Base station position prediction method and device and electronic equipment

Abstract

The embodiment of the invention discloses a base station position prediction method and device and electronic equipment, and the method comprises the steps: obtaining one or more pieces of service data associated with a target cell contained in a to-be-predicted target base station based on the association relationship between service data and the cell, and carrying out the prediction of the position of the target base station according to the position information contained in the one or more pieces of service data. Carrying out clustering processing on the service data to obtain a target service data group of which the number of the contained service data exceeds a preset threshold value; and determining the position of the target cell based on the position information contained in the service data in the target service data group, and further determining the position of the target base station based on the position of the target cell. Thus, the positions of the base stations are predicted through the service data, the positions of all the existing base stations in the specific area can be obtained, the positions of the base stations are directly predicted, the accuracy of base station prediction can be improved, and then the precision of base station distribution adjustment is improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a base station location prediction method, apparatus, and electronic device. Background Technology

[0002] Base stations are core devices in wireless communication networks. Acting as a "bridge" between mobile terminals and the core network, they are responsible for receiving, processing, and forwarding wireless signals, enabling communication functions such as voice calls, data transmission, and multimedia services. Adjusting the distribution of base stations based on their existing locations can optimize the communication network layout and expand network coverage.

[0003] Currently, the location prediction of existing base stations is usually based on the analysis of collected communication-related data. However, since communication-related data from other operators is unavailable, it is impossible to predict the locations of base stations deployed by other operators, or to know the locations of all existing base stations in a specific area. Consequently, it is impossible to accurately adjust the distribution of base stations. Therefore, a better base station location prediction scheme is needed to improve the accuracy of base station distribution adjustments. Summary of the Invention

[0004] The purpose of this invention is to provide a better base station location prediction scheme to improve the accuracy of base station distribution adjustment.

[0005] To solve the above-mentioned technical problems, the embodiments of the present invention are implemented as follows: In a first aspect, embodiments of the present invention provide a base station location prediction method, the method comprising: Based on the correlation between service data and cells, obtain one or more service data related to the target cell, where the target cell is the cell contained in the target base station to be predicted; Based on the location information contained in one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location of the target cell is determined based on the location information contained in the service data within the target service data group; The location of the target base station is determined based on the location of the target cell.

[0006] Secondly, embodiments of the present invention provide a base station location prediction device, the device comprising: The data processing module is used to obtain one or more service data associated with a target cell based on the correlation between service data and cells, wherein the target cell is the cell contained in the target base station to be predicted; and to perform clustering processing on the one or more service data according to the location information contained in the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location prediction module is used to determine the location of the target cell based on the location information contained in the service data within the target service data group; and to determine the location of the target base station based on the location of the target cell.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the base station location prediction method provided in the above embodiments.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the base station location prediction method provided in the above embodiments.

[0009] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the base station location prediction method provided in the above embodiments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present 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 only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a base station location prediction method according to an embodiment of the present invention. Figure 2 A flowchart illustrating the process of determining a target service array, as provided in another embodiment of the present invention; Figure 3 A schematic diagram of the structure of a service area provided in another embodiment of the present invention; Figure 4 A schematic diagram of a process for determining a target service area is provided in another embodiment of the present invention; Figure 5 This is a schematic diagram of another process for determining a target service area, provided in another embodiment of the present invention. Figure 6 This is a schematic diagram illustrating a process for grouping target cells, as provided in another embodiment of the present invention. Figure 7 A schematic diagram illustrating an exemplary target cell grouping result provided in another embodiment of the present invention; Figure 8 A logical schematic diagram of a base station location prediction method provided in another embodiment of the present invention; Figures 9a-9e A schematic diagram illustrating the process of determining a target service data group, provided in another embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of a base station location prediction device according to another embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of an electronic device provided in another embodiment of the present invention. Detailed Implementation

[0012] This invention provides a base station location prediction method, apparatus, and electronic device.

[0013] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0014] This specification provides a base station location prediction method, apparatus, and electronic device. Base stations are core devices in wireless communication networks, acting as a "bridge" between mobile terminals and the core network. They are responsible for receiving, processing, and forwarding wireless signals, enabling communication functions such as voice calls, data transmission, and multimedia services. Adjusting the distribution of base stations based on their existing locations can optimize the communication network layout and expand network coverage.

[0015] Currently, the location prediction of existing base stations is typically based on collected communication-related data, such as Measurement Reports (MR) and Minimization Drive Tests (MDTs). However, due to the lack of access to communication-related data from other operators, it is impossible to predict the locations of base stations deployed by other operators, or to know the locations of all existing base stations within a specific area, thus hindering accurate adjustments to base station distribution. Furthermore, communication-related data may not contain location information, making direct prediction of base station locations impossible. This necessitates first MR sampling point localization, then using the locations of the strongest neighboring signals from a neighboring cell list, and employing methods such as triangulation to predict the base station location. Additionally, the azimuth angle of the cells contained within a base station significantly impacts signal strength. Directly predicting cell locations without considering the influence of antenna azimuth angle on the results of triangulation and other methods will affect the accuracy of the prediction results. Therefore, a superior base station location prediction scheme is needed to improve the accuracy of base station distribution adjustments.

