CN114554592A - Optimized positioning method and device for combining communication big data rasterization algorithm with Gis engine - Google Patents
Optimized positioning method and device for combining communication big data rasterization algorithm with Gis engine Download PDFInfo
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Abstract
The invention belongs to the technical field of communication, and relates to an optimal positioning method and system for a communication big data rasterization algorithm combined with a Gis engine. According to the method, on the basis of optimizing the structure and the included information of a basic fingerprint library, a Gis engine in a PostGis spatial index technology is introduced into a communication big data rasterization positioning system, an intersection region R of all base station coverage regions covering sampling point positions acquired by the PostGis spatial index technology is used as a scanning region, then Euclidean distance calculation and sequencing are carried out on grids in the intersection region R, and finally a positioning result is obtained. The method utilizes the PostGis spatial index technology to greatly reduce the calculation times of the Euclidean distance, solves the problem of low data processing efficiency of the existing rasterization positioning system, greatly improves the performance efficiency of the rasterization positioning of the large communication data, and greatly reduces the system overhead.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to an optimal positioning method, system and device for a communication big data rasterization algorithm combined with a Gis engine and application thereof.
Background
With the continuous development of the scale of the mobile communication network, the market competition is increasingly intensified, and the requirements of users on the network coverage area and the network signal quality are higher and higher, so that the wireless network optimization work becomes the focus of the mobile communication network back-end operation system, and a grid positioning scheme based on the three-point positioning principle is produced. The network optimization system based on the rasterized data performs rasterization processing on huge analysis data, and performs statistical analysis by taking a geographic grid as a basic unit.
In terms of the current situation of optimization software at home and abroad, a rasterization positioning system with a mature market comprises a fingerprint database, a real-time MRO, signaling data and the like, and the technical architecture of the rasterization positioning system adopts a Hadoop ecological big data frame or a GreenPlum ecological data warehouse to realize sampling point correlation matching and minimum Euclidean distance calculation and return to a final positioning grid. However, when the rasterization positioning system is applied to a large city, because the area of the city is large, the number of grids and base stations is large, the coverage condition is complex, and the large data technology is difficult to realize rapid processing in the presence of a huge data volume, at present, data accumulated in one day often needs two days or even several days to complete calculation under the large data environment of a common operator server cluster, and if the daily data needs to be analyzed immediately on the same day, the low data processing efficiency becomes a problem which needs to be solved urgently.
Disclosure of Invention
In order to solve the problem that the data processing efficiency of the existing rasterization positioning system is low, the invention provides a set of solution.
Construction of the base fingerprint library (see fig. 1): the network coverage area is divided into a plurality of grids according to a preset rule, and the area and the shape of the grids are determined according to the data precision requirement of the application, the distribution position of the base station and the signal density of the area. The coverage area of the network comprises a plurality of base stations, each base station is different according to the transmitting frequency, and the coverage area of each base station is a circular area with different radiuses. All grids contained in a network coverage area jointly form a basic fingerprint library, and data information recorded in the basic fingerprint library comprises an identifier of each grid, a main service base station ID covering the grid, a main service base station level value RSRP and RSRQ, and an adjacent service base station ID, an adjacent service base station level value RSRP and RSRQ adjacent to the main service base station.
MR data acquisition of a communication base station (base station) for a sampling point: the sampling point MR (measurement result) data which needs to be collected by the base station comprises user information (unique identifier IMSI or IMEI), a main service base station ID + RSRP/RSRQ corresponding to the sampling point, a temporary service base station ID + RSRP/RSRQ and data reporting time. The sampling point must be covered by the main service base station and the adjacent service base station at the same time, and the number of the main service base station and the adjacent service base station is more than or equal to 3.
Rasterized three-point positioning principle (see fig. 2): knowing that the two-dimensional coordinate plane contains the point P (Px, Py) at known distances from the base stations a (Ax, Ay), B (Bx, By), C (Cx, Cy), the unique coordinates (Px, Py) of the point P can be determined By the three-point positioning principle. The rasterization positioning utilizes the three-point positioning principle to convert the distance into level values (ltescrrp, ltencrrp), and converts the unique intersection point (the P point where the three circles in fig. 2 strictly intersect) of the coverage area of the three base stations into the position of the grid with the minimum Euclidean distance with the P point as the positioning result.
