A method for collaborative calibration of high-energy proton data of GEO and SSO cross-platform satellites based on solar proton events

Abstract

This invention discloses a collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events. The method includes: acquiring high-energy proton observation data and space environment disturbance parameter data from GEO and SSO; preprocessing to obtain high-energy proton data, satellite spatiotemporal position data, and environmental parameters of a unified flux type; filtering high-energy proton data during events according to the definition of solar proton events, obtaining multiple event datasets and grouping them; aligning the filtered high-energy proton data from GEO and SSO; constructing energy matching relationships for each channel of GEO and SSO based on the power-law spectral characteristics of solar proton events; determining the effective energy and energy range of each channel of SSO through point-by-point spectral fitting and statistical inversion; recalculating the differential flux of GEO in the corresponding channel using the calibrated effective energy; establishing a point-by-point scatter matrix of the two types of satellite data; and performing linear fitting and correlation analysis to achieve calibration.

Description

Technical Field

[0001] This invention belongs to the field of space environment monitoring technology, and specifically relates to a collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events. Background Technology

[0002] High-energy proton radiation detection is a core technological means for building a space weather early warning and on-orbit protection system. Its real-time detection data directly drives emergency response decisions for space missions, including payload power-on / off commands and extravehicular activity (EVA) scheduling for manned spaceflight, and provides crucial quantitative data for spacecraft radiation protection design. Simultaneously, long-term observation data has irreplaceable value for retrieving solar activity patterns and understanding the dynamic evolution of radiation belts, serving as the scientific foundation for planning long-term missions such as deep space exploration and manned spaceflight.

[0003] Currently, due to inherent differences in the physical design and manufacturing processes of detection instruments, their performance evolves during on-orbit operation due to factors such as the space radiation environment. Furthermore, unavoidable differences exist between ground calibration conditions and the actual space environment. These factors collectively lead to a significant systematic bias in the observation data of different instruments, and even different orbital platforms, regarding the same space environment. This systematic bias restricts the effective fusion and mutual verification of multi-source detection data, directly reducing the accuracy and reliability of space environment state assessment, and becoming a major obstacle to achieving high-precision space weather early warning and deepening space physics research.

[0004] Geostationary orbit (GEO) satellites and sun-synchronous orbit (SSO) satellites form a crucial pillar of the space environment monitoring network. GEO satellites, acting as fixed-point outposts, continuously monitor the initiation, intensity, and temporal evolution of solar proton events; while SSO satellites effectively detect the deposition of high-energy protons along polar magnetic field lines. While functionally complementary, due to the aforementioned systemic biases, the observational data from these two orbital platforms are currently difficult to deeply integrate and collaboratively analyze on a unified physical benchmark. This limits the comprehensive understanding and accurate prediction of the entire chain of physical processes of solar proton events, from heliospheric propagation and magnetospheric intrusion to polar deposition. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events. GEO (Geostationary Earth Orbit) is a geostationary orbit, and SSO (Sun-Synchronous Orbit) is a sun-synchronous orbit.

[0006] In view of this, the present invention proposes a collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events, comprising: Step 1: Acquire high-energy proton observation data and space environment disturbance parameter data from geostationary orbit satellites and sun-synchronous orbit satellites, and preprocess them to obtain high-energy proton data of uniform flux type, satellite spatiotemporal position data and environmental parameters; Step 2: Filter high-energy proton data during the solar proton event according to the definition of the solar proton event to obtain multiple event datasets; and group the datasets according to solar wind dynamic pressure and geomagnetic Dst index; Step 3: Align the high-energy proton data of the selected geostationary orbit and sun-synchronous orbit satellites; Step 4: Based on the power-law spectral characteristics of solar proton events, construct the energy matching relationship between the energy channels of geostationary orbit satellites and sun-synchronous orbit satellites. Through point-by-point spectral fitting and statistical inversion, determine the effective energy and energy range of each energy channel of the sun-synchronous orbit satellite. Step 5: Using the calibrated effective energy, recalculate the differential flux of the geostationary orbit satellite in the corresponding energy channel, establish a point-by-point scatter matrix of the two types of satellite data, and perform linear fitting and correlation analysis to obtain the calibration relationship of cross-platform flux.

