A method and apparatus for identifying a coverage cell for a target location

Abstract

The embodiment of the present application provides a kind of target position's overlay cell identification method and device, by determining initial cell, obtains the first communication network switching frequency data for the initial cell;Determine suspected cell by the first communication network switching frequency data;Communication network access data for the suspected cell is obtained;Based on the communication network access data, the signal strength distribution characteristic parameter and access frequency distribution characteristic parameter for the suspected cell are calculated;Based on the signal strength distribution characteristic parameter and the access frequency distribution characteristic parameter, the overlay cell for the target position is determined from the suspected cell, to improve the accuracy and efficiency of the overlay cell identification for the target position is realized.

Description

Technical Field

[0001] This invention relates to the field of coverage cell identification technology for target locations, and in particular to a coverage cell identification method for a target location, a coverage cell identification device for a target location, an electronic device, and a computer-readable storage medium. Background Technology

[0002] With the rapid development of urban subway construction, various large shopping malls, shops, and food streets have been built in the underground extension areas adjacent to subway stations. The commercial points in the city are linked through the subway lines, transforming the consumption pattern from one-stop consumption to commercial district consumption. As the subway and its adjacent underground commercial districts expand, the coverage area of ​​the communication network also expands, making the maintenance and optimization of the communication network indispensable.

[0003] Therefore, how to identify communication cells in order to improve the efficiency of communication network maintenance and optimization has become a problem that needs to be overcome by those skilled in the art. Summary of the Invention

[0004] The present invention provides a method, apparatus, electronic device, and computer-readable storage medium for identifying coverage cells at a target location, in order to solve the problem of how to identify communication cells.

[0005] This invention discloses a method for identifying coverage cells at a target location, which may include:

[0006] Determine the initial cell and obtain the first communication network handover count data for the initial cell;

[0007] Suspected cells were identified using the handover count data from the first communication network.

[0008] Obtain communication network access data for the suspected cell;

[0009] Based on the communication network access data, the signal strength distribution characteristic parameters and access frequency distribution characteristic parameters for the suspected cell are calculated.

[0010] Based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters, the coverage cell for the target location is determined from the suspected cells.

[0011] Optionally, the first communication network handover count data includes the average daily handover count data of the communication network, and the step of determining the suspected cell through the first communication network handover count data may include:

[0012] Based on the average daily handover count data of the communication network, the sum and total number of communication network handovers for the initial cell are calculated.

[0013] Determine the centralized handover threshold;

[0014] Suspected cells are determined based on the number and value of communication network handovers, the total number of communication network handovers, and the centralized handover threshold.

[0015] Optionally, it may also include:

[0016] Construct a suspected cell set that includes multiple of the suspected cells;

[0017] Select multiple first cells from the set of suspected cells;

[0018] An initial cell group is determined based on multiple first cells; the initial cell group has corresponding left-end cells and right-end cells;

[0019] The left-end neighboring cells are determined based on the left-end cell and the handover count data corresponding to the left-end cell;

[0020] The right-end neighboring cells are determined based on the right-end cell and the handover count data corresponding to the right-end cell;

[0021] The final cell group is determined using the left-end neighboring cells and the right-end neighboring cells;

[0022] The cells belonging to the final cell group are designated as target cells.

[0023] Optionally, the communication network access data includes access distance, average access reference received power (RSRP), and average number of accesses. The step of calculating the signal strength distribution characteristic parameters and access frequency distribution characteristic parameters for the suspected cell based on the communication network access data may include:

[0024] Obtain suspected cell handover threshold parameters, and determine the threshold for adjacent access distance signal strength difference, access frequency distribution threshold, and maximum cell signal strength difference threshold.

[0025] The signal strength distribution characteristic parameters for the target cell are determined by using the average access reference signal received power (RSRP) value and the adjacent access distance signal strength difference threshold, or the maximum cell signal strength difference threshold.

[0026] The average number of accesses for the target cell is determined using the average number of accesses, the access distance, the average access reference signal received power (RSRP), and the handover threshold parameter.

[0027] The access frequency distribution characteristic parameters for the target cell are determined based on the mean access frequency and the access frequency distribution threshold.

[0028] Optionally, the step of determining the coverage cell for the target location from the suspected cells based on the signal strength distribution characteristic parameter and the access frequency distribution characteristic parameter may include:

[0029] Based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters, the subway tunnel-themed coverage cells are determined from the target cells;

[0030] Metro station platform cells and metro tunnel thematic cells were determined from multiple metro tunnel thematic coverage cells;

[0031] Obtain the list of indoor distributed antenna system (DAS) cell sources;

[0032] Remove the corresponding cells from the list of indoor distributed signal source cells in the subway station platform cells and the subway tunnel thematic cells to identify suspected subway underground commercial area cells;

[0033] Obtain data on the number of times the second communication network was switched for the suspected underground commercial area of ​​the subway.

[0034] Based on the second communication network handover count data, the indoor distribution signal source cell list, the subway station platform cell, and the suspected subway underground commercial area cell, a large subway underground commercial area cell is identified.

[0035] A set of large-scale underground commercial districts for subways is generated by using multiple such large-scale underground commercial districts;

[0036] Determine the threshold for the number of residential communities within a commercial district;

[0037] The set of underground commercial districts under the subway is determined based on the set of large-scale underground commercial districts and the threshold for the number of commercial districts.

[0038] The underground commercial districts of the subway will be used as coverage areas for the target location.

[0039] Optionally, before the step of determining the average number of accesses for the target cell using the average number of accesses, the access distance, the average access reference received power (RSRP), and the handover threshold parameter, the method may further include:

[0040] Abnormal data is determined from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter.

[0041] Remove the abnormal data.

[0042] This invention also discloses a coverage cell identification device for a target location, which may include:

[0043] A communication network handover count data acquisition device is used to determine an initial cell and acquire first communication network handover count data for the initial cell;

[0044] The suspected cell determination module is used to determine suspected cells based on the handover count data of the first communication network.

[0045] A communication network access data acquisition module is used to acquire communication network access data for the suspected cell;

[0046] The parameter calculation module is used to calculate the signal strength distribution characteristic parameters and access frequency distribution characteristic parameters for the suspected cell based on the communication network access data.

[0047] The coverage cell determination module is used to determine the coverage cell for the target location from the suspected cells based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters.

[0048] Optionally, the first communication network handover count data includes the average daily handover count data of the communication network, and the suspected cell determination module may include:

[0049] The sum-to-total value calculation submodule is used to calculate the sum of communication network handover times and the total number of communication network handover times for the initial cell based on the average daily handover times data of the communication network;

[0050] The centralized handover threshold determination submodule is used to determine the centralized handover threshold.

[0051] The suspected cell determination submodule is used to determine suspected cells based on the number and value of communication network handovers, the total number of communication network handovers, and the centralized handover threshold.

[0052] Optionally, it may also include:

[0053] The suspected cell set construction submodule is used to construct a suspected cell set containing multiple of the suspected cells;

[0054] The first cell selection submodule is used to select multiple first cells from the set of suspected cells;

[0055] The initial cell group determination submodule is used to determine an initial cell group based on multiple first cells; the initial cell group has corresponding left-end cells and right-end cells;

[0056] The left-end neighbor cell determination submodule is used to determine the left-end neighbor cell based on the left-end cell and the handover count data corresponding to the left-end cell;

[0057] The right-end neighbor cell determination submodule is used to determine the right-end neighbor cell based on the right-end cell and the handover count data corresponding to the right-end cell;

[0058] The final cell group determination submodule is used to determine the final cell group using the left-end neighboring cells and the right-end neighboring cells;

[0059] The cells belonging to the final cell group are designated as target cells.

[0060] Optionally, the communication network access data includes access distance, average access reference signal received power (RSRP), and average number of accesses. The parameter calculation module may include:

[0061] The threshold determination submodule is used to obtain the suspected cell handover threshold parameters and determine the threshold threshold for the signal strength difference between adjacent access distances, the threshold threshold for the distribution of access times, and the maximum threshold threshold for the cell signal strength difference.

