Grid matching method and device for cells with same coverage, and medium

Abstract

The invention provides a grid matching method and device for cells with the same coverage, and a medium. The method comprises the following steps: acquiring measurement report (MR) geography grid data sets of a first network type and a second network type; acquiring a first network type weak coverage raster data set from the MR geography raster data set of the first network type, and acquiring a second network type good coverage raster data set from the MR geography raster data set of the second network type; and matching each first network type cell in the first network type weak coverage raster data set with k second network type cells satisfying conditions in the second network type good coverage raster data set by adjusting a dynamic threshold, and outputting the same coverage raster data set of the first network type and the second network type. According to the method, the device and the medium, the problems that the user switching is not timely and the user perception is influenced due to the phenomena of neighbor cell missed matching, neighbor cell redundancy and the like which are easy to occur in neighbor cell matching in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a grid matching method, device and medium for cells with the same coverage. Background Art

[0002] The optimization of heterogeneous neighboring cells in wireless access networks is the basis of network quality and user experience, and directly affects the success rate of voice services, data continuity, interference level and resource utilization. Through precise neighboring cell planning, parameter tuning and dynamic management, the call drop rate can be significantly reduced, the switching success rate can be improved, and the service delay can be shortened.

[0003] However, there are still many problems in the neighboring cell planning between different network standards. For example, the 4G neighboring cell planning of 5G cells is usually done manually, matching neighboring cells according to the distance between cells, which is prone to neighboring cell omissions and redundant neighboring cells, resulting in untimely user switching and affecting user perception. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a grid matching method, device and medium for the same coverage area in view of the above-mentioned shortcomings of the prior art, so as to solve the problem that the prior art matching of neighboring areas is prone to neighboring area missing, redundant neighboring areas, etc., resulting in untimely user switching and affecting user perception.

[0005] In a first aspect, the present invention provides a grid matching method for cells with the same coverage, comprising:

[0006] Obtaining a measurement report MR geography raster dataset of a first network standard and a second network standard;

[0007] Acquire a first network standard poor coverage raster dataset from the MR geography raster dataset of the first network standard, and acquire a second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard;

[0008] By adjusting the dynamic threshold, each first network standard cell in the first network standard weak coverage raster dataset is matched with k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, and the same coverage raster datasets of the first network standard and the second network standard are output, where k is greater than or equal to 1.

[0009] Furthermore, the MR geo-based raster data sets of the first network standard and the second network standard both include: cell name, grid number, reference signal received power RSRP and number of sampling points, wherein the grid number in the MR geo-based raster data set of the first network standard is consistent with the grid number in the MR geo-based raster data set of the second network standard.

[0010] Further, the obtaining of the first network standard poor coverage raster dataset from the MR geography raster dataset of the first network standard, and the obtaining of the second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard specifically includes:

[0011] Selecting samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold from the MR geographic grid dataset of the first network standard as the weak coverage grid dataset of the first network standard;

[0012] Selecting samples with RSRP greater than a preset third threshold from the MR geographical grid dataset of the second network standard as the good coverage grid dataset of the second network standard.

[0013] Further, selecting samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold from the MR geographical grid data set of the first network standard as the weak coverage grid data set of the first network standard specifically includes:

[0014] Selecting a plurality of samples whose RSRP is less than the first threshold from the MR geographical grid data set of the first network standard;

[0015] Arrange the multiple samples in descending order according to the number of sampling points of the multiple samples, and calculate the percentile ranking of the number of sampling points of all samples in the multiple samples;

[0016] Define the second threshold as the number of sampling points corresponding to the preset percentile ranking;

[0017] The samples whose number of sampling points is less than the second threshold value among the multiple samples are filtered to obtain the first network standard weak coverage grid dataset.

[0018] Further, the step of adjusting the dynamic threshold to match each first network standard cell in the first network standard weak coverage grid data set with k second network standard cells that meet the conditions in the second network standard good coverage grid data set, and outputting the first network standard and the second network standard same coverage grid data sets specifically includes:

[0019] Using the raster number in the first network standard weak coverage raster dataset and the raster number in the second network standard good coverage raster dataset as keywords, the first network standard weak coverage raster dataset and the second network standard good coverage raster dataset are merged to generate the first network standard and the second network standard full coverage raster dataset;

[0020] Arrange the number of all the second network standard sampling points in the full data set of the same coverage grid in descending order, and calculate the percentile ranking of all the second network standard sampling points;

[0021] The percentile ranking of the number of preset second network standard sampling points is used as the initial dynamic threshold;

[0022] Define the number parameter of the second network standard cells that each first network standard cell needs to match as k;

[0023] A dynamic threshold algorithm is adopted, and k second network standard cells that meet the conditions are matched for each first network standard cell in the full set of same coverage grid data through the dynamic threshold, so as to generate the same coverage grid data set of the first network standard and the second network standard.

