Grading method for geomagnetic storm disaster caused by CME
By fusing and standardizing multi-source satellite data, the inversion of key parameters of CME in multiple dimensions and the accurate classification of geomagnetic storm levels were achieved, solving the problem of insufficient accuracy caused by a single data source in traditional methods and providing accurate early warning support.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YUNNAN OBSERVATORY CHINESE ACADEMY OF SCIENCES
- Filing Date
- 2026-01-06
- Publication Date
- 2026-05-08
AI Technical Summary
Traditional geomagnetic storm prediction techniques rely on data from a single satellite or ground observation station, resulting in insufficient comprehensiveness and granularity of data sources. This makes it impossible to accurately retrieve the multi-dimensional key parameters of the geomagnetic storm (CME), thus limiting the accuracy of geomagnetic storm trigger source location and disaster level classification.
Multi-source satellite data was simultaneously acquired by the Solar Dynamics Observatory, the Solar Terrestrial Relations Observatory, and the Parker Solar Probe. After standardized preprocessing, the multi-source satellite data was fused with the CME comprehensive parameter inversion formula to achieve a comprehensive inversion of the multi-dimensional key parameters of the CME. Combined with the geomagnetic storm triggering source convergence determination formula and the level determination formula, the disaster level was accurately classified.
It improves the accuracy and reliability of CME parameter inversion, enables precise location of geomagnetic storm triggering sources and accurate classification of disaster levels, solves the problem of insufficient data integration and analysis capabilities of traditional methods, and provides accurate early warning support.
Smart Images
Figure CN121995543A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of space weather disaster monitoring technology, specifically a method for classifying the level of geomagnetic storm disasters caused by CME. Background Technology
[0002] Geomagnetic storms, as a severe space weather phenomenon, can affect Earth's electromagnetic environment, satellite operations, power grid security, and even human health. As human exploration of space deepens and reliance on it increases, accurately predicting and assessing the severity of geomagnetic storms becomes particularly important. However, the triggering mechanisms of geomagnetic storms are complex and variable, mainly stemming from solar activity, especially coronal mass ejection (CME) events, which pose a significant challenge to their prediction.
[0003] However, traditional geomagnetic storm prediction techniques mainly rely on data from single satellites or ground observation stations. These methods have significant shortcomings in terms of the comprehensiveness of data sources and the precision of data processing. Specifically, single satellite observations often only acquire one dimension or local characteristics of CME events. For example, it is difficult to accurately invert the three-dimensional propagation direction and velocity of CMEs using only extreme ultraviolet imaging data, or it is impossible to accurately determine key parameters such as the mass and magnetic flux of CMEs by relying solely on stereoscopic observation data. This limitation of data sources leads to large errors in the inversion of multi-dimensional key parameters of CMEs by traditional methods, making it difficult to comprehensively and accurately characterize the physical properties of CMEs. At the same time, the lack of a collaborative acquisition and fusion inversion mechanism for multi-source satellite data makes it impossible for traditional methods to effectively integrate and analyze data from different satellite platforms and observation types when dealing with complex space weather events. This, in turn, limits the accuracy of geomagnetic storm trigger source location and the accuracy of disaster level classification.
[0004] Therefore, a method for classifying the disaster levels of geomagnetic storms induced by CME is to be developed. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for classifying the disaster level of geomagnetic storms caused by cyclone metamorphic events (CMEs). This invention uses multi-source satellite data collected simultaneously by the Solar Dynamics Observatory, the Solar-Earth Relations Observatory, and the Parker Solar Probe. After standardized preprocessing, it utilizes the CME comprehensive parameter inversion formula fused from the multi-source satellite data to achieve a comprehensive inversion of key parameters in multiple dimensions, such as CME outbreak time, source region location, three-dimensional propagation direction, initial velocity, acceleration, mass, and magnetic flux. This innovation directly addresses the shortcomings of traditional methods, such as single data source and incomplete inversion parameters. Through multi-source data fusion and mutual verification mechanisms, it improves the accuracy and reliability of CME parameter inversion, providing data support for the subsequent location of geomagnetic storm triggering sources and the classification of disaster levels.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for classifying the disaster level of geomagnetic storms induced by CME, the specific steps of which are as follows:
[0007] Data acquisition: Simultaneous acquisition of three types of satellite data through the atmospheric imaging component onboard the Solar Dynamics Observatory, the heliosphere imager and coronagraph onboard the Solar Terrestrial Relations Observatory, and the solar wind particle detector and magnetic field detector onboard the Parker Solar Probe.
