Method, system, device, medium and program product for monitoring dst index based on zhengheng no. 1 satellite

Abstract

This invention discloses a method, system, equipment, medium, and program product for monitoring the Dst index based on the Zhangheng-1 satellite. The method includes: acquiring magnetic field data collected by the Zhangheng-1 satellite; preprocessing the magnetic field data to obtain onboard observation data; subtracting the background magnetic field from the onboard observation data using a global geomagnetic field model to obtain the onboard magnetic field residual; and finally, inputting the onboard magnetic field residual into a ground-based Dst index mapping model to obtain the corrected onboard Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels. This invention achieves high-timeliness and high-precision monitoring of global geomagnetic storm indices through high-precision mapping from the original onboard observation residuals to the ground-based standard Dst index.

Description

Technical Field

[0001] This invention relates to the field of space environment monitoring technology, and in particular to a method, system, equipment, medium and program product for monitoring the Dst index based on the Zhang Heng-1 satellite. Background Technology

[0002] The solar-terrestrial space environment, as a complex and dynamically changing coupled system, is rife with geomagnetic storms triggered by the intense interaction between the solar wind and the Earth's magnetosphere. These storms are not only a core subject of space physics research into solar-driven processes but also a significant source of risk for modern high-tech societies. The intense effects of geomagnetic storms are multi-dimensionally destructive. In terrestrial power systems, geomagnetic induced currents (GIC) induced by violent fluctuations in the geomagnetic field can lead to transformer half-wave saturation and overheating (Liu et al., 2016; Love et al., 2016). In the aerospace field, the intense heating and expansion of the upper atmosphere caused by the energy injection of geomagnetic storms significantly increases the drag of low-Earth orbit spacecraft, leading to rapid orbital decay or even satellite crashes (Dang et al., 2022). Furthermore, the ionospheric storms and scintillation effects accompanying geomagnetic storms disrupt radio signal propagation paths, causing communication interruptions and GNSS navigation and positioning errors. The synchronously enhanced high-energy particle radiation environment can penetrate spacecraft shielding, directly threatening the lives of astronauts in orbit (Hapgood et al., 2021). As the most significant physical characteristic of geomagnetic storms within the magnetosphere, the enhancement of the equatorial ring current directly determines the intensity of geomagnetic disturbances. To quantify and monitor this crucial process, Masahisa Sugiura, using high-quality synchronous observation data accumulated by global geomagnetic networks during the International Geophysical Year (IGY, 1957-1958), first established the definition and algorithm system of the Disturbance Storm Time (Dst) index (Sugiura, 1964). This index, obtained by processing the horizontal component observations from global mid- and low-latitude geomagnetic stations, has long been recognized as a "standard ruler" for measuring the intensity of geomagnetic storms and the energy state of the magnetosphere, playing a vital role in space weather early warning and scientific research.

[0003] Although the ground-based Dst index is widely used, its limitations are becoming increasingly apparent as the demand for more refined space weather monitoring increases. In addition to ground-based observations, the successful launches of the European Space Agency's Swarm constellation, and especially my country's "Zhang Heng-1" (CSES, Shen et al., 2018) series and MSS-1 (Zhang et al., 2023) satellites in recent years, have provided massive amounts of high-precision global magnetic measurement data, opening up new data sources and methods for calculating the Dst index.

[0004] Therefore, there is an urgent need to invent a Dst index monitoring method based on the Zhangheng-1 satellite to solve the problem that existing Dst monitoring methods cannot meet the requirements for near real-time monitoring and early warning of the Dst index with high timeliness. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method, system, device, medium and program product for monitoring the Dst index based on the Zhangheng-1 satellite, which at least partially solves the problems existing in the prior art.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0008] According to a first aspect of the present invention, a method for monitoring the Dst index based on the Zhangheng-1 satellite is provided, the method comprising:

[0009] To acquire magnetic field data collected by the Zhang Heng-1 satellite;

[0010] The magnetic field data is preprocessed to obtain spaceborne observation data;

[0011] The background magnetic field was subtracted from the spaceborne observation data using a global geomagnetic field model to obtain the spaceborne magnetic field residual.

[0012] The residual of the spaceborne magnetic field is input into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels.

[0013] Furthermore, the magnetic field data collected by the Zhang Heng-1 satellite was obtained, including:

[0014] Obtain scalar magnetic field data collected by the Zhangheng-1 satellite and the corresponding mass labels of the scalar magnetic field data.

