; obtaining a final particle flux prediction value at the current position; obtaining the particle flux of the LEO orbital plane according to the final predicted value of the particle flux at each position to be predicted on the LEO orbital plane; and obtaining the single event upset rate of the LEO orbital plane according to the particle flux of the LEO orbital plane. The method can accurately predict the particle flux of the LEO orbital plane and the single event upset rate of the LEO orbital plane.

The invention belongs to the technical field of space meteorological prediction, and discloses a global ionosphere plate thickness prediction method based on multi-source data. According to the method, firstly, key input features are screened based on a physical mechanism and statistical correlation, then a data set containing time parameters, space parameters, ionosphere characteristic parameters and external environment parameters is constructed based on the key input features and a multi-source data fusion mechanism, and data preprocessing is performed on the data set; and then, constructing an ionosphere plate thickness prediction model based on a machine learning algorithm, and revealing regular changes of the ionosphere plate thickness prediction model along with solar activity, geomagnetic disturbance, latitude and seasons. According to the method, the machine learning algorithm is applied to global ionosphere plate thickness prediction for the first time, the spatial and temporal change rule of the global ionosphere plate thickness is effectively captured through feature conversion processing by means of multi-source data, the prediction precision of the global ionosphere plate thickness is improved, and the method shows high robustness under the extreme space weather condition.

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 application discloses an ionosphere total electron content prediction method, a model training method and a system, relates to the field of satellite geodesy and space weather prediction, and the model training method comprises: an ionosphere total electron content prediction model comprising an encoder part of a stacked autoencoder, an Informer network and a decoder part of the stacked autoencoder connected in sequence; wherein the model training comprises stacked autoencoder training and Informer network training. The ionosphere total electron content prediction model is constructed based on the Informer network and the stacked autoencoder, the ionosphere total electron content prediction model is used for ionosphere total electron content prediction, the time complexity is reduced, the memory occupation is reduced, and the ionosphere total electron content prediction efficiency is improved.

This invention discloses a device and method for simulating the space weathering of lunar rocks. The device includes a vacuum chamber, a stage, a vacuum control module, a temperature control module, a pulsed laser module, a high-energy ion beam module, a microscope module, and a control module. The vacuum chamber is hollow, forming a vacuum cavity. The stage is located inside the vacuum chamber. The vacuum control module is located on the vacuum chamber. The temperature control module, pulsed laser module, and high-energy ion beam module are all connected to the vacuum chamber. The microscope module is located inside the vacuum chamber, facing the stage. The control module is electrically connected to the vacuum control module, temperature control module, pulsed laser module, and high-energy ion beam module. By integrating and precisely controlling multiple modules, the device simulates the space weathering of lunar rocks, realizing a scenario where multiple space weathering factors act together, thus improving the realism of ground-based simulation experiments.

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 present application discloses a method and device for fusing space weather observation and simulation based on a 3D adversarial network, relating to the technical field. The method includes: interacting with a space weather simulation database, and obtaining simulation data based on a preset selection constraint; performing adaptive grid division on the simulation data based on adaptive grid technology to obtain divided adaptive grids, and storing the divided adaptive grids in an associated manner with the simulation data; obtaining observation data, and performing grid matching of the observation data based on the divided adaptive grids and the simulation data; inputting the observation data and the simulation data after grid matching into a 3D GAN generator to obtain generated data, wherein the generated data is marked with gradient information; activating a 3D GAN discriminator, performing data discrimination based on the generated data, and outputting fused data based on the data discrimination result. Thus, the technical effects of improving the data fusion accuracy and enhancing the fusion adaptability are achieved.

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 provides a high-latitude ionosphere phase flicker prediction method based on a segmented recurrent neural network, and belongs to the technical field of aviation navigation. According to the method, four kinds of heterogeneous data of a super dual aurora radar, an altimeter, a geomagnetic solar activity index and ionospheric scintillation are integrated, and a minute-level fusion data set is constructed through space-time registration; complementing missing values by using a self-attention neural network and keeping data distribution characteristics; a segmented recurrent neural network model is innovatively designed, cross-day scale long-range dependence and minute-level sudden flicker evolution characteristics are synchronously captured through a double-path state transfer architecture, and continuous high-precision prediction of flicker phases is achieved. A time decay weighted evaluation mechanism is adopted to carry out non-linear penalty on the deviation of the starting moment and the ending moment of flicker, and the robustness of the model in complex space weather is verified. The support efficiency of the dynamic suppression strategy of the navigation receiver is remarkably improved, and the positioning stability in the polar region ionosphere environment is enhanced.

The present invention discloses a method, device, equipment and medium for ionospheric data enhancement over the ocean, belonging to the technical field of ionospheric modeling. The method includes: obtaining vertical total electron content data at several positions of the ionosphere over the ocean through observations by low-Earth orbit satellites; combining the vertical total electron content data at several positions of the ionosphere over the ocean, as well as the longitude, latitude, observation time and space weather state data of the corresponding ionospheric piercing points as input feature data; inputting the input feature data into a first learner to obtain enhanced feature data; and inputting the input feature data and the enhanced feature data into a second learner to obtain the vertical total electron content data of the ionosphere over the ocean. The present invention enhances ionospheric data over the ocean through an integrated machine learning model, and can obtain virtual observation data of the ionosphere in unobserved areas over the ocean, effectively improving the ionospheric observation coverage over the ocean.

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 present invention belongs to the field of aerospace technology and relates to a TLE parameter prediction method and system based on TFT multi-source data fusion. The present invention generates a feature dataset based on historical TLE datasets, target static feature data, and space weather data of uncontrolled LEO space targets; uses a model to obtain angle parameters at the prediction moment based on TLE data at the moment before the prediction moment; uses a model to obtain core orbit parameters at the prediction moment based on the feature dataset; obtains temporary TLE orbit parameters at the prediction moment based on the angle parameters and the core orbit parameters at the prediction moment; updates the mean anomaly prediction value based on the current prediction step number and the temporary TLE orbit parameters at the previous moment; and uses the mean anomaly prediction value to replace the preliminary mean anomaly prediction value in the temporary TLE orbit parameters at the prediction moment to obtain the TLE orbit parameters at the prediction moment. This improves the accuracy, real-time performance, and efficiency of long-term orbit parameter prediction.