59 results about "Solar Activities" patented technology

The invention provides a low earth orbit satellite mobile communication ground experiment method and system. According to the influence of different orbit heights and solar activity periods, a diversified orbit parameter set is planned, and a virtual satellite network model is generated; based on the virtual satellite network model, calculating an optimal transmission path by using a path prediction algorithm, adjusting the characteristics of a transmitted signal through a real-time channel simulation technology, and generating a low-orbit satellite communication environment test scene; based on the low-orbit satellite communication environment test scene, evaluating communication performance data by using an adaptive performance evaluation algorithm, determining a power consumption mode by combining an energy efficiency optimization method, analyzing a relationship between battery consumption and communication quality, and generating a performance evaluation report; and integrating the power consumption data and the communication quality data, converting the integrated data into a practical solution, and generating a low-orbit satellite mobile communication ground experiment result. According to the technical scheme provided by the invention, the comprehensiveness and practicability of the low-orbit satellite mobile communication ground experiment method are improved.

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 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 embodiment of the invention discloses a training method, a using method and a related device of an electron content prediction model, and the method comprises the steps: carrying out the feature extraction of the total electron content data, geomagnetic activity index data and solar activity index data of an ionized layer collected by a Beidou satellite navigation system through a random forest algorithm; obtaining key feature data corresponding to the total electron content data; adopting a sliding window mechanism, taking the key feature data under a preset time window length as input time sequence data, taking the total electron content data at the last moment of the time window length as target output, and constructing a training sample pair; and performing prediction training of total electron content data according to the training sample pair and a preset deep learning model to obtain a trained electron content prediction model. In this way, the nonlinear dynamic characteristics of the ionosphere delay are effectively captured by the deep learning model, the prediction precision of the total electron content is improved, and the correction precision of the ionosphere delay is improved.

The invention discloses a regional ionosphere temporal-spatial resolution refinement method, which comprises the following steps: acquiring real-time GIM data, a solar activity index and a geomagnetic activity index, inputting the data into a trained forecasting model, and outputting a VTEC mean value of a grid center point; calculating an STEC value based on the output VTEC mean value; the training of the forecasting model comprises the following steps: acquiring original GIM data and CORS observation value data, and respectively preprocessing the original GIM data and the CORS observation value data to obtain GIM-VTEC calculated based on the GIM data and puncture point VTEC calculated based on the CORS observation value data; the GIM-VTEC obtained through preprocessing and the puncture point VTEC are matched according to the time and the grid range; acquiring a solar activity index and a geomagnetic activity index, matching the solar activity index and the geomagnetic activity index with the matched VTEC data according to time, and constructing a training sample set; constructing a BP neural network, taking time, longitude, latitude, GIM-VTEC, a solar activity index and a geomagnetic activity index as input, and taking a puncture point VTEC obtained by calculating CORS observation value data as output; and training is carried out on the constructed training sample set, and a trained forecasting model is output.

A TEC prediction method based on a Transform model embedded by spatio-temporal information comprises the following steps: firstly, extracting TEC historical data and spatio-temporal characteristic elements, calculating a mathematical expression related to the TEC historical data and the spatio-temporal characteristic elements, calculating indexes representing a solar activity intensity index and a geomagnetic activity intensity index as characteristic vectors of a TEC prediction model, and generating a corresponding query matrix, a key matrix and a value matrix; dividing the preprocessed data set into a training set and a test set according to a certain proportion, generating a multi-head self-attention score matrix for the training set by adopting a multi-head self-attention mechanism, calculating multi-head self-attention scores of the multi-head self-attention score matrix, merging multiple groups of output vectors to obtain an output vector, and using the test set for model performance evaluation. The trained prediction model can predict the TEC of the subsequent 24 hours based on the feature information of the first 120 hours. Compared with a traditional method, the method has the advantages that parallel computing can be achieved, efficiency is improved, meanwhile, the long-distance dependency capturing capacity of the model is further improved, and the prediction effect of an original model is effectively improved.

The application discloses a flare occurrence position determination method and device, electronic equipment and computer medium. The method comprises the following steps: when it is determined that a flare occurs, a flare peak time, a full-disk image corresponding to the flare peak time and initial disk positions of each solar active region are obtained; according to the flare peak time and the initial disk positions of each solar active region, first disk positions of each solar active region corresponding to the flare peak time are determined; the first disk positions of each solar active region are subjected to coordinate system conversion, and pixel positions of each solar active region in the full-disk image are determined; according to the pixel positions of each solar active region and the full-disk image, first pixel values of regions in which each solar active region is located in the full-disk image are respectively calculated; and the first disk position of a solar active region corresponding to a maximum value is determined as the occurrence position of the flare. The application can accurately determine the occurrence position of the flare when the flare occurs.

