CN113900137A

CN113900137A - Data processing method and system for high-energy particle detector

Info

Publication number: CN113900137A
Application number: CN202110875729.3A
Authority: CN
Inventors: 楚伟
Original assignee: National Institute of Natural Hazards
Current assignee: National Institute of Natural Hazards
Priority date: 2021-07-30
Filing date: 2021-07-30
Publication date: 2022-01-07

Abstract

The invention provides a data processing method and a data processing system for a high-energy particle detector. The method comprises the following steps: performing data format conversion and parameter correction on the 0-level data of the high-energy particle detector to generate a 1-level data product; generating a 2-level data product; obtaining a 3-level data product; and 4-level data products are generated. The data processing method and the data processing system for the high-energy particle detector provided by the invention can process the original detection data of the high-energy particle detector on the satellite to generate a practically usable data product.

Description

Data processing method and system for high-energy particle detector

Technical Field

The invention relates to the technical field of satellite remote sensing, in particular to a data processing method and system for a high-energy particle detector.

Background

The Zhanghenyi satellite is the first autonomously developed geophysical field satellite in China, and the high-energy particle detector is one of eight loads carried on the satellite. The electromagnetic satellite Zhanghenyi is successfully transmitted in 2018, 2.2.8.A radio occultation (GRO) receiver of a global navigation satellite system carried by the electromagnetic satellite has the occultation observation function of a Global Positioning System (GPS) satellite and a Beidou navigation system (BD) satellite.

Various load data transmitted back to the ground by the Zhang Heng I are original space detection data, so that the problems of low data precision and frequent data abnormality exist generally.

Disclosure of Invention

The invention aims to provide a data processing method and a data processing system for a high-energy particle detector, which can process original detection data of a Langmuir probe on a satellite to generate a practically usable data product.

In order to solve the technical problem, the invention provides a data processing method for a high-energy particle detector, which comprises the following steps: the method comprises the steps that 1, original binary 0-level data are obtained by frame synchronization, descrambling, error correction, duplicate removal and time arrangement of descending original data of a high-energy particle detector; performing decimal conversion on the acquired 0-level data, and then performing particle type identification, energy spectrum reconstruction, energy spectrum inversion, throwing angle inversion and the like according to calibration parameters to obtain an intermediate result and a final physical quantity so as to generate 1-level standard data; acquiring subsatellite point position information from a satellite affair data packet, acquiring magnetic field data through an electromagnetic field model, acquiring auxiliary information such as seismic records and space weather indexes, correspondingly overlapping the auxiliary information to level 1 data, and generating level 2 standard data; generating a time sequence analysis product as 3-level standard data by using the obtained current track and a plurality of previous revisiting periods; generating space product data which are 4-level standard data by using current orbits of the world and China regions and previous revisit data of a plurality of revisit periods;

the level-1 standard data is case-by-case electron and proton energy, throwing angle, electron and proton fixed time resolution flux, energy spectrum and throwing angle spectrum which are arranged according to time and obtained after format conversion, energy channel conversion and calibration are carried out on the level-0 data; the 2-level standard data is generated by performing coordinate transformation on the 1-level data, and comprises case-by-case electron and proton energies, throwing angles, fixed time resolution flux of the electrons and protons, energy spectrums and throwing angle spectrums with geographic and geomagnetic coordinate systems, time, position and attitude information; the 3-level standard data is a time sequence product which is used for resampling the electron flux and the proton flux on the basis of the 2-level data to generate revisit orbit observation data of a global scope and a Chinese area, and is labeled with earthquake and space weather index information; the 4-level standard data is based on 2-level data, and dynamic changes of electron and proton fluxes in the air over the global range and the Chinese area and the change amplitude of the electron and proton fluxes relative to the background field are generated.

In some embodiments, the level 1 data product comprises: scientific data, image products and data processing report parts; the level 2 product mainly comprises: a data product, an image product and a data processing report section; the grade 3 product mainly comprises: a data product, an image product and a data processing report section; the level 4 product mainly comprises: data products, image products, and data processing reports.

In some embodiments, the obtained 0-level data is firstly subjected to decimal conversion, and then particle type identification, energy spectrum reconstruction, energy spectrum inversion, throwing angle inversion and the like are performed according to calibration parameters to obtain an intermediate result and a final physical quantity, so as to generate 1-level standard data, including: a high energy section load process, a low energy break load process, and a solar X-ray monitor process.

In some embodiments, the treatment of the high-energy stage load comprises: reading the 0-level data after the duplicate removal operation, and selecting HEPP-H data according to the load code; selecting high-energy section load scientific data through a working mode; extracting the relevant state of the scientific data according to the extracted time information of the scientific data, further calculating the working state of each item, and judging whether each working state of the load is within the range of the upper limit and the lower limit so as to judge the credibility of the scientific data; carrying out energy spectrum construction and preliminary judgment in a physical quantity rapid processing mode; calculating the actual total incident case number of the current second by using a dead time correction formula, and inputting a detection energy spectrum after energy reconstruction and particle identification; carrying out further identification of the particle types; carrying out deconvolution operation on the detected electron spectrum and the detected proton spectrum by using the energy response matrix; and (4) judging the particle incidence direction by using a silicon strip detector loaded at a high energy section.

In some embodiments, the process of low energy outage loading comprises: reading 0-level scientific data after the deduplication operation; selecting a common scientific data packet of the HEPP-X and the HEPP-L according to the load code; judging the working state of the load according to the information such as the variable-speed telemetering amount, the slow-change telemetering amount and the like; performing energy spectrum reconstruction; performing dead time correction; performing further identification of the particle species; deconvoluting the detected electron spectrum and the detected proton spectrum by using the energy response matrix; an incident angle inversion is performed.

In some embodiments, the process of solar X-ray monitoring comprises: reading 0-level scientific data after the deduplication operation; selecting a common scientific data packet of the HEPP-X and the HEPP-L according to the load code; judging the integrity of energy spectrum data through E001-E005 in the working mode; acquiring binary scientific data according to a HEPP-X scientific data packet format; converting the binary data into decimal data to obtain detection energy spectrum or rate meter data; and performing dead time correction.

In some embodiments, obtaining the position information of the sub-satellite points from the satellite affair data packet, obtaining magnetic field data through an electromagnetic field model, obtaining auxiliary information such as seismic records and space weather indexes, and correspondingly stacking the auxiliary information to the level 1 data to generate level 2 standard data, including: and generating 2-level data according to the new data format and generating an image and a 2-level data processing report.

In some embodiments, generating a time series analysis product as level 3 standard data using the acquired current track and a previous plurality of revisit cycles comprises: obtaining a plurality of previous revisit cycles corresponding to the track, dividing the revisit cycles at intervals according to a preset latitude interval, and then calculating a median, upper and lower quartile points and a quartile difference in each interval; the single-track data observed at present is divided according to the intervals, and the median A is calculated₅(ii) a Calculating upper and lower limits by using the median, upper and lower quartile points and the inner difference of the quartile points of the previous multiple revisit cycle data; calculating the difference value between the current observation value and the upper and lower limits of a plurality of previous revisit periods in each area, marking each area point according to whether the current observation data exceeds the upper and lower limits, wherein the difference value is normally 0, and the difference value d between the current observation value and the upper and lower limits is marked when the current observation data exceeds the limits; calculating the above steps of each track by adopting a sliding mode for the current observed track data and the previous multiple revisit cycle data, and displaying the previous multiple revisit cycle data on the same graph; and marking information such as seismic records, spatial weather and the like to generate 3-level data, and finishing a data processing report.

In some embodiments, the spatial product data is generated as level 4 standard data using the current orbit of the global and chinese regions and previous revisit cycle revisit data, including: according to 30-day time interval and 5 ° (longitude) x 2.5 ° (latitude) space interval, 2-grade data over global and Chinese areas are selected in a sliding mode, and the median B of all tracks in each space interval is calculated_30iQuartering point and quantile difference, and upper and lower bounds D, by applying median B to each interval_30iCarrying out interpolation to obtain global and national spatial distribution background fields; the median B of all the orbits in each spatial interval is calculated at the current 5-day time interval and at the 5 (longitude) x 2.5 (latitude) spatial interval_5i(ii) a And by applying the median value B in each interval_5iCarrying out interpolation to obtain global and national space distribution maps, and updating the global and national space distribution maps every day in a sliding manner; the median B5i of the data in each interval of each day is calculated as the difference dB from the background median of the previous ones_i＝B_5i-B_30i(ii) a The difference in each bin is normalized gdB_i＝(B_5i-B_30i)/B_30iAnd by applying the median gdB to each interval_iCarrying out interpolation to obtain a global and national daily dynamic change spatial distribution map; calculating median B of data in each interval every day_5iMarking each interval according to whether the current observation data exceeds the upper limit and the lower limit, wherein the difference D between the current observation value and the upper limit and the lower limit is normally 0, and the difference D between the current observation value and the upper limit and the lower limit is marked when the current observation data exceeds the upper limit and the lower limit, and the interval exceeding the boundary is marked on a dynamic change diagram; and marking information such as seismic records, spatial weather and the like to generate 4-level data, and finishing a data processing report.