[0024] Furthermore, the dynamic threshold algorithm is adopted to match k second network standard cells that meet the conditions for each first network standard cell in the full data set of the same coverage grid by the dynamic threshold, and generate the same coverage grid data set of the first network standard and the second network standard, which specifically includes:

[0025] For each first network standard cell, match k second network standard cells for the first network standard cell according to the dynamic threshold, and add the successfully matched first network standard cell and the k second network standard cells to a matching set;

[0026] If k second network standard cells cannot be matched according to the dynamic threshold, the dynamic threshold is adjusted by increasing or decreasing the preset step value, and k second network standard cells are matched for the first network standard cell according to the adjusted dynamic threshold until enough matching items are found or the maximum or minimum percentile ranking is reached, and the data that fails to fully match but has a partial match is added to the partial match set;

[0027] The matching set and the partial matching set are merged to obtain the same coverage raster dataset of the first network standard and the second network standard.

[0028] Furthermore, the same coverage grid data set of the first network standard and the second network standard includes: the first network standard cell name, the first network standard grid number, the first network standard RSRP, the number of sampling points of the first network standard, the second network standard cell name, the second network standard grid number, the second network standard RSRP, the number of sampling points of the second network standard, and the second network standard percentile ranking.

[0029] In a second aspect, the present invention provides a grid matching device for cells with the same coverage, comprising:

[0030] A first acquisition module is used to acquire a measurement report MR geographic raster dataset of a first network standard and a second network standard;

[0031] a second acquisition module, connected to the first acquisition module, for acquiring a first network standard poor coverage raster dataset from the first network standard MR geography raster dataset, and acquiring a second network standard good coverage raster dataset from the second network standard MR geography raster dataset;

[0032] A matching module is connected to the second acquisition module, and is used to match each first network standard cell in the first network standard weak coverage grid data set with k second network standard cells that meet the conditions in the second network standard good coverage grid data set by adjusting the dynamic threshold, and output the same coverage grid data sets of the first network standard and the second network standard, wherein k is greater than or equal to 1.

[0033] In a third aspect, the present invention provides a grid matching device for cells with the same coverage, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the grid matching method for cells with the same coverage described in the first aspect.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the grid matching method for cells with the same coverage as described in the first aspect is implemented.

[0035] The present invention provides a method, device and medium for raster matching of cells with same coverage, firstly obtaining a measurement report MR geography raster dataset of a first network standard and a second network standard; then obtaining a first network standard weak coverage raster dataset from the MR geography raster dataset of the first network standard, and obtaining a second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard; finally, by adjusting a dynamic threshold, matching each first network standard cell in the first network standard weak coverage raster dataset with k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, and outputting the first network standard and second network standard same coverage raster datasets, wherein k is greater than or equal to 1. The present invention uses MR geo-localized raster datasets of the first network standard and the second network standard to obtain a first network standard weak coverage raster dataset and a second network standard good coverage raster dataset, and adjusts a dynamic threshold so that each first network standard cell in the first network standard weak coverage raster dataset can be matched to k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, thereby avoiding the occurrence of neighboring area omissions, redundant neighboring areas and the like, thereby ensuring that users can complete switching in a timely manner, optimizing user perception experience, and solving the problem that the prior art of matching neighboring areas is prone to neighboring area omissions, redundant neighboring areas and the like, resulting in untimely user switching and affecting user perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a grid matching method for cells with the same coverage according to Embodiment 1 of the present invention;

[0037] Figure 2 A flowchart of another grid matching method for the same coverage area according to an embodiment of the present invention;

[0038] Figure 3 A schematic structural diagram of a grid matching system for cells with the same coverage according to an embodiment of the present invention;

[0039] Figure 4 This is a schematic structural diagram of a grid matching device for cells with the same coverage according to Embodiment 2 of the present invention;

[0040] Figure 5 This is a schematic structural diagram of a grid matching device for cells with the same coverage according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0042] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0043] It can be understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other.

[0044] It can be understood that, for the convenience of description, the drawings of the present invention only show the parts related to the present invention, while the parts irrelevant to the present invention are not shown in the drawings.

[0045] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0046] It can be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0047] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0048] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0049] Embodiment 1:

[0050] This embodiment provides a grid matching method for the same coverage area, such as Figure 1 As shown, the method includes:

[0051] Step S11: Acquire MR (Measurement Report) geographic raster datasets of the first network standard and the second network standard.

[0052] It should be noted that the network standard refers to the type of communication network, for example, 5G, 4G, 3G or 2G, etc. The same coverage cell refers to an area that shares the same geographical range under different network standards, wherein the first network standard and the second network standard may be different network standards, for example, the first network standard may be 5G, and the second network standard may be 4G, and the MR geography grid data sets of the first network standard and the second network standard both include: cell name, grid number, RSRP (Reference Signal Receiving Power) and number of sampling points, etc., wherein the grid number in the MR geography grid data set of the first network standard is consistent with the grid number in the MR geography grid data set of the second network standard.

[0053] Step S12: acquiring a first network standard poor coverage raster dataset from the MR geography raster dataset of the first network standard, and acquiring a second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard.

