15 results about "Thermosphere" patented technology

The invention discloses a data fusion method for same-platform multi-means thermal layer atmospheric density observation. The data fusion method comprises the following steps: acquiring POD measured data and pressure intensity measured data of same-satellite observation; performing inversion based on the POD measured data to obtain a first type of thermal layer atmospheric density, and performing calculation based on the pressure intensity measured data to obtain a second type of thermal layer atmospheric density; verifying the validity of the two types of density data, and continuing the verification if the verification is passed; otherwise, ending; performing orbit stage division on all the thermal layer atmospheric density data to obtain three types of orbit stages; obtaining application conditions of the two types of thermal layer atmospheric density data by combining difference characteristics of the two types of thermal layer atmospheric density data according to the three types of orbit stages; dynamically correcting the two types of thermal layer atmospheric density data through cross calibration according to the difference characteristics and the application conditions, and determining corresponding calibration factors; and according to the calibration factor, completing fusion of the two types of thermal layer atmospheric density data for the three types of orbit stages.

The invention belongs to the technical field of atmospheric density prediction, and discloses a cross-source data fusion thermal layer atmospheric density hierarchical progressive full-scale prediction system and method, which utilize respective advantages of different gradient data sources to construct a reference density field-corrected density field-instantaneous refined density field three-level hierarchical fusion refinement architecture. Cross-source data calibration and physical constraint modeling are combined to realize month-year scale long-term prediction, day-week scale medium-short-term prediction and short-term prediction of the thermal layer atmospheric density. According to the method, through a three-level layered refinement architecture, the long-term coverage advantage of TLE data, the mesoscale variable rate description advantage of precise orbit data and the high-frequency and high-precision advantages of accelerometer data are fully mined, the defect of a single data source is avoided, full-scale and full-area density precise description is achieved, the thermal layer atmospheric density refinement precision and prediction suitability can be improved, and the method is suitable for large-scale and large-scale measurement. And data cost is reduced.

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.

This invention discloses a method for reconstructing the peak electron density of the ionosphere, comprising: S1, constructing a peak electron density reconstruction neural network SRON2NN model, which adopts a fully connected feedforward neural network and includes an input layer, several hidden layers, and an output layer; S2, constructing and introducing key physical features, including effective solar radiation EUV×cosχ and thermosphere ΣO / N2; S3, training the model using ionospheric F2 layer peak electron density NmF2 data, time information, geomagnetic index Dst, solar activity index F10.7, effective solar radiation EUV×cosχ, and ΣO / N2 obtained from COSMIC-1 radio occultation observations; S4, reconstructing and verifying the ionospheric F2 layer peak electron density NmF2 based on the trained SRON2NN model. This invention achieves high-precision and interpretable reconstruction of the ionospheric F2 layer peak electron density NmF2 globally by explicitly introducing effective solar radiation and thermosphere ΣO / N2.

This invention belongs to the field of lidar technology and discloses an iron resonance fluorescence Doppler lidar system. It employs a seed injection method, injecting a single-longitudinal-mode, frequency-stabilized seed light of a certain power into an oscillator stage unit. Through active cavity control technology, precise matching of the seed light frequency and the longitudinal-mode frequency of the oscillator stage is achieved, enabling the oscillator stage to output Q-switched pulses with good transverse-mode and frequency characteristics. Subsequently, the Q-switched pulses are amplified in the amplification stage unit using a MOPA configuration. After two nonlinear frequency transformations, single-longitudinal-mode narrow-linewidth laser output at a specific wavelength is achieved. This system uses iron atoms in the atmosphere as tracers, enabling high-precision detection of atmospheric temperature, wind field, and iron atom number density in the region from the top of the mesosphere to the bottom of the thermosphere.

