Intelligent identification method and system for pilot frequency co-station coverage cell, and medium

Abstract

The invention relates to the field of communication data, in particular to an intelligent identification method and system for pilot-frequency co-station coverage cells and a medium. An intelligent identification method for a pilot frequency co-station coverage cell is characterized by comprising the following steps that S1, network engineering parameter data are obtained, and the data at least comprise a base station identifier, an existing network base station name, a cell name, longitude and latitude and an azimuth angle; s2, preprocessing the engineering parameter data, S3, defining at least two matching rule groups with different priorities, each matching rule group comprising one or more matching conditions, the matching conditions are selected from the group consisting of the same physical station name, the same cell tail number, the same cell tail number modulus 3 value, the same longitude and latitude, the distance between the longitude and latitude within a preset threshold value, the same azimuth angle, the difference between the azimuth angles within the preset threshold value, and the target cell name including the main station physical station name; the problems of low efficiency, poor accuracy and weak adaptability in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the field of communication data, and in particular to an intelligent identification method, system and medium for inter-frequency co-site coverage cells. Background Technology

[0002] In mobile communication networks, especially with the development of multi-layered networks like 4G and 5G, there are a large number of cells that are co-located and provide coverage in the same direction but operate on different frequency bands (i.e., "hetero-frequency co-located coverage cells"). Accurately and efficiently identifying these cell pairs is a crucial foundation for precise network planning, optimized load balancing, improved handover success rates, and the realization of multi-layered network collaboration.

[0003] Currently, the industry primarily relies on manual experience or simple script tools to identify such cell pairs. Manual methods require engineers to compare information such as site names, latitude and longitude, and azimuth in engineering parameter tables, which is extremely inefficient and prone to inconsistencies due to differences in personnel experience. Existing script tools typically employ single, rigid matching rules (e.g., only checking if site names are exactly the same, or if latitude and longitude are absolutely identical), making it difficult to handle the complex and ever-changing real-world network engineering parameters. This is mainly reflected in the following aspects: Non-standard naming: The naming rules for site names and community names are inconsistent in engineering parameters of different provinces and cities and at different times. For example, the same physical site may be named "XX Building 1" or "XX Building A", which makes the method based on precise name matching fail.

[0004] Data errors exist: There are unavoidable measurement or input errors in engineering parameters such as latitude and longitude and azimuth. The matching rules based on absolute equality have poor fault tolerance and are prone to producing a large number of missed matches.

[0005] Unable to identify complex scenarios: Simple rules cannot effectively identify complex scenarios such as remote cells (whose cell names may contain the main station name but have different geographical locations).

[0006] Match confidence cannot be distinguished: Existing methods cannot classify the reliability of matching results, and all results are mixed together, which is not conducive to prioritizing subsequent optimization work.

[0007] Therefore, there is an urgent need in this field for an automated solution that can adaptively process non-standard data, tolerate parameter errors, and simulate complex expert judgment logic to overcome the shortcomings of existing technologies. Summary of the Invention

[0008] To address the problems of low efficiency, poor accuracy, and weak adaptability in existing technologies, this application provides an intelligent identification method, system, and medium for inter-frequency co-site coverage cells.

[0009] Firstly, this application provides an intelligent identification method for inter-frequency co-site coverage cells, which adopts the following technical solution: A method for intelligent identification of co-located coverage cells with different frequencies includes the following steps: S1: Obtain network engineering parameter data, which includes at least: base station identifier, existing network base station name, cell name, latitude and longitude, and azimuth. S2: Preprocess the engineering parameter data, including: S21: Based on the existing network base station names, generate physical site names by removing the last digits and / or letters; S22: Based on the cell name, extract the first consecutive number sequence from right to left to generate the cell last number; S3: Define at least two matching rule groups with different priorities, wherein the higher priority rule group is executed before the lower priority rule group; each of the matching rule groups contains one or more matching conditions, the matching conditions being selected from: same physical site name, same cell last digit, same cell last digit modulo 3 value, same latitude and longitude, distance between latitude and longitude within a preset threshold, same azimuth, difference in azimuth within a preset threshold, and the target cell name contains the main station physical site name; S4: Apply the matching rule groups to match cells in descending order of priority; for the currently executed matching rule group, if a pair of cells meets all the matching conditions of the rule group, mark the pair of cells as a co-site coverage cell pair and remove the matched cells from the set to be matched. S5: Output all marked co-site coverage cell pairs.

[0010] By adopting the above technical solution, the co-site cell identification process is fully automated, reducing the time required for manual analysis (which could take hours or even days) to minutes, thus greatly freeing up manpower.

[0011] Secondly, through preprocessing steps such as "physical site name generation" and "cell phone number tail extraction", non-standard network data is intelligently cleaned and standardized, laying the foundation for accurate matching in the future.

[0012] Finally, through the "priority-based progressive matching" mechanism, the system systematically ensures that high-confidence matches are completed first, and continues to mine in the remaining data, thereby maximizing the matching coverage while ensuring accuracy and avoiding missed matches caused by a single rule.

[0013] Optionally, in step S3, the high-priority rule group includes three matching conditions: "same physical site name", "same last digit of cell number", and "difference in azimuth angle is within the first preset threshold".

[0014] By adopting the above technical solution and jointly judging the three strict conditions of "same station name", "same last digit", and "same direction", it is possible to accurately match cell pairs with standardized engineering parameters and clear alignment of wireless coverage directions, producing results with the highest confidence level, and providing the most reliable data foundation for subsequent optimization of the core network.

[0015] Optionally, the range of the first preset threshold is 5 degrees to 15 degrees.

[0016] Optionally, in step S3, the low-priority rule group includes three matching conditions: "same physical site name", "difference in azimuth within a second preset threshold" and "distance between latitude and longitude within a third preset threshold", wherein the second preset threshold is greater than the first preset threshold.

[0017] By adopting the above technical solution, the requirement for the error-prone condition of "last digit of cell number" is relaxed, and instead, the three conditions of "same station name", "roughly the same direction" and "similar location" are relied upon. This can effectively capture co-station cells that are missed due to non-standard cell naming or incorrect last digits, and further improve the overall matching coverage.

[0018] Optionally, the second preset threshold ranges from 20 degrees to 40 degrees, and the third preset threshold ranges from 30 meters to 100 meters. By adopting the above technical solution, a reasonable tolerance range (azimuth angle 20-40 degrees, distance 30-100 meters) that has been tested in practice is provided for the fallback rule. This makes the implementation of the present invention more operable, and can effectively handle most real-world scenarios with slight latitude and longitude drift and azimuth deviation, avoiding the instability of results caused by overly subjective threshold settings.

[0019] Optionally, it also includes defining a matching rule group for identifying remote cells, which has a lower priority than the high-priority rule group. This rule group contains two matching conditions: "the target cell name contains the main station physical site name" and "the difference in azimuth angle is within a preset threshold".

[0020] By adopting the above technical solution, an effective solution is provided specifically for the special scenario of "remote cell" that is difficult to handle with existing technologies. By determining that "the target cell name contains the main station name" and combining it with azimuth verification, the coverage association between the remote cell and the main station cell can be intelligently established, filling the technical gap of traditional methods in this scenario and expanding the application scope of the present invention.

[0021] Secondly, the intelligent identification system for inter-frequency co-site coverage cells provided in this application adopts the following technical solution: A smart identification system for inter-frequency co-site coverage cells includes: The data acquisition and preprocessing module is used to acquire engineering parameter data and perform the preprocessing steps. A rule configuration module is used to receive user input to define the at least two sets of matching rules with different priorities; The core analysis engine is used to execute the matching rule groups in priority order and to perform matching tagging and set updates; The results output module is used to output the final co-site coverage cell pairs. By adopting the above technical solution, the functions protected by the method claims are implemented through a specific modular system.

[0022] Thirdly, the computer-readable storage medium provided in this application adopts the following technical solution: A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the above-described method.

[0023] By adopting the above technical solution, a carrier for the computer program of the above method is provided.

[0024] In summary, this application includes at least the following beneficial technical effects: 1. High degree of automation, significantly improving analysis efficiency: The tool automates the entire process of parameter import, data preprocessing, rule matching, and result output, reducing the time for large-scale parameter analysis from several days to minutes, significantly reducing the workload of engineers, and adapting to the high-efficiency needs of 4G / 5G collaborative optimization.

[0025] 2. Adaptable to non-standard data with strong fault tolerance: Through preprocessing steps such as physical site name generation and community last digit extraction, it can be compatible with the naming rules of engineering parameters in different provinces and cities. At the same time, by presetting reasonable latitude, longitude and azimuth tolerance thresholds, it can effectively deal with data measurement and input errors and avoid missing matches.

[0026] 3. Supports complex scene recognition and has wide applicability: A dedicated remote cell rule group can accurately identify the co-site coverage relationship between the remote cell and the main station. At the same time, the multi-priority rule group design can distinguish the confidence level of the matching results, providing a hierarchical basis for subsequent network optimization work, and adapting to various network scenarios such as macro stations and remote stations. Attached Figure Description

[0027] Figure 1 This is a flowchart of the intelligent identification method for inter-frequency co-site coverage cells in the embodiments of this application; Figure 2 This is a block diagram of the module structure of the intelligent recognition system in the embodiments of this application; Figure 3 This is a schematic diagram of the rule configuration interface of the tool in the embodiments of this application. Detailed Implementation

[0028] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1-3 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0029] This application discloses an intelligent identification method for co-located coverage cells of different frequencies. This method can be implemented based on the co-located coverage cell analysis tool (OverlayCellAnalyser.exe) and is applicable to the matching analysis of co-located coverage cells of 4G (LTE) and 5G (NR) networks. The following uses a 4G / 5G engineering parameter example from a certain area in Chengdu Eastern New Area to illustrate the execution process of this method in detail.

[0030] This application discloses an intelligent identification method for multi-frequency co-site coverage cells.

[0031] refer to Figure 1 A method for intelligent identification of co-located coverage cells using different frequencies includes the following steps: Step S1: Obtain network engineering parameter data Using the "Data Import" function of the inter-frequency co-site coverage cell analysis tool, 4G and 5G engineering parameter files (both in Excel format, with each file containing only one sheet) were imported. This example selects engineering parameter data from the WLHZ025 site and surrounding area in Chengdu Eastern New Area. The core field data for both 4G and 5G engineering parameters are shown in Table 1 below: Table 1. Core data of 4G / 5G network parameters for WLHZ025 site and surrounding areas in Chengdu Eastern New Area. When importing the tool, use the "Key Field Correspondence Setting" function to align the columns in the engineering parameter file with the tool's preset fields. For example, in the 5G engineering parameters, "NR Current Base Station Name" corresponds to "Current Base Station Name", "@Actual Survey Longitude" corresponds to "Longitude", and "@Actual Survey Azimuth" corresponds to "Azimuth". This ensures that the core fields are complete and accurately correspond, laying the foundation for subsequent analysis.

[0032] Step S2: Preprocessing of engineering parameter data The tool's data acquisition and preprocessing module automatically performs cleaning and standardization on the imported engineering parameter data. The specific process is as follows: S21: Generate physical site names. For the existing network base station names in Table 1, the tool automatically removes the trailing numbers, letters, and special symbols to obtain unified physical site names: after removing the trailing "_1" from "WLHZ025_1", it becomes "WLHZ025"; after removing the trailing "A" from "WLHZ025_A", it becomes "WLHZ025"; after removing the trailing "_remote" from "WLHZ025_remote", it becomes "WLHZ025". That is, the above 4 cells all correspond to the same physical site "WLHZ025".

[0033] S22: Extract the cell tail numbers. The tool searches for the first consecutive digit sequence from right to left based on the cell name as the cell tail number: the tail number of "WLHZ025_1_03" is 3, the tail number of "WLHZ025_A_03" is 3, the tail number of "WLHZ025_DU_03" is 3, and the tail number of "WLHZ025_remote_06" is 6. At the same time, the tool automatically calculates the modulo 3 value of the cell tail number. The modulo 3 value of the above tail number 3 is 0, and the modulo 3 value of the tail number 6 is also 0.

[0034] In addition, the tool automatically filters non-macro station data and only retains records with the cell type of "macro station". In this embodiment, all 4 cells are macro stations and are all retained in the待-matched set.

[0035] Step S3: Define a multi-priority matching rule group Through the "Analysis Settings" function of the tool, combined with the naming characteristics of the engineering parameters in the eastern new area of Chengdu and the actual network situation, define 3 groups of matching rule groups with different priorities. The priorities are from high to low in turn: high-priority rule group, remote cell rule group, low-priority rule group. The specific configuration is as follows (reference Figure 3 ) 1. High-priority rule group (priority 1): Includes three conditions: "the same physical site name", "the same cell tail number", and "the azimuth difference is within 10 degrees (the first preset threshold, taking the middle value of 5-15 degrees)", which is used to match the inter-frequency co-site cell pairs with the most standardized parameters and highest confidence.

[0036] 2. Remote cell rule group (priority 2): Includes two conditions: "the target cell name contains the physical site name of the main station" and "the azimuth difference is within 15 degrees", which is used to identify the co-site coverage relationship between the remote cell and the main station. The priority is lower than the high-priority rule group to avoid the remote cell occupying the high-confidence matching quota.

[0037] 3. Low-priority rule group (priority 3): Includes three conditions: "same physical site name", "difference in azimuth within 30 degrees (second preset threshold, taking the midpoint between 20-40 degrees)", and "latitude and longitude distance within 50 meters (third preset threshold, taking the midpoint between 30-100 meters)". It is used to capture missing cell pairs caused by non-standard naming or slight parameter deviations.

[0038] Step S4: Priority-based progressive matching The tool's core analysis engine applies rule groups for matching in descending order of priority, while simultaneously removing matched cells from the set to be matched. The specific process is as follows: 1. High-priority rule group matching: Matching is performed on 4 cells to be matched (2 4G cells and 2 5G cells) to determine if they meet the following conditions: "same physical site name (both WLHZ025), same cell last digit (both 3), and azimuth difference within 10 degrees". The azimuth difference is calculated: the difference between 4G cell 1 (120°) and 5G cell 1 (118°) is 2°, which meets the condition; the difference between 4G cell 2 (123°) and 5G cell 1 (118°) is 5°, which also meets the condition; 4G cell 1 and 4G cell 2 are co-frequency cells and are not included in the inter-frequency matching range. Therefore, "4G cell 1-5G cell 1" and "4G cell 2-5G cell 1" are marked as high-confidence inter-frequency co-site coverage cell pairs, and these 3 cells are removed from the set to be matched. The remaining 5G remote cell (last digit 6) remains in the set to be matched.

[0039] 2. Remote Cell Rule Group Matching: For the remaining 5G remote cells (WLHZ025_Remote_06), determine if there exists a target cell that satisfies the condition that "the name contains the main station physical site name (WLHZ025) and the azimuth difference is within 15 degrees". Select the already matched 4G cell 1 (azimuth 120°) as the main station cell. The azimuth of the 5G remote cell is 122°, the difference is 2°, and the cell name "WLHZ025_Remote_06" contains the main station physical site name "WLHZ025", thus satisfying the condition. Mark "4G cell 1-5G remote cell" as a remote-type inter-frequency co-site coverage cell pair, and remove the 5G remote cell from the set to be matched.

[0040] 3. Low-priority rule group matching: At this point, there are no remaining cells in the set to be matched, so this rule group does not need to be executed. If there are unmatched cells, further matching will be performed using latitude and longitude distance calculation (based on the Haversine formula) and azimuth and physical site name conditions to catch any missed matches.

[0041] Step S5: Output the matching results Using the tool's "Export Results" function, the matching results are exported to an Excel file. The results include detailed information about the cell pairs (base station identifier, cell name, frequency band, latitude and longitude, azimuth, matching rule group, confidence level, etc.). In this embodiment, three sets of inter-frequency co-site coverage cell pairs are finally output, of which two sets are high-confidence (high-priority rule group matching) and one set is remote (remote cell rule group matching), which facilitates engineers to carry out subsequent network optimization, load balancing adjustments, and other work.

[0042] Based on the same design concept, this embodiment also discloses an intelligent identification system for multi-frequency co-site coverage cells.

[0043] refer to Figure 2 Based on the above method, this embodiment discloses an intelligent identification system for inter-frequency co-site coverage cells. This system is integrated into an inter-frequency co-site coverage cell analysis tool and includes a data acquisition and preprocessing module 1, a rule configuration module 2, a core analysis engine 3, and a result output module 4. The functions and collaborative workflows of each module are as follows: 1. Data Acquisition and Preprocessing Module 1: This module reads user-imported 4G / 5G engineering parameter files (supports Excel format), automatically verifies the integrity of core fields (base station identifier, latitude and longitude, azimuth, etc.), and removes null values ​​and invalid data. Simultaneously, it performs preprocessing operations, generating physical site names, extracting cell tail numbers, and calculating the modulo 3 value of the tail number. The standardized data is then transmitted to the core analysis engine 3. This module is compatible with engineering parameter naming rules from different provinces and cities; for example, it can accurately extract physical site names from names such as "XX Site-2" and "XX Site B".

[0044] 2. Rule Configuration Module 2: Provides a visual configuration interface, supporting user-defined rule groups and priorities. Users can select preset matching conditions (such as identical physical sites, location deviation thresholds, azimuth deviation thresholds, etc.) or combine conditions to generate custom rule groups (such as "same physical site & location deviation within 100 meters & azimuth deviation within 20 degrees & same last three digits of the cell number"). It also supports operations such as moving rule groups up, down, adding, and resetting, adapting to the analysis needs of different network scenarios.

[0045] 3. Core Analysis Engine 3: As the core of the system, it receives standardized data and configured rule groups, executes matching logic according to priority, calculates latitude and longitude distance using the Haversine formula, calculates azimuth deviation using the angle difference algorithm, and determines whether cell pairs meet all conditions of the current rule group; it marks successfully matched cell pairs and updates the set to be matched (removing matched cells) to ensure that each cell participates in high-priority matching only once, thus improving the accuracy of the results.

[0046] 4. Result Output Module 4: Formats the matching results output by the core analysis engine 3, supporting export to Excel, CSV, and other formats. It also displays the matching progress and result statistics in real-time on the tool interface (such as the number of successfully matched sub-pairs, the number of matches in each rule group, etc.). If a matching anomaly occurs (such as missing parameter data or rule configuration conflicts), an error message will be generated to guide the user in troubleshooting.

[0047] This application also provides a computer-readable storage medium storing information that can be loaded and executed by a processor to perform the above steps.

[0048] The computer-readable storage medium includes, for example, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0049] Based on the same inventive concept, embodiments of this application provide a computer device, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the above-described method.

[0050] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0051] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0052] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0053] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0054] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0055] The above description of the embodiments is only used to provide a detailed introduction to the technical solutions of this application. However, the description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of this application, and should not be construed as a limitation of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application.

Claims

1. A method for intelligent identification of co-located coverage cells with different frequencies, characterized in that, Includes the following steps: S1: Obtain network engineering parameter data, which includes at least: base station identifier, existing network base station name, cell name, latitude and longitude, and azimuth. S2: Preprocess the engineering parameter data, including: S21: Based on the existing network base station names, generate physical site names by removing the last digits and / or letters; S22: Based on the cell name, extract the first consecutive number sequence from right to left to generate the cell last number; S3: Define at least two matching rule groups with different priorities, wherein the higher priority rule group is executed before the lower priority rule group; each of the matching rule groups contains one or more matching conditions, the matching conditions being selected from: same physical site name, same cell last digit, same cell last digit modulo 3 value, same latitude and longitude, distance between latitude and longitude within a preset threshold, same azimuth, difference in azimuth within a preset threshold, and the target cell name contains the main station physical site name; S4: Apply the matching rule groups to match cells in descending order of priority; for the currently executed matching rule group, if a pair of cells meets all the matching conditions of the rule group, mark the pair of cells as a co-site coverage cell pair and remove the matched cells from the set to be matched. S5: Output all marked co-site coverage cell pairs.

2. The method according to claim 1, characterized in that, In step S3, the high-priority rule group includes three matching conditions: "same physical site name", "same last digit of cell number", and "difference in azimuth angle within the first preset threshold".

3. The method according to claim 2, characterized in that, The first preset threshold ranges from 5 degrees to 15 degrees.

4. The method according to claim 1, characterized in that, In step S3, the low-priority rule group includes three matching conditions: "same physical site name", "difference in azimuth within a second preset threshold" and "distance between latitude and longitude within a third preset threshold", wherein the second preset threshold is greater than the first preset threshold.

5. The method according to claim 4, characterized in that, The second preset threshold ranges from 20 degrees to 40 degrees, and the third preset threshold ranges from 30 meters to 100 meters.

6. The method according to claim 1, characterized in that, It also includes defining a matching rule group for identifying remote cells, which has a lower priority than the high-priority rule group. This rule group contains two matching conditions: "the target cell name contains the main station physical site name" and "the difference in azimuth angle is within a preset threshold".

7. An intelligent identification system for inter-frequency co-site coverage cells applicable to the method described in any one of claims 1-6, characterized in that, include: The data acquisition and preprocessing module is used to acquire engineering parameter data and perform the preprocessing steps. A rule configuration module is used to receive user input to define the at least two sets of matching rules with different priorities; The core analysis engine is used to execute the matching rule groups in priority order and to perform matching tagging and set updates; The results output module is used to output the final co-site coverage cell pairs.

8. A computer-readable storage medium, characterized in that, The computer storage medium stores a computer program; when the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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