And (3) rasterization positioning process: performing correlation query on the level values of the main service base station and the adjacent service base station in the MR data of the specific sampling point and the level values of the main service base station and the adjacent service base station in the basic fingerprint database to obtain a result set; grouping (group by) the result set according to a specified rule (main service base station, timestamp, S1apid and the like), calculating and sequencing Euclidean distances of each group, and taking the minimum Euclidean distance in each group as a positioning result.
Gist spatial indexing technique: gist (generalized Search trees), a general Search tree, is a balanced tree structure access method, which can be used to implement any index pattern in the system as a basic template, and B-trees, R-trees and many other index patterns can be implemented by Gist. The Gist index is applicable to retrieval of multidimensional data types and aggregate data types, as well as other data types. The Gist multi-field index uses the index to scan when any subset of index fields is included in the query condition, and supports the storage of planar, three-dimensional and multidimensional data in an index structure. Quick calculation of geographic information spatial data is achieved using Gist indexing in PostGis (an open source Gis database that adds the ability to store administrative spatial data on an object relational database PostgreSQL). The invention aims to combine a Gist spatial index technology (Gis engine) with a conventional communication big data rasterization algorithm, and optimize the existing positioning method by using the Gis engine so as to achieve the aim of improving the data processing efficiency.
The invention aims to provide an optimized positioning method combining a communication big data rasterization algorithm with a Gis engine, which is characterized in that the Gis engine in a PostGis spatial index technology is introduced into a communication big data rasterization positioning system on the basis of optimizing the structure and the included information of a basic fingerprint library, an intersection region R of all base station coverage regions covering the position of a sampling point, which is acquired by using the PostGis spatial index technology, is used as a scanning region, then Euclidean distance calculation and sequencing are carried out on grids in the intersection region R, and finally a positioning result is obtained.
In a first aspect, the present invention provides an optimized positioning method combining a communication big data rasterization algorithm with a Gis engine, where the method includes:
constructing a basic fingerprint database of a network coverage area;
carrying out MR data acquisition on sampling points by each base station;
associating the longitude and latitude information of the base station stored in the basic fingerprint database by using the MR data of the sampling point acquired by the base station;
grouping sampling point MR data according to a preset rule, and then determining an intersection region R of coverage regions of all base stations covering the sampling point position by using a PostGis spatial index technology;
associating the intersection region R with a basic fingerprint library by using a PostGis spatial index technology to obtain all grids in the intersection region R, and then calculating the Euclidean distance of each grid;
and sequencing the Euclidean distances of the grids obtained by calculation, and taking the grid with the minimum Euclidean distance as a positioning result.
According to some embodiments of the present invention, the constructing the base fingerprint database of the network coverage area is performed according to the following method:
(1) grid division: dividing a network coverage area into a plurality of grids according to a preset rule;
(2) constructing a basic fingerprint database: all grids contained in a network coverage area jointly form a basic fingerprint library, and data information recorded in the basic fingerprint library comprises an identifier of each grid, a main service base station ID covering the grid, main service base station level values RSRP and RSRQ, and an adjacent service base station ID, an adjacent service base station level value RSRP and RSRQ adjacent to the main service base station and simultaneously covering the grid.
According to some embodiments of the present invention, the basic fingerprint database includes longitude and latitude information of all grids and base stations in a network coverage area, and the longitude and latitude information is stored as a spatial Geometry type and is queried and presented in a Geometry field form.