[0054] In this embodiment, the first network standard weak coverage grid data set includes attributes such as the first network standard cell name, the first network standard grid number, the first network standard RSRP, the first network standard sampling point number, the first network standard grid longitude, and the first network standard grid latitude. The second network standard good coverage grid data set includes attributes such as the second network standard cell name, the second network standard grid number, the second network standard RSRP, the second network standard sampling point number, the second network standard grid longitude, and the second network standard grid latitude.

[0055] In an optional embodiment, the step of obtaining a first network standard poor coverage raster dataset from the MR geography raster dataset of the first network standard, and obtaining a second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard specifically includes:

[0056] Selecting samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold from the MR geographic grid dataset of the first network standard as the weak coverage grid dataset of the first network standard;

[0057] Selecting samples with RSRP greater than a preset third threshold from the MR geographical grid dataset of the second network standard as the good coverage grid dataset of the second network standard.

[0058] Specifically, samples in the MR geography-based raster dataset of the first network standard whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold are selected to generate a first network standard weak coverage raster dataset, and samples in the MR geography-based raster dataset of the second network standard whose RSRP is greater than a preset third threshold are selected to generate a second network standard good coverage raster dataset, wherein the first threshold is preferably -105dBm and the third threshold is preferably -100dBm.

[0059] In an optional embodiment, selecting, from the MR geographical grid data set of the first network standard, samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold as the weak coverage grid data set of the first network standard, specifically includes:

[0060] Selecting a plurality of samples whose RSRP is less than the first threshold from the MR geographical grid data set of the first network standard;

[0061] Arrange the multiple samples in descending order according to the number of sampling points of the multiple samples, and calculate the percentile ranking of the number of sampling points of all samples in the multiple samples;

[0062] Define the second threshold as the number of sampling points corresponding to the preset percentile ranking;

[0063] The samples whose number of sampling points is less than the second threshold value among the multiple samples are filtered to obtain the first network standard weak coverage grid dataset.

[0064] Specifically, rows (i.e., multiple samples) with RSRP less than a first threshold are selected from the MR geography raster dataset of the first network standard, and the rows are arranged in descending order according to the number of sampling points, and the percentile ranking of the number of all sampling points is calculated; a second threshold parameter is defined, and the parameter value is equal to the number of sampling points corresponding to the preset percentile ranking, and the rows with RSRP less than the first threshold and the rows with the number of sampling points less than the second threshold are filtered out, and all the rasters of each first network standard cell in the filtered rows are sorted according to the number of sampling points and the percentile ranking; and the first network standard weak coverage raster dataset is output.

[0065] Step S13: By adjusting the dynamic threshold, each first network standard cell in the first network standard weak coverage grid data set is matched with k second network standard cells that meet the conditions in the second network standard good coverage grid data set, and the same coverage grid data sets of the first network standard and the second network standard are output, where k is greater than or equal to 1.

[0066] It should be noted that the same coverage grid data set of the first network standard and the second network standard includes: the first network standard cell name, the first network standard grid number, the first network standard RSRP, the number of sampling points of the first network standard, the second network standard cell name, the second network standard grid number, the second network standard RSRP, the number of sampling points of the second network standard, the second network standard percentile ranking, etc.

[0067] In an optional embodiment, the method of adjusting the dynamic threshold, matching each first network standard cell in the first network standard weak coverage grid data set with k second network standard cells that meet the conditions in the second network standard good coverage grid data set, and outputting the same coverage grid data sets of the first network standard and the second network standard, specifically includes:

[0068] Using the raster number in the first network standard weak coverage raster dataset and the raster number in the second network standard good coverage raster dataset as keywords, the first network standard weak coverage raster dataset and the second network standard good coverage raster dataset are merged to generate the first network standard and the second network standard full coverage raster dataset;

[0069] Arrange the number of all the second network standard sampling points in the full data set of the same coverage grid in descending order, and calculate the percentile ranking of all the second network standard sampling points;

[0070] The percentile ranking of the number of preset second network standard sampling points is used as the initial dynamic threshold;

[0071] Define the number parameter of the second network standard cells that each first network standard cell needs to match as k;

[0072] A dynamic threshold algorithm is adopted, and k second network standard cells that meet the conditions are matched for each first network standard cell in the full set of same coverage grid data through the dynamic threshold, so as to generate the same coverage grid data set of the first network standard and the second network standard.

[0073] Specifically, the first network standard weak coverage raster dataset and the second network standard good coverage raster dataset are merged based on the raster number as a keyword to generate the first network standard and the second network standard full coverage raster dataset, wherein the first network standard and the second network standard full coverage raster dataset includes the second network standard cell name, the second network standard grid number, the second network standard RSRP, the second network standard sampling point number, the second network standard grid longitude, the second network standard grid latitude, the first network standard cell name, the first network standard grid number, the first network standard RSRP, the first network standard sampling point number, the first network standard grid longitude, the first network standard grid latitude and other attributes.

