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

Abstract

The invention provides a base station work parameter calibration method, device and equipment and a computer program product. The method comprises the following steps: analyzing a minimization drive test report to obtain a plurality of effective sampling points; extracting minimum boundaries of the cells from the plurality of effective sampling points to obtain cell drive test coverage boundaries of the plurality of cells; determining an overlapping area of same-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the plurality of cells; according to the central position of the overlapping area of the same-frequency cells, determining the cell drive test simulation longitude and latitude of the base station; and calibrating the base station work parameter longitude and latitude of the base station based on the cell drive test simulation longitude and latitude of the base station. According to the base station work parameter calibration method provided by the invention, automatic calibration of the longitude and latitude of the work parameters of the base station can be realized; furthermore, according to the method, the minimization drive test report is adopted as a data source for evaluating the work parameters of the base station, the minimization drive test report is latest data directly reported by the user terminal, the effectiveness is high, and the timeliness of updating the longitude and latitude is improved.

Description

Technical Field

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

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

[0003] At present, the engineering parameter data of base stations mainly rely on manual maintenance. However, in the process of daily network cutover and optimization and adjustment, the accuracy of the engineering parameter data has always been difficult to guarantee due to the untimely and incomplete update of the engineering parameter data. The conventional means of rectification is to conduct a census of the engineering parameter data in stages, which still mainly relies on manual verification. This method is inefficient, costly and has a long cycle. Summary of the invention

[0004] The present invention provides a base station engineering parameter calibration method, device, equipment, medium and computer program product, which are used to solve the technical problem that the engineering parameter data of existing base stations still rely on manual maintenance and are difficult to be calibrated and updated in time, and realize automatic calibration of base station engineering parameters.

[0005] In a first aspect, the present invention provides a base station engineering parameter calibration method, comprising the following steps.

[0006] Analyze the minimization drive test report to obtain multiple valid sampling points; Extracting a minimum boundary of a cell from the multiple valid sampling points to obtain cell drive test coverage boundaries of the multiple cells; Determining, based on the cell drive test coverage boundaries of the multiple cells, an overlapping area of ​​co-frequency cells belonging to the same base station; Determining the cell drive test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cells; Based on the cell road test simulation longitude and latitude of the base station, the base station engineering parameter longitude and latitude of the base station are calibrated.

[0007] According to the base station engineering parameter calibration method provided by the present invention, after calibrating the base station engineering parameter longitude and latitude of the base station based on the cell drive test simulation longitude and latitude of the base station, the method further includes: A coverage circle is constructed with the location of the calibrated base station engineering parameter longitude and latitude as the center and the preset cell reference average coverage radius as the radius; Clustering the multiple valid sampling points until the sum of the intra-cluster square sums of all clusters within the coverage circle converges, and at this time, taking the cluster with the largest intra-cluster square sum as the main coverage cluster of the cell; Calculate the sum of the reference signal reception qualities of all valid sampling points in the cell main coverage cluster, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception qualities of all valid sampling points as the position centroid weight of each valid sampling point; Based on the centroid weights of the locations of the effective sampling points, weighting the locations of all the effective sampling points in the main coverage cluster of the cell, to obtain the longitude and latitude of the centroid location of the main coverage cluster of the cell; The cell simulation azimuth is calculated according to the latitude and longitude of the center of gravity point; Based on the cell simulation azimuth, the cell engineering parameter azimuth corresponding to the cell main coverage cluster is calibrated.

[0008] According to the base station engineering parameter calibration method provided by the present invention, clustering the multiple valid sampling points until the sum of the intra-cluster square sums of all clusters within the coverage circle converges specifically includes: Dividing the coverage circle into a plurality of sectors evenly; Clustering the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the centroid of each sector to obtain multiple clusters; Update the mean longitude and latitude of the valid sampling points in each cluster to the centroid of each cluster; Clustering the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the updated centroid of each cluster; Calculate the sum of the intra-cluster square sums of all clusters within the coverage circle after each round of clustering; Return to the step of updating the mean longitude and latitude of the valid sampling points in each cluster as the centroid of each cluster, until the sum of the intra-cluster sums of squares of all clusters within the covering circle converges.

[0009] According to the base station engineering parameter calibration method provided by the present invention, the parsing of the minimization of drive test reports to obtain multiple valid sampling points includes: Parse the minimization drive test report to obtain multiple initial sampling points; Noise points in the multiple initial sampling points are removed to obtain the multiple valid sampling points.

[0010] According to the base station engineering parameter calibration method provided by the present invention, removing the noise points in the multiple initial sampling points to obtain the multiple valid sampling points specifically includes: The multiple initial sampling points are clustered according to the density distribution between the initial sampling points, the initial sampling points that cannot be clustered are used as noise points, and the noise points are removed from the multiple initial sampling points to obtain the multiple valid sampling points.