[0062] The signal strength distribution characteristic parameter determination submodule is used to determine the signal strength distribution characteristic parameters for the target cell using the average access reference signal received power (RSRP) value and the adjacent access distance signal strength difference threshold, or the maximum cell signal strength difference threshold.

[0063] The average number of accesses determination submodule is used to determine the average number of accesses for the target cell using the average number of accesses value, the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter.

[0064] The access frequency distribution feature parameter determination submodule is used to determine the access frequency distribution feature parameters for the target cell based on the average access frequency and the access frequency distribution threshold.

[0065] Optionally, the coverage cell determination module may include:

[0066] The subway tunnel-themed coverage cell determination submodule is used to determine the subway tunnel-themed coverage cells from the target cells based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters;

[0067] The subway cell determination submodule is used to determine subway station platform cells and subway tunnel thematic cells from multiple subway tunnel thematic coverage cells;

[0068] The indoor distributed antenna system (DAS) source cell list acquisition submodule is used to acquire the indoor DAS source cell list;

[0069] The "Subway Underground Commercial Area Community Determination Submodule" is used to delete the corresponding communities in the subway station platform communities and the subway tunnel thematic communities from the indoor distributed signal source community list set, and determine the suspected subway underground commercial area communities.

[0070] The second communication network switching count data acquisition submodule is used to acquire the second communication network switching count data for the suspected underground commercial area of ​​the subway.

[0071] The large-scale subway underground commercial area identification submodule is used to identify large-scale subway underground commercial area communities based on the second communication network handover count data, the indoor distributed signal source cell list set, the subway station platform cells, and the suspected subway underground commercial area communities.

[0072] A sub-module for generating large-scale underground commercial districts within subway stations is used to generate a large-scale underground commercial district set from multiple such large-scale underground commercial districts.

[0073] The "Threshold Determination for the Number of Residential Communities in a Commercial Area" submodule is used to determine the threshold for the number of residential communities in a commercial area.

[0074] The "Subway Underground Commercial Area Set Determination Submodule" is used to determine the subway underground commercial area set based on the large subway underground commercial area set and the threshold for the number of commercial area units.

[0075] The coverage cell determination submodule is used to select the set of underground commercial districts in the subway as coverage cells for the target location.

[0076] Optionally, it may also include:

[0077] An abnormal data determination submodule is used to determine abnormal data from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter.

[0078] The abnormal data removal submodule is used to remove the abnormal data.

[0079] This invention also discloses an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0080] The memory is used to store computer programs;

[0081] When the processor executes a program stored in the memory, it implements the method described in the embodiments of the present invention.

[0082] This invention also discloses a computer-readable storage medium storing instructions that, when executed by one or more processors, cause the processors to perform the methods described in this invention.

[0083] The embodiments of the present invention have the following advantages:

[0084] In this embodiment of the invention, by determining an initial cell, first communication network handover count data for the initial cell is obtained; suspected cells are identified using the first communication network handover count data; communication network access data for the suspected cells is obtained; signal strength distribution characteristic parameters and access count distribution characteristic parameters for the suspected cells are calculated based on the communication network access data; and coverage cells for the target location are determined from the suspected cells based on the signal strength distribution characteristic parameters and the access count distribution characteristic parameters, thereby improving the accuracy and efficiency of coverage cell identification for the target location. Attached Figure Description

[0085] Figure 1 This is a flowchart illustrating the steps of a coverage cell identification method for a target location provided in an embodiment of the present invention;

[0086] Figure 2 This is a flowchart of another method for identifying coverage cells at a target location provided in an embodiment of the present invention;

[0087] Figure 3 This is a structural block diagram of a coverage cell identification device for a target location provided in an embodiment of the present invention;

[0088] Figure 4 This is a hardware structure block diagram of an electronic device provided in various embodiments of the present invention. Detailed Implementation

[0089] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0090] With economic development, subway construction in major and medium-sized cities has also developed rapidly. Subway stations, as gateways to the real economy, have gradually been recognized and developed by the market. Various large shopping malls, markets, restaurant streets, and other commercial entities have been established in the underground extension areas adjacent to some subway stations, forming new underground commercial districts and becoming one of the main drivers of regional consumption. The normal operation of subways and underground commercial districts cannot be separated from the support of communication networks. A crucial aspect of ensuring the quality of communication network services in subways and underground commercial districts is the maintenance and optimization of the communication network. During the maintenance and optimization process, the maintenance of information records for subway tunnel areas is mainly carried out manually. Due to the large workload and the potential impact of factors such as base station decommissioning, relocation, new station integration, cutover, and changes in maintenance and optimization personnel during network operation, there is a common problem of untimely updates to maintenance information. This leads to inaccuracies, omissions, and errors in the ledger information of subway tunnel coverage cells, making it impossible to accurately identify subway tunnel coverage cells and causing certain difficulties for daily network maintenance and optimization. Therefore, embodiments of the present invention combine communication network handover times, signal strength distribution characteristic parameters, and access frequency distribution characteristic parameters to identify coverage cells for target locations, thereby improving the accuracy and efficiency of coverage cell identification and further optimizing the maintenance and optimization of communication networks.

[0091] Reference Figure 1 The diagram illustrates a flowchart of a coverage cell identification method for a target location provided in an embodiment of the present invention, which may specifically include the following steps:

[0092] Step 101: Determine the initial cell and obtain the first communication network handover count data for the initial cell;

[0093] Step 102: Identify suspected cells using the handover count data from the first communication network;

[0094] Step 103: Obtain communication network access data for the suspected cell;

[0095] Step 104: Calculate the signal strength distribution characteristic parameters and access frequency distribution characteristic parameters for the suspected cell based on the communication network access data;

[0096] Step 105: Based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters, determine the coverage cell for the target location from the suspected cells.

[0097] In practical applications, a cell, also known as a cellular cell, refers to the area covered by one base station or a portion of a base station (fan antenna) in a cellular mobile communication system, within which mobile stations can reliably communicate with the base station via wireless channels.

[0098] In a specific implementation, the embodiments of the present invention can determine an initial cell, wherein the initial cell can be a cell that has a certain correlation with the target location but has not been identified by coverage. For example, assuming the target location is a commercial street of a subway station, the initial cell can be all the corresponding cells along the subway line.

[0099] Of course, the above example is only an illustration, and those skilled in the art can identify other cells that are related to the target location as the initial cell.

[0100] After identifying the initial cell, this embodiment of the invention can acquire first communication network handover count data for the initial cell; suspected cells are identified using the first communication network handover count data. The process of identifying suspected cells can be a preliminary identification process of coverage cells. The first communication network handover count data can be communication network handover count data for the initial cell. For example, when X is selected based on the target location... i and X j After being designated as the initial cell, it can then be accessed via cell X. i and X j Determine the cell handover pair X i,j SwitchTimes i,j Among them, the number of switches (SwitchTimes) i,j It can be used as a target for the initial cell X i and X j The first communication network switching count data, then, the switching count SwitchTimes can be selected. i,j The first communication network switching count data >0 will be compared with SwitchTimes. i,j X corresponding to the first communication network switching count data >0 i and X j As a suspected residential community.

[0101] Of course, those skilled in the art can determine suspected cells based on the first network handover count data in other ways, and the embodiments of the present invention do not limit this.

[0102] In this embodiment of the invention, by determining an initial cell, the first communication network handover count data for the initial cell is obtained; by using the first communication network handover count data, suspected cells are identified, thereby initially establishing the correlation between the initial cells, further identifying suspected cells, narrowing the data calculation scope, reducing workload, and laying the foundation for subsequent data analysis.

[0103] In a specific implementation, embodiments of the present invention can acquire communication network access data for suspected cells, such as access distance value, average access reference signal received power (RSRP) value, and average number of accesses value; calculate signal strength distribution characteristic parameters and access number distribution characteristic parameters for suspected cells based on the communication network access data; and determine the coverage cell for the target location from the suspected cells based on the signal strength distribution characteristic parameters and access number distribution characteristic parameters.