Furthermore, the present invention provides an energetic particle detector data processing system, the system comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the high energy particle detector data processing method according to the preceding.

After adopting such design, the invention has at least the following advantages:

according to the data processing method and system for the high-energy particle detector, provided by the invention, a series of operations such as carrying out the binary conversion, the calculation, the fitting and the like on the 0-level data of the high-energy particle detector are carried out, so that a data product with data precision and format meeting the requirements is generated.

Drawings

The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.

FIG. 1 is a schematic diagram of a level 0 data naming convention;

FIG. 2 is a schematic diagram of the naming rule of the data in stages 1-4;

FIG. 3 is a schematic illustration of scientific data processing report naming;

FIG. 4 is a schematic illustration of scientific data product image naming;

FIG. 5 is a HEPP payload data flow diagram;

FIG. 6 is a flow chart of HEPP-H, HEPP-L generating level 1 data from level 0 data;

FIG. 7 is a schematic diagram of the directional reconstruction of a particle hitting the front two layers of silicon strip detectors of the detector;

FIG. 8 is a HEPP-X generation level 1 data flow diagram from level 0 data;

FIG. 9 is a flow chart of HEPP payload generating level 2 data from level 1 data;

FIG. 10 is a 3-level data processing flow;

fig. 11 is a 4-level data processing flow.

Detailed Description

The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.

First, abbreviations to be used hereinafter are introduced. In particular, see table 1 for these abbreviations.

TABLE 1 abbreviations

1 introduction of high-energy particle detector and data output

The Zhang Heng I satellite (code number ZH-1) is a space test platform for monitoring physical quantities such as global space electromagnetic field, electromagnetic wave, ionized layer plasma, high-energy particles and the like, provides a new technical means for exploring earthquake precursor information, space environment monitoring and forecasting and earth system scientific research, and is technically prepared for establishing an electromagnetic monitoring satellite business system in the future.

The high energy particle detector is one of the payloads of the "Zhang Heyi" satellite. The high-energy particle detector aims to measure the incident direction and energy of high-energy charged particles, and is matched with spatial electromagnetic measurement to find possible correlation between the flux change of the spatial high-energy particles and electromagnetic radiation caused by an earthquake.

The data output of the high-energy particle detector is the high-energy charged particle species, energy and incident direction. The main energy ranges are high-energy-range electrons (2-50MeV), protons (15-200MeV), low-energy-range electrons (0.1-3MeV), protons (2-20MeV) and solar X-rays (1-20KeV), and the measurement of the throwing angle of the charged particles is realized.

2 standard data products

2.1 hierarchical definition

According to classification rules of satellite-to-ground observation data product classification (GB/T32453-2015) and classification and definition of electromagnetic monitoring satellite data products, the load observation data of the high-energy particle detector comprises 0-level, 1-level, 2-level, 3-level and 4-level data products, and HEPP standard data product definition is shown in Table 2.

Table 2 HEPP data product hierarchy definition and data product summary

2.2 naming rules

In order to facilitate the retrieval and query of each level of data products, the names of each level of data products should include satellite names, load names, track numbers, data start and stop times and other necessary identifications. Exemplary naming conventions for HEPP 0-4 class data products, images, and process reports are as follows.

The level 0 data naming convention is shown in figure 1.

The data naming rules of 1-4 levels are shown in FIG. 2.

Wherein:

(1) satellite name (4-bit character): denoted by CSES;

(2) satellite number (1 digit): starting from 1, increasing sequentially;

(3) payload encoding (3 characters): HPM, SCM, EFD, LAP, RPA, GRO, TBB, HEP represent 8 loads respectively;

(4) load number (1 digit): the method is used for distinguishing the condition that one load loads a plurality of probes with the same type of items, and sequentially increasing from 1, wherein 1 represents a probe with the number of 1,2 represents a probe with the number of 2, and sequentially increasing, and if the probes are not distinguished, the probes are represented by 0; except for the high-energy particle load, all other loads are 0; for the high-energy particle load, 1-4 sequentially represent a low-energy probe, a high-energy probe, an Italian load and an X ray;

(5) data scalable coding (1 bit sign 2 bit number): from left to right, the first bit is "L", the right two bits represent data levels, 0-4 levels are represented by 00, 01, 02/2A, 03, and 04, respectively;

(6) observed object code (2-digit number): and setting an observation object code according to the classification and code of the earthquake electromagnetic satellite survey items (submission). For 0-level data, the observed object is coded as 00;

(7) track number (5 digits): beginning with 00001, the data product used for organizing data files according to the track and incapable of marking track numbers is represented by 00000;

(8) lifting rail flag (1 digit): the ascending rail is 1, and the descending rail is 0;

(9) the data start time is represented by 14 digits, wherein the year (4 digits), the month (2 digits), the day (2 digits), the hour (2 digits), the minute (2 digits) and the second (2 digits);

(10) the end time of the data is in the same format (9);

(11) receiving station encoding (3-bit number): the magnetic field sensor is specially reserved for a tri-band beacon receiver, does not relate to ground receiving station information for an inductive magnetometer, and is marked as 000;

(12) file extension name: when the file extension is dat, representing a data file stored in binary format; when the file extension is h5, the representation data is stored in hdf format.

For scientific data processing report naming, a processing report is stored in an ASCII code format with txt as a file suffix, and the naming is formed by modifying partial fields and adding corresponding 'RP' suffixes on the basis of scientific data naming, as shown in FIG. 3.

Image naming for scientific data products is similar to process report file naming, which is based on scientific data naming by modifying part of the fields and adding the corresponding "_ xx.png" suffix, an example of which is shown in fig. 4.

(13) The expansion code for representing the observation object is composed of two bits of characters, wherein the first bit is used for distinguishing different areas: 1 represents a Chinese region, 2 represents a global, and 0 represents an undifferentiated region; the second position is used to distinguish multiple images of the same load, and the images are sequentially increased from 1.

2.3 data product introduction at level

2.3.10-level data product

And obtaining the observation data of the original binary high-energy particle detector by frame synchronization, descrambling, error correction, duplicate removal and time arrangement. The method mainly comprises the following steps of: the type of the detector (HEPP-H, HEPP-L, HEPP-X), the number of ADC channels, the number of detection units and the like. The observed packet format for the three loads is detailed in table 4. The HEPP load mode of operation is summarized in Table 3.

TABLE 3 HEPP LOAD WORKING MODE SUMMARY TABLE

2) Level 0 data processing report: the detailed description of the original data processing process includes unpacking the data of which tracks, determining whether the processing process is normal, recording error data found in the unpacking process, and the like.

2.3.21-level data product

The level 1 data product comprises: scientific data, images, and data processing reports.

1) Scientific data: the type attribute of a 1-level scientific data file of the high-energy particle detector is H5, and the data types comprise (HEPP-L, HEPP-H and HEPP-X), particle types, energy, incidence angles, flux and the like.

The specific format of the primary data is shown in table 4.

TABLE 4 HEPP-L1 File Attribute Specification Table

Serial number	Attribute name	Attribute content	Remarks for note
				1	PAYLOADID	Instrument code	146F0108H
2	ORBITNUM	Track number
				3	ORBITTYPE	Track classification	Lifting rail and lowering rail
4	SOFTVERSION	Program version number

TABLE 5 HEPP-L1-class data sheet description table

TABLE 6 HEPP-H1 File Attribute Specification Table

TABLE 7 HEPP-H1 level data sheet description table

TABLE 8 HEPP-X1 File Attribute Specification Table

TABLE 9 HEPP-X1-level data sheet description table

2) Image product

The image product comprises energy spectrum, throwing angle spectrum, flux and X-ray spectrum images of electrons and protons in each energy band.

3) Data processing report

1-level data processing report components of the high-energy particle detector:

1) v1.0 processing software version number

2) The starting time is yyymmdd HH and MM is SS

3) Inputting data: list its file name

4) Assistance data

5) Process of treatment

1. Is normal

2. Lack of numbers

3. Data file corruption

6) End time yyymmdd HH: MM: SS.ZZZ

7) Outputting data: and outputting the file name.