The invention discloses a method for predicting a long-term solar activity level. The method comprises the following steps: S1, establishing a solar activity level prediction model; s2, establishing a sunspot relative several-month mean value database; s3, obtaining a target prediction month; s4, using an RBF neural network to predict a sunspot relative several-year monthly average value of n months after the target month, and filling the sunspot relative several-month average value database with the sunspot relative several-month average value; s5, traversing the sunspot relative several-month mean value database, and obtaining the sunspot relative several-month mean value data of the target month and the previous several months; s6, substituting the mean value data of the relative multiple months of the sunspots of the target month and the previous multiple months into the solar activity level prediction model; and S7, outputting a monthly average value of the sunspots relative to several years and a prediction result thereof. According to the method, the average value of the relative several years of the sunspots in the current month and several months after the current month can be accurately predicted, and the average value can be used as a reference basis of the solar activity level and can also be used as an input parameter for forecasting other related parameters.

This invention discloses a method for refining the spatiotemporal resolution of the regional ionosphere, comprising: acquiring real-time GIM data, solar activity index, and geomagnetic activity index and inputting them into a trained forecast model, outputting the mean VTEC value of the grid center point; calculating the STEC value based on the output mean VTEC value; training the forecast model includes: acquiring raw GIM data and CORS observation data and preprocessing them respectively to obtain GIM-VTEC calculated based on GIM data and puncture point VTEC calculated based on CORS observation data; matching the preprocessed GIM-VTEC and puncture point VTEC according to time and grid range; acquiring solar activity index and geomagnetic activity index and matching them with the matched VTEC data according to time to construct a training sample set; constructing a BP neural network with time, longitude, latitude, GIM-VTEC, solar activity index, and geomagnetic activity index as inputs, and puncture point VTEC calculated from CORS observation data as output; training on the constructed training sample set and outputting the trained forecast model.

The invention discloses an information display method and device, electronic equipment and a readable storage medium, and belongs to the technical field of electronic equipment. The method comprises the following steps: determining meteorological information corresponding to a first position according to position information of the first position; the weather information comprises at least one of sun activity information and moon activity information; and displaying the meteorological identifier according to the meteorological information corresponding to the first position.

The invention provides an ionosphere parameter and short wave frequency band prediction method and electronic equipment, and the method comprises the steps: obtaining the ionosphere parameter data in a corresponding IRI model above the midpoint of a great circle through obtaining the geographic coordinates of two places of a communication link and the midpoint of the great circle, obtaining the geomagnetic solar activity data of an open-source data website, and obtaining the geomagnetic solar activity data of the open-source data website; the method comprises the following steps: carrying out ionosphere oblique detection by using ionosphere detectors at two communication places, obtaining an ionosphere oblique detection ionogram, obtaining frequency band data available for short-wave communication from the oblique detection ionogram, carrying out data preprocessing, selecting different tasks, setting corresponding Transform-LSTM model hyper-parameters, carrying out model training, and storing optimal model parameters; and performing an ionosphere parameter prediction task or a short-wave communication available frequency band prediction task by using the corresponding model. The ionosphere detection data, the environment monitoring data and the like are fused in the deep learning field and the short-wave communication field, and the method has important significance and application prospects.

The invention discloses a sky wave propagation time delay correction method for improving the equivalent height of an ionized layer. The method comprises the following steps: converting obtained geographical location information of a long-wave transmitting station and a signal receiver into latitude and longitude information; the latitude and the longitude corresponding to the position information of the sky wave signal ionosphere reflection point are obtained by combining a long wave transmitting station; obtaining an annual day and an hour moment corresponding to the emission time of the long-wave emission station for emitting the time service signal; inputting the solar activity F10.7 index and the geomagnetic activity planet index corresponding to the annual day, the hour moment and the emission time into an ionized layer equivalent height model to obtain an ionized layer equivalent height value; obtaining the sight distance extreme value of the radio signal through the ionosphere equivalent height value, and obtaining the actual signal propagation path length of the sky wave signal through the sight distance extreme value; and further acquiring the propagation time delay of the sky wave signal, and acquiring the complete propagation time of the sky wave signal from the long-wave transmitting station to the signal receiver through the propagation time delay. According to the invention, the propagation time delay precision of sky wave signals and the time service precision of a long-wave time service system are improved.