[0015] Furthermore, the magnetic field data is preprocessed to obtain spaceborne observation data, including:

[0016] Based on the quality label corresponding to the scalar magnetic field data, abnormal data are removed to obtain qualified magnetic measurement data;

[0017] From the qualified magnetic measurement data, near-magnetic equatorial observation point data of the satellite's night side orbit are selected to obtain the onboard observation data.

[0018] Furthermore, the background magnetic field of the spaceborne observation data is subtracted using a global geomagnetic field model to obtain the spaceborne magnetic field residual, including:

[0019] The background magnetic field intensity is obtained by calculating the magnetic field strength at the spatiotemporal location of the satellite using a global geomagnetic field model, which is either the CHAOS model or the CGGM model.

[0020] Based on the background magnetic field strength and the onboard observation data, the onboard magnetic field residual is calculated. The calculation formula is: ,in, The total magnetic field strength in the spaceborne observation data. The background magnetic field strength calculated for the global geomagnetic field model.

[0021] Furthermore, the spaceborne magnetic field residual is input into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index, including:

[0022] The residual of the onboard magnetic field is used as the original onboard Dst exponent. The input is fed into the ground-based Dst index mapping model, and the modified spaceborne Dst index is obtained. The corrected spaceborne Dst index The calculation formula is: ,in, This is the normalized solar radiation index. This is a set of regression coefficients obtained by fitting historical Dst index data in segments according to different geomagnetic storm levels using the multiple linear regression method.

[0023] Furthermore, the regression coefficient set is calculated as follows:

[0024] Based on the geomagnetic storm level, the historical Dst index dataset is segmented to obtain segmented datasets under different geomagnetic storm level intervals. The historical Dst index includes historical ground-based Dst index and historical original spaceborne Dst index.

[0025] For each of the aforementioned segmented datasets, the least squares method is used to apply the multiple linear regression equation. By fitting the data, the optimal regression coefficients for the geomagnetic storm level intervals corresponding to the segmented datasets are obtained. ,in, The Dst index is the foundation index.

[0026] The regression coefficient set is obtained by combining the optimal regression coefficients for each geomagnetic storm level range.

[0027] According to a second aspect of the present invention, a Dst index monitoring system based on the Zhangheng-1 satellite is provided, the system comprising:

[0028] The satellite data acquisition module is used to acquire magnetic field data collected by the Zhang Heng-1 satellite.

[0029] The data preprocessing module is used to preprocess the magnetic field data to obtain spaceborne observation data;

[0030] The magnetic field residual generation module is used to perform background magnetic field subtraction processing on the spaceborne observation data using a global geomagnetic field model to obtain the spaceborne magnetic field residual.

[0031] The spaceborne Dst correction module is used to input the spaceborne magnetic field residual into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels.

[0032] According to a third aspect of the present invention, a Dst index monitoring device based on the Zhangheng-1 satellite is provided, the device comprising: a processor and a memory;

[0033] The memory is used to store one or more program instructions;

[0034] The processor is configured to run one or more program instructions to perform the steps of a Dst index monitoring method based on the Zhang Heng-1 satellite as described in any of the preceding claims.

[0035] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program, when executed by a processor, implements the steps of a Dst index monitoring method based on the Zhang Heng-1 satellite as described in any of the preceding claims.

[0036] According to a fifth aspect of the present invention, a computer program product is provided, the computer program product comprising a computing program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to implement the steps of a Dst index monitoring method based on the Zhang Heng-1 satellite as described in any of the preceding claims.

[0037] This invention provides a method, system, equipment, medium, and program product for monitoring the Dst index based on the Zhangheng-1 satellite. The method includes: acquiring magnetic field data collected by the Zhangheng-1 satellite; preprocessing the magnetic field data to obtain onboard observation data; subtracting the background magnetic field from the onboard observation data using a global geomagnetic field model to obtain the onboard magnetic field residual; and finally, inputting the onboard magnetic field residual into a ground-based Dst index mapping model to obtain the corrected onboard Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels. This invention achieves high-timeliness and high-precision monitoring of global geomagnetic storm indices through high-precision mapping from the original onboard observation residuals to the ground-based standard Dst index. Attached Figure Description

[0038] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0039] Figure 1 A flowchart illustrating a Dst index monitoring method based on the Zhangheng-1 satellite provided in an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of a Dst index monitoring system based on the Zhangheng-1 satellite provided in an embodiment of the present invention;

[0041] Figure 3 A scatter plot of the correlation density between the original spaceborne Dst and the ground-based Dst index based on different geomagnetic field models provided for embodiments of the present invention.

