A base station co-site matching identification method and device and a 5G engineering parameter correction method

Abstract

The application discloses a base station co-site matching identification method and device and a 5G engineering parameter correction method. The matching identification method is a method for matching and identifying base station co-sites based on network side big data grids. The specific steps of the method comprise the following steps: firstly, 4G and 5G MR data are acquired, then the MR data is subjected to position backfilling based on a 4G MDT fingerprint library to obtain backfilled 4G and 5G MR data. Then, the backfilled data is subjected to gridding processing to generate corresponding 4G and 5G MR grid data. Subsequently, the 4G MR grid data is grouped to obtain 4G MR target data, and the 5G MR grid data is simultaneously grouped to obtain 5G MR target data. Finally, the 4G MR target data and the 5G MR target data are matched to identify co-sited 4G and 5G base stations, and grid matching and identification based on network side big data are realized. The method can quickly and accurately identify co-sited base stations, thereby providing support for the rapid correction of 5G network engineering parameter information.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of network communication, and particularly relates to a method and device for matching and identifying co-sited base stations, and a method for modifying 5G (the fifth generation of mobile communication technology) working parameters. BACKGROUND

[0002] In the field of wireless network planning and construction, the working parameter information of base stations plays a crucial role, as these information is directly related to the coverage range, signal quality, and actual user experience of the network. Accurate and complete working parameter information is the key to ensuring the authenticity and reliability of wireless network planning and simulation results, and it provides a solid scientific basis and clear guidance for subsequent network optimization strategies and actual construction.

[0003] Currently, in the actual operation of network construction, operators generally adopt the strategy of co-siting 4G (the fourth generation of mobile communication technology) and 5G base stations, i.e., setting these two types of base stations in the same geographic location, in order to effectively control construction costs, optimize the use of limited space resources, and significantly improve the coverage effect of the network. This co-siting method greatly reduces the capital investment in infrastructure construction, and optimizes the configuration and use efficiency of network resources, enabling operators to meet the growing communication needs of users as much as possible under the condition of relatively limited resources, and provide more stable and efficient service experience.

[0004] However, despite the advantages brought by the co-siting of 4G and 5G base stations, the current 5G network still has obvious shortcomings in the completeness of working parameter information compared to the more mature 4G network. This situation directly affects the accuracy of checking and verifying base station working parameters through mobile operation data (including MR data, which usually refers to "Mobile Report Data"), and hinders the efficient operation and precise optimization of 5G base stations.

[0005] Currently, for checking base station working parameters, one method is to conduct on-site checking of 5G base station working parameters by maintenance personnel. Although this method has a certain accuracy, its cost is relatively high, and the checking period is relatively long. At the same time, when entering the working parameter information, there may still be certain misoperations.

[0006] In order to replace manual work, the Chinese patent "A base station co-site identification method 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 in-depth analysis on the data collected by the foreign network frequency band signal in the wireless environment measurement report (MR), and on this basis, comprehensive data cleaning and processing are carried out. By skillfully using advanced machine learning technology, accurate classification and identification of co-site points are successfully achieved, effectively solving the problem of inaccurate and incomplete site information in traditional asset management systems, so that it can accurately identify whether a base station is a shared base station. However, in the actual implementation process of this technical solution, the high dependence on machine learning technology makes the data training process relatively cumbersome, and the calculation efficiency is not high.

[0007] Therefore, there is a need for a more efficient method to accurately identify co-located 4G and 5G base stations, thereby improving the performance of 5G networks in terms of the completeness of the work parameter information. SUMMARY

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

[0009] In a first aspect, the present application provides a method for matching and identifying base station co-sites based on network-side big data grid, which comprises the following steps:

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

[0011] Step S2: based on the 4G MDT fingerprint library, performing position backfilling on the 4G MR data and the 5G MR data to obtain backfilled 4G MR data and 5G MR data;

[0012] Step S3: performing grid processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR grid data and 5G MR grid 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: matching the 4G MR target data and the 5G MR target data to identify co-located 4G base stations and 5G base stations, thereby completing the matching and identification of base station co-sites based on network-side big data grid.

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

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

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

[0018] Step S01: acquiring network-side massive 4G MDT data in a preset period;

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

[0020] The attribute data comprises RSRP data and latitude and longitude data;

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

[0022] Further, after the step S3 and before the step S4, the method further comprises a step:

[0023] cleaning 4G MR grid data to ensure completeness and uniqueness of the 4G MR grid data; and, cleaning 5G MR grid data to ensure completeness and uniqueness of the 5G MR grid data.

