Communication big data rasterization algorithm combined with GIS engine optimization positioning method and device

Abstract

The present invention belongs to the field of communication technology, and relates to an optimization positioning method and system combining a communication big data rasterization algorithm with a Gis engine. Based on the optimization of the structure and included information of the basic fingerprint library, this method introduces the Gis engine in the PostGis spatial indexing technology into the communication big data rasterization positioning system, and uses the intersection area R of all base station coverage areas covering the sampling point location obtained by using the PostGis spatial indexing technology as the scanning area, and then calculates and sorts the Euclidean distance of the grids in this intersection area R, and finally obtains the positioning result. This method uses the PostGis spatial indexing technology to greatly reduce the number of Euclidean distance calculations, solves the problem of low data processing efficiency of the existing rasterization positioning system, greatly improves the performance efficiency of the communication big data rasterization positioning, and significantly reduces the system overhead.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to an optimization positioning method, system, device and application thereof that combines a communication big data rasterization algorithm with a GIS engine. Background Art

[0002] As the scale of mobile communication networks continues to grow, market competition is increasingly intensifying, and users have higher and higher requirements for network coverage and network signal quality. Therefore, wireless network optimization has become the focus of the back-end operation system of mobile communication networks, and the grid positioning solution based on the three-point positioning principle has emerged. The network optimization system based on rasterized data rasterizes a large amount of analysis data and performs statistical analysis based on geographic grids as the basic unit.

[0003] As for the current status of optimization software at home and abroad, the relatively mature grid positioning system in the market includes fingerprint library, real-time MRO, signaling data, etc. Its technical architecture adopts the big data framework of Hadoop ecology or the data warehouse of GreenPlum ecology to realize the correlation matching of sampling points, the calculation of the minimum Euclidean distance, and return the final positioning grid. However, when the above-mentioned grid positioning system is applied in large cities, due to the large area of ​​urban areas, the large number of grids and base stations and the complex coverage, it is difficult for big data technology to achieve rapid processing in the face of huge amounts of data. At present, in the big data environment of ordinary operator server clusters, it often takes two days or even several days to complete the calculation of the accumulated data of one day. If the daily data needs to be analyzed immediately on the same day, the low efficiency of data processing has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the problem of low data processing efficiency of the existing grid positioning system, the present invention proposes a set of solutions.

[0005] Construction of basic fingerprint library (see Figure 1 ): The network coverage area is divided into multiple grids according to pre-set rules. The area and shape of the grid are determined according to the data accuracy requirements of the application, the distribution location of the base station and the signal density of the area. The network coverage area contains multiple base stations, and each base station covers a circular area with a different radius according to different transmission frequencies. All grids contained in the network coverage area together constitute a basic fingerprint library. The data information included in the basic fingerprint library includes the identification of each grid, the main service base station ID covering the grid, the main service base station level values ​​RSRP and RSRQ, and the temporary service base station ID adjacent to the main service base station, and the temporary service base station level values ​​RSRP and RSRQ.

[0006] Communication base station (base station) for sampling point MR data collection: The sampling point MR (Measurement Result) data that the base station needs to collect includes user information (unique identifier IMSI or IMEI), the main service base station ID + RSRP / RSRQ corresponding to the sampling point, the temporary service base station ID + RSRP / RSRQ, and the data reporting time. Among them, the sampling point must be covered by both the main service base station and the temporary service base station, and the number of the main service base station + temporary service base station is ≥ 3.

[0007] Grid three-point positioning principle (see Figure 2 ): It is known that there is a point P (Px, Py) in the two-dimensional coordinate plane. The distances from this point to base station A (Ax, Ay), base station B (Bx, By), and base station C (Cx, Cy) are all known. Then, the unique coordinates (Px, Py) of point P can be determined by the three-point positioning principle. Raster positioning uses the above three-point positioning principle to convert the distance into a level value (LtescRsrp, LtencRsrp), and converts the unique intersection of the three base station coverage areas ( Figure 2 The point P where the three circles strictly intersect is converted into the grid position with the smallest Euclidean distance to the point P as the positioning result.

[0008] Raster positioning process: by associating and querying the level values ​​of the main service base station and the temporary service base station in the MR data of the above specific sampling points with the level values ​​of the main service base station and the temporary service base station in the basic fingerprint library, a result set is obtained; the above result set is grouped (group by) according to the specified rules (main service base station, timestamp, S1apid, etc.), the Euclidean distance of each group is calculated and sorted, and the one with the smallest Euclidean distance in each group is taken as the positioning result.

