Astronomical data processing system, method and device

Abstract

The present invention discloses an astronomical data processing system, method and device, the system comprising: a Beidou short message / very high frequency (VHF) packet processing module, used to extract burst data from short message packets and VHF packets; a digital transmission X-band data processing module, used to extract observation data from digital transmission X-band downlink data packets; an interval tree index construction module, used to construct an observation data interval tree index using observation data information, wherein each interval is defined by an observation start time and an observation end time, and is associated with an observation number; and a burst data query module, used to receive a query time period, query all observation data indexes overlapping with the query time period from the observation data interval tree index, and obtain the association relationship between the burst data and the observation data. By using the interval tree data structure to establish the observation information for indexing, the efficiency of burst data query in large-scale astronomical time series observation data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an astronomical data processing system, method and device. Background Art

[0002] With the innovation of space exploration technology, astronomers' ability to timely discover important celestial events and their ability to innovate in scientific research have been greatly improved. Remarkable achievements have been made in the fields of gravitational waves, fast radio bursts, and cosmic rays, accelerating the arrival of the era of multi-messenger and multi-band astronomy. In order to capture astronomical phenomena that occur randomly in space, the ground system needs to process astronomical data in a timely and accurate manner so as to conduct data analysis on astronomical phenomena in a timely manner, analyze the key information of these burst events, and generate burst data products to support multi-messenger and multi-band follow-up observations and joint observations.

[0003] At present, astronomical satellites can generally transmit a large amount of observation data through the digital transmission X-band, a small amount of telemetry data through the S-band, and alarm information through Beidou short messages and VHF (Very High Frequency) networks. When an important explosion event is discovered, the space astronomical satellite will transmit the explosion information to the ground system through Beidou short messages or VHF networks. Due to the data transmission constraints of Beidou short messages or VHF networks, only a very small amount of explosion data can be transmitted to the ground system. The ground system needs to quickly search for data from the digital transmission X-band based on these explosion data to find observation data related to the explosion.

[0004] The existing method for processing astronomical data is to organize astronomical data by the hour, and after processing the observation data, divide it into whole hour or full day data according to the time information of the data; or organize the data according to the observation number or observation event, which is similar to an experiment. For example, if scientists want to observe a certain area of ​​the celestial sphere in a certain time period, the satellite will adjust its attitude and observe the area, and the observation data will be organized according to this time period later.

[0005] Due to the large amount of data downlinked by astronomical observation satellites, the diverse data downlink strategies and the characteristics of organization based on observation numbers, the calculation of the observation data corresponding to the burst data is large, and the existing astronomical data processing methods cannot meet the timeliness requirements of burst data search. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the prior art, and an astronomical data processing system is proposed. The present invention also discloses an astronomical data processing method and device.

[0007] An astronomical data processing system, comprising:

[0008] A Beidou short message / VHF packet processing module is used to extract burst data from short message packets and VHF packets, wherein the burst data includes a burst start time, a burst end time and an associated burst number;

[0009] A digital transmission X-band data processing module, used to extract observation data from a digital transmission X-band downlink data packet, wherein the observation data includes: an observation start time, an observation end time and an associated observation number;

[0010] An interval tree index building module, used to build an observation data interval tree index using observation data information, wherein each interval is defined by an observation start time and an observation end time and is associated with an observation number; and

[0011] The outbreak data query module is used to receive a query time period, query all observation data indexes overlapping with the query time period from the observation data interval tree index, and obtain the association relationship between the outbreak data and the observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data, and the association relationship includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time.

[0012] As an improvement of the above system, the interval tree index construction module is specifically used to establish a ternary observation number array sequence based on the observation number, observation start time, and observation end time; sort the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array; select the midpoint according to the median of the ordered array as the root node of the interval tree to initialize the interval tree; divide the remaining part of the data sequence into a left interval and a right interval; and recursively construct an interval tree index.

[0013] As an improvement of the above system, the observation data further includes: observation type and / or observation content; the system further includes:

[0014] An observation data product generation module, used to sort and store the observation data extracted by the digital transmission X-band data processing module according to the observation time; and

[0015] The outbreak data product generation module is used to find the association relationship between each outbreak data and the observation data from the outbreak data query module. For each outbreak data, in the observation data product module, the observation data of the corresponding query time period is obtained from the observation data where the associated observation number is located, and the correspondence between each outbreak data and the obtained observation data is established and stored.