According to some embodiments of the present invention, in the process of constructing the basic fingerprint database of the network coverage area, the following processing is performed on the basic fingerprint database: dividing a three-dimensional space of a network coverage area corresponding to a basic fingerprint database into a plurality of grids (for example, grids of 5m x 5 m) with certain sizes according to actual application requirements, calculating signal intensity (namely level value) of a corresponding base station in each grid by using WinProp software, then establishing a database by using a plurality of groups of signal intensity fingerprint features received in each grid, and matching actual received sampling point MR data acquired by each base station with the signal intensity fingerprint features in the database to realize rapid positioning of sampling points.
According to some embodiments of the present invention, in the process of constructing the basic fingerprint library of the network coverage area, according to the longitude and latitude and the coverage radius of each base station, the dependency relationship between each grid and the base station is obtained by using the intersection and intercept function in the PostGis, and according to the dependency relationship between each grid and the base station, an array type field is added to the constructed basic fingerprint library, and the content of the array type field is the IDs of all base stations covering the grid corresponding to each grid and the key value pair of the coverage area of the corresponding base station (for example, if the grid in the area R in fig. 3 belongs to both the base station a and the base station B and belongs to the base station C, the content of the array type field is the base station ID of the base station B and the base station coverage area + the key value pair of the base station coverage area).
Considering that the MR data of one sample point includes the data of the main base station and the data of the neighboring base stations, the MR position must be within the circular range covered by the main base station, and at the same time, … … within the circular range covered by several neighboring base stations, so that it can be inferred that the MR position must be within a small intersection region R of the geotry regions covered by all the surrounding base stations (see fig. 3).
And associating the basic fingerprint library by using the MR data of the sampling points acquired by each base station, acquiring each Geometry field in the array type field according to the ID of the base station, calculating an intersection region R covered by a plurality of adjacent base stations where the sampling points are located by using a st _ interaction function in PostGis, and associating all grids belonging to the intersection region R through the intersection region R, wherein the association condition is st _ contacts (region R, grid) or st _ within (grid, region R) or st _ interactions (region R, grid), so as to acquire a grid positioning result of the sampling points in the intersection region R. By using the method, the data volume needing Euclidean distance calculation and sorting can be averagely reduced by one hundred to ten thousand times. In practical application, the process of constructing the basic fingerprint database by using the PostGis technology only needs to be carried out once, and the process of carrying out Euclidean distance calculation by real-time positioning is carried out constantly, so that the PostGis operation added during the generation of the basic fingerprint database does not occupy the time cost. The number of the base stations and the coverage condition of the base stations enable the intersection interception of the PostGis in the correlation process of the sampling point MR and the basic fingerprint database to be less time-consuming (the coverage base stations around one grid are usually within 10), so that the whole operation is realized, and the advantages are very obvious.
According to some embodiments of the invention, the MR data acquisition of the sample points by the base stations comprises:
(1) the MR data of the sampling points collected by each base station comprises user identification IMSI or IMEI, main service base station ID corresponding to the sampling points, main service base station level values RSRP and RSRQ, adjacent service base station ID adjacent to the main service base station, adjacent service base station level values RSRP and RSRQ and data reporting time;
(2) the sampling point must be covered by the main service base station and the adjacent service base station at the same time, and the number of the main service base station and the adjacent service base station is more than or equal to 3.
According to some embodiments of the invention, the inventive method uses a greenplus distributed database system as a storage system for raw communication big data, intermediate data and result data.
In a second aspect, the present invention provides an optimized positioning system combining a communication big data rasterization algorithm with a Gis engine, where the system includes:
a fingerprint library construction module: a basic fingerprint database used for constructing a network coverage area;
a data acquisition module: the MR data acquisition is carried out on the sampling points;
a data association module: the base station longitude and latitude information is used for associating the sampling point MR data with the base station longitude and latitude information stored in the basic fingerprint database;
a data grouping module: the system is used for grouping the MR data of the sampling points according to a preset rule;
PostGis orientation module: the method comprises the steps that an intersection region R of coverage regions of all base stations covering the position of a sampling point is determined through a PostGis spatial index technology;
PostGis correlation Module: the system comprises a base fingerprint database and a plurality of grids, wherein the base fingerprint database is used for associating the intersection region R with the base fingerprint database through a PostGis spatial index technology to obtain all grids in the intersection region R;
a distance calculation module: the Euclidean distance calculation method is used for calculating Euclidean distances of all grids in the intersection region R;
a distance sorting module: and the method is used for sequencing Euclidean distances of all grids in the intersection region R obtained by calculation and selecting the grid with the minimum Euclidean distance as a positioning result.
