A grid matching method, device and medium for co-covered cells

Abstract

This invention provides a method, apparatus, and medium for raster matching of co-coverage cells. The method includes: acquiring MR geospatial raster datasets of a first network standard and a second network standard; acquiring a weak coverage raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and acquiring a good coverage raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard; matching each cell of the first network standard in the weak coverage raster dataset of the first network standard with k cells of the second network standard in the good coverage raster dataset of the second network standard that meet the conditions by adjusting a dynamic threshold, and outputting a co-coverage raster dataset of the first and second network standards. This method, apparatus, and medium can solve the problems of missed neighbor cell matching and redundant neighbor cells that are prone to occur in existing technologies, resulting in untimely handover for users and affecting user experience.

Description

Technical Field

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

[0002] Inter-system neighbor cell optimization in wireless access networks is fundamental to network quality and user experience, directly impacting voice service success rate, data continuity, interference levels, and resource utilization. Through precise neighbor cell planning, parameter tuning, and dynamic management, call drop rates can be significantly reduced, handover success rates improved, and service latency shortened.

[0003] However, there are still many problems with neighbor cell planning between different network standards. For example, 4G neighbor cell planning for 5G cells is usually done manually, matching neighbor cells according to the distance between cells. This can easily lead to problems such as missing neighbor cells or redundant neighbor cells, resulting in untimely handover for users and affecting user experience. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art by providing a grid matching method, device and medium for the same coverage cell, so as to solve the problem that the existing technology is prone to the phenomenon of missing neighbor cells and redundant neighbor cells when matching neighbor cells, which leads to untimely user handover and affects user experience.

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

[0006] Obtain the MR geospatial raster dataset containing measurement reports for both the first and second network standards.

[0007] Obtain a weakly covered raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and obtain a well-covered raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard.

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

[0009] Furthermore, both the first network standard and the second network standard MR geospatial raster dataset include: cell name, raster number, reference signal received power (RSRP), and number of sampling points, wherein the raster number in the first network standard MR geospatial raster dataset is consistent with the raster number in the second network standard MR geospatial raster dataset.

[0010] Further, the step of obtaining a first network-format weakly covered raster dataset from the first network-format MR geospatial raster dataset and obtaining a second network-format well-covered raster dataset from the second network-format MR geospatial raster dataset specifically includes:

[0011] From the MR geospatial raster dataset of the first network standard, 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 as the weak coverage raster dataset of the first network standard.

[0012] From the MR geospatial raster dataset of the second network standard, select samples with RSRP greater than a preset third threshold as the well-covered raster dataset of the second network standard.

[0013] Further, the step of selecting samples with RSRP less than a preset first threshold and a number of filtered sampling points less than a preset second threshold from the MR geospatial raster dataset of the first network standard specifically includes:

[0014] Select multiple samples with RSRP less than the first threshold from the MR geospatial raster dataset of the first network standard;

[0015] Based on the number of sampling points of the multiple samples, the multiple samples are sorted in descending order, and the percentile ranking of the number of sampling points of all samples in the multiple samples is calculated.

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

[0017] The samples with fewer than the second threshold number of sampling points among the multiple samples are filtered out to obtain the first network-format weakly covered raster dataset.

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

[0019] Using the raster numbers in the first network standard weakly covered raster dataset and the raster numbers in the second network standard well covered raster dataset as keywords, the first network standard weakly covered raster dataset and the second network standard well covered raster dataset are merged to generate a full dataset of raster with the same coverage in both the first and second network standards.

[0020] Sort the number of sampling points of all second network types in the full dataset of the same coverage raster in descending order, and calculate the percentile ranking of the number of sampling points of all second network types.

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

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

[0023] A 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 dataset of the same coverage raster, thereby generating a same-coverage raster dataset of the first and second network standards.

[0024] Furthermore, the adoption of a dynamic threshold algorithm, which matches k second-network standard cells that meet certain conditions for each first-network standard cell in the full dataset of the same-coverage raster, to generate a raster dataset with the same coverage of the first and second network standards, specifically includes:

[0025] For each first network standard cell, k second network standard cells are matched for the first network standard cell according to the dynamic threshold, and the successfully matched first network standard cell and k second network standard cells are added to the 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 are not completely matched but partially matched are added to the partial matching set.

[0027] By merging the matching set and the partial matching set, a raster dataset with the same coverage for the first network standard and the second network standard is obtained.