[0007] As an improvement to the above method, step 1, acquiring high-energy proton observation data and space environment disturbance parameter data from geostationary orbit satellites and sun-synchronous orbit satellites, includes: High-energy proton observation data from geostationary orbit satellites and sun-synchronous orbit satellites in different energy channels; Data on observation time, location, and direction of observation for geostationary orbit satellites and sun-synchronous orbit satellites; Space environment disturbance parameter data include at least solar wind speed, solar wind proton mass density, and geomagnetic Dst index.

[0008] As an improvement to the above method, step 3 includes: Quasi-in-situ measurement sequence alignment processing is used to eliminate the influence of magnetospheric modulation and obtain high-energy proton observation data that satisfy the same solar proton event; Time alignment processing is used to map the observation data of sun-synchronous orbit satellites to a unified time series by weighted averaging, using the fixed sampling time interval of geosynchronous orbit satellites as the alignment grid. Background processing is used to calculate the average 5-minute count for each proton channel 24 hours before the event as its background value after determining the event time window; then, the observation count of each frame within the event window is subtracted from the background value of the corresponding channel to obtain the preliminary net flux; data cleaning is performed to obtain a physically significant net proton observation flux sequence.

[0009] As an improvement to the above method, the quasi-in-situ measurement sequence alignment process includes: Proton data for quasi-open magnetic field lines regions were selected from westward observation data by combining geosynchronous orbit satellite data with magnetospheric models or geomagnetic activity indices. By combining data from sun-synchronous orbit satellites with magnetospheric models or geomagnetic activity indices, the locations of open magnetic field lines are determined based on the changes in proton flux-magnetic shell parameter L, and proton data in quasi-open magnetic field line regions are selected.

[0010] As an improvement to the above method, the time alignment process includes: Using the fixed sampling time interval of geosynchronous orbit satellites as the alignment grid, a center time t_k is constructed. With each t_k as the center, the original data points of sun-synchronous orbit satellites within the time window are extracted. The time window is ±30 seconds, and the change of the magnetic shell parameter L value within the time window is less than 0.05. The inverse time weight is calculated for each data point within the time window, and the observation time and proton flux are weighted and averaged based on the weight to obtain the equivalent observation time and equivalent flux at time t_k.

[0011] As an improvement to the above method, the determination of the event time window in the background processing includes: When the high-energy channel is triggered, the system searches for three consecutive data points whose counts are all greater than the daily average value + 3σ, with the time corresponding to the first data point as the starting time; where the daily average value is the average count value of the previous 24 hours, and σ is the standard deviation. When the high-energy channel is not triggered, the downgrade is triggered and the channel is switched to the second-lower-energy channel. A more lenient statistical threshold and a set absolute channel lower limit are used as criteria. On the downgraded channel, the time of the first data point of three consecutive data points that meet the criteria is taken as the start time. Starting from the initial time, find 6 consecutive data points where the count is greater than the daily average value + 1σ, and take the time corresponding to the last data point as the end time; Determine the event time window based on the start and end times.

[0012] As an improvement to the above method, the background processing further includes: Within the defined event time window, perform the following processing: For each proton energy channel, the average 5-minute count value 24 hours before the event is calculated as the background value for that energy channel; Subtract the background value of the corresponding channel from the observation count of each frame in the event window to obtain the preliminary net flux. If the initial net flux is <0 or <2σ, then set this value to zero; This yields the net proton observation flux sequence after background subtraction and data cleaning.

[0013] As an improvement to the above method, step 4 includes: Step 4-1: Use the geometric mean as the geometric center energy of each channel of the geosynchronous orbit satellite; Step 4-2: Calculate the overlap length between the nominal energy range of each channel of the sun-synchronous orbit satellite and the energy range of each channel of the geostationary orbit satellite, and select the first 3 geostationary orbit channels in descending order of overlap length to complete energy matching; Step 4-3: For each alignment time point, the spectral index and intercept of the corresponding alignment time point are obtained by power-law spectral fitting using the flux of the three geostationary orbit channels that match the energy channels of the sun-synchronous orbit satellite and their geometric center energy. Step 4-4: Determine the effective energy, lower energy limit, and upper energy limit of each channel of the sun-synchronous orbit satellite through statistical inversion.

