A method for fast visualization of measurement report data based on geographic information

Abstract

The application provides a kind of based on geographic information's measurement report data quick visualization method, comprising S1: with zoom level as L, 2 L Low layer grid is divided into two of earth map;S2: longitude and latitude in measurement report data are extracted, and respectively conversion line value and column value, determine the position of measurement report data;S3: with zoom level as K, 2 K High layer grid is divided into two of earth map, and create block file, then measurement report data contained in high layer grid is stored in block file;S4: front end generates zoom level as M map window, and reads all measurement report data in block file in map window;S5: map window is divided into multiple window grid, and all measurement report data in S4 is positioned to window grid, and rendering is carried out.The application solves the problem of low efficiency of measurement report loading, aggregation and visualization in the process of map browsing in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geographic information systems and communications, and in particular to a method for fast visualization of measurement report data based on geographic information. BACKGROUND

[0002] Measurement report is the original data measured by a user terminal, and carries relevant information of uplink and downlink wireless links. In-depth analysis based on measurement report is one of effective means for network performance evaluation and optimization, such as network problem positioning, network coverage analysis, and neighbor optimization. Meanwhile, measurement report data can also provide dynamic population distribution information, and can be applied to various scenarios such as city planning and management.

[0003] Current geographic information technology has penetrated into various industries, and can provide objective and scientific basis for planning and management in city construction. The development of geographic information technology enables people to conveniently obtain and manage spatial state information related to their lives at any time, and it can provide support for the presentation of various data. It enables users to have a more intuitive, deeper and clearer understanding of data.

[0004] Measurement report includes longitude and latitude, RSCP, ISCP, BLER and transmission power, etc. There are massive measurement reports updated every day, and it is difficult to perform efficient query analysis by using conventional text files and databases, which leads to the fact that existing measurement report data is difficult to load and browse quickly at the city level and by index. Therefore, the application of measurement report data is not wide enough.

[0005] Therefore, a new technical means is needed to quickly process existing measurement report data, quickly and flexibly present data based on geographic information, enable users to efficiently and intuitively view measurement report data, improve the application scenarios of measurement report, and play a greater data value. SUMMARY

[0006] In view of the deficiencies in the prior art, the present application provides a method for fast visualization of measurement report data based on geographic information, which solves the problem of low efficiency of measurement report loading, aggregation and visualization in the process of map browsing in the prior art.

[0007] According to an embodiment of the present application, a method for fast visualization of measurement report data based on geographic information comprises:

[0008] S1: dividing the earth map into 2 L low layer grids according to longitude and latitude with a zoom level L, and numbering the row and column where each low layer grid is located;

[0009] S2: Extract the longitude and latitude in the measurement report data, and convert the longitude and latitude in the measurement report data into row and column values respectively to determine the low-level grid position of the measurement report data;

[0010] S3: With the scaling level K, the earth map is divided into 2 K high-level grids with the same starting point according to the longitude and latitude, each high-level grid is numbered by the row and column it is in, and the corresponding block file is created, and then the measurement report data in the low-level grid contained in the high-level grid is stored in the block file;

[0011] S4: The front end generates a map window with a scaling level M, converts the longitude and latitude range of the map window into a row and column value range, and reads all the measurement report data in the block file corresponding to the high-level grid in the row and column value range;

[0012] S5: The map window is equally divided into multiple window grids, and all the measurement report data read in S4 is positioned in the window grid, and the window grid is rendered.

[0013] Preferably, the scaling levels L, K, and M are all less than 21, and M≤L, K<L.

[0014] Preferably, in S4, the map window is a standard square, the row and column value range includes a row value range and a column value range, and the left upper corner longitude and latitude and the right lower corner longitude and latitude are converted into the minimum row value, the minimum column value, the maximum row value, and the maximum column value respectively, so that the row value range and the column value range can be obtained.

[0015] Preferably, in S3, the naming rule of the block file is: scaling level_row value_column value, and in S4, the block file is read according to the name of the block file, and the block file with the row value and the column value in the name in the row and column value range is read.

[0016] Preferably, in S2, the RSRP value of the measurement report data is also extracted, and in S5, the average value of the RSRP values of all the measurement report data in each window grid is calculated, and the window grid is colored.