[0104] In practical applications, signal strength and access frequency can be used to calculate whether multiple cells belong to the same coverage area in the same scenario. For example, when the suspected cell is X... i Then X can be obtained. i Access data from 7-9 AM on average each day within the community's statistical period is used as the communication network access data for suspected communities, such as the community's unique identifier X. i Access distance mr_distance i,j (j is the access distance identifier; a larger j indicates a longer access distance), the corresponding average access reference signal received power (RSRP) value avg_scrsrp for access distance identifier j. i,j The average number of accesses corresponding to the access distance identifier j, access_nums i,j Based on the access data, the calculation results for X i The signal strength distribution characteristic parameters and access frequency distribution characteristic parameters, and based on the suspected cell X i The signal strength distribution characteristic parameters and access frequency distribution characteristic parameters are used to determine the coverage cell Ce for the target location.

[0105] In this embodiment of the invention, by determining an initial cell, first communication network handover count data for the initial cell is obtained; suspected cells are identified using the first communication network handover count data; communication network access data for the suspected cells is obtained; signal strength distribution characteristic parameters and access count distribution characteristic parameters for the suspected cells are calculated based on the communication network access data; and coverage cells for the target location are determined from the suspected cells based on the signal strength distribution characteristic parameters and the access count distribution characteristic parameters. This improves the accuracy and efficiency of coverage cell identification for the target location and further optimizes the maintenance and optimization of the communication network.

[0106] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0107] In an optional embodiment of the present invention, the step of determining the suspected cell through the first communication network handover count data includes:

[0108] Based on the average daily handover count data of the communication network, the sum and total number of communication network handovers for the initial cell are calculated.

[0109] Determine the centralized handover threshold;

[0110] Suspected cells are determined based on the number and value of communication network handovers, the total number of communication network handovers, and the centralized handover threshold.

[0111] In practical applications, the first communication network handover count data can be the daily average handover count data of the communication network. For example, when the initial cell is X... i and X j (i≠j), and cell handover to X i,j Average daily number of SwitchTimes i,j When, it can be taken with X i X, the cell is being handed over j SwitchTimes Community i,j For cells with a value greater than 0, the SwitchTimes algorithm calculates the average daily number of handovers. i,j Sort by the number of SwitchTimes from largest to smallest, and calculate the average daily number of SwitchTimes. i,j The two largest X j1 and X j2 X j1 and X j2 The corresponding daily average number of SwitchTimes i,j Add them together to get the sum of the number of handovers in the communication network, and use this sum to represent the handover pairs of all cells X. i,j SwitchTimes (average daily number of switches) i,j The sum of these values ​​represents the total number of handovers in the communication network. Simultaneously, a centralized handover threshold is determined, which can be Threshold1 (values ​​can be 90% or higher). The ratio of the sum of the communication network handovers to the total number of handovers is calculated. If this ratio is greater than or equal to the centralized handover threshold Threshold1, then cell X can be considered for handover. i As a suspected residential community.

[0112] This invention calculates the sum and total number of handovers for the initial cell based on the average daily handover count data of the communication network; determines a centralized handover threshold; and identifies suspected cells based on the sum and total number of handovers, the total number of handovers, and the centralized handover threshold. This further refines the process of identifying suspected cells beyond the initial handover count data. By employing a method based on the sum and total number of handovers and the centralized handover threshold, it effectively eliminates initial cells that have handovers with suspected cells but are not concentrated or have low correlation, thus aiding in the accurate identification of subsequent coverage cells.

[0113] In an optional embodiment of the present invention, a suspected cell set comprising a plurality of the suspected cells may also be constructed;

[0114] Select multiple first cells from the set of suspected cells;

[0115] An initial cell group is determined based on multiple first cells; the initial cell group has corresponding left-end cells and right-end cells;

[0116] The left-end neighboring cells are determined based on the left-end cell and the handover count data corresponding to the left-end cell;

[0117] The right-end neighboring cells are determined based on the right-end cell and the handover count data corresponding to the right-end cell;

[0118] The final cell group is determined using the left-end neighboring cells and the right-end neighboring cells;

[0119] The cells belonging to the final cell group are designated as target cells.

[0120] In practical applications, in order to reduce the amount of data in a single operation and the time spent on overall analysis, as well as to increase the correlation and logic of the data and facilitate subsequent data processing, suspected cells can be scientifically grouped in a grouping manner to form a set with correlation.

[0121] In a specific implementation, embodiments of the present invention can construct a suspected cell set containing multiple suspected cells; select multiple first cells from the suspected cell set; determine an initial cell group based on the multiple first cells; the initial cell group has corresponding left-end cells and right-end cells; determine left-end neighbor cells based on the left-end cells and the handover count data corresponding to the left-end cells; determine right-end neighbor cells based on the right-end cells and the handover count data corresponding to the right-end cells; use the left-end neighbor cells, right-end neighbor cells, and the initial cell group to determine a final cell group for the suspected cell set; and use cells belonging to the final cell group as target cells.

[0122] For example, the final k-th cell group T is generated. k When the suspected community is X i and X j Then, it can be based on X i and X j Construct a set of suspected cells A, where X i X j ∈A, choose X i As the first community, based on X i Integrating into generated cell group T k , will T k As the initial cell group, i.e. T k ={X i}, taking values ​​n=1, m=1, and T k Having corresponding left-end cells and right-end cells, we can then take the initial cell group T. k The neighboring cell with the highest number of handovers among the neighboring cells of the left-hand cell As the left-hand neighbor, if the left-hand neighbor X i-n If ∈A and not grouped, then X i-n Incorporated into community group T k On the left end, and let n = n + 1, then repeat the process of taking the initial cell group T. k The neighboring cell with the highest number of handovers among the neighboring cells of the left-end cell is designated as the left-end neighboring cell, and this process continues until the left-end neighboring cell is reached. Or, until the left-hand neighboring region X i-n ∈A and X i-n It has been merged into another neighborhood group T w Then X i-n For the community group T k and T w The common community, denoted as T k,w Then the cell group T k The analysis of the left-end cell is complete; we will begin analyzing T. k Analyzing the right-hand cells, we can take cell group T. k The neighboring cell with the highest number of handovers among the neighboring cells of the right-hand cell As the right-end neighboring region, if the right-end neighboring region X i+m If ∈A and not grouped, then X i+m Incorporated into community group T k On the right end, let m = m + 1, and then repeat the process of taking cell group T. k The neighboring cell with the highest number of handovers among the neighboring cells of the right-end cell is designated as the right-end neighboring cell, and this process continues until the right-end neighboring cell is reached. Or, until the right-hand neighboring region X i+m ∈A and X i+m It has been merged into another neighborhood group T w Then X i+m For the community group T k and T wThe common community, denoted as T k，w Then the cell group T k Once the analysis of the right-hand cell is complete, the cell group T at this point... k As the final cell group, and T k The target community is the community in question.

[0123] This invention constructs a suspected cell set containing multiple suspected cells; selects multiple first cells from the suspected cell set; determines an initial cell group based on the multiple first cells; the initial cell group has corresponding left-end cells and right-end cells; determines left-end neighboring cells based on the left-end cells and their corresponding handover count data; determines right-end neighboring cells based on the right-end cells and their corresponding handover count data; uses the left-end neighboring cells and the right-end neighboring cells to determine a final cell group; and uses cells belonging to the final cell group as target cells, thereby scientifically and effectively associating a large number of suspected cells to form groups, thus facilitating subsequent data processing.

[0124] In an optional embodiment of the present invention, the step of calculating the signal strength distribution characteristic parameters and access frequency distribution characteristic parameters for the suspected cell based on the communication network access data includes:

[0125] Obtain suspected cell handover threshold parameters, and determine the threshold for adjacent access distance signal strength difference, access frequency distribution threshold, and maximum cell signal strength difference threshold.

[0126] The signal strength distribution characteristic parameters for the target cell are determined by using the average access reference signal received power (RSRP) value and the adjacent access distance signal strength difference threshold, or the maximum cell signal strength difference threshold.

[0127] The average number of accesses for the target cell is determined using the average number of accesses, the access distance, the average access reference signal received power (RSRP), and the handover threshold parameter.

[0128] The access frequency distribution characteristic parameters for the target cell are determined based on the mean access frequency and the access frequency distribution threshold.