[0014] As an improvement to the above method, step 5 includes: Set weight coefficients Given a value of 1, the energy can be obtained by inversely solving the following relationship. ;

[0015] in, Let i be the proton observation flux at the nominal energy channel i in the sun-synchronous orbit. For geosynchronous orbit in effective energy Proton flux observed at the location and in the SSO nominal channel The corresponding proton observation flux when they are equal; All the inverse solutions The energy channel is divided into segments according to a preset step size, and the median of the segment with the highest frequency is taken as the final effective energy. ; By adjusting the weighting coefficients The values ​​are 0.5 and 1.5, and the above process is repeated to obtain the lower energy limit of the energy channel. With the upper limit of energy .

[0016] Compared with the prior art, the advantages of the present invention are: 1. By utilizing the omnidirectional radiation characteristics of solar proton events with "same spectrum and same timing", the detection results of GEO and SSO satellites are directly mapped to a unified energy-flux coordinate system, breaking through the traditional orbital difference barrier and achieving cross-orbit accurate comparison without ephemeris correction.

[0017] 2. By employing an effective energy method and using the power-law spectrum of solar proton events as a bridge, the nominal energy channels of each instrument are inverted to a unified "single energy point," and the energy coordinates are calibrated in one go rather than the flux itself. This avoids the drift caused by bandgap misalignment, response function aging, and radiation damage in direct flux comparison, improves the consistency accuracy across platforms and lifecycles, and enables seamless stitching and trend tracking of long-term time-series data. Attached Figure Description

[0018] Figure 1 This is a flowchart of the collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events, according to the present invention. Detailed Implementation

[0019] The present invention aims to study a technical solution that can effectively eliminate or reduce the above-mentioned systematic deviations, so as to achieve accurate calibration and unification of high-energy proton detection data from multiple orbital platforms, thereby improving the accuracy and timeliness of space weather early warning.

[0020] This invention provides a collaborative calibration method for cross-orbit high-energy proton detection data based on solar proton events. By jointly processing continuous monitoring data from geostationary orbit satellites and polar observation data from sun-synchronous orbit satellites, a physical correlation is established between the observation data from the two platforms. A particle flux transfer function during solar proton events is constructed, forming a unified observation benchmark. This effectively eliminates systematic measurement biases caused by differences in inherent instrument characteristics, on-orbit performance evolution, and limitations of ground calibration, achieving high-precision fusion and mutual verification of multi-source detection data. The method includes the following steps: Step 1: Acquire high-energy proton observation data and space environment disturbance parameter data from geostationary orbit satellites and sun-synchronous orbit satellites, and preprocess them to obtain high-energy proton data of uniform flux type, satellite spatiotemporal position data and environmental parameters; Step 2: Filter high-energy proton data during the solar proton event according to the definition of the solar proton event to obtain multiple event datasets; and group the datasets according to solar wind dynamic pressure and geomagnetic Dst index; Step 3: Align the high-energy proton data of the selected geostationary orbit and sun-synchronous orbit satellites; Step 4: Based on the power-law spectral characteristics of solar proton events, construct the energy matching relationship between the energy channels of geostationary orbit satellites and sun-synchronous orbit satellites. Through point-by-point spectral fitting and statistical inversion, determine the effective energy and energy range of each energy channel of the sun-synchronous orbit satellite. Step 5: Using the calibrated effective energy, recalculate the differential flux of the geostationary orbit satellite in the corresponding energy channel, establish a point-by-point scatter matrix of the two types of satellite data, and perform linear fitting and correlation analysis to obtain the calibration relationship of cross-platform flux.

[0021] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0022] Example The embodiments of the present invention propose a collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events. The technical solution of the present invention is as follows: First: Acquire the data and perform preprocessing, including: Acquire high-energy proton observation data from geostationary orbit satellites and sun-synchronous orbit satellites with different energy channels; preprocess the high-energy proton observation data to convert it into differential directional flux in order to obtain high-energy proton data with a unified flux type; Acquire observation time, location, and direction data from geostationary orbit and sun-synchronous orbit satellites; based on the time and geographic coordinates of high-energy proton observation data from sun-synchronous orbit, calculate the magnetic shell parameters of sun-synchronous orbit using a geomagnetic field model to obtain its relative spatial location data within the Earth's magnetic field.

[0023] Acquire space environment data, including solar wind velocity and solar wind proton mass density data; calculate solar wind dynamic pressure data based on solar wind parameter data. Acquire geomagnetic Dst index data.