2.3.32-level data product

The level 2 product mainly comprises a data product, an image product and a data processing report part.

1) Scientific data

The main scientific data products include: 1) adding scientific data of coordinates and track information: data type (HEPP-L, HEPP-H, HEPP-X), particle type, energy, angle of incidence, flux.

The specific format of the secondary data is as follows:

TABLE 9 HEPP-L2 File Attribute Specification Table

TABLE 10 HEPP-L2 series data sheet description table

TABLE 11 HEPP-H2 File Attribute specification Table

Serial number	Attribute name	Attribute content	Remarks for note
				1	PAYLOADID	Instrument code	146F0107H
2	ORBITNUM	Track number
				3	ORBITTYPE	Track classification	Lifting rail and lowering rail
4	SOFTVERSION	Program version number

TABLE 12 HEPP-H2 series data sheet description Table

TABLE 13 HEPP-X2 File Attribute description Table

TABLE 14 HEPP-X2 series data sheet description Table

The calculation formula of the involved electron and proton cyclotron frequencies is as follows: ω 2 pi m/qB, where m represents the mass of the particles and q and B represent the electrical quantity of the particles and the intensity of the background field, respectively. The calculation formula of the McIlwain parameter L parameter is as follows: l ═ r/cos²(r), where γ represents the magnetic latitude and r represents the radial distance to the center of the earth. The calculation of the throwing angle of the particle requires detection data of a background magnetic field, so that calculation needs to be performed in combination with detection data of a high-precision magnetometer.

2) Image product

The image product comprises energy spectrum, throwing angle spectrum, flux and X-ray spectrum images of electrons and protons of all energy sections added with coordinate information.

3) Data processing report

The 2-level data processing report comprises the following components:

1. processing software version number: v1.0

2. Starting time: yyymmdd HH MM: SS.ZZZ

3. Inputting data: a primary data file name;

4. auxiliary data:

(1) calibration data: … txt, … txt, … txt

(2) Fitting parameters:

(3) correcting parameters:

(4) seismic events: seismic records of more than 6 levels within 100km and within 1 day before the track time

(5) Spatial weather index: kp, Dst, F107 of the corresponding time of the track

5. The treatment process comprises the following steps:

(1) the treatment process is normal

(2) Abnormal phenomena and possible causes

(3) Data file corruption: packet sequence number-time;

6. end time: yyymmdd HH MM: SS.ZZZ

7. Outputting data: listing file names of 2-level data

2.3.43-level data product

The level 3 product mainly comprises a data product, an image product and a data processing report part.

1) Scientific data product

The data product comprises: 1) electrons of the characteristic energy segment (tentatively the following energy segments: 100 to 200KeV, 500 to 800KeV, 1MeV to 2MeV), protons (tentatively the following energy segments: 2 MeV-5 MeV, 10 MeV-50 MeV, 120 MeV-200 MeV) and time series data of previous multiple revisiting periods.

The specific format of the three-level data is as follows:

TABLE 15 high energy particle Detector 3-level data File Attribute Specification

Serial number	Attribute name	Attribute content	Remarks for note
				1	PAYLOADID	Instrument code	146F0107/08H
2	ORBITNUM	Track number
				3	ORBITTYPE	Track classification	Lifting rail and lowering rail
4	SOFTVERSION	Program version number	1.1

TABLE 16 high energy particle Detector 3-level data Table Format description

2) Image product

The 3-level image product is an electron (the following sections are tentatively: 100-200 KeV, 500-800 KeV, 1 MeV-2 MeV), a proton (the following sections are tentatively: 2 MeV-5 MeV, 10 MeV-50 MeV, 120 MeV-200 MeV), flux, single track data and time sequence images of a plurality of previous revisit periods.

3) Data processing report

The type of the 3-level data processing report file is a TXT file, and the file names are exemplified as follows:

CSES_1_HEP_0_L03_A4_001230_20180101_002028_20180101_01002000 _RP.txt

the data processing report file comprises the following components:

1. processing software version number: v1.0

2. Starting time: yyymmdd HH MM: SS.ZZZ

3. Inputting data

1) Current track: current track data, listing the file name;

2) level 3 data of previous revisit cycles (tentative previous 5 revisit cycles): listing the file name;

3) missing data case

4. Assistance data

Earthquake catalogue: . . . . txt

Second, spatial weather index:

KP (3 hours), DST (1 hour), F107(1 day), AE (1 minute) for the current 5 days

5. The treatment method comprises the following steps:

a four-digit difference method of sampling at 0.1-degree intervals along the latitude and further sliding day by day;

6. end time: yyymmdd HH MM: SS.ZZZ

7. Outputting a product: listing file names of 3-level data products

8. Processing the results

1) The treatment process is normal

2) The lack of the number of each grid in the treatment process; there are problems in inversion;

9. description of abnormal situation

The latitude and longitude ranges of the points under the star are (23-33 degrees N, 110-112 degrees E) and exceed the boundary phenomenon from 34 minutes to 36 minutes from 3-23 days 01 to 23 days 01 of 2020. Further analysis is recommended.

② no abnormal phenomenon exists.

2.3.54-level data product

The level 4 product mainly comprises a data product, an image product and a data processing report part.

1) Scientific data product

The data product comprises: 1) electrons in the 30-day characteristic band of the global world and the chinese region (tentatively the following bands: 100 to 200KeV, 500 to 800KeV, 1MeV to 2MeV), protons (tentatively the following energy segments: 2 MeV-5 MeV, 10 MeV-50 MeV, 120 MeV-200 MeV) flux and energy spectrum background data; performing difference processing on the current 5-day observation value and the 30-day background image to obtain electron (the energy sections below the temporary are 100-200 KeV, 500-800 KeV and 1 MeV-2 MeV), proton (the energy sections below the temporary are 2 MeV-5 MeV, 10 MeV-50 MeV and 120 MeV-200 MeV) flux and energy spectrum space variation amplitude data of the Chinese overhead characteristic energy section;

the specific format of the four-level data is shown in table 17.

TABLE 17 high energy particle Detector 4-level data File Attribute Specification

Serial number	Attribute name	Attribute content	Remarks for note
				1	PAYLOADID	Instrument code	146F0107H
2	SOFTVERSION	Program version number	1.1
				3	ORBITTYPE	Track classification

Table 18 high energy particle detector 4 stage data table format description

2) Image product

The image product includes:

1) electrons in the global and chinese region 30-day characteristic bands (tentatively the following bands: 100 to 200KeV, 500 to 800KeV, 1MeV to 2MeV), protons (tentatively the following energy segments: 2 MeV-5 MeV, 10 MeV-50 MeV, 120 MeV-200 MeV) flux background map;

2) and performing difference processing on the current 5-day observation value and the 30-day background image to obtain electrons of the Chinese overhead characteristic energy segment (tentatively: 100 to 200KeV, 500 to 800KeV, 1MeV to 2MeV), protons (tentatively the following energy segments: 2 MeV-5 MeV, 10 MeV-50 MeV, 120 MeV-200 MeV) flux space variation amplitude diagram.

3) Data processing report

4-level data processing report component:

1. processing software version number: v1.0

2. Starting time: yyymmdd HH MM: SS.ZZZ

3. Inputting data

1) All level 2 data files within the current 35 days

2) Missing data case: 1 file in total, such as 12/month/01/32312 _1 in 2020, is missing.

4. Assistance data

1) Seismic catalogs: . . . . txt

2) Spatial weather index:

KP (3 hours), DST (1 hour), F107(1 day), AE (1 minute) for the current 5 days

5. The treatment method comprises the following steps:

global grid processing, calculating the variation of the value in each grid relative to the background value,

normalization extraction of anomalies;

6. end time: yyymmdd HH MM: SS.ZZZ

7. Outputting data: listing file names of 4-level data products;

8. processing the results

1) The treatment process is normal

2) Data redundancy overflow occurs in the processing process, the program is interrupted due to the occurrence of redundancy error when the program is stopped, and related pictures and analysis results are not generated

9. Description of abnormal situation

1) 34 minutes from 3/23/01 in 2020 to 36 minutes from 3/23/01 in 2020,

the out-of-range phenomenon occurs when the latitude and longitude range of the subsatellite point is (23-33 degrees N, 110-112 degrees E), and the out-of-range value is as follows: 25 MeV. In this case, Dst is-60 nT, kp is 4, and F107 maximum is 150.