[0129] In practical applications, communication network access data can include access distance, average access reference signal received power (RSRP), and average number of accesses. RSRP is a key parameter in LTE networks that represents the strength of the wireless signal and is one of the physical layer measurement requirements. It is the average signal power received on all REs (resource particles) carrying the reference signal within a certain symbol.

[0130] In specific implementations, suspected cell handover threshold parameters can be obtained to determine the adjacent access distance signal strength difference threshold, the access frequency distribution threshold, and the maximum cell signal strength difference threshold. The signal strength distribution characteristic parameters for the target cell are determined using the average access reference received power (RSRP) value and the adjacent access distance signal strength difference threshold, or the maximum cell signal strength difference threshold. The average number of accesses for the target cell is determined using the average number of accesses, access distance, average access reference received power (RSRP) value, and handover threshold parameters. Based on the average number of accesses and the access frequency distribution threshold, the access frequency distribution characteristic parameters for the target cell are determined. For example, when the target cell is X... i X i ∈T k At that time, it is possible to obtain information targeting X. i The community's access data is collected from an average of 7-9 AM daily during the statistical period. This period typically coincides with the morning rush hour, and selecting access data from this time effectively captures the data for X under conditions of maximum foot traffic. i Access data, for X i The access data may include the access distance mr_distance i,j (j is the access distance identifier; a larger j indicates a longer access distance), the corresponding average access reference signal received power (RSRP) value avg_scrsrp for access distance identifier j. i,j The average number of accesses corresponding to the access distance identifier j, access_nums i,j Simultaneously, the suspected cell handover threshold parameter RsrpThreshold can be obtained. iThe threshold values ​​are Threshold2 (adjacent access distance signal strength difference threshold) and Threshold4 (adjacent access number distribution threshold). Threshold2 is related to the signal frequency; the higher the frequency, the larger the value of Threshold2. For 4G networks, Threshold2 is usually between 3 and 4, and Threshold3 is usually between 2 and 5. The smaller the value, the more accurate it is; the larger the value, the greater the accuracy deviation. Threshold4 can be above -0.5. Of course, the above values ​​are only examples, and those skilled in the art can use other values ​​to participate in the calculation. This embodiment of the invention does not limit this.

[0131] Then, the formula for X can be used to determine... i Signal strength distribution characteristic parameters:

[0132] Formula 1:

[0133] and

[0134]

[0135] Formula 2:

[0136]

[0137]

[0138] Where, N i For X i The maximum access distance identifier for the corresponding cell.

[0139] If we are targeting community X i If the communication network access data conforms to Formula 1 or Formula 2 above, then for X i The signal strength distribution characteristic parameter is "consistent with the coverage cell of the target location".

[0140] Meanwhile, the following formula can be used for cell X i The average number of accesses (AvgAccess_nums) i Perform the calculation:

[0141] Formula 3:

[0142]

[0143] When k = 1, 2, ..., M i When the value is -1, calculate the average number of accesses (AvgAccess_nums) for each adjacent distance using the following formula. i The ratio:

[0144] Formula 4:

[0145] Avg2Ratio i,k =(access_nums i,k +access_nums i,k+1 ) / (2*AvgAccess_nums i )

[0146] Then, calculate the difference (Avg2Ratio). i,k -1), if (Avg2Ratio i,k The minimum value of -1 (Avg2Ratio) i,k -1) ≥ access frequency distribution threshold Threshold4, then for X i The access frequency distribution characteristic parameter is "consistent with the coverage cell of the target location".

[0147] This invention, through obtaining suspected cell handover threshold parameters, determines thresholds for adjacent access distance signal strength difference, access frequency distribution, and maximum cell signal strength difference. It uses the average access reference received power (RSRP) value and the adjacent access distance signal strength difference threshold, or the maximum cell signal strength difference threshold, to determine signal strength distribution characteristic parameters for the target cell. It uses the average access frequency value, the access distance value, the average access reference received power (RSRP) value, and the handover threshold parameters to determine the average number of accesses for the target cell. Finally, it determines the access frequency distribution characteristic parameters for the target cell based on the average number of accesses and the access frequency distribution threshold. This achieves correlation between the target cell and the coverage cell similarity at the target location based on communication network access data, further refining the coverage cell identification process and improving the accuracy of subsequent output results.

[0148] In an optional embodiment of the present invention, the step of determining the coverage cell for the target location from the suspected cells based on the signal strength distribution characteristic parameter and the access frequency distribution characteristic parameter includes:

[0149] Based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters, the subway tunnel-themed coverage cells are determined from the target cells;

[0150] Metro station platform cells and metro tunnel thematic cells were determined from multiple metro tunnel thematic coverage cells;

[0151] Obtain the list of indoor distributed antenna system (DAS) cell sources;

[0152] Remove the corresponding cells from the list of indoor distributed signal source cells in the subway station platform cells and the subway tunnel thematic cells to identify suspected subway underground commercial area cells;

[0153] Obtain data on the number of times the second communication network was switched for the suspected underground commercial area of ​​the subway.

[0154] Based on the second communication network handover count data, the indoor distribution signal source cell list, the subway station platform cell, and the suspected subway underground commercial area cell, a large subway underground commercial area cell is identified.

[0155] A set of large-scale underground commercial districts for subways is generated by using multiple such large-scale underground commercial districts;

[0156] Determine the threshold for the number of residential communities within a commercial district;

[0157] The set of underground commercial districts under the subway is determined based on the set of large-scale underground commercial districts and the threshold for the number of commercial districts.

[0158] The underground commercial districts of the subway will be used as coverage areas for the target location.

[0159] In practical applications, the main means of communication network coverage of subways is to use active distribution systems and lay leaky cables. Due to the large internal area and long tunnels of subways, multiple signal sources are allocated to compensate for the signal strength of each coverage area. As a result, there are multiple cell types in the subway. When terminals using the communication network switch between different cell types, the number of network handovers is generated. The number of network handovers reflects the correlation between different cell types to some extent.

[0160] For example, the suspected cell set A is known to include cell X. i Corresponding cell group T k When targeting community X i When both the signal strength distribution characteristic parameter and the access frequency distribution characteristic parameter are "consistent with the coverage cell of the target location", then cell X can be... i As a residential community covering subway tunnels, if X i ∈T k And X i In the community group T k If the proportion of the number of residential areas in the subway tunnel exceeds the threshold threshold of Threshold5, then T can also be... k As a group of cells covering subway tunnels. If the final analysis yields M groups of cells T covering subway tunnels. k (where k = 1, 2, ..., M), from each T kTwo end cells are identified. Each end cell can be a cell located at the edge of both ends of a cell group covering the subway tunnel. Each end cell corresponds to at most one end neighbor cell X with the largest number of handovers to its first communication network. k,r (where r = 1, 2 and ...) ), and count the T values ​​for each cell group. k The end neighbor region X k,r Using the end neighbor X k,r Generate a boundary neighbor group T, and count the neighboring cells X in T. k,r Based on the occurrence count, select the end neighboring cells that occur ≥ 2 times to generate the subway station platform cell set PlatformSet. If T k With T w All of these are community groups covered by the subway tunnel special topic, so T k，w Integrate into PlatformSet, where cells included in the PlatformSet (metro station platform cell set) can be used as metro station platform cells. Additionally, groups of metro tunnel cells with the same end neighbor cells can be merged into PlatformSet. k Merge the cells to generate a new cell group T covering the subway tunnel. e (e = 1, 2, ..., S), cell group T e The included cells are merged to generate a subway tunnel-themed cell set SubwaySet. Cells included in SubwaySet can be used as subway tunnel-themed cells. Then, the entire network indoor distribution source cell list set B is obtained as the indoor distribution source cell list set. Subway tunnel-themed cells corresponding to SubwaySet and subway platform cells corresponding to PlatformSet are deleted. Let e ​​= 1. If a subway platform cell X exists... i ∈ Metro station platform set PlatformSet, and X i Without analysis, X can be... i Integrate the network handover count data of all cells within the suspected underground commercial area of ​​the subway (Ce) as the second communication network handover count data, and select the cell X corresponding to the data where the second communication network handover count data is >0. n If X n If X is a list of all indoor distributed antenna system (DAS) cell sources in the entire network (B), then X will be... n Incorporate into the suspected subway underground commercial area cell Ce, and repeatedly perform the second communication network handover count data > 0 for all cells within the newly generated Ce, corresponding to cell X. n The selection, judgment, and incorporation process continues until the suspected underground commercial area community Ce does not have data corresponding to a second communication network handover count > 0. If such a community belongs to the entire network's indoor distribution signal source community list set B, then the subway station platform community X...i The analysis of the corresponding suspected underground commercial area Ce is completed. Let e ​​= e + 1, and repeat the steps of the e = 1 stage until “X” does not exist in the subway platform area set PlatformSet. i ∈ Metro station platform set PlatformSet, and X i When dealing with unanalyzed areas, if multiple suspected underground commercial districts (Ce) contain the same area, these suspected Ce areas are merged to determine a new underground commercial district set (Ce) as the large underground commercial district set. The corresponding area within this set is the large underground commercial district. Then, a threshold value (Threshold6) for the number of commercial district areas can be determined. Threshold6 can be any natural number greater than 4. This is merely an example; those skilled in the art can use other values ​​for calculation, and this embodiment of the invention does not impose limitations. At this point, the number of areas (n) contained in the large underground commercial district set (Ce) can be counted. If n > Threshold6, then the large underground commercial district set (Ce) can be used as the coverage area for the target location.