In a third aspect, the present invention provides an electronic positioning device, which includes a processor, a memory, a communication interface and a communication bus, where the processor, the memory and the communication interface complete mutual communication through the communication bus, the memory stores at least one executable instruction, and the processor executes the optimal positioning method combining the communication big data rasterization algorithm and the Gis engine according to the instruction.
In summary, the communication big data rasterization algorithm of the invention has the following advantages in combination with the optimal positioning method of the Gis engine:
(1) by utilizing a PostGis spatial index technology (Gis engine), the calculation times of Euclidean distance are greatly reduced, the problem that the existing rasterization positioning system is low in data processing efficiency is solved, the performance efficiency of rasterization positioning of large communication data is greatly improved, and the system overhead is greatly reduced.
(2) The large data processing capacity and efficiency of the GreenPlum data warehouse are compatible, and the high efficiency and the expandability of the large data system are inherited.
(3) Based on the storage optimization of the three-dimensional spatial fingerprint library and Gis spatial data, the Gis engine is convenient to use for calculation and analysis, the structure of the fingerprint library is optimized, and the application scene of the fingerprint library is enriched.
Drawings
In order to more clearly illustrate the technical solutions of the prior art and the embodiments of the present invention, the drawings used in the description of the prior art and the embodiments are briefly introduced below. It is to be understood that the drawings in the following description are illustrative of some, but not all embodiments of the invention, and that other drawings may be derived therefrom by those skilled in the art without the benefit of the teachings herein.
FIG. 1 is a schematic diagram of a basic fingerprint database construction mode.
Fig. 2 is a schematic diagram of a communication big data rasterization three-point positioning principle.
FIG. 3 is a schematic diagram of the communication big data rasterization positioning principle of the present invention combined with the Gis engine.
Fig. 4 is a schematic flow chart of an implementation of the optimal positioning method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the embodiments described are merely illustrative of some, but not all, of the present invention and that the invention may be embodied or carried out in various other specific forms, and that various modifications and changes in the details of the specification may be made without departing from the spirit of the invention.
Also, it should be understood that the scope of the invention is not limited to the particular embodiments described below; it is also to be understood that the terminology used in the examples is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.
Example 1: an optimized positioning method combining a communication big data rasterization algorithm with a Gis engine is characterized in that a distributed database system of GreenPlum is used as a storage system of original communication big data, intermediate data and result data.
The method comprises the following steps (see fig. 4):
s1: and constructing a basic fingerprint database of the network coverage area, wherein the basic fingerprint database comprises longitude and latitude information of all grids and base stations in the network coverage area, all the longitude and latitude information is stored as a space Geometry type, and is inquired and presented in a Geometry field form.
The basic fingerprint library is constructed as follows:
(1) grid division: dividing a network coverage area into a plurality of grids according to a preset rule, wherein the area and the shape of each grid are determined according to the data precision requirement of application, the distribution position of a base station and the signal density of the area; for example: in fig. 1, a 9km by 9km area is divided into 9 grids of 3km by 3km, there are 6 base stations in the area, the coverage area of each base station is close to a circle, and the basic fingerprint database is composed of 9 grids in the figure. In practical applications, the grid side length may be defined according to the data precision requirement of the application, for example, when the grid is used for high precision positioning, the grid may be divided into 5m × 5 m.
(2) Constructing a basic fingerprint database: all grids contained in a network coverage area jointly form a basic fingerprint library, and data information recorded in the basic fingerprint library comprises an identifier of each grid, a main service base station ID covering the grid, main service base station level values RSRP and RSRQ, and an adjacent service base station ID, an adjacent service base station level value RSRP and RSRQ adjacent to the main service base station and simultaneously covering the grid. For example: in fig. 1, the grid 4 is covered by the base station a, the base station B, and the base station D at the same time, three data records of the grid 4 are recorded in the basic fingerprint database, and the three data records correspond to the relative level values of the base station A, B, D, respectively.
