Space big data storage and management method based on equal-area grids

Abstract

The invention discloses a space big data storage and management method based on an equal-area grid, and relates to the technical field of space data storage and management. A background database architecture containing data such as a basic grid, associated satellites, collision early warning and a space environment is constructed, and a dynamic sub-table strategy is adopted to adapt to mass space data storage. Meanwhile, matched service management is provided, database operation query and a calling interface of a basic grid algorithm and grid coding and decoding are achieved, and meanwhile accurate mapping of geographic coordinates and basic grids and basic grid information extraction are supported. According to the method, the problems of uneven area and high latitude distortion of traditional longitude and latitude grids are solved, an efficient and accurate space big data organization and management framework is provided for scenes such as satellite operation and space situation awareness, and scientificity and efficiency of space target monitoring and intelligent management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of space data storage and management, and in particular to a space big data storage and management method based on equal-area grids. Background Art

[0002] With the rapid development of the global aerospace industry, the number of space objects (including various satellites, space debris, and rocket debris) is growing exponentially. These objects are distributed across a wide range of regions, extending from low-Earth orbit to deep space, creating a comprehensive coverage. This undoubtedly places unprecedented demands on the management of space big data.

[0003] Traditional spatial data management methods often use rectangular or trapezoidal grids based on longitude and latitude. However, because the Earth is a sphere and subject to curvature, this gridding method exhibits a serious flaw in high-latitude regions: significant grid distortion. For example, near the North Pole, a 1°×1° grid has only one-quarter the area of ​​a grid of the same size in the equatorial region. This uneven area directly triggers a series of chain reactions: it creates a large amount of redundancy in data storage; it is prone to bias in spatial statistics, such as overestimating the density of space targets in polar regions; and for dynamic space targets, their constantly changing positions require frequent grid reconstruction to adapt, which brings great inconvenience to management.

[0004] For example, invention application number 202310765061.6 discloses a method for storing and managing spatial big data based on BeiDou grid technology. This solution makes spatial big data more intuitive, understandable, expressive, and communicative. However, this solution still relies on traditional spatial data management methods, using a rectangular grid division method based on longitude and latitude.

[0005] These drawbacks make traditional methods incapable of meeting the demands of modern high-precision space situational awareness and collision warning. Therefore, a method for storing and managing space big data based on equal-area grids is needed. Using an adaptive partitioning algorithm, a grid with uniform area at the same altitude across the globe can be constructed, fundamentally addressing the spatial distortion and uneven area problems inherent in traditional longitude and latitude grids. Summary of the Invention

[0006] In response to the above-mentioned problems, the purpose of the present invention is to provide a space big data storage and management method based on equal-area grids to solve the spatial distortion and area unevenness problems existing in traditional longitude and latitude grids in space big data storage and management.

[0007] An embodiment of the present invention provides a space big data storage and management method based on equal-area grids, comprising the following steps: S1. Divide the basic grid cells with equal areas at the same height; S2. Form a space big data storage database architecture based on basic grid units; S3, provides supporting service management for space big data based on database architecture; Among them, the database architecture uses Mysql as the backend database, and the database includes a basic library and an extended library. The basic library includes a basic grid library and a satellite association library, and the extended library includes a collision warning library and a space environment library.

[0008] In one embodiment of the present invention, the extension library can be extended based on actual space big data.

[0009] In one embodiment of the present invention, the library tables of the basic grid library include a grid division result table, a satellite basic information table, a station basic information table, and a station capability grid mapping table.

[0010] In one embodiment of the present invention, the satellite association library table includes: association time, grid code, association object code and association object type fields.

[0011] In one embodiment of the present invention, the collision warning library and the space environment library are stored in dynamic sub-tables;

[0012] Among them, the collision warning library single table stores one year's data, and the space environment library single table stores one day's data.

[0013] In one embodiment of the present invention, the division comprises dividing into basic grid units of equal area at the same height;

[0014] Based on the ORDEM2010 model, the basic grid unit division includes basic grid division and height dimension division.