[0161] In this embodiment of the invention, the following steps are taken: First, a subway tunnel-specific coverage cell is determined from the target cell based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters. Second, subway station platform cells and subway tunnel-specific cells are determined from multiple subway tunnel-specific coverage cells. Third, a list of indoor distributed antenna system (DAS) source cells is obtained. Fourth, cells corresponding to the subway station platform cells and subway tunnel-specific cells are deleted from the indoor DAS source cell list to identify suspected subway underground commercial area cells. Fifth, second, communication network handover frequency data is obtained for the suspected subway underground commercial area cells. Sixth, based on the second, communication network handover frequency data and the... The system identifies large underground commercial area cells by using a list of indoor distributed antenna system (DAS) source cells, subway station platform cells, and suspected underground commercial area cells. It then generates a large underground commercial area cell set using multiple of these cells. A threshold for the number of commercial area cells is determined. Based on this large underground commercial area cell set and the threshold threshold, a subway underground commercial area cell set is determined. This set is then used as the coverage cell set for the target location. This is combined with the indoor DAS source cell list for layer-by-layer correlation analysis, making the coverage cell identification results for the target location more scientific and accurate.

[0162] In an optional embodiment of the present invention, before the step of determining the average number of accesses for the target cell using the average access count value, the access distance value, the average access reference received power (RSRP) value, and the handover threshold parameter, the method further includes:

[0163] Abnormal data is determined from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter.

[0164] Remove the abnormal data.

[0165] In the specific implementation, when the cell is X i Regarding X i The access data may include the access distance mr_distance i,j (j is the access distance identifier; the larger j is, the farther the access distance), corresponding to the average access reference signal received power (RSRP) value avg_scrsrp of the access distance identifier j. i,j Meanwhile, the suspected cell handover threshold parameter was found to be RsrpThreshold. i At that time, mr_distance can be used i,j The access data corresponding to the minimum value j is designated as abnormal data 1. Simultaneously, avg_scrsrp... i,j -RsrpThreshold i >0 and avg_scrsrp i,j -RsrpThreshold i The access data corresponding to j with the minimum value is designated as abnormal data 2, and abnormal data 1 and abnormal data 2 are deleted.

[0166] This invention identifies and removes abnormal data from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter. This process cleans and organizes the data, facilitating subsequent data processing, reducing interference from abnormal data, and decreasing the computational load, thereby improving the accuracy and efficiency of coverage cell identification.

[0167] To enable those skilled in the art to better understand the embodiments of the present invention, a complete example is used below to illustrate the embodiments of the present invention.

[0168] With economic development, the construction of subways in major and medium-sized cities has also developed rapidly. As a traffic entrance for the real economy, subway stations have gradually been developed and recognized by the market. Various large shopping malls, markets, restaurant streets and so on have been built in the underground extension areas adjacent to some subway stations, forming new underground commercial circles and becoming one of the main driving forces for regional consumption. In order to better carry out the maintenance and optimization of mobile communication networks in subways and their adjacent underground commercial circles and improve user experience, a method for identifying coverage cells for target locations is needed to improve the accuracy and efficiency of coverage cell identification for target locations.

[0169] refer to Figure 2 , Figure 2 This is a flowchart of another method for identifying coverage cells at a target location provided in an embodiment of the present invention;

[0170] To achieve the above objectives, the present invention provides the following technical solution:

[0171] Step S001: By analyzing the distribution of handover frequency of cell handover pairs in the mobile communication network, output a set of cells A suspected to cover the subway tunnel. Utilize the relationship between neighboring cell handover pairs to group the cells in set A into several cell groups T. k .

[0172] Specifically, handover count data between cells in the mobile communication network can be extracted within a statistical period, the average daily handover count between cell pairs can be calculated, and a mobile communication network cell handover pair data table can be generated. The table's fields must include at least the cell's unique identifier X. i X j (i≠j) and cell handover pair X i,j Average daily number of SwitchTimes i,j (where SwitchTimes) i,j >0);

[0173] Take X i For cell handover pairs with a daily average handover count greater than zero, sort them from largest to smallest daily average handover count, and calculate the percentage of the sum of the handover counts of the two cells with the highest daily average handover counts relative to the total number of handover pairs for cell X. i The percentage of total cell handovers, if greater than or equal to the centralized handover threshold Threshold1 (a suggested value of 90% or higher), then X will be... i The residential community was incorporated into the special report covering community group A, which is suspected to be a subway tunnel.

[0174] The cells in cell set A, which are suspected to be covered by the subway tunnel topic, are grouped into groups. The grouping steps are as follows:

[0175] S0011: If X exists in cell set A, which is suspected to be a subway tunnel,... i ∈A and X i If not yet grouped, then X i Incorporated into community group T k That is, T k ={X i}, with values ​​n=1 and m=1, jump to execute S0012; if all cells in the suspected subway tunnel topic coverage cell set A have been grouped, then jump to execute step four;

[0176] S0012: Get cell group Tk The other neighboring cell X with a high number of handovers on the left end of the cell i-n (in );

[0177] If X i-n If ∈A and not grouped, then X i-n Incorporated into community group T k On the left, n = n + 1, jump to execute S0012:;

[0178] like Then the cell group T k The left-end cell analysis is complete; proceed to execute S0013.

[0179] If X i-n ∈A and X i-n It has been merged into T w Group, then X i-n For T k Group and T w The public community of the group is denoted as T. k,w If the analysis of the left-hand neighboring region is complete, proceed to execute S0013.

[0180] S0013: Get cell group T k X, another neighboring cell with a high number of handovers on the right end of the cell. i+m (in ):

[0181] If X i+m If ∈A and not grouped, then X i+m Incorporated into community group T k On the right, m ​​= m + 1, jump to execute S0013;

[0182] like Then the cell group T k Once the analysis of the right-end cell is complete, k = k + 1, and proceed to execute S0011;

[0183] If X i+m ∈A and X i+m It has been merged into T v Group, then X i+m For T k Group and T v The public community of the group is denoted as T. k,v Once the analysis of the right-hand neighboring region is complete, k = k + 1, and the process jumps to execute S0011.

[0184] Step S002: For cell group T k The signal distribution characteristics of the medium-sized cells are analyzed to determine whether the cell signal distribution characteristics are consistent with the subway tunnel scenario.