2) Further analysis is recommended.

3 data processing flow

The flow of the data processing of the stages of the loading of the high-energy particle detector is shown in figure 5. The basic process comprises the following steps: the downlink original data is subjected to frame synchronization, descrambling, error correction, duplicate removal and time arrangement to obtain original binary 0-level data; performing decimal conversion on the acquired 0-level data, and then performing particle type identification, energy spectrum reconstruction, energy spectrum inversion, throwing angle inversion and the like according to calibration parameters to obtain an intermediate result and a final physical quantity so as to generate 1-level standard data; acquiring information such as the position of a satellite point from a satellite affair data packet, acquiring magnetic field data through an electromagnetic field model (or a high-precision magnetometer), acquiring auxiliary information such as seismic records and space weather indexes, correspondingly superposing the auxiliary information to level 1 data, and generating level 2 standard data; generating a time sequence analysis product as 3-level standard data by using the obtained current track and a plurality of previous revisiting periods (5 previous revisiting periods are tentatively set); the spatial product data is generated by using the current orbit of the global and Chinese areas and previous revisit data of a plurality of revisit periods (5 revisit periods before the provisional), and the spatial product data is a 4-level standard product. The detailed data processing method will be described in detail in the next section, and includes algorithms, formulas, and the like.

4 data processing method

4.1 Generation of 0-level data from raw data

Defining: the 0-level data product is the observation data of the high-energy particle detector obtained by frame synchronization, descrambling, error correction, duplicate removal and time arrangement.

The treatment method comprises the following steps: and after frame synchronization, descrambling, error correction and duplicate removal are carried out on the acquired satellite downlink original data, extracting a high-energy particle detector load scientific data block, carrying out correction sequencing according to sampling time, and carrying out file segmentation according to the track number. And finally, reading according to a fixed format of the detection original code, and storing according to 30 half tracks every day.

Inputting parameters: and satellite downlink data.

Outputting parameters: 1) class 0 data files (HEPP-L, HEPP-H, HEPP-X);

2) and (6) reporting data processing.

4.20 level data Generation of level 1 data

Defining: the 1-level data product is obtained by carrying out format conversion, energy channel conversion and calibration on 0-level data, and then carrying out case-by-case electron and proton energy, throwing angle, electron and proton fixed time resolution flux, energy spectrum and throwing angle spectrum which are arranged according to time.

4.2.1 high energy band load (HEPP-H) 0-level data to 1-level data processing method

Inputting parameters: level 0 data (scientific data and engineering parameters), calibration data packets, geomagnetic field models, and the like

Outputting parameters: 1) a level 1 data file;

2) a level 1 image file;

3) level 1 data processing reports.

The basic data processing process can be divided into the following steps:

data reading

1) Firstly, judging whether data are available or not through monitoring data;

2) the particle type is primarily identified, and whether the incident particles are electrons or protons is judged;

3) a rapid processing mode of physical quantity, energy spectrum construction and preliminary judgment;

4) correcting dead time;

5) and (4) energy spectrum inversion. The incident spectrum is generated by deconvolving the detected spectrum.

6) And judging the incident angle.

Secondly, the detailed processing procedure is as follows

1) Level 0 data read.

And reading the 0-level data after the operation of deduplication and the like, and selecting the HEPP-H data according to the load code number (146F 0107).

2) And (5) scientific data reading.

Selecting high-energy section load science data through the working mode (a1b 1);

3) and judging whether the data is available or not by monitoring the data.

The insertion of data per second is selected by the operating mode (a2b 2). And extracting the relevant state of the scientific data according to the time information of the scientific data extracted in the last step, further calculating the working state (temperature value, voltage value and the like) of each item through a formula, and judging whether the working state of each item of the load is within the range of the upper limit and the lower limit so as to judge the credibility of the scientific data.

4) And (4) energy spectrum reconstruction.

(1) Selecting scientific data in the table (each bag of scientific data comprises 47 single cases)

(2) Reading the whole second time Ti (i is 1,2,3, … …) corresponding to the example;

(3) the temperature of the corresponding load is obtained by the above-mentioned time, since the calibration data relate to the temperature, which is obtained from the monitoring data.

(4) Reading binary data of 5 anticoincidence, 2 DSSDs and 5 energy meters according to the high-energy single case table sequence for each case;

(5) respectively converting into decimal data; if the reading of the anti-coincidence is 1, the data is unusable, if the reading of the anti-coincidence is 0, then the next work is carried out;

(6) and (5) preliminarily identifying the particle types.

The energy deposition in the two-layer detector is related to the deposition energy of electrons and protons in the two-layer detector along with the change of incident energy. The method comprises the steps of reading a double-layer energy track value of a detector (a calibration database contains a judged energy track value, tentatively 0.5MeV, less than or equal to 0.5MeV is electrons, and more than 0.5MeV is protons), and preliminarily judging whether the type of incident particles is electrons or protons.

Example (1): assuming the energy of the two-layer detector DSSD1 and DSSD2, where DSSD1 is 2 × 0.008 × Ex (where Ex is the reading of a single particle, see the table of single particle events for high-energy particle detectors), DSSD2 is 2 × 0.008 Ex, and the sum of the two is deltaE — DSSD1+ DSSD2, if deltaE is less than or equal to 0.5, it is determined as an electron, otherwise it is determined as a proton.

(7) And (4) energy inversion.

According to the judgment result (electron or proton) of the particle type, a group of energy linearity under the normal working state provided by a load party (comprising 7 groups of energy linearity functions of 2 DSSDs and 5 energy meters, and the specific format is shown in a table 19. because the calibration data relates to the temperature, the related temperature is obtained from the monitoring data, and for the high-energy-stage load, 3 groups of energy linearity parameters under the working condition of temperature are tentatively given in total, 3 groups of temperature values are respectively-35 ℃, 20 ℃ and-5 ℃, the detector numbers are expressed by HEPP-H, the detector units are successively from the incidence window to the back, DSSD1, DSSD2, PSCAL, CSICAL1, CSICAL2, CSICAL3 and CSICAL 4.) respectively calculate the energy deposition of the 2 DSSDs and the 5 energy meters; the calculation formula is Energy ═ a × CHN + B, where a denotes slope, B denotes intercept, and CHN denotes number of ADC channels.

TABLE 19 HEPP-H energy Linear calibration data

Using the calibration database data, the Energy corresponding to each detector is reconstructed as Energy _ i ═ a × CHN _ i + B, a slope, B intercept, CHN _ i denotes the number of ADC channels per detector, i ═ 1,2, …, 7. Summing all Energy _ i to obtain the total detection Energy, i.e. the detected incident particle Energy;

summing Energy _1 and Energy _2 to obtain delta E; energy _3, … and Energy _7 are summed to obtain Energy Ec of the Energy measuring device, and then further identification of particle types is carried out;

accumulating the energy of 7 detection units in Ti seconds to the respective detection energy spectrums EDSSD1_ i, EDSSD2_ i, EPSCAL _ i, ECsI1_ i, ECsI2_ i, ECsI3_ i and ECsI4_ i; the detection energy of the electrons and protons at the time of Ti is obtained.

(8) Energy spectrum reconstruction

Accumulating and calculating the counting rate corresponding to each detection energy spectrum of each detection unit to obtain total count;

the detection energy spectra of electrons and protons are obtained by second accumulation according to the particle species.

5) And (5) correcting the dead time.

(1) Reading the HEPP-H Rate Meter data inserted into the data sheet per second, denoted F_cr；

(2) Calculating the actual total incident case number F of the second by using a dead time correction formula (1)_tot；

R_in: actual incident particle count rate over detection time; r_t: a detected particle count rate over a detection time; τ: detector dead time (τ R)_t<<1) Where τ is given by the calibration database; r_tData of corresponding rate F_cr，R_inRepresents the number of actual incident total instances F of the second_tot。

The dead time scaling data is shown in table 20.

TABLE 20 HEPP-H dead time calibration data

Serial number	Item	Unit of	Data format	Remarks for note
					1	Identification	-	2D	Corresponding sub-detector number and telescope number
2	Dead time	s	0.10F

(3) Inputting detection energy spectrums (electron spectrums and proton spectrums) after energy reconstruction and particle identification;

(a) the electron spectrum and the proton spectrum are integrated separately,

i.e. the sum of all detected particle numbers at the same time,

obtaining total electron detection flux FDe and total proton detection flux FDp corresponding to the energy spectrum;

(b) assuming Spec _ det _ ele as an electron energy spectrum, the electron energy spectrum after dead time correction is as follows:

Spec_det_ele_cor＝Spec_det_ele*Ftot/Fcr；

the corrected total electron flux is: FDe _ cr (FDe) Ftot/Fcr

(c) Assuming Spec _ det _ pro as the proton spectrum, the dead time corrected proton spectrum is:

Spec_det_pro_cor＝Spec_det_pro*Ftot/Fcr；

the corrected proton flux is: FDp _ cr ═ FDp × Ftot/Fcr

6) The particle species were further identified.

