Method and device for determining complaint problem of mobile communication network and storage medium

Abstract

The invention provides a method and a device for determining a complaint problem of a mobile communication network and a storage medium, relates to the technical field of communication, and is used for solving the problem of how to determine a network problem. The method comprises: obtaining network problems of a plurality of candidate cells and a target factor of each candidate cell, the target factor being used for reflecting an association degree between the candidate cell and a user complaint; and for each candidate cell, determining a target score of the candidate cell based on the target factor of the candidate cell to obtain the target score of each candidate cell, the target score being used for indicating the probability that the candidate cell is a complained cell. And determining a complained cell and a network problem of the complained cell from the plurality of candidate cells based on the target score of each candidate cell and the network problem of each candidate cell.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device and storage medium for determining complaint issues in a mobile communication network. Background Art

[0002] How to identify network problems by analyzing network problem complaints is a complex process. In the traditional workflow for identifying network problems, it is difficult to determine the real cause of network problems. For example, it is necessary to conduct on-site testing first to simulate user complaint scenarios. After that, it is necessary to find the problem by checking the statistical data of various indicators in the background and detailed signaling records. Finally, targeted testing is required to verify the improvement. Due to the complex workflow for identifying network problems, complaint handlers need to spend a lot of time in the stages of discovering and finding problems.

[0003] Therefore, how to identify network problems becomes a technical issue that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method, device and storage medium for determining complaint problems in a mobile communication network, which are used to solve the problem of how to determine network problems.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a method for determining complaint problems in a mobile communication network. In the method, network problems of multiple candidate cells and a target factor of each candidate cell are obtained, and the target factor is used to reflect the degree of correlation between the candidate cell and the user complaint. For each candidate cell, a target score of the candidate cell is determined based on the target factor of the candidate cell to obtain a target score for each candidate cell, and the target score is used to indicate the probability that the candidate cell is a complained cell. Based on the target score of each candidate cell and the network problem of each candidate cell, a complained cell and the network problem of the complained cell are determined from multiple candidate cells.

[0007] Based on the above technical solution, the network problems of multiple candidate cells and the target factor of each candidate cell are obtained. In this way, the candidate cells can be associated with user complaints. For each candidate cell, the target score of the candidate cell is determined based on the target factor of the candidate cell to obtain the target score of each candidate cell. By obtaining the target score of each candidate cell, the probability that each candidate cell is a complained cell can be obtained. Afterwards, based on the target score of each candidate cell and the network problems of each candidate cell, the complained cell and the network problems of the complained cell are determined from multiple candidate cells. In this way, by sorting the target score of each candidate cell and obtaining the cell with the highest probability of being complained, the source of the problem can be accurately located and the accuracy of network problem analysis can be improved. Furthermore, it can reduce the troubleshooting time and improve the efficiency of network problem analysis.

[0008] In a possible design, network problems of multiple candidate cells and a target factor of each candidate cell are obtained, and the target factor is used to reflect the degree of correlation between the candidate cell and the user complaint. For each candidate cell, a target score of the candidate cell is determined based on the target factor of the candidate cell to obtain a target score of each candidate cell, and the target score is used to indicate the probability that the candidate cell is a complained cell. Based on the target score of each candidate cell and the network problem of each candidate cell, a complained cell and the network problem of the complained cell are determined from the multiple candidate cells.

[0009] In one possible design, the target factor includes at least one of the following: a distance factor, a dwell time factor, a time consistency factor, a cell abnormal frequency factor, a sampling point factor, and a weak coverage grid factor. Based on each target factor of the candidate cell, a score corresponding to each target factor is determined. Based on the score corresponding to each target factor and the weight value of each target factor, a target score of the candidate cell is determined.

[0010] In a possible design, the user's complaint time and complaint location are obtained, and multiple candidate cells are determined based on the complaint time and complaint location.

[0011] In one possible design, based on the complaint time, multiple first cells are determined, where the first cells are cells that the user passes through within a target time period, and the target time period is related to the complaint time. Based on the complaint location, multiple second cells are determined, where the second cells are cells within a preset distance threshold from the complaint location. Multiple candidate cells are determined, where the candidate cells are cells that have intersections among the multiple first cells and the multiple second cells.

[0012] In a possible design, at least one alarm data of the candidate cell is obtained, and one alarm data corresponds to an alarm time period. For each alarm data, if the user's complaint time is within the alarm time period corresponding to the alarm data, the alarm data is regarded as a network problem of the candidate cell.

[0013] In a possible design, if the alarm time period corresponding to at least one alarm data does not include the complaint time, the network indicator of the candidate cell is obtained. Based on the network indicator of the candidate cell, the network problem of the candidate cell is determined.