[0024] Further, in the step S5, the 4G MR target data and the 5G MR target data are matched to identify co-located 4G base stations and 5G base stations, specifically comprising 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 vector shape, area, and grid level of the first outer bounding box to obtain a combined point of a 4G base station; and, combining vector shape, area, and grid level of the second outer bounding box to obtain a combined point of a 5G base station;

[0027] Step S53: matching the combined points of the 4G base stations and the combined points of the 5G base stations two by two to identify co-located 4G base stations and 5G base stations.

[0028] Further, the step S53 specifically comprises the following steps:

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

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

[0031]

[0032] Step S533: grouping a combined point of the 4G base station and a combined point of the 5G base station, which have a distance less than a preset threshold, into a matching pair set to obtain a preliminary matching result;

[0033] Step S534: based on the preliminary matching result, a matching score M is calculated according to a vector shape, an area, and a grid level 1-2 , and a calculation formula is as follows:

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

[0035] S1 represents a vector shape similarity between the combined point of the 4G base station and the combined point of the 5G base station;

[0036] S2 represents an area similarity between the combined point of the 4G base station and the combined point of the 5G base station;

[0037] S3 represents a grid level similarity between the combined point of the 4G base station and the combined point of the 5G base station;

[0038] W1 represents a vector shape similarity weight between the combined point of the 4G base station and the combined point of the 5G base station;

[0039] W2 represents an area similarity weight between the combined point of the 4G base station and the combined point of the 5G base station;

[0040] W3 represents a grid level similarity weight between the combined point of the 4G base station and the combined point of the 5G base station;

[0041] Step S535: eliminating a matching result with a matching score less than a preset matching value, sorting matching result scores, and selecting a highest matching result as an optimal matching result;

[0042] Step S536: identifying the co-sited 4G base station and the 5G base station through the optimal matching result.

[0043] In a second aspect, the present application provides a 5G base station parameter correction method, and the method comprises the following steps:

[0044] acquire a target 5G base station;

[0045] The method for identifying the co-sited base stations based on the network-side large data grid matching of the first aspect identifies the 4G base station co-sited with the target 5G base station.

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

[0047] In a third aspect, the present application provides a device for identifying the co-sited base stations based on the network-side large data grid matching, which comprises:

[0048] An acquisition unit is configured to acquire 4G MR data and 5G MR data.

[0049] A first processing unit is connected to the acquisition unit and is 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 is connected to the first processing unit and is configured to perform grid processing on the backfilled 4G MR data and 5G MR data, to obtain 4G MR grid data and 5G MR grid data.

[0051] A grouping unit is connected to the second processing unit and is configured to group the 4G MR grid data to obtain 4G MR target data, and 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 configured to match the 4G MR target data and the 5G MR target data to identify the co-sited 4G base station and 5G base station, thereby completing the identification of the co-sited base stations based on the network-side large data grid matching.

[0053] Further, the device further comprises:

[0054] A construction unit is connected to the first processing unit and is configured to construct a 4G MDT fingerprint library.

[0055] The construction unit comprises:

[0056] An acquisition module is configured to acquire network-side mass 4G MDT data within a preset period.

[0057] An extraction module is connected to the acquisition module and is configured to extract attribute data from the 4G MDT data.

[0058] The attribute data comprises RSRP data and latitude and longitude data.

[0059] The elimination module is connected with the acquisition module and is configured to eliminate 4G MDT data without latitude and longitude information.

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

[0061] Further, the matching unit comprises:

[0062] The processing module is configured to perform GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box, and perform GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box.

[0063] The combination module is connected with the processing module and is configured to combine a vector shape, an area, and a raster level of the first outer bounding box to obtain a combination point of a 4G base station, and combine a vector shape, an area, and a raster level of the second outer bounding box to obtain a combination point of a 5G base station.

[0064] The matching module is connected with the combination module and is configured to match the combination point of the 4G base station and the combination point of the 5G base station two by two to obtain co-located 4G base stations and 5G base stations.

[0065] Further, the matching module comprises:

[0066] The acquisition submodule is configured to acquire a first center point coordinate (X1, Y1) of a vector shape of the first outer bounding box, and acquire a second center point coordinate (X2, Y2) of a vector shape of the second outer bounding box.

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

[0068] In the first calculation submodule, the following formula is stored:

[0069]

[0070] The combination submodule is connected with the first calculation submodule and is configured to form a matching pair set by combining the combination point of the 4G base station and the combination point of the 5G base station with a distance less than a preset threshold value to obtain a preliminary matching result.