TABLE 21 HEPP-H particle discrimination threshold calibration data

The specific process is as follows:

in the energy inversion in the energy spectrum reconstruction:

summing Energy _1 and Energy _2 to obtain delta E;

summing Energy _3, … and Energy _7 to obtain Energy measurer Energy Ec;

read the data threshold Th in Table 21 above according to Δ E_i(ii) a1, 2, …, 256, which is DSSD energy address;

comparison of Ec and Th_i，Ec<Th_iIt is judged as an electron, and conversely, it is judged as a proton.

7) And (4) incident spectrum reconstruction.

The detected lines are the convolution of the incident lines and the energy response matrix in order to obtain the incident electron spectrum and the incident proton spectrum. The energy response matrix is required to deconvolute the detected electron spectrum and the detected proton spectrum, mainly electrons.

The specific process is as follows:

(1) reading the second item and the eighth item of temperature state in the table according to the HEPP-H insert data format per second;

(2) respectively selecting a temperature-energy response matrix database corresponding to the energy response matrix according to the temperature state (the specific format is shown in the following table, and table 19 HEPP-H energy response matrix calibration data);

electron/proton incident spectrum reconstruction

(3) Selecting an energy response matrix of electrons/protons;

(4) selecting a detected electron spectrum/proton spectrum;

(5) the detected electron spectrum/proton spectrum is the convolution result of the incident electron spectrum/proton spectrum and the response of the detector;

the electron/proton spectra are deconvoluted with an energy response matrix (the function deconv in Matlab can perform a deconvolution operation).

Obtaining an incident electron spectrum/proton spectrum after deconvolution, and outputting the incident electron spectrum/proton spectrum;

TABLE 22 HEPP-H energy response matrix calibration data

8) Determination of incident angle

In the coordinate system of the detector, assuming that the distance between two layers of detectors is z0, the coordinate positions of the particle hitting the upper and lower layers are (x1, y1) and (x2, y2), respectively, and the coordinate values are calculated according to the positions of the detection units recorded by the detectors, as shown in fig. 7.

The polar angle θ and the azimuth angle φ of the incident particle relative to the direction of the detector are:

according to the calculation result, the zenith angle and the azimuth angle of the incident particle direction can be calculated through coordinate transformation with a universal coordinate system.

The specific method comprises the following steps: and (4) judging the particle incidence direction by using a silicon strip detector loaded at a high energy section. Two double-sided read-out silicon strip detectors form a telescope, measure the direction of incident particles, generate a trigger signal at the same time, and start the data acquisition of the detectors. The X coordinate (DSSD1-X) and the Y coordinate (DSSD1-Y) of the maximum signal of DSSD1 are recorded. The X coordinate (DSSD2-X) and the Y coordinate (DSSD2-Y) of the maximum signal of DSSD2 are recorded.

The incident angles of the particles are therefore:

constructing a unit vector in the HEPP-H coordinate system along the incidence direction of the particle and recording the unit vector as (e) according to the incidence angle of the particle_xh,e_yh,e_zh). Converting the unit vector into a satellite coordinate system to obtain a corresponding unit vector as (e)_xs,e_ys,e_zs). Suppose that the vector of the magnetic field strength detected in the satellite coordinate system is (B)_x,B_y,B_z) The casting angle alpha cosine of the particles can be obtained as

4.2.2 Low energy stage load (HEPP-L) 0-level data to 1-level data processing method

Fast processing mode of physical quantity:

1) level 0 data read.

Reading 0-level scientific data after operations such as duplicate removal and the like;

2) and selecting the HEPP-L packet data.

And selecting a common scientific data packet of the HEPP-X and the HEPP-L according to the load code (146F 0108H). Selecting HEPP-L load scientific data according to the working mode (8000H);

3) and (4) judging the load state.

And judging the working state of the load according to the information such as the variable-speed telemetering amount, the slow-speed telemetering amount and the like.

4) And (4) energy spectrum reconstruction.

TABLE 22 HEPP-L energy Linear scaling data

(for low energy load, tentative total given energy linearity parameters for each detection unit for 3 sets of temperature conditions 3.3 sets of temperature values-35 ℃, -20 ℃ and-5 ℃. the detector numbers are denoted HEPP-L, and the detector units are in series TELE1_ SD1, TELE1_ SD2, TELE2_ SD1, TELE2_ SD2, TELE3_ SD1, TELE3_ SD2, TELE4_ SD1, TELE4_ SD2, TELE5_ SD1, TELE5_ SD2, TELE6_ SD1, TELE6_ SD2, TELE7_ SD1, TELE7_ SD2, TELE8_ SD1, TELE8_ 2, TELE9_ SD1, TELE9_ SD 2. Each detection unit given energy linearity parameters for each detection unit according to the linear structure table 22.)

(1) Reading scientific data of low-energy section loads in the table;

(3) reading binary data of the two layers of silicon wafer detectors according to the sequence of the low-energy single case table for each case;

(4) respectively converting into decimal data; if the reading of the anti-coincidence is 1, the data is unusable, if the reading of the anti-coincidence is 0, then the next work is carried out;

(5) and (5) preliminarily identifying the particle types.

The energy deposition in the two-layer detector is related to the deposition energy of electrons and protons in the two-layer detector along with the change of incident energy. By reading the dual-layer energy track value of the detector (the calibration data base contains the determined energy track value), whether the incident particle species is electrons or protons is preliminarily determined.

(6) Respectively calculating the energy deposition of 18 silicon wafer detectors according to a group of energy linearity (see table 23. containing 9 thin silicon wafer detectors and 9 thick silicon wafer detectors) provided by a load side under a normal working state and according to the particle types; the calculation formula is as follows: energy _ i _ j ═ a × CHN _ i + B, a slope, B intercept, i ═ 1,2, …, 9; j is 1,2

(7) Summing all Energy _ i _1 and Energy _ i _2 to obtain the total detection Energy of Energy _ i _ tot;

(8) energy of 18 detection units at Ti seconds is respectively accumulated to respective detection spectrums ESD1_1_ i, ESD1_2_ i, ESD2_1_ i, ESD2_2_ i, ESD3_1_ i, ESD3_2_ i, ESD4_1_ i, ESD4_2_ i, ESD5_1_ i, ESD5_2_ i, ESD6_1_ i, ESD6_2_ i, ESD7_1_ i, ESD7_2_ i, ESD8_1_ i, ESD8_2_ i, ESD9_1_ i and ESD9_2_ i;

(9) meanwhile, the counting rates corresponding to the detection energy spectrums of the detection units are accumulated and calculated;

(10) taking Energy _ i _1 as delta E; taking Energy _ i _2 as Ec; Δ E and Ec were used for particle identification.

4) And (5) correcting the dead time.

See the dead time correction procedure for the high energy segments described above.

5) The particle species were further identified.

The specific process is as follows:

summing Energy _1 and Energy _2 to obtain delta E;

summing Energy _3, … and Energy _7 to obtain Energy measurer Energy Ec;

Δ E and Ec were used for particle identification:

read data thresholds Th in the Table below according to Δ E_i(ii) a1, 2, …, 256, which is DSSD energy address;

TABLE 23 HEPP-L particle discrimination threshold calibration data

6) And (4) incident spectrum reconstruction.

In order to obtain the incident electron spectrum and the incident proton spectrum. The detected electron spectrum and the detected proton spectrum need to be deconvoluted (mainly electrons) with an energy response matrix.

The specific process is as follows:

(1) reading the temperature state according to the HEPP-L per second insertion data format;

(2) respectively selecting a temperature-energy response matrix database corresponding to the energy response matrix according to the temperature state;

electron/proton incident spectrum reconstruction

(3) Selecting an energy response matrix of electrons/protons;

(4) selecting a detected electron spectrum/proton spectrum;

the electron/proton spectra are deconvoluted with an energy response matrix.

TABLE 24 HEPP-L energy response matrix calibration data

7) The angle of incidence is inverted.

The direction of incidence of the HEPP-L particles is determined by the direction of the 9 probes.