[0008] Data standardization preprocessing: The three types of satellite data collected are subjected to preprocessing operations in sequence, including denoising, format unification, time base normalization and sampling frequency adjustment, and observation coordinate alignment, to obtain standardized data;
[0009] Parameter fusion inversion: Based on standardized data, CME multi-dimensional key parameter inversion is performed by combining multi-source satellite data with the CME comprehensive parameter inversion formula, and mutual verification and calibration are performed through three types of satellite data.
[0010] Trigger source location: Combining the multi-dimensional key parameters of CME with the spatial geometric relationship between the Sun and Earth, the convergence determination index is calculated using the geomagnetic storm trigger source convergence determination formula, and the CME is determined as a geomagnetic storm trigger source based on the convergence determination index;
[0011] Level prediction correction: Based on historical data, a dataset is constructed that correlates CME parameters with geomagnetic storm levels, and the geomagnetic storm level is determined by the geomagnetic storm level determination formula.
[0012] Furthermore, in the collaborative data acquisition, the three types of satellite data include multi-band extreme ultraviolet coronal imaging data, stereo observation data, and near-solar exploration data; multi-band extreme ultraviolet coronal imaging data is acquired through atmospheric imaging components, covering multiple extreme ultraviolet bands of 131Å, 171Å, 211Å, 304Å, and 335Å; stereo observation data is acquired through heliosphere imagers and coronagraphs, covering the heliocentric angle range of 0°-150°; and near-solar exploration data is acquired through solar wind particle detectors and magnetic field detectors, including parameters such as solar wind plasma density, velocity, temperature, and magnetic field strength and direction.
[0013] Furthermore, in the parameter fusion inversion, the formula for the CME integrated parameter inversion of multi-source satellite data fusion is as follows: ,in, For CME comprehensive feature parameters, The CME brightness characteristic is obtained by statistically analyzing the difference between the mean gray level of the CME region and the background gray level, based on preprocessed SDO multi-band extreme ultraviolet corona imaging data. The azimuth angle of the CME propagation direction is obtained by analyzing the CME's azimuth in the heliocentric coordinate system based on preprocessed stereo observation data. The CME material properties are obtained by integrating observations of solar wind plasma density and magnetic field strength based on preprocessed near-solar probe data. , , To merge weights, and It is determined through calibration using historical data.
[0014] Furthermore, in the parameter fusion and inversion, based on The key parameters of the CME in multiple dimensions were extracted and inverted from the corresponding satellite data sources. These parameters include the CME burst time, the latitude and longitude of the burst source region, the three-dimensional propagation direction, the initial velocity, the acceleration, the mass, and the magnetic flux. The burst time and the latitude and longitude of the burst source region were obtained from multi-band extreme ultraviolet coronal imaging data, the three-dimensional propagation direction and the initial velocity were obtained from stereo observation data, and the mass and magnetic flux were obtained from near-solar exploration data.
[0015] Furthermore, in the trigger source localization, the formula for determining the convergence of geomagnetic storm trigger sources is: ,in, For determining the intersection index, For CME comprehensive feature parameters, The propagation speed of CME is obtained by inversion based on stereoscopic observation data. To keep the Earth-Sun distance constant, The angle between the CME propagation trajectory and Earth's orbit is obtained by inversion based on stereoscopic observation data.
[0016] Furthermore, in the trigger source location, based on To determine whether the CME's propagation trajectory will intersect with Earth's orbit, when 1.0×10 -4 s -1 At that time, the CME's propagation trajectory intersected with Earth's orbit, indicating that the CME was the trigger source for the risk of geomagnetic storms; when 1.0×10 -4 s -1 At that time, the CME's propagation trajectory is unlikely to intersect with Earth's orbit, so it is determined that the CME will not trigger a geomagnetic storm, and the classification process ends directly.
[0017] Furthermore, in the aforementioned level prediction and correction process, based on historical data of CME events, the multi-dimensional parameters of CME and the corresponding geomagnetic storm levels for each event are compiled to construct a dataset linking CME parameters and geomagnetic storm levels. The geomagnetic storm level index is then calculated using the geomagnetic storm level determination formula. .