[0185] Specifically, X can be taken. i ∈Tk Get X i The average access data for the community from 7:00 to 9:00 AM daily within the statistical period includes the community's unique identifier X. i mr_distance i,j (Access distance, where j is the access distance identifier; a larger value for j indicates a longer access distance), avg_scrsrp i,j (represents the average RSRP value corresponding to access distance identifier j), access_nums i,j (Indicates the average number of accesses corresponding to access distance identifier j), etc., to obtain X. i Cell handover threshold parameter RsrpThreshold i ;

[0186] Then, for X i Cell signal distribution characteristic analysis: X is considered to be satisfied if one of the following two conditions is met. i The cell signal distribution characteristics are consistent with those of a subway tunnel coverage scenario:

[0187] (1) Calculate X i The difference in signal strength received by a cell at any two adjacent distances, if satisfying the following relationship:

[0188] (where N) i For X i (Maximum access distance identifier for the cell), if the following conditions are met:

[0189] and

[0190] The threshold value of the signal strength difference between adjacent access distances, Threshold2, is related to the signal frequency. The higher the frequency, the larger the value of Threshold2. For 4G networks, Threshold2 is usually between 3 and 4.

[0191] (2) Calculate X i The difference between the maximum and minimum average signal strength at different access distances within the cell is less than or equal to the threshold value of signal strength difference between adjacent access distances (Threshold3).

[0192] Right now like Threshold3 takes a value greater than 0. Big data analysis results show that Threshold3 is usually between 2 and 5. The smaller the value, the more accurate it is, and the larger the value, the greater the accuracy deviation.

[0193] Step S003: For cell group T kThe distribution characteristics of access frequency in medium-sized cells are analyzed to determine whether the distribution characteristics of access frequency in medium-sized cells are consistent with the subway tunnel scenario.

[0194] Specifically, it is possible to target X i Analysis of the distribution characteristics of cell access frequency, first removing X i For low-probability or abnormal data in the cell, the specific deletion method is as follows: delete the access data corresponding to the minimum access distance, and delete avg_scrsrp. i,j -RsrpThreshold i >0 and avg_scrsrp i,j -RsrpThreshold i The access data corresponding to the minimum value, for mobile communication network X i The cell access data information is sorted in ascending order of access distance, and assigned sorting numbers Key1 = 1, 2, ..., M. i ;

[0195] Calculate X i The formula for the average number of cell access attempts is as follows:

[0196]

[0197] When k = 1, 2, ..., M i When -1, calculate the average of the two access counts for each adjacent distance and AvgAccess_nums. i The ratio;

[0198] Right now:

[0199] Avg2Ratio i,k =(access_nums i,k +access_nums i,k+1 ) / (2*AvgAccess_nums i )

[0200] Calculate the difference (Avg2Ratio) i,k -1), if (Avg2Ratio i,k -1) The minimum value is greater than or equal to the access frequency distribution threshold Threshold4, where Threshold4 can take values ​​greater than -0.5, then X i The distribution characteristics of cell access frequency are consistent with those of the subway tunnel coverage scenario.

[0201] Step S004: Cover the subway tunnel topic cell group T k End-neighbor cell analysis outputs the subway tunnel thematic coverage cell set SubwaySet and the subway station platform cell set PlatformSet;

[0202] Specifically, if i If the cell signal distribution characteristics and access frequency distribution characteristics are consistent with the scenario of covering a subway tunnel, then X i The community is a community covered by a subway tunnel. If the community group T k If the proportion of cells containing coverage areas of subway tunnels exceeds the threshold threshold of 5, then the cell group T is considered to be... k The special feature on subway tunnels covers residential communities;

[0203] Assuming that M thematic coverage cell groups for subway tunnels are ultimately obtained, namely T k (where k = 1, 2, ..., M), each T k There are two end cells, and each end cell corresponds to at most one end neighbor cell X with the highest number of handovers. k,r (where r = 1, 2 and ...) ), and group all the cell groups T k The end neighbor region X k,r Place them in the boundary neighbor group T, count the frequency of each cell in T, and take the neighbor cells that appear more than or equal to 2 times, then add them to the subway station platform cell set PlatformSet; if T k With T v All of these are community groups covered by the subway tunnel special topic, so T k,v Integrate into the subway station platform cell set PlatformSet; simultaneously, group cells T with the same end neighbor cells will be merged. k Merge the data to generate a new subway-themed coverage area group T. e (e=1,2,…,S), group T of cells e The merged communities yielded the SubwaySet, a thematic coverage set for subway tunnels.

[0204] Step S005: Combine the indoor distribution system's information source list with data analysis of the handover pairs of each cell in the subway station platform cell set (PlatformSet), and output the suspected subway underground commercial area cell set C. e Includes a list of residential communities, outputting the final set of communities within the subway underground commercial area, C. e .

[0205] Specifically, the analysis of potential underground commercial area cell sets in subway stations involves obtaining a list of indoor distributed antenna system (DAS) cell sets B across the entire network, deleting cells belonging to SubwaySet and PlatformSet, and setting the value e=1. The algorithm implementation steps for the potential underground commercial area cell set in subway stations can be as follows:

[0206] S0051: If platform area X exists i ∈PlatformSet and X i No analysis was performed on X.i Incorporated into the suspected underground commercial area of ​​the subway, community group C e If it does not exist, proceed to step S0053.

[0207] S0052: Obtaining and C e Cell X has a handover count greater than zero in all cells. n If X exists n ∈B, then X n Merged into C e If it does not exist, proceed to step S0052; otherwise, proceed to platform cell X. i The corresponding C e Analysis complete, e = e + 1, jump to execute S0051;

[0208] S0053: If different suspected underground commercial areas of subway stations are clustered together... e If the same residential area is included, the commercial area residential area set will be merged to generate a new large-scale subway underground commercial area residential area set C. e ;

[0209] S0054: Suspected underground commercial area near subway station, residential complex C e The more communities a subway underground commercial area contains, the larger its potential scale. When the number of communities it contains exceeds the threshold of 6 for the number of communities in a subway underground commercial area, it can be considered as a set of communities in a subway underground commercial area (usually, Threshold6 can be a natural number of 4 or higher).

[0210] This invention eliminates the need for manual maintenance of subway tunnel cell ledgers. It directly analyzes data, cell access signal strength, and access frequency distribution characteristics through mobile communication network cell handover, outputting a thematic coverage cell set for subway tunnels and a subway platform cell set. This significantly reduces manual maintenance workload and greatly supports daily network maintenance and optimization. Furthermore, in the context of mobile communication network maintenance and optimization gradually entering the era of big data and artificial intelligence, traditional manual maintenance and management of subway tunnel cell ledger information is no longer adequate. This invention, through cell group T... k The identification and correlation analysis of neighboring cells successfully further subdivided subway scene cells into the subway tunnel-themed coverage cell set SubwaySet and the subway platform cell set PlatformSet, enabling refined management of subway scene cells and better meeting the needs of big data and artificial intelligence optimization. Furthermore, considering the characteristics of the subway underground commercial area scenario, by analyzing the handover relationships between each cell in the subway platform cell set PlatformSet and its neighboring cells, combined with the correlation analysis of indoor distributed antenna system (DAS) source cells, the subway underground commercial area cell set C is output. e This provides strong support for the optimization of the underground commercial area network, through the C-level cluster of communities within the underground commercial area of ​​the subway.e This study aims to provide value-added service revenue opportunities for the commercial market, possessing strong potential commercial value. Through analysis of data distribution characteristics, signal strength distribution characteristics, and access frequency distribution characteristics of mobile communication network cell handover, a method for identifying coverage cells in subway tunnels is developed. Based on the neighboring cells of the subway tunnel coverage cell group, the system accurately outputs the subway platform cell set (PlatformSet) and the subway tunnel coverage cell set (SubwaySet). Combining the handover relationships between each cell in the PlatformSet and its neighboring cells, and integrating the correlation analysis of indoor distributed antenna system (DAS) source cells, the system outputs the underground commercial area cell set (C). e .

[0211] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0212] Reference Figure 3 The diagram illustrates a structural block diagram of a coverage cell identification device for a target location provided in an embodiment of the present invention, which may specifically include the following modules:

[0213] Communication network handover count data acquisition device 301 is used to determine an initial cell and acquire first communication network handover count data for the initial cell;

[0214] The suspected cell determination module 302 is used to determine suspected cells based on the handover count data of the first communication network.

[0215] The communication network access data acquisition module 303 is used to acquire communication network access data for the suspected cell;

[0216] Parameter calculation module 304 is used to calculate the signal strength distribution characteristic parameters and access frequency distribution characteristic parameters for the suspected cell based on the communication network access data;

[0217] The coverage cell determination module 305 is used to determine the coverage cell for the target location from the suspected cells based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters.