[0011] According to the base station engineering parameter calibration method provided by the present invention, extracting the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells includes: The concave hull algorithm is used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells.

[0012] According to the base station engineering parameter calibration method provided by the present invention, the cell simulation azimuth for: ; in, is the longitude of the centroid point O of the cell main coverage cluster, It is the latitude of the centroid point O of the main coverage cluster of the cell.

[0013] According to the base station working parameter calibration method provided by the present invention, the preset cell reference average coverage radius is obtained by utilizing big data analysis, and the cell reference average coverage radius in different scenarios or different frequency bands is different.

[0014] In a second aspect, the present invention further provides a base station engineering parameter calibration device, comprising the following modules: A drive test report parsing module, used to parse the minimized drive test report to obtain multiple valid sampling points; A cell boundary extraction module, used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells; A cell overlap area determination module, configured to determine an overlap area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells; A cell drive test simulation longitude and latitude calculation module, used to determine the cell drive test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cell; The base station engineering parameter calibration module is used to calibrate the base station engineering parameter longitude and latitude of the base station based on the cell road test simulation longitude and latitude of the base station.

[0015] In a third aspect, the present invention further provides an electronic device, comprising a processor and a memory storing a computer program, wherein when the processor executes the program, the steps of the base station working parameter calibration method described in the first aspect are implemented.

[0016] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the base station engineering parameter calibration method described in the first aspect are implemented.

[0017] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the base station engineering parameter calibration method described in the first aspect.

[0018] The base station engineering parameter calibration method, device, equipment, medium and computer program product provided by the present invention obtain multiple valid sampling points by parsing the minimized road test report; extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell road test coverage boundary of the multiple cells; determine the overlapping area of ​​the same-frequency cells belonging to the same base station based on the cell road test coverage boundary of the multiple cells; determine the cell road test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cells; calibrate the base station engineering parameter longitude and latitude of the base station based on the cell road test simulation longitude and latitude of the base station. The method can realize the automatic calibration of the longitude and latitude of the base station engineering parameters; further, the method adopts the minimized road test report as the data source for evaluating the base station engineering parameters. The minimized road test report is the latest data directly reported by the user terminal, which is highly effective and improves the timeliness of the longitude and latitude update; the data carries geographic location information, can directly reflect the user distribution, and is the minimum granularity sampling point. Compared with the traditional method based on raster data granularity calculation, the minimized measurement report data sample is sufficient and the error is small, which can improve the accuracy of the base station longitude and latitude evaluation; further, it also provides more accurate base station longitude and latitude for the subsequent calibration of the cell azimuth, thereby improving the accuracy of the cell azimuth. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is one of the flow charts of the base station working parameter calibration method provided by the present invention.

[0021] Figure 2 It is a schematic diagram of extracting boundaries of effective sampling points before denoising provided by the present invention.

[0022] Figure 3 It is a schematic diagram of the overlapping area of ​​the cell drive test coverage boundary provided by the present invention.

[0023] Figure 4 This is the second flow chart of the base station engineering parameter calibration method provided by the present invention

[0024] Figure 5 It is a schematic diagram of the coverage circle provided by the present invention.

[0025] Figure 6 It is a schematic diagram of the initial clustering cluster provided by the present invention.

[0026] Figure 7 It is a schematic diagram of the main coverage area of ​​a cell obtained after clustering provided by the present invention.

[0027] Figure 8 This is a schematic diagram of extracting the boundary of effective sampling points after denoising provided by the present invention.

[0028] Fig. 9 This is the third flow chart of the base station working parameter calibration method provided by the present invention.

[0029] Fig.10 It is a structural schematic diagram of the base station working parameter calibration device provided by the present invention.

[0030] Fig.11 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Combine the following Figure 1-Figure 11 Specific embodiments of the present invention are described.

[0033] Figure 1 It is one of the flow charts of the base station working parameter calibration method provided by the present invention, such as Figure 1 As shown, the method includes the following.

[0034] Step 101: parse the minimization of drive test report to obtain multiple valid sampling points.

[0035] Among them, the Minimization Drive Test (MDT) is a measurement report reported by the user terminal to the base station, which is then reported to the network management system by the base station. The user terminal will obtain geographical location information, communication signal parameters and other information at the sampling point at irregular or regular intervals and report them to the base station. The effective sampling point refers to the sampling point used for subsequent data analysis.