[0042] Figure 4 This is one of the comparison charts between the original spaceborne Dst index and the ground-based Dst index provided in the embodiments of the present invention;

[0043] Figure 5 This is the second comparison chart of the original spaceborne Dst index and the ground-based Dst index provided in the embodiments of the present invention;

[0044] Figure 6 A statistical analysis chart showing the variation of the original spaceborne Dst index and the ground-based Dst index with the F10.7 index, provided for embodiments of the present invention.

[0045] Figure 7 A joint density distribution map of the difference between the F10.7 index and the star-ground Dst index provided for embodiments of the present invention;

[0046] Figure 8 One of the comparison charts of the original spaceborne Dst index, the corrected spaceborne Dst index, and the ground-based Dst index provided in an embodiment of the present invention;

[0047] Figure 9 The second comparison chart of the original spaceborne Dst index, the corrected spaceborne Dst index, and the ground-based Dst index provided for embodiments of the present invention;

[0048] Figure 10 A comparison chart of the difference between the star-ground Dst index before and after correction as a function of the F10.7 index, provided for embodiments of the present invention;

[0049] Figure 11 Histogram of the distribution of the difference between the star-ground Dst index before and after correction, provided in an embodiment of the present invention;

[0050] Figure 12 This is a schematic diagram of the joint observation results of two stars provided in an embodiment of the present invention;

[0051] Figure 13 This is a schematic diagram of the smoothed binary star observation results provided in an embodiment of the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0053] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] Figure 1 A flowchart of a Dst index monitoring method based on the Zhangheng-1 satellite according to an embodiment of the present invention is shown.

[0055] like Figure 1 As shown, the Dst index monitoring method based on the Zhangheng-1 satellite according to an embodiment of the present invention may include steps S100, S200, S300 and S400.

[0056] In step S100, the magnetic field data collected by the Zhang Heng-1 satellite is obtained.

[0057] Specifically, the above steps include:

[0058] The Zhangheng-1 satellite (CSES) operates in a sun-synchronous orbit at an altitude of approximately 507 kilometers with an inclination of 97.4°. Its orbit is designed with fixed local time characteristics: the local time at the descending node is 14:00, and the local time at the ascending node is 02:00. CSES has a 5-day revisit period, meaning it covers the same ground trajectory every 5 days. The platform carries eight scientific instruments used to acquire various physical quantities such as electromagnetic fields and plasma parameters. Among them, the high-precision magnetometer (HPM, Yang et al., 2021) is the core payload for achieving precise measurements of the Earth's magnetic field.

[0059] The HPM payload is mounted on a 5-meter-long extension rod to reduce magnetic interference from the satellite itself. The payload mainly consists of two fluxgate magnetometers (FGMs) and one coupled dark-state magnetometer (CDSM). The FGMs measure vector magnetic fields from DC to 15 Hz; the CDSM measures scalar magnetic fields from DC to 0.5 Hz and performs on-orbit calibration of the FGMs to ensure the long-term stability and accuracy of the vector data.

[0060] This invention uses a calibrated HPM2 Level 2 scalar scientific data product. This data product contains three quality flags that can identify and label magnetic interference generated by the operation of the magnetic torquer (FLAG_MT), boom thermal deformation caused by satellite exiting the shadow region (FLAG_SHW), and the operation of the tri-frequency beacon (TBB).

[0061] Next, in step S200, the magnetic field data is preprocessed to obtain spaceborne observation data.

[0062] Specifically, the above steps include:

[0063] First, based on the quality label corresponding to the scalar magnetic field data, abnormal data are removed to obtain qualified magnetic measurement data.

[0064] In the Dst index inversion process, the embodiments of the present invention strictly eliminate the interfered data segments according to the above-mentioned quality label, and retain only high-quality night side magnetometry data, thereby ensuring the purity and reliability of the source data for spaceborne Dst index calculation.

[0065] To accurately capture the magnetic field disturbances caused by the loop current, the near-magnetic equator (magnetic equatorial plane in solar magnetic coordinate system) observation point data of the satellite's night side orbit were selected from the qualified magnetic measurement data to obtain the onboard observation data.

[0066] In step S300, the background magnetic field of the spaceborne observation data is subtracted using the global geomagnetic field model to obtain the spaceborne magnetic field residual.