[0071] The second calculation submodule is connected with the combination submodule and is configured to calculate a matching score M based on the preliminary matching result according to a vector shape, an area, and a raster level. 1-2 ;

[0072] The second calculation sub-module stores the following calculation formula:

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

[0074] S1 represents the vector shape similarity between the combined points of the 4G base station and the combined points of the 5G base station;

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

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

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

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

[0079] W3 represents the grid level similarity weight between the combined points of the 4G base station and the combined points of the 5G base station;

[0080] The preferred sub-module is connected with the second calculation sub-module, used for rejecting the matching result with a matching score less than a preset matching value, and used for sorting the matching result scores and selecting the highest matching result as the optimal matching result;

[0081] The identification sub-module is connected with the preferred sub-module, used for identifying the co-located 4G base station and 5G base station through the optimal matching result.

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

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

[0084] 2. Precise positioning capability: through position backfilling, the 4G and 5G MR data obtained by the present application is more accurate, providing a reliable data basis for subsequent grid processing and matching identification, thereby significantly improving the accuracy of co-location identification.

[0085] 3. Reduced computational complexity: The present invention rasterizes MR data, dividing massive data into smaller units, simplifying the complexity of data analysis, and making subsequent grouping and matching processes more efficient.

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

[0087] 5. Real-time updating capability: The present invention performs real-time analysis based on big data in dynamic network environments, enabling timely identification of newly emerging co-located base stations, ensuring the timeliness and effectiveness of data.

[0088] 6. Resource optimization and management: By accurately identifying co-located base stations, the present invention helps operators more reasonably allocate resources, optimize network layout, and improve network service quality, thereby meeting user demand for network coverage.

[0089] 7. Strong adaptability: The present invention can flexibly adjust rasterization processing and matching mechanisms 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 future identification of base station co-location for new technologies (such as 6G), ensuring its forward-looking position in wireless network management. BRIEF DESCRIPTION OF DRAWINGS

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

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

[0093] Figure 3 A flowchart for identifying base station co-location based on network-side big data raster matching in an embodiment of the present invention;

[0094] Figure 4 A co-location matching diagram for 4G base stations and 5G base stations in an embodiment of the present invention;

[0095] Figure 5 A device diagram for identifying base station co-location based on network-side big data raster matching in an embodiment of the present invention;

[0096] Wherein, the reference signs: 10, acquisition unit, 20, first processing unit, 30, second processing unit, 40, grouping unit, 50, matching unit. DETAILED DESCRIPTION

[0097] In order to better understand the technical solutions of the present application, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0098] It can be understood that the specific embodiments and drawings described herein are merely intended to explain the present application, but not to limit the present application.

[0099] It can be understood that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0100] It can be understood that, for the convenience of description, only the parts related to the present application are shown in the drawings of the present application, and the parts unrelated to the present application are not shown in the drawings.

[0101] It can be understood that each unit and module involved in the embodiments of the present application can correspond to only one entity structure, or can be composed of multiple entity structures, or multiple units and modules can also be integrated into one entity structure.

[0102] It can be understood that the functions and steps marked in the flowcharts and block diagrams of the present application can occur in an order different from that marked in the drawings without conflict.

[0103] It can be understood that in the flowcharts and block diagrams of the present application, the system, device, equipment, method according to the embodiments of the present application are shown as the possible implementation architecture, function and operation. Each block in the flowchart or block diagram can represent a unit, module, program segment, code, which contains executable instructions for realizing the specified function. Moreover, each block or combination of blocks in the block diagram and flowchart can be realized by a hardware-based system for realizing the specified function, or 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 application can be realized in the form of software or hardware, for example, the units and modules can be located in a processor.

[0105] Embodiment 1:

[0106] As Figure 1As shown, the present embodiment provides a method for identifying co-located base stations based on network-side big data grid matching. This 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, the network coverage and signal quality can be improved. In addition, this method can also be used for network expansion and upgrading, helping operators to reasonably allocate resources and reduce redundant construction; in areas where communication is not popular, the network coverage can be quickly improved by determining the best construction point; in base station maintenance and optimization, the working parameters can be adjusted; and in emergency management and post-disaster recovery, the smoothness of the communication network can be ensured to support disaster relief work. In summary, this 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 for recording and storing user equipment measurement data in a 4G wireless network, mainly containing signal strength, signal quality and location information and other data. These data are automatically collected by user terminals and uploaded to the server of the operator, aiming to optimize network performance, evaluate base station performance and reduce the need for traditional on-site testing. By analyzing the data in the MDT fingerprint library, the operator can identify hotspots and blind spots of network coverage, so as to reasonably plan and deploy base stations and improve user experience and network service quality.