The application discloses a method for predicting long-term solar activity level, comprising the following steps: S1, establishing a solar activity level prediction model; S2, establishing a relative sunspot number monthly average database; S3, obtaining a target prediction month; S4, using an RBF neural network to predict the relative sunspot number yearly moving monthly average of the target prediction month and the following n months, and filling in the relative sunspot number monthly average database; S5, traversing the relative sunspot number monthly average database to obtain the relative sunspot number monthly average data of the target prediction month and the previous several months; S6, substituting the relative sunspot number monthly average data of the target prediction month and the previous several months into the solar activity level prediction model; and S7, outputting the relative sunspot number yearly moving monthly average and the prediction result thereof. The application can accurately predict the relative sunspot number yearly moving monthly average of the current month and the following months, and can be used as a reference basis for the solar activity level and an input parameter for the prediction of other related parameters.

The invention discloses a 4K solar extreme ultraviolet image prediction model construction method based on BandSwinUnet, and relates to the technical field of solar observation and space weather forecast, and the method comprises the following steps: S1, screening original data, and processing the data to obtain training data; s2, segmenting the calculation task and the weight in parallel by using an operator, and dividing the calculation task and the weight to a plurality of devices to execute the calculation task; s3, acquiring a device number, uniformly dividing training data to a plurality of devices, independently completing forward propagation and back propagation calculation by each device, and synchronizing gradients; s4, building a BandSwinUnet model on each device, and training the BandSwinUnet model based on the data in the step S1 and the parallel strategy set in the step S2; and S5, analyzing and optimizing the model in combination with errors. The problems that when an existing method is applied to 4K-resolution image prediction, the model generalization ability is insufficient, the adaptability to specific solar activity conditions is poor, and the calculation complexity is extremely high are solved.

A building automation system includes a ceiling-mounted sensor module comprising a millimeter-wave radar sensor, thermal array sensor, and ambient light sensor. The millimeter-wave radar sensor detects stationary occupants by measuring chest movements corresponding to breathing patterns and generates three-dimensional position coordinates for each detected occupant. A control unit processes sensor data locally and automatically controls a lighting system, window covering system, climate control system, and audio system based on occupancy patterns and environmental conditions. The control unit adjusts lighting brightness and color temperature according to a solar schedule determined from GPS coordinates and time of day, and prevents lighting activation when ambient light levels exceed a predetermined threshold despite detected occupancy. The system operates independently during network disruptions through local data processing within the residential structure.

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 invention discloses a flare occurrence position determination method and device, electronic equipment and a computer medium. The method comprises the following steps: when it is determined that a flare occurs, acquiring flare peak time, a full-solar-surface image corresponding to the flare peak time and an initial solar-surface position of each solar active area; according to the solar flare peak time and the initial solar surface position of each solar active area, determining a first solar surface position corresponding to each solar active area at the solar flare peak time; performing coordinate system conversion on the first solar surface position of each solar activity area, and determining the pixel position of each solar activity area in the full solar surface image; according to the pixel position of each solar activity area and the full-solar-surface image, respectively calculating a first pixel value of each solar activity area in an area where the full-solar-surface image is located; and determining the first solar surface position of the solar active area corresponding to the maximum value as the occurrence position of the flare. According to the invention, the occurrence position of the flare can be accurately determined when the flare occurs.

The invention belongs to the technical field of ionosphere physics, and particularly relates to a Chinese low-latitude region ionosphere TEC empirical model based on multi-source data fusion, and the modeling process comprises the steps: selecting multi-source TEC data of a solar activity cycle in a Chinese low-latitude region as a multi-source data set; determining the weight of each type of data through a Hall model variance component estimation method, and determining the weight of each type of data; carrying out data fusion on various data in the multi-source data set by utilizing the determined weight through a form of a 15-order 15-order spherical harmonic function to obtain a modeling data set; setting components of the TEC empirical model, and establishing a nonlinear mathematical expression to represent all the components; and calculating to-be-estimated parameters in the nonlinear mathematical expression of each component through nonlinear least square fitting, forming a TEC empirical model, and finishing final modeling. According to the method, the modeling precision of the Chinese low-latitude region is effectively improved, and the temporal and spatial change rule of the TEC can be described more accurately.