[0067] Specifically, the above steps include:

[0068] The magnetic field strength at the satellite's location in time and space was calculated using a global geomagnetic field model to obtain the background magnetic field strength (the contribution values ​​of the main magnetic field and the crustal field). The global geomagnetic field model mentioned above is either the CHAOS (Finlay et al., 2016; Olsen et al., 2006) model or the CGGM model.

[0069] The background magnetic field strength is then subtracted from the spaceborne observation data to calculate the residual spaceborne magnetic field near the magnetic equator for each orbit.

[0070] Since the change in total magnetic field strength at the magnetic equator is mainly contributed by the horizontal component, the residual of the onboard magnetic field directly reflects the change characteristics of the equatorial ring current intensity and can be used as the original onboard Dst index.

[0071] The above-mentioned spaceborne magnetic field residual The calculation formula is: ,in, The total magnetic field strength in the spaceborne observation data. The background magnetic field strength calculated for the global geomagnetic field model.

[0072] Furthermore, based on the above calculation results of the spaceborne magnetic field residuals, a database of the original spaceborne Dst index corresponding to the spaceborne magnetic field residuals was constructed. The database spans from April 1, 2018 to September 12, 2024. The database structure includes both image and text components. The image data, in .png format, visually displays the monthly trend of the Dst index. The text data records detailed index values ​​in .txt format, with specifications detailed in Table 1. The text files contain the following fields: observation date, time, ground-based Dst index interpolated based on satellite time (Dst_ground), spaceborne Dst calculated based on the CHAOS model (Dst_CHAOS), and spaceborne Dst calculated based on the CGGM model (Dst_CGGM).

[0073] surface Zhangheng-1 Satellite Dst Index Database File Format Instructions

[0074]

[0075] Furthermore, this invention conducts a difference analysis based on the original onboard Dst index of the Zhang Heng-1 satellite and the ground-based Dst index. The analysis process is as follows:

[0076] Based on the magnetic field observation data of the Zhangheng-1 01 satellite from April 1, 2018 to September 12, 2024, this invention presents a systematic statistical comparative analysis of the onboard Dst index and the ground-based Dst index calculated using different background field models such as CHAOS and CGGM.

[0077] Statistical results show that the spaceborne Dst index and the ground-based Dst index maintain a high degree of consistency in overall trend. For example... Figure 3 As shown in the density scatter plot, the two exhibit a significant linear correlation, with Pearson correlation coefficients exceeding 0.80. This indicates that the magnetic field residuals observed by the Zhang Heng-1 satellite can effectively reflect the main variation characteristics of the equatorial ring current intensity, verifying the feasibility of calculating the Dst exponent from satellite data. Furthermore, Figure 3 This also shows that the correlations obtained based on the CHAOS model and the CGGM model are very close, verifying the ability of the CGGM model in calculating the Dst exponent.

[0078] The embodiments of the present invention have verified the high statistical correlation between the original spaceborne Dst index and the ground-based Dst index through the above analysis. However, there are still differences between their absolute values. Existing technologies only use simple linear fitting to describe the conversion relationship between the two sets of indices, but lack systematic long-term research on the sources of their differences. In particular, they ignore the complex deviations introduced by satellites due to differences in orbit and space environment, which leads to a large deviation in the conversion results.

[0079] In actual observations, the deviation between the two may fluctuate significantly in different years or at different levels of geomagnetic activity. This deviation, which changes dynamically over time, can even reach tens of nT in extreme cases.

[0080] Figure 4 The image shows a comparison of the original spaceborne Dst index and the ground-based Dst index from August 2018. Figure 5 The image shows a comparison of the original spaceborne Dst index and the ground-based Dst index in August 2024, as shown below. Figure 4 and Figure 5 As shown, the differences are large in certain years or periods, while they are small in other periods. The black curve is the ground-based Dst index, and the blue and red curves are the original satellite-borne Dst indices of Zhang Heng-1 calculated using the CHAOS model and the CGGM model, respectively.

[0081] To address this, this invention aims to deeply analyze the causes of discrepancies between space-based and ground-based Dst indices and provide a more accurate conversion relationship between space-based and ground-based indices, thereby truly achieving unification between space-based and ground-based indices. Through systematic research and analysis, it was found that the main sources of discrepancies between space-based and ground-based Dst observations are as follows:

[0082] (1) Baseline deviation of the star-ground Dst index

[0083] The baseline deviation of the satellite-to-ground Dst index was first discovered through analysis of Magsat satellite data. Langel & Estes (1985) used spherical harmonic analysis to separate the internal and external source fields, establishing a quantitative linear relationship between the first-order spherical harmonic coefficients of the external source field and the ground-based Dst index. Even during the magnetic quiescence period when the ground-based Dst is zero, the satellite could still observe an external magnetic field of approximately 20 nT. This work confirmed the objective existence of the "magnetic quiescence loop current" and also revealed for the first time an absolute baseline deviation of 20 nT between the ground-based Dst index and the absolute quantity observed by space. This typical value has been confirmed by subsequent satellite data. It should be noted that the offset of 10–20 nT is within the expected range. The ground-based Dst index is calculated relative to an unknown reference offset, while the satellite measurement has a correct reference offset. In practice, the ground-based Dst index is calculated from station observation data, and contributions from non-magnetic sources (such as the core field, lithospheric magnetic field, and ionospheric currents like Sq and the equatorial electric jet) need to be subtracted. However, it should be noted that since the Zhangheng-1 satellite calculation in this invention only uses nighttime data, the ionospheric field subtraction will be ignored. The core field can indeed be subtracted using the latest core field model, but due to the influence of local magnetic sources, the lithospheric magnetic field of ground stations is difficult to measure accurately.

[0084] In practice, it is usually assumed that only the Earth's core and lithosphere magnetic fields contribute during periods of geomagnetic quiescence, thus subtracting them from the station data. Therefore, according to the definition of ground-based Dst, magnetic field disturbances under quiescent conditions are forced to zero, which constitutes the baseline used in practice. The true baseline can only be determined through satellite data.

[0085] (2) The impact of solar activity

[0086] Figure 6 The figure shows a statistical analysis of the deviation between the original spaceborne Dst index and the ground-based Dst index as a function of the F10.7 index from April 2018 to September 2024.

[0087] like Figure 6As shown, the green curve represents the monthly average value of the ground-based Dst index, the blue curve represents the monthly average value of the original spaceborne Dst index calculated by the CGGM model, the red curve represents the monthly average value of the original spaceborne Dst index calculated by the CHAOS model, the fluorescent blue curve represents the monthly average value of the difference between the original spaceborne Dst index calculated by the CGGM model and the ground-based Dst index, and the fluorescent pink curve represents the monthly average value of the difference between the original spaceborne Dst index calculated by the CHAOS model and the ground-based Dst index. The histogram shows the monthly average value of F10.7.

[0088] Figure 6 The statistical results clearly reveal a significant positive correlation between the spaceborne and ground-based Dst deviations and the F10.7 index. During years of low solar activity, i.e., periods with lower F10.7 indexes, the difference between spaceborne and ground-based Dst is smaller; during years of high solar activity, i.e., periods with higher F10.7 indexes, the difference between spaceborne and ground-based Dst is larger. Figure 7 The joint density distribution map of the difference between the F10.7 index and the satellite-to-ground Dst index (original satellite-borne Dst minus ground-based Dst) further confirms this phenomenon, showing that the deviation has a non-fixed systematic characteristic in different Dst intervals. This statistical regularity is consistent with... Figure 4 and Figure 5 The temporal evolution characteristics shown, namely that the star-to-ground Dst deviation is not a random error but a systematic deviation modulated by the level of solar activity, corroborate each other, indicating that the star-to-ground Dst deviation is not a random error, but a systematic deviation essentially modulated by the level of solar activity. It should be noted that, in existing research and prior art, no work has yet discussed the difference in the star-to-ground Dst index caused by solar activity; therefore, this invention clarifies the source of this difference for the first time.

[0089] Then, in step S400, the spaceborne magnetic field residual is input into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index.

[0090] Specifically, the above steps include:

[0091] To address the factors affecting the original spaceborne Dst index and the ground-based Dst index identified in the above analysis, and to achieve consistency between spaceborne data and ground-based standards, this invention simultaneously considers the solar radiation index (F10.7) and baseline deviation, and employs a piecewise multiple linear regression method to construct a mapping model from the original spaceborne Dst index of Zhang Heng-1 to the ground-based Dst index:

[0092]

[0093] in, The original onboard Dst index, Represents the Dst index of the foundation. This is the normalized solar radiation index. This is a set of regression coefficients obtained by fitting historical Dst index data in segments according to different geomagnetic storm levels using the multiple linear regression method.

[0094] The above regression coefficient set is calculated as follows:

[0095] Considering the different response characteristics of the magnetosphere under different disturbance levels, the historical Dst index dataset is first segmented according to the geomagnetic storm level (referring to the national standard "Geomagnetic Storm Level" (GB / T 31160-2014) proposed by the China Meteorological Administration, and the geomagnetic storm level is determined based on the magnitude of the ground-based Dst index). This results in segmented datasets for different geomagnetic storm level intervals. The aforementioned historical Dst index includes historical ground-based Dst index and historical original spaceborne Dst index.