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

[0110] Step S01: Obtain network-side mass 4G MDT data within a preset period;

[0111] Step S02: Extract attribute data from the 4G MDT data; and, eliminate 4G MDT data without latitude and longitude information;

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

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

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

[0115] Step S2: Based on the 4G MDT fingerprint library, perform location backfilling on the 4G MR data and the 5G MR data to obtain backfilled 4G MR data and 5G MR data.

[0116] Step S3: Rasterizing the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data.

[0117] In implementation, after obtaining the 4G MR raster data and the 5G MR raster data, the 4G MR raster data and the 5G MR raster data need to be cleaned respectively. Cleaning the 4G MR raster data is to ensure the integrity and uniqueness of the 4G MR raster data, and cleaning the 5G MR raster data is to ensure the integrity and uniqueness of the 5G MR raster data. The cleaning of the 4G MR raster data aims to ensure the integrity and uniqueness of the data, specifically including checking and filling missing latitude and longitude information, removing duplicate records, processing outliers, and unifying data formats, so as to make the data set more accurate and reliable; and the cleaning of the 5G MR raster data also focuses on ensuring its integrity and uniqueness, through similar steps, to ensure that all necessary fields are complete and have no duplicates, so as to provide a high-quality data basis for subsequent analysis and decision-making.

[0118] Step S4: Grouping the 4G MR raster data to obtain 4G MR target data, and grouping the 5G MR raster data to obtain 5G MR target data.

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

[0120] As a specific implementation, in step S5, the 4G MR target data and the 5G MR target data are matched to identify co-located 4G base stations and 5G base stations, specifically including 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: Combining the vector shape, area, and raster level of the first outer bounding box to obtain a combined point of the 4G base station, and combining the vector shape, area, and raster level of the second outer bounding box to obtain a combined point of the 5G base station.

[0123] Step S53: Pairwise matching the combined point of the 4G base station and the combined point of the 5G base station to identify co-located 4G base stations and 5G base stations.

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

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

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

[0127]

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

[0129] Step S533: grouping the combined point of the 4G base station and the combined point of the 5G base station whose distance is less than the preset threshold value to form a matching pair set, and obtaining a preliminary matching result.

[0130] Step S534: based on the preliminary matching result, calculating a matching score M according to the vector shape, the area, and the grid level 1-2 , and the calculation formula is as follows:

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

[0132] Wherein, S1 represents the vector shape similarity between the combined point of the 4G base station and the combined point of the 5G base station;

[0133] S2 represents the area similarity between the combined point of the 4G base station and the combined point of the 5G base station;

[0134] S3 represents the grid level similarity between the combined point of the 4G base station and the combined point of the 5G base station;

[0135] W1 represents the vector shape similarity weight between the combined point of the 4G base station and the combined point of the 5G base station;

[0136] W2 represents the area similarity weight between the combined point of the 4G base station and the combined point of the 5G base station;

[0137] W3 represents the grid level similarity weight between the combined point of the 4G base station and the combined point of the 5G base station.

[0138] Step S535: eliminating the matching result whose matching score is less than the preset matching value, sorting the matching result scores, and selecting the highest matching result as the optimal matching result.

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

[0140] Here are the detailed explanations and examples for this step:

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

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

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

[0144] Calculate similarity

[0145] Vector shape similarity (Shape sim ): By comparing the shape of A1 and B1, use shape index or contour similarity algorithm to evaluate. Assume

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

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

[0148] Grid level similarity (Level sim ): Compare the grid level of A1 and B1, assume the level of A1 is 3 and the level of B1 is 2, then use the absolute difference of level to reflect the similarity calculation to get Level sim (A1, B1) = 0.7.

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

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

[0151] Calculate matching score

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

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

[0154] Substitute the values into the formula:

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

[0156] Similarly, calculate the matching scores of other combination points (e.g. A2 / B2, A3 / B3).

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

[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 eliminate the matching of A2 and B2, leaving A1-B1 and A3-B3.

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

[0164] Through the optimal matching result A1-B1, the co-located 4G base station A1 and 5G base station B1 are identified. This result shows that A1 and B1 have high similarity in indicators such as vector shape, area, and grid level, so they are likely to be co-located base stations.