[0074] Specifically, the number of all second network standard sampling points corresponding to the grid of each first network standard cell in the full data set of the same coverage grid of the first network standard and the second network standard are arranged in descending order, the percentile ranking of the number of all second network standard sampling points corresponding to the grid of each first network standard cell is calculated, and an attribute percentile ranking is generated and added to the full data set of the same coverage grid of the first network standard and the second network standard; an initial dynamic threshold parameter is defined, and the initial dynamic threshold parameter value is the percentile ranking of the number of preset second network standard sampling points; a parameter of the number of second network standard cells that the first network standard cell needs to match is defined as k; by adjusting the dynamic threshold, each first network standard cell matches k second network standard cells that meet the conditions, and the same coverage grid data set of the first network standard and the second network standard is generated.

[0075] In an optional embodiment, the dynamic threshold algorithm is used to match k second network standard cells that meet the conditions for each first network standard cell in the full data set of the same coverage grid by the dynamic threshold, so as to generate the same coverage grid data set of the first network standard and the second network standard, specifically including:

[0076] For each first network standard cell, match k second network standard cells for the first network standard cell according to the dynamic threshold, and add the successfully matched first network standard cell and the k second network standard cells to a matching set;

[0077] If k second network standard cells cannot be matched according to the dynamic threshold, the dynamic threshold is adjusted by increasing or decreasing the preset step value, and k second network standard cells are matched for the first network standard cell according to the adjusted dynamic threshold until enough matching items are found or the maximum or minimum percentile ranking is reached, and the data that fails to fully match but has a partial match is added to the partial match set;

[0078] The matching set and the partial matching set are merged to obtain the same coverage raster dataset of the first network standard and the second network standard.

[0079] Specifically, each first network standard cell is first matched to k second network standard cells according to the number of first network standard sampling points and then according to the initial dynamic threshold, and added to the matching set. The first network standard cell and the second network standard cell matched to it will no longer participate in subsequent processing. Define the dynamic threshold increment (or decrement) step parameter. If a sufficient number of matches cannot be found under the current percentile ranking dynamic threshold, the dynamic threshold is incremented (or decremented) by the step parameter until sufficient matches are found or the maximum or minimum percentile ranking is reached; when processing each first network standard cell, the result is divided into two parts, the fully matched data and the partially matched data, according to whether the number of second network standard cells that the first network standard cell needs to match is met. The data that is not fully matched but partially matched is saved to the partially matched set, the matching set and the partially matched set are merged, and the same coverage raster dataset of the first network standard and the second network standard is output.

[0080] In a specific embodiment, taking the first network standard as 5G and the second network standard as 4G as an example, Figure 2 As shown, the grid matching method for the same coverage cells includes the following steps:

[0081] Step 101: Collect daily MR geography grid raw data within the historical time period

[0082] Specifically, MR refers to the process in which the UE monitors the wireless environment according to the measurement parameters configured by the network, and reports the monitoring results to the network side through a dedicated signaling channel. These measurement results generally include but are not limited to signal strength, signal quality, neighboring information, etc., and are intended to help the network side make more reasonable resource allocation and connection management decisions. MR geographic grid refers to the process of spatially dividing the measurement report (MR) in the mobile communication network according to the geographical location, and summarizing and counting the relevant wireless performance indicators in each divided area (grid unit). In this way, the quality status, user distribution and potential problem points of the wireless network in a specific area can be intuitively displayed. Each 4G or 5G cell includes multiple grids, and the 4G MR geographic grid original data set collected in this embodiment includes 4G cell name, 4G grid number, 4GRSRP, 4G sampling point number, 4G grid longitude, 4G grid latitude and other attributes. The 5G MR geographic grid original data set includes 5G cell name, 5G grid number, 5GRSRP, 5G sampling point number, 5G grid longitude, 5G grid latitude and other attributes. In this embodiment, the grid numbers in the 4G and 5G MR geo-raster original data sets are completely consistent, that is, the grid division areas of the 4G and 5G cells are completely identical in geographical location.

[0083] It should be noted that the grid matching method of the same coverage area is applied to Figure 3 The grid matching system of the same coverage cells shown includes an original data collection module 201, a 5G weak coverage grid processing module 202, a 4G good coverage grid processing module 203, a 4G5G same coverage grid full processing module 204, and a 4G5G same coverage grid processing module 205.

[0084] Specifically, the collected 4G MR geo-rasterized original data set and 5G MR geo-rasterized original data set are input into the original data collection module 201 .

[0085] Step 102: Select samples whose 5G RSRP is less than -105dBm and whose number of filtered 5G sampling points is less than the minimum sampling point number threshold to generate a 5G weak coverage grid dataset.

[0086] It should be noted that the threshold, also known as the critical value, refers to the lowest or highest value that an effect can produce. In data analysis, the threshold is mainly used for data classification, filtering, and judgment. By setting the threshold, we can refine the data, filter out data that meets specific conditions, and conduct further in-depth analysis. At the same time, the choice of threshold also directly affects the accuracy and practicality of the analysis results.