[0014] In a second aspect, the present application provides a device for determining complaint issues in a mobile communication network, the device comprising an acquisition module and a processing module.

[0015] The acquisition module is used to acquire network problems of multiple candidate cells and a target factor of each candidate cell, where the target factor is used to reflect the degree of association between the candidate cell and user complaints. The processing module is used to determine a target score of each candidate cell based on the target factor of the candidate cell to obtain a target score of each candidate cell. The target score is used to indicate the probability that the candidate cell is a complained cell. The processing module is also used to determine a complained cell and a network problem of the complained cell from multiple candidate cells based on the target score of each candidate cell and the network problem of each candidate cell.

[0016] In one possible design, the target factor includes at least one of the following: a distance factor, a dwell time factor, a time consistency factor, a cell abnormality frequency factor, a sampling point factor, and a weak coverage grid factor. The processing module is used to determine the score corresponding to each target factor based on each target factor of the candidate cell. The processing module is also used to determine the target score of the candidate cell based on the score corresponding to each target factor and the weight value of each target factor.

[0017] In a possible design, the acquisition module is used to obtain the user's complaint time and complaint location. The processing module is used to determine multiple candidate cells based on the complaint time and complaint location.

[0018] In one possible design, the processing module is used to determine multiple first cells based on the complaint time, where the first cells are cells that the user passes through within a target time period, and the target time period is related to the complaint time. The processing module is also used to determine multiple second cells based on the complaint location, where the second cells are cells within a preset distance threshold from the complaint location. The processing module is also used to determine multiple candidate cells, where the candidate cells are cells that have intersections among the multiple first cells and the multiple second cells.

[0019] In a possible design, the acquisition module is used to acquire at least one alarm data of the candidate cell, and one alarm data corresponds to an alarm time period. The processing module is used to, for each alarm data, if the user's complaint time is within the alarm time period corresponding to the alarm data, then regard the alarm data as a network problem of the candidate cell.

[0020] In one possible design, the acquisition module is used to acquire the network indicator of the candidate cell if the alarm time period corresponding to at least one alarm data does not include the complaint time. The processing module is used to determine the network problem of the candidate cell based on the network indicator of the candidate cell.

[0021] In a third aspect, the present application provides a device for determining a complaint problem in a mobile communication network, the device comprising: a processor and a memory; the processor and the memory are coupled; the memory is used to store one or more programs, the one or more programs comprising computer execution instructions, and when the device for determining a complaint problem in a mobile communication network is running, the processor executes the computer execution instructions stored in the memory to implement the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed on a computer, the computer executes the method described in the above-mentioned first aspect and any possible implementation of the first aspect.

[0023] In a fifth aspect, the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run a computer program or instructions to implement the method described in the first aspect and any possible implementation of the first aspect.

[0024] In a sixth aspect, the present application provides a computer program product comprising instructions, which, when executed by a computer, enables the computer to execute the method described in the above-mentioned first aspect and any possible implementation manner of the first aspect.

[0025] In the above scheme, the technical problems that can be solved and the technical effects achieved by the device for determining mobile communication network complaint problems, computer equipment, computer storage media, chips or computer program products can be referred to the technical problems and technical effects solved by the above-mentioned first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for determining a mobile communication network complaint problem provided in an embodiment of the present application;

[0027] Figure 2A schematic diagram of an example of scene division provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of another example of scene division provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of an example of a method for determining a grid radius provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of an example of a cell grid distribution provided in an embodiment of the present application;

[0031] Figure 6 A schematic diagram of another example of cell grid distribution provided in an embodiment of the present application;

[0032] Figure 7 A schematic diagram of another example of cell grid distribution provided in an embodiment of the present application;

[0033] Figure 8 A flowchart of another method for determining a mobile communication network complaint problem provided in an embodiment of the present application;

[0034] Fig. 9 A schematic diagram of an example of a method for determining a complained cell provided in an embodiment of the present application;

[0035] Fig.10 A schematic diagram of another example of a method for determining a complained cell provided in an embodiment of the present application;

[0036] Fig.11 A schematic diagram of another example of a method for determining a complained cell provided in an embodiment of the present application;

[0037] Fig.12 A schematic diagram of the structure of a device for determining a mobile communication network complaint problem provided in an embodiment of the present application;

[0038] Fig.13 A schematic diagram of the structure of another device for determining a mobile communication network complaint problem provided in an embodiment of the present application;

[0039] Fig.14 A conceptual partial view of a computer program product provided for an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] The terms “first” and “second” in the description and claims of the present application are used to distinguish different objects rather than to describe a specific order of the objects.

