CN114048204A - Beidou grid space indexing method and device based on database inverted index - Google Patents
Beidou grid space indexing method and device based on database inverted index Download PDFInfo
- Publication number
- CN114048204A CN114048204A CN202111143748.3A CN202111143748A CN114048204A CN 114048204 A CN114048204 A CN 114048204A CN 202111143748 A CN202111143748 A CN 202111143748A CN 114048204 A CN114048204 A CN 114048204A
- Authority
- CN
- China
- Prior art keywords
- grid
- beidou
- space
- level
- database
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000004590 computer program Methods 0.000 claims description 7
- DMBHHRLKUKUOEG-UHFFFAOYSA-N diphenylamine Chemical compound C=1C=CC=CC=1NC1=CC=CC=C1 DMBHHRLKUKUOEG-UHFFFAOYSA-N 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004806 packaging method and process Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000007493 shaping process Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Remote Sensing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the disclosure provides a Beidou grid space indexing method and device based on database inverted index, wherein the method comprises the following steps: acquiring a target query range, and determining a Beidou grid set corresponding to the target query range; and for each Beidou grid in the Beidou grid set, determining a space object corresponding to the Beidou grid in a relational database based on a GIN inverted index, traversing all the Beidou grids in the Beidou grid set, determining the corresponding space object, and generating a space object set. In this way, the amount of index data can be reduced in the process of querying the space object, and the calculation overhead is reduced.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of geographic information processing, and more particularly to a Beidou grid spatial indexing method and device based on database inverted index.
Background
A traditional spatial database is combined with an R-Tree to construct an index method aiming at spatial data types (geometry), so that the requirement for improving the retrieval performance is met.
The core idea of the R-Tree index is to aggregate nodes with similar distances and represent the nodes as the minimum bounding rectangles of the nodes at the upper layer of the Tree structure, and the minimum bounding rectangles become the nodes of the upper layer. Since all nodes are in their minimum bounding rectangle, queries that are disjoint from a rectangle must be disjoint from all nodes in that rectangle. Each rectangle on a leaf node represents an object, the nodes are aggregates of objects, and the more objects aggregated up the top level.
All the R-Tree index table records are stored on leaf nodes, the space occupied by the leaf nodes reaches one page in the database, the leaf nodes are divided to generate intermediate nodes, the space occupied by the intermediate nodes reaches one page in the database, and the intermediate nodes are continuously divided, so that more intermediate nodes are generated along with the increase of the data volume, and the index is gradually increased.
Meanwhile, the minimum circumscribed rectangle can only estimate the relationship between spatial data with coarse granularity, and has a certain precision error, so that in order to meet the requirement of correctness, recheck operation needs to be further executed to accurately position the relationship between the calculated data, which increases the calculation overhead and reduces the retrieval performance.
Disclosure of Invention
According to the embodiment of the disclosure, the Beidou grid spatial index scheme based on the database inverted index is provided, and the index data volume is small, and the calculation cost is low.
In a first aspect of the disclosure, a Beidou grid space indexing method based on database inverted index is provided, which includes:
acquiring a target query range, and determining a Beidou grid set corresponding to the target query range;
and for each Beidou grid in the Beidou grid set, determining a space object corresponding to the Beidou grid in a relational database based on a GIN inverted index, traversing all the Beidou grids in the Beidou grid set, determining the corresponding space object, and generating a space object set.
In some embodiments, the Beidou grid set corresponding to the target query range comprises grids of the same and/or different grid levels.
In some embodiments, the coordinate information in the relational database is stored in a grid cell form, the grid cell form is stored by using a tb _ location table as a model, the tb _ location table includes longitude and latitude and elevation fields of a coordinate system and grid fields, and the grid fields include a grid level field level, a longitude and latitude subdivision coding field code and an elevation subdivision coding field zcode.
In some embodiments, the method further comprises:
the spatial objects in the relational database are stored in the form of grid unit sets, wherein the grid unit sets include longitude and latitude subdivision coding fields code and grid level field level of grid units in corresponding two-dimensional spatial objects, or the longitude and latitude subdivision coding fields code, the grid level field level and the elevation subdivision coding field zcode of the grid units in three-dimensional spatial objects.