[0218] Optionally, the first communication network handover count data includes the average daily handover count data of the communication network, and the suspected cell determination module may include:

[0219] The sum-to-total value calculation submodule is used to calculate the sum of communication network handover times and the total number of communication network handover times for the initial cell based on the average daily handover times data of the communication network;

[0220] The centralized handover threshold determination submodule is used to determine the centralized handover threshold.

[0221] The suspected cell determination submodule is used to determine suspected cells based on the number and value of communication network handovers, the total number of communication network handovers, and the centralized handover threshold.

[0222] Optionally, it may also include:

[0223] The suspected cell set construction submodule is used to construct a suspected cell set containing multiple of the suspected cells;

[0224] The first cell selection submodule is used to select multiple first cells from the set of suspected cells;

[0225] The initial cell group determination submodule is used to determine an initial cell group based on multiple first cells; the initial cell group has corresponding left-end cells and right-end cells;

[0226] The left-end neighbor cell determination submodule is used to determine the left-end neighbor cell based on the left-end cell and the handover count data corresponding to the left-end cell;

[0227] The right-end neighbor cell determination submodule is used to determine the right-end neighbor cell based on the right-end cell and the handover count data corresponding to the right-end cell;

[0228] The final cell group determination submodule is used to determine the final cell group using the left-end neighboring cells and the right-end neighboring cells;

[0229] The cells belonging to the final cell group are designated as target cells.

[0230] Optionally, the communication network access data includes access distance, average access reference signal received power (RSRP), and average number of accesses. The parameter calculation module may include:

[0231] The threshold determination submodule is used to obtain the suspected cell handover threshold parameters and determine the threshold threshold for the signal strength difference between adjacent access distances, the threshold threshold for the distribution of access times, and the maximum threshold threshold for the cell signal strength difference.

[0232] The signal strength distribution characteristic parameter determination submodule is used to determine the signal strength distribution characteristic parameters for the target cell using the average access reference signal received power (RSRP) value and the adjacent access distance signal strength difference threshold, or the maximum cell signal strength difference threshold.

[0233] The average number of accesses determination submodule is used to determine the average number of accesses for the target cell using the average number of accesses value, the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter.

[0234] The access frequency distribution feature parameter determination submodule is used to determine the access frequency distribution feature parameters for the target cell based on the average access frequency and the access frequency distribution threshold.

[0235] Optionally, the coverage cell determination module may include:

[0236] The subway tunnel-themed coverage cell determination submodule is used to determine the subway tunnel-themed coverage cells from the target cells based on the signal strength distribution characteristic parameters and the access frequency distribution characteristic parameters;

[0237] The subway cell determination submodule is used to determine subway station platform cells and subway tunnel thematic cells from multiple subway tunnel thematic coverage cells;

[0238] The indoor distributed antenna system (DAS) source cell list acquisition submodule is used to acquire the indoor DAS source cell list;

[0239] The "Subway Underground Commercial Area Community Determination Submodule" is used to delete the corresponding communities in the subway station platform communities and the subway tunnel thematic communities from the indoor distributed signal source community list set, and determine the suspected subway underground commercial area communities.

[0240] The second communication network switching count data acquisition submodule is used to acquire the second communication network switching count data for the suspected underground commercial area of ​​the subway.

[0241] The large-scale subway underground commercial area identification submodule is used to identify large-scale subway underground commercial area communities based on the second communication network handover count data, the indoor distributed signal source cell list set, the subway station platform cells, and the suspected subway underground commercial area communities.

[0242] A sub-module for generating large-scale underground commercial districts within subway stations is used to generate a large-scale underground commercial district set from multiple such large-scale underground commercial districts.

[0243] The "Threshold Determination for the Number of Residential Communities in a Commercial Area" submodule is used to determine the threshold for the number of residential communities in a commercial area.

[0244] The "Subway Underground Commercial Area Set Determination Submodule" is used to determine the subway underground commercial area set based on the large subway underground commercial area set and the threshold for the number of commercial area units.

[0245] The coverage cell determination submodule is used to select the set of underground commercial districts in the subway as coverage cells for the target location.

[0246] Optionally, it may also include:

[0247] An abnormal data determination submodule is used to determine abnormal data from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter.

[0248] The abnormal data removal submodule is used to remove the abnormal data.

[0249] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0250] In addition, this invention also provides 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 various processes of the above-described embodiments of the coverage cell identification method for a target location and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0251] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described embodiments of the coverage cell identification method for a target location, achieving the same technical effects. To avoid repetition, these details are not repeated here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0252] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0253] The electronic device 400 includes, but is not limited to, components such as: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will understand that... Figure 4The electronic device structures shown are not intended to limit the electronic device. An electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. In embodiments of the present invention, the electronic device includes, but is not limited to, mobile phones, tablet computers, laptops, PDAs, in-vehicle terminals, wearable devices, and pedometers.

[0254] It should be understood that, in this embodiment of the invention, the radio frequency unit 401 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink data from the base station and processes it with the processor 410; additionally, it transmits uplink data to the base station. Typically, the radio frequency unit 401 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc. Furthermore, the radio frequency unit 401 can also communicate with networks and other devices through a wireless communication system.

[0255] The electronic device provides users with wireless broadband internet access through network module 402, such as helping users send and receive emails, browse web pages, and access streaming media.

[0256] The audio output unit 403 can convert audio data received by the radio frequency unit 401 or the network module 402 or stored in the memory 409 into audio signals and output them as sound. Furthermore, the audio output unit 403 can also provide audio output related to specific functions performed by the electronic device 400 (e.g., call signal reception sound, message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, and a receiver, etc.

[0257] Input unit 404 is used to receive audio or video signals. Input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042. The GPU 4041 processes image data of still images or videos acquired by an image capture device (such as a camera) in video capture mode or image capture mode. The processed image frames can be displayed on display unit 406. The image frames processed by GPU 4041 can be stored in memory 409 (or other storage medium) or transmitted via radio frequency unit 401 or network module 402. Microphone 4042 can receive sound and process such sound into audio data. The processed audio data can be converted into a format that can be transmitted to a mobile communication base station via radio frequency unit 401 in telephone call mode.

[0258] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 4061 according to the ambient light level, and the proximity sensor can turn off the display panel 4061 and / or backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used to identify the posture of the electronic device (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. The sensor 405 may also include a fingerprint sensor, pressure sensor, iris sensor, molecular sensor, gyroscope, barometer, hygrometer, thermometer, infrared sensor, etc., which will not be described in detail here.

[0259] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0260] User input unit 407 can be used to receive input numerical or character information, and generate key signal inputs related to user settings and function control of electronic devices. Specifically, user input unit 407 includes a touch panel 4071 and other input devices 4072. Touch panel 4071, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near touch panel 4071). Touch panel 4071 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 410, which receives and executes commands from the processor 410. In addition, touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. Besides touch panel 4071, user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here.

[0261] Furthermore, the touch panel 4071 can cover the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it transmits the information to the processor 410 to determine the type of touch event. Subsequently, the processor 410 provides corresponding visual output on the display panel 4061 based on the type of touch event. Although in Figure 4 In this embodiment, the touch panel 4071 and the display panel 4061 are two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device. The specific implementation is not limited here.

[0262] Interface unit 408 serves as an interface for connecting external devices to electronic device 400. For example, external devices may include a wired or wireless headphone port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, and so on. Interface unit 408 can be used to receive input from external devices (e.g., data, power, etc.) and transmit the received input to one or more components within electronic device 400, or it can be used to transmit data between electronic device 400 and external devices.

[0263] The memory 409 can be used to store software programs and various data. The memory 409 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback, image playback, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory 409 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0264] The processor 410 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 409, and by calling data stored in the memory 409, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 410.

[0265] The electronic device 400 may also include a power supply 411 (such as a battery) for supplying power to various components. Preferably, the power supply 411 can be logically connected to the processor 410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system.

[0266] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0267] It should be noted that, in this document, 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. Unless otherwise specified, 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 that element.