[0009] Gist spatial index technology: Gist (Generalized Search Trees) is a generalized search tree, which is an access method for a balanced tree structure. As a basic template in the system, it can be used to implement any index mode. B-trees, R-trees and many other index modes can be implemented with Gist. Gist index is not only suitable for the retrieval of multidimensional data types and set data types, but also for other data types. Gist multi-field index will use the index for scanning when the query condition contains any subset of the index field, and supports the storage of plane, three-dimensional and multi-dimensional data in one index structure. PostGis (an open source Gis database that adds the ability to store and manage spatial data on the object-relational database PostgreSQL) uses Gist index to realize the rapid calculation of geographic information spatial data. The present invention aims to combine Gist spatial index technology (Gis engine) with conventional communication big data rasterization algorithm, and use Gis engine to optimize the current positioning method to achieve the purpose of improving data processing efficiency.

[0010] The purpose of the present invention is to propose an optimization positioning method combining a communication big data rasterization algorithm with a Gis engine. On the basis of optimizing the structure and included information of a basic fingerprint library, the method introduces the Gis engine in the PostGis spatial indexing technology into the communication big data rasterization positioning system. The intersection area R of all base station coverage areas covering the sampling point position obtained by using the PostGis spatial indexing technology is used as the scanning area, and then the Euclidean distance of the grids in the intersection area R is calculated and sorted, and finally the positioning result is obtained.

[0011] In a first aspect, the present invention provides an optimization positioning method combining a communication big data rasterization algorithm with a GIS engine, the method comprising:

[0012] Build a basic fingerprint database for network coverage areas;

[0013] Each base station collects MR data at the sampling point;

[0014] The MR data of the sampling points collected by the base station are used to associate the latitude and longitude information of the base station stored in the basic fingerprint library;

[0015] The MR data of the sampling points are grouped according to the pre-set rules, and then the intersection area R of all base station coverage areas covering the sampling point locations is determined using PostGis spatial index technology;

[0016] Use PostGis spatial index technology to associate the above intersection area R with the basic fingerprint library, obtain all grids in the intersection area R, and then calculate the Euclidean distance of each grid;

[0017] The calculated Euclidean distances of each grid are sorted, and the grid with the smallest Euclidean distance is taken as the positioning result.

[0018] According to some embodiments of the present invention, the construction of the basic fingerprint library of the network coverage area is performed according to the following method:

[0019] (1) Grid division: The network coverage area is divided into multiple grids according to pre-set rules;

[0020] (2) Construction of basic fingerprint library: The basic fingerprint library is composed of all grids included in the network coverage area. The data information recorded in the basic fingerprint library includes the identification of each grid, the main service base station ID covering the grid, the main service base station level values ​​RSRP and RSRQ, and the temporary service base station ID, RSRP and RSRQ of the temporary service base station adjacent to the main service base station and covering the grid at the same time.

[0021] According to some embodiments of the present invention, the basic fingerprint library contains the longitude and latitude information of all grids and base stations in the network coverage area, and the longitude and latitude information is stored as a spatial Geometry type and is queried and presented in the form of Geometry fields.

[0022] According to some embodiments of the present invention, in the process of constructing a basic fingerprint library of a network coverage area, the basic fingerprint library is processed as follows: the three-dimensional space of the network coverage area corresponding to the basic fingerprint library is divided into multiple grids of a certain size (for example, a grid of 5m*5m*5m) according to actual application needs, and WinProp software is used to calculate the signal strength (i.e., level value) of the corresponding base station in each grid, and then a database is established with multiple groups of signal strength fingerprint features received in each grid, and the sampling point MR data actually received and collected by each base station is matched with the signal strength fingerprint features in the database to achieve rapid positioning of the sampling point.

[0023] According to some embodiments of the present invention, in the process of constructing the basic fingerprint library of the network coverage area, the intersection and interception functions in PostGis are used to obtain the subordinate relationship between each grid and the base station according to the longitude and latitude of each base station and the coverage radius, and an array type field is added to the constructed basic fingerprint library according to the subordinate relationship between each grid and the base station. 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 pairs of the corresponding base station coverage area (such as Figure 3 The grid in area R belongs to base station A, base station B and base station C at the same time. Then the content of the array type field is the key-value pair of base station ID of base station A, base station B and base station C + base station coverage area).