[0015] In one embodiment of the present invention, the height dimension division includes:

[0016] Low altitude area, the altitude is 0 meters to 186,000 meters, and the altitude step is 6,000 meters;

[0017] Low orbit range: 186,000 meters to 2,286,000 meters, with an altitude step of 25,000 meters;

[0018] Medium orbit range: 2,286,000 meters to 34,786,000 meters, with an altitude step of 500,000 meters;

[0019] High orbit region, 34,786,000 meters to, altitude step length 50,000 meters, number of altitude divisions 40;

[0020] In one embodiment of the present invention, the basic grid division mathematical model includes:

[0021] The objective function is:

[0022]

[0023] in, is the span difference between different latitude zones;

[0024] The function limiting the number of units divided by latitude bands is:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] in, For the The number of longitude grids divided by the layer latitude band, is the number of latitude zones that the Northern Hemisphere will be divided into, The total number of grid cells in the Northern Hemisphere, Represents a set of integers.

[0033] The latitude band height calculation function is:

[0034] in, For the The length from the intersection of the latitude belt and the Earth's rotation axis to the North Pole;

[0035] The calculation function of the latitude band eigenvalue is:

[0036]

[0037] in, For the The boundary latitude of the layer latitude band close to the equator;

[0038] The latitude band span calculation function is:

[0039]

[0040] in, For the The boundary latitude value of the layer latitude band close to the equator.

[0041] In one embodiment of the present invention, the S3 performs supporting service management for space big data based on the database architecture, including performing grid coding based on the longitude, latitude and altitude of geographic coordinates to obtain a basic grid index, including the steps of:

[0042] S31. Determine the grid index in the latitude direction of the northern hemisphere;

[0043] S32, determining the grid index of the longitude direction of the northern hemisphere;

[0044] S33, adjust the grid index to adapt to the southern hemisphere;

[0045] S34, determining a grid index in a height direction;

[0046] S35. Finally, the longitude grid index, the latitude grid index, and the altitude grid index are spliced ​​together according to a fixed format to obtain a basic grid index.

[0047] In one embodiment of the present invention, the S3 performs supporting service management for space big data based on a database architecture, including performing grid decoding based on a basic grid index to obtain latitude boundaries, longitude boundaries, and altitude boundaries, including the steps of:

[0048] S36. Extracting index values ​​of longitude, latitude, and altitude from the grid index code;

[0049] S37, determine the northern and southern hemispheres, and convert the value into the northern hemisphere index;

[0050] S38. Determine the latitude boundary, longitude boundary, and altitude boundary based on the index value.

[0051] Beneficial effects of the present invention:

[0052] 1. This invention fundamentally eliminates the problem of grid distortion in high-latitude areas through equal-area grid division, avoids data storage redundancy and spatial statistical deviation, ensures the accuracy of statistical results such as the spatial distribution and density of space targets, and provides a reliable data foundation for high-precision space situational awareness.

[0053] 2. The present invention adopts dynamic table partitioning, satellite-related data by day, collision warning data by year, space environment data by day, and a hierarchical storage strategy, combined with grid encoding and decoding technology to achieve rapid mapping of geographic coordinates and basic grid units, greatly improving the storage, query and correlation analysis efficiency of massive space data, and efficiently supporting core scenarios such as satellite orbit prediction and collision risk calculation.

[0054] 3. The present invention realizes dynamic adaptation of space targets in all-dimensional areas from low-Earth orbit to deep space through multi-level grid division and supporting service interfaces. It can meet the needs of diverse scenarios such as station resource scheduling and space environment modeling without frequent grid reconstruction, and provides a scientific and efficient data organization framework for global monitoring and intelligent management of space targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of the space big data storage and management method of the present invention;

[0056] Figure 2 It is a schematic diagram of the structure of the database framework of the present invention;

[0057] Figure 3 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0058] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings. The same or similar symbols throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0059] Existing space big data management methods have the following problems: grid division based on longitude and latitude, due to the curvature of the earth, high-latitude grids are severely distorted, resulting in data storage redundancy and spatial statistical deviation. Dynamic target adaptation requires frequent reconstruction of the grid, which is difficult to meet high-precision requirements; dynamic data management is insufficient, and the high-frequency change data associated with satellites and grids can easily cause single table overload, affecting real-time monitoring; grid characteristics are poorly adapted to business needs, and cannot accurately support collision warning and orbit prediction. There is also a lack of a unified interface, which increases the difficulty of system integration and restricts the promotion of practical applications.

