Base station co-location matching identification method and device and 5G work parameter correction method

Abstract

The invention discloses a base station co-location matching identification method and device and a 5G work parameter correction method. The matching identification method is a method for identifying base station co-location based on network side big data grid matching, and comprises the following specific steps: firstly obtaining 4G and 5G MR data, and then performing position backfilling on the MR data based on a 4G MDT fingerprint database to obtain backfilled 4G and 5G MR data. Then, carrying out rasterization processing on the backfill data to generate corresponding 4G MR raster data and 5G MR raster data; and then, grouping the 4G MR raster data to obtain 4G MR target data, and meanwhile, grouping the 5G MR raster data to obtain 5G MR target data. And finally, through matching the 4G MR target data with the 5G MR target data, identifying co-located 4G and 5G base stations, and realizing grid matching identification based on network side big data. According to the method, the co-located base station can be quickly and accurately identified, so that support is provided for quick correction of 5G network work parameter information.

Description

Technical Field

[0001] The present invention belongs to the field of network communication technology, and in particular, relates to a base station co-location matching identification method, device and 5G (fifth generation mobile communication technology) engineering parameter correction method. Background Art

[0002] In the field of wireless network planning and construction, the engineering parameter information of base stations plays a vital role, because this information is directly related to the network coverage, signal quality and the actual user experience. Accurate and complete engineering parameter information is the key to ensure the authenticity and reliability of wireless network planning and simulation results. It provides a solid scientific basis and clear guidance for subsequent network optimization strategies and actual construction.

[0003] At present, in the actual operation of network construction, in order to effectively control construction costs, optimize the use of limited space resources, and significantly improve network coverage, operators generally adopt the strategy of co-locating 4G (fourth-generation mobile communication technology) and 5G base stations, that is, setting these two types of base stations in the same geographical location. This co-location method greatly reduces the capital investment in infrastructure construction, while optimizing the configuration and utilization efficiency of network resources, allowing operators to meet the growing communication needs of users as much as possible under relatively limited resources, and provide a more stable and efficient service experience.

[0004] However, despite the many advantages of co-location of 4G and 5G base stations, the 5G network still has obvious shortcomings in terms of the integrity of the engineering parameter information compared to the more mature 4G network. This situation directly affects the accuracy of checking and verifying the engineering parameters of base stations through mobile operation data (including MR data, which usually refers to "measurement report data of mobile operators", i.e. MobileReport Data), thus hindering the efficient operation and precise optimization of 5G base stations.

[0005] Currently, one way to check the working parameters of base stations is to have maintenance personnel conduct on-site verification of the working parameters of 5G base stations. Although this method has a certain degree of accuracy, its cost is relatively high and the verification cycle is long. At the same time, some errors may still occur when entering working parameter information.

[0006] In order to replace manual work, the Chinese patent "A method for identifying base station co-sites based on big data" (application number: CN202110509326.7) proposes a method for accurately identifying base station co-sites. The core of this method is to conduct an in-depth analysis of the data collected from the different network frequency band signals involved in the wireless environment measurement report (MR), and to conduct comprehensive data cleaning and processing on this basis. By cleverly using advanced machine learning technology, the accurate classification and identification of co-site points have been successfully achieved, effectively solving the problems of inaccurate and incomplete site information that are common in traditional asset management systems, enabling it to accurately identify whether a base station is a shared base station. Despite this, in the actual implementation of this technical solution, it relies heavily on machine learning technology, which makes the data training process relatively cumbersome and the computational efficiency is not high.

[0007] Therefore, a more efficient method is needed to accurately identify co-located 4G and 5G base stations, thereby improving the performance of 5G networks in terms of engineering parameter information integrity. Summary of the invention

[0008] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art and propose a base station co-location matching identification method, device and 5G engineering parameter correction method. The base station co-location matching identification method can quickly and accurately identify co-located 4G and 5G base stations, thereby realizing rapid correction of the engineering parameter information of the 5G network.

[0009] In a first aspect, the present invention provides a method for identifying base station co-location based on network-side big data grid matching, the method comprising the following steps:

[0010] Step S1: Acquire 4G MR data and 5G MR data;

[0011] Step S2: Based on the 4G MDT fingerprint library, the 4G MR data and the 5G MR data are backfilled to obtain backfilled 4G MR data and 5G MR data;

[0012] Step S3: performing rasterization processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data;

[0013] Step S4: grouping the 4G MR grid data to obtain 4G MR target data; and grouping the 5G MR grid data to obtain 5G MR target data;

[0014] Step S5: Match the 4G MR target data and the 5G MR target data to identify the co-located 4G base stations and 5G base stations, thereby completing the identification of base station co-location based on network-side big data grid matching.

[0015] Further, before the step S2, the method further includes a step S0;

[0016] Step S0: Build a 4G MDT fingerprint library;

[0017] The step S0 specifically includes the following steps:

[0018] Step S01: Acquire massive 4G MDT data on the network side within a preset period;

[0019] Step S02: extracting attribute data from the 4G MDT data; and removing 4G MDT data without longitude and latitude information;

[0020] Wherein, the attribute data includes RSRP data and latitude and longitude data;

[0021] Step S03: Integrate 4G MDT data according to the attribute data to obtain a 4G MDT fingerprint library, thereby completing the construction of the 4G MDT fingerprint library.