[0024] We believe that the MR data of a sampling point contains the data of the main base station and the neighboring base stations. Therefore, the location of MR must be within the circular range covered by the main base station and within the circular range covered by several neighboring base stations. Based on this, it can be inferred that the location of MR must be within a very small intersection area R of the Geometry areas covered by all surrounding base stations (see Figure 3 ).

[0025] The above basic fingerprint library is associated with the sampling point MR data collected by each base station, and each Geometry field in the above array type field is obtained according to the base station ID. Then, the st_intersection function in PostGis is used to calculate the intersection area R covered by multiple adjacent base stations where the sampling point is located, and then all grids belonging to the intersection area R are associated with the intersection area R. The association condition is st_contains (region R, grid) or st_within (grid, region R) or st_intersects (region R, grid), and the grid positioning result of the sampling point in the intersection area R is obtained. Using the above method, the amount of data that needs to be calculated and sorted for Euclidean distance can be reduced by an average of one hundred to ten thousand times. In practical applications, the process of using PostGis technology to build a basic fingerprint library only needs to be done once, and the process of real-time positioning and calculating Euclidean distance is performed all the time, so the PostGis operation added when the basic fingerprint library is generated does not take up time cost. The number and coverage of base stations make it less time-consuming to perform PostGis intersection extraction in the process of associating the sampling point MR with the basic fingerprint library (the number of base stations covering a grid is usually less than 10), so the overall operation has obvious advantages.

[0026] According to some embodiments of the present invention, the collecting MR data of the sampling points by each base station includes:

[0027] (1) The MR data of the sampling point collected by each base station includes the user identity IMSI or IMEI, the ID of the primary service base station corresponding to the sampling point, the level values ​​RSRP and RSRQ of the primary service base station, the ID of the temporary service base station adjacent to the primary service base station, the level values ​​RSRP and RSRQ of the temporary service base station, and the data reporting time;

[0028] (2) The sampling point must be covered by both the primary serving base station and the temporary serving base station, and the number of the primary serving base station + the temporary serving base station must be ≥ 3.

[0029] According to some embodiments of the present invention, the method of the present invention uses the GreenPlum distributed database system as a storage system for original communication big data, intermediate data and result data.

[0030] In a second aspect, the present invention provides an optimization positioning system combining a communication big data rasterization algorithm with a GIS engine, the system comprising:

[0031] Fingerprint library construction module: used to build the basic fingerprint library of the network coverage area;

[0032] Data acquisition module: used to collect MR data of sampling points;

[0033] Data association module: used to associate the MR data of the sampling point with the latitude and longitude information of the base station stored in the basic fingerprint library;

[0034] Data grouping module: used to group the sampling point MR data according to pre-set rules;

[0035] PostGis positioning module: used to determine the intersection area R of all base station coverage areas covering the sampling point location through PostGis spatial index technology;

[0036] PostGis association module: used to associate the intersection area R with the basic fingerprint library through PostGis spatial index technology to obtain all grids in the intersection area R;

[0037] Distance calculation module: used to calculate the Euclidean distance of all grids in the intersection area R;

[0038] Distance sorting module: used to sort the Euclidean distances of all grids in the calculated intersection area R, and select the grid with the smallest Euclidean distance as the positioning result.

[0039] In the third aspect, the present invention provides an electronic positioning device, including a processor, a memory, a communication interface and a communication bus. The processor, memory and communication interface communicate with each other through the communication bus. The memory stores at least one executable instruction. The processor executes the above-mentioned communication big data rasterization algorithm combined with the GIS engine according to the instruction. The optimized positioning method.

[0040] In summary, the communication big data rasterization algorithm combined with the optimization positioning method of the GIS engine of the present invention has the following advantages:

[0041] (1) The use of PostGis spatial index technology (Gis engine) greatly reduces the number of Euclidean distance calculations, solves the problem of low data processing efficiency of the existing raster positioning system, greatly improves the performance efficiency of raster positioning of communication big data, and significantly reduces system overhead.

[0042] (2) Compatible with the big data processing capabilities and efficiency of the GreenPlum data warehouse, and inherits the efficiency and scalability of the big data system itself.