As an improvement, in the process of constructing a basic fingerprint library of a network coverage area, according to the longitude and latitude and the coverage radius of each base station, the dependency relationship between each grid and the base station is obtained by using the intersection and intercept functions in the PostGis, and an array type field is added in the constructed basic fingerprint library according to the dependency relationship between each grid and the base station, and the content of the array type field is the IDs of all base stations covering the grid corresponding to each grid and the key value pair of the coverage area of the corresponding base station (for example, the grid in the area R in fig. 3 belongs to both the base station a and the base station B and belongs to the base station C, the content of the array type field is the base station ID of the base station a, the base station B, the base station C and the key value pair of the coverage area of the base station).
Considering that the MR data of one sample point includes the data of the main base station and the data of the neighboring base stations, the MR position must be within the circular range covered by the main base station, and at the same time, … … within the circular range covered by several neighboring base stations, so that it can be inferred that the MR position must be within a small intersection region R of the geotry regions covered by all the surrounding base stations (see fig. 3).
And associating the basic fingerprint library by using the MR data of the sampling points acquired by each base station, acquiring each Geometry field in the array type field according to the ID of the base station, calculating an intersection region R covered by a plurality of adjacent base stations where the sampling points are located by using a st _ interaction function in PostGis, and associating all grids belonging to the intersection region R through the intersection region R, wherein the association condition is st _ contacts (region R, grid) or st _ within (grid, region R) or st _ interactions (region R, grid), and acquiring a grid positioning result of the sampling points in the intersection region R. By using the method, the data volume needing Euclidean distance calculation and sorting can be averagely reduced by one hundred to ten thousand times. In practical application, the process of constructing the basic fingerprint database by using the PostGis technology only needs to be carried out once, and the process of carrying out Euclidean distance calculation by real-time positioning is carried out constantly, so that the PostGis operation added during the generation of the basic fingerprint database does not occupy the time cost. The number of the base stations and the coverage condition of the base stations enable the intersection interception of the PostGis in the correlation process of the sampling point MR and the basic fingerprint database to be less time-consuming (the coverage base stations around one grid are usually within 10), so that the whole operation is realized, and the advantages are very obvious.
In addition, in order to realize the fast calculation of the intersection region R, the following processing may be performed on the basic fingerprint library in the process of constructing the basic fingerprint library of the network coverage region: dividing a three-dimensional space of a network coverage area corresponding to a basic fingerprint database into a plurality of grids (for example, grids of 5m x 5 m) with certain sizes according to actual application requirements, calculating signal intensity (namely level value) of a corresponding base station in each grid by using WinProp software, then establishing a database by using a plurality of groups of signal intensity fingerprint features received in each grid, and matching actual received sampling point MR data acquired by each base station with the signal intensity fingerprint features in the database to realize rapid positioning of sampling points.
S2: and carrying out MR data acquisition on the sampling points by each base station.
(1) The MR data of the sampling point collected by each base station comprises a user identifier IMSI or IMEI, a main service base station ID, a main service base station level value RSRP and RSRQ corresponding to the sampling point, a temporary service base station ID adjacent to the main service base station, a temporary service base station level value RSRP and RSRQ and data reporting time.
(2) The sampling point must be covered by the main service base station and the adjacent service base station at the same time, and the number of the main service base station and the adjacent service base station is more than or equal to 3.
S3: and associating the latitude and longitude information of the base station stored in the basic fingerprint database by using the MR data of the sampling point acquired by the base station.
S4: and grouping the MR data of the sampling points according to a preset rule, and then determining an intersection region R of coverage regions of all base stations covering the positions of the sampling points by using a PostGis spatial index technology.