[0022] Furthermore, after step S3 and before step S4, the method further includes the steps of:

[0023] The 4G MR grid data is cleaned to ensure the integrity and uniqueness of the 4G MR grid data; and the 5G MR grid data is cleaned to ensure the integrity and uniqueness of the 5G MR grid data.

[0024] Further, in step S5, matching the 4G MR target data with the 5G MR target data to identify the co-located 4G base station and 5G base station specifically includes the following steps:

[0025] Step S51: performing GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box; and performing GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box;

[0026] Step S52: combining the vector shape, area, and grid level of the first outer frame to obtain a combination point of a 4G base station; and combining the vector shape, area, and grid level of the second outer frame to obtain a combination point of a 5G base station;

[0027] Step S53: Match the combination points of the 4G base stations and the combination points of the 5G base stations in pairs to identify the co-located 4G base stations and 5G base stations.

[0028] Furthermore, the step S53 specifically includes the following steps:

[0029] Step S531: obtaining the first center point coordinates (X1, Y1) of the vector shape of the first outer bounding box; and obtaining the second center point coordinates (X2, Y2) of the vector shape of the second outer bounding box;

[0030] Step S532: Calculate the distance D between the first center point coordinates and the second center point coordinates c1-c2 , and its calculation formula is as follows:

[0031]

[0032] Step S533: The combination points of the 4G base stations and the combination points of the 5G base stations whose distances are less than the preset threshold form a matching pair set to obtain an initial matching result;

[0033] Step S534: Based on the initial matching result, the matching score M is calculated according to the vector shape, area, and grid level. 1-2 , and its calculation formula is as follows:

[0034] M 1-2 =S1*W1+S2*W2+S3*W3;

[0035] Among them, S1 represents the vector shape similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0036] S2 represents the area similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0037] S3 represents the grid level similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0038] W1 represents the vector shape similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0039] W2 represents the area similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0040] W3 represents the grid-level similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0041] Step S535: Eliminate the matching results whose matching scores are less than the preset matching value, sort the matching result scores, and select the highest matching result as the optimal matching result;

[0042] Step S536: Identify the co-located 4G base stations and 5G base stations through the optimal matching result.

[0043] In a second aspect, the present invention provides a method for correcting working parameters of a 5G base station, characterized in that the method comprises the following steps:

[0044] Get the target 5G base station;

[0045] The method for identifying base station co-location by grid matching of big data on the network side described in the first aspect is adopted to identify the 4G base station co-located with the target 5G base station;

[0046] According to the working parameters of the 4G base station, the working parameters of the target 5G base station are corrected.

[0047] In a third aspect, the present invention provides a device for identifying base station co-location based on network-side big data grid matching, the device comprising:

[0048] An acquisition unit, used for acquiring 4G MR data and 5G MR data;

[0049] A first processing unit, connected to the acquisition unit, configured to perform position backfilling on the 4G MR data and the 5G MR data based on a 4G MDT fingerprint library to obtain backfilled 4G MR data and 5G MR data;

[0050] A second processing unit, connected to the first processing unit, for performing rasterization processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data;

[0051] a grouping unit connected to the second processing unit, configured to group the 4G MR grid data to obtain 4G MR target data; and, configured to group the 5G MR grid data to obtain 5G MR target data;

[0052] A matching unit is connected to the grouping unit and is used to match the 4G MR target data with the 5G MR target data to identify the co-located 4G base stations and 5G base stations, thereby completing the identification of base station co-location based on network-side big data grid matching.

[0053] Furthermore, the device also includes:

[0054] A construction unit, connected to the first processing unit, configured to construct a 4G MDT fingerprint library;

[0055] The building block comprises:

[0056] An acquisition module is used to acquire massive 4G MDT data on the network side within a preset period;

[0057] An extraction module, connected to the acquisition module, for extracting attribute data from the 4G MDT data;

[0058] Wherein, the attribute data includes RSRP data and latitude and longitude data;

[0059] A rejection module, connected to the acquisition module, for rejecting 4G MDT data without longitude and latitude information;

[0060] The integration module is connected to the extraction module and the elimination module respectively, and is used to integrate the 4G MDT data according to the attribute data to obtain the 4G MDT fingerprint library, that is, to complete the construction of the 4G MDT fingerprint library.

[0061] Furthermore, the matching unit includes:

[0062] A processing module, used for performing GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box; and used for performing GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box;

[0063] A combination module, connected to the processing module, for combining the vector shape, area, and grid level of the first outer frame to obtain a combination point of a 4G base station; and combining the vector shape, area, and grid level of the second outer frame to obtain a combination point of a 5G base station;

[0064] A matching module is connected to the combination module and is used to match the combination points of the 4G base stations and the combination points of the 5G base stations in pairs to obtain a co-located 4G base station and a 5G base station.

[0065] Furthermore, the matching module includes:

[0066] An acquisition submodule, used to acquire the first center point coordinates (X1, Y1) of the vector shape of the first outer bounding box; and used to acquire the second center point coordinates (X2, Y2) of the vector shape of the second outer bounding box;

[0067] The first calculation submodule is used to calculate the distance D between the first center point coordinates and the second center point coordinates. c1-c2 ;

[0068] The first calculation submodule stores the following formula:

[0069]

[0070] A combination submodule, connected to the first calculation submodule, for forming a matching pair set from the combination points of the 4G base stations and the combination points of the 5G base stations whose distances are less than a preset threshold, to obtain an initial matching result;

[0071] The second calculation submodule is connected to the combination submodule and is used to calculate the matching score M based on the initial matching result according to the vector shape, area, and grid level. 1-2 ;

[0072] The second calculation submodule stores the following calculation formula:

[0073] M 1-2 =S1*W1+S2*W2+S3*W3;

[0074] Among them, S1 represents the vector shape similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0075] S2 represents the area similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0076] S3 represents the grid level similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0077] W1 represents the vector shape similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0078] W2 represents the area similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0079] W3 represents the grid-level similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0080] A preferred submodule, connected to the second calculation submodule, for eliminating matching results with matching scores less than a preset matching value, and for sorting the matching result scores, and selecting the highest matching result as the optimal matching result;

[0081] An identification submodule is connected to the preferred submodule and is used to identify co-located 4G base stations and 5G base stations through the optimal matching result.