The application discloses a method for calculating foF2 affected by weak geomagnetic activity and relates to the technical field of ionospheric physical research and application. The method comprises the following steps: acquiring an Ap index and a Dst index, wherein the Ap index and the Dst index satisfy a first condition; acquiring an ionospheric solar activity index S by processing an F10.7 index; calculating a solar activity influence value T by using a Fourier polynomial; acquiring an actual observation value R; and acquiring a weak geomagnetic activity influence value G by using the actual observation value R and the solar activity influence value T. The method can effectively separate the influence of weak geomagnetic activity on foF2 when studying foF2 by improving the F10.7 index and further putting forward the ionospheric solar activity index S, and is favorable for more directly studying the influence of weak geomagnetic activity on foF2 and the ionosphere. The method is easy to operate, simple in data selection and easy to realize.

The application discloses a method for extracting middle layer top temperature multi-period characteristics and analyzing influencing factors, and belongs to the technical field of atmospheric detection and meteorological data analysis. The method first extracts time series data of the middle layer top temperature based on the 90km height and the lowest temperature point standard, then completes time domain trend and mutation analysis through seasonal departure and Mann-Kendall test, adopts discrete wavelet decomposition to obtain four main oscillation periods of 3 years, 7 years, 11 years and 22 years, then uses continuous wavelet to obtain the length of the fine-calibrated period, and finally performs cross wavelet analysis on the quasi-biennial oscillation, the El Nino effect and the solar activity index to obtain the final correlation and time lag results. The application realizes joint time-frequency domain analysis of discrete wavelet transform, continuous wavelet transform and cross wavelet transform, effectively improves the cycle recognition accuracy, quantifies the multi-scale correlation and time sequence hysteresis of each factor, and provides a new research idea for high-altitude atmospheric climate influencing factor analysis.

The application discloses a method for stabilizing a full-disk extreme ultraviolet time sequence image of the sun, and relates to the technical field of solar observation image processing. The method comprises the following steps: acquiring a full-disk extreme ultraviolet time sequence image of the sun, and extracting gradient information of each frame of image; determining a solar edge region of each frame of image based on the gradient information; calculating a regional gradient difference value of a frame to be stabilized and a reference frame in the solar edge region, and minimizing the regional gradient difference value to obtain a first offset; extracting a matching point pair between the two frames; adjusting the matching point pair based on the first offset, determining an offset change amount based on a residual of the matching point pair and a matching point pair weight, and obtaining a second offset; and compensating the frame to be stabilized by using the second offset to obtain a stabilized frame. The application significantly suppresses the problems of mismatching and error accumulation caused by the rapid evolution of the solar active region by combining gradient difference minimization and anti-difference selection weight iteration optimization of the matching point pair, and improves the robustness and accuracy of the stabilization processing.

The present invention relates to the field of astronomical calculation technology, specifically a method and system for calculating the relationship between the sun and planets, which includes the following steps: collecting solar activity observation data, analyzing the time series, intensity, and frequency information of the data, extracting the characteristics of solar activity in different cycles, summarizing the change trends, and calculating the correlation between the cycle and the activity intensity to obtain solar activity correlation data. In the present invention, by extracting the characteristics of solar activity in different cycles and combining with the magnetohydrodynamics method, the velocity and density parameters of electromagnetic radiation and particle flow are deeply analyzed, the perturbation characteristics of the planetary magnetic field are scientifically evaluated, the interference of solar activity on the planetary orbit deviation is clarified by analyzing the gravitational perturbation, the dynamic changes of the orbital velocity and orbital intersection are traced, the correlation between magnetic storms and orbital deviation is incorporated into the prediction analysis, and the analysis of the orbital change trend is further improved by combining the rotation period and gravitational perturbation data, providing a more refined reference basis for predicting planetary motion and astronomical phenomena.

The present invention provides a method for ionospheric enhanced RTK positioning service based on a single reference station mode, which relates to the field of RTK positioning service. Aiming at the problem that the ambiguity fixing effect is poor in traditional single reference station real-time positioning service during periods of intense ionospheric activity, and taking into account that the current single reference station service RTK devices on the market do not support receiving external ionospheric data for real-time ionospheric correction, the following method is proposed: Step 1, calculate the geometric distance difference between stations for the same satellite. Step 2, calculate the ionospheric difference between stations. Step 3, calculate the corrected observation values. Using the corrected observation values obtained by the present invention for real-time kinematic positioning can weaken the influence of large fluctuations in ionospheric delay on the positioning result during periods of intense solar activity, and further improve the positioning accuracy and ambiguity fixing effect of the single reference station mode RTK. At the same time, this method only corrects the observation values of the reference station and does not require additional broadcast of correction messages during the transmission process.