[0042] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include other steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0043] In addition, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present concepts in a specific way.

[0044] User complaints are a common phenomenon in mobile communication network operations. The main reasons for user complaints include network problems, package problems, fee problems, terminal problems, business problems, and service problems. Among them, network problems account for a large proportion of user complaints. Network problems include fault or alarm problems, coverage problems, interference problems, and capacity problems. These problems are divided into maintenance, optimization, and construction problems according to the processing process or responsible department.

[0045] However, since most users are not professional technicians, they are also influenced by their subjective feelings, which may result in incomplete or misleading descriptions of problems. For example, the description of the time when the problem occurred and the type of service network is inaccurate, the evaluation of the signal strength or network speed is not objective, and the problem of the terminal or subscriber identity module (SIM) card is attributed to the network problem.

[0046] In addition, it is necessary to have certain professional capabilities to analyze network problems. The response of complaint handlers to user complaints must also be completed within a certain period of time. For example, for network problems, the average response time of complaint handlers to user complaints must be controlled within 24 hours, and the maximum cannot exceed 72 hours. However, because the network analysis capabilities of some complaint handlers are not up to professional levels, it is difficult to quickly analyze and accurately identify network problems when handling network problem complaints.

[0047] In the traditional network problem complaint analysis workflow, network problem complaint analysis is a complicated process. For example, it is necessary to first conduct on-site testing to simulate user complaint scenarios. After that, it is necessary to find problems by checking the statistical data of various indicators in the background and detailed signaling records. Finally, targeted testing is required to verify the improvement. Due to the complex network problem analysis workflow, complaint handling personnel need to spend a lot of work time in the stages of discovering and finding problems. As a result, it is more difficult to determine the mobile communication network complaint problems.

[0048] Currently, complaints can be analyzed through AI models. However, this method is difficult to implement in practical applications due to factors such as data quality, computing resources, model generalization ability and unexplainability. In addition, the results of mobile network complaint analysis often output multiple possible abnormal cells, and it is impossible to directly determine the cell that caused the complaint and the network problem of the cell. Some network problem complaint analysis methods pay too much attention to the complaint phenomenon and lack a comprehensive and in-depth analysis of network problems. In addition, there is a lack of fault-tolerant methods and measures for problems such as complaint time and inaccurate problem description.

[0049] In some embodiments, effective processing of key complaint information output by users can improve the accuracy of information recording, reduce the time required for complaint processing, and handle user complaints quickly and effectively.

[0050] However, the accuracy of this processing method for analyzing network problems needs to be improved. If the problem is incorrectly characterized in the initial stage, the cell corresponding to the network problem may also be incorrect. Therefore, the preset model needs to be continuously updated and corrected. As a result, in actual applications, the network problem analysis workflow is long and cumbersome, which not only affects the overall closed-loop processing efficiency, but also increases the workload of experts. Moreover, the cell associated with this method cannot be guaranteed to be the cell actually occupied by the user. There is a lack of necessary connection between the cell and the user's complaint problem, and the accuracy of the user complaint handling results cannot be guaranteed.

[0051] In response to the technical problems pointed out in the background technology section of this application, an embodiment of the present application provides a method for determining complaint problems in a mobile communication network. In this method, network problems of multiple candidate cells and a target factor of each candidate cell are obtained. In this way, the candidate cells can be associated with user complaints. For each candidate cell, the target score of the candidate cell is determined based on the target factor of the candidate cell to obtain the target score of each candidate cell. By obtaining the target score of each candidate cell, the probability that each candidate cell is a complained cell can be obtained. Afterwards, based on the target score of each candidate cell and the network problems of each candidate cell, the complained cell and the network problems of the complained cell are determined from multiple candidate cells. In this way, by sorting the target scores of each candidate cell, the cell with the highest probability of being complained is obtained, the source of the problem can be accurately located, and the accuracy of network problem analysis can be improved. Furthermore, it can reduce the troubleshooting time and improve the efficiency of network problem analysis.

[0052] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0053] like Figure 1 As shown, a method for determining a mobile communication network complaint problem provided by an embodiment of the present application includes:

[0054] S101. Obtain network problems of multiple candidate cells.

[0055] The network problem is used to indicate the network problem of the candidate cell, including construction problems, fault alarm problems, grid weak coverage problems, cell weak coverage problems, cell strong interference problems and cell capacity limited problems.

[0056] In a possible implementation, the user's complaint time and complaint location are obtained, and multiple candidate cells are determined based on the complaint time and complaint location.