[0017] Compared with the prior art, the present application has the following beneficial effects:

[0018] By dividing the measurement report data into grid block files that are more conducive to map rendering, reading and aggregation are facilitated, and then the corresponding block file is obtained according to the visible range of the map window and aggregated according to the drawing grid, and in the aggregation, the measurement report data in the block file can be statistically calculated, so that the measurement report rendering is more flexible, without the need to load and query all the measurement report data, the server load is smaller, the loading efficiency is higher, and the speed of measurement report reading and aggregation in map browsing is improved. Attached Figure Description

[0019] Figure 1 This is a system flowchart of an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the grid division principle of an embodiment of the present invention.

[0021] Figure 3 A file partitioning diagram for the measurement report data in this embodiment of the invention.

[0022] Figure 4 This is a schematic diagram illustrating the data loading principle of the map window in an embodiment of the present invention.

[0023] Figure 5 This is a map raster rendering image according to an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] like Figure 1 As shown in the figure, this invention proposes a method for rapid visualization of measurement report data based on geographic information, including:

[0026] S1: Divide the Earth map into 2 based on latitude and longitude, with a zoom level of L. L Each low-level grid is numbered according to the row and column in which it is located;

[0027] like Figure 2 As shown, to improve the efficiency of searching raster files in the measurement report, the commonly used TMS map service specifications are adopted to divide the map into grids, similar to a pyramid shape. The larger the zoom level value, the lower the level; the smaller the value, the higher the level. The grid area is divided into squares with longitude [-180°, 180°] and latitude [-85.051129°, 85.051129°], and the origin is [-180°, 85.05°]. The number of grids is 2. n indivual.

[0028] Each measurement report grid width is approximately 40075016.6855784 / (2) at the equator. n The scaling factor (n) represents the scaling level, which can be adjusted according to the required accuracy of spatial location. A larger n results in higher accuracy. However, since the positioning accuracy of the measurement report is approximately within 10 meters, the maximum value of n can be 21. A grid near the equator is approximately 19.1 meters wide. In this embodiment, the scaling level of the measurement report data is L = 20, and the Earth map is divided into 2... 20 Each low-level grid cell is numbered according to its row and column position.

[0029] S2: Extract the longitude and latitude in the measurement report data, and convert the longitude and latitude in the measurement report data into row and column values respectively to determine the low-level grid position of the measurement report data;

[0030] As shown in Figure 3 , a raw measurement report data includes longitude, latitude, RSRP, RSRQ, time, RSCP, ISCP, and BLER, etc. In the present application, longitude, latitude, time, and RSRP and RSRQ are the data that need to be concerned or used.

[0031] The longitude and latitude in each measurement report data are converted into row and column values respectively using the following two formulas to determine which low-level grid the measurement report data is located in. Each low-level grid can contain one or more measurement report data.

[0032]

[0033]

[0034] Where col is the row value, row is the column value, lat is the latitude, and lng is the longitude.

[0035] S3: With a zoom level of K, the earth map is divided into 2 K high-level grids with the same starting point according to the longitude and latitude, each high-level grid is numbered by its row and column, and a corresponding block file is created. Then the measurement report data in the low-level grids contained in the high-level grid is stored in the block file.

[0036] Then the zoom level is increased, and in this embodiment, K=15 is used. At this time, the earth map is divided into 2 15 high-level grids with the same starting point as the lowest level (L=20) in S1 according to the longitude and latitude. Each high-level grid includes 2 21-15 = 2 6 low-level grids, for example, the high-level grid with row and column values of (0, 1) includes 8*8=64 low-level grids with row and column value ranges of 0-7 and 8-15 respectively. Then a corresponding block file is created for each high-level grid, and the naming rule is zoom level_grid_row value_grid column value.

[0037] After the creation, the measurement report data in the low-level grids contained in the high-level grid is stored in the block file, as shown in Figure 3 , by dividing the measurement report data into grid block files that are more conducive to map rendering, it is convenient for reading and aggregation.

[0038] S4: The front end generates a map window with a zoom level of M, converts the latitude and longitude range of the map window into a row and column value range, and reads the measurement report data in the block file corresponding to all high-level grids in the row and column value range;

[0039] The map window viewed by the user is generated in the front end, which is a standard square and dynamically adjusts the size of the generated square. Similarly, the shape of the map window also conforms to the TMS grid. The zoom level of the generated map window is M, and M≤L.