In some embodiments, the method further comprises:
the method comprises the steps of establishing indexes of space objects and grid cells in advance, and establishing GIN (graphic information network) inverted indexes of the space objects and the grid cells based on grid cells gridcell.
In some embodiments, the pre-indexing spatial objects and the index for establishing grid cells, and the establishing of the GIN inverted index for spatial objects and grid cells based on grid cells gridcell includes:
the method comprises the steps of determining a minimum grid level grid unit contained in each space object in advance, establishing indexes of the space objects and the minimum grid level grid unit, and establishing GIN (graphic information network) inverted indexes of the space objects and the grid units based on the minimum grid level grid unit.
In some embodiments, the method further comprises:
and establishing indexes among grid cells gridcell based on a hierarchical relationship in an interval range corresponding to the grid level corresponding to the minimum grid level grid cell.
In a second aspect of the present disclosure, a Beidou grid spatial indexing device based on database inverted index is provided, including:
the query request receiving module is used for acquiring a target query range and determining a Beidou grid set corresponding to the target query range;
and the space object set generation module is used for determining a space object corresponding to each Beidou grid in the Beidou grid set in a relational database based on the GIN inverted index, traversing all the Beidou grids in the Beidou grid set, determining the corresponding space object and generating a space object set.
In a third aspect of the present disclosure, an electronic device is provided, comprising a memory having stored thereon a computer program and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method as set forth above.
According to the Beidou grid space indexing method based on the database inverted index, the index data volume can be reduced, and the calculation cost is reduced.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
fig. 1 shows a flowchart of a first method for indexing a Beidou grid space based on an inverted database index according to an embodiment of the present disclosure;
fig. 2 shows a flowchart of a method for generating a spatial database based on a Beidou grid relational database according to a second embodiment of the disclosure;
fig. 3 shows a schematic structural diagram of a third example of the present disclosure, which is based on a Beidou grid spatial indexing device with inverted database indexing;
fig. 4 shows a schematic structural diagram of a Beidou grid spatial indexing device based on database inverted index in the fourth embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
According to the Beidou grid space indexing method based on the inverted index of the database, the index data volume can be reduced in the process of inquiring the space object, and the calculation cost is reduced. Specifically, as shown in fig. 1, the method is a flowchart of a first method for indexing a Beidou grid space based on a database inverted index according to an embodiment of the present disclosure. As can be seen from fig. 1, the Beidou grid space indexing method based on the database inverted index of the embodiment may include the following steps:
s101: and acquiring a target query range, and determining a Beidou grid set corresponding to the target query range.
The method of this embodiment may be applied to query space objects in a certain area range, such as how many schools are in a certain urban area, or how many cells are in the same area. The urban area is the target query range mentioned in this embodiment, and the school (or cell) is the space object mentioned in this embodiment. The method of this embodiment is based on a pre-constructed Beidou grid relational database, and when constructing the Beidou grid relational database, reference may be made to a flowchart of a method for generating a spatial database based on the Beidou grid relational database shown in fig. 2, and specifically includes the following steps:
s201: acquiring two-dimensional coordinate information or three-dimensional coordinate information in a relational database, wherein the two-dimensional coordinate information or the three-dimensional coordinate information is stored in the relational database in a tb _ location table form.
The method comprises the steps of firstly obtaining two-dimensional coordinate information or three-dimensional coordinate information in a relational database, wherein the two-dimensional coordinate information or the three-dimensional coordinate information is stored in the relational database in a tb _ location table form.
S202: and determining the corresponding Beidou subdivision grid code hierarchy according to the actual application scene of the two-dimensional coordinate information or the three-dimensional coordinate information, and generating a corresponding hierarchy field level.
After the two-dimensional coordinate information or the three-dimensional coordinate information in the relational database is obtained, the corresponding Beidou subdivision grid code hierarchy needs to be further determined, and a corresponding hierarchy field level is generated.
S203: and converting the two-dimensional coordinate information or the three-dimensional coordinate information into Beidou subdivision grid codes of corresponding levels, and generating corresponding longitude and latitude subdivision code fields and elevation subdivision code fields zcode.