[0057] In a possible design, multiple first cells are determined based on the complaint time. Multiple second cells are determined based on the complaint location. Then, multiple candidate cells are determined. The first cells are cells that the user passes through within a target time period, and the target time period is related to the complaint time. The second cells are cells that are within a preset distance threshold from the complaint location. The candidate cells are cells that have intersections among the multiple first cells and the multiple second cells.

[0058] Among them, the complaint time is the time when the abnormality is reported when the complaint is made.

[0059] Exemplarily, the target time period may be 24 hours before the complaint time.

[0060] In some embodiments, the preset distance threshold includes a first preset distance threshold and a second preset distance threshold, and the second cell includes a first location cell and a second location cell. The first location cell is a candidate cell divided based on the scene where the complaint location is located, and the second location cell is a candidate cell divided based on the grid where the complaint location is located. The first location cell is determined based on the first preset distance threshold, and the second location cell is determined based on the second preset distance threshold.

[0061] In a possible implementation, based on the complaint location, the target scene where the complaint location is located is determined, thereby determining the first preset distance threshold corresponding to the target scene. The first location cell is obtained based on the first preset distance threshold. The target scene is used to indicate the network coverage of the area where the complaint location is located. The target scenes include urban scenes, county scenes, township scenes, and rural scenes.

[0062] In a possible design, the device for determining a mobile communication network complaint problem pre-stores a first preset distance threshold corresponding to each target scenario.

[0063] Exemplarily, the first preset distance threshold for urban scenarios may be 300 meters, the first preset distance threshold for county scenarios may be 500 meters, the first preset distance threshold for township scenarios may be 800 meters, and the first preset distance threshold for rural scenarios may be 1200 meters.

[0064] It should be noted that before determining the target scenario of the complaint location, it is necessary to divide the scenario of the area where the complaint location is located. Obtain regional information, which includes the urbanization development level, population density, network resources, network coverage and business traffic of each area. Based on the regional information, divide the scenario of the area where the complaint location is located.

[0065] For example, Figure 2 As shown, it is a schematic diagram of an example of scene division provided by an embodiment of the present application. Among them, the urban scene 201 is a concentrated black area, the county scene 202 is three black areas, the remaining black areas are the township scene 203, and the rural scene 204 is a white area. The division area of ​​the rural scene 204 is larger than the division area of ​​the urban scene 201. The division of the urban scene 201 is more concentrated, the division of the county scene 202 is more dispersed, and the division of the township scene 203 is more dispersed than the division of the county scene 202.

[0066] For example, Figure 3As shown, it is a schematic diagram of another example of scene division provided by an embodiment of the present application. Among them, the urban scene 301 is a concentrated black area, the county scene 302 is three black areas, the remaining black area is the township scene 303, and the rural scene 304 is a white area. The division area of ​​the rural scene 304 is larger than the division area of ​​the urban scene 301. The division of the urban scene 301 is more concentrated, the division of the county scene 302 is more dispersed, and the division of the township scene 303 is more dispersed than the division of the county scene 302. Figure 3 The urban scene in the partition area is smaller than Figure 2 The division areas of the urban scene in Figure 2. Figure 3 The rural scene in the partition area is larger than Figure 2 The divided areas of the rural scene. Figure 3 The area of ​​the county scene in the Figure 2 The division areas of the county town scene.

[0067] In another possible design, the average station distance of existing 4G / 5G sites in the target scenario is calculated. Based on the average station distance of existing 4G / 5G sites in the target scenario, a first preset distance threshold corresponding to each scenario is obtained.

[0068] In another possible implementation, there are multiple grids. With the target grid where the complaint location is located as the center, multiple first grids are obtained. The first grid is a grid whose center is within a second preset distance threshold range from the target grid. The base station cell with coverage in the first grid is used as the second location cell.

[0069] In a possible design, the grid side length is obtained and based on the grid side length, a second preset distance threshold is determined.

[0070] In the embodiment of the present application, the second preset distance threshold is A value between times and 2.5 times the grid side length.

[0071] Exemplarily, the second preset distance threshold may be three times the grid side length.

[0072] Exemplarily, if the grid side length is 50 meters, the second preset distance threshold may be 150.875 meters.

[0073] For example, Figure 4 As shown, it is a schematic diagram of an example of a method for determining a grid radius provided in an embodiment of the present application. Obtain the grid side length (i.e., d). Obtain a second preset distance threshold based on the grid side length. With the grid 401 where the complaint location is located as the center, multiple second grids 402 are obtained based on the second preset distance threshold. The base station cell with a coverage range in the second grid 402 is taken as the second location cell.

[0074] In a possible design, based on obtaining measurement report (MR) data, base station cells with coverage in a first grid are obtained.