[0016] Therefore, this specification provides a better base station location prediction scheme. It identifies the target cells contained in the target base station to be predicted, clusters one or more service data associated with the target cell to obtain a target service data group containing more than a preset threshold of service data, and determines the location of the target cell based on the location information corresponding to the service data contained in the target service data group, thereby determining the location of the target base station. In this way, predicting base station location through service data ignores the influence of operators on base stations, obtains the locations of all existing base stations in a specific area, and improves the accuracy of base station distribution adjustment. Based on the service data, the base station ID can be directly obtained to directly predict the base station location, avoiding the influence of azimuth angle on the prediction results, thus improving the accuracy of base station prediction and consequently improving the accuracy of base station distribution adjustment.

[0017] Figure 1 This is a flowchart illustrating a base station location prediction method according to an embodiment of the present invention. Figure 1 As shown, the execution subject of this method can be a terminal device or a server. The terminal device can be a mobile terminal device such as a mobile phone, tablet, or smartwatch, or a terminal device such as a computer. The server can be a standalone server or a server cluster composed of multiple servers. Specifically, the method may include the following steps: In step S102, based on the association between service data and cells, one or more service data associated with the target cell are obtained. The target cell is the cell contained in the target base station to be predicted.

[0018] A base station is a fixed facility in a mobile communication network, and a base station can contain one or more cells. When a device with wireless communication capabilities is within the coverage area of ​​a cell, it can interact with the base station of that cell via radio waves to achieve functions such as voice calls and data transmission.

[0019] Furthermore, service data refers to data provided to users through the internet for various application services. Service data can be OTT (Over-The-Top) data, which refers to user behavior, content consumption, and device operation data generated through OTT terminals (such as internet TVs and mobile phones). In other words, there is a mapping relationship between OTT data and OTT terminals. OTT terminals generate OTT data through base stations, and the OTT terminals are located within the cells included by the base stations; therefore, there is a correlation between OTT terminals and cells. Based on the mapping relationship between OTT data and OTT terminals, and the correlation between OTT terminals and cells, it can be determined that there is a correlation between OTT data and cells; that is, there is a correlation between service data and cells.

[0020] In implementation, the target base station to be predicted can be identified first, and the cells contained within the target base station can be designated as target cells. The number of target cells can be one or more. Based on the correlation between service data and cells, a data acquisition request can be sent to a third-party data service provider to obtain one or more service data associated with the target cell. The service data may include communication information, location information, etc. Communication information may include operator, network standard, etc., and location information may include cell CI, latitude and longitude information, etc.

[0021] In step S104, based on the location information contained in one or more service data, clustering is performed on one or more service data to obtain a target service data group containing more than a preset threshold number of service data.

[0022] Clustering aims to divide objects in a dataset into several clusters, such that objects within the same cluster have high similarity, while objects in different clusters have low similarity. Examples of clustering methods include K-Means clustering, DBSCAN clustering, distribution-based clustering methods, and grid-based clustering methods.

[0023] In implementation, based on the location information contained in each piece of service data, one or more pieces of service data associated with the target cell are clustered, dividing the one or more pieces of service data associated with the target cell into several clusters. The number of service data contained in each cluster is counted, and the clusters with the number of service data exceeding a preset threshold are determined as the target service data group. The target service data group is usually one, but it can also be multiple; this embodiment does not limit this.

[0024] It should be noted that in practical applications, if the number of target service groups is determined to be one, the cluster containing the most service data can also be selected as the target service data group.

[0025] In step S106, the location of the target cell is determined based on the location information contained in the service data within the target service data group.

[0026] Since the target service data group is obtained by clustering based on location information, the location information of the service data contained in the target service data is similar.

[0027] In implementation, the location information of the service data includes latitude and longitude information. This latitude and longitude information allows the service data to be mapped to a data point on a map. Since the location information of the service data within the target service data group is similar, mapping the target data group onto the map yields at least one data point exhibiting a clustered distribution. Based on the distribution of these data points on the map, the geometric center of these data points can be determined as the location of the target cell.

[0028] In step S108, the location of the target base station is determined based on the location of the target cell.

[0029] In implementation, different methods can be used to determine the location of the target base station depending on the number of target cells. If there is only one target cell, the location of the target cell can be used as the location of the target base station. If there are multiple target cells, the location of the base station can be calculated based on the locations of multiple target cells. For example, the average of the locations of multiple target cells can be used as the location of the target base station, or the equidistant center point of the locations of multiple target cells can be used as the location of the target base station.

[0030] This specification provides a base station location prediction method. Based on the correlation between service data and cells, one or more service data associated with a target cell are obtained. The target cell is the cell contained in the target base station to be predicted. Based on the location information corresponding to one or more service data, the one or more service data are clustered to obtain a target service data group containing more than a preset threshold of service data. Based on the location information corresponding to the service data contained in the target service data group, the location of the target cell is determined, and then the location of the target base station is determined based on the location of the target cell. In this way, predicting base station locations through service data can ignore the influence of operators on base stations, obtain the locations of all existing base stations in a specific area, and improve the accuracy of base station distribution adjustment. Furthermore, the base station ID can be directly obtained from the service data to directly predict the base station location, avoiding the influence of azimuth angle on the prediction results, thus improving the accuracy of base station prediction and consequently improving the accuracy of base station distribution adjustment.

[0031] In the above or following embodiments, the processing method for clustering one or more service data based on the location information corresponding to one or more service data in step S104 to obtain a target service array containing more than a preset threshold of service data is varied. One optional processing method is provided below, such as... Figure 2 As shown, this processing method may specifically include the following steps S202 to S206: In step S202, based on the location information contained in one or more service data, the service area containing the location information is obtained, and the service area is rasterized to obtain multiple raster areas.