[0028] Furthermore, the co-coverage raster dataset of the first network standard and the second network standard includes: cell name of the first network standard, raster number of the first network standard, RSRP of the first network standard, number of sampling points of the first network standard, cell name of the second network standard, raster number of the second network standard, RSRP of the second network standard, number of sampling points of the second network standard, and percentile ranking of the second network standard.

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

[0030] The first acquisition module is used to acquire the MR geospatial raster dataset of measurement reports for the first and second network standards.

[0031] The second acquisition module, connected to the first acquisition module, is used to acquire a first network-based weakly covered raster dataset from the first network-based MR geospatial raster dataset, and to acquire a second network-based well-covered raster dataset from the second network-based MR geospatial raster dataset.

[0032] The matching module, connected to the second acquisition module, is used to match each first network standard cell in the first weak coverage raster dataset with k second network standard cells in the second good coverage raster dataset that meet the conditions by adjusting the dynamic threshold, and output the same coverage raster dataset of the first and second network standards, where k is greater than or equal to 1.

[0033] Thirdly, the present invention provides a grid matching device for co-coverage cells, 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 co-coverage cells described in the first aspect.

[0034] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the grid matching method for co-coverage cells described in the first aspect.

[0035] The present invention provides a method, apparatus, and medium for raster matching of co-coverage cells. First, it acquires MR geospatial raster datasets of a first network standard and a second network standard. Then, it acquires a weak coverage raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and a good coverage raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard. Finally, by adjusting a dynamic threshold, it matches each cell of the first network standard in the weak coverage raster dataset with k cells of the second network standard in the good coverage raster dataset of the second network standard that meet certain conditions, outputting a co-coverage raster dataset of the first and second network standards, where k is greater than or equal to 1. This invention uses MR geospatial raster datasets of a first network standard and a second network standard to obtain a weak coverage raster dataset of the first network standard and a good coverage raster dataset of the second network standard. By adjusting dynamic thresholds, each cell of the first network standard in the weak coverage raster dataset can be matched with k cells of the second network standard in the good coverage raster dataset that meet the conditions. This avoids phenomena such as missing neighbor cells or redundant neighbor cells, thereby ensuring that users can complete handover in a timely manner, optimizing the user experience, and solving the problem that existing technologies often result in missing neighbor cells or redundant neighbor cells when matching neighbor cells, leading to untimely handover and affecting user experience. Attached Figure Description

[0036] Figure 1 This is a flowchart of a grid matching method for co-coverage cells according to Embodiment 1 of the present invention;

[0037] Figure 2 This is a flowchart of another grid matching method for cells with the same coverage area according to an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the structure 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 diagram of the structure of a grid matching device for the same coverage cell according to Embodiment 2 of the present invention;

[0040] Figure 5 This is a schematic diagram of the structure of a grid matching device for the same coverage cell according to Embodiment 3 of the present invention. Detailed Implementation

[0041] 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 is understood that the specific embodiments and accompanying drawings described herein are merely for explaining the invention and are not intended to limit the invention.

[0043] It is understood that, without conflict, the various embodiments and features in the embodiments of the present invention can be combined with each other.

[0044] It is understood that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, while the parts unrelated to the present invention are not shown in the drawings.

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

[0046] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this invention may occur in a different order than that marked in the accompanying drawings.

[0047] It is understood that the flowcharts and block diagrams of this invention illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this invention. Each block in the flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagram and flowchart can be implemented using a hardware-based system to achieve the specified function, or using a combination of hardware and computer instructions.

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

[0049] Example 1:

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

[0051] Step S11: Obtain MR (Measurement Report) geospatial raster datasets of the first and second network formats.

[0052] It should be noted that network standard refers to the type of communication network, such as 5G, 4G, 3G, or 2G. Co-coverage cells refer to areas sharing the same geographical range under different network standards. The first and second network standards can be different; for example, the first network standard could be 5G and the second network standard could be 4G. Both the first and second network standard MR geospatial raster datasets include: cell name, raster number, RSRP (Reference Signal Receiving Power), and number of sampling points. The raster numbers in the first network standard MR geospatial raster dataset are consistent with those in the second network standard MR geospatial raster dataset.

[0053] Step S12: Obtain a weakly covered raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and obtain a well-covered raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard.