[0075] Optionally, the complaint location may be a location where the complaint is initiated or a location where an abnormality occurs.

[0076] It should be noted that the preset distance threshold obtained again is greater than the preset distance threshold obtained for the first time.

[0077] S102: Obtain a target factor for each candidate cell.

[0078] Among them, the target factor is used to reflect the correlation between the candidate cell and the user complaint.

[0079] In a possible implementation manner, measurement report (MR) data and key performance indicator (KPI) data are obtained, and based on the MR data and the KPI data, a target factor of each candidate cell is obtained.

[0080] In one possible design, the target factors include at least one of the following: a distance factor, a residence time factor, a time consistency factor, a cell anomaly frequency factor, a sampling point factor, and a weak coverage grid factor. The distance factor is used to indicate the degree of correlation between the distance between the candidate cell and the complaint location and the user complaint, and the residence time factor is used to indicate the degree of correlation between the residence time of the user in the candidate cell and the user complaint. The time consistency factor is used to indicate the degree of correlation between the difference between the time period when the candidate cell has an abnormal indicator and the complaint time and the user complaint. The cell anomaly frequency factor is used to indicate the degree of correlation between the time period when the candidate cell has an abnormal indicator and the user complaint, the sampling point factor is used to indicate the degree of correlation between the coverage network of the candidate cell and the user complaint, and the weak coverage grid factor is used to indicate the degree of correlation between the signal strength of the candidate cell and the user complaint.

[0081] It should be noted that in mobile communications, the strength of wireless signals decreases as the distance increases. To ensure the continuity and quality of communication, users preferentially stay in cells with stronger signals through cell reselection or switching mechanisms. Therefore, cells closer to users are usually more relevant to users. If users complain about signal problems or communication quality problems, cells closer to the user's complaint location may be the root cause of these problems. Therefore, the distance factor is an important reference indicator for determining the complained cell.

[0082] It should be noted that if users stay in a cell for a long time, it means that the network quality of the cell has a significant impact on the user experience. Therefore, if a cell with a high proportion of residence time has network problems, it may be the root cause of user complaints. Therefore, the residence time factor is an important reference indicator for determining the cell that is complained about.

[0083] It should be noted that if the time when the user's complaint occurs is highly coincident or close to the user's residence time in a certain cell, the probability that the cell is directly related to the complaint is higher. When an indicator of a certain cell in the user's trajectory is found to be abnormal, time consistency needs to be evaluated. Therefore, the time consistency factor is an important reference indicator for determining the cell being complained about.

[0084] It should be noted that if the complaining user stays in a certain cell for multiple periods of time in a day (i.e., 24 hours before the time when the complaint occurred), and the indicators of this cell are abnormal in most periods of time, then the cell is likely to be the complained cell. Therefore, the cell abnormal frequency factor is an important reference indicator for determining the complained cell.

[0085] It should be noted that within the range around the complaint location, if a cell has more sampling points, then this cell contributes more to the network coverage and communication services of the complaint area, and may be the main service cell of the area. Therefore, there is a high correlation between this cell and the user's complaint in the area, that is, the user's complaint is likely to be related to the network coverage and service quality provided by the cell. Therefore, the sampling point factor is an important reference indicator for determining the cell being complained.

[0086] It should be noted that weak coverage grids are grids with poor signals or signal blind spots, and these grids are often high-incidence areas for network problems. For a cell, the proportion of weak coverage grids in the cell is one of the important indicators for evaluating the quality of cell network coverage. There is a direct correlation between weak coverage grids and complaint issues, and it is an important basis for determining the cells that have been complained about. Therefore, the weak coverage grid factor is an important reference indicator for determining the cells that have been complained about.

[0087] S103. For each candidate cell, determine a target score of the candidate cell based on the target factor of the candidate cell to obtain a target score of each candidate cell.

[0088] The target score is used to indicate the probability that the candidate cell is a complained cell.

[0089] In a possible implementation, based on each target factor of the candidate cell, a score corresponding to each target factor is determined. Based on the score corresponding to each target factor and a weight value of each target factor, a target score of the candidate cell is determined.

[0090] Exemplarily, the distance between the base station cell and the complaint location may satisfy Formula 1.

[0091]

[0092] Where L is the distance between the base station cell and the complaint location, R is the average radius of the earth, lon1 is the longitude of the base station cell, lat1 is the latitude of the base station cell, lon2 is the longitude of the complaint location, and lat2 is the latitude of the complaint location.

[0093] Optionally, R is 6371393 meters.

[0094] In one possible design, a first preset distance threshold and the distance between the base station cell and the complaint location are calculated to obtain a distance score.