[0060] Example 1:

[0061] To address the above issues, this embodiment discloses a space big data storage and management method based on equal-area grids, which constructs a backend database architecture containing data such as basic grids, associated satellites, collision warnings, and space environment. The database architecture is equipped with backend service management, providing interface applications such as grid division generation, satellite / station target management and query, collision warning, and space environment information management. Figure 1 As shown, the method includes the steps of:

[0062] S1. Divide the basic grid units with equal areas at the same height.

[0063] The division of basic grid cells can be based on a customized space grid division algorithm based on the equal-area airspace grid transformation of the ORDEM2010 model. The division of basic grid cells includes two parts: basic grid division and height dimension division.

[0064] Basic gridding is the process of dividing the ORDEM2010 model surface or a specific altitude area into several basic grids based on the principle of equal area. These basic grids have the same area to facilitate uniform distribution and management of data.

[0065] Altitude partitioning involves dividing a space region vertically into layers at regular intervals. Each layer corresponds to a specific altitude range, thus achieving a gridded three-dimensional space.

[0066] S2. Form a space big data storage database architecture based on basic grid units.

[0067] like Figure 2 As shown in the figure, the database architecture consists of two main parts: data storage solution design and supporting services. The data storage solution in this architecture uses MySQL as the backend database and consists of two parts: a basic library and an extended library. The basic library includes a basic grid library and a satellite association library, while the extended library includes a collision warning library and a space environment library, which can be expanded based on actual data. The satellite association library, collision warning library, and space environment library are all linked to the basic grid library.

[0068] S3: Provide supporting service management for space big data based on database architecture.

[0069] Supporting service management includes implementing functions such as operational queries of data in the database architecture, including obtaining the basic grid index based on the longitude, latitude and altitude of geographic coordinates, and extracting the index values ​​of the three parts of longitude, latitude and altitude by splitting the grid index code based on the basic grid index, and then locating the specific grid through the index value to obtain the boundary, volume and other information of the grid.

[0070] Supporting service management includes the operation interface of the database framework, which is mainly used to implement the basic operation functions of the library table, including database creation, table creation, single and batch addition of table data, data query, data association query and other functions.

[0071] Supporting service management also includes calling interfaces for basic grid algorithms and grid encoding and decoding, which facilitates access and calling by external systems.

[0072] Based on the above data storage architecture and supporting service management approach, efficient and accurate space big data processing can be achieved. By integrating advanced database technologies, it can support the rapid storage and retrieval of large-scale space data, ensuring data integrity and accuracy. Furthermore, utilizing supporting service management, it can provide diverse data manipulation and query capabilities to meet the space big data needs of different users. Furthermore, the adopted database architecture provides flexible scalability to accommodate future growth in space big data volumes and the emergence of new business scenarios.

[0073] Example 2:

[0074] Based on Example 1, this example discloses a space big data storage and management method based on equal-area grids, which refines the database tables that make up the database architecture and further optimizes the database architecture. Specifically:

[0075] The basic library includes the basic grid library and the satellite association library, including:

[0076] The basic grid library contains four basic data tables, as shown in Table 1:

[0077] Table 1 Basic grid library table structure

[0078]

[0079] The basic grid library contains four basic data tables, which respectively store grid division results (EQGRID), satellite basic information (SATELLITE), station basic information (STATION) and station capability grid mapping (STATIONGRID); the grid division results mainly store the six boundary values ​​of each grid, as well as the grid number, volume and other information; the satellite basic information stores the basic orbit information of the satellite and some additional information. Since the stored orbit is the number of TLE roots, it needs to be updated at least once a day to update the database; the station basic information stores the station point information and capability range; the station capability grid mapping contains the relationship between the station capability range and the grid.