[0054] In this embodiment, the first network standard weak coverage raster dataset includes attributes such as the first network standard cell name, the first network standard raster number, the first network standard RSRP, the number of sampling points in the first network standard, the longitude of the first network standard raster, and the latitude of the first network standard raster. The second network standard good coverage raster dataset includes attributes such as the second network standard cell name, the second network standard raster number, the second network standard RSRP, the number of sampling points in the second network standard, the longitude of the second network standard raster, and the latitude of the second network standard raster.

[0055] In an optional embodiment, obtaining a first network-format weakly covered raster dataset from the first network-format MR geospatial raster dataset and obtaining a second network-format well-covered raster dataset from the second network-format MR geospatial raster dataset specifically includes:

[0056] From the MR geospatial raster dataset of the first network standard, 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 as the weak coverage raster dataset of the first network standard.

[0057] From the MR geospatial raster dataset of the second network standard, select samples with RSRP greater than a preset third threshold as the well-covered raster dataset of the second network standard.

[0058] Specifically, samples with RSRP less than a preset first threshold and a number of filtered sampling points less than a preset second threshold in the MR geospatial raster dataset of the first network standard are selected to generate a weakly covered raster dataset of the first network standard, and samples with RSRP greater than a preset third threshold in the MR geospatial raster dataset of the second network standard are selected to generate a well-covered raster dataset of the second network standard. The first threshold is preferably -105dBm and the third threshold is preferably -100dBm.

[0059] In an optional embodiment, selecting samples with RSRP less than a preset first threshold and a number of filtered sampling points less than a preset second threshold from the MR geospatial raster dataset of the first network standard as the weakly covered raster dataset of the first network standard specifically includes:

[0060] Select multiple samples with RSRP less than the first threshold from the MR geospatial raster dataset of the first network standard;

[0061] Based on the number of sampling points of the multiple samples, the multiple samples are sorted in descending order, and the percentile ranking of the number of sampling points of all samples in the multiple samples is calculated.

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

[0063] The samples with fewer than the second threshold number of sampling points among the multiple samples are filtered out to obtain the first network-format weakly covered raster dataset.

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

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

[0066] It should be noted that the co-coverage raster dataset of the first network standard and the second network standard includes: cell name of the first network standard, raster number of the first network standard, RSRP of the first network standard, number of sampling points of the first network standard, cell name of the second network standard, raster number of the second network standard, RSRP of the second network standard, number of sampling points of the second network standard, percentile ranking of the second network standard, etc.

[0067] In an optional embodiment, the step of adjusting a dynamic threshold to match each cell of the first network standard in the weak coverage raster dataset with k cells of the second network standard in the good coverage raster dataset that meet the conditions, and outputting a raster dataset with the same coverage of the first and second network standards, specifically includes:

[0068] Using the raster numbers in the first network standard weakly covered raster dataset and the raster numbers in the second network standard well covered raster dataset as keywords, the first network standard weakly covered raster dataset and the second network standard well covered raster dataset are merged to generate a full dataset of raster with the same coverage in both the first and second network standards.

[0069] Sort the number of sampling points of all second network types in the full dataset of the same coverage raster in descending order, and calculate the percentile ranking of the number of sampling points of all second network types.

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

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

[0072] A 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 dataset of the same coverage raster, thereby generating a same-coverage raster dataset of the first and second network standards.

[0073] Specifically, the weak coverage raster dataset of the first network standard and the good coverage raster dataset of the second network standard are merged based on the raster number as the keyword to generate a full dataset of raster with the same coverage of the first and second network standards. The full dataset of raster with the same coverage of the first and second network standards includes attributes such as cell name of the second network standard, raster number of the second network standard, RSRP of the second network standard, number of sampling points of the second network standard, longitude of the second network standard raster, latitude of the second network standard, cell name of the first network standard, raster number of the first network standard, RSRP of the first network standard, number of sampling points of the first network standard, longitude of the first network standard raster, and latitude of the first network standard raster.

[0074] Specifically, in the full dataset of co-coverage raster for both first and second network standards, the number of all second network standard sampling points corresponding to each first network standard cell is sorted in descending order. The percentile ranking of the number of all second network standard sampling points corresponding to each first network standard cell is calculated, and this percentile ranking is added to the full dataset of co-coverage raster for both first and second network standards. An initial dynamic threshold parameter is defined, which is set to the percentile ranking of the number of second network standard sampling points. The number of second network standard cells that a 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, generating a co-coverage raster dataset for both first and second network standards.