[0095] For example, if the distance is less than 0.1 times the first preset distance threshold, 100 points are awarded, and 10 points are deducted for each increase of 0.1 times the first preset distance threshold until the score is deducted to 0. The maximum value of the score range is set to 100 points, and the minimum value is 0 points.

[0096] Exemplarily, the distance score may satisfy Formula 2.

[0097]

[0098] Wherein, D1 is the first preset distance threshold, and INT() is the rounding function.

[0099] Exemplarily, as shown in Table 1, multiple distance scores are shown.

[0100] Table 1

[0101]

[0102] Among them, when the regional scenario is an urban area, the first preset distance threshold (i.e., the coverage radius D1) is 500 meters, and 0.1 times the first preset distance threshold is 50 meters, the distance between the base station cell and the complaint location (i.e., the distance between the cell and the complaint point) is in the range of 0 to 49 meters, and the distance score is 100; the distance between the base station cell and the complaint location is in the range of 50 to 99 meters, and the distance score is 90; the distance between the base station cell and the complaint location is in the range of 100 to 149 meters, and the distance score is 80; the distance between the base station cell and the complaint location is in the range of 400 to 449 meters, and the distance score is 20; the distance between the base station cell and the complaint location is in the range of 450 to 499 meters, and the distance score is 10; the distance between the base station cell and the complaint location is greater than 500 meters, and the distance score is 0.

[0103] In a possible design, based on the user complaint time, user trajectory information is obtained, and the user trajectory information is used to indicate the length of time the user stayed in each candidate cell within 24 hours before the user complaint time. The length of time the user stayed in each candidate cell is summed to obtain the total length of time the user stayed. The ratio between the length of time the user stayed in each candidate cell and the total length of time the user stayed is calculated to obtain the length of stay ratio. The candidate cells are sorted according to the length of stay ratio to obtain the length of stay score for each candidate cell.

[0104] For example, the maximum value of the dwell time score range may be 100 points, and the minimum value may be 0 points. The cell with the largest dwell time ratio is ranked first, with a score of 100. The second-ranked cell has a score of 90. The cell with the smallest dwell time ratio is ranked last, with a score of 0. And so on, 10 points are deducted for each subsequent ranking until the score is deducted to 0.

[0105] Exemplarily, the dwell time score may satisfy Formula 3.

[0106]

[0107] Among them, n is the ranking of the residence time ratio of the candidate cells, and the candidate cells are sorted from high to low according to the residence time ratio.

[0108] Exemplarily, as shown in Table 2, multiple dwell time scores are shown.

[0109] Table 2

[0110]

[0111] Among them, the complaint number is 185xxxx1111, the resident cell ID is Cell_1, the cell network type is 4G, the cell resident time is 39313s, the total user resident time is 85366s, and the resident time ranking is 1st, so the resident time score is 100. The complaint number is 185xxxx1111, the resident cell ID is Cell_2, the cell network type is 4G, the cell resident time is 14393s, the total user resident time is 85366s, and the resident time ranking is 2nd, so the resident time score is 90.

[0112] For the introduction of other residence time scores in Table 2, please refer to the above introduction of residence time scores, which will not be repeated here.

[0113] In one possible design, the residence period and the abnormality of the network indicators are obtained. Based on the complaint time and each time point in the residence period (i.e., the residence time point), multiple first time differences are obtained. Each candidate cell is sorted based on the first time difference and the abnormality of the network indicators to obtain a time consistency score for each candidate cell. The residence period is the time period when the user resides. The first time difference is the hour difference between the complaint time and the residence time point. Network abnormalities include normal and abnormal network indicators. The network indicators include cell-level indicators, and the cell-level indicators include weak coverage indicators, strong interference indicators, and capacity-limited indicators.

[0114] Exemplarily, the maximum value of the time consistency score is 100 points, and the minimum value is 0 points. If the first time difference corresponding to a time point of the candidate cell is 0, and the network indicator is abnormal, the candidate cell scores 100 points at this time point. For every hour that the first time difference increases, 10 points are reduced. If the first time difference of the candidate cell is 10 hours, the candidate cell scores 0 at this time point.

[0115] It should be noted that, when a candidate cell has multiple dwell time points, multiple first time difference scores of the candidate cell are obtained, and the highest score among them is taken as the final score of the candidate cell.

[0116] Exemplarily, the temporal consistency score may satisfy Formula 4.

[0117]

[0118] Wherein, Δt is the first time difference.

[0119] Exemplarily, Δt may satisfy Formula 5.

[0120] Δt=|T1-T2|Formula 5.

[0121] Among them, T1 is the complaint time and T2 is the residence time point.