[0087] Specifically, the 5G MR geo-rasterized original data set selects rows with 5GRSRP less than -105dBm, arranges them in descending order according to the number of sampling points, and calculates the percentile ranking of all the sampling points; for each 5G cell, the number of sampling points and the average percentile ranking of its grid number are calculated, and the calculation results are stored in the 5G cell group; a minimum sampling point number threshold parameter is defined, and the parameter value is equal to the number of sampling points corresponding to the percentile ranking of all the sampling points. The percentile ranking used by this parameter can be adjusted. In this embodiment, the percentile ranking is set to 22% (in this embodiment, the number of sampling points corresponding to the percentile ranking of 0% is the largest), assuming that the corresponding number of sampling points is 6; each 5G cell in the 5G cell group filters out the grids with the number of sampling points less than the minimum sampling point number threshold, and each 5G cell in the 5G cell group is sorted according to the number of sampling points and the percentile ranking; outputs a 5G weak coverage raster data set. This data set includes attributes such as 5G cell name, 5G grid number, 5G RSRP, 5G sampling point number, 5G grid longitude, and 5G grid latitude.

[0088] Step 103: Select samples with 4G RSRP greater than -100dBm to generate a 4G good coverage grid dataset

[0089] Specifically, the 4G MR geo-raster original dataset selects rows with 4GRSRP greater than -100dBm and outputs a 4G good coverage raster dataset, which includes attributes such as 4G cell name, 4G grid number, 4GRSRP, number of 4G sampling points, 4G grid longitude, and 4G grid latitude.

[0090] Step 104: Use the 4G grid number and the 5G grid number as keywords to merge and generate a full dataset of 4G5G grids with the same coverage.

[0091] Specifically, the 4G good coverage raster dataset and the 5G weak coverage raster dataset are merged according to the 4G raster number and the 5G raster number, and the 4G5G same coverage raster full dataset is output. The fields of this dataset include 4G cell name, 4G grid number, 4GRSRP, number of 4G sampling points, 4G grid longitude, 4G grid latitude, 5G cell name, 5G grid number, 5GRSRP, number of 5G sampling points, 5G grid longitude, 5G grid latitude and other attributes.

[0092] Step 105: Adjust the dynamic threshold, match each 5G cell with k 4G cells, and generate a 4G5G same coverage grid dataset

[0093] Specifically, define a parameter of the number of 4G cells that the 5G cell needs to match. This parameter value can be adjusted and is set to 3 in this embodiment; the number of 4G sampling points of the full data set of the 4G5G same coverage grid is arranged in descending order, the percentile ranking of all 4G sampling points is calculated, and an attribute percentile ranking is generated and added to the full data set of the 4G5G same coverage grid; each 5G cell is arranged in descending order according to the number of 5G sampling points; define a percentile ranking dynamic threshold parameter, and this parameter value is the percentile ranking of the number of 4G sampling points. This parameter value can be adjusted and is set to 0% in this embodiment, assuming that the corresponding number of sampling points is 228304; each 5G cell is first matched according to the size of the number of 5G sampling points, and then according to the dynamic threshold of the percentile ranking of the number of 4G sampling points, which is 0% in this embodiment, to 3 4G cells (that is, to the 4G cells corresponding to the 3 largest numbers of 4G sampling points), and they are added to the matching set, and the 5G cell and the 4G cell matched to it no longer participate in subsequent processing. Define the percentile ranking dynamic threshold increment (or decrement) step parameter, this parameter value can be adjusted, in this embodiment, it is set to 1%; if a sufficient number of matches are not found under the current percentile ranking dynamic threshold, the percentile ranking parameter is incremented by 1% until enough matches are found or the maximum percentile ranking is reached; when processing each 5G cell, according to whether the number of 4G cells that the 5G cell needs to match is met, the result is divided into two parts, the fully matched data and the data that is not fully matched but partially matched, the data that is not matched but partially matched is saved to the partial match set, the match set and the partial match set are merged, and the 4G5G same coverage grid dataset is output. This dataset field includes 4G cell name, 4G grid number, 4GRSRP, 4G sampling point number, 4G grid longitude, 4G grid latitude, 5G cell name, 5G grid number, 5GRSRP, 5G sampling point number, 5G grid longitude, 5G grid latitude, percentile ranking and other attributes.

[0094] Specifically, the specific process of the dynamic threshold algorithm is as follows:

[0095] Input: Sample set D = {x1, x2…x m};

[0096] Minimum number of matches k;

[0097] Initial threshold p (0≤p≤1);

[0098] Threshold step s.