The invention discloses a method for calculating the electron density of a large-scale structure of a medium weft slot in an ionized layer. The method comprises the following steps: step 1, determining calculation time and a maximum value of the strength of the medium weft slot; step 2, acquiring solar activity data and geomagnetic activity indexes; 3, calculating the electron density of the background ionized layer when the weft slot structure in the ionized layer is not considered; 4, determining the latitude position of a latitude slot in the ionized layer; 5, calculating the zenith angle of the sun; and step 6, determining the longitude position of the weft slot in the ionized layer. According to the method, the influence of the solar geomagnetic activity on the ionosphere midlatitudinal slot is fully considered, the electron density distribution model considering the midlatitudinal slot structure is constructed according to the midlatitudinal slot electron density spatial-temporal change rule obtained through early-stage data statistical analysis, and the calculation precision of an ionosphere model in a related region is 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.

The application discloses a method, device, medium and product for predicting the total electron content of the ionosphere, which comprises the following steps: acquiring geomagnetic disturbance data of a region where a single-station global navigation satellite system is located, wherein the geomagnetic disturbance data comprises a magnetic north component and a magnetic east component; calculating the horizontal geomagnetic disturbance intensity based on the magnetic north component and the magnetic east component; inputting the horizontal geomagnetic disturbance intensity, the geomagnetic disturbance data, the acquired solar activity index and the historical observation sequence of the total electron content into a preset prediction model, so as to obtain a plurality of subspace attention features through a multi-head attention mechanism, and to obtain an encoding feature matrix through linear change processing; decoding the encoding feature matrix through a decoder of the prediction model to obtain a context vector, and performing autoregressive processing on the context vector until a target total electron content prediction sequence is obtained. The application can improve the prediction accuracy of the total electron content in the ionosphere above the single-station global navigation satellite system.

The invention provides a multi-modal F10.7 index prediction method based on image features and time sequence analysis, and mainly solves the problems of low prediction precision and insufficient efficiency in the prior art. According to the implementation scheme, an SDO / HMI solar activity image and F10.7 index time sequence data are sequentially subjected to preprocessing, time alignment and storage to obtain multi-modal data, and the multi-modal data are divided into a training set and a test set; constructing a multi-modal prediction model comprising a three-dimensional convolutional neural network, a time convolutional network, a self-attention module and a full-connection network, and adopting a mean square error as a loss function of the model; inputting the training set into a multi-modal model, and training the multi-modal model through an Adam optimizer; and inputting the test set into the trained multi-modal prediction model, and outputting an F10.7 index prediction value. According to the method, the prediction precision of the F10.7 index is improved, the calculation cost is reduced, the calculation efficiency is improved, and the method can be used for aerospace, communication and space weather early warning.

The application provides a low-orbit satellite mobile communication ground experiment method and system. According to the influence of different orbit heights and solar activity periods, a diversified orbit parameter set is planned to generate a virtual satellite network model; based on the virtual satellite network model, an optimal transmission path is calculated by using a path prediction algorithm, the characteristics of the transmitted signal are adjusted through real-time channel simulation technology, and a low-orbit satellite communication environment test scene is generated; based on the low-orbit satellite communication environment test scene, a self-adaptive performance evaluation algorithm is used to evaluate communication performance data, an energy efficiency optimization method is used to determine a power consumption mode, the relationship between battery consumption and communication quality is analyzed, and a performance evaluation report is generated; the power consumption data and the communication quality data are integrated, and after integration, the integrated data are converted into a practical solution, and a low-orbit satellite mobile communication ground experiment result is generated. The technical scheme provided by the application improves the comprehensiveness and practicality of the low-orbit satellite mobile communication ground experiment method.

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 invention provides a geomagnetic storm event extraction method based on a wavelet scale spectrum, and relates to the technical field of space weather event monitoring. The method comprises the steps that Dst index data and SYM-H index data are acquired and preprocessed, and a Dst index time sequence and an SYM-H index time sequence are obtained; according to the Dst index time sequence, geomagnetic storm event features defined by the Dst index are extracted; according to the geomagnetic storm event characteristics defined by the Dst index, extracting the geomagnetic storm event characteristics defined by the SYM-H index from the SYM-H index time sequence based on a wavelet scale spectrum method; and constructing a geomagnetic storm event list according to the geomagnetic storm event characteristics defined by the Dst index and the geomagnetic storm event characteristics defined by the SYM-H index. According to the method, the time node information of the initial phase, the main phase and the recovery phase of the geomagnetic storm event can be extracted more accurately and automatically, and a complete geomagnetic storm event list of nearly four solar activity cycles is constructed.