[0032] The location information of the service data includes latitude and longitude coordinates. Based on these coordinates, the service data can be mapped to a data point on a map. The service area refers to a geographical region containing one or more data points mapped from the service data. Rasterization processing involves dividing the service area into M*N raster regions of the same size for ease of calculation.

[0033] In implementation, the geographical area containing data points mapped to the service data can be extracted from the map of the city where the target base station is located as the service area. The grid unit can be determined as n, and the service area can be divided into M*N grid regions, each with a size of n*n. Here, n is typically in meters, and its value can be flexibly set according to actual conditions.

[0034] In step S204, based on the location information contained in one or more service data, the target raster area where each service data is located in the service area is determined.

[0035] The service area contains multiple raster regions, each with latitude and longitude information. The latitude and longitude range of a raster region can be determined based on the latitude and longitude of its four vertices. The raster region containing service data within the service area is then designated as the target raster region, determined by the latitude and longitude information contained within the service data.

[0036] In implementation, the latitude and longitude information of the service data can be compared with the latitude and longitude range corresponding to the target raster area. By determining whether the latitude and longitude information of the service data falls within the latitude and longitude range corresponding to the target raster area, the target raster area where the service data is located in the service area can be determined. Alternatively, a planar coordinate system can be established within the service area, and the latitude and longitude information of the service data can be converted into planar coordinates in meters. By locating the position of the planar coordinates in the coordinate system, the target raster area where each piece of service data is located in the service area can be determined. This embodiment does not limit this approach.

[0037] In step S206, based on the distribution of target raster regions in the service region, target service regions containing more than a preset threshold of consecutive target raster regions are determined from the service region, and target service data groups are constructed based on the service data contained in the target service regions.

[0038] Within the service area, the target raster regions are randomly distributed. For example... Figure 3 As shown, the service area contains gray target raster areas and white raster areas. The target raster areas include discretely distributed target raster areas 1 and 2, and continuously distributed target raster areas 3, 4, and 5.

[0039] In implementation, at least one continuous raster region can be identified from the service region based on the distribution of the target raster region within the service region. The target raster regions within each continuous raster region are continuously distributed. The number of continuously distributed target raster regions within each continuous raster region is counted, and continuous raster regions with a number exceeding a preset threshold are selected as target service regions. There can be one or more target service regions, but typically one is chosen. Then, service data located within the target service regions can be grouped together to form a target service data group. When multiple target service regions are selected, a separate target service data group can be constructed for each target service region.

[0040] In this processing method, step S206, based on the distribution of target raster regions in the service area, determines target service areas within the service area whose number of consecutive target raster regions exceeds a preset threshold. There are various possible implementation methods; one optional implementation is provided below. Figure 4 As shown, this implementation method may specifically include the following steps S402 to S408: In step S402, a first target raster region is selected from the target raster regions contained in the service region, the first target raster region is marked in the service region, and the first target raster region is added to the raster segmentation set corresponding to the first target raster region.

[0041] The first target grid region can be any one of the target grid regions within the service area. There are multiple ways to select the first target grid region. For example, a target grid region can be randomly selected from the target grid regions included in the service area as the first target grid region; or, a sequence identifier can be preset for the target grid regions included in the service area, and the first grid region can be selected as the first target grid region in ascending order of the sequence identifier.

[0042] In implementation, a first target raster region can be selected from the target raster regions contained in the service area according to a pre-specified selection method, the first target raster region can be marked as "calculated", and the first target raster region can be added to the raster segmentation set corresponding to the first target raster region.

[0043] In step S404, the adjacent target raster regions adjacent to the first target raster region are found in the service region, the adjacent target raster regions are marked in the service region, and the adjacent target raster regions are added to the raster segmentation set corresponding to the first target raster region.

[0044] The adjacent target grid regions, which are adjacent to the first target grid region, refer to the target grid regions located around the first target grid region. For example, if the first target grid region is the middle square in a 3x3 grid, then the eight squares surrounding the middle square are the adjacent target grid regions adjacent to the first target grid region.

[0045] In implementation, after finding adjacent target raster regions adjacent to the first target raster region, it is determined whether the adjacent target raster regions are "calculated". Unmarked adjacent target raster regions are marked as "calculated", and the marked adjacent target raster regions are added to the raster segmentation set corresponding to the first target raster region. If the found adjacent target raster region has already been marked as "calculated", it indicates that the adjacent target raster region may also be an adjacent raster region of another target raster region, and the adjacent target raster region has already been added to the raster segmentation set, so there is no need to mark and add it again.

[0046] In step S406, the search continues in the service area for adjacent target raster regions that are adjacent to the adjacent target raster region until the search result is empty. A second target raster region is selected from the unmarked target raster regions in the service area, and the raster segmentation set corresponding to the second target raster region is determined until the service area does not contain any unmarked target raster regions.

[0047] In implementation, among at least one adjacent target grid region adjacent to the first target grid region, an adjacent target grid region can be randomly selected or selected according to the order of being placed in the queue as the new first target grid region, and step S404 is executed for this new first target grid region until the found adjacent target grid regions are empty, indicating that this part of the continuous target grid regions has been marked as "calculated" and has been added to the grid segmentation set corresponding to the first target grid region.

[0048] Then, a second target raster region can be selected from the unmarked target raster regions within the service area. The method for selecting the second target raster region is similar to that for selecting the first target raster region, and will not be repeated here. Following the method used to determine the corresponding raster segmentation set for the first target raster region, the raster segmentation set corresponding to the second target raster region is determined. This allows processing of another contiguous portion of the raster region within the service area to be completed. All of this contiguous portion of the target raster region is then marked as "calculated," and all of this contiguous portion of the target raster region is added to the raster segmentation set corresponding to the second target raster region.

[0049] Then, a second target raster region can be reselected from the unmarked target raster regions within the service area until there are no unmarked target raster regions within the service area, resulting in at least one raster segmentation set. Each raster segmentation set can represent a portion of the target raster regions that are continuously distributed within the service area.

[0050] In step S408, from at least one raster segmentation set, a target raster segmentation set containing more than a preset threshold of service data is selected, and the area occupied by the target raster segmentation set in the service area is determined as the target service area.

[0051] In implementation, each raster segment set contains target raster regions, and each target raster region contains service data. The number of service data items contained in each raster segment set can be counted. The counted number is compared with a preset threshold, and the raster segment sets containing more service data than the preset threshold are selected as target raster sets. There can be one or more target raster segment sets. Furthermore, the area occupied by the target raster regions in the target raster segment sets within the service area is determined as the target service area.

[0052] In practical applications, there is usually only one target raster segment set. The raster segment set containing the most service data can be selected as the target raster segment set.

[0053] Figure 5 This is a schematic diagram of another process for determining a target service area according to another embodiment of the present invention, such as... Figure 5 As shown, the process of determining the target service area includes the following steps: Step A02: Create a new grid queue and a grid segment set. Initialize the grid queue and grid segment set to empty. Initialize all target grid regions within the service area to "uncalculated". Also, initialize the maximum value of the sum of the number of service data within the continuously distributed target grid regions, Smax=0.

[0054] Step A04: Determine if there are any uncalculated target raster regions within the service area; If it exists, proceed to step A06, select any uncalculated target raster region and add it to the raster queue, and mark the target raster region as "calculated".

[0055] Step A08: Determine if the grid queue is empty; If the grid queue is not empty, proceed to step A10: retrieve the first target grid region from the grid queue, add it to the grid segmentation set, and remove the target grid region from the grid queue; traverse the adjacent target grid regions of the target grid region, and if they are not calculated, add the adjacent target grid regions to the queue and mark them as calculated; If the grid queue is empty, proceed to step A12 to calculate the number of service data in all grid regions within the grid segmentation set and S. If S > Smax, then execute Smax = S and use this grid segmentation set as the target grid segmentation set. Create a new empty grid segmentation set and jump to step A04 to determine if there is an uncalculated target grid region within the service area. If it does not exist, proceed to step A14, the calculation terminates, and the output is the target raster segmentation set containing the largest number of service data.

[0056] This specification provides a base station location prediction method. By searching for the service data with the most contiguous areas among the service data associated with the target cell, the effective service data for location prediction is selected. The selected effective service data allows for the identification of concentrated areas of the target cell's location, improving the accuracy of target cell location prediction and thus further enhancing the precision of target base station location prediction.

[0057] In the above or following embodiments, there are multiple ways to determine the location of the target cell based on the location information contained in the service data within the target service data group in step S106. The following provides an optional processing method, which may specifically include the following steps: Step S1062: Determine the maximum power value based on the power value contained in each service data in the target service data group; Step S1064: Calculate the difference between the power value and the maximum power value contained in each service data, and obtain the difference corresponding to each service data; Step S1066: Based on the difference corresponding to each service data and the preset power threshold, delete the service data corresponding to the difference that is greater than the preset power threshold from the target service data group; Step S1066: Determine the location of the target cell based on the location information corresponding to the remaining service data in the target service data group.

[0058] The power value can be the Reference Signal Receiving Power (RSRP), which refers to the linear average of the cell common reference signal power received by the terminal and can be used to characterize the wireless signal strength in the communication network. Each service data contains a corresponding power value, and the power values ​​contained in the service data within the target service data group can be compared to select the maximum power value.

[0059] In implementation, the difference between the power value and the maximum power value in each service data entry can be calculated sequentially, and this calculated difference is used as the difference corresponding to the service data. The difference is an absolute value. When the difference corresponding to a service data entry is greater than a preset power threshold, it indicates that the service data has poor availability. Therefore, based on the difference corresponding to each service data entry and the preset power threshold, service data in the target service data group with differences exceeding the preset power threshold can be deleted. After comparing the difference corresponding to the last service data entry with the preset power threshold, the location of the target cell can be determined based on the location information corresponding to the remaining service data in the target service data group. For example, latitude and longitude information can be extracted from the location information corresponding to the remaining service data, and the average value of this latitude and longitude information can be calculated as the location of the target cell.

[0060] In the above or following embodiments, there are various ways to calculate the location of the target base station based on the location of the target cell in step S108. The following provides an optional processing method, which may specifically include the following steps: Step S1082: When there are multiple target cells, the multiple target cells are grouped to obtain at least one cell group; Step S1084: Calculate the location of each cell group based on the location of each target cell in at least one cell group, and use the location of at least one cell group as the location of the target base station.

[0061] It should be noted that due to networking methods such as Centralized Radio Access Network (CRAN), some base stations may have multiple cell groups, each located in a different location. Therefore, predicting the location of each cell group is crucial for accurately predicting the base station's location.

[0062] In implementation, multiple target cells contained in the target base station can be grouped to obtain at least one cell group. When grouping multiple target cells, target cells with similar locations can be assigned to the same cell group based on the predicted location of each target cell, or other grouping methods can be used to group multiple target cells.