[0040] As shown in Figure 4 According to the latitude and longitude of the upper left corner of the map window (106.237792, 29.372601) and the latitude and longitude of the lower right corner (106.226806, 29.334298), the minimum row value, minimum column value, maximum row value, and maximum column value are converted, respectively, to 13585, 26053, 13588, and 26056. Since the zoom level does not affect the latitude and longitude, it does not affect the row and column values, therefore, the block file with the row value range of 13585-13588 and the column value range of 26053-26056 can be directly retrieved by searching the name of the block file, and the measurement report data in each block file can be loaded. After that, the loaded measurement report data is parsed, and the LRU cache algorithm is used to manage the loaded block files and measurement report data.

[0041] In addition, since the map window is dynamically generated, its four endpoints may fall within the high-level grid with a zoom level of 15. At the edge of the map window, it may not completely contain the entire high-level grid, but in this embodiment, the block file of such an incomplete high-level grid still needs to be loaded.

[0042] S5: Equally divide the map window into multiple window grids, and position all the measurement report data read in S4 to the window grids, and render the window grids.

[0043] Similarly, the map window is divided into grids, the number of which is determined according to actual needs, but must be an exponential of 2. In this embodiment, the map window is divided into 2 4 = 16 window grids. For example, by substituting the row value col and the column value row in the measurement report data into the formula, the row and column values under the zoom level M can be obtained, which are positioned to the window grids to determine which measurement report data each window grid contains.

[0044]

[0045] Wherein y is the row or col value corresponding to the measurement report data when the scaling level is L=20, x is the row or col value corresponding to the measurement report data when the scaling level is M, the int() function is an integer function, and if the x value is a decimal, the integer part of the decimal is taken to obtain the position (i.e. in which window grid) of the corresponding measurement data report in the map window.

[0046] In the measurement report data, RSRP is the signal receiving power, the RSRP value of all measurement data reports contained in each window grid can be averaged, and the window grid is rendered by coloring, as shown in Figure 5 The specific color description uses RGB16 binary representation to avoid confusion.

[0047] If The window grid color is #00ff00.

[0048] If The window grid color is #0000ff.

[0049] If The window grid color is #00ffff.

[0050] If The window grid color is #ffff00.

[0051] If The window grid color is #ff00ff.

[0052] If The window grid color is #ff0000.

[0053] The specific color used to color the window grid is designed according to actual requirements.

[0054] According to the visible range of the map window, the corresponding block file is obtained and aggregated according to the drawing grid. In the aggregation, the measurement report data in the block file can be statistically calculated to make the measurement report rendering more flexible, without loading and querying all measurement report data, the server load is smaller, the loading efficiency is higher, and the speed of measurement report reading and aggregation in map browsing is improved.

[0055] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for fast visualization of measurement report data based on geographic information, characterized in that: Comprise: S1: Divide the Earth map into sections based on latitude and longitude, with zoom level L as the zoom level. Each low-level grid is numbered according to the row and column in which it is located; S2: extract the longitude and latitude in the measurement report data, and convert the longitude and latitude in the measurement report data into row and column values respectively, to determine the low-level grid position of the measurement report data; S3: with the scaling level K, the earth map is divided into high-level grids with the same starting point according to the latitude and longitude, each high-level grid is numbered by the row and column where it is located, and the corresponding sub-block file is created, and then the measurement report data in the low-level grids contained in the high-level grid is stored in the sub-block file; S4: the front end generates a map window with a zoom level of M, converts the longitude and latitude range of the map window into a row and column value range, and reads the measurement report data in the sub-block file corresponding to all high-level grids in the row and column value range; S5: divide the map window into multiple window grids, and locate all the measurement report data read in S4 into the window grids, and render the window grids; The zoom levels L, K and M are all less than 21, and M≤L, K In S3, the naming rule of the sub-block file is: zoom level_row value_column value, and in S4, the sub-block file is read by searching according to the name of the sub-block file, and the sub-block file with row value and column value in the row and column value range is read; In S4, LRU cache algorithm is used to manage the loaded sub-block files and measurement report data; In S2, the RSRP value in the measurement report data is also extracted, and in S5, the average value of the RSRP values in all measurement report data in each window grid is calculated, and the window grid is colored.

2. The method for fast visualization of measurement reporting data based on geographic information according to claim 1, characterized in that: In S4, the map window is a standard square, the row and column value range includes the row value range and the column value range, and the left upper corner longitude and latitude and the right lower corner longitude and latitude are converted into the minimum row value, the minimum column value, the maximum row value and the maximum column value respectively, so that the row value range and the column value range can be obtained.

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