[0268] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0269] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of the present invention.

[0270] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0271] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0272] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0273] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0274] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0275] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0276] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for identifying coverage cells at a target location, characterized in that, include: Determine the initial cell and obtain the first communication network handover count data for the initial cell; The target location is a subway station, and the initial residential area is a residential area along the subway line; When the initial cell is and X j i≠j, and cell handover is correct. The average number of daily switches is At that time, take and give Cell handover In the community For cells with a value >0, based on the average daily handover frequency... Sort by largest to smallest, and take the average daily number of switches. The two largest X i,j The corresponding daily average number of switches Add them together to get the sum of the number of handovers in the communication network, and use this sum to represent the handover pairs of all cells. Average daily switching frequency The sums are used as the total number of handovers in the communication network. Simultaneously, a centralized handover threshold is determined. Calculate the ratio of the sum of communication network handover counts to the total number of communication network handover counts, and determine if this ratio is greater than or equal to the centralized handover threshold. Then the community As a suspected residential community; Determine the centralized handover threshold; Construct a suspected cell set that includes multiple of the suspected cells; Select multiple first cells from the set of suspected cells; An initial cell group is determined based on multiple first cells, and the left-end cell and right-end cell of the initial cell group are determined; The neighboring cells with the highest number of handovers with the left-end cell and the right-end cell, respectively, are selected as the left-end neighboring cells and the right-end neighboring cells. The left-end neighboring cells and the right-end neighboring cells are then merged into the initial cell group. Based on the left-end cells and the right-end cells of the merged initial cell group, new left-end neighboring cells and right-end neighboring cells are determined and merged into the initial cell group again. This process continues until the left-end neighboring cells and right-end neighboring cells have been merged into another initial cell group or are no longer in the suspected cell set, thus obtaining the final cell group. The cells belonging to the final cell group are designated as target cells; Obtain communication network access data for the target cell; The communication network access data includes access distance value, average access reference signal received power (RSRP) value, and average number of accesses value. Obtain suspected cell handover threshold parameters, and determine the threshold for adjacent access distance signal strength difference, access frequency distribution threshold, and maximum cell signal strength difference threshold. Based on communication network access data, the signal strength distribution characteristics and access frequency characteristics of the target cell are determined. It is then determined whether both the strength distribution characteristics and the access frequency characteristics of the target cell conform to the subway tunnel scenario. If both conform, the target cell is identified as a subway tunnel-specific coverage cell. Metro station platform cells and metro tunnel thematic cells were determined from multiple metro tunnel thematic coverage cells; Obtain the list of indoor distributed antenna system (DAS) cell sources; Remove the corresponding cells from the list of indoor distributed signal source cells in the subway station platform cells and the subway tunnel special cells to identify suspected subway underground commercial area cells; Obtain data on the number of times the second communication network was switched for the suspected underground commercial area of ​​the subway. Based on the second communication network handover count data, the indoor distribution signal source cell list, the subway station platform cell, and the suspected subway underground commercial area cell, a large subway underground commercial area cell is identified. A set of large-scale underground commercial districts for subways is generated by using multiple such large-scale underground commercial districts; Determine the threshold for the number of residential communities within a commercial district; The set of underground commercial districts under the subway is determined based on the set of large-scale underground commercial districts and the threshold for the number of commercial districts. The underground commercial districts of the subway will be used as coverage areas for the target location.

2. The method according to claim 1, characterized in that, Before the step of determining the average number of accesses for the target cell using the average number of accesses, the access distance, the average access reference received power (RSRP), and the handover threshold parameter, the method further includes: Abnormal data is determined from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter. Remove the abnormal data.

3. A coverage cell identification device for a target location, characterized in that, include: A communication network handover count data acquisition device is used to determine an initial cell and acquire first communication network handover count data for the initial cell; The target location is a subway station, and the initial residential area is a residential area along the subway line; The suspected cell determination module is used when the initial cell is and X j i≠j, and cell handover is correct. The average number of daily switches is At that time, take and give Cell handover In the community For cells with a value >0, based on the average daily handover frequency... Sort by largest to smallest, and take the average daily number of switches. The two largest X i,j The corresponding daily average number of switches Add them together to get the sum of the number of handovers in the communication network, and use this sum to represent the handover pairs of all cells. Average daily switching frequency The sums are used as the total number of handovers in the communication network. Simultaneously, a centralized handover threshold is determined. Calculate the ratio of the sum of communication network handover counts to the total number of communication network handover counts, and determine if this ratio is greater than or equal to the centralized handover threshold. Then the community As a suspected residential community; The centralized handover threshold determination submodule is used to determine the centralized handover threshold. The suspected cell set construction submodule is used to construct a suspected cell set containing multiple of the suspected cells; The first cell selection submodule is used to select multiple first cells from the set of suspected cells; The final cell group determination submodule is used to construct a set of suspected cells that includes multiple of the suspected cells; Multiple first cells are selected from the suspected cell set; an initial cell group is determined based on the multiple first cells, and the left-end cell and right-end cell of the initial cell group are determined; the neighboring cell with the largest number of handovers with the left-end cell and right-end cell respectively is selected as the left-end neighboring cell and right-end neighboring cell, and the left-end neighboring cell and right-end neighboring cell are merged into the initial cell group. Based on the left-end cell and right-end cell of the merged initial cell group, new left-end neighboring cells and right-end neighboring cells are determined and merged into the initial cell group again, until the left-end neighboring cells and right-end neighboring cells have been merged into another initial cell group or are no longer in the suspected cell set, thereby obtaining the final cell group; the cell belonging to the final cell group is designated as the target cell. The communication network access data acquisition module is used to acquire communication network access data for the target cell. The communication network access data includes access distance value, average access reference signal received power (RSRP) value, and average number of accesses value. The threshold determination submodule is used to obtain the suspected cell handover threshold parameters and determine the threshold threshold for the signal strength difference between adjacent access distances, the threshold threshold for the distribution of access times, and the maximum threshold threshold for the cell signal strength difference. The "Metro Tunnel Thematic Coverage Cell Determination Submodule" is used to determine the signal strength distribution characteristics and access frequency characteristics of the target cell based on communication network access data. It determines whether both the strength distribution characteristics and the access frequency characteristics of the target cell conform to the metro tunnel scenario. When both conform, the target cell is determined as the metro tunnel thematic coverage cell. The subway cell determination submodule is used to determine subway station platform cells and subway tunnel thematic cells from multiple subway tunnel thematic coverage cells; The indoor distributed antenna system (DAS) source cell list acquisition submodule is used to acquire the indoor DAS source cell list; The "Subway Underground Commercial Area Community Determination Submodule" is used to delete the corresponding communities in the subway station platform communities and the subway tunnel thematic communities from the indoor distributed signal source community list set, and determine the suspected subway underground commercial area communities. The second communication network switching count data acquisition submodule is used to acquire the second communication network switching count data for the suspected underground commercial area of ​​the subway. The large-scale subway underground commercial area identification submodule is used to identify large-scale subway underground commercial area communities based on the second communication network handover count data, the indoor distributed signal source cell list set, the subway station platform cells, and the suspected subway underground commercial area communities. A sub-module for generating large-scale underground commercial districts within subway stations is used to generate a large-scale underground commercial district set from multiple such large-scale underground commercial districts. The "Threshold Determination for the Number of Residential Communities in a Commercial Area" submodule is used to determine the threshold for the number of residential communities in a commercial area. The "Subway Underground Commercial Area Set Determination Submodule" is used to determine the subway underground commercial area set based on the large subway underground commercial area set and the threshold for the number of commercial area units. The coverage cell determination submodule is used to select the set of underground commercial districts in the subway as coverage cells for the target location.

4. The apparatus according to claim 3, characterized in that, Also includes: An abnormal data determination submodule is used to determine abnormal data from the communication network access data based on the access distance value, the average access reference signal received power (RSRP) value, and the handover threshold parameter. The abnormal data removal submodule is used to remove the abnormal data.

5. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes a program stored in the memory, it implements the method as described in any one of claims 1-2.

6. A computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the method as described in any one of claims 1-2.

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