[0036] Specifically, the network management system parses the Minimized Drive Test Report (MDT) reported by the User Equipment (UE) to obtain all sampling point information, including the latitude and longitude of the sampling point, the level strength (rsrp, Reference Signal Receiving Power, also known as reference signal received power), and the cell CGI (Cell Global Identifier) ​​information, where srsrp minus 140 is the real level value. Valid sampling points are screened from all sampling points.

[0037] Step 102: extract the minimum boundary of a cell from the multiple valid sampling points to obtain cell drive test coverage boundaries of multiple cells.

[0038] Each base station can have multiple cells. On the map, the minimum external boundary is extracted from the valid sampling points belonging to the same cell to obtain the cell road test coverage boundary of the cell, such as Figure 2 As shown, Figure 2 A schematic diagram showing the cell drive test coverage boundary of a cell.

[0039] Step 103: Determine an overlapping area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells.

[0040] Specifically, since the cell coverage ranges of different sites are different, the cell coverage radius of different frequency bands of the same base station is also different. Therefore, among all the cell drive test coverage boundaries, the cell drive test coverage boundaries of the same frequency band belonging to the same base station are searched, and their overlapping areas are calculated, such as Figure 3 As shown, the overlapping area of ​​the three cell road test coverage boundaries CELLA, CELLB and CELLC is A.

[0041] Step 104: Determine the cell drive test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cells.

[0042] Specifically, the longitude and latitude coordinates of the center position of the overlapping area A are used as the longitude and latitude of the cell drive test simulation of the base station.

[0043] Step 105: calibrate the base station engineering parameter longitude and latitude of the base station based on the cell drive test simulation longitude and latitude of the base station.

[0044] Specifically, the base station engineering parameters have been saved in the engineering parameter database in advance, including the Chinese name of the cell, cell CGI, station type, working frequency band, scenario category, RRU (Remote Radio Unit) longitude, RRU latitude, engineering parameter azimuth, etc. of the entire network 4 / 5G base station. The base station engineering parameter longitude and latitude of the saved base station are retrieved from the engineering parameter database, and the distance between the two points is calculated based on the base station engineering parameter longitude and latitude and the cell simulation longitude and latitude. If the distance between the two points is greater than the preset distance threshold DL meters (DL is taken as 50 in this solution), the base station engineering parameter longitude and latitude is considered abnormal, and the above-mentioned cell road test simulation longitude and latitude calibration and update are required. The calibrated longitude and latitude are the cell location information.

[0045] In the above embodiment, multiple valid sampling points are obtained by parsing the minimized drive test report; the minimum boundary of the cell is extracted from the multiple valid sampling points to obtain the cell drive test coverage boundary of multiple cells; based on the cell drive test coverage boundary of multiple cells, the overlapping area of ​​the same frequency cells belonging to the same base station is determined; according to the center position of the overlapping area of ​​the same frequency cells, the cell drive test simulation longitude and latitude of the base station is determined; based on the cell drive test simulation longitude and latitude of the base station, the base station engineering parameter longitude and latitude of the base station is calibrated. This method can realize the automatic calibration of the base station engineering parameter longitude and latitude; further, this method uses the minimized drive test report as the data source for evaluating the base station engineering parameter, and the minimized drive test report is the latest data directly reported by the user terminal, which has high validity and improves the timeliness of the longitude and latitude update; this data carries geographic location information, can directly reflect the user distribution, and is the minimum granularity sampling point. Compared with the traditional method based on raster data granularity calculation, the minimized measurement report data sample is sufficient, the error is small, and the accuracy of the base station longitude and latitude evaluation can be improved; further, it also provides more accurate base station longitude and latitude for subsequent calibration of the cell azimuth, thereby improving the accuracy of the cell azimuth.

[0046] In one embodiment, if Figure 4 As shown, Figure 4 A flow chart of an engineering parameter azimuth calibration method in another embodiment is shown, that is, after the above step 105, it also includes.

[0047] Step 401, constructing a coverage circle with the calibrated longitude and latitude of the base station as the center and a preset cell reference average coverage radius as the radius.

[0048] Among them, the pre-set cell reference average coverage radius is obtained in advance through big data statistics. Because the coverage distances of different scenarios and different frequency bands are different, in order to obtain a more accurate cell simulation azimuth, it is necessary to refine the frequency band scenario coverage radius relationship mapping table. The following Table 1 is the cell reference average coverage radius obtained by using a large amount of MDT sampling point data and big data calculation.

[0049] Table 1 Frequency band scene radius relationship mapping table

[0050] Specifically, taking the location of the calibrated base station engineering parameter longitude and latitude as the center of the circle, the reference average coverage radius of the cell corresponding to the base station is found from the above-mentioned frequency band scene radius relationship mapping table to construct a coverage circle, such as Figure 5 shown.