[0016] An astronomical data processing method, the above-mentioned astronomical data processing system comprises:

[0017] The Beidou short message / VHF packet processing module extracts burst data from the short message packet and the VHF packet, wherein the burst data includes a burst start time, a burst end time and an associated burst number;

[0018] The digital transmission X-band data processing module extracts observation data from the digital transmission X-band downlink data packet, wherein the observation data includes: observation start time, observation end time and associated observation number;

[0019] The interval tree index building module uses the observation data information to build the observation data interval tree index, where each interval is defined by the observation start time and the observation end time, and is associated with an observation number;

[0020] The outbreak data query module receives a query time period, queries all observation data indexes overlapping with the query time period from the observation data interval tree index, and obtains the association relationship between the outbreak data and the observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data, and the association relationship includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time.

[0021] As an improvement of the above astronomical data processing method, the interval tree index construction module uses the observation data information to construct the observation data interval tree index, including:

[0022] The interval tree index construction module establishes a ternary observation number array sequence based on the observation number, observation start time, and observation end time; sorts the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array; selects the midpoint according to the median of the ordered array as the root node of the interval tree and initializes the interval tree; divides the remaining part of the data sequence into left interval and right interval; and recursively constructs the interval tree index.

[0023] As an improvement of the above astronomical data processing method, the astronomical data processing system further includes: a burst data product generation module; the method further includes:

[0024] The outbreak data product generation module obtains the association relationship between each outbreak data and the observation data from the outbreak data query module; for each outbreak data, in the observation data product module, the observation data of the corresponding query time period is obtained from the observation data where the associated observation number is located; the correspondence between each outbreak data and the obtained observation data is established and stored.

[0025] An astronomical data processing method, comprising:

[0026] According to the observation number, observation start time and observation end time, a three-element observation number array sequence is established;

[0027] According to the observation start time of the observation data, the observation number array sequence is sorted to construct an ordered time series array;

[0028] Select the midpoint according to the median of the ordered array as the root node of the interval tree and initialize the interval tree; divide the remaining part of the data sequence into left interval and right interval;

[0029] The observation data interval tree index is recursively constructed to facilitate querying the association between the outbreak data and the observation data, which includes the relationship between the outbreak number, outbreak start time and outbreak end time and the observation number, observation start time and observation end time.

[0030] An astronomical data processing device, comprising:

[0031] An array sequence establishing unit is used to establish a ternary observation number array sequence according to the observation number, the observation start time, and the observation end time;

[0032] A sorting unit is used to sort the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array;

[0033] A node establishment unit, used for selecting a midpoint according to the median of the ordered array as the root node of the interval tree, initializing the interval tree, and dividing the remaining part of the data sequence into a left interval and a right interval; and

[0034] The interval tree index establishment unit is used to call the sorting unit and the node calling unit to recursively construct the observation data interval tree index to facilitate the query of the association between the outbreak data and the observation data, which includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time.

[0035] An astronomical data processing method, comprising:

[0036] Obtaining the correlation between each outbreak data and the observation data, the correlation between the outbreak data and the observation data includes: the relationship between the outbreak number, outbreak start time and outbreak end time and the observation number, observation start time and observation end time;

[0037] For each outbreak data, in the observation data product module, determine the observation data with the associated observation number;

[0038] From the determined observation data, the observation data corresponding to the query time period is obtained, which is determined by the outbreak start time and the outbreak end time of the outbreak data;

[0039] Establish the corresponding relationship between each burst data and the obtained observation data.

[0040] An astronomical data processing device, comprising:

[0041] The association relationship obtaining unit is used to obtain the association relationship between each outbreak data and the observation data, wherein the association relationship between the outbreak data and the observation data includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time;

[0042] A determination unit, for determining, for each piece of outbreak data, in the observation data product module, the observation data to which the associated observation number belongs;

[0043] an observation data obtaining unit, for obtaining observation data corresponding to a query time period from the determined observation data, determined by an outbreak start time and an outbreak end time of the outbreak data; and

[0044] The outbreak data product establishment unit is used to establish a corresponding relationship between each outbreak data and the obtained observation data.