[0122] It should be noted that the time consistency score includes a weak coverage time consistency score, a strong interference time consistency score and a capacity-limited time consistency score.

[0123] Exemplarily, as shown in Table 3, taking the cell weak coverage indicator as an example, multiple weak coverage time consistency scores are shown.

[0124] Table 3

[0125]

[0126] Among them, the complaint number is 185xxxx1111, the resident cell id is Cell_1, the complaint period is 21:00 on July 21, 2024, the user resident period is 01:00 on July 22, 2024, the time difference between the complaint period and the user resident period is 6 hours, the cell is normal, and the weak coverage time consistency score of this resident time point is not calculated. The complaint number is 185xxxx1111, the resident cell id is Cell_1, the complaint period is 21:00 on July 21, 2024, the user resident period is 21:00 on July 21, 2024, the time difference between the complaint period and the user resident period is 0, the cell weak coverage is abnormal, and the weak coverage time consistency score of this resident time point is 100. Since the weak coverage time consistency score of this resident time point is the largest among the multiple weak coverage time consistency scores of Cell_1, the weak coverage time consistency score of Cell_1 is 100. The complaint number is 185xxxx1111, the resident cell ID is Cell_2, the complaint period is 21:00 on July 21, 2024, the user resident period is 01:00 on July 22, 2024, the time difference between the complaint period and the user resident period is 4 hours, the cell has abnormal weak coverage, and the weak coverage time consistency score at this resident time point is 60. Since the largest of the multiple weak coverage time consistency scores of Cell_2 is 80, not 60, the weak coverage time consistency score of Cell_2 is 80.

[0127] For the introduction of other time consistency scores in Table 3, please refer to the above introduction of time consistency scores, which will not be repeated here.

[0128] In one possible design, the total number of dwell periods (i.e., the total number of dwell periods) is used as the denominator, and the number of periods in which the cell has abnormal network indicators (i.e., the number of abnormal cell periods) is used as the numerator to calculate the cell abnormality frequency. The cell abnormality frequency is multiplied by 100 and rounded to the nearest integer to obtain the cell abnormality frequency score. The number of abnormal cell periods includes the number of weak cell coverage periods, the number of strong cell interference periods, and the number of cell capacity limited periods.

[0129] Exemplarily, the cell abnormality frequency score may satisfy Formula 6.

[0130]

[0131] Optionally, the maximum value of the cell abnormality frequency score is 100 points and the minimum value is 0 points.

[0132] It should be noted that the cell abnormal frequency score includes the cell weak coverage abnormal frequency score, the cell strong interference abnormal frequency score and the cell capacity limited abnormal frequency score.

[0133] Exemplarily, as shown in Table 4, taking the cell weak coverage indicator as an example, it shows multiple weak coverage anomaly frequency scores.

[0134] Table 4

[0135]

[0136] Among them, the complaint number is 185xxxx1111, the resident cell id is Cell_1, the complaint period is 21:00 on July 21, 2024, the total number of resident periods is 6, Cell_1 has weak cell coverage in 2 periods, and the cell weak coverage abnormality frequency score is 33.33. The complaint number is 185xxxx1111, the resident cell id is Cell_2, the complaint period is 21:00 on July 21, 2024, the total number of resident periods is 6, Cell_2 has weak cell coverage in 4 periods, and the cell weak coverage abnormality frequency score is 66.67.

[0137] In a possible design, the first grid is obtained, and the MR sampling points of all candidate cells falling in the first grid are used as the sampling denominator. The MR sampling points of a candidate cell falling in the first grid are used as the sampling numerator to obtain the sampling point ratio of each candidate cell. The sampling point ratio is the ratio of the sampling numerator to the sampling denominator. The sampling point ratio of each candidate cell is sorted from high to low to obtain the sampling point score.

[0138] For example, the maximum value of the sampling point score is 100 points, and the minimum value is 0 points. The candidate cell with the largest sampling point ratio ranks first and scores 100 points. From the second place onwards, the score decreases by 10 points for each drop in rank until the score decreases to 0 points. The candidate cell with the smallest sampling point ratio ranks last and scores 0 points.

[0139] Exemplarily, the sampling point score may satisfy Formula 7.

[0140]

[0141] Among them, n is the ranking of the sampling point proportion, and the sampling points are sorted from high to low.

[0142] In one possible design, the first grid is obtained. The total number of grids in which all candidate cells fall in the first grid is used as the coverage denominator, and the number of weak coverage grids in which a candidate cell is judged to have weak coverage in the total number of grids is used as the coverage numerator to obtain the weak coverage grid ratio. The weak coverage grid ratio is the ratio of the coverage numerator to the coverage denominator. The weak coverage grid ratio of each candidate cell is sorted from high to low to obtain a weak coverage grid score.