[0063] Based on this, the location of each cell group can be calculated according to the location of each target cell in the cell group. For example, the average location of all target cells in a cell group can be calculated as the location of that cell group; or, based on the location of each target cell in the cell group, geometrically equidistant points of these locations can be calculated as the location of that cell group, and so on. Then, the location of each cell group can be used as the location of the target base station.

[0064] In this processing method, the grouping of multiple target cells in step S1082 to obtain at least one cell group can be done in various ways, such as... Figure 6 As shown, one optional grouping method may specifically include the following steps: In step S602, the cell identifier corresponding to each target cell among the multiple target cells is obtained.

[0065] The cell identifier is used to locate the cell's position. The cell identifier can be ECI or NCGI, etc. ECI is a code used to uniquely identify a cell in a communication network. ECI includes a base station identifier to determine the base station and a region identifier to determine the area within the base station. NCGI includes a mobile country code to determine the country, a mobile network code to determine the operator, and a network cell identifier to determine the geographical area.

[0066] In implementation, the cell identifier corresponding to each target cell among the multiple target cells contained in the target base station can be obtained, such as... Figure 7 As shown, the cell identifier can be an ECI, which includes the base station ID and the cell ID. The base station IDs in the cell identifiers of these target cells are the same.

[0067] In step S604, the difference between the cell identifiers corresponding to any two target cells is calculated; Specifically, the cell group to which a cell belongs can be determined by its cell identifier, and the difference between the cell identifiers of two target cells can reflect the locational differences between the two target cells. For example, when the cell identifier is ECI, the difference between the cell identifiers can be the difference between the cell IDs.

[0068] Furthermore, prior to step S604, multiple target cells can be sorted based on their cell identifiers. For example, when the cell identifier is ECI, the base station IDs in the cell identifiers of these target cells are the same, and these target cells can be sorted in ascending order of cell ID. Afterward, for each target cell, only the difference between that target cell and the next target cell needs to be calculated, greatly reducing the computational load of the difference value and thus improving grouping efficiency.

[0069] In step S606, multiple target cells are grouped by assigning two target cells whose difference values ​​meet the grouping criteria to the same cell group and two target cells whose difference values ​​do not meet the grouping criteria to different cell groups, thereby obtaining at least one cell group.

[0070] When establishing cell groups for base stations, the number of cells in each cell group is fixed. For example, traditional base stations typically use a three-sector design, dividing the 360° coverage area into three 120° sectors using directional antennas. Each sector corresponds to an independent cell, thus forming a group of three cells per sector. Of course, in densely populated areas, a six-sector design may be used, with six cells per sector, increasing capacity through narrower beams. However, this requires a more complex antenna system and enhanced interference coordination techniques. The grouping conditions are related to the number of cells in each cell group. When the number of cells in a cell group is three, the preset grouping threshold can be set to two, and the grouping condition is that the difference value is less than the preset grouping threshold.

[0071] In implementation, the differences between different cell groups are usually quite significant. Each difference value corresponds to two target cells. By determining whether each difference value meets the grouping criteria, the cell groups to which the two target cells corresponding to the difference value belong are determined. For example, when the difference value meets the grouping criteria, the two target cells corresponding to that difference value are assigned to the same cell group; when the difference value does not meet the grouping criteria, the two target cells corresponding to that difference value are assigned to different cell groups. By grouping multiple target cells by determining whether the difference value meets the grouping criteria, when all the difference values ​​have been determined, the grouping of multiple target cells is completed, resulting in at least one cell group.

[0072] In practical applications, grouping multiple target cells under a target base station can effectively address the situation where one base station ID corresponds to multiple sites, accurately predict the multiple locations corresponding to the base station, and greatly improve the accuracy of base station location prediction results.

[0073] like Figure 7 As shown, in this embodiment, the total amount of cell data and the total amount of effective cell data in each target cell can also be counted. Generally, the more service data there is, the more accurate the prediction result will be. The accuracy of the prediction result can be evaluated through these two parameters.

[0074] The total cell data volume represents the amount of service data falling within the coverage area of ​​the target cell, while the total effective cell data volume represents the amount of effective service data falling within the coverage area of ​​the target cell. Effective service data refers to the service data in the target service data group corresponding to the target cell.

[0075] In addition, when the difference between the total amount of cell data in the target cell and the total amount of effective cell data exceeds the screening threshold, the target cell can be removed from the target cells included in the target base station to reduce noise interference in the base station location prediction process and improve the accuracy of base station location prediction.

[0076] Figure 8 This is a schematic diagram illustrating a scenario logic for a base station location prediction method according to another embodiment of the present invention. In this application scenario, the service data is OTT data, the target base station typically employs a three-sector design, and the target base station contains multiple cell groups, with each cell group containing three target cells. For example... Figure 8 As shown, the process of predicting the location of a target base station may specifically include the following steps: Step S802: Rasterize the map of the city where the target base station is located; The grid division unit can be determined to be n meters. The city map is divided according to the grid division unit, and the city map is divided into several grid areas of size n*n.