[0080] The satellite association library table is shown in Table 2:

[0081] Table 2 Satellite association database table structure

[0082]

[0083] Because association information is strongly tied to time, storing data consumes a significant amount of storage space, a typical case of trading space for time. Therefore, the database table design utilizes a dynamic table creation model, storing data on a daily basis, with one table created per day. This ensures data integrity while avoiding the reduction in query efficiency caused by excessive data volume. A single table includes the association time, grid code, association object code, and association object type. This table transforms conventional target association calculations into database table queries, enabling rapid retrieval of association relationships through budgeting and discretized storage.

[0084] The extended library includes the collision warning library and the space environment library, including:

[0085] The collision warning library table is shown in Table 3.

[0086] Table 3 Collision warning library table structure

[0087]

[0088] Collision warning data can include massive amounts of computational data from 1970 to the present. The data volume is so huge that it is not suitable for single-table storage. It can be stored in the same dynamic partitioned table as satellite-related data, with a single table storing one year's data.

[0089] The space environment library table is shown in Table 4:

[0090] Table 4 Space environment library table structure

[0091]

[0092] Space environment data can include massive amounts of computing data from 1970 to the present, and is not suitable for single-table storage. It can be stored in the same dynamic partitioned table method as satellite-related data, with a single table storing one day's data.

[0093] By creating the structure of basic grid library table, satellite association library table, collision warning library table and space environment library table, it is possible to associate the satellite association library table, collision warning library table and space environment library table with the basic grid library table, and build a space big data storage system with clear hierarchy and close association: the basic grid library table serves as a "spatial benchmark", storing core spatial parameters such as grid boundaries, center coordinates, and volume, providing a unified spatial index basis for other libraries.

[0094] The satellite association table uses grid code fields to associate the spatiotemporal relationships between satellites and corresponding grids, leveraging the spatial boundaries of the underlying grid library to rapidly locate satellite orbital dynamics. The collision warning table utilizes grid codes to associate collision targets with spatial grids. Combined with the area uniformity of the underlying grid, this facilitates the precise calculation of collision probability and warning range. The space environment table uses grid codes as a link to anchor environmental parameters such as atmospheric density and gravitational field to specific grids. Leveraging the full coverage of the underlying grid, it supports spatial analysis and modeling of environmental data.

[0095] Example 3:

[0096] Based on Example 1 or 2, this embodiment discloses a space big data storage and management method based on equal-area grids, which refines the process of equally dividing basic grid units by area. Specifically:

[0097] The basic grid unit division is mainly a customized space grid division algorithm based on the equal-area division of the ORDEM2010 model and the transformation of the airspace grid. Its implementation is divided into two parts: basic grid division and height dimension division.

[0098] Among them, the height dimension division scheme is shown in Table 5:

[0099] Table 5 Basic grid unit height dimension division table

[0100]

[0101] The altitude dimension division scheme in Table 5 not only ensures the precision and coverage of different orbital altitude domains, but also balances data accuracy and storage efficiency through differentiated step sizes, providing a scientific altitude dimension division basis for the hierarchical management, orbit prediction and environmental adaptation of space targets.

[0102] The basic grid division mathematical model includes:

[0103] The objective function is:

[0104]

[0105] in, It represents the span difference between different latitude bands. The ultimate goal is to obtain the result with the smallest latitude span difference among all the screened solutions.

[0106] Based on the realization of the above objective function, the latitude band division unit number limit function is:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] in, For the The number of longitude grids divided by the layer latitude band, is the number of latitude zones that the Northern Hemisphere will be divided into, The total number of grid cells in the Northern Hemisphere, Represents a set of integers.

[0115] The latitude band height calculation function is:

[0116] in, For the The length from the intersection of the latitude belt and the Earth's rotation axis to the North Pole;

[0117] The calculation function of the latitude band eigenvalue is:

[0118]

[0119] in, For the The boundary latitude of the layer latitude band close to the equator;

[0120] The latitude band span calculation function is:

[0121]

[0122] in, For the The boundary latitude value of the layer latitude band close to the equator.