[0096] Then, for each segmented dataset, the least squares method is used to apply the multiple linear regression equation. By fitting the data, the optimal regression coefficients for the geomagnetic storm level intervals corresponding to the segmented datasets are obtained. Based on the optimal regression coefficients for each geomagnetic storm level range, regression coefficient sets were obtained. Table 2 shows the regression coefficient sets for different geomagnetic storm level ranges.

[0097] surface Zhang Heng-1 satellite Dst index sub-geomagnetic storm fitting range and regression coefficients

[0098]

[0099] Finally, the fitted regression coefficient set, the measured F10.7, and the original onboard Dst index were compared. Substitute model The mapping yields the corrected onboard Dst index. .

[0100] The spaceborne Dst index before and after correction was compared with the ground-based Dst index. Figure 8 The graph shows a comparison of the original spaceborne Dst index, the corrected spaceborne Dst index, and the ground-based Dst index in May 2024. Figure 9 The graph shows a comparison of the original satellite-borne Dst index, the corrected satellite-borne Dst index, and the ground-based Dst index in August 2024. The black curve represents the ground-based Dst index, the blue curve represents the original satellite-borne Dst index before correction, and the red curve represents the corrected satellite-borne Dst index.

[0101] Depend on Figure 8 and Figure 9It can be seen that the corrected spaceborne Dst index is closer to the ground-based Dst index in both trend and magnitude. The model performs well under different levels of geomagnetic activity. It can also effectively suppress background noise during magnetically quiescent periods, and its fit is significantly better than that of the original satellite observation data.

[0102] Furthermore, Figure 10 The graph shows a comparison of the difference between the satellite-to-ground Dst index (dDst, satellite-borne Dst minus ground-based Dst) before and after correction as a function of the F10.7 index. Figure 11 The distribution histogram of the difference between the star-ground Dst index before and after correction is shown.

[0103] Depend on Figure 10 and Figure 11 It can be seen that after applying the ground-based Dst index mapping model, the correlation between the dDst distribution (spaceborne Dst minus ground-based Dst) and the F10.7 index is significantly weakened, and the overall distribution tends to converge and fluctuate around zero. This result shows that the ground-based Dst index mapping model provided in this embodiment of the invention effectively eliminates the influence of solar activity background noise characterized by F10.7 on satellite observations, significantly reduces systematic bias, and verifies the effectiveness of the model.

[0104] Preferably, since the time resolution of the onboard Dst index calculated solely by Zhang Heng-1 01 satellite is approximately 1.5 hours, it is sometimes difficult to capture rapid changes during geomagnetic storms. On June 14, 2025, Zhang Heng-1 02 satellite was successfully launched, forming excellent spatiotemporal complementarity with 01 satellite. Utilizing the advantages of network observation by both satellites can break through the single-satellite time resolution limit, further improving the resolution to 45 minutes.

[0105] This invention explores the feasibility of joint observation of the Dst index by two satellites in order to improve the resolution of the onboard Dst index.

[0106] Figure 12 The results of joint observations by the two satellites during the geomagnetic storm in November 2025 are shown. The black curve represents the Dst index of the ground-based station, the blue curve represents the original satellite Dst index of CSES-01 and CSES-02 without model correction, and the red curve represents the satellite Dst index of CSES-01 and CSES-02 after mapping model correction.

[0107] Depend on Figure 12 It can be seen that the fusion of the two satellites does significantly increase the density of data sampling points and effectively improves the temporal resolution. However, due to slight differences in the orbits of the two satellites, the Dst index after direct fusion exhibits obvious "sawtooth" fluctuations at alternating sampling points, which affects the continuity of the data to some extent.

[0108] To address the aforementioned problems, this invention employs a two-point moving average algorithm to smooth the fused sequence, specifically by calculating the arithmetic mean of the data from the current observation time and the previous sampling time. The processed result is as follows: Figure 13 (The black curve in the figure represents the ground-based Dst index, and the red curve represents the binary fusion Dst index after model correction and processing using a two-point moving average algorithm.) As shown, the "sawtooth" fluctuations have been effectively suppressed, and the consistency between the satellite-borne Dst index and the ground-based Dst index has been significantly improved. Notably, during the sudden onset (SSC) phase of the geomagnetic storm on November 12th, the high-resolution binary fusion index successfully captured the pulse-like abrupt changes in the geomagnetic field, and its linear response characteristics were more sensitive than those of single-satellite data. This result indicates that multi-satellite collaborative observation can not only fill observational blind spots but also describe the dynamics of geomagnetic storm development in greater detail. In the future, further multi-point joint observations using satellites such as the ESA's Swarm satellite and my country's MSS-1 satellite are expected to further eliminate orbital observation blind spots and achieve satellite-borne Dst index monitoring with a resolution better than 30 minutes.