[0051] Step 402: cluster the multiple valid sampling points until the sum of the intra-cluster square sums of all clusters within the coverage circle converges, and then the cluster with the largest intra-cluster square sum is used as the main coverage cluster of the cell.

[0052] The cluster refers to one of the multiple sets obtained by the clustering method.

[0053] Specifically, in the present application, the above 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 is calculated, that is, TATALCSS. The calculation formula of the cluster sum of squares CSS of each cluster is as follows: ; in, is the position coordinate of the i-th feature parameter of the j-th valid sampling point, m represents the total number of valid sampling points in a cluster, and n represents the number of features in each valid sampling point. In this scheme, only the distance feature is used, so represents the position (i.e. longitude and latitude) of the jth valid sampling point, is the location (i.e. longitude and latitude) of the centroid of the current cluster.

[0054] Centroid The calculation formula for longitude and latitude is: ; in, Represents the centroid of the current cluster The longitude, Represents the centroid of the current cluster The latitude, represents the longitude of the jth valid sampling point, Indicates the latitude of the jth valid sampling point.

[0055] The calculation formula for the sum of the intra-cluster sums of squares of all clusters, TATALCSS, is: ; in, represents the intra-cluster sum of squares of the kth cluster, and p represents the total number of clusters.

[0056] In step 402, the effective sampling points can be clustered by a variety of clustering methods, such as the kmeans clustering method, which requires continuous iteration of the kmeans clustering algorithm. In each round of iteration, the latitude and longitude mean of the sampling points is the centroid point of the next round of clustering. If the TATALCSS value of the clustering result of the next round of iteration does not change and is the same as the result of the previous round (that is, TATALCSS reaches convergence), then the TATALCSS value is optimal. At this time, the cluster with the largest intra-cluster square sum CSS is used as the main coverage cluster of the cell.

[0057] Step 403, calculate the sum of the reference signal reception qualities of all valid sampling points in the cell primary coverage cluster, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception qualities of all valid sampling points as the position centroid weight of each valid sampling point.

[0058] Specifically, the cell main coverage cluster is identified in step 402, and then the sum of rsrq (Reference Signal Receiving Quality) of all points in the cluster is calculated, and the reference signal receiving quality of the i-th valid sampling point is calculated. The ratio of the sum of the reference signal reception quality of all sampling points rsrqall is used as the position centroid weight of the i-th valid sampling point : ; Step 404: based on the centroid weights of the locations of the respective valid sampling points, weight the locations of all valid sampling points in the cell main coverage cluster to obtain the centroid latitude and longitude of the location of the cell main coverage cluster.

[0059] Specifically, based on the above position center weight , get the i-th valid sampling point The longitude is , latitude is , then the longitude and latitude of the center of gravity of the cluster for( , ); m represents the total number of valid sampling points in a cluster.

[0060] Step 405, calculating the cell simulation azimuth according to the longitude and latitude of the center of gravity point.

[0061] The simulated azimuth of the cell represents the angle between the line connecting the centroid and the cell location and the true north direction. Its practical meaning is the strongest azimuth in the cell radiation direction and can be used to characterize the actual working parameter azimuth of the cell antenna.

[0062] Specifically, according to the definition of the above-mentioned cell simulation azimuth, the cell simulation azimuth can be calculated using the following formula. Since the longitude and latitude are in the WGS84 (World Geodetic System 1984 Coordinate System, 1984 World Geodetic Coordinate System) coordinate system, the cell longitude and latitude coordinates need to be converted into Mercator projection coordinates first, and then the cell simulation azimuth is calculated according to the following geometric formula: for: ; in, is the longitude of the centroid point O of the cell main coverage cluster, It is the latitude of the centroid point O of the main coverage cluster of the cell.

[0063] Step 406: based on the cell simulation azimuth, calibrate the cell engineering parameter azimuth corresponding to the cell main coverage cluster.

[0064] Specifically, the cell engineering parameter azimuth saved in the engineering parameter database is compared with the above-mentioned cell simulation azimuth, and then according to the scene mapping difference threshold, if the difference between the simulation azimuth and the engineering parameter azimuth exceeds the corresponding preset azimuth threshold, the cell engineering parameter azimuth is considered abnormal, and the azimuth data in the engineering parameter database needs to be modified according to the cell simulation azimuth. Assume that the engineering parameter azimuth of a cell is , calculate the simulation azimuth as The difference between the two is , The calculation formula is as follows: ; The formula for judging the abnormality of the engineering parameter azimuth is as follows: ; in, is the preset azimuth threshold.

[0065] Abnormal thresholds in different scenarios As shown in Table 2 below.