[0123] The basic gridding mathematical model embodies the ultimate pursuit of uniform grid spatial distribution and proactively compensates for the effects of Earth curvature. By minimizing the difference in latitude spans through an objective function, the division of each latitude band is ensured to be as balanced as possible. By constraining the number of grid cells in each latitude band, the number of grid cells in low-latitude regions is greater than that in high-latitude regions, adapting to the area differences caused by spherical curvature. Through precise calculation of latitude band heights, eigenvalues, and spans, the boundary latitudes are derived layer by layer starting from the North Pole, ultimately achieving strict equality of grid area at the same altitude around the world. This provides a solid mathematical foundation for unbiased statistics of space data and efficient adaptation to dynamic targets, fundamentally overcoming the spatial distortion defects of traditional longitude and latitude grids.

[0124] Example 4:

[0125] Based on Example 1, 2, or 3, this embodiment discloses a space big data storage and management method based on equal-area grids, and provides a detailed description of the content implementation of supporting service management, specifically:

[0126] Supporting service management includes functions such as implementing operational queries on data in the database architecture, including grid coding based on the longitude, latitude, and altitude of geographic coordinates to obtain basic grid indexes. Specifically:

[0127] The principle of grid coding is to determine the specific grid in which the coordinates are located based on the geographic coordinates (longitude, latitude, altitude) and grid division rules, and represent them as a grid index code in the form of a string. The parameter variables used in the process include:

[0128] lon, the longitude of the geographic coordinate, in degrees (usually -180°~180°);

[0129] lat, the latitude of the geographic coordinate, in degrees (usually -90°~90°);

[0130] alt, the height of the geographic coordinate, in meters;

[0131] level, the number of latitude zones in the Northern Hemisphere.

[0132] Based on the above variables, the implementation steps include:

[0133] S31. Determine the grid index (latindex) in the latitude direction.

[0134] The latitude bands are numbered starting from the North Pole, and there are a total of 2*level latitude bands.

[0135] First, take the absolute value of the latitude to determine the latitude band it is in.

[0136] Traverse the latitude bands divided by the basic grid, and check one by one whether the current latitude is greater than or equal to the boundary value of the latitude band: if the condition is met, preliminarily determine that the latitude is in the current latitude band, and record the index of the latitude band latindex (starting from 1).

[0137] Then, further within the current latitude band, according to the longitude division rule, a specific longitude grid index is determined.

[0138] S32. Determine the grid index (lonindex) in the longitude direction.

[0139] Convert longitude values ​​from [-180°, 180°] to the interval [0°, 360°] by adding 180.0 to all longitudes.

[0140] In the current latitude band, the width of each longitude grid is calculated according to the number of longitude grids in the latitude band divided by the basic grid.

[0141] Traverse the longitude grids within the latitude band and determine in which grid range the converted longitude value falls, thereby determining the longitude grid index lonindex (starting from 1).

[0142] S33. Adjust the latitude index (latindex) to adapt to the northern and southern hemispheres.

[0143] If the input latitude lat is a negative value (that is, it is located in the Southern Hemisphere), the latitude index needs to be converted using a formula so that the numbering rule starting from the North Pole is adapted to the Southern Hemisphere. The formula is:

[0144]

[0145] S34. Determine the grid index (altindex) in the height direction.

[0146] According to the tabular results of the altitude dimension division scheme, the altitude direction is divided into multiple levels: low altitude domain (LAS), low orbit domain (LEO), medium orbit domain (MEO) and high orbit domain (GEO).

[0147] First, determine whether the height alt exceeds the range of the highest layer (GEO): If alt is greater than the maximum height, set the height index to 999, indicating that it exceeds the grid range. Otherwise, check the layers where alt is located from low to high:

[0148] Low Altitude Area (LAS): If alt is within the low altitude range, the grid index within this level is obtained by calculating the difference between alt and the lowest altitude of the low altitude and dividing it by the grid step size of the low altitude.

[0149] Low Earth Orbit (LEO): If alt exceeds the low altitude range but is within the LEO range, the number of grids in the low altitude is accumulated first, and then the grid index in the LEO range is calculated.

[0150] Medium Orbit (MEO): If alt is outside the LEO range but within the MEO range, the number of grids in low altitude and LEO is accumulated first, and then the grid index in MEO is calculated.

[0151] High orbit domain (GEO): If alt is outside the MEO range but within the GEO range, first accumulate the number of grids in low altitude, LEO and MEO, and then calculate the grid index in GEO.