[0018] Furthermore, in the aforementioned level prediction correction, the formula for determining the geomagnetic storm level is: ,in, This is a geomagnetic storm level index. , The weights for the levels are determined by associating CME parameters with the geomagnetic storm level dataset. For CME comprehensive feature parameters, This is the index for determining the intersection.
[0019] Furthermore, in the aforementioned level prediction correction, the geomagnetic storm level index is used as the basis. Determining the level of a geomagnetic storm: When At that time, it was G1 level; when At that time, it was G2 level; when At that time, it was G3 level; when At that time, it was G4 level; when At that time, it was G5 level.
[0020] Compared with existing technologies, this method for classifying the disaster levels of geomagnetic storms induced by CME has the following advantages:
[0021] I. This invention utilizes multi-source satellite data acquired simultaneously by the Solar Dynamics Observatory, the Solar Terrestrial Relations Observatory, and the Parker Solar Probe. After standardized preprocessing, it employs a comprehensive CME parameter inversion formula fused from multi-source satellite data to achieve a comprehensive inversion of key parameters across multiple dimensions, including CME outburst time, source region location, three-dimensional propagation direction, initial velocity, acceleration, mass, and magnetic flux. This innovation directly addresses the shortcomings of traditional methods, such as single data sources and incomplete inversion parameters. Through multi-source data fusion and mutual verification mechanisms, it improves the accuracy and reliability of CME parameter inversion, providing data support for subsequent geomagnetic storm trigger source location and disaster level classification.
[0022] Second, this invention utilizes the multi-dimensional key parameters of the CME obtained through inversion and their relationship with the Sun-Earth spatial geometry to calculate the convergence determination index using the geomagnetic storm triggering source determination formula. This allows for accurate determination of whether a CME is a geomagnetic storm triggering source. Simultaneously, based on a dataset linking CME parameters and geomagnetic storm levels constructed from historical data, the geomagnetic storm level index is scientifically calculated using the geomagnetic storm level determination formula, achieving accurate classification of geomagnetic storm disaster levels. This innovation effectively solves the problem of insufficient data integration and analysis capabilities in traditional methods when dealing with complex space weather events. Through multi-source data collaborative processing and determination mechanisms, it improves the efficiency of geomagnetic storm triggering source location and the accuracy of disaster level classification, providing precise and effective decision support for geomagnetic storm early warning and response.
[0023] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0025] Figure 1 A flowchart illustrating a method for classifying the severity of geomagnetic storms induced by CME;
[0026] Figure 2 A framework diagram for a method of classifying the disaster levels of geomagnetic storms induced by CME;
[0027] Figure 3 This is a flowchart of the trigger source location and level prediction correction in a method for classifying geomagnetic storm disaster levels caused by CME. Detailed Implementation
[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0029] Example 1:
[0030] In the scenario of determining the geomagnetic storm level during a high-risk CME (Central Magnetic Mechanism) during a peak solar activity year, the ground monitoring center issued an emergency observation command, dispatching the atmospheric imaging component carried by the Solar Dynamics Observatory to activate the full-band observation mode and simultaneously acquire multi-band extreme ultraviolet coronal imaging data in multiple extreme ultraviolet bands at 131Å, 171Å, 211Å, 304Å, and 335Å. At the same time, the heliosphere imager and coronagraph carried by the Solar Terrestrial Relations Observatory were dispatched to conduct a stereo scan in the range of heliocentric angle from 0° to 150° to complete the acquisition of stereo observation data. The Parker Solar Probe simultaneously activated the solar wind particle detector and magnetic field detector in its perihelion orbit to capture near-solar detection data including solar wind plasma density, velocity, temperature, and magnetic field strength and direction. After completing the synchronous data acquisition, the data was transmitted to the ground processing center.
[0031] After receiving the raw data, the ground processing center initiates a standardized preprocessing procedure. First, denoising is performed on the three types of satellite data to filter out electronic noise from the instruments themselves and cosmic background clutter from the Sun-Earth space. Next, the heterogeneous data formats from the Solar Dynamics Observatory, the Solar-Earth Relations Observatory, and the Parker Solar Probe are unified into the universally applicable FITS format for astronomical observations. Then, using the observation time of the Solar Dynamics Observatory as a reference, the time reference for the three types of data is normalized, and the sampling frequency is uniformly adjusted to once per minute to ensure consistency in the time dimension. Finally, the observation coordinates of all data are uniformly converted to the heliocentric coordinate system, completing the observation coordinate alignment and forming standardized data that can be directly used for subsequent analysis, such as... Figure 1 As shown.