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

[0166] Figure 3A flowchart for network-side big data grid matching to identify base station co-site is shown, and 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 a "fingerprint" of the base station signal. Based on this fingerprint library, the 4G and 5G mobile records (MR) can be position backfilled to ensure that each record can accurately match the corresponding base station signal characteristics. Subsequently, these mobile records are further cleaned and grouped by base station ID to facilitate subsequent data analysis and processing. Finally, the grouped 4G and 5G mobile record data is output for in-depth research and application. For example, after collecting signal strength data from multiple base stations in a city, the user's mobile records at different locations can be matched with the base station signal characteristics through the construction of the fingerprint library, thereby accurately backfilling the user's location and analyzing their mobile behavior and network coverage.

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

[0168] Step one: network-side 4G MDT fingerprint library construction, MR position backfilling and grouping

[0169] Based on the 4G MDT, a fingerprint library is constructed, and then the 4 / 5G MR is position backfilled and processed:

[0170] First step: By collecting network-side massive 4G MDT data for 5 consecutive days in a city, the frequency, pci, rsrp, longitude and latitude attributes of the 4G MDT sample point main zone and adjacent zone are parsed, and the data without longitude and latitude is excluded to construct a 4G MDT fingerprint library;

[0171] Second step: Collect 4 / 5G MR data for 3 consecutive days in the city, parse the MR data, and then perform position backfilling based on the 4G MDT fingerprint library;

[0172] Third step: Clean the backfilled MR data, exclude data without backfilled longitude and latitude information, and then group the 4 / 5G MR by base station ID.

[0173] Step two: 4 / 5G backfilled MR gridding and 4G base station operating parameter estimation

[0174] First step: Based on the backfilled and grouped MR data, gridding is performed respectively with a grid size of 8 levels, and the average level of each grid is calculated;

[0175] Second step: Based on the backfilled MR data, estimate the 4 / 5G base station operating parameters.

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

[0177] As shown, the grouped 4 / 5G gridded data is matched, and the matched results are taken as the 4 / 5G base station co-site: Figure 4

[0178] First step: The grouped 4 / 5G base station grid data is respectively processed by GIS convex hull to an outer package frame, the area of the outer package frame is calculated, and the outer package frame vector shape, area, and grid level are taken 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] Second step: The 4 / 5G base station grid combination points are matched two by two, and the matching steps are as follows:

[0180] (1) Initial matching: The 4 / 5G base station grid outer package frame vector center point coordinates are calculated, the distance between the 4 / 5G base station center points is calculated, the threshold is set to 300 meters, and the base station combination points with a distance less than 300 meters are combined to form a matching pair set, 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 attribute (outer package frame shape, area, grid level);

[0181] (2) Fine matching: Each matching pair is further matched, the matching weight is set, the matching attribute [outer package frame shape, area, grid level] is set, and the corresponding weight [0.3, 0.2, 0.5] is set.

[0182] (3) Screening the optimal matching result: The matching score of each matching pair is calculated, the threshold of the score is set to 0.6, the matching pairs with a score less than the threshold are removed, and if the scores of the matching pairs are all less than 0.6, it is proved that the 4 / 5G base stations do not co-site. If the remaining matching results are removed, the matching result scores are sorted, and the result with the highest score is taken as the optimal matching result, and then the 4 / 5G base stations in the matching pair co-site.

[0183] Third step: Output the 4 / 5G co-site optimal matching result set.

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

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

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

[0187] ​Attributes of the combined point elements in the set: (raster shape, bounding box area, raster level), matching score weights: (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] Optimal matching result:

[0197] PR a = (a, C)

[0198] PR b = null (no matching result)

[0199] The raster shape refers to the geometric shape formed by the raster cells (usually regular squares or rectangles) within a certain spatial area in data analysis. Each raster cell represents data information in a specific area, such as signal strength, land use, etc. In base station co-location analysis, raster shape helps visualize and compare signal coverage in the area. The bounding box area refers to the area of the smallest rectangular box that encloses a certain area (such as the raster shape). The boundary of this rectangular box is usually aligned with the boundary of the raster, and it can completely wrap the raster shape. The bounding box area is used to measure the size of a specific area in spatial analysis, which is very important for calculation and spatial relationship analysis. Raster level refers to the data value or feature within a raster cell, which can represent multiple aspects of information, such as signal strength, coverage quality or network performance indicators. Raster level is usually a continuous numerical value representing the relative strength of a certain characteristic (such as telecommunication signal strength) in the area. Different values of raster level can be used to analyze the coverage of the area and its application in network optimization and planning.