[0082] The present invention is based on the network-side big data grid matching technology, which can quickly and accurately identify co-located 4G and 5G base stations, thereby providing effective support for quickly correcting the engineering parameter information of the 5G network. The specific beneficial effects are as follows:

[0083] 1. Efficient data processing: The present invention uses the MDT fingerprint library to perform position backfilling of 4G and 5G MR data, which can quickly integrate data from different networks, thereby effectively shortening the time for data processing and analysis.

[0084] 2. Accurate positioning capability: Through position backfilling, the 4G and 5G MR data obtained by the present invention are more accurate, providing a reliable data basis for subsequent rasterization processing and matching identification, thereby significantly improving the accuracy of co-site identification.

[0085] 3. Reduce computational complexity: The present invention performs rasterization processing on MR data, divides the massive data into smaller units, simplifies the complexity of data analysis, and makes the subsequent grouping and matching process more efficient.

[0086] 4. Automation and intelligence: The present invention can realize automatic identification of co-located base stations by grouping and matching 4G and 5G MR data, reduce dependence on manual intervention, and thus reduce the risk of human error.

[0087] 5. Real-time update capability: The present invention performs real-time analysis based on big data in a dynamic network environment, and can promptly identify newly emerging co-located base stations to ensure the timeliness and effectiveness of the data.

[0088] 6. Resource optimization and management: By accurately identifying co-located base stations, the present invention helps operators to allocate resources more reasonably, optimize network layout, improve network service quality, and thus meet users' needs for network coverage.

[0089] 7. Strong adaptability: The present invention can flexibly adjust the rasterization processing and matching mechanism according to the characteristics of different regions to adapt to diverse network environments and needs.

[0090] 8. Good scalability and foresight: With the continuous development of network technology, the present invention has good scalability and can support the co-location identification of base stations of future new technologies (such as 6G), ensuring its forward-looking position in wireless network management. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A schematic diagram of a method for identifying base station co-location based on network-side big data grid matching in an embodiment of the present invention;

[0092] Figure 2 This is an overall framework diagram for identifying base station co-location based on network-side big data grid matching in an embodiment of the present invention;

[0093] Figure 3 It is a schematic diagram of a process of identifying base station co-location based on network-side big data grid matching in an embodiment of the present invention;

[0094] Figure 4 This is a schematic diagram of co-location matching of a 4G base station and a 5G base station in an embodiment of the present invention;

[0095] Figure 5 It is a schematic diagram of a device for identifying base station co-location on the network side by big data grid matching in an embodiment of the present invention;

[0096] Among them, the figure numbers are: 10, acquisition unit, 20, first processing unit, 30, second processing unit, 40, grouping unit, 50, matching unit. DETAILED DESCRIPTION

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Embodiment 1:

[0106] like Figure 1As shown, this embodiment provides a method for identifying base station co-location based on network-side big data grid matching. The method is applicable to multiple scenarios, including urban wireless networks and new base station construction planning. By quickly and accurately identifying co-located 4G and 5G base stations, network coverage and signal quality can be improved. In addition, the method can also be used for network expansion and upgrading, helping operators to rationally allocate resources and reduce duplicate construction; in areas where communication popularization is slow, network coverage can be quickly improved by determining the best construction point; in base station maintenance and optimization, the adjustment of working parameters can be achieved; and in emergency management and post-disaster recovery, the smooth communication network is ensured to support disaster relief work. In short, the method provides strong technical support for the planning, construction and operation of wireless networks.

[0107] The method comprises the following steps:

[0108] Step S0: Construct a 4G MDT fingerprint library; the 4G MDT fingerprint library is a database used to record and store user equipment measurement data in 4G wireless networks, mainly including data such as signal strength, signal quality and location information. These data are automatically collected by user terminals and uploaded to the operator's server, aiming to optimize network performance, evaluate base station performance and reduce the need for traditional field testing. By analyzing the data in the MDT fingerprint library, operators can identify hot spots and blind spots in network coverage, thereby rationally planning and deploying base stations to improve user experience and network service quality.

[0109] The step S0 specifically includes the following steps:

[0110] Step S01: Acquire massive 4G MDT data on the network side within a preset period;

[0111] Step S02: extracting attribute data from the 4G MDT data; and removing 4G MDT data without longitude and latitude information;

[0112] Wherein, the attribute data includes RSRP data and latitude and longitude data;

[0113] Step S03: Integrate 4G MDT data according to the attribute data to obtain a 4G MDT fingerprint library, thereby completing the construction of the 4G MDT fingerprint library.

[0114] Step S1: Acquire 4G MR data and 5G MR data.

[0115] Step S2: Based on the 4G MDT fingerprint library, the positions of the 4G MR data and the 5G MR data are backfilled to obtain backfilled 4G MR data and 5G MR data.

[0116] Step S3: performing rasterization processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data.

[0117] In specific implementation, after obtaining 4G MR raster data and 5G MR raster data, it is necessary to clean the 4G MR raster data and 5G MR raster data respectively. Cleaning the 4G MR raster data is used to ensure the integrity and uniqueness of the 4G MR raster data; cleaning the 5G MR raster data is used to ensure the integrity and uniqueness of the 5G MR raster data. The cleaning of 4G MR raster data aims to ensure the integrity and uniqueness of the data, including checking and filling in missing longitude and latitude information, removing duplicate records, processing outliers, and unifying data formats, so as to make the data set more accurate and reliable; while the cleaning of 5G MR raster data also focuses on ensuring its integrity and uniqueness. Through similar steps, it is ensured that all necessary fields are complete and without duplications, so as to provide a high-quality data foundation for subsequent analysis and decision-making.

[0118] Step S4: grouping the 4G MR grid data to obtain 4G MR target data; and grouping the 5G MR grid data to obtain 5G MR target data.

[0119] Step S5: Match the 4G MR target data and the 5G MR target data to identify the co-located 4G base stations and 5G base stations, thereby completing the identification of base station co-location based on network-side big data grid matching.

[0120] As a specific implementation, in step S5, matching the 4G MR target data with the 5G MR target data to identify the co-located 4G base station and 5G base station specifically includes the following steps:

[0121] Step S51: performing GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box; and performing GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box.

[0122] Step S52: Combine the vector shape, area, and grid level of the first outer frame to obtain the combination point of the 4G base station; and combine the vector shape, area, and grid level of the second outer frame to obtain the combination point of the 5G base station.

[0123] Step S53: Match the combination points of the 4G base stations and the combination points of the 5G base stations in pairs to identify the co-located 4G base stations and 5G base stations.

[0124] Specifically, step S53 includes the following steps:

[0125] Step S531: obtaining the first center point coordinates (X1, Y1) of the vector shape of the first bounding box; and obtaining the second center point coordinates (X2, Y2) of the vector shape of the second bounding box.

[0126] Step S532: Calculate the distance D between the first center point coordinates and the second center point coordinates c1-c2 , and its calculation formula is as follows:

[0127]

[0128] This formula is based on the Pythagorean theorem in a rectangular coordinate system and can effectively calculate the straight-line distance between two points.

[0129] Step S533: The combination points of the 4G base stations and the combination points of the 5G base stations whose distance is less than the preset threshold are formed into a matching pair set to obtain an initial matching result.

[0130] Step S534: Based on the initial matching result, the matching score M is calculated according to the vector shape, area, and grid level. 1-2 , and its calculation formula is as follows:

[0131] M 1-2 =S1*W1+S2*W2+S3*W3;

[0132] Among them, S1 represents the vector shape similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0133] S2 represents the area similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0134] S3 represents the grid level similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0135] W1 represents the vector shape similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0136] W2 represents the area similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0137] W3 represents the grid-level similarity weight between the combination points of 4G base stations and the combination points of 5G base stations.

[0138] Step S535: Eliminate the matching results whose matching scores are less than the preset matching value, sort the matching result scores, and select the highest matching result as the optimal matching result.

[0139] Step S536: Identify the co-located 4G base stations and 5G base stations through the optimal matching result.

[0140] The following is a detailed description and example of the above step:

[0141] This step mainly evaluates the matching degree between the combination points of 4G base stations and 5G base stations based on four indicators and calculates the matching score. Assume that there are the following sets of data:

[0142] 4G base station combination points: A1, A2, A3

[0143] 5G base station combination points: B1, B2, B3

[0144] Calculating Similarity

[0145] Vector shape similarity (Shape sim ): Evaluated by comparing the shapes of A1 and B1 using a shape index or contour similarity algorithm. Assume

[0146] shape sim (A1,B1)=0.8.

[0147] Area similarity sim ): Compare the areas of A1 and B1. Assuming the area of ​​A1 is 100 units and the area of ​​B1 is 90 units, use the relative area difference calculation formula to get Area sim (A1,B1)=0.9.

[0148] Grid level similarity sim ): Compare the grid levels of A1 and B1. Assuming that the level of A1 is 3 and the level of B1 is 2, the absolute difference in the levels can be used to reflect the similarity and calculate the Level sim (A1,B1)=0.7.

[0149] Weight: Set the corresponding weight value, assuming:

[0150] Weight Shape =0.5,Weight Area =0.3,Weight Level =0.2

[0151] Calculate the matching score

[0152] The formula for the comprehensive matching score is:

[0153] Score(A1,B1)=Shapesim(A1,B1)×WeightShape+Areasim(A1,B1)×WeightArea+Levelsim(A1,B1)×WeightLevel

[0154] Substituting the values ​​into the formula:

[0155] Score(A1,B1)=0.8×0.5+0.9×0.3+0.7×0.2=0.4+0.27+0.14=0.81;

[0156] Similar calculations are performed on other combination points (such as A2 / B2, A3 / B3) to obtain their respective matching scores.

[0157] After the matching scores of all combination points are calculated, a preset matching value, such as 0.75, is set. In this step, matching results with scores lower than 0.75 are eliminated.

[0158] If the sequence is:

[0159] Score(A1,B1)=0.81

[0160] Score(A2,B2)=0.65

[0161] Score(A3,B3)=0.78

[0162] Then the matches of A2 and B2 are eliminated, leaving A1-B1 and A3-B3.