[0099] process:

[0100] 1: The attribute values ​​of attribute B in sample set D are arranged in descending order

[0101] 2: Calculate the percentile ranking r of attribute B attribute value B

[0102] 3: Assign the initial threshold p to the current threshold p c

[0103] 4: The attribute values ​​of attribute A in sample set D are arranged in descending order

[0104] 5: Let the matching sample set

[0105] 6: For each attribute A

[0106] 7: while (matching number <k)

[0107] 8: Filter out r B Satisfy the current threshold p c Sample x i ;

[0108] 9: if there is no x that meets the conditions i And the current threshold p c ≥0(or p c ≤1); increase (or decrease) the threshold step s to update the current threshold p c ;

[0109] 10: Select samples x with matching number ≤ k i , stored in the matching sample set G

[0110] 11: Remove the matched sample x i , continue processing the remaining parts

[0111] 12: if the number of matches ≥ k or there are no more samples available

[0112] Break

[0113] 13: return (attribute A, matching sample set G)

[0114] Output: matching sample set G = {x1, x2…x n}(n <m)

[0115] It is worth mentioning that this embodiment uses the MR data of the 5G cell and the 4G cell to identify the weak coverage grid of the 5G cell, find the good coverage grid of the 4G cell with the same coverage as the 5G cell, set the percentile ranking of the number of sampling points in the 4G cell good coverage grid as the neighboring cell selection threshold, and select the 4G cell good coverage grid with the largest number of sampling points through iterative calculation and dynamic update of the threshold. Thereby, each 5G cell can be matched to k 4G cells with the same coverage, thereby improving the accuracy of 4G neighboring area planning of the 5G cell. The present invention adopts the initial percentile ranking as the threshold, adjusts the percentile ranking by increasing or decreasing step values, iteratively calculates and updates the threshold, and realizes dynamic adjustment of the threshold, so that the attribute A in the data set (that is, the first network standard cell) can be matched to k attributes B (that is, the second network standard cell), which can solve the problem that the attribute A cannot fully match the attribute B due to the fixed threshold, and the attribute value of the attribute A can match the maximum attribute value of the attribute B. The present invention uses the percentile to determine the initial threshold. The percentile is an objective indicator based on the data distribution, which indicates how many proportions of data points in the data set are less than or equal to the value. Therefore, the threshold setting based on percentiles is objective and not affected by the subjective judgment of the analyst. The percentiles of the dynamic thresholds matching parameter 1 (i.e., the first network standard cell) and parameter 2 (i.e., the second network standard cell) of the present invention are not approximately equal. The percentiles of the dynamic thresholds are iteratively calculated so that parameter 1 can match N parameters 2, and the threshold can be adaptively adjusted. Therefore, the problem of inability to fully match due to changes in data distribution can be reduced, and parameter 1 can match the maximum value of parameter 2.

[0116] The raster matching method for cells with the same coverage provided in an embodiment of the present invention first obtains a measurement report MR geography raster dataset of a first network standard and a second network standard; then obtains a first network standard weak coverage raster dataset from the MR geography raster dataset of the first network standard, and obtains a second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard; finally, by adjusting a dynamic threshold, each first network standard cell in the first network standard weak coverage raster dataset is matched with k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, and outputs the first network standard and the second network standard same coverage raster dataset, wherein k is greater than or equal to 1. The present invention uses MR geo-localized raster datasets of the first network standard and the second network standard to obtain a first network standard weak coverage raster dataset and a second network standard good coverage raster dataset, and adjusts a dynamic threshold so that each first network standard cell in the first network standard weak coverage raster dataset can be matched to k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, thereby avoiding the occurrence of neighboring area omissions, redundant neighboring areas and the like, thereby ensuring that users can complete switching in a timely manner, optimizing user perception experience, and solving the problem that the prior art of matching neighboring areas is prone to neighboring area omissions, redundant neighboring areas and the like, resulting in untimely user switching and affecting user perception.

[0117] Embodiment 2:

[0118] like Figure 4 As shown, this embodiment provides a grid matching device for cells with the same coverage, which is used to execute the above-mentioned grid matching method for cells with the same coverage, including:

[0119] The first acquisition module 11 is used to acquire the measurement report MR geographic raster dataset of the first network standard and the second network standard;

[0120] A second acquisition module 12, connected to the first acquisition module 11, is used to acquire a first network standard poor coverage raster dataset from the first network standard MR geographic raster dataset, and acquire a second network standard good coverage raster dataset from the second network standard MR geographic raster dataset;

[0121] The matching module 13 is connected to the second acquisition module 12, and is used to match each first network standard cell in the first network standard weak coverage raster dataset with k second network standard cells that meet the conditions in the second network standard good coverage raster dataset by adjusting the dynamic threshold, and output the same coverage raster datasets of the first network standard and the second network standard, where k is greater than or equal to 1.

[0122] Furthermore, the MR geo-based raster data sets of the first network standard and the second network standard both include: cell name, grid number, reference signal received power RSRP and number of sampling points, wherein the grid number in the MR geo-based raster data set of the first network standard is consistent with the grid number in the MR geo-based raster data set of the second network standard.

[0123] Furthermore, the second acquisition module 12 specifically includes:

[0124] A first selection unit is configured to select samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold from the MR geographic grid data set of the first network standard as the weak coverage grid data set of the first network standard;

[0125] The second selection unit is configured to select samples with RSRP greater than a preset third threshold from the MR geographical grid data set of the second network standard as the good coverage grid data set of the second network standard.