[0066] Table 2 Mapping table of cell engineering parameter azimuth relationship by frequency band and scenario

[0067] An example of the results of the cell simulation azimuth verification is shown in Table 3 below.

[0068] Table 3 Frequency band scene azimuth relationship mapping table

[0069] In the above embodiment, by clustering the effective sampling points after filtering the cell MDT noise points and identifying the main coverage cluster, the cell simulation azimuth is obtained after calculating the centroid of the main coverage cluster, thereby realizing automatic calibration of the cell engineering parameter azimuth, reducing labor costs and improving calibration efficiency.

[0070] In one embodiment, the above-mentioned step 402 includes: dividing the coverage circle into multiple sectors on an even basis; clustering the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the centroid of each sector to obtain multiple clusters; updating the mean longitude and latitude of the valid sampling points in each cluster to the centroid of each cluster; clustering the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the updated centroid of each cluster; calculating the sum of the intra-cluster sums of squares of all clusters in the coverage circle after each round of clustering; returning to the step of updating the mean longitude and latitude of the valid sampling points in each cluster to the centroid of each cluster, until the sum of the intra-cluster sums of squares of all clusters in the coverage circle converges.

[0071] In detail, this embodiment adopts the kmeans clustering algorithm, takes the updated base station engineering parameter longitude and latitude as the center, and takes the preset cell reference average coverage radius as the radius to construct a coverage circle, such as Figure 5 As shown; the coverage circle is evenly divided into multiple sectors as initial clustering clusters, such as Figure 6 As shown, named as 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 of the longitude and latitude 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, and the sum of the intra-cluster sum of squares CSS of all clusters in the coverage circle after each round of clustering is calculated T The smaller the ATALCSS and TATALCSS are, the smaller the clustering error is and the more accurate the clustering result is. In order to obtain the minimum TATALCSS value, it is necessary to continuously iterate the kmeans clustering algorithm (using the latitude and longitude mean of the effective sampling points as the centroid point of the next round of clustering). If the TATALCSS value of the clustering result of the next round of iteration does not change and is the same as the result of the previous round, then the TATALCSS value is optimal. After obtaining the optimal TATALCSS value, the cluster with the largest CSS value among the N clusters of the cell is the main coverage cluster of the cell, such as Figure 7 shown.

[0072] In the above embodiment, the kmeans algorithm is used to cluster all valid sampling points within the base station coverage circle, so that the main coverage cluster of the cell can be identified, providing a data basis for calculating the simulated azimuth of the cell.

[0073] In one embodiment, the step 101 includes: parsing the minimization of drive tests report to obtain a plurality of initial sampling points; and removing noise points from the plurality of initial sampling points to obtain the plurality of valid sampling points.

[0074] Specifically, the network management system parses the minimized drive test (MDT) report reported by the user terminal (UE) to obtain information on all sampling points, which are called initial sampling points. Since the initial sampling points may include points with data errors, which will affect subsequent analysis results, it is necessary to perform data denoising on the initial sampling points. Generally, the sampling points belonging to the same base station cell are generally concentrated, and 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, valid sampling points are obtained.

[0075] In the above embodiment, noise points are removed from the initial sampling points, so that the cell boundary is located more accurately and the calibration accuracy is improved.

[0076] In one embodiment, the removing noise points from the multiple initial sampling points to obtain the multiple valid sampling points specifically includes: clustering the multiple initial sampling points according to the density distribution between the initial sampling points, treating the initial sampling points that cannot be clustered as noise points, and removing the noise points from the multiple initial sampling points to obtain the multiple valid sampling points.

[0077] Specifically, the present application distinguishes different cells according to the density distribution of sampling points, but since the initial sampling points may contain some erroneous data, the subsequent cell boundary extraction is inaccurate, so it is necessary to remove noise points. The present application removes noise points according to the density distribution, mainly focusing on the density distribution between data points, and forming clusters by identifying dense areas 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 find clusters of arbitrary shapes. It does not require the number of clusters to be specified in advance, 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 the core points (that is, the data points with at least MinPts points in its r neighborhood), and then builds clusters based on density accessibility (points connected by a series of core points). Points that do not belong to any cluster are regarded as noise. Specifically, the density clustering algorithm DBSCAN (r, MinPts) clusters the MDT sampling points. In this scheme, r is the reference average coverage radius of the cell, MinPts is the density threshold, that is, the minimum number of clustering sampling points. In this scheme, Minpts is 3. Points that cannot be clustered are identified as noise points. The noise points are removed to obtain multiple valid sampling points.

[0078] In the above embodiment, the initial sampling point denoising is achieved by using a density clustering algorithm, which is beneficial to the subsequent extraction of cell boundaries and improves the accuracy of boundary extraction.