S204: and packaging the level field level, the longitude and latitude subdivision coding field code and the elevation subdivision coding field zcode into a same field grid, storing the field grid in a tb _ location table corresponding to two-dimensional coordinate information or three-dimensional coordinate information in the relational database, and generating grid cells.
The grid cells gridcell are of a composite type and are used for representing a 2D or 3D unit grid in the Beidou subdivision, and the 2D or 3D unit grid is coordinate information in the relational data. The Beidou subdivision grid is divided into a 2D grid and a 3D grid, wherein the 2D grid is subjected to binary subdivision through latitude and longitude respectively, and then combined into one-dimensional binary codes through Morton codes. The grid level is from 1-32 levels, each level is expressed by 2 binary bits, and 32-level two-dimensional codes can be stored by using 64-bit unsigned Long shape (unsigned Long). The 3D grid is added with elevation subdivision codes on the basis of the 2D grid, the range of elevation subdivision is also 1-32 levels, and 1 binary bit represents the first-level subdivision. A 32-bit unsigned Int may be used to store a 32-level program code. The hierarchy ranges from 1 to 32, and an unscheduled char can be used to store the hierarchy; since gridcell is compatible with 2D/3D, a type mark dim needs to be specified to distinguish two-dimensional grids from three-dimensional grids.
In summary, the structure of gridcell data types is as follows:
the variables in the structure are as follows:
in either 32-bit or 64-bit operating systems, after memory alignment, the memory space occupied by a gridcell object is 16 bytes.
And two three-dimensional gridcell generating functions are respectively realized, and the prototype is as follows:
gridcell ST_AsGridcell(double lng,double lat,integer level)
gridcell ST _ AsGridcell3D (double ng, double lat, double height, integer level) takes ST _ AsGridcell3D as an example to illustrate the calculation steps:
tb _ location is a table in the relational database, and long, lat, height are coordinates longitude, latitude, elevation, respectively.
2. Adding a field named grid with the type of gridcell to the tb _ location table
3. Determining the level of the grid according to the business requirement
4. The grid field in tb _ location is updated by the update statement.
Update tb_location set grid＝st_AsGridcell(lng,lat,height,level)。
5. And in st _ AsGridcell, for the lng, lat, height and level transmitted into each group, generating a corresponding longitude and latitude subdivision code and an elevation subdivision code zcode through a Beidou mesh subdivision algorithm, and packaging the longitude and latitude subdivision code and the elevation subdivision code together with the level into a gridcell object to return.
Serializing each gridcell object by an Update statement and writing the serialized gridcell object into a grid field
7. The execution loops until all rows in the table are executed.
By the method, the coordinate information in the relational database can be converted into the form of the Beidou subdivision grid code and stored. And the coordinate information in the Beidou grid code form and the original coordinate information in the relational database are packaged together, so that the data packaging property and consistency are ensured, the data management is convenient, and meanwhile, the conversion technology of the coordinate information coding form is completed in the database, so that the parallel computing capability of the database can be fully utilized.
And in addition, a space object is further defined in the Beidou grid relational database, the space object in the relational database is stored in a form of grid unit sets georgrid, and the grid unit sets georgrid comprise longitude and latitude subdivision coding fields code and grid level fields level of grid units in corresponding two-dimensional space objects, or the longitude and latitude subdivision coding fields code, the grid level fields level and the elevation subdivision coding fields zcode of the grid units in the three-dimensional space objects. Different levels of unit grids may exist in a grid unit set georgrid, and the level of the grid unit with the highest level is the level of the georgrid object and is called detailLevel. That is, in the beidou grid relational database, the spatial objects are in one-to-one correspondence with the grid cell sets georgrids. In order to realize the one-to-one correspondence between the space object and the grid cell set georgrid, indexes of the space object and the grid cells can be established, and GIN inverted indexes of the space object and the grid cells are established based on the grid cells gridcell. Specifically, a minimum grid level grid unit contained in each space object is predetermined, indexes of the space objects and the minimum grid level grid unit are established, and GIN inverted indexes of the space objects and the grid units are established based on the minimum grid level grid unit. And establishing indexes among grid cells gridcell based on a hierarchical relationship in the interval range corresponding to the grid level corresponding to the minimum grid level grid cell.