[0045] Compared with the prior art, the advantages of the present invention are:

[0046] 1. Aiming at the demand of quickly extracting burst event data from large-scale time-series astronomical observation data, the present invention proposes an astronomical data processing method. Based on the characteristics of space astronomical satellite observation data and the correlation between observation data and burst events, the efficiency of burst data query in large-scale astronomical time-series observation data is effectively improved by establishing observation information based on interval tree data structure for indexing.

[0047] 2. The present invention first pre-processes the astronomical satellite observation data, and then quickly generates the process steps and system design of burst data products based on the observation organization data. The burst data products can be generated more quickly through the design of a parallel architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic diagram of the structure of an astronomical processing system according to Embodiment 1 of the present invention;

[0049] Figure 2 is a flow chart of an astronomical data processing method according to Embodiment 2 of the present invention;

[0050] Figure 3 is a flow chart of the astronomical processing method according to Embodiment 3 of the present invention;

[0051] Figure 4 It is a schematic diagram of the structure of the interval tree index construction module of Example 4 of the present invention;

[0052] Figure 5 is a flow chart of the astronomical processing method according to Embodiment 5 of the present invention;

[0053] Figure 6 Schematic diagram of the structure of the outbreak data product generation module of Example 6 of the present invention;

[0054] Figure 7 is a flow chart of the astronomical processing system according to Embodiment 7 of the present invention;

[0055] Figure 8 This is a diagram of the original data format of the X-band downlink according to Embodiment 7 of the present invention;

[0056] Fig. 9 is a schematic diagram of the structure of the interval tree of Embodiment 7 of the present invention;

[0057] Fig.10 is a schematic diagram of the correlation between the outbreak data and the observation data of Example 7 of the present invention;

[0058] Fig.11 It is a schematic diagram of the outbreak data product generation process of Example 7 of the present invention. DETAILED DESCRIPTION

[0059] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0060] Example 1

[0061] In order to facilitate understanding of the solution of the embodiment of the present application, the observation data and burst data included in the astronomical data in the embodiment of the present application are described in detail. The observation data can be obtained by parsing the raw data downlinked by the X-band, and the burst data can be obtained by processing the raw data downlinked by the Beidou and VHF channels.

[0062] The following uses Figure 1 The astronomical data processing system provided in the embodiment of the present application is described, including: a Beidou short message / very high frequency (VHF) packet processing module, a digital transmission X-band data processing module, an interval tree index construction module and a burst data query module; wherein:

[0063] The Beidou short message / VHF packet processing module is used to extract burst data from the short message packet and the VHF packet, wherein the burst data includes the burst start time, the burst end time and the associated burst number.

[0064] The above-mentioned burst data may also include information such as the right ascension and declination of the burst source.

[0065] The digital transmission X-band data processing module is used to extract observation data from the digital transmission X-band downlink data packet, and the observation data includes: observation start time, observation end time and associated observation number.

[0066] The above observation start time, observation end time and the associated observation number are attribute information contained in the observation data. The observation data also includes scientific data such as payload detection events and energy spectrum data.

[0067] The interval tree index construction module is used to construct the observation data interval tree index using the observation data information, wherein each interval is defined by an observation start time and an observation end time and is associated with an observation number.

[0068] Preferably, to ensure accurate time matching, the end time of the interval includes the boundary time point by adding one second to the observation end time.

[0069] The outbreak data query module is used to receive the query time period, query all observation data indexes overlapping with the query time period from the observation data interval tree index, and obtain the association relationship between the outbreak data and the observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data.

[0070] In order to quickly obtain burst data and observation data and obtain the correlation between the two, the Beidou short message / VHF packet processing module can process the Beidou short message packet and VHF packet respectively at the same time; at the same time, the digital transmission X-band data processing module also processes the data packets downlinked from the digital transmission X-band.

[0071] Specifically, the interval tree index construction module is specifically used to establish a ternary observation number array sequence based on the observation number, observation start time, and observation end time; sort the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array; select the midpoint according to the median of the ordered array as the root node of the interval tree to initialize the interval tree; divide the remaining part of the data sequence into a left interval and a right interval; and recursively construct the interval tree index.

[0072] Preferably, considering that the observation data may overlap due to observation priority, the interval tree index construction module is also specifically used to split the observation data; according to the observation start time of the observation data, the observation number array sequence obtained after the split is sorted to construct an ordered time series array.