[0079] In one embodiment, the step 102 includes: using a concave hull algorithm to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells.

[0080] Among them, the concave hull algorithm is a method 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, which is opposite to the convex hull, which requires that all boundaries are convex.

[0081] Specifically, the present application uses the concave hull algorithm to extract the boundaries of the valid sampling points in each cell, and obtains the cell road test coverage boundaries of each cell, such as Figure 8 shown.

[0082] In the above embodiment, the cell boundary is extracted by the concave hull algorithm, which provides data for the subsequent calculation of the cell center of gravity.

[0083] In one embodiment, the above-mentioned preset reference average coverage radius of the cell is obtained by utilizing big data analysis, and the reference average coverage radius of the cell in different scenarios or different frequency bands is different.

[0084] Specifically, because the coverage distances of different scenarios and different frequency bands are different, in order to obtain a more accurate cell simulation azimuth, it is necessary to refine the mapping table of the frequency band and scenario coverage radius relationship. The following table is the reference average coverage radius of the cell obtained by using a large amount of MDT sampling point data and big data calculation.

[0085] Table 4 Frequency band scene radius relationship mapping table

[0086] The above embodiment obtains the reference average coverage radius of the cell at different sites and different frequency bands through the data mining technology of big data, provides a data basis for the subsequent calculation of the longitude and latitude of the cell and the simulated azimuth of the cell, and further improves the level of automated calibration of the longitude and latitude of the base station engineering parameters and the azimuth of the cell engineering parameters.

[0087] In one embodiment, if Fig. 9 As shown, Fig. 9 The complete flow chart of the base station working parameter calibration method provided by this application is shown. This solution first collects the base station working parameter data and organizes these data. The 4 / 5G working parameters of the entire network need to include the Chinese name of the cell, the cell CGI, the station type, the working frequency band, the scenario category, the RRU longitude, the RRU latitude, and the working parameter azimuth. Parse the MDT data reported by the terminal, and store the relevant data in the database for calling, which mainly includes the longitude, latitude, level strength srsrp, and cell CGI information of the sampling point; use a large amount of MDT sampling point data and the cell reference average coverage radius calculated by big data to make a cell reference average coverage radius mapping table; cluster the MDT sampling points according to the density clustering algorithm DBSCAN (r, MinPts), and remove the points that cannot be clustered as noise points; use the concave hull algorithm to extract the cell MDT coverage boundary from the set of sampling points where the noise points are removed, calculate the MDT coverage simulation diagram of the cell with the same station and the same frequency on a station basis, and then calculate the coverage of the cell under the station. The overlapping area of ​​the cell coverage simulation map is calculated, and then the center position of the overlapping area is calculated as the cell simulation longitude and latitude of the site; calibrate the cell longitude and latitude: calculate the distance between the two points according to the engineering parameter longitude and longitude and the simulation longitude and longitude. If the distance between the two points is greater than DL meters (DL is 50 in this solution), it is considered that the engineering parameter longitude and longitude are abnormal and need to be calibrated and updated using the MDT simulation longitude and longitude; use the clustering algorithm to identify the main coverage cluster of the cell, and calculate the center of gravity of the main coverage cluster of the cell, obtain the connection between the center of gravity and the base station, and calculate the angle between the connection and the north direction as the cell simulation azimuth. Finally, update the engineering parameter azimuth in the engineering parameter database according to the difference between the cell simulation azimuth and the engineering parameter azimuth.

[0088] The above-mentioned embodiment can quickly realize the calibration of the working parameter azimuth angle, which will greatly improve the accuracy of the wireless network optimization solution, thereby improving the network quality and user perception. The method has high accuracy in identifying the working parameter azimuth angle of abnormal cells, high calculation efficiency and low investment cost.

[0089] The base station industrial parameter calibration device provided by the present invention is described below. The base station industrial parameter calibration device described below and the base station industrial parameter calibration method described above can be referenced to each other.

[0090] The present application provides a base station industrial parameter calibration device, such as Fig.10 As shown, including: The drive test report parsing module 1001 is used to parse the minimization drive test report to obtain multiple valid sampling points; 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 boundaries of the multiple cells; The cell overlapping area determination module 1003 is used to determine the overlapping area of ​​the same-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 longitude and latitude calculation module 1004 is used to determine the cell drive test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cell; The base station engineering parameter calibration module 1005 is used to calibrate the base station engineering parameter longitude and latitude of the base station based on the cell drive test simulation longitude and latitude of the base station.