[0075] In an optional embodiment, the step of employing a dynamic threshold algorithm, using the dynamic threshold to match k second-network standard cells that meet certain conditions for each first-network standard cell in the full dataset of the same-coverage raster, generates a raster dataset of the first and second network standards with the same coverage, specifically including:

[0076] For each first network standard cell, k second network standard cells are matched for the first network standard cell according to the dynamic threshold, and the successfully matched first network standard cell and k second network standard cells are added to the 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 are not completely matched but partially matched are added to the partial matching set.

[0078] By merging the matching set and the partial matching set, a raster dataset with the same coverage for the first network standard and the second network standard is obtained.

[0079] Specifically, for each cell of the first network standard, k cells of the second network standard are matched according to the number of sampling points of the first network standard and the initial dynamic threshold. These are then added to the matching set. The first network standard cells and their matched second network standard cells are no longer involved in subsequent processing. A step parameter for increasing (or decreasing) the dynamic threshold is defined. If a sufficient number of matches are not found under the current percentile ranking dynamic threshold, the dynamic threshold is increased (or decreased) by the step parameter until a sufficient number of matches are found or the maximum or minimum percentile ranking is reached. When processing each cell of the first network standard, the result is divided into two parts according to whether the requirement for the number of second network standard cells to be matched by the first network standard cell is met: fully matched data and partially matched data. The partially matched data is saved to a partially matched set. The matching set and the partially matched set are merged, and the co-coverage raster dataset of the first and second network standards is output.

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

[0081] Step 101: Collect daily-level raw MR geospatial raster data within a historical time period.

[0082] Specifically, MR (Measurement Reporting) refers to the process by which the UE monitors the wireless environment based on measurement parameters configured by the network and reports the monitoring results to the network side through a dedicated signaling channel. These measurement results typically include, but are not limited to, signal strength, signal quality, and neighbor cell information, aiming to help the network side make more reasonable resource allocation and connection management decisions. MR geogrid refers to the process of spatially dividing measurement reports (MR) in the mobile communication network according to geographical location, and summarizing and statistically analyzing relevant wireless performance indicators within each divided area (grid cell). In this way, the quality status of the wireless network, user distribution, and potential problem areas can be visually displayed. Each 4G or 5G cell includes multiple grids. The original 4G MR geogrid dataset collected in this embodiment includes attributes such as 4G cell name, 4G grid number, 4GRSRP, number of 4G sampling points, 4G grid longitude, and 4G grid latitude. The original 5G MR geogrid dataset includes attributes such as 5G cell name, 5G grid number, 5GRSRP, number of 5G sampling points, 5G grid longitude, and 5G grid latitude. In this embodiment, the raster numbers in the original datasets of 4G and 5G MR geodesic raster are completely identical, meaning that the raster division areas of 4G and 5G cells are geographically identical.

[0083] It should be noted that this grid matching method for cells with the same coverage area is applied to, for example... Figure 3 The grid matching system for co-coverage cells shown includes a raw data collection module 201, a 5G weak coverage grid processing module 202, a 4G good coverage grid processing module 203, a 4G / 5G co-coverage grid full data processing module 204, and a 4G / 5G co-coverage grid processing module 205.

[0084] Specifically, the collected 4G MR geodesic raster raw dataset and 5G MR geodesic raster raw dataset are input into the raw data collection module 201.

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

[0086] It's important to note that a threshold, also called a critical value, refers to the minimum or maximum value at which an effect can be produced. In data analysis, thresholds are primarily used for data classification, filtering, and judgment. By setting thresholds, we can refine data processing, selecting data that meets specific conditions for further in-depth analysis. Furthermore, the choice of threshold directly impacts the accuracy and usability of the analysis results.

[0087] Specifically, the raw dataset of 5G MR geospatial raster selects rows where 5GRSRP is less than -105dBm, sorts them in descending order of the number of sampling points, and calculates the percentile ranking of all sampling point counts. For each 5G cell, the number of sampling points and the average percentile ranking of its raster number are calculated, and the results are stored in the 5G cell group. A minimum sampling point count threshold parameter is defined, the parameter value of which is equal to the number of sampling points corresponding to the percentile ranking of all sampling point counts. The percentile ranking used for 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. In each 5G cell group, rasters with a number of sampling points less than the minimum sampling point count threshold are filtered out. Each 5G cell in the 5G cell group is sorted according to the number of sampling points and the percentile ranking. The 5G weak coverage raster dataset is output. This dataset includes attributes such as 5G cell name, 5G raster number, 5GRSRP, number of 5G sampling points, 5G raster longitude, and 5G raster latitude.