For HEPP-L: the unit vector of the probe number from 1 to 9 surface normals in the coordinate system of the detector is expressed as a spherical polar coordinate (1, theta, phi), wherein 1,3,5,7,9 half opening angles are 6.5 degrees, 2,4,6,8 half opening angles are 15 degrees. The central 5 th probe normal direction corresponds to the-Y direction of the probe coordinate system, the particle incidence direction being opposite to the probe normal direction. The range of the throwing angle alpha of the particles is 0-90, and when the throwing angle alpha is larger than 180, the throwing angle alpha is 180-alpha. Each probe normal unit vector is expressed in spherical polar coordinates as:

TABLE 25 HEPP-L CYLINDER COORDINATE POSITIONS

The conversion relation from the spherical polar coordinate to the rectangular coordinate system is as follows:

and obtaining the representation of each normal in the satellite coordinate system through the coordinate transformation between the detector coordinate system and the satellite star coordinate system:

for HEPP-L, the incident particle throwing angle direction is determined by the included angle between the normal of each probe and the local magnetic field direction.

Assuming that the magnetometer detects a local magnetic field of B in the satellite constellation coordinate system, the throwing angle α is determined by:

4.2.3 solar X-ray monitor (HEPP-X) 0-level data to 1-level data processing method

For HEPP-X, the semiconductor refrigeration is carried by the HEPP-X, and the working temperature is only one. The detector number is denoted HEPP-X, and there are only 1 detector unit. When the energy is inverted, the method is carried out according to the following procedures:

1) and reading the data.

2) and selecting HEP-X packet data.

And selecting a common scientific data packet of the HEPP-X and the HEPP-L according to the load code (146F 0108H). Selecting HEPP-X load science data according to the working mode (E0H: energy spectrum data (E001-E005); E1H: rate meter data (E101-E103));

3) the integrity of the data packet is determined.

Since five packets are required for the HEPP-X to obtain a complete energy spectrum data, and three packets are required for obtaining a complete rate meter data, the integrity determination of the packets is required. The method specifically comprises the steps that according to the packet serial number in the HEPP-X data transmission scientific data packet format, energy spectrum data integrity is judged through E001-E005 in the working mode, and if the working mode is adopted, the data packets are arranged according to the E001-E005 in sequence, the energy spectrum data packets are judged to be complete. And (4) carrying out rate counting data integrity judgment through E101-E103 in the working mode, and if the working mode is adopted, arranging data packets according to E101-E103 in sequence, judging that the rate counting data packets are complete.

4) Scientific data is acquired.

And acquiring binary scientific data according to the HEPP-X scientific data packet format.

5) And (5) carrying out binary conversion. Converting the binary data into decimal data to obtain detection energy spectrum or rate meter data;

6) and (5) correcting the dead time.

The dead time correction formula (1).

The method comprises the following specific steps:

read rate count data Fcr;

calculating the actual incident total case number Ftot of the second by using a dead time correction formula;

inputting an X-ray detection energy spectrum;

summing the X-ray counts at the same time to obtain a flux Fx corresponding to the energy spectrum;

let Spec _ det _ X be the X-ray energy spectrum

The dead time corrected X-ray energy spectrum is:

Spec_det_x_cor＝Spec_det_x*Ftot/Fx

the corrected total X-ray flux is:

FDx_cr＝FDx*Ftot/Fx

4.31-level data Generation 2-level data

Defining: the 2-level data product is generated by performing coordinate transformation on the 1-level data, and has case-by-case electron and proton energies, throwing angles, and fixed time resolution fluxes, energy spectrums and throwing angle spectrums of the electrons and the protons with geographic and geomagnetic coordinate systems, time, position and attitude information.

The treatment method comprises the following steps: and generating 2-level data according to the new data format and generating an image and a 2-level data processing report.

And (3) parameter calculation: McIlwain parameter L parameter r/cos²(γ), where r represents the magnetic latitude and r represents the radial distance to the center of the earth; the particle cyclotron frequency ω is 2 π m/qB, where m represents the mass of the particle and q and B represent the amount of charge of the particle and the intensity of the background magnetic field, respectively.

Inputting parameters: level 1 data, transfer functions of satellite, geographic and geomagnetic coordinate systems, and satellite high-precision magnetometer probe data.

Outputting parameters: 1) data: including UT and LT times, spatial coordinates (geographical longitude, geographical latitude, geomagnetic longitude, geomagnetic latitude, altitude, L value), single particle species, energy, and throw angle.

2) Image files (including electron, proton angle of throw spectra, energy spectra, flux, and X-ray flux);

3) a level 2 data processing report;

4.42-level data Generation of 3-level data

Defining: the 3-level data product is a time sequence product which resamples electron and proton flux on the basis of 2-level data to generate revisit orbit observation data of a global area and a Chinese area, and marks earthquake and space weather index information.

The treatment method comprises the following steps: based on the semi-orbit 2-level data and the previous multiple revisiting period data, a significance analysis method is applied according to the average particle count rate of the previous multiple revisiting period data (6 revisiting orbit data in 30 days before tentative), the average value and the significance are labeled, and disturbance data are extracted. And gives identification to the events of occurrence of magnetic storm, violent solar activity, earthquake and the like in the same day.

The following products were produced:

1. electronic flux single-track data of different energy sections and time sequence data and images of a plurality of previous revisiting periods (6 pieces of revisiting track data in 30 days before the tentative period);

2. proton flux single-track data of different energy sections and time sequence data and images of a plurality of previous revisiting periods (6 pieces of revisiting track data 30 days before the tentative period);

3. a 3-level data processing report;

inputting data: level 2 data, seismic catalogue, magnetic index, F107 index.

Outputting data: 1) electronic flux single-track data of different energy sections and time sequence data and images of a plurality of previous revisiting periods (6 pieces of revisiting track data in 30 days before the tentative period);

2) proton flux single-track data of different energy sections and time sequence data and images of a plurality of previous revisiting periods (6 pieces of revisiting track data 30 days before the tentative period);

3) level 3 data processing reports.

(1) Obtaining a plurality of previous revisit cycles (6 pieces of revisit track data 30 days before the tentative) corresponding to the track, dividing the revisit cycles at intervals according to a certain latitude interval (0.1 degree of the tentative), and then calculating a median, upper and lower quartile points and a quartile difference in each interval.

The specific steps of the quartile and the quartile range are as follows:

firstly, sequencing original sequence data from small to large to obtain a sequence A_kK is 1,2, … N, and Q is obtained₁、Q₃At the position L₁、L₃；

Secondly, the corresponding mark value Q is determined according to the position₁、Q₃；

Let | L_iL represents L_iThe integer part of (a) is,

|dL_i|＝L_i-|L_i| (8)

then:

the quartile range is also called inner distance and quartile range, and means that the variable values are arranged in the order from small to large, then the number sequence is divided into four equal parts, and the difference between the value in the third quartile and the value in the first quartile is obtained. Calculating Q₁And Q₃The difference is the quartile range.

IQR＝Q₃-Q₁ (10)

(2) The single-track data of the current observation is also divided by the above interval, and the median a5 is calculated.

(3) And calculating upper and lower limits by using the median, upper and lower quartile points and the inner difference of the quartile of the data of a plurality of previous revisit periods (6 revisit tracks in 30 days before provisional). The calculation method of the upper and lower limits comprises the following steps:

D＝A4₀(median) ± IQR (11)

(4) Calculating the difference value between the current observation value and the upper and lower limits of a plurality of previous revisit periods (6 revisit tracks in 30 days before the tentative period) in each area; and marking each region point according to whether the current observation data exceeds the upper and lower limits, wherein the normal value is 0, and if the current observation data exceeds the boundary, marking the difference value d between the current observation value and the upper and lower boundaries.

d＝A₅-D (12)

(5) The current observation orbit data and the previous multiple revisit cycle (6 revisit orbits before the tentative 30 days) data are calculated in a sliding mode, and the previous multiple revisit cycle (6 revisit orbits before the tentative 30 days) data can be displayed on the same graph.

(6) And marking information such as seismic records, spatial weather and the like to generate 3-level data, and finishing a data processing report. The 3-level data product comprises: scientific data and its images and data processing reports.

4.52 level data Generation of 4 level data

Defining: the 4-level data product generates dynamic changes of electron and proton fluxes in the air over global and Chinese areas and the change amplitude of the electron and proton fluxes relative to a background field on the basis of 2-level data.

The treatment method comprises the following steps: and on the basis of 2-level standard data, generating the dynamic change of the current space environment of global and Chinese overhead physical parameters and the change amplitude of the current space environment relative to a background field. A spatial background map of electron and proton fluxes for specific energy segments worldwide and above china is given. Proton and electron flux levels were analyzed for specific energy segments worldwide and above china, giving significant level changes relative to the near-one-month background value. And gives identification to the events of occurrence of magnetic storm, violent solar activity, earthquake and the like in the same day.

The following products were produced:

1) background images of specific energy section electrons and proton fluxes of a plurality of revisiting periods (6 revisiting tracks 30 days before tentative) in China;

2) performing significance analysis processing on the current 5-day observation value and background images of previous revisit periods (6 revisit orbits before tentative 30 days) to obtain a spatial variation amplitude image of the electron and proton flux of a specific energy section above China;

3) specific energy band electron and proton flux background maps of a plurality of revisiting cycles (6 revisiting tracks 30 days before tentative) over the world;

4) carrying out significance analysis processing on the current 5-day observation value and background images of a plurality of previous revisiting periods (6 revising orbits before tentative 30 days) to obtain a global daily specific energy section electron and proton flux spatial variation amplitude image;

5) level 4 data processing reports.

Inputting data: level 2 data, seismic catalogue, magnetic index, F107 index.

Outputting data: 1) electron and proton flux background maps of multiple revisit cycles (6 revisit orbits before tentative 30 days) before the sky in china;

2) performing significance analysis processing on the current 5-day observation value and background images of a plurality of previous revisiting periods (6 revising orbits before provisional 30 days) to obtain a space variation amplitude image of the electron and proton flux of a specific energy section above China;

3) background images of electron and proton flux of specific energy segments of a plurality of revisiting periods (6 revisiting orbits 30 days before the tentative) over the world;

5) level 4 data processing reports.

(1) According to 30-day time interval and 5 ° (longitude) multiplied by 2.5 ° (latitude) space interval, 2-level data above the global and Chinese regions (0-60 ° latitude; 60-140 ° longitude) are selected in a sliding mode, and the median B of all tracks in each space interval is calculated_30iQuartering point and quantile difference, and upper and lower limits D, which are the same as formulas (7) to (12); and by applying the median value B in each interval_30iCarrying out interpolation to obtain global and national spatial distribution background fields;

(2) the median B of all the orbits in each spatial interval is calculated at the current 5-day time interval and at the 5 (longitude) x 2.5 (latitude) spatial interval_5i(ii) a Interpolating the median B5i in each interval to obtain global and national spatial distribution maps; sliding and updating the daily global and nationwide spatial distribution map;

(3) calculating median B of data in each interval every day_5iDifference dB from the background median of the previous ones_i＝B_5i-B_30i；

(4) The difference in each bin is normalized gdB_i＝(B_5i-B_30ii)/B_30iAnd by applying the median gdB to each interval_iCarrying out interpolation to obtain a global and national daily dynamic change spatial distribution map;

(5) calculating median B of data in each interval every day_5iDifference D from the previous 30 days revisit track upper and lower bounds D, as in equation (12); according to whether the current observed data exceedsAnd (4) passing through the upper and lower limits, marking each interval, wherein the interval is normally 0, if the interval exceeds the boundary, marking the difference d between the current observed value and the upper and lower boundaries, and marking the interval exceeding the boundary on the dynamic change diagram calculated in the step (4).

(6) And marking information such as seismic records, spatial weather and the like to generate 4-level data, and finishing a data processing report. The 4-level data product comprises: scientific data and its images and data processing reports.

5 data quality evaluation method

Two methods are available for evaluating the data quality of the high-energy particle detector:

1) and comparing and verifying the data with the data of other existing satellites in orbit.

Different platform load observation conditions have great difference in the space-time range of detection. Therefore, in order to perform cross-checking, the data acquired by different platform loads needs to be generalized firstly, so as to ensure that the observation data acquired under the same conditions acquired by different satellite platforms are compared.

The plane space matching processing comprises two steps, firstly, the detection load with approximate orbit height is selected to eliminate the detection data difference caused by vertical space mismatching, and the data of different satellite platforms are projected to the same space height by utilizing the existing high-energy particle space distribution model. And then selecting data in the same time period and the same geographical position, selecting particles with the same energy period and the same throwing angle, performing average counting rate distribution of the particles, and performing comparison verification.

Considering the respective detection errors of different platform satellite detectors, model errors during generalization data, complex space environment and other factors, the change rules of particle flux detected by different satellites at a certain space-time are the same, and the data processing result is generally considered to be reliable and reasonable.

2) And (4) cross checking the high-energy detector and the low-energy detector.

(1) Comparing the change trends of three loads, namely a high-energy section, a low-energy section and X-ray;

(2) repeating energy segment comparison: the energy particle detector is divided into a high-energy HEPP-H detector and a low-energy HEPP-L detector. The example ranges detected are: high-energy electrons (2-50MeV), protons (15-200MeV), low-energy electrons (0.1-3MeV), and protons (2-20 MeV). The particle flux distributions of the two detectors which are crossed in energy and in the same throwing angle range can be compared for cross inspection.

The specific method is that data of high-energy and low-energy detectors with the superposition energy range and the same throw angle range are respectively selected on the same orbit or a certain time period (such as a certain geographical range), the average counting rate is compared, and cross inspection is carried out.

6 general algorithm

6.1 coordinate transformation

(1) Satellite coordinate system-geographic coordinate system conversion matrix

Let the three euler angles between the satellite coordinate system with respect to the geographic coordinate system be phi, psi, theta due to changes in the satellite attitude. The transformation matrix from the satellite coordinate system to the geographic coordinate system is set to M, and then M can be expressed as:

(2) conversion matrix of geographic coordinate system-geomagnetic coordinate system

The conversion relation between the geographic coordinate system GEO and the geomagnetic coordinate system MAG is set as

Wherein, the ratio of theta,

respectively the geographical latitude, longitude of the dipole axis.

6.2 method for extracting time sequence abnormity

In a 3-level data product, statistical processing (for convenience of description, a four-bit method is taken as an example) is mainly performed around a time sequence of a certain parameter to obtain abnormal change characteristics on the time sequence. The main process comprises the following steps:

revisiting track search

Directly obtaining the revisit track number according to the current track number, wherein the specific formula is as follows:

S＝L-n*M(n＝1,2,3,4,5,6) (15)

wherein S is the revisit track number, L is the current track number, and M is the revisit track fixation difference.

Partitioning a grid

For the 2A-level geomagnetic field total field and geomagnetic field vector data of the half-track, the grids are divided by 0.5 ° (latitude interval) × 5 ° (longitude interval) (chinese region, latitude [0 °,55 ° ], longitude [70 °,140 ° ]) and 1 ° (latitude interval) × 5 ° (longitude interval) (other regions).

Statistical process analysis

Solving a median value falling in each grid point for the current track; and solving a median value and upper and lower quartile values in each grid point for all the revisited tracks 30 days before the current track. The calculation method comprises the following steps:

suppose there are n data B arranged from small to large in each grid₁,B₂,…,B_n. Median value B when n +1 is an integer multiple of 4_mAnd upper and lower quartile values B₁，B₃The calculation formula is as follows:

B_m＝B_(n+1)/2

B₁＝B_(n+1)/4

B₃＝B_3(n+1)/4 (16)

when n +1 is not an integer multiple of 4, the quartile position calculated by the above formula carries a decimal number, and at this time, the decimal number is rounded.

6.3 spatial anomaly extraction method

The 4-level data product mainly processes the spatial statistical variation characteristics (for convenience of description, a four-bit method is taken as an example) around a certain parameter to obtain the spatial abnormal variation of the product. The main process comprises the following steps:

mesh partitioning

The 4-stage data processing divides the world into several grids at certain spatial intervals (e.g., 1 ° (latitude) x 5 ° (longitude)).

Ambient field data processing

According to the divided grids, a plurality of replay periods before the current replay period are selected as time intervals, and statistical parameters (such as mean value and mean square error) of physical quantities in each grid are respectively obtained to obtain background fields in the global range and the space above the Chinese area.

Physical quantity distribution characteristics in current playback period

And selecting the previous 5 days (current 5 days for short) including the current day as time intervals, and respectively calculating statistical parameters (such as mean value and variance) of the physical quantities in each grid to obtain statistical parameters of the global range and the Chinese regional upper space.

Dynamic change handling

And calculating the dynamic change amplitude of the physical quantity according to the obtained background field and the current 5-day result. The calculation formula is as follows:

wherein B is_a，B_abStatistics (e.g., mean) of the parameters for the current 5 days and previous playback periods, respectively.

7 auxiliary data

The text format records the seismic event, but the format standard can be directly called by a program to form a 3-level and 4-level data product, and the seismic event text comprises fields such as a table 26.

Table 26 table of seismic event field information

Dst index

The Dst index needs to be updated from the network in real time, one data is given every hour, the data format is standard and standard, and the standard can be directly called by a program to form a 3-level and 4-level data product.

Kp index

The Kp index is required to be updated from the network in real time, one data is given every 3 hours, the data format is standard and standard, and the standard can be directly called by a program to form a 3-level and 4-level data product.

F107 index

The F107 index needs to be updated from the internet in real time, one data is given every day, the data format is standard, and the standard can be directly called by a program to form a 3-level data product and a 4-level data product.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.

Claims

1. A method for processing high energy particle detector data, comprising:

the method comprises the steps that 1, original binary 0-level data are obtained by frame synchronization, descrambling, error correction, duplicate removal and time arrangement of descending original data of a high-energy particle detector;

performing decimal conversion on the acquired 0-level data, and then performing particle type identification, energy spectrum reconstruction, energy spectrum inversion, throwing angle inversion and the like according to calibration parameters to obtain an intermediate result and a final physical quantity so as to generate 1-level standard data;

acquiring subsatellite point position information from a satellite affair data packet, acquiring magnetic field data through an electromagnetic field model, acquiring auxiliary information such as seismic records and space weather indexes, correspondingly overlapping the auxiliary information to level 1 data, and generating level 2 standard data;

generating a time sequence analysis product as 3-level standard data by using the obtained current track and a plurality of previous revisiting periods;

generating space product data which are 4-level standard data by using current orbits of the world and China regions and previous revisit data of a plurality of revisit periods;

the level-1 standard data is case-by-case electron and proton energy, throwing angle, electron and proton fixed time resolution flux, energy spectrum and throwing angle spectrum which are arranged according to time and obtained after format conversion, energy channel conversion and calibration are carried out on the level-0 data;

the 2-level standard data is generated by performing coordinate transformation on the 1-level data, and comprises case-by-case electron and proton energies, throwing angles, fixed time resolution flux of the electrons and protons, energy spectrums and throwing angle spectrums with geographic and geomagnetic coordinate systems, time, position and attitude information;

the 3-level standard data is a time sequence product which is used for resampling the electron flux and the proton flux on the basis of the 2-level data to generate revisit orbit observation data of a global scope and a Chinese area, and is labeled with earthquake and space weather index information;

the 4-level standard data is based on 2-level data, and dynamic changes of electron and proton fluxes in the air over the global range and the Chinese area and the change amplitude of the electron and proton fluxes relative to the background field are generated.

2. The method of claim 1, wherein the level 1 data product comprises: scientific data, image products and data processing report parts;

the level 2 product mainly comprises: a data product, an image product and a data processing report section;

the grade 3 product mainly comprises: a data product, an image product and a data processing report section;

the level 4 product mainly comprises: data products, image products, and data processing reports.

3. The data processing method of the high-energy particle detector according to claim 1, wherein the obtained 0-level data is firstly subjected to decimal conversion, and then subjected to particle type identification, energy spectrum reconstruction, energy spectrum inversion, throwing angle inversion and the like according to calibration parameters to obtain an intermediate result and a final physical quantity, so as to generate 1-level standard data, and the method comprises the following steps: a high energy section load process, a low energy break load process, and a solar X-ray monitor process.

4. The method of claim 3, wherein the processing of the high energy segment loading comprises:

reading the 0-level data after the duplicate removal operation, and selecting HEPP-H data according to the load code;

selecting high-energy section load scientific data through a working mode;

extracting the relevant state of the scientific data according to the extracted time information of the scientific data, further calculating the working state of each item, and judging whether each working state of the load is within the range of the upper limit and the lower limit so as to judge the credibility of the scientific data;

carrying out energy spectrum construction and preliminary judgment in a physical quantity rapid processing mode;

calculating the actual total incident case number of the current second by using a dead time correction formula, and inputting a detection energy spectrum after energy reconstruction and particle identification;

carrying out further identification of the particle types;

carrying out deconvolution operation on the detected electron spectrum and the detected proton spectrum by using the energy response matrix;

and (4) judging the particle incidence direction by using a silicon strip detector loaded at a high energy section.

5. The method of claim 3, wherein the processing of the low energy interrupt load comprises:

reading 0-level scientific data after the deduplication operation;

selecting a common scientific data packet of the HEPP-X and the HEPP-L according to the load code;

judging the working state of the load according to the information such as the variable-speed telemetering amount, the slow-change telemetering amount and the like;

performing energy spectrum reconstruction;

performing dead time correction;

performing further identification of the particle species;

deconvoluting the detected electron spectrum and the detected proton spectrum by using the energy response matrix;

an incident angle inversion is performed.

6. The method of claim 3, wherein the processing procedure of the solar X-ray detector comprises:

reading 0-level scientific data after the deduplication operation;

judging the integrity of energy spectrum data through E001-E005 in the working mode;

acquiring binary scientific data according to a HEPP-X scientific data packet format;

converting the binary data into decimal data to obtain detection energy spectrum or rate meter data;

and performing dead time correction.

7. The data processing method of the high-energy particle detector as claimed in claim 1, wherein the method comprises the steps of obtaining position information of a subsatellite point from a satellite affairs data packet, obtaining magnetic field data through an electromagnetic field model, obtaining auxiliary information such as seismic records and space weather indexes, correspondingly stacking the auxiliary information to level 1 data, and generating level 2 standard data, and comprises the following steps:

and generating 2-level data according to the new data format and generating an image and a 2-level data processing report.

8. The method for processing high energy particle detector data as claimed in claim 1, wherein the step of generating a time sequence analysis product as 3-level standard data by using the acquired current orbit and a plurality of previous revisiting periods comprises:

obtaining a plurality of previous revisit cycles corresponding to the track, dividing the revisit cycles at intervals according to a preset latitude interval, and then calculating a median, upper and lower quartile points and a quartile difference in each interval;

the single-track data observed at present is divided according to the intervals, and the median A is calculated₅；

Calculating upper and lower limits by using the median, upper and lower quartile points and the inner difference of the quartile points of the previous multiple revisit cycle data;

calculating the difference value between the current observation value and the upper and lower limits of a plurality of previous revisit periods in each area, marking each area point according to whether the current observation data exceeds the upper and lower limits, wherein the difference value is normally 0, and the difference value d between the current observation value and the upper and lower limits is marked when the current observation data exceeds the limits;

calculating the above steps of each track by adopting a sliding mode for the current observed track data and the previous multiple revisit cycle data, and displaying the previous multiple revisit cycle data on the same graph;

and marking information such as seismic records, spatial weather and the like to generate 3-level data, and finishing a data processing report.

9. The data processing method of the high energy particle detector as claimed in claim 1, wherein the spatial product data is generated by using the current orbit of the global and Chinese areas and the previous revisit cycle revisit data, and is 4-level standard data, comprising:

according to 30-day time interval and 5 ° (longitude) x 2.5 ° (latitude) space interval, 2-grade data over global and Chinese areas are selected in a sliding mode, and the median B of all tracks in each space interval is calculated_30iQuartering point and quantile difference, and upper and lower bounds D, by applying median B to each interval_30iCarrying out interpolation to obtain global and national spatial distribution background fields;

the median B of all the orbits in each spatial interval is calculated at the current 5-day time interval and at the 5 (longitude) x 2.5 (latitude) spatial interval_5i(ii) a And by applying the median value B in each interval_5iCarrying out interpolation to obtain global and national space distribution maps, and updating the global and national space distribution maps every day in a sliding manner;

the median B5i of the data in each interval of each day is calculated as the difference dB from the background median of the previous ones_i＝B_5i-B_30i；

The difference in each bin is normalized gdB_i＝(B_5i-B_30i)/B_30iAnd by applying the median gdB to each interval_iInterpolation is carried out to obtain global and nationalDynamically changing the spatial profile each day;

calculating median B of data in each interval every day_5iMarking each interval according to whether the current observation data exceeds the upper limit and the lower limit, wherein the difference D between the current observation value and the upper limit and the lower limit is normally 0, and the difference D between the current observation value and the upper limit and the lower limit is marked when the current observation data exceeds the upper limit and the lower limit, and the interval exceeding the boundary is marked on a dynamic change diagram;

and marking information such as seismic records, spatial weather and the like to generate 4-level data, and finishing a data processing report.

10. An energy particle detector data processing system, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement an energetic particle detector data processing method according to any one of claims 1 to 9.