[0152] S35. Finally, the longitude grid index lonindex, the latitude grid index latindex, and the altitude grid index altindex are concatenated into a string in a fixed format. For example, the format is: "%02d%02d%03d", that is:

[0153] The longitude index occupies 2 digits, and if it is less than the value, it will be padded with 0 in front. The latitude index occupies 2 digits, and if it is less than the value, it will be padded with 0 in front. The altitude index occupies 3 digits, and if it is less than the value, it will be padded with 0 in front.

[0154] Furthermore, the implementation of data operation and query functions in the database architecture also includes the principle of grid decoding based on grid index, which is to extract the index values ​​of the three parts (longitude, latitude, and altitude) by splitting the grid index code, and then locate the specific grid through the index value to obtain the boundary, volume and other information of the grid. The steps include:

[0155] S36. Extract the index values ​​of the three parts (longitude, latitude, and altitude) from the grid index code:

[0156] Longitude index (lonindex): Take the first two characters and convert them to an integer (e.g. "05" → 5).

[0157] Latitude index (latindex): Take the middle two characters and convert them to an integer (for example, "12" → 12).

[0158] Altindex: Take the last three characters and convert them to an integer (e.g. "003" → 3).

[0159] S37. Determine the northern and southern hemispheres: If latindex is greater than the number of divided latitude bands, it means that the grid is located in the southern hemisphere and needs to be converted to the corresponding northern hemisphere index value trueLatIndex:

[0160]

[0161] S38. Determine the latitude boundary according to the index value.

[0162] First, obtain the latitude band object: According to trueLatIndex, obtain the detailed information of the corresponding latitude band from the grid division result, which includes the boundary latitude characteristic value lat and the number of longitude grids num.

[0163] Handling special latitude bands: the upper boundary latitude of the first latitude band is fixed at 90° (North Pole), and the lower boundary latitude is the latitude characteristic value of the first latitude band in the grid division result; the last latitude band: the upper boundary latitude is the negative latitude characteristic value of the first latitude band, and the lower boundary latitude is fixed at -90° (South Pole).

[0164] Regular latitude band (not the first or last): Gets the latitude characteristic value (upper boundary) of the previous latitude band and the latitude characteristic value (lower boundary) of the latitude band pointed to by the current index value.

[0165] Then, determine the longitude boundary. Based on the latitude band information located in the previous step, the number of grids in the current latitude band can be obtained from the basic grid division information. The longitude span of each grid, i.e. the longitude step length, is calculated as for:

[0166]

[0167] Then calculate the longitude range of the current grid, the upper boundary for:

[0168]

[0169] Lower boundary for:

[0170]

[0171] Then, determine the height boundaries.

[0172] Positioning altitude domain: Determine the altitude layer based on altindex. If altindex is less than the number of altitude divisions in the low-altitude domain, it is determined to be in the low-altitude domain. If altindex is less than the sum of the number of low-altitude domain and low-orbit domain divisions and greater than the number of low-altitude domain divisions, it is determined to be in the low-orbit domain. This is repeated to finally determine the altitude domain.

[0173] Locate the upper (maxalt) and lower (minalt) height boundaries:

[0174] The target grid is obtained by the index value of the height in the current height domain. For example, when the current index value is located in the middle orbital domain, its height level in the middle orbital domain is altindex minus the sum of the height divisions of the low orbital domain and the low airspace domain. Let it be index and the lower limit of the middle orbital domain height is minaltInMEO, and the step size of the middle orbital domain height division is stepInMEO, then the upper and lower boundaries can be calculated:

[0175]

[0176]

[0177] Similarly, the processing method for locating other height domains is the same as the above method, and the height boundary of the grid can be obtained.

[0178] This embodiment achieves accurate mapping of geographic coordinates and grids through specific steps, providing key technical support for efficient management and query of space big data.

[0179] The present invention also provides an electronic device, Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the electronic device may include: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor may call logic instructions in the memory, for example, to execute the following method:

[0180] S1. Divide the basic grid cells with equal areas at the same height;

[0181] S2. Form a space big data storage database architecture based on basic grid units;

[0182] S3: Provide supporting service management for space big data based on database architecture.