[0163] Next, the remaining matching results (A1-B1 and A3-B3) are sorted by score, and the matching result with the highest score is A1-B1.

[0164] The best matching result A1-B1 identifies the co-located 4G base station A1 and 5G base station B1. This result shows that A1 and B1 have high similarity in terms of vector shape, area, and grid level, so they are likely to be co-located base stations.

[0165] like Figure 2 As shown, this embodiment proposes a method for identifying 4 / 5G base station co-location based on massive 4G MDT and 4 / 5G MR data on the network side, including network side 4G MDT fingerprint library construction, 4 / 5G MR sampling point position backfilling, backfill result data cleaning and grouping, grouped backfill result rasterization processing, 4 / 5G base station engineering parameter estimation, 4 / 5G base station grid matching and co-location identification, and 5G engineering parameter verification.

[0166] Figure 3The flow chart of identifying base station co-location based on network-side big data grid matching is shown. The specific implementation process is as follows: First, the 4G MDT (mobile data terminal) raw data is parsed and cleaned to ensure the accuracy and reliability of the data. Then, a 4G MDT fingerprint library containing different base station signal characteristics and environmental information is constructed to form the "fingerprint" of the base station signal. Based on this fingerprint library, the location of 4G and 5G mobile records (MR) can be backfilled to ensure that each record can accurately match the corresponding base station signal characteristics. Subsequently, these mobile records are further cleaned and grouped according to the base station ID for subsequent data analysis and processing. Finally, the grouped 4G and 5G mobile record data are output for in-depth research and application. For example, in a city, after collecting the signal strength data of multiple base stations, by building a fingerprint library, the user's mobile records at different locations can be matched with the base station signal characteristics, so as to accurately backfill the user's location and analyze their mobile behavior and network coverage.

[0167] The working principle and workflow of this embodiment specifically include the following steps:

[0168] Step 1: Build 4G MDT fingerprint library on the network side, backfill MR positions and group them

[0169] Build a fingerprint library based on 4G MDT, and then backfill and process the 4 / 5G MR:

[0170] Step 1: Collect massive 4G MDT data from the network side of a city for 5 consecutive days, parse out the frequency, PCI, RSRP, longitude and latitude and other attributes of the neighboring areas of the main area of ​​the 4G MDT sample point, remove the data without longitude and latitude, and build a 4G MDT fingerprint library;

[0171] Step 2: Collect 4 / 5G MR data of the city for three consecutive days, parse the MR data, and then backfill the location based on the 4G MDT fingerprint library;

[0172] Step 3: Clean the backfilled MR data, remove the data without backfilled latitude and longitude information, and then group the 4 / 5G MRs by base station ID.

[0173] Step 2: 4 / 5G backfill MR rasterization and 4G base station engineering parameter estimation Rasterize the backfill grouped 4 / 5G MR data and estimate the 4G base station engineering parameters:

[0174] Step 1: Based on the MR data after backfilling grouping, rasterization is performed separately, with a grid size of 8 levels, and the average level of each grid is calculated;

[0175] Step 2: Estimate 4 / 5G base station engineering parameters based on the backfilled MR data.

[0176] Step 3: 4 / 5G base station grid matching and co-site identification

[0177] like Figure 4 As shown, the grouped 4 / 5G gridded data is matched, and the matching result is used as the 4 / 5G base station co-location:

[0178] Step 1: Perform GIS convex hull processing on the grouped 4 / 5G base station raster data into an outer box, calculate the area of ​​the outer box, and use the outer box vector shape, area, and grid level as a combination point, such as: 4G base station combination point set (base station a, base station b...), 5G base station combination point set (base station A, base station B...);

[0179] Step 2: Match the 4 / 5G base station grid combination points in pairs. The matching steps are as follows:

[0180] (1) Initial matching: Calculate the coordinates of the center point of the 4 / 5G base station grid outer frame vector, calculate the distance of the 4 / 5G base station center point, set the threshold to 300 meters, and form a matching pair set of base station combination points with a distance less than 300 meters, such as: matching pair 1 (4G combination point a, 5G combination point A), matching pair 2 (4G combination point a, 5G combination point B)..., matching pair 2 (4G combination point b, 5G combination point A), matching pair 2 (4G combination point b, 5G combination point B)..., combination point attributes (outer frame shape, area, grid level);

[0181] (2) Precision matching: further match each matching pair, set the matching weight, match attributes [box shape, area, grid level], corresponding weight [0.3, 0.2, 0.5];

[0182] (3) Screening the best matching result: Calculate the matching score of each matching pair, set the score threshold to 0.6, and remove the matching pairs with a score less than the threshold. If the scores of the matching pairs in this group are all less than 0.6, it proves that the 4 / 5G base stations in this group are not co-located. If there are remaining matching results after removal, sort the matching result scores, and the result with the highest score is regarded as the best matching result, and the 4 / 5G base stations in this matching pair are co-located.

[0183] Step 3: Output the optimal matching result set of 4 / 5G co-location.

[0184] 4 / 5G base station grid collection:

[0185] U 4G ={a,b,c...}

[0186] U 5G ={A,B,C...}

[0187] The properties of the combined point elements in the collection are: (grid shape, bounding box area, grid level), and the matching score weights are: (0.3, 0.2, 0.5).

[0188] Matching pair set:

[0189] PP a ={(a,A),(a,B),(a,C)...}

[0190] PP b ={(b,A),(b,B),(b,C)...} ...

[0192] Matching result set:

[0193] PR a ={0.5,0.8,0.9...}

[0194] PR b ={0.5,0.3,0.4...} ...

[0196] Best matching result:

[0197] PR a =(a,C)

[0198] PR b =null (no matching results) ...

[0200] Note that the grid shape refers to the geometric shape formed by the grid cells (usually regular squares or rectangles) in a certain spatial area in data analysis. Each grid cell represents data information in a specific area, such as signal strength, land use, etc. In base station co-location analysis, the grid shape helps to visualize and compare the signal coverage in the area. The outer box area refers to the area of ​​the smallest rectangular box that encloses a specific area (such as a grid shape). The border of this rectangular box is usually aligned with the border of the grid and can completely wrap the grid shape. The outer box area is used to measure the size of a specific area in spatial analysis and is very important for calculation and spatial relationship analysis. The grid level refers to the data value or feature within the grid cell, which can represent multiple aspects of information, such as signal strength, coverage quality, or network performance indicators. The grid level is usually a continuous value that indicates the relative strength of a certain characteristic (such as telecommunications signal strength) in the area. Different values ​​of the grid level can be used to analyze the coverage area and for applications in network optimization and planning.

[0201] Step 4: Correct the 5G working parameters by using the 4G base station working parameters at the same site

[0202] Based on the output results of step three, the co-located 4G engineering parameter information (base station longitude and latitude, base station height, and cell azimuth) is assigned to the 5G engineering parameter information to perform 5G engineering parameter verification and improve the accuracy of 5G engineering parameter estimation.

[0203] This embodiment relies on rich data sources and low acquisition costs, and uses massive data to identify the co-location of 4G and 5G base stations, effectively avoiding the impact of erroneous information in the industrial parameters on the recognition results. First, the backfilled MR data is rasterized, and GIS convex hull processing is performed on each set of raster data to convert it into an outer box, thereby achieving image similarity matching. Next, through the co-location grid similarity matching of 4G and 5G base stations, the shape, area and level of the grid outer box are used as matching attributes, and a matching weight is assigned to each attribute, which can effectively avoid extreme value fluctuations that may occur in single attribute matching. Not only that, the matching process is divided into preliminary matching and precise matching, and the optimal matching result is finally screened out. This calculation process is simple and efficient, with high matching efficiency and low resource consumption. Through the above steps, this embodiment significantly improves the accuracy of 5G industrial parameter verification, thereby further improving the accuracy of wireless network planning and simulation.

[0204] Embodiment 2:

[0205] like Figure 2 As shown, this embodiment provides a method for correcting working parameters of a 5G base station, characterized in that the method comprises the following steps:

[0206] Get the target 5G base station;

[0207] The method for identifying base station co-location by big data grid matching on the network side described in Example 1 is adopted to identify the 4G base station co-located with the target 5G base station;

[0208] According to the working parameters of the 4G base station, the working parameters of the target 5G base station are corrected.

[0209] This embodiment provides a method for correcting the working parameters of a 5G base station, and the specific steps are as follows: First, the system obtains the basic information of the target 5G base station, including its location information and current working parameter settings. Next, the network-side big data grid matching technology described in Example 1 is used to identify the 4G base stations co-located with the target 5G base station to obtain relevant 4G base station locations and performance data. Finally, combined with the working parameter information of these co-located 4G base stations, the working parameters of the target 5G base station are analyzed and corrected to ensure that their parameters are more accurate and optimized, thereby improving the performance and stability of the entire 5G network. This method not only improves the efficiency of the working parameter correction of the target base station, but also provides important data support for the overall planning and improvement of the network.

[0210] Embodiment 3:

[0211] like Figure 5 As shown, this embodiment provides a device for identifying base station co-location based on network-side big data grid matching, the device comprising:

[0212] An acquisition unit 10, configured to acquire 4G MR data and 5G MR data;

[0213] The first processing unit 20 is connected to the acquisition unit 10, and is used to perform position backfilling on the 4G MR data and the 5G MR data based on the 4G MDT fingerprint library to obtain backfilled 4G MR data and 5G MR data;

[0214] The second processing unit 30 is connected to the first processing unit 20 and is used to perform rasterization processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data;

[0215] The grouping unit 40 is connected to the second processing unit 30, and is used to group the 4G MR grid data to obtain 4G MR target data; and is used to group the 5G MR grid data to obtain 5G MR target data;

[0216] The matching unit 50 is connected to the grouping unit 40, and is used to match the 4G MR target data with the 5G MR target data to identify the co-located 4G base stations and 5G base stations, thereby completing the identification of base station co-location based on network-side big data grid matching.

[0217] As a specific implementation, the device further includes:

[0218] A construction unit, connected to the first processing unit, configured to construct a 4G MDT fingerprint library;

[0219] The building block comprises:

[0220] An acquisition module is used to acquire massive 4G MDT data on the network side within a preset period;

[0221] An extraction module, connected to the acquisition module, for extracting attribute data from the 4G MDT data;

[0222] Wherein, the attribute data includes RSRP data and latitude and longitude data;

[0223] A rejection module, connected to the acquisition module, for rejecting 4G MDT data without longitude and latitude information;

[0224] The integration module is connected to the extraction module and the elimination module respectively, and is used to integrate the 4G MDT data according to the attribute data to obtain the 4G MDT fingerprint library, that is, to complete the construction of the 4G MDT fingerprint library.

[0225] As a specific implementation, the matching unit 50 includes:

[0226] A processing module, used for performing GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box; and used for performing GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box;

[0227] A combination module, connected to the processing module, for combining the vector shape, area, and grid level of the first outer frame to obtain a combination point of a 4G base station; and combining the vector shape, area, and grid level of the second outer frame to obtain a combination point of a 5G base station;

[0228] A matching module is connected to the combination module and is used to match the combination points of the 4G base stations and the combination points of the 5G base stations in pairs to obtain a co-located 4G base station and a 5G base station.

[0229] As a specific implementation, the matching module includes:

[0230] An acquisition submodule, used to acquire the first center point coordinates (X1, Y1) of the vector shape of the first outer bounding box; and used to acquire the second center point coordinates (X2, Y2) of the vector shape of the second outer bounding box;

[0231] The first calculation submodule is used to calculate the distance D between the first center point coordinates and the second center point coordinates. c1-c2 ;

[0232] The first calculation submodule stores the following formula:

[0233]

[0234] A combination submodule, connected to the first calculation submodule, for forming a matching pair set from the combination points of the 4G base stations and the combination points of the 5G base stations whose distances are less than a preset threshold, to obtain an initial matching result;

[0235] The second calculation submodule is connected to the combination submodule and is used to calculate the matching score M based on the initial matching result according to the vector shape, area, and grid level. 1-2 ;

[0236] The second calculation submodule stores the following calculation formula:

[0237] M1-2 =S1*W1+S2*W2+S3*W3;

[0238] Among them, S1 represents the vector shape similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0239] S2 represents the area similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0240] S3 represents the grid level similarity between the combination points of 4G base stations and the combination points of 5G base stations;

[0241] W1 represents the vector shape similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0242] W2 represents the area similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0243] W3 represents the grid-level similarity weight between the combination points of 4G base stations and the combination points of 5G base stations;

[0244] A preferred submodule, connected to the second calculation submodule, for eliminating matching results with matching scores less than a preset matching value, and for sorting the matching result scores, and selecting the highest matching result as the optimal matching result;

[0245] An identification submodule is connected to the preferred submodule and is used to identify co-located 4G base stations and 5G base stations through the optimal matching result.

[0246] The device in this embodiment can execute the method in embodiment 1.

[0247] 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 method for identifying base station co-location based on network-side big data grid matching, characterized in that: The method comprises the following steps: Step S1: Acquire 4G MR data and 5G MR data; Step S2: Based on the 4G MDT fingerprint library, the 4G MR data and the 5G MR data are backfilled to obtain backfilled 4G MR data and 5G MR data; Step S3: performing rasterization processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data; Step S4: grouping the 4G MR grid data to obtain 4G MR target data; and grouping the 5G MR grid data to obtain 5G MR target data; Step S5: Match the 4G MR target data and the 5G MR target data to identify the co-located 4G base stations and 5G base stations, thereby completing the identification of base station co-location based on network-side big data grid matching.

2. The method for identifying base station co-location based on network-side big data grid matching according to claim 1 is characterized in that: Before step S2, the method further includes step S0; Step S0: Build a 4G MDT fingerprint library; The step S0 specifically includes the following steps: Step S01: Acquire massive 4G MDT data on the network side within a preset period; Step S02: extracting attribute data from the 4G MDT data; and removing 4G MDT data without longitude and latitude information; Wherein, the attribute data includes RSRP data and latitude and longitude data; Step S03: Integrate 4G MDT data according to the attribute data to obtain a 4G MDT fingerprint library, thereby completing the construction of the 4G MDT fingerprint library.

3. The method for identifying base station co-location based on network-side big data grid matching according to claim 1, characterized in that: After step S3 and before step S4, the method further comprises the steps of: The 4G MR grid data is cleaned to ensure the integrity and uniqueness of the 4G MR grid data; and the 5G MR grid data is cleaned to ensure the integrity and uniqueness of the 5G MR grid data.

4. The method for identifying base station co-location based on network-side big data grid matching according to any one of claims 1 to 3, characterized in that: In step S5, matching the 4G MR target data with the 5G MR target data to identify the co-located 4G base station and 5G base station specifically includes the following steps: Step S51: performing GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box; and performing GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box; Step S52: combining the vector shape, area, and grid level of the first outer frame to obtain a combination point of a 4G base station; and combining the vector shape, area, and grid level of the second outer frame to obtain a combination point of a 5G base station; Step S53: Match the combination points of the 4G base stations and the combination points of the 5G base stations in pairs to identify the co-located 4G base stations and 5G base stations.

5. The method for identifying base station co-location based on network-side big data grid matching according to claim 4 is characterized in that: The step S53 specifically includes the following steps: Step S531: obtaining the first center point coordinates (X1, Y1) of the vector shape of the first outer bounding box; and obtaining the second center point coordinates (X2, Y2) of the vector shape of the second outer bounding box; Step S532: Calculate the distance D between the first center point coordinates and the second center point coordinates c1-c2 , and its calculation formula is as follows: Step S533: The combination points of the 4G base stations and the combination points of the 5G base stations whose distances are less than the preset threshold form a matching pair set to obtain an initial matching result; Step S534: Based on the initial matching result, the matching score M is calculated according to the vector shape, area, and grid level. 1-2 , and its calculation formula is as follows: <h2 style=";text-align:left;direction:ltr">M<h2 style=";text-align:left;direction:ltr"> 1-2 <h2 style=";text-align:left;direction:ltr"> (S1*W1+S2*W2+S3*W3) Among them, S1 represents the vector shape similarity between the combination points of 4G base stations and the combination points of 5G base stations; S2 represents the area similarity between the combination points of 4G base stations and the combination points of 5G base stations; S3 represents the grid level similarity between the combination points of 4G base stations and the combination points of 5G base stations; W1 represents the vector shape similarity weight between the combination points of 4G base stations and the combination points of 5G base stations; W2 represents the area similarity weight between the combination points of 4G base stations and the combination points of 5G base stations; W3 represents the grid-level similarity weight between the combination points of 4G base stations and the combination points of 5G base stations; Step S535: Eliminate the matching results whose matching scores are less than the preset matching value, sort the matching result scores, and select the highest matching result as the optimal matching result; Step S536: Identify the co-located 4G base stations and 5G base stations through the optimal matching result.

6. A method for correcting working parameters of a 5G base station, characterized in that: The method comprises the following steps: Get the target 5G base station; Adopting the method for identifying base station co-location by big data grid matching on the network side as described in any one of claims 1 to 4, identifying the 4G base station co-located with the target 5G base station; According to the working parameters of the 4G base station, the working parameters of the target 5G base station are corrected.

7. A device for identifying base station co-location based on network-side big data grid matching, characterized in that: include: An acquisition unit, used for acquiring 4G MR data and 5G MR data; A first processing unit, connected to the acquisition unit, configured to perform position backfilling on the 4G MR data and the 5G MR data based on a 4G MDT fingerprint library to obtain backfilled 4G MR data and 5G MR data; A second processing unit, connected to the first processing unit, for performing rasterization processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data; a grouping unit connected to the second processing unit, configured to group the 4G MR grid data to obtain 4G MR target data; and, configured to group the 5G MR grid data to obtain 5G MR target data; A matching unit is connected to the grouping unit and is used to match the 4G MR target data with the 5G MR target data to identify the co-located 4G base stations and 5G base stations, thereby completing the identification of base station co-location based on network-side big data grid matching.

8. The device for identifying base station co-location based on network-side big data grid matching according to claim 7, characterized in that: The device also includes: A construction unit, connected to the first processing unit, configured to construct a 4G MDT fingerprint library; The building block comprises: An acquisition module is used to acquire massive 4G MDT data on the network side within a preset period; An extraction module, connected to the acquisition module, for extracting attribute data from the 4G MDT data; Wherein, the attribute data includes RSRP data and latitude and longitude data; A rejection module, connected to the acquisition module, for rejecting 4G MDT data without longitude and latitude information; The integration module is connected to the extraction module and the elimination module respectively, and is used to integrate the 4G MDT data according to the attribute data to obtain the 4G MDT fingerprint library, that is, to complete the construction of the 4G MDT fingerprint library.

9. The device for identifying base station co-location based on network-side big data grid matching according to claim 7 or 8, characterized in that: The matching unit comprises: A processing module, used for performing GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box; and used for performing GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box; A combination module, connected to the processing module, for combining the vector shape, area, and grid level of the first outer frame to obtain a combination point of a 4G base station; and combining the vector shape, area, and grid level of the second outer frame to obtain a combination point of a 5G base station; A matching module is connected to the combination module and is used to match the combination points of the 4G base stations and the combination points of the 5G base stations in pairs to obtain a co-located 4G base station and a 5G base station.

10. The device for identifying base station co-location based on network-side big data grid matching according to claim 9, characterized in that: The matching module comprises: An acquisition submodule, used to acquire the first center point coordinates (X1, Y1) of the vector shape of the first outer bounding box; and used to acquire the second center point coordinates (X2, Y2) of the vector shape of the second outer bounding box; The first calculation submodule is used to calculate the distance D between the first center point coordinates and the second center point coordinates. c1-c2 ; The first calculation submodule stores the following formula: A combination submodule, connected to the first calculation submodule, for forming a matching pair set from the combination points of the 4G base stations and the combination points of the 5G base stations whose distances are less than a preset threshold, to obtain an initial matching result; The second calculation submodule is connected to the combination submodule and is used to calculate the matching score M based on the initial matching result according to the vector shape, area, and grid level. 1-2 ; The second calculation submodule stores the following calculation formula: <h2 style=";text-align:left;direction:ltr">M<h2 style=";text-align:left;direction:ltr"> 1-2 <h2 style=";text-align:left;direction:ltr"> (S1*W1+S2*W2+S3*W3) Among them, S1 represents the vector shape similarity between the combination points of 4G base stations and the combination points of 5G base stations; S2 represents the area similarity between the combination points of 4G base stations and the combination points of 5G base stations; S3 represents the grid level similarity between the combination points of 4G base stations and the combination points of 5G base stations; W1 represents the vector shape similarity weight between the combination points of 4G base stations and the combination points of 5G base stations; W2 represents the area similarity weight between the combination points of 4G base stations and the combination points of 5G base stations; W3 represents the grid-level similarity weight between the combination points of 4G base stations and the combination points of 5G base stations; A preferred submodule, connected to the second calculation submodule, for eliminating matching results with matching scores less than a preset matching value, and for sorting the matching result scores, and selecting the highest matching result as the optimal matching result; An identification submodule is connected to the preferred submodule and is used to identify co-located 4G base stations and 5G base stations through the optimal matching result.

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