[0077] Step S804: Select the target service area with the largest contiguous OTT data volume for each target cell. The target service area contains at least one grid area. Specifically, step S804 may include the following: First, based on the multiple OTT data associated with each target cell, calculate the metric latitude and longitude corresponding to the latitude and longitude information contained in each OTT data. Divide the calculated metric latitude and longitude by n and round down to determine the grid area to which each OTT data belongs. Secondly, based on the grid region to which each OTT data associated with the target cell belongs, at least one target grid region associated with the target cell is determined; such as Figure 9a As shown, the gray grid area in the upper left part of the figure and the blue grid area in the middle right part of the figure represent at least one target grid area associated with the target cell. The gray grid area and the blue grid area contain OTT data. The bottom row of green grid areas represents the grid queue. The green grid area does not exist in the city map and is only used for recording here. Then, select gray grid region 1 and add it to the grid queue, and mark gray grid region 1 as "calculated". Gray grid region 1 turns yellow, and the result is as follows. Figure 9b As shown; Take gray grid region 1 from the grid queue, add the adjacent, uncalculated gray grid regions 2, 6, 7, and 8 to the grid queue, and mark gray grid regions 2, 6, 7, and 8 as "calculated". Gray grid regions 2, 6, 7, and 8 turn yellow, resulting in the following: Figure 9c As shown; Next, following the order of the grid regions in the grid queue, gray grid region 2 is retrieved from the queue. The adjacent, uncalculated gray grid regions 3, 4, and 9 are then added to the grid queue, and simultaneously marked as "calculated." Gray grid regions 3, 4, and 9 turn yellow, resulting in the following outcome: Figure 9d As shown; repeat the above steps until the raster queue is empty. At this point, all gray raster areas are marked as "calculated," and all gray raster areas turn yellow. All OTT data within the yellow raster area belongs to a contiguous region, resulting in the following: Figure 9e As shown; Finally, the blue grid area is processed in the same way as the gray grid area, and the sum of the number of OTT data in the blue grid area and the yellow grid area is calculated separately. If the sum of the number of OTT data in the gray grid area is the largest, it indicates that the gray grid area is the target service area with the largest number of contiguous OTT data.

[0078] Step S806: Filter OTT data within the target service area and calculate the location of the target cell; In this step, the maximum RSRP among all OTT data in the target service area can be determined, and all OTT data whose difference from the maximum RSRP is less than a preset power threshold can be selected. For example, the preset power threshold can be 2 dB. The average latitude and longitude of the selected OTT data can be calculated as the location of the target cell.

[0079] Step S808: Group the multiple target cells contained in the target base station; Specifically, step S808 may include the following: a) For all target cells included in the target base station, sort these target cells in ascending order of cell ECI; b) i represents the position of the target cell in the sorting result, and g represents the group number corresponding to the target cell. Initialize i=1 and g=1 for the first target cell. c) Starting from i=1, calculate the ECI difference between the (i+1)th target cell and the ith target cell; d) If the calculated ECI difference is less than 3, then determine the group number g of the (i+1)th target cell as 1, execute i=i+1, and repeat step c). e) If the calculated ECI difference is greater than 2, then determine the group number g of the (i+1)th target cell as g=g+1, execute i=i+1, and re-execute step c). f) When i is greater than the total number of target cells, the grouping ends and at least one cell group is obtained.

[0080] Step S810: For each cell group of the target base station, predict the location of the cell group, and predict the location of the target base station based on the location of the cell group; In this step, the average location of all target cells within the cell group can be calculated as the location of the cell group, and the location of at least one cell group can be used as at least one location corresponding to the target base station.

[0081] This specification provides a base station location prediction method. Using OTT data, the base station ID can be directly obtained, allowing for direct prediction of the location of a single base station. This avoids the problem of incomplete competitor information collection in the network measurement report and also avoids the influence of antenna azimuth angle on the prediction results when predicting the location of a single cell. Furthermore, considering the issue of one base station ID corresponding to multiple sites, a scheme is proposed to group the cells under the base station to predict the site location, significantly improving the accuracy of the base station location prediction results.

[0082] The above describes the base station location prediction method provided in the embodiments of this specification. Based on the same idea, the embodiments of this specification also provide a base station location prediction device, such as... Figure 10 As shown.

[0083] The base station location prediction device includes: a data processing module 1001 and a location prediction module 1002, wherein: The data processing module 1001 is used to obtain one or more service data associated with a target cell based on the correlation between service data and cells, wherein the target cell is the cell contained in the target base station to be predicted; and to perform clustering processing on the one or more service data according to the location information contained in the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location prediction module 1002 is used to determine the location of the target cell based on the location information contained in the service data within the target service data group; and to determine the location of the target base station based on the location of the target cell.

[0084] In the embodiments described in this specification, the data processing module 1001 includes a first processing unit and a second processing unit, wherein: The first processing unit is configured to obtain a service area containing the location information pointed to by the location information based on the location information contained in one or more service data, and to perform rasterization processing on the service area to obtain multiple raster areas; and to determine the target raster area in the service area where each service data is located based on the location information contained in one or more service data. The second processing unit is configured to determine, based on the distribution of target raster regions in the service region, a target service region containing more than a preset threshold of consecutive target raster regions, and construct a target service data group based on the service data contained in the target service region.

[0085] In the embodiments of this specification, the second processing unit is further configured to: Select a first target raster region from the target raster regions included in the service area, mark the first target raster region in the service area, and add the first target raster region to the raster segmentation set corresponding to the first target raster region; In the service area, find the adjacent target grid area that is adjacent to the first target grid area, mark the adjacent target grid area in the service area, and add the adjacent target grid area to the grid segmentation set corresponding to the first target grid area; Continue searching for adjacent target raster regions in the service area until the search results are empty. Select a second target raster region from the unmarked target raster regions in the service area and determine the raster segmentation set corresponding to the second target raster region until the service area does not contain any unmarked target raster regions. From at least one raster segmentation set, select a target raster segmentation set containing more than a preset threshold of service data, and determine the area occupied by the target raster segmentation set in the service area as the target service area.