[0088] Step 103: Select samples with 4G RSRP greater than -100dBm to generate a 4G well-covered raster dataset.

[0089] Specifically, the raw dataset of 4G MR geodesic raster selects rows with 4G RSRP greater than -100dBm to output a 4G good coverage raster dataset. This dataset includes attributes such as 4G cell name, 4G raster number, 4G RSRP, number of 4G sampling points, 4G raster longitude, and 4G raster latitude.

[0090] Step 104: Merge the 4G and 5G raster numbers using the 4G and 5G raster numbers as keys to generate a full dataset of 4G and 5G co-coverage rasters.

[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 to output the full dataset of 4G and 5G co-coverage rasters. This dataset includes fields such as 4G cell name, 4G raster number, 4G RSRP, number of 4G sampling points, 4G raster longitude, 4G raster latitude, 5G cell name, 5G raster number, 5G RSRP, number of 5G sampling points, 5G raster longitude, and 5G raster latitude.

[0092] Step 105: Adjust the dynamic threshold, match each 5G cell with k 4G cells, and generate a 4G / 5G co-coverage raster dataset.

[0093] Specifically, a parameter is defined to specify the number of 4G cells that a 5G cell needs to match. This parameter value can be adjusted; in this embodiment, it is set to 3. The 4G sampling point counts of the full 4G / 5G co-coverage grid dataset are sorted in descending order. The percentile ranking of all 4G sampling point counts is calculated, and an attribute percentile ranking is generated and added to the full 4G / 5G co-coverage grid dataset. Each 5G cell is sorted in descending order based on the number of 5G sampling points. A dynamic threshold parameter for percentile ranking is defined. This parameter value is the percentile ranking of the number of 4G sampling points. This parameter value can be adjusted; in this embodiment, it is set to 0%, assuming the corresponding number of sampling points is 228304. Each 5G cell is first matched according to the size of the 5G sampling point count, and then according to the dynamic threshold for the percentile ranking of the number of 4G sampling points (0% in this embodiment). Three 4G cells are matched (i.e., the three 4G cells corresponding to the largest number of 4G sampling points are matched), and these are added to the matching set. The 5G cell and its matched 4G cells no longer participate in subsequent processing. Define a step parameter for increasing (or decreasing) the dynamic threshold for percentile ranking. 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 dynamic threshold for percentile ranking, the percentile ranking parameter is increased in increments of 1% until a sufficient number of matches are found or the maximum percentile ranking is reached. When processing each 5G cell, the results are divided into two parts based on whether the requirement for the number of 4G cells to match the 5G cell is met: fully matched data and partially matched data. The partially matched data is saved to a partially matched set. The matched set and the partially matched set are merged to output a 4G / 5G co-coverage raster dataset. This dataset includes fields such as 4G cell name, 4G raster number, 4GRSRP, number of 4G sampling points, 4G raster longitude, 4G raster latitude, 5G cell name, 5G raster number, 5GRSRP, number of 5G sampling points, 5G raster longitude, 5G raster latitude, and percentile ranking.

[0094] Specifically, the dynamic threshold algorithm's process is as follows:

[0095] Input: Sample set D = {x1, x2, ..., xn} 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 sorted in descending order.

[0101] 2: Calculate the percentile ranking r of attribute B. 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 sorted in descending order.

[0104] 5: Let the matching sample set

[0105] 6: For each attribute A

[0106] 7: while(number of matches) <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 condition i And the current threshold p c ≥0 (or p) c ≤1); Increase (or decrease) the threshold step s and update the current threshold p. c ;

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

[0110] 11: Remove matched samples x i Continue processing the remaining parts

[0111] 12: if the number of matches is greater than or equal to k or there are no more samples to choose from.