[0126] Furthermore, the first selection as a unit specifically includes:

[0127] A selection unit, configured to select a plurality of samples having an RSRP less than the first threshold from the MR geographical grid data set of the first network standard;

[0128] A first arrangement calculation unit, configured to arrange the plurality of samples in descending order according to the number of sampling points of the plurality of samples, and calculate the percentile ranking of the number of sampling points of all samples in the plurality of samples;

[0129] A first definition unit, configured to define the second threshold as the number of sampling points corresponding to a preset percentile ranking;

[0130] The filtering unit is used to filter the samples whose number of sampling points is less than the second threshold value among the multiple samples to obtain the first network standard weak coverage grid data set.

[0131] Furthermore, the matching module 13 specifically includes:

[0132] A merging generation unit, used to merge the first network standard weak coverage raster dataset and the second network standard good coverage raster dataset using the raster number in the first network standard weak coverage raster dataset and the raster number in the second network standard good coverage raster dataset as keywords, to generate a full raster dataset with the same coverage of the first network standard and the second network standard;

[0133] A second arrangement calculation unit is used to arrange the number of all second network standard sampling points in the full data set of the same coverage grid in descending order, and calculate the percentile ranking of all the second network standard sampling points;

[0134] As a unit, used to adopt the percentile ranking of the number of preset second network standard sampling points as an initial dynamic threshold;

[0135] A second definition unit is used to define a number parameter k of cells of the second network standard that each cell of the first network standard needs to match;

[0136] The matching generation unit is used to adopt a dynamic threshold algorithm to match k second network standard cells that meet the conditions for each first network standard cell in the full data set of the same coverage grid through the dynamic threshold, so as to generate the same coverage grid data set of the first network standard and the second network standard.

[0137] Furthermore, the matching generation unit specifically includes:

[0138] A first matching and adding unit, configured to match k second network standard cells for each first network standard cell according to the dynamic threshold, and add the successfully matched first network standard cells and k second network standard cells to a matching set;

[0139] A second matching adding unit is used for adjusting the dynamic threshold by increasing or decreasing a preset step value if the k second network standard cells cannot be matched according to the dynamic threshold, and matching the k second network standard cells for the first network standard cell according to the adjusted dynamic threshold until enough matching items are found or the maximum or minimum percentile ranking is reached, and adding the data that cannot be fully matched but has a partial match to the partial matching set;

[0140] A merging unit is used to merge the matching set and the partial matching set to obtain the same coverage raster dataset of the first network standard and the second network standard.

[0141] Furthermore, the same coverage grid data set of the first network standard and the second network standard includes: the first network standard cell name, the first network standard grid number, the first network standard RSRP, the number of sampling points of the first network standard, the second network standard cell name, the second network standard grid number, the second network standard RSRP, the number of sampling points of the second network standard, and the second network standard percentile ranking.

[0142] Embodiment 3:

[0143] refer to Figure 5This embodiment provides a grid matching device for cells with the same coverage, including a memory 21 and a processor 22. The memory 21 stores a computer program, and the processor 22 is configured to run the computer program to execute the grid matching method for cells with the same coverage in Example 1.

[0144] The memory 21 is connected to the processor 22. The memory 21 may be a flash memory, a read-only memory or other memory. The processor 22 may be a central processing unit or a single-chip microcomputer.

[0145] Embodiment 4:

[0146] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the grid matching method for cells with the same coverage in the above-mentioned embodiment 1 is implemented.

[0147] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0148] To summarize, the raster matching method, device and medium for cells with the same coverage provided in the embodiments of the present invention first obtain the MR geography raster dataset of the measurement reports of the first network standard and the second network standard; then obtain the first network standard weak coverage raster dataset from the MR geography raster dataset of the first network standard, and obtain the second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard; finally, by adjusting the dynamic threshold, each first network standard cell in the first network standard weak coverage raster dataset is matched with k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, and the same coverage raster dataset of the first network standard and the second network standard is output, where k is greater than or equal to 1. The present invention uses MR geo-localized raster datasets of the first network standard and the second network standard to obtain a first network standard weak coverage raster dataset and a second network standard good coverage raster dataset, and adjusts a dynamic threshold so that each first network standard cell in the first network standard weak coverage raster dataset can be matched to k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, thereby avoiding the occurrence of neighboring area omissions, redundant neighboring areas and the like, thereby ensuring that users can complete switching in a timely manner, optimizing user perception experience, and solving the problem that the prior art of matching neighboring areas is prone to neighboring area omissions, redundant neighboring areas and the like, resulting in untimely user switching and affecting user perception.

[0149] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A grid matching method for the same coverage area, characterized in that: The method comprises: Obtaining a measurement report MR geography raster dataset of a first network standard and a second network standard; Acquire a first network standard poor coverage raster dataset from the MR geography raster dataset of the first network standard, and acquire a second network standard good coverage raster dataset from the MR geography raster dataset of the second network standard; By adjusting the dynamic threshold, each first network standard cell in the first network standard weak coverage raster dataset is matched with k second network standard cells that meet the conditions in the second network standard good coverage raster dataset, and the same coverage raster datasets of the first network standard and the second network standard are output, where k is greater than or equal to 1.