[0086] In the embodiments described in this specification, the location prediction module 1002 includes a calculation unit, a processing unit, and a prediction unit, wherein: The calculation unit is used to determine the maximum power value based on the power value contained in each service data in the target service data group; and to calculate the difference between the power value contained in each service data and the maximum power value to obtain the difference corresponding to each service data. The processing unit is used to delete service data corresponding to a difference greater than the preset power threshold from the target service data group based on the difference corresponding to each service data and the preset power threshold. The prediction unit is used to determine the location of the target cell based on the location information corresponding to the remaining service data in the target service data group.

[0087] In this embodiment of the specification, the location prediction module 1002 further includes a grouping unit, wherein: The grouping unit is used to group the multiple target cells to obtain at least one cell group when there are multiple target cells. The prediction unit is used to calculate the location of each cell group based on the location of each target cell in the at least one cell group, and to use the location of at least one cell group as the location of the target base station.

[0088] In the embodiments of this specification, the grouping unit is further used for: Obtain the cell identifier corresponding to each target cell among multiple target cells; Calculate the difference between the cell identifiers of any two target cells; Multiple target cells are grouped by assigning two target cells whose difference values ​​meet the grouping criteria to the same cell group, and assigning two target cells whose difference values ​​do not meet the grouping criteria to different cell groups, thus obtaining at least one cell group.

[0089] This specification provides a base station location prediction device. Based on the association between service data and cells, it obtains one or more service data associated with a target cell, where the target cell is a cell contained in the target base station to be predicted. According to the location information corresponding to the one or more service data, it performs clustering processing on the multiple service data to obtain a target service data group containing more than a preset threshold of service data. Based on the location information corresponding to the service data contained in the target service data group, it determines the location of the target cell, and then determines the location of the target base station based on the location of the target cell. In this way, predicting base station locations through service data can ignore the influence of operators on base stations, obtain the locations of all existing base stations in a specific area, and improve the accuracy of base station distribution adjustment. Furthermore, the base station ID can be directly obtained from the service data to directly predict the base station location, avoiding the influence of azimuth angle on the prediction result, thus improving the accuracy of base station prediction and consequently improving the accuracy of base station distribution adjustment.

[0090] The above are examples of base station location prediction devices provided in this specification. Based on the same concept, this specification also provides an electronic device, such as... Figure 11 As shown.

[0091] The electronic device can provide a terminal device or server, etc., for the above embodiments.

[0092] Electronic devices can vary considerably due to differences in configuration or performance. They may include one or more processors 1101 and memory 1102, with memory 1102 storing one or more application programs or data. Memory 1102 may be temporary or persistent storage. The application programs stored in memory 1102 may include one or more modules (not shown), each module including a series of computer-executable instructions for the electronic device. Furthermore, processor 1101 may be configured to communicate with memory 1102 and execute the series of computer-executable instructions stored in memory 1102 on the electronic device. The electronic device may also include one or more power supplies 1103, one or more wired or wireless network interfaces 1104, one or more input / output interfaces 1105, and one or more keyboards 1106.

[0093] Specifically, in this embodiment, the electronic device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for use in the electronic device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: Based on the correlation between service data and cells, obtain one or more service data related to the target cell, where the target cell is the cell contained in the target base station to be predicted; Based on the location information contained in one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location of the target cell is determined based on the location information contained in the service data within the target service data group; The location of the target base station is determined based on the location of the target cell.

[0094] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the electronic device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0095] This specification provides an electronic device that, based on the association between service data and cells, obtains one or more service data associated with a target cell, where the target cell is a cell contained in the target base station to be predicted. Based on the location information corresponding to the one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. Based on the location information corresponding to the service data contained in the target service data group, the location of the target cell is determined, and then the location of the target base station is determined based on the location of the target cell. In this way, predicting base station locations through service data can ignore the influence of operators on base stations, obtain the locations of all existing base stations in a specific area, and improve the accuracy of base station distribution adjustment. Furthermore, the base station ID can be directly obtained from the service data to directly predict the base station location, avoiding the influence of azimuth angle on the prediction result, thus improving the accuracy of base station prediction and consequently improving the accuracy of base station distribution adjustment.

[0096] Furthermore, based on the above Figures 1 to 6 The method shown in this specification, along with one or more embodiments, also provides a storage medium for storing computer-executable instruction information. In one specific embodiment, the storage medium can be a USB flash drive, optical disc, hard disk, etc. When the computer-executable instruction information stored in the storage medium is executed by a processor, it can achieve the following process: Based on the correlation between service data and cells, obtain one or more service data related to the target cell, where the target cell is the cell contained in the target base station to be predicted; Based on the location information contained in one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location of the target cell is determined based on the location information contained in the service data within the target service data group; The location of the target base station is determined based on the location of the target cell.

[0097] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the above-described storage medium embodiment is basically similar to the method embodiment, so the description is relatively simple; relevant parts can be referred to the description of the method embodiment.

[0098] This specification provides a storage medium that, based on the association between service data and cells, obtains one or more service data associated with a target cell, where the target cell is a cell contained in the target base station to be predicted. Based on the location information corresponding to one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. Based on the location information corresponding to the service data contained in the target service data group, the location of the target cell is determined, and then the location of the target base station is determined based on the location of the target cell. In this way, predicting base station locations through service data can ignore the influence of operators on base stations, obtain the locations of all existing base stations in a specific area, and improve the accuracy of base station distribution adjustment. Furthermore, the base station ID can be directly obtained from the service data to directly predict the base station location, avoiding the influence of azimuth angle on the prediction result, thus improving the accuracy of base station prediction and consequently improving the accuracy of base station distribution adjustment.