[0024] Secondly: Filtering and grouping of solar proton event datasets, including: Based on the definition of solar proton events, including filtering high-energy proton data during the event, multiple high-energy proton datasets for various events were obtained; Based on solar wind dynamic pressure and geomagnetic Dst index, the high-energy proton datasets are grouped to obtain high-energy proton datasets under different solar wind dynamic pressure levels and different geomagnetic disturbance levels.

[0025] Thirdly: Data alignment processing, including: 1) Quasi-in-situ measurement sequence alignment processing. This process removes magnetospheric modulation effects and obtains high-energy proton observation data that satisfy the same solar proton event. This includes: From geostationary orbit satellite data, westward observed proton fluxes are screened, and combined with magnetosphere models or geomagnetic activity indices, proton fluxes located in quasi-open magnetic field line regions are extracted.

[0026] From sun-synchronous orbit satellite data, the locations of open magnetic field lines are determined based on the variation of proton flux and magnetic shell parameter L. Proton data in quasi-open magnetic field line regions are then selected. This includes: sorting the proton flux at each sampling point according to the calculated L value in ascending order; in one embodiment, the magnetic shell parameter L is uniformly divided into intervals with a step size of 0.5, and the sliding window width is 0.5L. If the flux fluctuation within a continuous 0.5L interval is ≤ 0.15, the starting point of this interval is considered the entrance to the open magnetic field line. The satellite-measured location is an open magnetic field line greater than this starting point.

[0027] 2) Time alignment processing.

[0028] Data from both types of orbits are mapped to a unified event timeline. Using the fixed sampling time intervals of geostationary orbit satellites as the alignment grid, a series of center times t_k are generated. For each center time, raw observation data points from sun-synchronous orbits within a ±30 s time window (with L-value variation within the same magnetic shell <0.05) are extracted. An inverse time weight is calculated for each sun-synchronous orbit sample within the window. A weighted average of these weights is then applied to the sampling times to obtain the equivalent observation time within the window, used to record the representative time of that alignment point. Simultaneously, flux is weighted with the same weights to obtain the equivalent flux at time t_k.

[0029] 3) Background processing.

[0030] (a) Determination of start and end times The 5-minute average of the GEO proton channel was used uniformly. The start time was the moment when the average of three consecutive points was first greater than the daily average of the previous 24 hours + 3σ. If the high-energy band count was too low, it was downgraded to the second-lowest energy channel, the threshold was relaxed, and a minimum flux limit was set. The end time was the last point when the average of the following 30 minutes (6 points) was less than the daily average of the previous 24 hours + 1σ. The time window was determined based on the start and end times. σ represents the standard deviation.

[0031] (b) Perform the following processing in each time window: Preliminary net flux: For each channel, the average count of 5 minutes on a quiet day 24 hours before the event is taken as the background for that channel. The corresponding background is subtracted from the observation count of each frame within the event time window to obtain the preliminary net flux. Data cleaning: If the initial net flux is negative or <2σ, then set the value to zero; The final result is a physically significant net proton observation flux sequence.

[0032] Fourth: Energy-gear cross-calibration.

[0033] (a) Effective energy calculation of each channel in GEO, using the geometric mean as the center of each channel. energy.

[0034] (b) SSO and GEO energy matching. Read the nominal boundary [E_min] of SSO channel i. SSO (i), E_max SSO (i)] Calculate the overlap length ΔE between its energy range and each channel of GEO, and select the first 3 in descending order of ΔE to complete the energy matching of SSO channel i to GEO.

[0035] (c) Pointwise power-law spectral fitting. For each time point j, extract the differential fluxes F_G1(t_j), F_G2(t_j), and F_G3(t_j) of the three GEO channels matching SSO channel i at the same time. Take the natural logarithm of the geometric center energies E_G1, E_G2, and E_G3 of the three channels with the corresponding fluxes to obtain three points: (ln E_Gk, ln F_Gk), k = 1, 2, 3. Perform a linear fitting using least squares: ln F = ln α + r ln E, giving the spectral index r(t_j) and intercept lnα(t_j) at that time.