[0073] Preferably, in order to facilitate the use of burst data to support multi-messenger, multi-band follow-up observations and joint observations, the observation data also includes: observation type and / or observation content; the association between burst data and observation data includes: the relationship between the burst number, burst start time and burst end time and the observation number, observation start time and observation end time; the system also includes:

[0074] The observation data product generation module is used to store the observation data extracted by the digital transmission X-band data processing module;

[0075] The outbreak data product generation module is used to obtain the association relationship between each outbreak data and the observation data from the outbreak data query module. For each outbreak data, in the observation data product module, the observation data of the corresponding query time period is obtained from the observation data where the associated observation number is located; the correspondence between each outbreak data and the obtained observation data is established and stored.

[0076] Preferably, since the observation time period of each observation data obtained by the query does not necessarily completely overlap with the query time period, in order to ensure that each outbreak data established is completely matched with the obtained observation data, the outbreak data product generation module is also used to split and combine the obtained corresponding query observation data according to the query time period; establish and store the corresponding relationship between each outbreak data and the observation data after splitting and combining.

[0077] The corresponding relationship between each outbreak data and the observation data after segmentation and combination can be outbreak number, observation number, outbreak start time, outbreak end time, and outbreak data content.

[0078] The solution of the embodiment of the present invention processes multi-channel downlink data in parallel, processes the massive observation data of the digital transmission X-band downlink, extracts the observation number information in the data and establishes an index of the interval tree structure, quickly processes the alarm information quickly downlinked through the Beidou and VHF channels, extracts the burst data and performs a rapid query on the interval tree index structure, and after obtaining the corresponding observation information index, divides and produces the burst data product based on the observation data, thereby improving the production timeliness of the astronomical burst event data product and helping astronomers to quickly carry out scientific data analysis of astronomical phenomena.

[0079] Example 2

[0080] like Figure 2 FIG. 1 is a diagram showing an astronomical data processing method provided by an embodiment of the present invention, which describes a process of processing astronomical data by the astronomical data processing system in Embodiment 1, including the following steps:

[0081] Step 201: The Beidou short message / VHF packet processing module extracts burst data from the short message packet and the VHF packet, wherein the burst data includes a burst start time, a burst end time and an associated burst number;

[0082] Step 202: The digital transmission X-band data processing module extracts observation data from the digital transmission X-band downlink data packet, wherein the observation data includes: observation start time, observation end time and associated observation number;

[0083] The above steps 201 and 202 may be executed in series or in parallel. When executed in parallel, the data processing efficiency is increased.

[0084] Step 203: The interval tree index construction module constructs an observation data interval tree index using the observation data information, wherein each interval is defined by an observation start time and an observation end time, and is associated with an observation number;

[0085] Preferably, the interval tree index construction module establishes a ternary observation number array sequence based on the observation number, observation start time, and observation end time; sorts the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array; selects the midpoint according to the median of the ordered array as the root node of the interval tree to initialize the interval tree; divides the remaining part of the data sequence into a left interval and a right interval; and recursively constructs the interval tree index.

[0086] Step 204: The outbreak data query module receives the query time period, queries all observation data indexes overlapping with the query time period from the observation data interval tree index, and obtains the association relationship between the outbreak data and the observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data.

[0087] Step 205: the outbreak data product generation module obtains the association relationship between each outbreak data and the observation data from the outbreak data query module;

[0088] Step 206: The outbreak data product generation module obtains the observation data corresponding to the query time period from the observation data where the associated observation number is located in the observation data product module for each outbreak data;

[0089] Step 207: The outbreak data product generation module obtains the observation data corresponding to the query time period; establishes and stores the corresponding relationship between each outbreak data and the obtained observation data.

[0090] Example 3

[0091] like Figure 3 As shown, an astronomical data processing method provided by Embodiment 3 of the present invention is described, specifically, a process of processing observation data by an interval tree index construction module is described, including the following steps:

[0092] Step 301: Establish a ternary observation number array sequence according to the observation number, observation start time, and observation end time;

[0093] Step 302: sorting the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array;

[0094] Step 303: Select the midpoint according to the median of the ordered array as the root node of the interval tree, and initialize the interval tree; divide the remaining part of the data sequence into a left interval and a right interval;

[0095] Step 304: recursively construct an observation data interval tree index to facilitate querying the association between the outbreak data and the observation data.