[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 MR data from 5G and 4G cells to identify weak coverage grids of 5G cells, find good coverage grids of 4G cells that are covered by the same 5G cells, set the percentile ranking of the number of sampling points in the good coverage grids of 4G cells as the neighbor cell selection threshold, and select the good coverage grids of 4G cells with the most sampling points through iterative calculation and dynamic updating of the threshold. This allows each 5G cell to be matched with k 4G cells under the same coverage, improving the accuracy of 4G neighbor cell planning for 5G cells. This invention uses an initial percentile ranking as a threshold, adjusts the percentile ranking by incrementing or decrementing step values, and iteratively calculates and updates the threshold to achieve dynamic adjustment of the threshold. This ensures that attribute A (i.e., the first network standard cell) in the dataset can be matched with k attributes B (i.e., the second network standard cell). This solves the problem that attribute A cannot fully match attribute B due to a fixed threshold, and the attribute value of attribute A can match the maximum attribute value of attribute B. This invention uses percentiles to determine the initial threshold. Percentiles are an objective indicator based on data distribution, representing the proportion of data points in the dataset that are less than or equal to that value. Therefore, the percentile-based threshold setting is objective and not affected by the analyst's subjective judgment. The percentile of the dynamic threshold matching parameter 1 (i.e., the first network standard cell) and parameter 2 (i.e., the second network standard cell) in this invention is not subject to any approximate equal restriction. The percentile of the dynamic threshold is calculated iteratively so that parameter 1 can match N parameters 2. The threshold can be adaptively adjusted, thus reducing the problem of incomplete matching caused by changes in data distribution. In addition, parameter 1 can match the maximum value among parameters 2.

[0116] The raster matching method for co-coverage cells provided in this embodiment of the invention first obtains MR geospatial raster datasets of a first network standard and a second network standard; then, it obtains a weak coverage raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and obtains a good coverage raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard; finally, by adjusting a dynamic threshold, it matches each cell of the first network standard in the weak coverage raster dataset with k cells of the second network standard in the good coverage raster dataset of the second network standard that meet the conditions, and outputs a co-coverage raster dataset of the first network standard and the second network standard, where k is greater than or equal to 1. This invention uses MR geospatial raster datasets of a first network standard and a second network standard to obtain a weak coverage raster dataset of the first network standard and a good coverage raster dataset of the second network standard. By adjusting dynamic thresholds, each cell of the first network standard in the weak coverage raster dataset can be matched with k cells of the second network standard in the good coverage raster dataset that meet the conditions. This avoids phenomena such as missing neighbor cells or redundant neighbor cells, thereby ensuring that users can complete handover in a timely manner, optimizing the user experience, and solving the problem that existing technologies often result in missing neighbor cells or redundant neighbor cells when matching neighbor cells, leading to untimely handover and affecting user experience.

[0117] Example 2:

[0118] like Figure 4 As shown, this embodiment provides a grid matching device for co-coverage cells, used to perform the above-described grid matching method for co-coverage cells, including:

[0119] The first acquisition module 11 is used to acquire the MR geospatial raster dataset of the first network standard and the second network standard.

[0120] The second acquisition module 12 is connected to the first acquisition module 11 and is used to acquire a first network-based weakly covered raster dataset from the first network-based MR geospatial raster dataset and to acquire a second network-based well-covered raster dataset from the second network-based MR geospatial raster dataset.

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

[0122] Furthermore, both the first network standard and the second network standard MR geospatial raster dataset include: cell name, raster number, reference signal received power (RSRP), and number of sampling points, wherein the raster number in the first network standard MR geospatial raster dataset is consistent with the raster number in the second network standard MR geospatial raster dataset.

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

[0124] The first selection is used as a unit to select samples from the MR geospatial 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 as the weakly covered raster dataset of the first network standard.

[0125] The second selection is used as a unit to select samples with RSRP greater than a preset third threshold from the MR geospatial raster dataset of the second network standard as the well-covered raster dataset of the second network standard.

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

[0127] The selection unit is used to select multiple samples whose RSRP is less than the first threshold from the MR geospatial raster dataset of the first network standard.

[0128] The first sorting calculation unit is used to sort the multiple samples in descending order according to the number of sampling points of the multiple samples, and to calculate the percentile ranking of the number of sampling points of all samples in the multiple samples.

[0129] The first defining unit is used 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 samples among the plurality of samples whose number of sampling points is less than the second threshold, thereby obtaining the first network-format weakly covered raster dataset.