In short, one spatial data record corresponds to one piece of georgrid data, one piece of georgrid data corresponds to a plurality of pieces of gridcell data, and each piece of gridcell data and grid data related to the gridcell are stored in the index structure as key values of the GIN index.
After the description of the data structure in the Beidou grid relational database, a specific example is taken as an example below to further describe the scheme of the embodiment of the present disclosure. Firstly, establishing an association relation between a space object and a grid unit set georgrid, wherein the space object is not limited to schools and cells, but also can be other objects such as districts, lakes, overpasses and the like. Taking schools as an example, the Beidou grids included in each school are determined, and the included Beidou grids are the grids with the lowest hierarchy, for example, an 8-level grid includes 4 9-level grids, and the 8-level grid and the 4 9-level grids included in the 8-level grid are all in the region of the school, only the association relationship between the school and the 8-level grid is established, and the association relationship between the school and the other 4 9-level grids is not established, that is, the region in the school is represented by codes corresponding to the 8-level grids, and the region in the school is represented by codes corresponding to the 4 9-level grids. Through the mode, the Beidou grids contained in all schools are established, and similarly, the Beidou grids contained in all cells can be determined. Then according to the same method, Beidou grids contained in a region larger than a school region, such as a city, a village and a town, a county, a city, a province and other prefectures, or Beidou grids contained in a region smaller than the school region, such as a dormitory, a dining room, a teaching building and the like, can be established, and Beidou grids contained in parking spaces, green belts and the like can be established in a cell. Then, indexes of the space objects and the minimum grid level grid units are established, and GIN inverted indexes of the space objects and the grid units are established based on the minimum grid level grid units. And establishing indexes among grid cells gridcell based on a hierarchical relationship in the interval range corresponding to the grid level corresponding to the minimum grid level grid cell. GIN (Generalized Inverted Index) is an Index structure that stores a set of pairs (keys), where a key is a key value and a nesting list is a set of TIDs (thread control symbols) in which the key has occurred. For each attribute in the table, each item may be resolved into multiple keys when the index is built, the same TID may appear in multiple posting lists, and each key value is stored only once, so the GIN index is very compact in the case that the same key appears in the item multiple times.
S102: and for each Beidou grid in the Beidou grid set, determining a space object corresponding to the Beidou grid in a relational database based on a GIN inverted index, traversing all the Beidou grids in the Beidou grid set, determining the corresponding space object, and generating a space object set.
In this embodiment, the target query range may be one spatial object or a set of several spatial objects, or may be one region, and after the Beidou grid set corresponding to the target query range is determined, for each Beidou grid (the Beidou grid is the Beidou grid of the minimum grid level) in the Beidou grid set, based on the GIN inverted index, the spatial object corresponding to the Beidou grid, that is, the spatial object having an index relationship with the grid is determined in the relational database, for example, if the grid is in one spatial object, the spatial object having an index relationship is one, the grid is partially in one spatial object, and is partially in another spatial object, the spatial objects having an index relationship are two, and similarly, the spatial objects having an index relationship with the grid may also be multiple. And traversing all the Beidou grids in the Beidou grid set according to the same mode, determining corresponding spatial objects, and generating a spatial object set so as to determine the pre-query spatial objects existing in the target query range.
The above process of this embodiment is only an illustration of the principle of the query process, and for querying different types of spatial objects, for example, how to determine whether the queried spatial object is a cell or a school, a field may be added in the grid cell set georgrid corresponding to the spatial object, where the field represents the attribute of the control object, such as a school, a cell, and the like, so that, in the process of querying the spatial object, the query may be performed only from the school or only from the cell.
Because the number of the grid units in the whole world is also fixed and unchanged, the phenomenon of abrupt increase of index data can not occur along with the increase of data volume, so that the index data volume can be reduced, the accurate judgment on the Beidou split code grid index relationship can be realized, the data relationship can be accurately matched through a binary bit operation method of shaping data in the index retrieval process, further relationship judgment is not needed, the calculation cost is reduced, and the query performance is improved.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily essential to the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
As shown in fig. 3, is a schematic structural diagram of a third example of the present disclosure of a Beidou grid spatial indexing device based on database inverted index. The big dipper grid space indexing means based on database inverted index of this embodiment includes:
the query request receiving module 301 is configured to obtain a target query range, and determine a Beidou grid set corresponding to the target query range.