[0096] In Example 3 of the present application, the observation data is organized and stored through interval tree index construction, and the observation time is used as the interval, which is conducive to improving the query efficiency of querying the observation data by using the outbreak time period in the outbreak data.

[0097] Example 4

[0098] like Figure 4 As shown, an astronomical data processing device provided by Embodiment 4 of the present invention is provided. Specifically, a schematic diagram of the structure of an interval tree index construction module is provided, including:

[0099] An array sequence establishing unit 401 is used to establish a ternary observation number array sequence according to the observation number, the observation start time, and the observation end time;

[0100] A sorting unit 402 is used to sort the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array;

[0101] A node establishment unit 403 is used to select a midpoint according to the median of the ordered array as the root node of the interval tree, initialize the interval tree, and divide the remaining part of the data sequence into a left interval and a right interval;

[0102] The interval tree index building unit 404 is used to call the sorting unit and the node calling unit to recursively build the observation data interval tree index to facilitate querying the association relationship between the outbreak data and the observation data.

[0103] Example 5

[0104] like Figure 5 As shown, an astronomical data processing method provided by Embodiment 4 of the present invention is described, specifically, the process of processing observation data by the burst data product generation module is described, including the following steps:

[0105] Step 501: Obtain the association relationship between each outbreak data and the observation data, the association relationship between the outbreak data and the observation data includes: the relationship between the outbreak number, outbreak start time and outbreak end time and the observation number, observation start time and observation end time;

[0106] Step 502: for each piece of outbreak data, in the observation data product module, determine the observation data where the associated observation number is located;

[0107] Step 503: Obtaining observation data corresponding to a query time period from the determined observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data;

[0108] Step 504: Establish a corresponding relationship between each burst data and the obtained observation data.

[0109] Example 6

[0110] like Figure 6 As shown, an astronomical data processing device provided by Embodiment 6 of the present invention is provided. Specifically, a schematic diagram of the structure of a burst data product generation module is provided, including:

[0111] The association relationship obtaining unit 601 is used to obtain the association relationship between each outbreak data and the observation data, wherein the association relationship between the outbreak data and the observation data includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time;

[0112] A determination unit 602 is used to determine, for each piece of outbreak data, in the observation data product module, the observation data where the associated observation number is located;

[0113] An observation data obtaining unit 603 is used to obtain observation data corresponding to a query time period from the determined observation data, where the query time period is determined by an outbreak start time and an outbreak end time of the outbreak data;

[0114] The outbreak data product establishing unit 604 is used to establish a corresponding relationship between each outbreak data and the obtained observation data.

[0115] The above embodiments introduce the system, method and device of the present invention. The data processing method of the present invention is described in detail below through a specific example in Embodiment 7.

[0116] Example 7

[0117] Attached Figure 7This is a flow chart of the astronomical processing system of the present invention, which explains the various functional modules included in the present invention and the flow relationship between the modules. It should be pointed out that the astronomical satellite downlink AOS transmission frame and payload source packet structure proposed in this document are example data structures. The current popular format is the AOS transmission frame layer and source packet layer format definition agreed upon by the Consultative Committee for Space Data System (CCSDS). However, there is no unified format definition and specification for storing observation types (OBS_TYPE) and observation numbers (OBS_ID) for astronomical satellites, and each satellite mission may cut and change the standard format during the data design process. Therefore, this document gives an astronomical satellite data format that is a format sample that can store observation type and observation number information. The detailed description is as follows:

[0118] Step 1: Analyze the raw data of the X-band downlink. The format is as follows: Figure 8 As shown, the data of the AOS transmission frame is subjected to consistency check of fields such as "version number" and "spacecraft identifier" and RS check, and the AOS frame data that passes the check is separated into different virtual channel data according to the "virtual channel identifier" field.

[0119] Step 2: Extract the payload source packet data in the "data domain" field of the AOS transmission frame, separate the payload source packet data of different application processes according to the "application process identifier", and sort the payload source packet data according to the "source packet sequence count" and "time code" to form ordered payload source packet data.

[0120] Step 3: Extract "TYPE" and "OBSID" from the "observation task" identifier of each payload source package, mark the observation type (OBS_TYPE) and observation number (OBS_ID) respectively, classify the payload source package data according to OBS_TYPE and OBS_ID, and form payload source package data products based on different observation organizations. Based on the time code information of the first payload source package and the time code information of the last payload source package of the product, obtain the start time (OBS_STARTTIME) and end time (OBS_ENDTIME) of the observation data.

[0121] Step 4: Process the raw data transmitted by the astronomical satellite through the Beidou and VHF channels respectively, sort and splice the Beidou short message downlink data according to the batch number + package number; interpret the VHF packet type of the VHF data, and finally extract the burst data information detected by the satellite, and use (burst number, burst start time, burst end time) as the burst data query information.

[0122] Step 5: Create an empty interval tree and insert the observation number (OBS_ID), observation start time (OBS_STARTTIME), and observation end time (OBS_ENDTIME) as intervals into the interval tree. Each interval is defined by a start time and an end time, and is associated with an observation number. To ensure an accurate match of time, the end time is added by one second to include the boundary time point, and finally an interval tree structure storing the observation number information is constructed. The specific algorithm is as follows:

[0123] 1) Input: Based on the observation number OBS_ID and the start time (OBS_STARTTIME) and end time (OBS_ENDTIME) of the observation data, create a three-element array sequence D, D = {(obsid i ,[s i , e i ])}, where obsid i represents the observation number, [s i , e i ] represents an observed time region, s i represents the observation start time, e i Indicates the end time of the observation.

[0124] Assume that the observation data is the following 8 records (serial number, observation start time, observation end time, observation ID):

[0125] 1, 20231001T000000, 20231001T012833, 2017

[0126] 2, 20231001T003338, 20231001T011148, 2290

[0127] 3, 20231001T012834, 20231001T024429, 863

[0128] 4, 20231001T014258, 20231001T023212, 2954

[0129] 5, 20231001T024430, 20231001T031228, 1678

[0130] 6, 20231001T031229, 20231001T031415, 106

[0131] 7, 20231001T031416, 20231001T033832, 1456

[0132] 8, 20231001T033833, 20231001T040619, 1666

[0133] 2) Preprocessing: Traverse the observation data information sequence and split the overlapping data (overlapping data is usually caused by high-priority observations interrupting low-priority observations first, and then answering low-priority observations again after completing the high-priority observation task). Fig.11 As shown, the data corresponding to OBS-i is (obsid i , [T0, T3]), and the data corresponding to OBS-j is (obsid j , [T1, T2]), where T2~T3 overlaps with T0~T3, and the OBS-id data needs to be split to form (obsid i , [T0, T1]) and (obsid i , [T2, T3]), the observation number array sequence is calculated according to the start time s of the observation data i Sort and construct ordered time series data.

[0134] Using the above example, we can see from the sequence that observation number 2017 and observation number 863 overlap with observation number 2290 and 2954 respectively, so the overlapping observation data needs to be split. Then re-sort them to form the following processing results:

[0135] 1. 20231001T000000, 20231001T003337, 2017

[0136] 2. 20231001T003338, 20231001T011148, 2290

[0137] 3. 20231001T011149, 20231001T012833, 2017

[0138] 4. 20231001T012834,20231001T014257,863

[0139] 5. 20231001T014258, 20231001T023212, 2954

[0140] 6. 20231001T023213, 20231001T024429,863

[0141] 7. 20231001T024430, 20231001T031228, 1678

[0142] 8. 20231001T031229, 20231001T031415, 106

[0143] 9. 20231001T031416, 20231001T033832, 1456

[0144] 10. 20231001T033833, 20231001T040619, 1666

[0145] 3) Determine the midpoint: Select the midpoint according to the median of the ordered array as the root node of the interval tree and initialize the interval tree T.

[0146] Continuing with the above example, the median is selected based on the start time of the observation number, and (20231001T014258, 20231001T023212, 2954) is determined as the root node of the interval tree.

[0147] 4) Interval division: Divide the remaining part of the data sequence into left interval and right interval.

[0148] Left interval:

[0149] 1. 20231001T000000, 20231001T003337, 2017

[0150] 2. 20231001T003338, 20231001T011148, 2290

[0151] 3. 20231001T011149, 20231001T012833, 2017

[0152] 4. 20231001T012834, 20231001T014257, 863

[0153] Right interval:

[0154] 6. 20231001T023213, 20231001T024429, 863

[0155] 7. 20231001T024430, 20231001T031228, 1678

[0156] 8. 20231001T031229, 20231001T031415, 106

[0157] 9. 20231001T031416, 20231001T033832, 1456

[0158] 10. 20231001T033833, 20231001T040619, 1666

[0159] 5) Recursively construct the interval tree: recursively construct the data sequence of the left interval and the right interval, and traverse the observation data of each observation interval (obsid i ,[s i , e i ]), in order to ensure the exact time match, the end time is increased by one second to include the boundary time point, thus obtaining (obsid i ,[s i , e i +1]), repeat the process of 3) and 4) to build the left and right interval trees. If the start time of the data to be inserted [s, e] is less than the start time of the current node, then (obsid i ,[s i , e i +1]) as the left child of the current node. Otherwise, (obsid i ,[s i , e i +1]) is inserted as the right child of the current node.

[0160] Using the above example, we construct the interval tree for the left subinterval and the right subinterval using the same method. Fig. 9 The interval tree structure shown.

[0161] In addition, to ensure the balance of the tree, a balance factor balance_factor is defined for each node, which represents the difference between the number of nodes in the left subtree and the number of nodes in the right subtree. When a new node is inserted, the balance factor of each node can be checked and a rotation operation can be performed as needed to adjust the balance of the tree.

[0162] Step 6: Define the basic information block of burst data, Q = (burstId, q s ,q e ), where burstId is the ID number of the burst event, [q s ,q e ] are the start time and end time of the outbreak respectively. Execute the outbreak data query operation Search(T, [q s ,q e ]), traverse the interval tree starting from the root node:

[0163] If the time interval of the current node is s ,q e ] If there is an overlap, the observation identifier obsid of the current node is i Add to the result set.

[0164] If the left child of the current node is not empty and is equal to [q s ,q e ] If there is overlap, recursively execute Search(T left , [q s ,q e ])Query operation.

[0165] If the right child of the current node is not empty and is equal to [q s ,q e ] If there is overlap, recursively execute Search(T right , [q s ,q e ])Query operation.

[0166] Finally, the query results are obtained to obtain the correlation sequence information between the outbreak data and the observation data. Since each outbreak event may be associated with one or two observation number information, such as Fig.10 As shown, a burst event may occur within one observation period, or between two different observation periods, or two burst events may occur in the same observation period, so the result may be Result = (burstId, q s ,q e ,{[obsid i , (q s ,q e )]})or

[0167] Result = (burstId, q s ,q e ,{[obsid i , (q s , e i +1)],[obsid j ,(s j ,q e )]}).

[0168] Continuing with the above example, assuming the outbreak data is (20231001T023290, 20231001T029000, 345), 20231001T023290 is the start time of the outbreak, 20231001T029000 is the end time of the outbreak, and 345 is the outbreak number, then traversing the interval tree from the root node, the query result is (345, 20231001T023290, 20231001T029000, {[863, (20231001T023213, 20231001T024430)[,

[0169] [1678, (20231001T024430, 20231001T031228)]}.

[0170] Step 7: Based on the query Result information, obtain the data of the corresponding query time period from the observation data with the corresponding observation number obsid, split the data according to the outbreak data range covered in the data of each observation number, and sort the split data by time into outbreak data products organized based on outbreak information.

[0171] Using the above example, since the query time period is (20231001T023290, 20231001T029000), which is not exactly the same as the start time and end time of the observation data, in order to accurately correspond to the observation data, it is necessary to query the observation data [863, (20231001T023213, 20231001T024430)],

[0172] The data content of [1678, (20231001T024430, 20231001T031228)] is split to obtain the observed data [863, (20231001T023290, 20231001T024430)],

[0173] The data content corresponding to [1678, (20231001T024430, 20231001T029000)].

[0174] The generated data product based on the outbreak event organization is also the data product of the correspondence between the outbreak data sorted by time and the obtained observation data, such as Fig.11 shown.