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

[0132] The merging and generation unit is used to merge the first network standard weakly covered raster dataset and the second network standard well covered raster dataset using the raster number in the first network standard weakly covered raster dataset and the raster number in the second network standard well covered raster dataset as keywords, to generate a full dataset of raster with the same coverage in both the first and second network standards.

[0133] The second sorting calculation unit is used to sort the number of all second network standard sampling points in the full dataset of the same coverage raster in descending order, and to calculate the percentile ranking of the number of all second network standard sampling points.

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

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

[0136] The matching generation unit is used to use a dynamic threshold algorithm to match k second network standard cells that meet the conditions for each first network standard cell in the full dataset of the same coverage raster, thereby generating a raster dataset of the first and second network standards with the same coverage.

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

[0138] The first matching and adding unit is used 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 cell and k second network standard cells to the matching set;

[0139] The second matching addition unit is used to adjust the dynamic threshold by incrementing or decrementing a preset step value if k second network standard cells cannot be matched according to the dynamic threshold, and match 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 add the data that is not completely matched but partially matched to the partial matching set.

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

[0141] Furthermore, the co-coverage raster dataset of the first network standard and the second network standard includes: cell name of the first network standard, raster number of the first network standard, RSRP of the first network standard, number of sampling points of the first network standard, cell name of the second network standard, raster number of the second network standard, RSRP of the second network standard, number of sampling points of the second network standard, and percentile ranking of the second network standard.

[0142] Example 3:

[0143] refer to Figure 5This embodiment provides a grid matching device for co-coverage cells, 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 co-coverage cells in Embodiment 1.

[0144] The memory 21 is connected to the processor 22. The memory 21 can be a flash memory, a read-only memory or other memory, and the processor 22 can be a central processing unit or a microcontroller.

[0145] Example 4:

[0146] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the grid matching method for co-coverage cells in Embodiment 1 above.

[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 technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0148] In summary, the raster matching method, apparatus, and medium for co-coverage cells provided in this embodiment of the invention first acquires MR geospatial raster datasets of a first network standard and a second network standard; then, it acquires a weak coverage raster dataset of a first network standard from the MR geospatial raster dataset of the first network standard, and acquires a good coverage raster dataset of a second network standard from the MR geospatial raster dataset of the second network standard; finally, by adjusting a dynamic threshold, it matches each cell of the first network standard in the weak coverage raster dataset of the first network standard with k cells of the second network standard in the good coverage raster dataset of the second network standard that meet the conditions, and outputs a co-coverage raster dataset of the first network standard and the second network standard, where k is greater than or equal to 1. This invention uses MR geospatial raster datasets of a first network standard and a second network standard to obtain a weak coverage raster dataset of the first network standard and a good coverage raster dataset of the second network standard. By adjusting dynamic thresholds, each cell of the first network standard in the weak coverage raster dataset can be matched with k cells of the second network standard in the good coverage raster dataset that meet the conditions. This avoids phenomena such as missing neighbor cells or redundant neighbor cells, thereby ensuring that users can complete handover in a timely manner, optimizing the user experience, and solving the problem that existing technologies often result in missing neighbor cells or redundant neighbor cells when matching neighbor cells, leading to untimely handover and affecting user experience.

[0149] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled 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 co-coverage cells, characterized in that, The method includes: Obtain the MR geospatial raster dataset containing measurement reports for both the first and second network standards. Obtain a weakly covered raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and obtain a well-covered raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard. By adjusting the dynamic threshold, each cell of the first network standard in the weak coverage raster dataset of the first network standard is matched with k cells of the second network standard in the good coverage raster dataset of the second network standard that meet the conditions, and the first network standard and the second network standard co-coverage raster dataset are output, where k is greater than or equal to 1. The step of adjusting a dynamic threshold to match each cell of the first network standard in the weak coverage raster dataset with k cells of the second network standard in the good coverage raster dataset that meet the conditions, and outputting a raster dataset with the same coverage of the first and second network standards, specifically includes: Using the raster numbers in the first network standard weakly covered raster dataset and the raster numbers in the second network standard well covered raster dataset as keywords, the first network standard weakly covered raster dataset and the second network standard well covered raster dataset are merged to generate a full dataset of raster with the same coverage in both the first and second network standards. Sort the number of sampling points of all second network types in the full dataset of the same coverage raster in descending order, and calculate the percentile ranking of the number of sampling points of all second network types. The percentile ranking of the number of sampling points in the preset second network standard is used as the initial dynamic threshold; Define the number of second-network standard cells that each first-network standard cell needs to match as parameter k; For each first network standard cell, k second network standard cells are matched for the first network standard cell according to the dynamic threshold, and the successfully matched first network standard cell and k second network standard cells are added to the 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 are not completely matched but partially matched are added to the partial matching set. By merging the matching set and the partial matching set, a raster dataset with the same coverage for the first network standard and the second network standard is obtained.