[0200] Step four: co-located 4G base station parameter correction 5G parameter

[0201] According to the output result of step three, the co-sited 4G work parameter information (base station latitude and longitude, base station height, and cell azimuth angle) is assigned to the 5G work parameter information to perform 5G work parameter checking and improve the accuracy of 5G work parameter estimation.

[0202] This embodiment relies on rich data sources and low acquisition cost to identify the co-sited situation of 4G and 5G base stations using massive data, effectively avoiding the influence of incorrect information in the work parameters on the identification result. First, the backfilled MR data is rasterized, and GIS convex hull processing is performed on each group of raster data to convert it into an outer bounding box, thereby realizing image similarity matching. Next, through co-sited raster similarity matching of 4G and 5G base stations, the shape, area, and level of the raster outer bounding box are used as matching attributes, and each attribute is assigned a matching weight, which can effectively avoid extreme value fluctuations that may occur in single attribute matching. Moreover, the matching process is divided into preliminary matching and accurate matching, and the optimal matching result is finally selected. This calculation process is simple and efficient, with high matching efficiency and low resource consumption. Through the above steps, the accuracy of 5G work parameter checking is significantly improved, thereby further improving the accuracy of wireless network planning and simulation.

[0203] Embodiment 2

[0204] As shown in Figure 2 , the embodiment provides a 5G base station work parameter correction method, characterized in that the method comprises the following steps:

[0205] Obtain a target 5G base station;

[0206] Using the method of identifying co-sited base stations based on network-side big data raster matching in embodiment 1, identify the 4G base station co-sited with the target 5G base station;

[0207] According to the work parameters of the 4G base station, correct the work parameters of the target 5G base station.

[0208] The embodiment provides a 5G base station work parameter correction method, 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 work parameter setting. Next, using the network-side big data raster matching technology described in embodiment 1, identify the 4G base station co-sited with the target 5G base station to obtain relevant 4G base station location and performance data. Finally, analyze and correct the work parameters of the target 5G base station in combination with the work parameter information of these co-sited 4G base stations to ensure that the parameters are more accurate and optimized, thereby improving the performance and stability of the entire 5G network. This method not only improves the work parameter correction efficiency of the target base station, but also provides important data support for the overall planning and improvement of the network.

[0209] Embodiment 3

[0210] As Figure 5 shown, the embodiment provides a device for identifying co-sited base stations based on network-side big data grid matching, which comprises:

[0211] An acquisition unit 10 is configured to acquire 4G MR data and 5G MR data.

[0212] A first processing unit 20 is connected with the acquisition unit 10 and is configured to perform location 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.

[0213] A second processing unit 30 is connected with the first processing unit 20 and is configured to perform grid processing on the backfilled 4G MR data and 5G MR data to obtain 4G MR grid data and 5G MR grid data.

[0214] A grouping unit 40 is connected with the second processing unit 30 and is configured to group the 4G MR grid data to obtain 4G MR target data and to group the 5G MR grid data to obtain 5G MR target data.

[0215] A matching unit 50 is connected with the grouping unit 40 and is configured to match the 4G MR target data and the 5G MR target data to identify co-sited 4G base stations and 5G base stations, thereby completing the identification of co-sited base stations based on network-side big data grid matching.

[0216] As a specific implementation, the device further comprises:

[0217] A construction unit is connected with the first processing unit and is configured to construct a 4G MDT fingerprint library.

[0218] The construction unit comprises:

[0219] An acquisition module is configured to acquire network-side massive 4G MDT data within a preset period.

[0220] An extraction module is connected with the acquisition module and is configured to extract attribute data from the 4G MDT data.

[0221] The attribute data comprises RSRP data and latitude and longitude data.

[0222] A rejection module is connected with the acquisition module and is configured to reject 4G MDT data without latitude and longitude information.

[0223] The integration module is connected with the extraction module and the elimination module respectively, and is configured to integrate the 4G MDT data according to the attribute data to obtain a 4G MDT fingerprint library, that is, to complete the construction of the 4G MDT fingerprint library.

[0224] As a specific implementation, the matching unit 50 comprises:

[0225] The processing module is configured to perform GIS convex hull processing on the 4G MR target data to obtain a first outer bounding box, and perform GIS convex hull processing on the 5G MR target data to obtain a second outer bounding box.