[0043] (3) Based on the storage optimization of the three-dimensional fingerprint library combined with Gis spatial data, it is convenient to use the Gis engine for calculation and analysis, optimize the fingerprint library structure, and enrich the application scenarios of the fingerprint library. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the prior art and the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the prior art and the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, not all embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 Schematic diagram of the construction mode of the basic fingerprint library.

[0046] Figure 2 Schematic diagram of the rasterized three-point positioning principle for communication big data.

[0047] Figure 3 This is a schematic diagram of the raster positioning principle of communication big data combined with the GIS engine of the present invention.

[0048] Figure 4 The figure is a schematic diagram of the implementation process of the positioning optimization method of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. The present invention can also be implemented or applied through other different specific implementation methods. The details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0050] At the same time, it should be understood that the protection scope of the present invention is not limited to the specific embodiments described below; it should also be understood that the terms used in the embodiments of the present invention are for describing specific embodiments rather than for limiting the protection scope of the present invention.

[0051] Embodiment 1: A communication big data rasterization algorithm combined with a GIS engine optimization positioning method, in which the GreenPlum distributed database system is used as the storage system for original communication big data, intermediate data and result data.

[0052] The method comprises the following steps (see Figure 4 ):

[0053] S1: Build a basic fingerprint library of the network coverage area. The basic fingerprint library contains the longitude and latitude information of all grids and base stations in the network coverage area. All longitude and latitude information is stored as a spatial Geometry type and is queried and presented in the form of Geometry fields.

[0054] The construction process of the basic fingerprint library is as follows:

[0055] (1) Grid division: The network coverage area is divided into multiple grids according to pre-set rules. The area and shape of each grid are determined according to the data accuracy requirements of the application, the distribution location of the base stations, and the signal density of the area; for example: Figure 1 The 9km*9km area is divided into 9 grids of 3km*3km. There are 6 base stations in the area, and the coverage of each base station is close to a circle. The basic fingerprint library is composed of the 9 grids in the figure. In practical applications, the grid side length can be defined according to the data accuracy requirements of the application. For example, when used for high-precision positioning, the grid can be divided into 5m*5m.

[0056] (2) Construction of basic fingerprint library: All grids included in the network coverage area together constitute the basic fingerprint library. The data information recorded in the basic fingerprint library includes the identification of each grid, the main service base station ID covering the grid, the main service base station level value RSRP and RSRQ, and the temporary service base station ID adjacent to the main service base station and covering the grid at the same time, and the temporary service base station level value RSRP and RSRQ. For example: Figure 1 If the middle grid 4 is covered by base stations A, B and D at the same time, the basic fingerprint library will store three data records of grid 4, corresponding to the relative level values ​​of base stations A, B and D respectively.

[0057] As an improvement, in the process of constructing the basic fingerprint library of the network coverage area, the intersection and interception functions in PostGis are used to obtain the subordinate relationship between each grid and the base station according to the latitude and longitude and coverage radius of each base station, and an array type field is added to the constructed basic fingerprint library according to the subordinate relationship between each grid and the base station. 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 pairs of the corresponding base station coverage area (such as Figure 3 The grid in area R belongs to base station A, base station B and base station C at the same time. Then the content of the array type field is the key-value pair of base station ID of base station A, base station B and base station C + base station coverage area).

[0058] We believe that the MR data of a sampling point contains the data of the main base station and the neighboring base stations. Therefore, the location of MR must be within the circular range covered by the main base station and within the circular range covered by several neighboring base stations. Based on this, it can be inferred that the location of MR must be within a very small intersection area R of the Geometry areas covered by all surrounding base stations (see Figure 3 ).

[0059] The above basic fingerprint library is associated with the sampling point MR data collected by each base station, and each Geometry field in the above array type field is obtained according to the base station ID. Then, the st_intersection function in PostGis is used to calculate the intersection area R covered by multiple adjacent base stations where the sampling point is located, and then all grids belonging to the intersection area R are associated with the intersection area R. The association condition is st_contains (region R, grid) or st_within (grid, region R) or st_intersects (region R, grid), and the grid positioning result of the sampling point in the intersection area R is obtained. Using the above method, the amount of data that needs to be calculated and sorted for Euclidean distance can be reduced by an average of one hundred to ten thousand times. In practical applications, the process of using PostGis technology to build a basic fingerprint library only needs to be done once, and the process of real-time positioning and calculating Euclidean distance is performed all the time, so the PostGis operation added when the basic fingerprint library is generated does not take up time cost. The number and coverage of base stations make it less time-consuming to perform PostGis intersection extraction in the process of associating the sampling point MR with the basic fingerprint library (the number of base stations covering a grid is usually less than 10), so the overall operation has obvious advantages.