S5: and associating the intersection region R with the basic fingerprint library by using a PostGis spatial index technology to obtain all grids in the intersection region R, and then calculating the Euclidean distance of each grid.
S6: and sequencing the Euclidean distances of the grids obtained by calculation, and taking the grid with the minimum Euclidean distance as a positioning result.
Example 2: an optimized positioning system combining a communication big data rasterization algorithm and a Gis engine comprises:
a fingerprint library construction module: a basic fingerprint database used for constructing a network coverage area;
a data acquisition module: the MR data acquisition is carried out on the sampling points;
a data association module: the base station longitude and latitude information is used for associating the sampling point MR data with the base station longitude and latitude information stored in the basic fingerprint database;
a data grouping module: the device is used for grouping the sampling point MR data according to a preset rule;
PostGis orientation module: the method comprises the steps that an intersection region R of coverage regions of all base stations covering the position of a sampling point is determined through a PostGis spatial index technology;
PostGis correlation Module: the system comprises a base fingerprint database and a plurality of grids, wherein the base fingerprint database is used for associating the intersection region R with the base fingerprint database through a PostGis spatial index technology to obtain all grids in the intersection region R;
a distance calculation module: the Euclidean distance calculation method is used for calculating Euclidean distances of all grids in the intersection region R;
a distance sorting module: and the method is used for sequencing Euclidean distances of all grids in the intersection region R obtained by calculation and selecting the grid with the minimum Euclidean distance as a positioning result.
The modules are operated and implemented according to the technical scheme of the embodiment 1.
Example 3: the practical application efficiency of the method of the invention is compared with that of the prior rasterization positioning method, and the following table shows that:
the above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, replacement, or the like that comes within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (9)
1. An optimized positioning method combining a communication big data rasterization algorithm and a Gis engine is characterized by comprising the following steps:
constructing a basic fingerprint database of a network coverage area;
carrying out MR data acquisition on sampling points by each base station;
associating the longitude and latitude information of the base station stored in the basic fingerprint database by using the MR data of the sampling point acquired by the base station;
grouping sampling point MR data according to a preset rule, and then determining an intersection region R of coverage regions of all base stations covering the positions of the sampling points by using a PostGis spatial index technology;
associating the intersection region R with the basic fingerprint library by using a PostGis spatial index technology to obtain all grids in the intersection region R, and then calculating the Euclidean distance of each grid;
and sequencing the Euclidean distances of the grids obtained by calculation, and taking the grid with the minimum Euclidean distance as a positioning result.
2. The communication big data rasterization algorithm combined with the Gis engine optimized positioning method as recited in claim 1, wherein the construction of the basic fingerprint library of the network coverage area is performed according to the following steps:
(1) grid division: dividing a network coverage area into a plurality of grids according to a preset rule;
(2) constructing a basic fingerprint database: all grids contained in a network coverage area jointly form a basic fingerprint library, and data information recorded in the basic fingerprint library comprises an identifier of each grid, a main service base station ID covering the grid, main service base station level values RSRP and RSRQ, and an adjacent service base station ID, an adjacent service base station level value RSRP and RSRQ adjacent to the main service base station and simultaneously covering the grid.
3. The optimal positioning method combining the communication big data rasterization algorithm and the Gis engine according to claim 2, wherein the basic fingerprint library comprises longitude and latitude information of all grids and base stations in a network coverage area, and the longitude and latitude information is stored as a space Geometry type and is inquired and presented in a Geometry field form.
4. The communication big data rasterization algorithm combined with the Gis engine optimized positioning method according to claim 2, wherein the basic fingerprint library of the network coverage area is constructed by performing the following processing on the basic fingerprint library: dividing a three-dimensional space of a network coverage area corresponding to a basic fingerprint database into a plurality of grids with certain sizes according to actual application requirements, calculating the signal intensity of a corresponding base station in each grid by using WinProp software, then establishing a database by using a plurality of groups of signal intensity fingerprint characteristics received in each grid, and matching the MR data of sampling points acquired by each base station which are actually received with the signal intensity fingerprint characteristics in the database to realize the rapid positioning of the sampling points.