The spatial object set generating module 302 is configured to determine, based on the GIN inverted index, a spatial object corresponding to each beidou mesh in the beidou mesh set in the relational database, traverse all beidou meshes in the beidou mesh set, determine a corresponding spatial object, and generate a spatial object set.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 4 shows a schematic block diagram of an electronic device 400 that may be used to implement an embodiment method of the present disclosure. As shown, the arrangement 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)402 or loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A Beidou grid space indexing method based on database inverted indexes is characterized by comprising the following steps:
acquiring a target query range, and determining a Beidou grid set corresponding to the target query range;
and for each Beidou grid in the Beidou grid set, determining a space object corresponding to the Beidou grid in a relational database based on a GIN inverted index, traversing all the Beidou grids in the Beidou grid set, determining the corresponding space object, and generating a space object set.
2. The spatial indexing method for Beidou grids based on inverted database indexes according to claim 1, wherein the Beidou grid sets corresponding to the target query range comprise grids of the same and/or different grid levels.
3. The Beidou grid space indexing method based on inverted indexes of the database as claimed in claim 1, wherein coordinate information in the relational database is stored in a grid unit gridcell form, the grid unit gridcell is stored by taking a tb _ location table as a model, the tb _ location table comprises longitude and latitude and elevation fields and grid fields of a coordinate system, wherein the grid field comprises a grid level field level, a longitude and latitude subdivision coding field code and an elevation subdivision coding field zcode.
4. The method of claim 3, further comprising:
the spatial objects in the relational database are stored in the form of grid unit sets, wherein the grid unit sets include longitude and latitude subdivision coding fields code and grid level field level of grid units in corresponding two-dimensional spatial objects, or the longitude and latitude subdivision coding fields code, the grid level field level and the elevation subdivision coding field zcode of the grid units in three-dimensional spatial objects.
5. The method of claim 4, further comprising:
the method comprises the steps of establishing indexes of space objects and grid cells in advance, and establishing GIN (graphic information network) inverted indexes of the space objects and the grid cells based on grid cells gridcell.
6. The Beidou grid space indexing method based on the database inverted index as set forth in claim 5, wherein the pre-indexing of the space object and the index for establishing the grid cells and the establishing of the GIN inverted index of the space object and the grid cells based on the grid cells gridcell comprise:
the method comprises the steps of determining a minimum grid level grid unit contained in each space object in advance, establishing indexes of the space objects and the minimum grid level grid unit, and establishing GIN (graphic information network) inverted indexes of the space objects and the grid units based on the minimum grid level grid unit.
7. The method of claim 6, further comprising:
and establishing indexes among grid cells gridcell based on a hierarchical relationship in an interval range corresponding to the grid level corresponding to the minimum grid level grid cell.
8. The utility model provides a big dipper net space indexing device based on database inverted index which characterized in that includes:
the query request receiving module is used for acquiring a target query range and determining a Beidou grid set corresponding to the target query range;
and the space object set generation module is used for determining a space object corresponding to each Beidou grid in the Beidou grid set in a relational database based on the GIN inverted index, traversing all the Beidou grids in the Beidou grid set, determining the corresponding space object and generating a space object set.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111143748.3A CN114048204A (en) | 2021-09-28 | 2021-09-28 | Beidou grid space indexing method and device based on database inverted index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111143748.3A CN114048204A (en) | 2021-09-28 | 2021-09-28 | Beidou grid space indexing method and device based on database inverted index |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114048204A true CN114048204A (en) | 2022-02-15 |
Family
ID=80204998