[0091] In one embodiment, the base station engineering parameter calibration module 1005 is further used to: construct a coverage circle with the location of the calibrated base station engineering parameter longitude and latitude as the center of the circle and a preset cell reference average coverage radius as the radius; cluster the multiple valid sampling points until the sum of the intra-cluster square sums of all cluster clusters within the coverage circle converges, and at this time, the cluster cluster with the largest intra-cluster square sum is used as the cell main coverage cluster cluster; calculate the sum of the reference signal reception qualities of all valid sampling points in the cell main coverage cluster cluster, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception qualities of all valid sampling points as the position centroid weight of each valid sampling point; based on the position centroid weight of each valid sampling point, weight the positions of all valid sampling points in the cell main coverage cluster cluster to obtain the longitude and latitude of the centroid position of the cell main coverage cluster cluster; calculate the cell simulation azimuth according to the longitude and latitude of the centroid position; based on the cell simulation azimuth, calibrate the cell engineering parameter azimuth corresponding to the cell main coverage cluster cluster.

[0092] In one embodiment, the above-mentioned base station engineering parameter calibration module 1005 is further used to: divide the coverage circle into multiple sectors on an even basis; cluster the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the centroid of each sector to obtain multiple cluster clusters; update the mean longitude and latitude of the valid sampling points in each cluster cluster to the centroid of each cluster cluster; cluster the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the updated centroid of each cluster cluster; calculate the sum of the intra-cluster sums of squares of all cluster clusters in the coverage circle after each round of clustering; return to the step of updating the mean longitude and latitude of the valid sampling points in each cluster cluster to the centroid of each cluster cluster, until the sum of the intra-cluster sums of squares of all cluster clusters in the coverage circle converges.

[0093] In one embodiment, the drive test report parsing module 1001 is further used to: parse the minimization of drive test reports to obtain a plurality of initial sampling points; and remove noise points from the plurality of initial sampling points to obtain the plurality of valid sampling points.

[0094] In one embodiment, the drive test report parsing module 1001 is further used to: cluster the multiple initial sampling points according to the density distribution between the initial sampling points, use the initial sampling points that cannot be clustered as noise points, remove the noise points from the multiple initial sampling points, and obtain the multiple valid sampling points.

[0095] In one embodiment, the cell boundary extraction module 1002 is further configured to: extract the minimum boundary of the cell from the multiple valid sampling points using a concave hull algorithm to obtain the cell drive test coverage boundaries of the multiple cells.

[0096] In one embodiment, the cell simulation azimuth for: ; in, is the longitude of the centroid point O of the cell main coverage cluster, It is the latitude of the centroid point O of the main coverage cluster of the cell.

[0097] In one embodiment, the preset reference average coverage radius of the cell is obtained by using big data analysis, and the reference average coverage radius of the cell in different scenarios or different frequency bands is different.

[0098] Fig.11 An example of a physical structure diagram of an electronic device is shown in FIG. Fig.11As shown, the electronic device may include: a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120 and the memory 1130 communicate with each other through the communication bus 1140. The processor 1110 may call the logic instructions in the memory 1130 to execute the base station engineering parameter calibration method, which includes: parsing the minimization drive test report to obtain multiple valid sampling points; extracting the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundary of the multiple cells; determining the overlapping area of ​​the same 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 longitude and latitude of the base station according to the center position of the overlapping area of ​​the same frequency cells; calibrating the base station engineering parameter longitude and latitude of the base station based on the cell drive test simulation longitude and latitude of the base station.

[0099] In addition, the logic instructions in the above-mentioned memory 1130 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0100] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program 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, which includes: parsing the minimized road test report to obtain multiple valid sampling points; extracting the minimum boundary of the cell from the multiple valid sampling points to obtain the cell road test coverage boundary of multiple cells; based on the cell road test coverage boundary of multiple cells, determining the overlapping area of ​​the same-frequency cells belonging to the same base station; determining the cell road test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cells; and calibrating the base station engineering parameter longitude and latitude of the base station based on the cell road test simulation longitude and latitude of the base station.

[0101] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the base station engineering parameter calibration method provided by the above-mentioned methods, the method comprising: parsing the minimized road test report to obtain multiple valid sampling points; extracting the minimum boundary of the cell from the multiple valid sampling points to obtain the cell road test coverage boundary of the multiple cells; determining the overlapping area of ​​the same-frequency cells belonging to the same base station based on the cell road test coverage boundary of the multiple cells; determining the cell road test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cells; and calibrating the base station engineering parameter longitude and latitude of the base station based on the cell road test simulation longitude and latitude of the base station.