[0032] Based on standardized data, the CME integrated parameter inversion formula for multi-source satellite data fusion was used to carry out parameter inversion. The CME integrated parameter inversion formula for multi-source satellite data fusion is as follows: ,in, For CME comprehensive feature parameters, The CME brightness characteristic is obtained by statistically analyzing the difference between the mean gray level of the CME region and the background gray level, based on preprocessed SDO multi-band extreme ultraviolet corona imaging data. The azimuth angle of the CME propagation direction is obtained by analyzing the CME's azimuth in the heliocentric coordinate system based on preprocessed stereo observation data. The CME material properties are obtained by integrating observations of solar wind plasma density and magnetic field strength based on preprocessed near-solar probe data. , , To merge weights, and The parameters were determined through calibration using historical data; then, parameter verification and calibration were completed through cross-comparison of three types of satellite data, and then the burst time, latitude and longitude of the burst source region, three-dimensional propagation direction, initial velocity, acceleration, mass and magnetic flux of the CME were obtained by decomposition and inversion.
[0033] Combining the acquired multi-dimensional key parameters of the CME with the spatial geometric relationship between the Sun and Earth, the convergence determination index is calculated using the formula for determining the convergence of geomagnetic storm triggering sources. The formula for determining the convergence of geomagnetic storm triggering sources is as follows: ,in, For determining the intersection index, For CME comprehensive feature parameters, The propagation speed of CME is obtained by inversion based on stereoscopic observation data. To keep the Earth-Sun distance constant, The angle between the CME's propagation trajectory and Earth's orbit was obtained through inversion based on stereo observation data; the intersection determination index of this CME was calculated. Reaching 1.0×10 -4 s -1 Therefore, the CME was determined to be the trigger source for the geomagnetic storm risk, and the subsequent level prediction and correction process was initiated, such as... Figure 2 As shown.
[0034] By retrieving historical data of similar CME events from the past 50 years, we compiled multi-dimensional CME parameters and corresponding geomagnetic storm levels for each event, constructing a dataset linking CME parameters and geomagnetic storm levels. Based on this dataset, we determined the level weights and then calculated the geomagnetic storm level index using the geomagnetic storm level determination formula. The geomagnetic storm level determination formula is as follows: ,in, This is a geomagnetic storm level index. , The weights for the levels are determined by associating CME parameters with the geomagnetic storm level dataset. For CME comprehensive feature parameters, This is used to determine the convergence index; and is based on the geomagnetic storm level index. Determining the level of a geomagnetic storm, such as Figure 3 As shown: When At that time, it was G1 level; when At that time, it was G2 level; when At that time, it was G3 level; when At that time, it was G4 level; when At that time, it was classified as G5; the geomagnetic storm level index was calculated. The intensity was 3.6, indicating that this CME would trigger a G3-level geomagnetic storm. The ground warning center immediately issued a geomagnetic storm warning of the corresponding level to relevant departments.
[0035] In summary, for the geomagnetic storm (GMG) level determination scenario of high-risk CMEs during solar activity peak years, the following approach was first adopted: Data from three types of satellites—Solar Dynamics Observatory, Solar-Earth Relations Observatory, and Parker Solar Probe—were simultaneously acquired collaboratively. Then, standardized preprocessing was performed on the data, including denoising, format unification, time reference normalization, sampling frequency adjustment, and observation coordinate alignment. Next, multi-dimensional key parameters were inverted and calibrated using the CME comprehensive parameter inversion formula based on multi-source satellite data fusion. Finally, the GMG trigger source convergence determination formula was used to confirm that the CME was a trigger source for GMG risk. Finally, based on the CME parameter and GMG level correlation dataset, the GMG level determination formula was applied to determine that a G3-level GMG would occur, providing accurate technical support for relevant departments to issue early warnings.