2. The method according to claim 1, characterized in that, Both the first and second network standard MR geospatial raster datasets include: cell name, raster number, reference signal received power (RSRP), and number of sampling points. The raster number in the first network standard MR geospatial raster dataset is the same as the raster number in the second network standard MR geospatial raster dataset.

3. The method according to claim 2, characterized in that, The step of obtaining a weakly covered raster dataset of the first network standard from the MR geospatial raster dataset of the first network standard, and obtaining a well-covered raster dataset of the second network standard from the MR geospatial raster dataset of the second network standard, specifically includes: From the MR geospatial raster dataset of the first network standard, 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 as the weak coverage raster dataset of the first network standard. From the MR geospatial raster dataset of the second network standard, select samples with RSRP greater than a preset third threshold as the well-covered raster dataset of the second network standard.

4. The method according to claim 3, characterized in that, The step of selecting samples with RSRP less than a preset first threshold and a number of filtered sampling points less than a preset second threshold from the MR geospatial raster dataset of the first network standard specifically includes: Select multiple samples with RSRP less than the first threshold from the MR geospatial raster dataset of the first network standard; Based on the number of sampling points of the multiple samples, the multiple samples are sorted in descending order, and the percentile ranking of the number of sampling points of all samples in the multiple samples is calculated. The second threshold is defined as the number of sampling points corresponding to the preset percentile ranking; The samples with fewer than the second threshold number of sampling points among the multiple samples are filtered out to obtain the first network standard weakly covered raster dataset.

5. The method according to claim 1, characterized in that, The co-coverage raster dataset of the first network standard and the second network standard includes: cell name of the first network standard, raster number of the first network standard, RSRP of the first network standard, number of sampling points of the first network standard, cell name of the second network standard, raster number of the second network standard, RSRP of the second network standard, number of sampling points of the second network standard, and percentile ranking of the second network standard.

6. A grid matching device for a cell with the same coverage area, characterized in that, include: The first acquisition module is used to acquire the MR geospatial raster dataset of measurement reports for the first and second network standards. The second acquisition module, connected to the first acquisition module, is used to acquire a first network-based weakly covered raster dataset from the first network-based MR geospatial raster dataset, and to acquire a second network-based well-covered raster dataset from the second network-based MR geospatial raster dataset. The matching module, connected to the second acquisition module, is used to match each first network standard cell in the first network standard weak coverage raster dataset with k second network standard cells in the second network standard good coverage raster dataset that meet the conditions by adjusting the dynamic threshold, and output the same coverage raster dataset of the first network standard and the second network standard, where k is greater than or equal to 1. The matching module specifically includes: The merging and generation unit is used to merge the first network standard weakly covered raster dataset and the second network standard well covered raster dataset using the raster number in the first network standard weakly covered raster dataset and the raster number in the second network standard well covered raster dataset as keywords, to generate a full dataset of raster with the same coverage in both the first and second network standards. The second sorting calculation unit is used to sort the number of all second network standard sampling points in the full dataset of the same coverage raster in descending order, and to calculate the percentile ranking of the number of all second network standard sampling points. As a unit, it is used to adopt the percentile ranking of the number of sampling points of the preset second network standard as the initial dynamic threshold; The second definition unit is used to define the number of second network standard cells that each first network standard cell needs to match as parameter k; The first matching and adding unit is used 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 cell and k second network standard cells to the matching set; The second matching addition unit is used to adjust the dynamic threshold by incrementing or decrementing a preset step value if k second network standard cells cannot be matched according to the dynamic threshold, and match 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 add the data that is not completely matched but partially matched to the partial matching set. The merging unit is used to merge the matching set and the partial matching set to obtain a raster dataset with the same coverage for the first network standard and the second network standard.

7. A grid matching device for a cell with the same coverage area, characterized in that, It includes 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 co-coverage cells as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the grid matching method for co-coverage cells as described in any one of claims 1-5.

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