[0175] Fig.11 At the same time, a more intuitive burst data product generation process is given: the first step: sort the observation data in the observation data product. It can be sorted according to the observation start time to obtain the time series observation data; the observation data OBS-Data in the observation data product includes: OBS-Type observation type, OBS-ID observation number, OBS-StartTime observation start time, OBS-EndTime observation end time and OBS-DataContent observation data content. The observation data content here refers to the detection cases of the payload and scientific data such as energy spectrum data. Fig.11In the figure, OBS-j is the high-priority observation data, which interrupts the low-priority observation data OBS-i, causing the OBS-i observation data to be divided into two sections. When the observation data is sorted according to the observation start time, the observation start time of the observation data is reflected; the second step is to find the association relationship between the observation data corresponding to each burst data based on the interval tree, that is, to use the burst start time and end time information BursrInfo in the burst data to find the observation data with overlapping time; the third step: use the found association relationship to generate burst product data. The data in the burst data product Alert Data Info includes: Burst-ID burst number, OBS-ID observation number, Burst-StartTime burst start time, Burst-EndTime burst end time and Burst-DataContent burst data content. Most of the content in the burst data product here comes from the observation data, including the location information of the burst source (right ascension, declination) and the detection cases of the payload as well as scientific data such as energy spectrum data.

[0176] The solution of the embodiment of the present invention designs a data structure for storing astronomical observations based on an interval tree structure. Through simulation experimental comparison, when there are no less than 200,000 observation records, the search efficiency of explosion event information based on this data structure is 40 times higher than that based on the search method based on time information index.

[0177] 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 the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. An astronomical data processing system, characterized in that: include: A Beidou short message / VHF packet processing module is used to extract burst data from short message packets and VHF packets, wherein the burst data includes a burst start time, a burst end time and an associated burst number; A digital transmission X-band data processing module, used to extract observation data from a digital transmission X-band downlink data packet, wherein the observation data includes: an observation start time, an observation end time and an associated observation number; An interval tree index building module, used to build an observation data interval tree index using observation data information, wherein each interval is defined by an observation start time and an observation end time and is associated with an observation number; and The outbreak data query module is used to receive the query time period, query all the observation data indexes overlapping with the query time period from the observation data interval tree index, and obtain the association relationship between the outbreak data and the observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data, and the association relationship includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time; The interval tree index construction module is specifically used to establish a ternary observation number array sequence according to the observation number, the observation start time, and the observation end time; sort the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array; select the midpoint according to the median of the ordered array as the root node of the interval tree to initialize the interval tree; divide the remaining part of the data sequence into a left interval and a right interval; and recursively construct the interval tree index; The observation data further includes: observation type and / or observation content; the system further includes: An observation data product generation module, used to sort and store the observation data extracted by the digital transmission X-band data processing module according to the observation time; and The outbreak data product generation module is used to find the association relationship between each outbreak data and the observation data from the outbreak data query module. For each outbreak data, in the observation data product module, the observation data of the corresponding query time period is obtained from the observation data where the associated observation number is located, and the correspondence between each outbreak data and the obtained observation data is established and stored.

2. An astronomical data processing method, based on the astronomical data processing system according to claim 1, comprising: The Beidou short message / VHF packet processing module extracts burst data from the short message packet and the VHF packet, wherein the burst data includes a burst start time, a burst end time and an associated burst number; The digital transmission X-band data processing module extracts observation data from the digital transmission X-band downlink data packet, wherein the observation data includes: observation start time, observation end time and associated observation number; The interval tree index building module uses the observation data information to build the observation data interval tree index, where each interval is defined by the observation start time and the observation end time, and is associated with an observation number; The outbreak data query module receives the query time period, queries all observation data indexes overlapping with the query time period from the observation data interval tree index, and obtains the association relationship between the outbreak data and the observation data, wherein the query time period is determined by the outbreak start time and the outbreak end time of the outbreak data, and the association relationship includes: the relationship between the outbreak number, the outbreak start time and the outbreak end time and the observation number, the observation start time and the observation end time; The interval tree index construction module uses the observation data information to construct the observation data interval tree index, including: The interval tree index construction module establishes a ternary observation number array sequence based on the observation number, observation start time, and observation end time; sorts the observation number array sequence according to the observation start time of the observation data to construct an ordered time series array; selects the midpoint according to the median of the ordered array as the root node of the interval tree and initializes the interval tree; divides the remaining part of the data sequence into left intervals and right intervals; and recursively constructs the interval tree index; The astronomical data processing system further comprises: a burst data product generation module; the method further comprises: The outbreak data product generation module obtains the association relationship between each outbreak data and the observation data from the outbreak data query module; for each outbreak data, in the observation data product module, the observation data of the corresponding query time period is obtained from the observation data where the associated observation number is located; the correspondence between each outbreak data and the obtained observation data is established and stored.

Images