[0109] In addition, this invention also provides a Dst index monitoring system based on the Zhangheng-1 satellite. Figure 2 This diagram illustrates the structure of a Dst index monitoring system based on the Zhangheng-1 satellite, according to an embodiment of the present invention. The system specifically includes:

[0110] The satellite data acquisition module is used to acquire magnetic field data collected by the Zhang Heng-1 satellite.

[0111] The data preprocessing module is used to preprocess the magnetic field data to obtain spaceborne observation data;

[0112] The magnetic field residual generation module is used to perform background magnetic field subtraction on spaceborne observation data using a global geomagnetic field model to obtain the spaceborne magnetic field residual.

[0113] The spaceborne Dst correction module is used to input the spaceborne magnetic field residual into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels.

[0114] In addition, this embodiment of the invention also provides a Dst index monitoring device based on the Zhangheng-1 satellite. The device includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to perform the steps of the Dst index monitoring method based on the Zhangheng-1 satellite as described above.

[0115] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a Dst index monitoring method based on the Zhang Heng-1 satellite as described above.

[0116] In addition, embodiments of the present invention also provide a computer program product, which includes computer program instructions that, when executed by a processor, implement the steps of a Dst index monitoring method based on the Zhang Heng-1 satellite as described above.

[0117] In summary, this invention verifies the feasibility of monitoring equatorial ring current variations using low-Earth orbit satellite observation data from the Zhang Heng-1 satellite. Statistical analysis shows that the onboard Dst index acquired by the Zhang Heng-1 satellite and the ground-based Dst index exhibit a strong correlation in trend (correlation coefficient approximately 0.85), but there are significant and non-constant differences in magnitude. These differences mainly stem from two aspects: first, a systematic bias due to different baseline definitions, resulting in a baseline deviation of several to tens of nT between ground-based and space-based observations; second, the influence of periodic solar activity modulation, with smaller differences in low solar activity years and significantly larger differences in high solar activity years.

[0118] To address the aforementioned discrepancies, this invention comprehensively considers baseline deviation and solar activity intensity factors to construct a ground-based Dst index mapping model. This model effectively eliminates the influence of solar activity background noise, characterized by F10.7, on satellite observations, successfully achieving a high-precision mapping from the residuals of the original spaceborne observations to the ground-based standard Dst index. The corrected and transformed spaceborne Dst index is more consistent with the ground-based observation results in both amplitude and trend. It not only eliminates the systematic error that drifts over time in terms of magnitude, but its residual distribution also exhibits an ideal normal distribution with a mean of zero. This result confirms that using a single low-Earth orbit satellite in conjunction with a physical correction model can achieve highly timely and accurate autonomous monitoring of global geomagnetic storm indices, providing key technical support for building an independent space weather early warning system.

[0119] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in this embodiment of the invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in this embodiment of the invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods. The storage medium can be memory, for example, volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory. Those skilled in the art will recognize that the functions described in the above examples can be implemented using a combination of hardware and software. When applied software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers. Although the invention has been described in detail above with general description and specific embodiments, modifications or improvements can be made to it, which will be apparent to those skilled in the art. Therefore, such modifications or improvements made without departing from the spirit of the invention are all within the scope of protection claimed by the invention.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, or alterations made by those skilled in the art using the disclosed technical content shall fall within the protection scope of the present invention.