[0183] In addition, the logical instructions in the aforementioned memory can be implemented in the form of a software functional unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0184] An embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method provided in each of the above embodiments is implemented, for example, including:

[0185] S1. Divide the basic grid cells with equal areas at the same height;

[0186] S2. Form a space big data storage database architecture based on basic grid units;

[0187] S3: Provide supporting service management for space big data based on database architecture.

[0188] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0189] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A space big data storage and management method based on equal-area grids, characterized in that: include: S1. Divide the basic grid cells with equal areas at the same height; S2. Form a space big data storage database architecture based on basic grid units; S3, provides supporting service management for space big data based on database architecture; Among them, the database architecture uses Mysql as the backend database, and the database includes a basic library and an extended library. The basic library includes a basic grid library and a satellite association library, and the extended library includes a collision warning library and a space environment library.

2. The space big data storage and management method based on equal-area grids according to claim 1 is characterized in that: The extension library can be expanded based on actual space big data.

3. The space big data storage and management method based on equal-area grids according to claim 2 is characterized in that: The database tables of the basic grid database include a grid division result table, a satellite basic information table, a station basic information table and a station capability grid mapping table.

4. The space big data storage and management method based on equal-area grids according to claim 2 is characterized in that: The satellite association library table includes: association time, grid code, association object code and association object type fields.

5. The space big data storage and management method based on equal-area grids according to claim 2 is characterized in that: The collision warning library and the space environment library are stored in dynamic sub-tables; Among them, the collision warning library single table stores one year's data, and the space environment library single table stores one day's data.

6. The space big data storage and management method based on equal-area grids according to claim 1 is characterized in that: The division is performed into basic grid units of equal area at the same height; Based on the ORDEM2010 model, the basic grid unit division includes basic grid division and height dimension division.

7. The space big data storage and management method based on equal-area grids according to claim 6 is characterized in that: The height dimension division includes: Low altitude area, the altitude is 0 meters to 186,000 meters, and the altitude step is 6,000 meters; Low orbit range: 186,000 meters to 2,286,000 meters, with an altitude step of 25,000 meters; Medium orbit range: 2,286,000 meters to 34,786,000 meters, with an altitude step of 500,000 meters; High orbit area, 34786000 meters ~, altitude step length 50000 meters, number of altitude divisions 40.

8. The space big data storage and management method based on equal-area grids according to claim 6 is characterized in that: The basic grid division mathematical model includes: The objective function is: ； in, is the span difference between different latitude zones; The function limiting the number of units divided by latitude bands is: ； ； ； ； ； ； ； in, For the The number of longitude grids divided by the layer latitude band, is the number of latitude zones that the Northern Hemisphere will be divided into, The total number of grid cells in the Northern Hemisphere, Represents a set of integers; The latitude band height calculation function is: ； in, For the The length from the intersection of the latitude belt and the Earth's rotation axis to the North Pole; The calculation function of the latitude band eigenvalue is: ； in, For the The boundary latitude of the layer latitude band close to the equator; The latitude band span calculation function is: ； in, For the The boundary latitude value of the layer latitude band close to the equator.

9. The space big data storage and management method based on equal-area grids according to claim 1 is characterized in that: The S3 performs supporting service management for space big data based on the database architecture, including grid coding based on the longitude, latitude and altitude of geographic coordinates to obtain a basic grid index, including the following steps: S31. Determine the grid index in the latitude direction of the northern hemisphere; S32, determining the grid index of the longitude direction of the northern hemisphere; S33, adjust the grid index to adapt to the southern hemisphere; S34, determining a grid index in a height direction; S35. Finally, the longitude grid index, the latitude grid index, and the altitude grid index are spliced ​​together according to a fixed format to obtain a basic grid index.

10. The space big data storage and management method based on equal-area grids according to claim 1, characterized in that: The S3 performs supporting service management for space big data based on the database architecture, including grid decoding based on the basic grid index to obtain latitude boundaries, longitude boundaries and altitude boundaries, including the following steps: S36. Extracting index values ​​of longitude, latitude, and altitude from the grid index code; S37, determine the northern and southern hemispheres, and convert the value into the northern hemisphere index; S38. Determine the latitude boundary, longitude boundary, and altitude boundary based on the index value.

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