[0102] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calibrating base station parameters, characterized in that: The method comprises: Analyze the minimization drive test report to obtain multiple valid sampling points; Extracting a minimum boundary of a cell from the multiple valid sampling points to obtain cell drive test coverage boundaries of the multiple cells; Determining, based on the cell drive test coverage boundaries of the multiple cells, an overlapping area of ​​co-frequency cells belonging to the same base station; Determining the cell drive test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cells; Based on the cell road test simulation longitude and latitude of the base station, the base station engineering parameter longitude and latitude of the base station are calibrated.

2. The base station engineering parameter calibration method according to claim 1, characterized in that: After calibrating the base station engineering parameter longitude and latitude of the base station based on the cell drive test simulation longitude and latitude of the base station, the method further includes: A coverage circle is constructed with the location of the calibrated base station engineering parameter longitude and latitude as the center and the preset cell reference average coverage radius as the radius; Clustering the multiple valid sampling points until the sum of the intra-cluster square sums of all clusters within the coverage circle converges, and at this time, taking the cluster with the largest intra-cluster square sum as the main coverage cluster of the cell; Calculate the sum of the reference signal reception qualities of all valid sampling points in the cell main coverage cluster, and use the ratio of the reference signal reception quality of each valid sampling point to the sum of the reference signal reception qualities of all valid sampling points as the position centroid weight of each valid sampling point; Based on the centroid weights of the locations of the effective sampling points, weighting the locations of all the effective sampling points in the main coverage cluster of the cell, to obtain the longitude and latitude of the centroid location of the main coverage cluster of the cell; The cell simulation azimuth is calculated according to the latitude and longitude of the center of gravity point; Based on the cell simulation azimuth, the cell engineering parameter azimuth corresponding to the cell main coverage cluster is calibrated.

3. The base station engineering parameter calibration method according to claim 2, characterized in that: The clustering of the multiple valid sampling points until the sum of the intra-cluster square sums of all clusters within the coverage circle converges specifically includes: Dividing the coverage circle into a plurality of sectors evenly; Clustering the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the centroid of each sector to obtain multiple clusters; Update the mean longitude and latitude of the valid sampling points in each cluster to the centroid of each cluster; Clustering the multiple valid sampling points based on the Euclidean distance from each valid sampling point to the updated centroid of each cluster; Calculate the sum of the intra-cluster square sums of all clusters within the coverage circle after each round of clustering; Return to the step of updating the mean longitude and latitude of the valid sampling points in each cluster as the centroid of each cluster, until the sum of the intra-cluster sums of squares of all clusters within the covering circle converges.

4. The base station engineering parameter calibration method according to claim 1, characterized in that: The parsing of the minimization of drive test reports obtains multiple valid sampling points, including: Parse the minimization drive test report to obtain multiple initial sampling points; Noise points in the multiple initial sampling points are removed to obtain the multiple valid sampling points.

5. The base station engineering parameter calibration method according to claim 4, characterized in that: The removing of noise points from the plurality of initial sampling points to obtain the plurality of valid sampling points specifically includes: The multiple initial sampling points are clustered according to the density distribution between the initial sampling points, the initial sampling points that cannot be clustered are taken as noise points, and the noise points are removed from the multiple initial sampling points to obtain the multiple valid sampling points.

6. The base station engineering parameter calibration method according to claim 1, characterized in that: The extracting the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells includes: The concave hull algorithm is used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells.

7. The base station engineering parameter calibration method according to claim 2, characterized in that: The cell simulation azimuth for: ； in, is the longitude of the centroid point O of the cell main coverage cluster, It is the latitude of the centroid point O of the main coverage cluster of the cell.

8. The base station engineering parameter calibration method according to claim 2, characterized in that: The preset reference average coverage radius of the cell is obtained by using big data analysis, and the reference average coverage radius of the cell in different scenarios or different frequency bands is different.

9. A base station engineering parameter calibration device, characterized in that: include: A drive test report parsing module, used to parse the minimized drive test report to obtain multiple valid sampling points; A cell boundary extraction module, used to extract the minimum boundary of the cell from the multiple valid sampling points to obtain the cell drive test coverage boundaries of the multiple cells; A cell overlap area determination module, configured to determine an overlap area of ​​co-frequency cells belonging to the same base station based on the cell drive test coverage boundaries of the multiple cells; A cell drive test simulation longitude and latitude calculation module, used to determine the cell drive test simulation longitude and latitude of the base station according to the center position of the overlapping area of ​​the same-frequency cell; The base station engineering parameter calibration module is used to calibrate the base station engineering parameter longitude and latitude of the base station based on the cell road test simulation longitude and latitude of the base station.

10. An electronic device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the base station engineering parameter calibration method described in any one of claims 1 to 8 is implemented.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the base station engineering parameter calibration method as described in any one of claims 1 to 8 is implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the base station engineering parameter calibration method described in any one of claims 1 to 8 is implemented.

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