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111143748.3A Pending CN114048204A (en) | 2021-09-28 | 2021-09-28 | Beidou grid space indexing method and device based on database inverted index |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114048204A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114238384A (en) * | 2022-02-24 | 2022-03-25 | 阿里云计算有限公司 | Area positioning method, device, equipment and storage medium |
| CN114820989A (en) * | 2022-06-24 | 2022-07-29 | 中国空气动力研究与发展中心计算空气动力研究所 | Method for quickly establishing non-structural grid coplanar relation based on inverted index |
| CN115374237A (en) * | 2022-10-26 | 2022-11-22 | 北京数字政通科技股份有限公司 | Vector space data storage and query method based on Beidou grid code |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714145A (en) * | 2013-12-25 | 2014-04-09 | 中国地质大学（武汉） | Relational and Key-Value type database spatial data index method |
| CN109992636A (en) * | 2019-03-22 | 2019-07-09 | 中国人民解放军战略支援部队信息工程大学 | Space-time code method, temporal index and querying method and device |
| CN110147377A (en) * | 2019-05-29 | 2019-08-20 | 大连大学 | General polling algorithm based on secondary index under extensive spatial data environment |
| CN112347118A (en) * | 2021-01-08 | 2021-02-09 | 阿里云计算有限公司 | Data storage, query and generation method, database engine and storage medium |
-
2021
- 2021-09-28 CN CN202111143748.3A patent/CN114048204A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714145A (en) * | 2013-12-25 | 2014-04-09 | 中国地质大学（武汉） | Relational and Key-Value type database spatial data index method |
| CN109992636A (en) * | 2019-03-22 | 2019-07-09 | 中国人民解放军战略支援部队信息工程大学 | Space-time code method, temporal index and querying method and device |
| CN110147377A (en) * | 2019-05-29 | 2019-08-20 | 大连大学 | General polling algorithm based on secondary index under extensive spatial data environment |
| CN112347118A (en) * | 2021-01-08 | 2021-02-09 | 阿里云计算有限公司 | Data storage, query and generation method, database engine and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114238384A (en) * | 2022-02-24 | 2022-03-25 | 阿里云计算有限公司 | Area positioning method, device, equipment and storage medium |
| CN114820989A (en) * | 2022-06-24 | 2022-07-29 | 中国空气动力研究与发展中心计算空气动力研究所 | Method for quickly establishing non-structural grid coplanar relation based on inverted index |
| CN115374237A (en) * | 2022-10-26 | 2022-11-22 | 北京数字政通科技股份有限公司 | Vector space data storage and query method based on Beidou grid code |
| CN115374237B (en) * | 2022-10-26 | 2023-01-03 | 北京数字政通科技股份有限公司 | Vector space data storage and query method based on Beidou grid code |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114048204A (en) | Beidou grid space indexing method and device based on database inverted index | |
| CN104199986B (en) | Vector data space index method based on hbase and geohash | |
| CN112860689B (en) | Platform system based on space-time grid data | |
| CN107153711A (en) | Geographic information data processing method and processing device | |
| CN108009265B (en) | Spatial data indexing method in cloud computing environment | |
| CN103425772A (en) | Method for searching massive data with multi-dimensional information | |
| CN114048203A (en) | Beidou grid space indexing and retrieval method and device based on database B-tree index | |
| CN104216961A (en) | Method and device for data processing | |
| CN114676368B (en) | Geographic entity information display method, device, equipment and medium based on identification code | |
| CN113268557B (en) | Rapid spatial index method suitable for display-oriented visual analysis | |
| CN112214472B (en) | Meteorological lattice data storage and query method, device and storage medium | |
| CN114048271A (en) | Storage method and device of Beidou grid data model in database | |
| CN106991149B (en) | Massive space object storage method fusing coding and multi-version data | |
| US20230281182A1 (en) | R-tree index merging and updating method and apparatus based on hilbert curve, and medium | |
| WO2022147923A1 (en) | Block network safety organization storage mapping method for spatial data | |
| Zhang et al. | Improving NoSQL storage schema based on Z-curve for spatial vector data | |
| CN116126942B (en) | Multi-dimensional space meteorological grid data distributed storage query method | |
| Jing et al. | An improved distributed storage and query for remote sensing data | |
| Li et al. | Method for managing and querying geo-spatial data using a grid-code-array spatial index | |
| CN106503811A (en) | A kind of infrastructure full life cycle management method based on big data | |
| CN106649425B (en) | Vector space data coding method considering spatial proximity | |
| Wu et al. | A spatiotemporal trajectory data index based on the Hilbert curve code | |
| CN110297952B (en) | Grid index-based parallelization high-speed railway survey data retrieval method | |
| CN110765298B (en) | Tile coding method for decoupling geometric attributes of vector data | |
| CN103106254B (en) | The parallel joining method of polygon vector data file |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |