Base station parameter calibration method, device, equipment, medium and computer program product

Abstract

The application provides a base station working parameter calibration method, device, equipment and computer program product. The method comprises the following steps: analyzing a minimization of drive test report to obtain a plurality of effective sampling points; extracting a minimum boundary of a cell from the plurality of effective sampling points to obtain a cell drive test coverage boundary of a plurality of cells; determining an overlapping area of same-frequency cells belonging to a same base station based on the cell drive test coverage boundary of the plurality of cells; determining a cell drive test simulation latitude and longitude of the base station according to a center position of the overlapping area of the same-frequency cells; and calibrating base station working parameter latitude and longitude of the base station based on the cell drive test simulation latitude and longitude of the base station. The base station working parameter calibration method provided by the application can realize automatic calibration of base station working parameter latitude and longitude. Furthermore, the method uses a minimization of drive test report as a data source for evaluating base station working parameters. The minimization of drive test report is the latest data directly reported by a user terminal, and is highly effective, thereby improving the timeliness of latitude and longitude updating.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile communications, and in particular to a method, device, equipment, medium and computer program product for calibrating engineering parameters of a base station. Background Art

[0002] Engineering parameters are the industry term abbreviations for engineering parameters in the field of wireless network optimization, specifically referring to parameters such as longitude, latitude, azimuth angle, and station height determined during the engineering construction of a site. The accuracy of engineering parameter data directly affects many tasks such as optimization analysis, planning design simulation, performance special analysis, and complaint handling. In particular, if the location and azimuth angle data of a base station are inaccurate, it may lead to mistakes in optimization plans and errors in planning decisions. Once these incorrect plans are implemented, it will directly affect the quality of the wireless network, resulting in a poor user experience and potentially triggering a large number of complaints.

[0003] Currently, the engineering parameter data of base stations mainly relies on manual maintenance. However, during the daily network cutover and optimization adjustment processes, due to reasons such as untimely and incomplete updates of engineering parameter data, it has always been a problem difficult to guarantee the accuracy of engineering parameter data. The conventional rectification method is to conduct a general survey of engineering parameter data in stages, still mainly relying on manual verification. This method is inefficient, costly, and has a long cycle. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and computer program product for calibrating engineering parameters of a base station to solve the technical problem that the engineering parameter data of existing network base stations still relies on manual maintenance and is difficult to be calibrated and updated in a timely manner, and to achieve automatic calibration of the engineering parameters of base stations.

[0005] In a first aspect, the present invention provides a method for calibrating engineering parameters of a base station, including the following steps.

[0006] Parse the minimized drive test report to obtain multiple valid sampling points;

[0007] Extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of multiple cells;

[0008] Based on the cell drive test coverage boundaries of the multiple cells, determine the overlapping area of co-frequency cells belonging to the same base station;

[0009] According to the central position of the overlapping area of the co-frequency cells, determine the drive test simulation longitude and latitude of the cell of the base station;

[0010] Based on the drive test simulation longitude and latitude of the cell of the base station, calibrate the engineering parameter longitude and latitude of the base station.

[0011] According to the base station engineering parameter calibration method provided by the present invention, after calibrating the base station engineering parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station, the method further includes:

[0012] A coverage circle is constructed with the calibrated base station latitude and longitude coordinates as the center and the pre-set average coverage radius of the cell reference as the radius.

[0013] Cluster the multiple valid sampling points until the sum of the sums of squares within all clusters within the coverage circle converges. At this point, the cluster with the largest sum of squares within the cluster is taken as the main coverage cluster of the cell.

[0014] Calculate the sum of the reference signal reception quality of all valid sampling points within the main coverage cluster of the cell, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception quality of all valid sampling points as the centroid weight of each valid sampling point.

[0015] Based on the centroid weights of each valid sampling point, the positions of all valid sampling points within the main coverage cluster of the cell are weighted to obtain the latitude and longitude of the centroid position of the main coverage cluster of the cell.

[0016] The simulated azimuth angle of the cell is calculated based on the latitude and longitude of the centroid location.

[0017] Based on the simulated azimuth angle of the cell, calibrate the azimuth angle of the cell corresponding to the main coverage cluster of the cell.

[0018] According to the base station parameter calibration method provided by the present invention, the step of clustering the plurality of valid sampling points until the sum of the in-cluster squares of all clusters within the coverage circle converges specifically includes:

[0019] The covering circle is divided into multiple sectors on an even basis;

[0020] Based on the Euclidean distance from each valid sampling point to the centroid of each sector, the multiple valid sampling points are clustered to obtain multiple clusters.

[0021] The mean latitude and longitude of the valid sampling points in each cluster are used to update the centroid of each cluster;

[0022] The multiple valid sampling points are clustered based on the Euclidean distance from each valid sampling point to the centroid of each updated cluster.

[0023] Calculate the sum of the in-cluster sums of squares of all clusters within the covering circle after each round of clustering;

[0024] Return to the step of updating the centroid of each cluster with the mean latitude and longitude of the valid sample points in each cluster, until the sum of the in-cluster sums of squares of all clusters within the covering circle converges.

[0025] According to the base station engineering parameter calibration method provided by the present invention, the analytical minimization of the drive test report yields multiple effective sampling points, including:

[0026] The minimized road test report is analyzed to obtain multiple initial sampling points;

[0027] Noise points are removed from the plurality of initial sampling points to obtain the plurality of valid sampling points.

[0028] According to the base station parameter calibration method provided by the present invention, the step of removing noise from the plurality of initial sampling points to obtain the plurality of effective sampling points specifically includes:

[0029] Based on the density distribution among the initial sampling points, the multiple initial sampling points are clustered. The initial sampling points that cannot be clustered are regarded as noise points. The noise points are removed from the multiple initial sampling points to obtain the multiple effective sampling points.

[0030] According to the base station engineering parameter calibration method provided by the present invention, the step of extracting the minimum boundary of the cell from the plurality of valid sampling points to obtain the cell drive test coverage boundary of the plurality of cells includes:

[0031] The minimum boundary of the cell is extracted from the multiple valid sampling points using the concave hull algorithm, thus obtaining the cell drive test coverage boundary of multiple cells.

[0032] According to the base station engineering parameter calibration method provided by the present invention, the simulated azimuth angle α of the cell is:

[0033]

[0034] Among them, lng o Let lat be the longitude of the centroid O of the main coverage cluster of the cell. o Let O be the latitude of the centroid point O of the main coverage cluster of the cell.

[0035] According to the base station engineering parameter calibration method provided by the present invention, the pre-set cell reference average coverage radius is obtained by big data analysis, and the cell reference average coverage radius is different in different scenarios or different frequency bands.

[0036] Secondly, the present invention also provides a base station engineering parameter calibration device, comprising the following modules:

[0037] The road test report parsing module is used to parse the minimized road test report to obtain multiple valid sampling points;

[0038] The cell boundary extraction module is used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of multiple cells;

[0039] The cell overlap area determination module is used to determine the overlap area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells;

[0040] The cell drive test simulation latitude and longitude calculation module is used to determine the cell drive test simulation latitude and longitude of the base station based on the center position of the overlapping area of ​​the co-frequency cells;

[0041] The base station engineering parameter calibration module is used to calibrate the base station engineering parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station.

[0042] Thirdly, the present invention also provides an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the base station engineering parameter calibration method described in the first aspect.

[0043] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the base station engineering parameter calibration method described in the first aspect.

[0044] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the base station engineering parameter calibration method described in the first aspect.

[0045] The base station engineering parameter calibration method, apparatus, equipment, medium, and computer program product provided by this invention obtain multiple effective sampling points by parsing and minimizing drive test reports; extract the minimum boundary of the cell from the multiple effective sampling points to obtain the cell drive test coverage boundary of multiple cells; determine the overlapping area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundary of multiple cells; determine the cell drive test simulation latitude and longitude of the base station according to the center position of the overlapping area of ​​co-frequency cells; and calibrate the base station engineering parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station. This method enables automated calibration of base station operational parameters (latitude and longitude). Furthermore, it employs minimized drive test reports as the data source for evaluating base station operational parameters. These reports are the latest data directly reported by user terminals, ensuring high validity and improving the timeliness of latitude and longitude updates. This data includes geographic location information, directly reflecting user distribution, and represents the smallest granularity sampling point. Compared to traditional methods based on grid data granularity calculations, minimized measurement report data samples are sufficient and have small errors, improving the accuracy of base station latitude and longitude assessment. Moreover, it provides more accurate base station latitude and longitude for subsequent cell azimuth calibration, enhancing the accuracy of cell azimuth. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is one of the flowcharts illustrating the base station engineering parameter calibration method provided by the present invention.

[0048] Figure 2 This is a schematic diagram of the extraction of the boundary of the effective sampling points before denoising provided by the present invention.

[0049] Figure 3 This is a schematic diagram of the overlapping area of ​​the community road test coverage boundary provided by the present invention.

[0050] Figure 4 This is the second flowchart of the base station engineering parameter calibration method provided by the present invention.

[0051] Figure 5 This is a schematic diagram of the covering circle provided by the present invention.

[0052] Figure 6 This is a schematic diagram of the initial cluster provided by the present invention.

[0053] Figure 7 This is a schematic diagram of the main coverage area of ​​the cell obtained after clustering provided by the present invention.

[0054] Figure 8 This is a schematic diagram of the extraction boundary of effective sampling points after denoising provided by the present invention.

[0055] Figure 9 This is the third flowchart of the base station engineering parameter calibration method provided by the present invention.

[0056] Figure 10 This is a schematic diagram of the base station parameter calibration device provided by the present invention.

[0057] Figure 11 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0059] The following is combined with Figures 1-11 Specific embodiments of the present invention are described below.

[0060] Figure 1 This is one of the flowcharts illustrating the base station engineering parameter calibration method provided by the present invention, such as... Figure 1 As shown, the method includes the following.

[0061] Step 101: parse the minimized road test report to obtain multiple valid sampling points.

[0062] The Minimization Drive Test (MDT) report is a measurement report submitted by the user terminal to the base station, which then submits it to the network management system. The user terminal periodically or periodically collects geographical location information, communication signal parameters, and other data from sampling points and reports this information to the base station. Valid sampling points refer to those used for subsequent data analysis.

[0063] Specifically, the network management system parses the Minimum Drive Test Report (MDT) reported by the User Equipment (UE) to obtain information on all sampling points. This information primarily includes the latitude and longitude of the sampling points, the voltage level (RSRP, Reference Signal Receiving Power), and the cell CGI (Cell Global Identifier) ​​information. The actual voltage level is obtained by subtracting 140 from the RSRP value. Valid sampling points are then selected from all sampling points.

[0064] Step 102: Extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of the multiple cells.

[0065] Each base station can have multiple cells. On the map, the minimum bounding boundary is extracted from the valid sampling points belonging to the same cell to obtain the cell's drive test coverage boundary, such as... Figure 2 As shown, Figure 2 A schematic diagram showing the coverage boundary of a community's road test is displayed.

[0066] Step 103: Based on the cell drive test coverage boundaries of the multiple cells, determine the overlapping area of ​​co-frequency cells belonging to the same base station.

[0067] Specifically, since the cell coverage areas of different sites are different, and the cell coverage radii of different frequency bands of the same base station are also different, therefore, in the entire cell drive test coverage boundary, we need to find the cell drive test coverage boundaries belonging to the same frequency band of the same base station, and calculate their overlapping areas, such as... Figure 3 As shown, the overlapping area of ​​the three cell road test coverage boundaries CELLA, CELLB and CELLC is A.

[0068] Step 104: Determine the cell drive test simulation latitude and longitude of the base station based on the center location of the overlapping area of ​​the co-frequency cells.

[0069] Specifically, the latitude and longitude coordinates of the center of the overlapping area A are used as the simulated latitude and longitude coordinates of the cell drive test for the base station.

[0070] Step 105: Based on the cell drive test simulation latitude and longitude of the base station, calibrate the base station engineering parameter latitude and longitude of the base station.

[0071] Specifically, base station operating parameters are pre-stored in the operating parameter database, including the Chinese name of the cell, cell CGI, station type, operating frequency band, scene category, RRU (Remote Radio Unit) longitude, RRU latitude, and operating parameter azimuth angle of all 4 / 5G base stations in the network. The base station operating parameter longitude and latitude of the already stored base stations are retrieved from the operating parameter database. The distance between two points is calculated based on the base station operating parameter longitude and latitude and the simulated longitude and latitude of the cell. If the distance between the two points is greater than the preset distance threshold DL meters (DL is taken as 50 in this scheme), the base station operating parameter longitude and latitude are considered abnormal, and the above-mentioned cell drive test simulated longitude and latitude calibration and update are required. The calibrated longitude and latitude are the cell location information.

[0072] The above embodiment obtains multiple effective sampling points by parsing the minimized drive test report; extracts the minimum cell boundary from the multiple effective sampling points to obtain the cell drive test coverage boundary of multiple cells; determines the overlapping area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundary of multiple cells; determines the cell drive test simulation latitude and longitude of the base station according to the center position of the overlapping area of ​​co-frequency cells; and calibrates the base station's operating parameter latitude and longitude based on the base station's cell drive test simulation latitude and longitude. This method can realize the automated calibration of base station operating parameter latitude and longitude; furthermore, this method uses the minimized drive test report as the data source for evaluating base station operating parameters. The minimized drive test report is the latest data directly reported by the user terminal, which has high validity and improves the timeliness of latitude and longitude updates; this data carries geographical location information, which can directly reflect user distribution, and is the smallest granularity sampling point. Compared with the traditional method based on grid data granularity calculation, the minimized measurement report data sample is sufficient and the error is small, which can improve the accuracy of base station latitude and longitude evaluation; furthermore, it also provides more accurate base station latitude and longitude for subsequent cell azimuth angle calibration, improving the accuracy of cell azimuth angle.

[0073] In one embodiment, such as Figure 4 As shown, Figure 4 A flowchart illustrating the azimuth calibration method for engineering parameters in another embodiment is shown, which includes the following steps after step 105 above:

[0074] Step 401: Construct a coverage circle with the location of the calibrated base station's latitude and longitude as the center and the pre-set average coverage radius of the cell reference as the radius.

[0075] The pre-set average coverage radius of the cell is obtained in advance through big data statistics. Because the coverage distance is different for different scenarios and frequency bands, in order to obtain a more accurate simulated azimuth angle of the cell, it is necessary to refine the frequency band scenario coverage radius relationship mapping table. Table 1 below shows the average coverage radius of the cell obtained by using a large amount of MDT sampling point data and big data calculation.

[0076] Table 1. Mapping Table of Frequency Band Scene Radius Relationship

[0077]

[0078]

[0079] Specifically, using the location of the calibrated base station's latitude and longitude as the center, the corresponding cell reference average coverage radius is found in the frequency band scene radius relationship mapping table to construct a coverage circle, such as... Figure 5 As shown.

[0080] Step 402: Cluster the multiple valid sampling points until the sum of the sum of squares within all clusters within the coverage circle converges. At this point, the cluster with the largest sum of squares within the cluster is taken as the main coverage cluster of the cell.

[0081] In this context, a cluster refers to one of several sets obtained through clustering methods.

[0082] Specifically, in this application, the aforementioned multiple valid sampling points can be clustered to obtain multiple clusters. The cluster sum of squares (CSS) of each cluster is calculated, and then the sum of the cluster sums of squares of all clusters, i.e., TATALCSS, is calculated. The formula for calculating the cluster sum of squares (CSS) of each cluster is as follows:

[0083]

[0084] Where, x ij Let x be the coordinates of the i-th feature parameter of the j-th valid sampling point, m represent the total number of valid sampling points in a cluster, and n represent the number of features in each valid sampling point. In this scheme, only the distance feature is used, therefore x ij μ represents the location (latitude and longitude) of the j-th valid sampling point. i This represents the location (i.e., latitude and longitude) of the centroid of the current cluster.

[0085] centroid μ i The formula for calculating latitude and longitude is:

[0086]

[0087] Among them, lng μ μ represents the centroid of the current cluster. i longitude, lat μ μ represents the centroid of the current cluster. i latitude, This represents the longitude of the j-th valid sampling point. This represents the latitude of the j-th valid sampling point.

[0088] The formula for calculating the sum of squares within all clusters (TATALCSS) is:

[0089]

[0090] Among them, CSS k Let represent the sum of squares within the k-th cluster, and p represent the total number of clusters.

[0091] In step 402, effective sampling points can be clustered using various clustering methods, such as the k-means clustering method. This requires iterative k-means clustering. In each iteration, the average latitude and longitude of the sampling points is used as the centroid of the next iteration. If the TATALCSS value remains unchanged in the next iteration (i.e., the TATALCSS converges), then the TATALCSS value is optimal. At this point, the cluster with the largest sum of squares (CSS) within the cluster is selected as the primary coverage cluster for the cell.

[0092] Step 403: Calculate the sum of the reference signal reception quality of all valid sampling points within the main coverage cluster of the cell, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception quality of all valid sampling points as the centroid weight of each valid sampling point.

[0093] Specifically, step 402 identifies the main coverage cluster of the cell, then calculates the sum of rsrq (Reference Signal Receiving Quality) of all points within the cluster, rsrqall, and sets the reference signal receiving quality rsrq of the i-th valid sampling point. i The ratio of the sum of the received quality of the reference signals at all sampling points (rsrqall) is used as the centroid weight W of the i-th valid sampling point. i :

[0094] W i =rsrq i / rsrqall;

[0095] Step 404: Based on the centroid weights of each valid sampling point, the positions of all valid sampling points within the main coverage cluster of the cell are weighted to obtain the latitude and longitude of the centroid position of the main coverage cluster of the cell.

[0096] Specifically, based on the aforementioned position centroid weight W i The i-th valid sampling point x is obtained. i The longitude is latitude Then the centroid location of this cluster is determined by its latitude and longitude (lng). o ,lat o )for m represents the total number of valid sampling points in a cluster.

[0097] Step 405: Calculate the simulated azimuth angle of the cell based on the latitude and longitude of the centroid location.

[0098] The simulated azimuth of a cell represents the angle between the line connecting the centroid and the cell's location and the due north direction. In practice, it represents the strongest azimuth of the cell's radiation direction and can be used to characterize the actual azimuth of the cell antenna.

[0099] Specifically, based on the definition of the simulated azimuth angle of the cell as described above, the simulated azimuth angle of the cell can be calculated using the following formula. Since the latitude and longitude are in the WGS84 (World Geodetic System 1984 Coordinate System) coordinate system, it is first necessary to convert the cell's latitude and longitude coordinates to Mercator projection coordinates. Then, the simulated azimuth angle α of the cell can be calculated using the following geometric formula:

[0100] α=atan(abs(lng o / lat o ))*180 / π;

[0101] Among them, lng o Let lat be the longitude of the centroid O of the main coverage cluster of the cell. o Let O be the latitude of the centroid point O of the main coverage cluster of the cell.

[0102] Step 406: Based on the simulated azimuth angle of the cell, calibrate the azimuth angle of the cell corresponding to the main coverage cluster of the cell.

[0103] Specifically, the azimuth angles of the cell stored in the engineering parameter database are compared with the simulated azimuth angles of the aforementioned cell. Then, based on the scene mapping difference threshold, if the difference between the simulated azimuth angle and the engineering parameter azimuth angle exceeds the corresponding preset azimuth angle threshold, the cell's engineering parameter azimuth angle is considered abnormal, and the azimuth angle data in the engineering parameter database needs to be modified according to the cell's simulated azimuth angle. Assume the engineering parameter azimuth angle of a certain cell is azimuth. gc The calculated simulation azimuth angle is azimuth. fz The difference between the two is value. j value j The calculation formula is as follows:

[0104]

[0105] The formula for judging anomalies in engineering parameter azimuth is as follows:

[0106]

[0107] Where, k j This is the preset azimuth threshold.

[0108] Anomaly threshold k in different scenarios j As shown in Table 2 below.

[0109] Table 2. Mapping Table of Cell Engineering Parameter Azimuth Relationships by Frequency Band and Scenario

[0110] station type Scene categories <![CDATA[Preset azimuth threshold k j > Macro Station Main urban area 5 Macro Station General urban areas 10 Macro Station county seat 15 Macro Station Townships 20 Macro Station rural areas 30 Mini-site Regardless of the scene 15

[0111] The following table 3 shows an example of the results of verifying the azimuth angle using the simulated azimuth angle of the community.

[0112] Table 3. Frequency Band Scene Azimuth Relationship Mapping Table

[0113] Community CGI Engineering parameter azimuth Simulated azimuth Remark 460-00-186844-65 120 162 The azimuth angle of the engineering parameters is abnormal and needs to be corrected to 162 degrees. 460-00-305546-131 240 210 The azimuth anomaly in the engineering parameters needs to be corrected to 210 degrees.

[0114] The above embodiments achieve automated calibration of cell engineering parameter azimuth angle by clustering and identifying the main coverage clusters of effective sampling points after MDT noise filtering in the cell, and calculating the centroid of the main coverage cluster. This reduces labor costs and improves calibration efficiency.

[0115] In one embodiment, step 402 includes: dividing the covering circle into multiple sectors on an average basis; clustering the multiple effective sampling points based on the Euclidean distance from each effective sampling point to the centroid of each sector to obtain multiple clusters; updating the centroid of each cluster with the mean latitude and longitude of the effective sampling points in each cluster; clustering the multiple effective sampling points based on the Euclidean distance from each effective sampling point to the updated centroid of each cluster; calculating the sum of the sum of squares within all clusters in the covering circle after each round of clustering; returning to the step of updating the centroid of each cluster with the mean latitude and longitude of the effective sampling points in each cluster, until the sum of the sum of squares within all clusters in the covering circle converges.

[0116] In detail, this embodiment uses the k-means clustering algorithm to construct a coverage circle with the updated base station latitude and longitude coordinates as the center and a pre-set average coverage radius of the cell reference as the radius. Figure 5 As shown; the covering circle is evenly divided into multiple sectors as initial clusters, such as... Figure 6As shown, the data are named cluster 1, cluster 2, ..., cluster N. Each sector has a centroid. Based on the Euclidean distance from each valid sampling point to the centroid of each sector, the multiple valid sampling points are clustered to obtain multiple new clusters. The centroid of each cluster is updated, that is, the mean latitude and longitude of the valid sampling points in each cluster is updated to the centroid of each cluster. Clustering is performed again based on the Euclidean distance from each valid sampling point to the new centroid. The sum of the squared in-cluster sums (CSS) of all clusters within the coverage circle after each round of clustering is calculated as T. ATALCSS is a key indicator of clustering accuracy. A smaller ATALCSS value results in less clustering error and more precise clustering. To obtain the minimum ATALCSS value, the k-means clustering algorithm needs to be iterated (using the mean latitude and longitude of the effective sampling points as the centroid of the next clustering iteration). The optimal ATALCSS value is achieved when the ATALCSS value remains unchanged in the next iteration (i.e., the same as the previous result). After obtaining the optimal ATALCSS value, the cluster with the largest ATALCSS value among the N clusters of the cell is the main coverage cluster of the cell. Figure 7 As shown.

[0117] The above embodiment uses the k-means algorithm to cluster all valid sampling points within the base station coverage circle, thus identifying the main coverage cluster of the cell and providing a data foundation for calculating the simulated azimuth angle of the cell.

[0118] In one embodiment, step 101 includes: parsing the minimized road test report to obtain multiple initial sampling points; removing noise from the multiple initial sampling points to obtain the multiple valid sampling points.

[0119] Specifically, the network management system parses the Minimum Drive Test Report (MDT) reported by the User Terminal (UE) to obtain all sampling point information, which is called the initial sampling point. Since the initial sampling point may include data errors that affect the subsequent analysis results, it is necessary to denoise the initial sampling point. Generally, sampling points belonging to the same base station cell are usually relatively concentrated. If there are points in the cell that are far away from the base station, they can be considered as noise points. After removing such noise points, the effective sampling points are obtained.

[0120] The above embodiments remove noise from the initial sampling points, making the cell boundary positioning more accurate and improving the calibration accuracy.

[0121] In one embodiment, the above-mentioned removal of noise from the plurality of initial sampling points to obtain the plurality of effective sampling points specifically includes: clustering the plurality of initial sampling points according to the density distribution among the initial sampling points, taking the initial sampling points that cannot be clustered as noise, removing the noise from the plurality of initial sampling points to obtain the plurality of effective sampling points.

[0122] Specifically, this application distinguishes different cells based on the density distribution of sampling points. However, since the initial sampling points may contain some erroneous data, subsequent cell boundary extraction may be inaccurate, thus requiring noise removal. This application removes noise based on density distribution, focusing primarily on the density distribution between data points, and forming clusters by identifying dense regions in the initial sampling points. Specifically, this embodiment uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to implement density clustering. DBSCAN is a density-based spatial clustering algorithm that can identify noise points and discover clusters of arbitrary shapes. It does not require pre-specifying the number of clusters, but defines the density of data points through two core parameters (neighborhood radius r and minimum number of points MinPts). Its working principle: The algorithm first identifies core points (i.e., data points with at least MinPts points in their r-neighborhood), and then constructs clusters based on density reachability (points connected through a series of core points). Points not belonging to any cluster are considered noise. Specifically, the density clustering algorithm DBSCAN(r, MinPts) clusters MDT sampling points. In this scheme, r is the reference average coverage radius of the cell, and MinPts is the density threshold, i.e., the minimum number of clustered sampling points. In this scheme, Minpts is set to 3. Points that cannot be clustered are identified as noise points. After removing noise points, multiple valid sampling points are obtained.

[0123] The above embodiments use density clustering algorithms to denoise the initial sampling points, which is beneficial for subsequent extraction of cell boundaries and improves the accuracy of boundary extraction.

[0124] In one embodiment, step 102 includes: using a concave hull algorithm to extract the minimum boundary of the cell from the plurality of valid sampling points to obtain the cell drive test coverage boundary of the plurality of cells.

[0125] The concave hull algorithm is used in computational geometry to generate a polygon that encloses a given set of points. The boundary of the polygon is composed of a subset of the point set, and the polygon is allowed to have concave parts. In contrast to the convex hull, which requires all boundaries to be convex.

[0126] Specifically, this application uses the concave hull algorithm to extract the boundaries of effective sampling points in each cell, thereby obtaining the cell drive test coverage boundaries of each cell, such as... Figure 8 As shown.

[0127] The above embodiments use a concave hull algorithm to extract cell boundaries, providing data for subsequent calculation of cell centroids.

[0128] In one embodiment, the aforementioned pre-set cell reference average coverage radius is obtained using big data analysis, and the cell reference average coverage radius varies in different scenarios or frequency bands.

[0129] Specifically, because the coverage distance varies in different scenarios and frequency bands, a refined frequency band scenario coverage radius relationship mapping table needs to be created in order to obtain a more accurate simulated azimuth angle of the cell. The table below shows the reference average coverage radius of the cell obtained by using a large amount of MDT sampling point data and big data calculation.

[0130] Table 4. Frequency Band Scene Radius Relationship Mapping Table

[0131]

[0132] The above embodiments use big data mining technology to obtain the reference average coverage radius of cells under different sites and frequency bands, providing a data foundation for subsequent calculation of cell latitude and longitude and cell simulated azimuth angle, and further improving the automated calibration level of base station engineering parameter latitude and longitude and cell engineering parameter azimuth angle.

[0133] In one embodiment, such as Figure 9 As shown, Figure 9This paper presents a complete flowchart of the base station technical parameter calibration method provided in this application. The method first collects and organizes base station technical parameter data. The technical parameters for the entire 4G / 5G network must include the cell's Chinese name, cell CGI, station type, operating frequency band, scene category, RRU longitude, RRU latitude, and technical parameter azimuth. It then parses the MDT data reported by the terminal and stores the relevant data in a database for later use. This data mainly includes the longitude, latitude, signal strength (SRSRP), and cell CGI information of the sampling points. Using a large amount of MDT sampling point data and the cell reference average coverage radius calculated through big data analysis, a cell reference average coverage radius mapping table is created. The MDT sampling points are clustered using the density clustering algorithm DBSCAN(r, MinPts), and points that cannot be clustered are removed as noise. The concave hull algorithm is used to extract the cell MDT coverage boundary from the noise-removed sampling point set. An MDT coverage simulation diagram of the same frequency cell at the same station is calculated on a station-by-station basis, and then the coverage of the next station is calculated. The overlapping areas of the cell coverage simulation map are identified, and the center position of these overlapping areas is calculated as the cell simulation latitude and longitude of the site. Cell latitude and longitude calibration: the distance between two points is calculated based on the engineering parameter latitude and longitude and the simulation latitude and longitude. If the distance between the two points is greater than DL meters (DL is taken as 50 in this scheme), the engineering parameter latitude and longitude is considered abnormal and needs to be updated using MDT simulation latitude and longitude calibration. The main coverage cluster of the cell is identified using a clustering algorithm, and the centroid of the main coverage cluster is calculated. The line connecting the centroid and the base station is obtained, and the angle between this line and due north is calculated as the cell simulation azimuth. Finally, the engineering parameter azimuth in the engineering parameter database is updated based on the difference between the cell simulation azimuth and the engineering parameter azimuth.

[0134] The above embodiments can quickly achieve azimuth calibration of engineering parameters, which will greatly improve the accuracy of wireless network optimization schemes, thereby improving network quality and user experience. This method has high accuracy in identifying the azimuth of abnormal cells, high computational efficiency, and low investment cost.

[0135] The base station parameter calibration device provided by the present invention is described below. The base station parameter calibration device described below can be referred to in correspondence with the base station parameter calibration method described above.

[0136] This application provides a base station engineering parameter calibration device, such as... Figure 10 As shown, it includes:

[0137] The road test report parsing module 1001 is used to parse the minimized road test report to obtain multiple valid sampling points;

[0138] The cell boundary extraction module 1002 is used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of the multiple cells;

[0139] The cell overlap area determination module 1003 is used to determine the overlap area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells.

[0140] The cell drive test simulation latitude and longitude calculation module 1004 is used to determine the cell drive test simulation latitude and longitude of the base station based on the center position of the overlapping area of ​​the co-frequency cells;

[0141] The base station engineering parameter calibration module 1005 is used to calibrate the base station engineering parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station.

[0142] In one embodiment, the base station engineering parameter calibration module 1005 is further configured to: construct a coverage circle with the calibrated base station engineering parameter latitude and longitude location as the center and a pre-set cell reference average coverage radius as the radius; cluster the multiple effective sampling points until the sum of the sums of squares within all clusters within the coverage circle converges, at which point the cluster with the largest sum of squares within the cluster is taken as the main coverage cluster of the cell; calculate the sum of the reference signal reception quality of all effective sampling points within the main coverage cluster of the cell, and use the ratio of the reference signal reception quality of each effective sampling point to the sum of the reference signal reception quality of all effective sampling points as the centroid weight of each effective sampling point; based on the centroid weight of each effective sampling point, weight the positions of all effective sampling points within the main coverage cluster of the cell to obtain the latitude and longitude of the centroid point of the main coverage cluster of the cell; calculate the cell simulated azimuth angle based on the centroid point's latitude and longitude; and calibrate the cell engineering parameter azimuth angle corresponding to the main coverage cluster of the cell based on the cell simulated azimuth angle.

[0143] In one embodiment, the base station parameter calibration module 1005 is further configured to: divide the coverage circle into multiple sectors on an average basis; cluster the multiple effective sampling points based on the Euclidean distance from each effective sampling point to the centroid of each sector to obtain multiple clusters; update the centroid of each cluster with the mean latitude and longitude of the effective sampling points in each cluster; cluster the multiple effective sampling points based on the Euclidean distance from each effective sampling point to the updated centroid of each cluster; calculate the sum of the sum of squares within all clusters in the coverage circle after each round of clustering; return to the step of updating the centroid of each cluster with the mean latitude and longitude of the effective sampling points in each cluster, until the sum of the sum of squares within all clusters in the coverage circle converges.

[0144] In one embodiment, the aforementioned road test report parsing module 1001 is further configured to: parse the minimized road test report to obtain multiple initial sampling points; and remove noise from the multiple initial sampling points to obtain the multiple valid sampling points.

[0145] In one embodiment, the aforementioned road test report parsing module 1001 is further configured to: cluster the plurality of initial sampling points according to the density distribution among the initial sampling points, treat the initial sampling points that cannot be clustered as noise points, remove the noise points from the plurality of initial sampling points, and obtain the plurality of valid sampling points.

[0146] In one embodiment, the cell boundary extraction module 1002 is further configured to: extract the minimum boundary of the cell from the plurality of valid sampling points using a concave hull algorithm, thereby obtaining the cell drive test coverage boundary of the plurality of cells.

[0147] In one embodiment, the simulated azimuth angle α of the cell is:

[0148] α=atan(abs(lng o / lat o ))*180 / π;

[0149] Among them, lng o Let lat be the longitude of the centroid O of the main coverage cluster of the cell. o Let O be the latitude of the centroid point O of the main coverage cluster of the cell.

[0150] In one embodiment, the pre-set cell reference average coverage radius is obtained using big data analysis, and the cell reference average coverage radius is different in different scenarios or frequency bands.

[0151] Figure 11 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 11 As shown, the electronic device may include a processor 1110, a communications interface 1120, a memory 1130, and a communication bus 1140. The processor 1110, communications interface 1120, and memory 1130 communicate with each other via the communication bus 1140. The processor 1110 can call logical instructions in the memory 1130 to execute a base station operating parameter calibration method. This method includes: parsing a minimized drive test report to obtain multiple valid sampling points; extracting the minimum boundary of the cells from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells; determining the overlapping area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells; determining the cell drive test simulation latitude and longitude of the base station based on the center position of the overlapping area of ​​the co-frequency cells; and calibrating the base station operating parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station.

[0152] Furthermore, the logical instructions in the aforementioned memory 1130 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a 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.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes 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.

[0153] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the base station engineering parameter calibration method provided by the above methods. The method includes: parsing a minimized drive test report to obtain multiple effective sampling points; extracting the minimum boundary of the cell from the multiple effective sampling points to obtain the cell drive test coverage boundary of the multiple cells; determining the overlapping area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundary of the multiple cells; determining the cell drive test simulation latitude and longitude of the base station according to the center position of the overlapping area of ​​the co-frequency cells; and calibrating the base station engineering parameter latitude and longitude of the base station based on the cell drive test simulation latitude and longitude of the base station.

[0154] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the base station engineering parameter calibration method provided by the methods described above. This method includes: parsing a minimized drive test report to obtain multiple valid sampling points; extracting the minimum boundary of a cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of the multiple cells; determining the overlapping area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundary of the multiple cells; determining the cell drive test simulation latitude and longitude of the base station according to the center position of the overlapping area of ​​the co-frequency cells; and calibrating the base station engineering parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station.

[0155] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0156] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calibrating base station operating parameters, characterized in that, The method includes: The minimized road test report was analyzed to obtain multiple valid sampling points; The minimum boundary of the cell is extracted from the multiple valid sampling points to obtain the cell drive test coverage boundary of the multiple cells; Based on the cell drive test coverage boundaries of the multiple cells, the overlapping area of ​​co-frequency cells belonging to the same base station is determined; Based on the center location of the overlapping area of ​​the co-frequency cells, the cell drive test simulation latitude and longitude of the base station are determined; Based on the cell drive test simulation latitude and longitude of the base station, calibrate the base station engineering parameter latitude and longitude of the base station; A coverage circle is constructed with the calibrated base station latitude and longitude coordinates as the center and the pre-set average coverage radius of the cell reference as the radius. Cluster the multiple valid sampling points until the sum of the sums of squares within all clusters within the coverage circle converges. At this point, the cluster with the largest sum of squares within the cluster is taken as the main coverage cluster of the cell. Calculate the sum of the reference signal reception quality of all valid sampling points within the main coverage cluster of the cell, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception quality of all valid sampling points as the centroid weight of each valid sampling point. Based on the centroid weights of each valid sampling point, the positions of all valid sampling points within the main coverage cluster of the cell are weighted to obtain the latitude and longitude of the centroid position of the main coverage cluster of the cell. The simulated azimuth angle of the cell is calculated based on the latitude and longitude of the centroid location. Based on the simulated azimuth angle of the cell, calibrate the azimuth angle of the cell corresponding to the main coverage cluster of the cell.

2. The base station operating parameter calibration method according to claim 1, characterized in that, The process of clustering the multiple valid sampling points until the sum of the in-cluster squares of all clusters within the covering circle converges specifically includes: The covering circle is divided into multiple sectors on an even basis; Based on the Euclidean distance from each valid sampling point to the centroid of each sector, the multiple valid sampling points are clustered to obtain multiple clusters. The mean latitude and longitude of the valid sampling points in each cluster are used to update the centroid of each cluster; The multiple valid sampling points are clustered based on the Euclidean distance from each valid sampling point to the centroid of each updated cluster. Calculate the sum of the in-cluster sums of squares of all clusters within the covering circle after each round of clustering; Return to the step of updating the centroid of each cluster with the mean latitude and longitude of the valid sample points in each cluster, until the sum of the in-cluster sums of squares of all clusters within the covering circle converges.

3. The base station operating parameter calibration method according to claim 1, characterized in that, The analytical minimization of the road test report yields multiple valid sampling points, including: The minimized road test report is analyzed to obtain multiple initial sampling points; Noise points are removed from the plurality of initial sampling points to obtain the plurality of valid sampling points.

4. The base station operating parameter calibration method according to claim 3, characterized in that, The step of removing noise from the plurality of initial sampling points to obtain the plurality of valid sampling points specifically includes: Based on the density distribution among the initial sampling points, the multiple initial sampling points are clustered. The initial sampling points that cannot be clustered are regarded as noise points. The noise points are removed from the multiple initial sampling points to obtain the multiple effective sampling points.

5. The base station operating parameter calibration method according to claim 1, characterized in that, The step of extracting the minimum boundary of a cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of multiple cells includes: The minimum boundary of the cell is extracted from the multiple valid sampling points using the concave hull algorithm, thus obtaining the cell drive test coverage boundary of multiple cells.

6. The base station operating parameter calibration method according to claim 1, characterized in that, The simulated azimuth angle α of the cell is: α=attan(abs(lng o / lat o ))*180 / π; Among them, lng o Let lat be the longitude of the centroid O of the main coverage cluster of the cell. o Let O be the latitude of the centroid point O of the main coverage cluster of the cell.

7. The base station operating parameter calibration method according to claim 1, characterized in that, The pre-set cell reference average coverage radius is obtained using big data analysis, and the cell reference average coverage radius varies in different scenarios or frequency bands.

8. A base station operating parameter calibration device, characterized in that, include: The road test report parsing module is used to parse the minimized road test report to obtain multiple valid sampling points; The cell boundary extraction module is used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of multiple cells; The cell overlap area determination module is used to determine the overlap area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells; The cell drive test simulation latitude and longitude calculation module is used to determine the cell drive test simulation latitude and longitude of the base station based on the center position of the overlapping area of ​​the co-frequency cells; The base station engineering parameter calibration module is used to calibrate the base station engineering parameter latitude and longitude based on the cell drive test simulation latitude and longitude of the base station; The base station engineering parameter calibration module is further configured to construct a coverage circle with the calibrated base station engineering parameter latitude and longitude location as the center and a pre-set cell reference average coverage radius as the radius; cluster the multiple effective sampling points until the sum of the sums of squares within all clusters within the coverage circle converges, at which point the cluster with the largest sum of squares within the cluster is taken as the cell's main coverage cluster; calculate the sum of the reference signal reception quality of all effective sampling points within the cell's main coverage cluster, and use the ratio of the reference signal reception quality of each effective sampling point to the sum of the reference signal reception quality of all effective sampling points as the centroid weight of each effective sampling point; Based on the centroid weights of each valid sampling point, the positions of all valid sampling points within the main coverage cluster of the cell are weighted to obtain the latitude and longitude of the centroid position of the main coverage cluster of the cell. The simulated azimuth angle of the cell is calculated based on the latitude and longitude of the centroid location; based on the simulated azimuth angle of the cell, the engineering parameter azimuth angle of the cell corresponding to the main coverage cluster of the cell is calibrated.

9. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the base station parameter calibration method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the base station parameter calibration method as described in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the base station engineering parameter calibration method according to any one of claims 1 to 7.

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