[0060] In addition, in order to realize the quick calculation of the intersection area R, the basic fingerprint library can be processed as follows in the process of constructing the basic fingerprint library of the network coverage area: the three-dimensional space of the network coverage area corresponding to the basic fingerprint library is divided into multiple grids of a certain size (for example, a grid of 5m*5m*5m) according to actual application needs, and the WinProp software is used to calculate the signal strength (i.e., level value) of the corresponding base station in each grid, and then a database is established with multiple groups of signal strength fingerprint features received in each grid, and the sampling point MR data actually received from each base station is matched with the signal strength fingerprint features in the database to realize the rapid positioning of the sampling point.

[0061] S2: Each base station collects MR data at the sampling point.

[0062] (1) The MR data of the sampling point collected by each base station includes the user identity IMSI or IMEI, the ID of the main service base station corresponding to the sampling point, the level values ​​RSRP and RSRQ of the main service base station, the ID of the temporary service base station adjacent to the main service base station, the level values ​​RSRP and RSRQ of the temporary service base station, and the data reporting time.

[0063] (2) The sampling point must be covered by both the primary serving base station and the temporary serving base station, and the number of the primary serving base station + the temporary serving base station must be ≥ 3.

[0064] S3: Use the sampling point MR data collected by the base station to associate the base station longitude and latitude information stored in the basic fingerprint library.

[0065] S4: Group the sampling point MR data according to a preset rule, and then use the PostGis spatial index technology to determine the intersection area R of all base station coverage areas covering the sampling point location.

[0066] S5: Use PostGis spatial index technology to associate the above intersection area R with the basic fingerprint library, obtain all grids in the intersection area R, and then calculate the Euclidean distance of each grid.

[0067] S6: Sort the calculated Euclidean distances of the grids, and take the grid with the smallest Euclidean distance as the positioning result.

[0068] Embodiment 2: A communication big data rasterization algorithm combined with a GIS engine optimized positioning system, the system includes:

[0069] Fingerprint library construction module: used to build the basic fingerprint library of the network coverage area;

[0070] Data acquisition module: used to collect MR data of sampling points;

[0071] Data association module: used to associate the MR data of the sampling point with the latitude and longitude information of the base station stored in the basic fingerprint library;

[0072] Data grouping module: used to group the sampling point MR data according to pre-set rules;

[0073] PostGis positioning module: used to determine the intersection area R of all base station coverage areas covering the sampling point location through PostGis spatial index technology;

[0074] PostGis association module: used to associate the intersection area R with the basic fingerprint library through PostGis spatial index technology to obtain all grids in the intersection area R;

[0075] Distance calculation module: used to calculate the Euclidean distance of all grids in the intersection area R;

[0076] Distance sorting module: used to sort the Euclidean distances of all grids in the calculated intersection area R, and select the grid with the smallest Euclidean distance as the positioning result.

[0077] The above modules are implemented according to the technical solution of Example 1.

[0078] Example 3: The actual application efficiency comparison between the method of the present invention and the existing grid positioning method is shown in the following table:

[0079]

[0080]