2. The method according to claim 1, characterized in that The MR geographic grid data sets of the first network standard and the second network standard both include: cell name, grid number, reference signal received power RSRP and number of sampling points, wherein the grid number in the MR geographic grid data set of the first network standard is consistent with the grid number in the MR geographic grid data set of the second network standard.

3. The method according to claim 2, characterized in that The step of acquiring a first network standard poorly covered raster dataset from the MR geography raster dataset of the first network standard, and acquiring a second network standard well covered raster dataset from the MR geography raster dataset of the second network standard specifically includes: Selecting samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold from the MR geographic grid dataset of the first network standard as the weak coverage grid dataset of the first network standard; Select samples with RSRP greater than a preset third threshold from the MR geographical grid dataset of the second network standard as the good coverage grid dataset of the second network standard.

4. The method according to claim 3, characterized in that The selecting, from the MR geographical grid data set of the first network standard, samples whose RSRP is less than a preset first threshold and whose number of filtered sampling points is less than a preset second threshold as the weak coverage grid data set of the first network standard specifically includes: Selecting a plurality of samples whose RSRP is less than the first threshold from the MR geographical grid data set of the first network standard; Arrange the multiple samples in descending order according to the number of sampling points of the multiple samples, and calculate the percentile ranking of the number of sampling points of all samples in the multiple samples; Define the second threshold as the number of sampling points corresponding to the preset percentile ranking; The samples whose number of sampling points is less than the second threshold value among the multiple samples are filtered to obtain the first network standard weak coverage grid dataset.

5. The method according to claim 2, characterized in that: The step of adjusting the dynamic threshold to match each first network standard cell in the first network standard weak coverage grid data set with k second network standard cells that meet the conditions in the second network standard good coverage grid data set, and outputting the first network standard and the second network standard same coverage grid data sets specifically includes: Using the raster number in the first network standard weak coverage raster dataset and the raster number in the second network standard good coverage raster dataset as keywords, the first network standard weak coverage raster dataset and the second network standard good coverage raster dataset are merged to generate the first network standard and the second network standard full coverage raster dataset; Arrange the number of all the second network standard sampling points in the full data set of the same coverage grid in descending order, and calculate the percentile ranking of all the second network standard sampling points; The percentile ranking of the number of preset second network standard sampling points is used as the initial dynamic threshold; Define the number parameter of the second network standard cells that each first network standard cell needs to match as k; A dynamic threshold algorithm is adopted, and k second network standard cells that meet the conditions are matched for each first network standard cell in the full set of same coverage grid data through the dynamic threshold, so as to generate the same coverage grid data set of the first network standard and the second network standard.

6. The method according to claim 5, characterized in that The adopting of the dynamic threshold algorithm, matching k second network standard cells that meet the conditions for each first network standard cell in the full set of grid data with same coverage by the dynamic threshold, and generating the grid data sets with same coverage of the first network standard and the second network standard, specifically includes: For each first network standard cell, match k second network standard cells for the first network standard cell according to the dynamic threshold, and add the successfully matched first network standard cell and the k second network standard cells to a matching set; If k second network standard cells cannot be matched according to the dynamic threshold, the dynamic threshold is adjusted by increasing or decreasing the preset step value, and k second network standard cells are matched for the first network standard cell according to the adjusted dynamic threshold until enough matching items are found or the maximum or minimum percentile ranking is reached, and the data that fails to fully match but has a partial match is added to the partial match set; The matching set and the partial matching set are merged to obtain the same coverage raster dataset of the first network standard and the second network standard.

7. The method according to claim 1, characterized in that The same coverage grid data set of the first network standard and the second network standard includes: the first network standard cell name, the first network standard grid number, the first network standard RSRP, the number of sampling points of the first network standard, the second network standard cell name, the second network standard grid number, the second network standard RSRP, the number of sampling points of the second network standard, and the second network standard percentile ranking.

8. A grid matching device for the same coverage area, characterized in that: include: A first acquisition module is used to acquire a measurement report MR geographic raster dataset of a first network standard and a second network standard; a second acquisition module, connected to the first acquisition module, for acquiring a first network standard poor coverage raster dataset from the first network standard MR geography raster dataset, and acquiring a second network standard good coverage raster dataset from the second network standard MR geography raster dataset; A matching module is connected to the second acquisition module, and is used to match each first network standard cell in the first network standard weak coverage grid data set with k second network standard cells that meet the conditions in the second network standard good coverage grid data set by adjusting the dynamic threshold, and output the same coverage grid data sets of the first network standard and the second network standard, wherein k is greater than or equal to 1.

9. A grid matching device for the same coverage area, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the grid matching method for cells with the same coverage as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the grid matching method for cells with the same coverage as described in any one of claims 1 to 7 is implemented.

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