The invention discloses a data fusion method for thermospheric atmospheric density observation by multiple means on the same platform, comprising: obtaining POD measured data and pressure measured data observed on the same satellite; obtaining a first type of thermospheric atmospheric density based on the POD measured data by inversion, and obtaining a second type of thermospheric atmospheric density based on the pressure measured data by calculation; verifying the validity of the two types of density data, and continuing if the verification is passed; otherwise, ending; dividing all thermospheric atmospheric density data into orbital phases to obtain three types of orbital phases; obtaining applicable conditions of the two types of thermospheric atmospheric density data based on the three types of orbital phases and in combination with difference characteristics of the two types of thermospheric atmospheric density data; dynamically correcting the two types of thermospheric atmospheric density data through cross calibration according to the difference characteristics and the applicable conditions, and determining corresponding calibration factors; and completing the fusion of the two types of thermospheric atmospheric density data for the three types of orbital phases according to the calibration factors.

The invention belongs to the technical field of atmospheric measurement, and discloses a measurement method for acquiring atmospheric parameters by using iron resonance fluorescence echo signals, which comprises the following steps of: quantitatively describing a physical model of an iron atom resonance fluorescence spectral line, and establishing a warm wind two-dimensional lookup table; the iron resonance fluorescence Doppler laser radar echo signals after data preprocessing are substituted into a two-dimensional lookup table, and atmospheric temperature and radial wind speed measurement results are obtained. By means of the measuring method, the atmospheric temperature and the vertical distribution profiles of the warp wind and the weft wind can be obtained at the same time, the detection precision is determined, and an effective data processing means is provided for fine research of the atmosphere in the area from the top of the middle layer to the bottom of the thermal layer.

The invention discloses a thermal layer atmospheric density calibration method and system, and solves the technical problem of poor precision of a thermal layer atmospheric density calibration result caused by the fact that an existing thermal layer atmospheric density calibration method depends on a single technical path to carry out calibration work. The method comprises the following steps: acquiring actually measured real density, space environment data and an empirical model, and fitting to obtain daily factor data of the empirical model at the current moment; calculating a residual error by combining the actually measured real density and the original calculation density at the current moment, calculating a priori model predicted value by relying on the residual error and daily factor data, and comparing to obtain a calibrated residual error of the priori model; then training a residual fusion recurrent neural network to be trained by using the residual and the spatial environment data based on an adaptive moment estimation optimizer, and determining a trained network; and finally, outputting a residual prediction value by the network, superposing the residual prediction value with a priori model prediction value, and outputting the final calibration density of the thermal atmosphere.

The present invention relates to the field of data processing technology, and provides a method and system for rapid simulation of key parameters of space weather, including: using a space weather model to simulate and obtain a multi-source key data set of the ionosphere and thermosphere; standardizing the multi-source key data set based on a preprocessing strategy, and testing the SWIN Transformer with multi-source key small sample data; iteratively optimizing the 3D convolution operator of the SWIN Transformer based on data test effect information; performing model adjustment and optimization of the Transformer channel based on a predetermined learning strategy, constructing a thermosphere mass density simulation prediction model, and simulating key parameters of space weather through the thermosphere mass density simulation prediction model. The method solves the technical problem that the numerical simulation model of space physics in the prior art is complex in operation, resulting in the simulation of thermosphere mechanism parameters that are not timely and accurate enough.

The application belongs to the technical field of atmospheric density prediction, and discloses a thermosphere atmospheric density layered progressive full-scale prediction system and method based on cross-source data fusion, which utilizes respective advantages of different gradient data sources, constructs a three-level layered fusion refinement architecture of a benchmark density field, a correction density field and an instantaneous refined density field, and realizes month-year scale long-term prediction, day-week scale medium and short-term prediction and short-term prediction of thermosphere atmospheric density by combining cross-source data calibration and physical constraint modeling. Through the three-level layered refinement architecture, the application fully excavates the long-term coverage advantage of TLE data, the mesoscale variability description advantage of precise orbit data and the high-frequency high-precision advantage of accelerometer data, avoids defects of a single data source, realizes accurate description of full-scale and full-area density, and can improve refinement accuracy and prediction adaptability of thermosphere atmospheric density and reduce data cost.