[0081] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, replacement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A communication big data rasterization algorithm combined with a GIS engine optimization positioning method, characterized in that: The method comprises: Construct a basic fingerprint library of the network coverage area; the basic fingerprint library contains the longitude and latitude information of all grids and base stations in the network coverage area, and the longitude and latitude information are stored as spatial Geometry type and queried and presented in the form of Geometry fields; Each base station collects MR data at the sampling point; The MR data of the sampling points collected by the base station are used to associate the latitude and longitude information of the base station stored in the basic fingerprint library; The MR data of the sampling points are grouped according to the pre-set rules, and then the intersection area R of all base station coverage areas covering the sampling point locations is determined using PostGis spatial index technology; In the process of constructing the basic fingerprint library of the network coverage area, the intersection and interception functions in PostGis are used to obtain the subordinate relationship between each grid and the base station according to the latitude and longitude and coverage radius of each base station, and an array type field is added to the constructed basic fingerprint library according to the subordinate relationship between each grid and the base station. The content of the array type field is the key-value pair of the ID of all base stations covering the grid corresponding to each grid and the corresponding base station coverage area; The MR data of the sampling points collected by each base station are associated with the above basic fingerprint library, and each Geometry field in the above array type field is obtained according to the base station ID. Then, the st_intersection function in PostGis is used to calculate the intersection area R covered by multiple adjacent base stations where the sampling point is located. Then, all grids belonging to the intersection area R are associated with the intersection area R to obtain the grid positioning result of the sampling point in the intersection area R. Use PostGis spatial index technology to associate the above intersection area R with the basic fingerprint library, obtain all grids in the intersection area R, and then calculate the Euclidean distance of each grid; The calculated Euclidean distances of each grid are sorted, and the grid with the smallest Euclidean distance is taken as the positioning result.

2. The communication big data rasterization algorithm combined with the optimization positioning method of the GIS engine according to claim 1 is characterized in that: To build a basic fingerprint database for the network coverage area, follow these steps: (1) Grid division: Divide the network coverage area into multiple grids according to pre-set rules; (2) Construction of basic fingerprint library: The basic fingerprint library is composed of all grids contained in the network coverage area. The data information recorded in the basic fingerprint library includes the identification of each grid, the main service base station ID covering the grid, the main service base station level values ​​RSRP and RSRQ, and the temporary service base station ID, RSRP and RSRQ of the temporary service base station adjacent to the main service base station and covering the grid at the same time.

3. The communication big data rasterization algorithm combined with the optimization positioning method of the GIS engine according to claim 2 is characterized in that: In the process of constructing the basic fingerprint library of the network coverage area, the basic fingerprint library is processed as follows: the three-dimensional space of the network coverage area corresponding to the basic fingerprint library is divided into multiple grids of a certain size according to actual application needs, and the WinProp software is used to calculate the signal strength of the corresponding base station in each grid. Then, a database is established with multiple groups of signal strength fingerprint features received in each grid, and the MR data of the sampling points collected by each base station actually received are matched with the signal strength fingerprint features in the database to achieve rapid positioning of the sampling points.

4. The communication big data rasterization algorithm combined with the optimization positioning method of the GIS engine according to claim 1 is characterized in that: The MR data collection of the sampling points by each base station includes: (1) The MR data collected by each base station at the sampling point includes the user identifier IMSI or IMEI, the ID of the main service base station corresponding to the sampling point, the level values ​​RSRP and RSRQ of the main service base station, the ID of the temporary service base station adjacent to the main service base station, the level values ​​RSRP and RSRQ of the temporary service base station, and the data reporting time; (2) The sampling point must be covered by both the primary serving base station and the temporary serving base station, and the number of primary serving base stations + temporary serving base stations must be ≥ 3.

5. The communication big data rasterization algorithm combined with the optimization positioning method of the GIS engine according to claim 1 is characterized in that: The method uses the GreenPlum distributed database system as a storage system for original communication big data, intermediate data and result data.

6. A communication big data rasterization algorithm combined with a GIS engine optimized positioning system, characterized in that: When the system is running, the communication big data rasterization algorithm combined with the optimization positioning method of the GIS engine according to any one of claims 1 to 5 is implemented, and the system includes: Fingerprint library construction module: used to build the basic fingerprint library of the network coverage area; Data acquisition module: used to collect MR data of sampling points; Data association module: used to associate the MR data of the sampling point with the latitude and longitude information of the base station stored in the basic fingerprint library; Data grouping module: used to group the sampling point MR data according to pre-set rules; PostGis positioning module: used to determine the intersection area R of all base station coverage areas covering the sampling point location through PostGis spatial index technology; PostGis association module: used to associate the intersection area R with the basic fingerprint library through PostGis spatial index technology to obtain all grids in the intersection area R; Distance calculation module: used to calculate the Euclidean distance of all grids in the intersection area R; Distance sorting module: used to sort the Euclidean distances of all grids in the calculated intersection area R, and select the grid with the smallest Euclidean distance as the positioning result.

7. 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 via the communication bus, characterized in that: At least one executable instruction is stored in the memory, and the processor executes the communication big data rasterization algorithm combined with the optimized positioning method of the GIS engine according to any one of claims 1-5.

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