[0036] (d) Statistical inversion of effective energy. Through... In weighting coefficient a _i,j Take 1, and solve E_j inversely, where, Let i be the proton observation flux at the nominal energy channel i in the sun-synchronous orbit. For geosynchronous orbit in effective energy Proton flux observed at the location and in the SSO nominal channel The corresponding proton observation flux when they are equal; All E_j are divided into segments of 0.5MeV, and the median of the segment with the highest frequency is taken as the final E_eff(i) of that channel.

[0037] (e) Energy lower and upper limits. Weighting coefficient a _i,j Set the value to 0.5 and repeat the above operation to obtain the lower energy limit E_min(i) of the SSO calibration channel; weighting coefficient a _i,j Take 1.5 and repeat the above operation to obtain the lower energy limit E_max(i) of the SSO calibration channel.

[0038] Fifth: Comparison of proton flux scatter plots and verification of linear relationship.

[0039] Using the same effective energy as the SSO, the differential flux of the GEO was recalculated to obtain the effective energy flux of the GEO corresponding one-to-one with the SSO channels. A scatter matrix corresponding point-by-point to the dual platforms was established, and linear fitting parameters and correlation coefficients were systematically extracted. Least squares linear fitting was used, and the slope k and intercept b were given; simultaneously, the Pearson correlation coefficient (PCC) (R) and the coefficient of determination R0 were calculated. 2 It is worth noting that in the embodiments of the above system, the various modules are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional module are only for easy distinction between each other and are not used to limit the scope of protection of the present invention.

[0040] It provides a seamless, uncertainty-quantified proton input for radiation belt models, astronaut dose calculations, and satellite radiation-resistant design, reducing spacecraft radiation design errors; Establish a universal high-energy particle calibration framework encompassing deep space, GEO, polar orbit, and HEO, using solar proton events as a natural benchmark to form standardized calibration procedures that are process-oriented and reproducible, thereby breaking down data barriers between high and low orbits and high and low latitudes in one go.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A collaborative calibration method for high-energy proton data from GEO and SSO cross-platform satellites based on solar proton events, comprising: Step 1: Acquire high-energy proton observation data and space environment disturbance parameter data from geostationary orbit satellites and sun-synchronous orbit satellites, and preprocess them to obtain high-energy proton data of uniform flux type, satellite spatiotemporal position data and environmental parameters; Step 2: Filter high-energy proton data during the solar proton event according to the definition of the solar proton event to obtain multiple event datasets; and group the datasets according to solar wind dynamic pressure and geomagnetic Dst index; Step 3: Align the high-energy proton data of the selected geostationary orbit and sun-synchronous orbit satellites; Step 4: Based on the power-law spectral characteristics of solar proton events, construct the energy matching relationship between the energy channels of geostationary orbit satellites and sun-synchronous orbit satellites. Through point-by-point spectral fitting and statistical inversion, determine the effective energy and energy range of each energy channel of the sun-synchronous orbit satellite. Step 5: Using the calibrated effective energy, recalculate the differential flux of the geostationary orbit satellite in the corresponding energy channel, establish a point-by-point scatter matrix of the two types of satellite data, and perform linear fitting and correlation analysis to obtain the calibration relationship of cross-platform flux. Step 4 includes: Step 4-1: Use the geometric mean as the geometric center energy of each channel of the geosynchronous orbit satellite; Step 4-2: Calculate the overlap length between the nominal energy range of each channel of the sun-synchronous orbit satellite and the energy range of each channel of the geostationary orbit satellite, and select the first 3 geostationary orbit channels in descending order of overlap length to complete energy matching; Step 4-3: For each alignment time point, the spectral index and intercept of the corresponding alignment time point are obtained by power-law spectral fitting using the flux of the three geostationary orbit channels that match the energy channels of the sun-synchronous orbit satellite and their geometric center energy. Step 4-4: Determine the effective energy, lower energy limit, and upper energy limit of each channel of the sun-synchronous orbit satellite through statistical inversion; Step 5 includes: Step 5-1: Set weighting coefficients =1; Step 5-2: Solve for the energy using the following relationship. : ； in, Let i be the proton observation flux at the nominal energy channel i in the sun-synchronous orbit. For geosynchronous orbit in effective energy Proton flux observed at the location and in the SSO nominal channel The corresponding proton observation flux when they are equal; Step 5-3: Solve all the inverse solutions The energy channel is divided into segments according to a preset step size, and the median of the segment with the highest frequency is taken as the final effective energy. ; By adjusting the weighting coefficients The values ​​are 0.5 and 1.5, and steps 5-2 and 5-3 are repeated to obtain... These are the lower limits of the energy of this energy channel. With upper limit of energy .