[0099] Furthermore, based on the above Figures 1 to 6 The method shown in this specification, along with one or more embodiments, also provides a computer program product including a computer program that, when executed by a processor, performs the following process: Based on the correlation between service data and cells, obtain one or more service data related to the target cell, where the target cell is the cell contained in the target base station to be predicted; Based on the location information contained in one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location of the target cell is determined based on the location information contained in the service data within the target service data group; The location of the target base station is determined based on the location of the target cell.

[0100] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the above-described embodiment of a computer program product is relatively simple in description because it is fundamentally similar to the method embodiment; relevant parts can be referred to the description of the method embodiment.

[0101] This specification provides a computer program product that, based on the association between service data and cells, obtains one or more service data associated with a target cell, where the target cell is a cell contained in the target base station to be predicted. Based on the location information corresponding to one or more service data, it performs clustering processing on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. Based on the location information corresponding to the service data contained in the target service data group, it determines the location of the target cell, and then determines the location of the target base station based on the location of the target cell. In this way, predicting base station locations through service data can ignore the influence of operators on base stations, obtain the locations of all existing base stations in a specific area, and improve the accuracy of base station distribution adjustment. Furthermore, the base station ID can be directly obtained based on the service data to directly predict the base station location, avoiding the influence of azimuth angle on the prediction result, thus improving the accuracy of base station prediction and consequently improving the accuracy of base station distribution adjustment. The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0102] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0103] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0104] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0105] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0106] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] Embodiments in this specification are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable parallel device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable parallel device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable fraud device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0111] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0112] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0113] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0114] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0116] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0117] The above description is merely an embodiment of this specification and is not intended to limit this document. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A base station location prediction method, characterized in that, The method includes: Based on the correlation between service data and cells, obtain one or more service data related to the target cell, where the target cell is the cell contained in the target base station to be predicted; Based on the location information contained in one or more service data, clustering is performed on the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location of the target cell is determined based on the location information contained in the service data within the target service data group; The location of the target base station is determined based on the location of the target cell.

2. The method according to claim 1, characterized in that, The step of clustering one or more service data points based on location information contained in those service data points to obtain a target service data group containing more than a preset threshold includes: Based on the location information contained in one or more service data, a service area containing the location information is obtained, and the service area is rasterized to obtain multiple raster areas. Based on the location information contained in one or more service data, determine the target grid area where each service data is located in the service area; Based on the distribution of target grid regions in the service area, target service areas containing more than a preset threshold of consecutive target grid regions are determined from the service area, and target service data groups are constructed based on the service data contained in the target service areas.

3. The method according to claim 2, characterized in that, The step of determining, based on the target raster regions contained in the service area, a target service area containing more than a preset threshold of consecutive target raster regions includes: Select a first target raster region from the target raster regions included in the service area, mark the first target raster region in the service area, and add the first target raster region to the raster segmentation set corresponding to the first target raster region; In the service area, find the adjacent target grid area that is adjacent to the first target grid area, mark the adjacent target grid area in the service area, and add the adjacent target grid area to the grid segmentation set corresponding to the first target grid area; Continue searching for adjacent target raster regions in the service area until the search results are empty. Select a second target raster region from the unmarked target raster regions in the service area and determine the raster segmentation set corresponding to the second target raster region until the service area does not contain any unmarked target raster regions. From at least one raster segmentation set, select a target raster segmentation set containing more than a preset threshold of service data, and determine the area occupied by the target raster segmentation set in the service area as the target service area.

4. The method according to claim 1, characterized in that, Determining the location of the target cell based on the location information corresponding to the service data contained in the target service data group includes: The maximum power value is determined based on the power value contained in each service data item in the target service data group; Calculate the difference between the power value contained in each service data and the maximum power value to obtain the difference corresponding to each service data; Based on the difference corresponding to each service data and the preset power threshold, delete the service data corresponding to the difference that is greater than the preset power threshold from the target service data group; The location of the target cell is determined based on the location information corresponding to the remaining service data in the target service data group.

5. The method according to claim 5, characterized in that, Calculating the location of the target base station based on the location of the target cell includes: When there are multiple target cells, the multiple target cells are grouped to obtain at least one cell group; Based on the location of each target cell in the at least one cell group, the location of each cell group is calculated, and the location of at least one cell group is used as the location of the target base station.

6. The method according to claim 5, characterized in that, The process of grouping multiple target cells to obtain at least one cell group includes: Obtain the cell identifier corresponding to each target cell among multiple target cells; Calculate the difference between the cell identifiers of any two target cells; Multiple target cells are grouped by assigning two target cells whose difference values ​​meet the grouping criteria to the same cell group, and assigning two target cells whose difference values ​​do not meet the grouping criteria to different cell groups, thus obtaining at least one cell group.

7. A base station location prediction device, characterized in that, The device includes: The data processing module is used to obtain one or more service data associated with a target cell based on the correlation between service data and cells, wherein the target cell is the cell contained in the target base station to be predicted; and to perform clustering processing on the one or more service data according to the location information contained in the one or more service data to obtain a target service data group containing more than a preset threshold of service data. The location prediction module is used to determine the location of the target cell based on the location information contained in the service data within the target service data group; and to determine the location of the target base station based on the location of the target cell.

8. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the base station location prediction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the base station location prediction method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the base station location prediction method according to any one of claims 1 to 6.

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