[0226] The combination module is connected with the processing module, and is configured to combine the vector shape, area and raster level of the first outer bounding box to obtain a combined point of the 4G base station, and combine the vector shape, area and raster level of the second outer bounding box to obtain a combined point of the 5G base station.

[0227] The matching module is connected with the combination module, and is configured to match the combined points of the 4G base station and the combined points of the 5G base station two by two to obtain co-located 4G base stations and 5G base stations.

[0228] As a specific implementation, the matching module comprises:

[0229] The acquisition sub-module is configured to acquire a first center point coordinate (X1, Y1) of the vector shape of the first outer bounding box, and acquire a second center point coordinate (X2, Y2) of the vector shape of the second outer bounding box.

[0230] The first calculation sub-module is configured to calculate a distance D between the first center point coordinate and the second center point coordinate. c1-c2 ;

[0231] The first calculation sub-module stores the following formula:

[0232]

[0233] The combination sub-module is connected with the first calculation sub-module, and is configured to form a matching pair set by combining the combined points of the 4G base station and the combined points of the 5G base station whose distance is less than a preset threshold value, to obtain a preliminary matching result.

[0234] The second calculation sub-module is connected with the combination sub-module, and is configured to calculate a matching score M according to the vector shape, area and raster level based on the preliminary matching result. 1-2 ;

[0235] The second calculation sub-module stores the following calculation formula:

[0236] M1-2 = S1*W1 + S2*W2 + S3*W3;

[0237] S1 represents a vector shape similarity between the combined points of the 4G base stations and the combined points of the 5G base stations;

[0238] S2 represents an area similarity between the combined points of the 4G base stations and the combined points of the 5G base stations;

[0239] S3 represents a grid level similarity between the combined points of the 4G base stations and the combined points of the 5G base stations;

[0240] W1 represents a vector shape similarity weight between the combined points of the 4G base stations and the combined points of the 5G base stations;

[0241] W2 represents an area similarity weight between the combined points of the 4G base stations and the combined points of the 5G base stations;

[0242] W3 represents a grid level similarity weight between the combined points of the 4G base stations and the combined points of the 5G base stations;

[0243] The preferred sub-module, connected with the second calculation sub-module, is used for eliminating the matching result whose matching score is less than a preset matching value, and is used for sorting the matching result scores and selecting the highest matching result as the optimal matching result;

[0244] The identification sub-module, connected with the preferred sub-module, is used for identifying the co-located 4G base stations and 5G base stations through the optimal matching result.

[0245] The device in the embodiment can execute the method in Embodiment 1.

[0246] It can be understood that the above embodiments are only exemplary embodiments adopted for illustrating the principles of the present application, and the present application is not limited thereto. Various modifications and improvements can be made by those skilled in the art without departing from the spirit and essence of the present application, and these modifications and improvements are also considered as the protection scope of the present application.

Claims

1. A method for identifying base station co-location based on network-side big data grid matching, characterized in that, The method includes the following steps: Step S1: Acquire 4G MR data and 5G MR data; Step S2: Based on the 4G MDT fingerprint database, perform location backfilling on the 4G MR data and the 5G MR data to obtain the backfilled 4G MR data and 5G MR data; Step S3: Rasterize the backfilled 4G MR data and 5G MR data to obtain 4G MR raster data and 5G MR raster data; Step S4: Group the 4G MR grid data to obtain 4G MR target data; and group 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 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. In step S5, the 4G MR target data and the 5G MR target data are matched to identify co-located 4G base stations and 5G base stations, specifically including the following steps: Step S51: Perform GIS convex hull processing on the 4G MR target data to obtain a first bounding box; and perform GIS convex hull processing on the 5G MR target data to obtain a second bounding box; 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. Step S53: Match the combination points of the 4G base station and the combination points of the 5G base station in pairs to identify the co-located 4G base station and 5G base station.

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

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 includes the following steps: Cleaning is performed on 4G MR raster data to ensure its integrity and uniqueness; and cleaning is performed on 5G MR raster data to ensure its integrity and uniqueness.