Claims

1. A method for monitoring the Dst index based on the Zhangheng-1 satellite, characterized in that, The method includes: To acquire magnetic field data collected by the Zhang Heng-1 satellite; The magnetic field data is preprocessed to obtain spaceborne observation data; The background magnetic field was subtracted from the spaceborne observation data using a global geomagnetic field model to obtain the spaceborne magnetic field residual. The spaceborne magnetic field residual is input into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels. The spaceborne magnetic field residual is input into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index, including: The residual of the onboard magnetic field is used as the original onboard Dst exponent. The input is fed into the ground-based Dst index mapping model, and the modified spaceborne Dst index is obtained. The corrected spaceborne Dst index The calculation formula is: ,in, This is the normalized solar radiation index. This is a set of regression coefficients obtained by fitting historical Dst index data in segments according to different geomagnetic storm levels using the multiple linear regression method; The regression coefficient set is calculated as follows: Based on the geomagnetic storm level, the historical Dst index dataset is segmented to obtain segmented datasets under different geomagnetic storm level intervals. The historical Dst index includes historical ground-based Dst index and historical original spaceborne Dst index. For each of the aforementioned segmented datasets, the least squares method is used to apply the multiple linear regression equation. By fitting the data, the optimal regression coefficients for the geomagnetic storm level intervals corresponding to the segmented datasets are obtained. ,in, The Dst index is the foundation index. The regression coefficient set is obtained by combining the optimal regression coefficients for each geomagnetic storm level range.

2. The Dst index monitoring method based on Zhang Heng-1 satellite according to claim 1, characterized in that, Obtain magnetic field data collected by the Zhang Heng-1 satellite, including: Obtain scalar magnetic field data collected by the Zhangheng-1 satellite and the corresponding mass labels of the scalar magnetic field data.

3. The Dst index monitoring method based on Zhang Heng-1 satellite according to claim 2, characterized in that, The magnetic field data is preprocessed to obtain spaceborne observation data, including: Based on the quality label corresponding to the scalar magnetic field data, abnormal data are removed to obtain qualified magnetic measurement data; From the qualified magnetic measurement data, near-magnetic equatorial observation point data of the satellite's night side orbit are selected to obtain the onboard observation data.

4. The Dst index monitoring method based on Zhang Heng-1 satellite according to claim 1, characterized in that, The background magnetic field of the spaceborne observation data was subtracted using a global geomagnetic field model to obtain the spaceborne magnetic field residual, including: The background magnetic field intensity is obtained by calculating the magnetic field strength at the spatiotemporal location of the satellite using a global geomagnetic field model, which is either the CHAOS model or the CGGM model. Based on the background magnetic field strength and the onboard observation data, the onboard magnetic field residual is calculated. The calculation formula is: ,in, The total magnetic field strength in the spaceborne observation data. The background magnetic field strength calculated by the global geomagnetic field model.

5. A Dst index monitoring system based on the Zhangheng-1 satellite, characterized in that, The system includes: The satellite data acquisition module is used to acquire magnetic field data collected by the Zhang Heng-1 satellite. The data preprocessing module is used to preprocess the magnetic field data to obtain spaceborne observation data; The magnetic field residual generation module is used to perform background magnetic field subtraction processing on the spaceborne observation data using a global geomagnetic field model to obtain the spaceborne magnetic field residual. The spaceborne Dst correction module is used to input the spaceborne magnetic field residual into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index. The ground-based Dst index mapping model is a multiple linear regression model obtained by fitting historical Dst index data and solar radiation index in segments according to different geomagnetic storm levels. The spaceborne magnetic field residual is input into the ground-based Dst index mapping model to obtain the corrected spaceborne Dst index, including: The residual of the onboard magnetic field is used as the original onboard Dst exponent. The input is fed into the ground-based Dst index mapping model, and the modified spaceborne Dst index is obtained. The corrected spaceborne Dst index The calculation formula is: ,in, This is the normalized solar radiation index. This is a set of regression coefficients obtained by fitting historical Dst index data in segments according to different geomagnetic storm levels using the multiple linear regression method; The regression coefficient set is calculated as follows: Based on the geomagnetic storm level, the historical Dst index dataset is segmented to obtain segmented datasets under different geomagnetic storm level intervals. The historical Dst index includes historical ground-based Dst index and historical original spaceborne Dst index. For each of the aforementioned segmented datasets, the least squares method is used to apply the multiple linear regression equation. By fitting the data, the optimal regression coefficients for the geomagnetic storm level intervals corresponding to the segmented datasets are obtained. ,in, The Dst index is the foundation index. The regression coefficient set is obtained by combining the optimal regression coefficients for each geomagnetic storm level range.

6. A Dst index monitoring device based on the Zhangheng-1 satellite, characterized in that, The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions to perform the steps of the Dst index monitoring method based on the Zhangheng-1 satellite as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the Dst index monitoring method based on the Zhangheng-1 satellite as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes computer program instructions that, when executed by a processor, implement the steps of a Dst index monitoring method based on the Zhangheng-1 satellite as described in any one of claims 1 to 4.

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