5. The communication big data rasterization algorithm combined with the Gis engine optimized positioning method according to claim 3, characterized in that in the process of constructing a basic fingerprint library of a network coverage area, according to the longitude and latitude and the coverage radius of each base station, the dependency relationship between each grid and the base station is obtained by using the intersection and intercept function in the PostGis, and an array type field is added in the constructed basic fingerprint library according to the dependency relationship between each grid and the base station, and the content of the array type field is the ID of all base stations covering the grid corresponding to each grid and the key value pair of the coverage area of the corresponding base station;
and associating the basic fingerprint library by using the MR data of the sampling points acquired by each base station, acquiring each Geometry field in the array type field according to the ID of the base station, calculating an intersection region R covered by a plurality of adjacent base stations where the sampling points are located by using a st _ interaction function in PostGis, associating all grids belonging to the intersection region R through the intersection region R, and acquiring a grid positioning result of the sampling points in the intersection region R.
6. The communication big data rasterization algorithm combined with the Gis engine optimized positioning method according to claim 1, wherein the performing, by each base station, MR data acquisition on the sampling points comprises:
(1) the MR data of the sampling points collected by each base station comprises user identification IMSI or IMEI, main service base station ID corresponding to the sampling points, main service base station level values RSRP and RSRQ, adjacent service base station ID adjacent to the main service base station, adjacent service base station level values RSRP and RSRQ and data reporting time;
(2) the sampling point must be covered by the main service base station and the adjacent service base station at the same time, and the number of the main service base station and the adjacent service base station is more than or equal to 3.
7. The communication big data rasterization algorithm combined with the Gis engine optimized positioning method as claimed in claim 1, wherein the method uses a GreenPlum distributed database system as a storage system for the original communication big data, the intermediate data and the result data.
8. An optimized positioning system combining a communication big data rasterization algorithm and a Gis engine, which is characterized by comprising:
a fingerprint library construction module: a basic fingerprint database used for constructing a network coverage area;
a data acquisition module: the MR data acquisition is carried out on the sampling points;
a data association module: the base station longitude and latitude information storage module is used for associating the sampling point MR data with the base station longitude and latitude information stored in the base fingerprint database;
a data grouping module: the system is used for grouping the MR data of the sampling points according to a preset rule;
PostGis orientation module: the method comprises the steps that an intersection region R of coverage regions of all base stations covering the position of a sampling point is determined through a PostGis spatial index technology;
PostGis correlation Module: the system comprises a base fingerprint database and a plurality of grids, wherein the base fingerprint database is used for associating the intersection region R with the base fingerprint database through a PostGis spatial index technology to obtain all grids in the intersection region R;
a distance calculation module: the Euclidean distance calculation method is used for calculating Euclidean distances of all grids in the intersection region R;
a distance sorting module: and the method is used for sequencing Euclidean distances of all grids in the intersection region R obtained by calculation and selecting the grid with the minimum Euclidean distance as a positioning result.
9. An electronic positioning device, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus, and the memory stores at least one executable instruction, and the processor executes the communication big data rasterization algorithm combined with the Gis engine optimized positioning method according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115114356A (en) * | 2022-06-28 | 2022-09-27 | 河北平普数政科技有限公司 | Real-time decryption method based on vector data front-end display |
| CN115358641A (en) * | 2022-10-20 | 2022-11-18 | 杭州明启数智科技有限公司 | Property post working hour calculation system and method based on global positioning and stream calculation |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115114356A (en) * | 2022-06-28 | 2022-09-27 | 河北平普数政科技有限公司 | Real-time decryption method based on vector data front-end display |
| CN115358641A (en) * | 2022-10-20 | 2022-11-18 | 杭州明启数智科技有限公司 | Property post working hour calculation system and method based on global positioning and stream calculation |
| CN115358641B (en) * | 2022-10-20 | 2023-05-02 | 杭州明启数智科技有限公司 | Property post man-hour computing system and method based on global positioning and stream computing |
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