[0036] Example 2:
[0037] In the scenario of ruling out geomagnetic storms in low-risk CMEs during routine sky surveys, the ground monitoring system initiates multi-satellite collaborative observation commands. The atmospheric imaging component onboard the Solar Dynamics Observatory switches to the conventional observation bands to collect extreme ultraviolet coronal imaging data in multiple bands of 131Å, 171Å, 211Å, 304Å, and 335Å. The heliosphere imager and coronagraph of the Solar Terrestrial Relations Observatory collect stereoscopic observation data with heliocentric angles from 0° to 150° according to the predetermined sky survey range. The Parker Solar Probe simultaneously activates its instruments in its conventional orbit to acquire near-solar detection data including solar wind plasma density, velocity, temperature, and magnetic field strength and direction. The three types of satellites complete data acquisition according to the preset 12-hour observation cycle and automatically transmit the data to the ground data processing center.
[0038] After receiving the raw data from the three types of satellites, the ground data processing center conducts preprocessing according to a standardized process. First, all data is denoised to remove inherent noise generated by instrument operation and interference signals from the space background. Then, the heterogeneous data from different satellites are uniformly converted into the FITS format to achieve data format standardization. Subsequently, the time reference of the three types of data is normalized based on the observation time of the Sun-Earth Relations Observatory, and the sampling frequency is uniformly adjusted to once every five minutes to match the data analysis rhythm of routine monitoring. Finally, the observation coordinates of all data are converted to the heliocentric coordinate system to complete coordinate alignment and generate standardized data.
[0039] Based on standardized data, parameter inversion is performed using the CME integrated parameter inversion formula for multi-source satellite data fusion. The CME integrated parameter inversion formula for multi-source satellite data fusion is as follows: After mutual verification and calibration of three types of satellite data, the burst time, latitude and longitude of the burst source region, three-dimensional propagation direction, initial velocity, acceleration, mass and magnetic flux of the CME were further decomposed and inverted to obtain key parameters in multiple dimensions.
[0040] Combining the multi-dimensional key parameters of the CME with the spatial geometric relationship between the Sun and Earth, the convergence determination index is calculated using the formula for determining the convergence of geomagnetic storm triggering sources. The formula for determining the convergence of geomagnetic storm triggering sources is as follows: The CME's convergence determination index was calculated. 0.8×10 -4 s -1 Therefore, it was determined that the propagation trajectory of the CME was unlikely to intersect with Earth's orbit and would not trigger a geomagnetic storm. The classification process was then terminated directly, avoiding meaningless subsequent calculations. This improved the overall efficiency of monitoring and judgment and enabled the accurate screening of non-risk CMEs. The ground monitoring center only recorded the event in the solar activity database and did not issue a geomagnetic storm warning.
[0041] In summary, for the geomagnetic storm exclusion scenario of low-risk CMEs in routine sky surveys, the Solar Dynamics Observatory, the Solar Terrestrial Relations Observatory, and the Parker Solar Probe were first coordinated to complete the acquisition of data from the three types of satellites. Then, the data was standardized and preprocessed according to the established procedure. Subsequently, the multi-dimensional key parameters of the CME were inverted and calibrated by the CME comprehensive parameter inversion formula of multi-source satellite data fusion. Then, the convergence determination index was calculated by the geomagnetic storm triggering source convergence determination formula. If it was determined that the CME would not trigger a geomagnetic storm, the subsequent process was directly terminated. This not only achieved accurate screening of non-risk CMEs, but also improved the overall execution efficiency of geomagnetic storm risk monitoring. The event was only recorded in the solar activity database for storage.
[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A method for classifying the disaster level of geomagnetic storms induced by CME, characterized in that, The specific steps of this method are as follows: Data acquisition: Simultaneous acquisition of three types of satellite data through the atmospheric imaging component onboard the Solar Dynamics Observatory, the heliosphere imager and coronagraph onboard the Solar Terrestrial Relations Observatory, and the solar wind particle detector and magnetic field detector onboard the Parker Solar Probe. Data standardization preprocessing: The three types of satellite data collected are subjected to preprocessing operations in sequence, including denoising, format unification, time base normalization and sampling frequency adjustment, and observation coordinate alignment, to obtain standardized data; Parameter fusion inversion: Based on standardized data, CME multi-dimensional key parameter inversion is performed by combining multi-source satellite data with the CME comprehensive parameter inversion formula, and mutual verification and calibration are performed through three types of satellite data. Trigger source location: Combining the multi-dimensional key parameters of CME with the spatial geometric relationship between the Sun and Earth, the convergence determination index is calculated using the geomagnetic storm trigger source convergence determination formula, and the CME is determined as a geomagnetic storm trigger source based on the convergence determination index; Level prediction correction: Based on historical data, a dataset is constructed that correlates CME parameters with geomagnetic storm levels, and the geomagnetic storm level is determined by the geomagnetic storm level determination formula.
2. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 1, characterized in that, In the collaborative data acquisition, three types of satellite data are included: multi-band extreme ultraviolet corona imaging data, stereo observation data, and near-solar exploration data; multi-band extreme ultraviolet corona imaging data are acquired through atmospheric imaging components, covering multiple extreme ultraviolet bands of 131Å, 171Å, 211Å, 304Å, and 335Å; stereo observation data are acquired through heliosphere imagers and coronagraphs, covering the heliocentric angle range of 0°-150°; and near-solar exploration data are acquired through solar wind particle detectors and magnetic field detectors, including parameters such as solar wind plasma density, velocity, temperature, and magnetic field strength and direction.
3. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 1, characterized in that, In the parameter fusion inversion, the formula for CME comprehensive parameter inversion based on multi-source satellite data fusion is as follows: ,in, For CME comprehensive feature parameters, The CME brightness characteristic is obtained by statistically analyzing the difference between the mean gray level of the CME region and the background gray level, based on preprocessed SDO multi-band extreme ultraviolet corona imaging data. The azimuth angle of the CME propagation direction is obtained by analyzing the CME's azimuth in the heliocentric coordinate system based on preprocessed stereo observation data. The CME material properties are obtained by integrating observations of solar wind plasma density and magnetic field strength based on preprocessed near-solar probe data. , , To merge weights, and It is determined through calibration using historical data.
4. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 3, characterized in that, In the parameter fusion inversion, based on The key parameters of CME in multiple dimensions are extracted and inverted from the corresponding satellite data sources. These parameters include CME burst time, latitude and longitude of the burst source region, three-dimensional propagation direction, initial velocity, acceleration, mass and magnetic flux. The eruption time and latitude and longitude of the eruption source region were obtained by inversion from multi-band extreme ultraviolet corona imaging data, the three-dimensional propagation direction and initial velocity were obtained by inversion from stereo observation data, and the mass and magnetic flux were obtained by inversion from near-solar exploration data.
5. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 1, characterized in that, In the location of the triggering source, the formula for determining the convergence of geomagnetic storm triggering sources is as follows: ,in, For determining the intersection index, For CME comprehensive feature parameters, The propagation speed of CME is obtained by inversion based on stereoscopic observation data. To keep the Earth-Sun distance constant, The angle between the CME propagation trajectory and Earth's orbit is obtained by inversion based on stereoscopic observation data.
6. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 5, characterized in that, In the trigger source location, based on To determine whether the CME's propagation trajectory will intersect with Earth's orbit, when 1.0×10 -4 s -1 At that time, the CME's propagation trajectory intersected with Earth's orbit, indicating that the CME was the trigger source for the risk of geomagnetic storms; when 1.0×10 -4 s -1 At that time, the CME's propagation trajectory is unlikely to intersect with Earth's orbit, so it is determined that the CME will not trigger a geomagnetic storm, and the classification process ends directly.
7. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 1, characterized in that, In the level prediction and correction process, based on historical data of CME events, the multi-dimensional parameters of CME and the corresponding geomagnetic storm level for each event are compiled to construct a dataset relating CME parameters and geomagnetic storm levels. The geomagnetic storm level index is then calculated using the geomagnetic storm level determination formula. .
8. The method for classifying the disaster level of CME-induced geomagnetic storms according to claim 7, characterized in that, In the aforementioned level prediction correction, the formula for determining the geomagnetic storm level is: ,in, This is a geomagnetic storm level index. , The weights for the levels are determined by associating CME parameters with the geomagnetic storm level dataset. For CME comprehensive feature parameters, This is the index for determining the intersection.
9. A method for classifying the disaster level of CME-induced geomagnetic storms according to claim 8, characterized in that, In the aforementioned level prediction correction, the geomagnetic storm level index is used as the basis. Determining the level of a geomagnetic storm: When At that time, it was G1 level; when At that time, it was G2 level; when At that time, it was G3 level; when At that time, it was G4 level; when At that time, it was G5 level.