The invention relates to the technical field of thermal layer atmosphere in-situ measurement, in particular to a thermal layer atmosphere in-situ measurement detector which comprises a detector body and a baffle, and a first atmosphere particle inlet is formed in one end of the detector body. A preset distance is reserved between the baffle and the end face of the detector body. A second atmospheric particle inlet right opposite to the first atmospheric particle inlet is formed in the baffle. On one hand, a gas high-density accumulation area is transferred to the side, away from the detector body, of the baffle by means of the baffle, and the gas high-density accumulation area is prevented from being formed at the first atmospheric particle inlet of the detector body. On the other hand, the baffle is used for replacing a collimator, one part of the atmospheric particles penetrating through the second atmospheric particle inlet are diffused into the space, the other part of the atmospheric particles flow into the first atmospheric particle inlet, the particles moving in the unexpected direction are limited from flowing into the first atmospheric particle inlet, and the thermal layer atmospheric density measurement precision is greatly improved on the whole.

A computer-implemented method for determining an orbital effect on a low-Earth orbiting object includes, forming an ensemble of thermosphere-ionosphere state models to represent thermosphere-ionosphere state model uncertainty, assimilating measured thermosphere-ionosphere data into a select thermosphere-ionosphere state model of the ensemble in accordance with a Kalman gain derived from statistics of the ensemble, computing drag on the low-Earth orbiting object from elements of the assimilated select model of thermosphere-ionosphere state models and randomizing elements of the assimilated select thermosphere-ionosphere model in accordance with statistics of the ensemble to generate a new ensemble of thermosphere-ionosphere state models. A system is arranged to perform the method. A computer program product is arranged to cause one or more computers to perform the method.

The present application provides a LIPF-based super-resolution method and system for key space weather parameters, which relates to the field of data analysis technology. Key space weather parameters are obtained based on a target area, and dual-version data are outputted respectively using an ionosphere-thermosphere numerical simulation model. A super-resolution model is built based on the LIIF algorithm, and supervised training and cross-validation are performed on the model using the dual-version data to obtain a converged super-resolution model. Finally, the hyper-parameters of the model are retrieved and optimized based on a genetic algorithm, which solves the technical problems in the prior art of image super-resolution algorithms when applied to the reconstruction of thermospheric atmospheric parameters, such as insufficient model generalization ability, high computational complexity, low practicality and efficiency, and poor adaptability. This achieves the technical effect of providing an efficient, accurate, and adaptable method for reconstructing thermospheric atmospheric parameters, which can effectively improve the resolution and accuracy of thermospheric atmospheric parameters, reduce computational complexity, and improve the practicality and efficiency of the algorithm.

The invention provides a method for simultaneously detecting 80-100 km temperature and wind field and 0-30 km wind field based on a satellite-borne iron laser radar, and relates to the technical field of atmosphere remote sensing, and the method comprises the steps: transmitting laser with the wavelength of 372 nm to an atmosphere UMLT region through the satellite-borne iron laser radar, and calculating the number of echo photons generated by the resonance fluorescence effect of received iron atoms; a temperature response function and a wind speed response function are defined according to the echo photon number, the atmospheric temperature of the 80-100 km area is calculated according to the temperature response function, and the atmospheric wind speed of the 80-100 km area is calculated according to the wind speed response function; and calculating the atmospheric radial wind speed of 0-30km according to the number of echo photons based on the Doppler principle. According to the method provided by the invention, atmospheric parameters of 0-30 km and 80-100 km are detected simultaneously based on the satellite-borne iron radar, and wind field and temperature profile information can be acquired in a high-precision manner all day long.

The present application belongs to the technical field of thermosphere mass density prediction method and space exploration and information processing, and specifically discloses a thermosphere mass density short-term prediction method and system based on ensemble learning and an electronic device, step 1: pre-processing and standard normalization of feature data; step 2: constructing an ensemble learning model MBiLE, integrating a multivariate perceptron model and a bidirectional long short-term memory neural network model, and training; step 3: selecting the optimal model and model parameters according to the loss value size of the proposed adaptive loss function RMSE-Huber; step 4: combining the feature data, and predicting the thermosphere mass density in a wider height range by using the optimal model. The proposed MBiLE model has strong stability and high reliability, can short-term predict the thermosphere mass density at a lower height, and is conducive to further realizing the prediction of the thermosphere mass density at any time in the future.