2. The method for collaborative calibration of GEO and SSO cross-platform satellite high-energy proton data based on solar proton events according to claim 1, characterized in that, Step 1 involves acquiring high-energy proton observation data and space environment disturbance parameter data from geostationary orbit satellites and sun-synchronous orbit satellites, including: High-energy proton observation data from geostationary orbit satellites and sun-synchronous orbit satellites in different energy channels; Data on observation time, location, and direction of observation for geostationary orbit satellites and sun-synchronous orbit satellites; Space environment disturbance parameter data include at least solar wind speed, solar wind proton mass density, and geomagnetic Dst index.

3. The method for collaborative calibration of GEO and SSO cross-platform satellite high-energy proton data based on solar proton events according to claim 1, characterized in that, Step 3 includes: Quasi-in-situ measurement sequence alignment processing is used to eliminate the influence of magnetospheric modulation and obtain high-energy proton observation data that satisfy the same solar proton event; Time alignment processing is used to map the observation data of sun-synchronous orbit satellites to a unified time series by weighted averaging, using the fixed sampling time interval of geosynchronous orbit satellites as the alignment grid. Background processing is used to calculate the average 5-minute count for each proton channel 24 hours before the event as its background value after determining the event time window; then, the observation count of each frame within the event window is subtracted from the background value of the corresponding channel to obtain the preliminary net flux; data cleaning is performed to obtain a physically significant net proton observation flux sequence.

4. The method for collaborative calibration of GEO and SSO cross-platform satellite high-energy proton data based on solar proton events according to claim 3, characterized in that, The quasi-in-situ measurement sequence alignment process includes: Proton data for quasi-open magnetic field lines regions were selected from westward observation data by combining geosynchronous orbit satellite data with magnetospheric models or geomagnetic activity indices. By combining data from sun-synchronous orbit satellites with magnetospheric models or geomagnetic activity indices, the locations of open magnetic field lines are determined based on the changes in proton flux-magnetic shell parameter L, and proton data in quasi-open magnetic field line regions are selected.

5. The method for collaborative calibration of GEO and SSO cross-platform satellite high-energy proton data based on solar proton events according to claim 3, characterized in that, The time alignment process includes: Using the fixed sampling time interval of geosynchronous orbit satellites as the alignment grid, a center time t_k is constructed. With each t_k as the center, the original data points of sun-synchronous orbit satellites within the time window are extracted. The time window is ±30 seconds, and the change of the magnetic shell parameter L value within the time window is less than 0.

05. The inverse time weight is calculated for each data point within the time window, and the observation time and proton flux are weighted and averaged based on the weight to obtain the equivalent observation time and equivalent flux at time t_k.

6. The method for collaborative calibration of GEO and SSO cross-platform satellite high-energy proton data based on solar proton events according to claim 3, characterized in that, The determination of the event time window in the background processing includes: When the high-energy channel is triggered, the system searches for three consecutive data points whose counts are all greater than the daily average value + 3σ, with the time corresponding to the first data point as the starting time; where the daily average value is the average count value of the previous 24 hours, and σ is the standard deviation. When the high-energy channel is not triggered, the downgrade is triggered and the channel is switched to the second-lower-energy channel. A more lenient statistical threshold and a set absolute channel lower limit are used as criteria. On the downgraded channel, the time of the first data point of three consecutive data points that meet the criteria is taken as the start time. Starting from the initial time, find 6 consecutive data points where the count is greater than the daily average value + 1σ, and take the time corresponding to the last data point as the end time; Determine the event time window based on the start and end times.

7. The method for collaborative calibration of GEO and SSO cross-platform satellite high-energy proton data based on solar proton events according to claim 6, characterized in that, The background processing also includes: Within the defined event time window, perform the following processing: For each proton energy channel, the average 5-minute count value 24 hours before the event is calculated as the background value for that energy channel; Subtract the background value of the corresponding channel from the observation count of each frame in the event window to obtain the preliminary net flux. If the initial net flux is < 0 or < 2σ, then set this value to zero; This yields the net proton observation flux sequence after background subtraction and data cleaning.

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