4. The method for identifying base station co-location based on network-side big data grid matching according to claim 1, characterized in that, Step S53 specifically includes the following steps: Step S531: Obtain the coordinates (X1, Y1) of the first center point of the vector shape of the first outer frame; and obtain the coordinates (X2, Y2) of the second center point of the vector shape of the second outer frame. Step S532: Calculate the distance D between the coordinates of the first center point and the coordinates of the second center point. c1-c2 The calculation formula is as follows: Step S533: Combine the combination points of 4G base stations with a distance less than a preset threshold and the combination points of 5G base stations to form a matching pair set, and obtain the initial matching result; Step S534: Based on the initial matching result, calculate the matching score M according to the vector shape, area, and grid level. 1-2 The calculation formula is as follows: M 1-2 S1*W1+S2*W2+S3*W3; Wherein, 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 point of a 4G base station and the combination point of a 5G base station; S3 represents the grid level similarity between the combination point of a 4G base station and the combination point of a 5G base station; 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: Remove matching results with a matching score lower than the preset matching value, sort the matching results by score, and select the matching result with the highest score as the optimal matching result; Step S536: Identify co-located 4G and 5G base stations based on the optimal matching results.

5. A method for correcting the operating parameters of a 5G base station, characterized in that, The method includes the following steps: Acquire the target 5G base station; Using the method for identifying base station co-location through big data grid matching on the network side as described in any one of claims 1 to 4, a 4G base station co-located with the target 5G base station is identified; Based on the operating parameters of the 4G base station, the operating parameters of the target 5G base station are corrected.

6. A device for identifying base station co-location based on network-side big data grid matching, characterized in that, include: Acquisition unit, used to acquire 4G MR data and 5G MR data; The first processing unit, connected to the acquisition unit, is used to perform location backfilling on the 4G MR data and the 5G MR data based on the 4G MDT fingerprint database, to obtain the backfilled 4G MR data and 5G MR data. The second processing unit, connected to the first processing unit, 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. A grouping unit, connected to the second processing unit, is used to group the 4G MR grid data to obtain 4G MR target data; and to group the 5G MR grid data to obtain 5G MR target data. A matching unit, connected to the grouping unit, is used to match the 4G MR target data and the 5G MR target data to identify 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. The matching unit includes: The processing module is used to perform GIS convex hull processing on the 4G MR target data to obtain a first bounding box; and to perform GIS convex hull processing on the 5G MR target data to obtain a second bounding box. A combination module, connected to the processing module, is used to combine the vector shape, area, and grid level of the first outer frame to obtain the combination point of the 4G base station; and to combine the vector shape, area, and grid level of the second outer frame to obtain the combination point of the 5G base station. A matching module, connected to the combination module, is used to match the combination points of the 4G base station and the combination points of the 5G base station in pairs to obtain co-located 4G base stations and 5G base stations.

7. The apparatus for identifying base station co-location based on network-side big data grid matching according to claim 6, characterized in that, The device further includes: A construction unit, connected to the first processing unit, is used to construct a 4G MDT fingerprint database; The building unit includes: The acquisition module is used to acquire massive amounts of 4G MDT data from the network side within a preset period; An extraction module, connected to the acquisition module, is used to extract attribute data from the 4G MDT data; The attribute data includes RSRP data and latitude and longitude data; The rejection module, connected to the acquisition module, is used to reject 4G MDT data that lacks latitude and longitude information; The integration module, connected to the extraction module and the elimination module respectively, is used to integrate 4G MDT data according to the attribute data to obtain the 4G MDT fingerprint database, thus completing the construction of the 4G MDT fingerprint database.

8. The apparatus for identifying base station co-location based on network-side big data grid matching according to claim 6, characterized in that, The matching module includes: The acquisition submodule is used to acquire the coordinates (X1, Y1) of the first center point of the vector shape of the first outer frame; and to acquire the coordinates (X2, Y2) of the second center point of the vector shape of the second outer frame. The first calculation submodule is used to calculate the distance D between the coordinates of the first center point and the coordinates of the second center point. c1-c2 ; The first calculation submodule stores the following formula: A combination submodule, connected to the first calculation submodule, is used to form a matching pair set by combining the combination points of 4G base stations with a distance less than a preset threshold and the combination points of 5G base stations to obtain an initial matching result. The second calculation submodule, connected to the combined submodule, 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: M 1-2 S1*W1+S2*W2+S3*W3; Wherein, 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 point of a 4G base station and the combination point of a 5G base station; S3 represents the grid level similarity between the combination point of a 4G base station and the combination point of a 5G base station; 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; The preferred submodule, connected to the second calculation submodule, is used to remove matching results with a matching score less than a preset matching value, and to sort the matching result scores and select the highest matching result as the optimal matching result. The identification submodule, connected to the optimization submodule, is used to identify co-located 4G base stations and 5G base stations based on the optimal matching result.

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