[0143] For example, the maximum score of the weak coverage grid is 100 points, and the minimum score is 0 points. The candidate cell with the largest proportion of weak coverage grids ranks first and scores 100. From the second place, the score decreases by 10 points for each drop in rank until the score decreases to 0 points. The candidate cell with the smallest proportion of weak coverage grids ranks last and scores 0.

[0144] For example, Figure 5 As shown, it is a schematic diagram of an example of a cell grid distribution provided by an embodiment of the present application. In two circles of grids (i.e., 25 grids) with a second preset distance as a radius around the complaint location, there are 14 grids 501 of the Cell_1 cell, of which there are 7 weak coverage grids 502.

[0145] For example, Figure 6 As shown, it is a schematic diagram of another example of cell grid distribution provided in an embodiment of the present application. In two circles of grids (i.e., 25 grids) with a second preset distance as a radius around the complaint location, there are 12 grids 601 of the Cell_2 cell. Among them, there are 6 weak coverage grids 602.

[0146] For example, Figure 7 , which is a schematic diagram of another example of cell grid distribution provided in an embodiment of the present application. After the Cell_1 cell and the Cell_2 cell are superimposed, there are 19 grids 701 and 11 weak coverage grids 702.

[0147] Exemplarily, the weak coverage grid score of Cell_1 may satisfy Formula 8.

[0148]

[0149] Exemplarily, the number score of weakly covered cells in Cell_2 may satisfy Formula 9.

[0150]

[0151] Exemplarily, the weak coverage grid score may satisfy Formula 10.

[0152]

[0153] Among them, n is the ranking of the proportion of weak coverage grids, and the weak coverage grids are sorted from high to low.

[0154] In a possible implementation manner, a weight coefficient of each target factor is obtained, and a target score of each candidate cell is obtained based on the weight coefficient and the score of the target factor.

[0155] In one possible design, based on the network problem of the candidate cell, a corresponding target factor and a weight coefficient of the corresponding target factor are determined. Based on the weight coefficient of the corresponding target factor and the score of the corresponding target factor, a target score for each candidate cell is obtained.

[0156] Exemplarily, as shown in Table 5, the weight coefficients of the target factors are shown.

[0157] Table 5

[0158]

[0159]

[0160] Among them, the distance score is a general indicator with a weight of 30%, and the residence time score is a general indicator with a weight of 30%.

[0161] For the introduction of the weight coefficients of other target factors in Table 5, please refer to the introduction of the weight coefficients of the target factors mentioned above, which will not be repeated here.

[0162] It should be noted that the distance and dwell time indicators are derived from user trajectory data, which are more accurate and refined than grid and cell indicator data and can more directly reflect the actual situation of users. Therefore, a higher weight coefficient is considered.

[0163] Exemplarily, the target score of the candidate cell for the cell weak coverage problem may satisfy Formula 11.

[0164] Final total score =

[0165] Distance score × 30% + residence time score × 30% + time consistency score_cell weak coverage × 20% +

[0166] Indicator abnormal frequency score_cell weak coverage × 20% Formula 11.

[0167] S104: Determine a complained cell and the network problem of the complained cell from multiple candidate cells based on the target score of each candidate cell and the network problem of each candidate cell.

[0168] In a possible implementation, multiple candidate cells are ranked based on the target score of each candidate cell. Among the multiple candidate cells, the candidate cell with the largest target score is the complained cell. Multiple candidate cells ranked after the complained cell and having a candidate complained cell score greater than or equal to 60 points are candidate complained cells.

[0169] It is understandable that, in this way, if the network problem of the complained cell is different from the network problem complained by the user, the complained cell and the network problem of the complained cell can be quickly determined from multiple candidate complained cells.

[0170] In some embodiments, after determining the complained cell and the network problem of the complained cell, a reply text is output.

[0171] It should be noted that the device for determining mobile communication network complaint problems stores answer texts corresponding to network problems, and the answer texts include at least one of the following: maintenance answer texts, construction answer texts, and optimization answer texts. If the preset distance threshold is re-acquired and multiple candidate cells still cannot be determined, it is determined that there is a coverage blind spot at the location where the complaint occurred, and the network problem at the user's complaint location is determined to be a construction problem, and a construction answer text is output.

[0172] Based on the above-mentioned scheme, the network problems of multiple candidate cells and the target factor of each candidate cell are obtained. In this way, the candidate cells can be associated with user complaints. For each candidate cell, the target score of the candidate cell is determined based on the target factor of the candidate cell to obtain the target score of each candidate cell. By obtaining the target score of each candidate cell, the probability that each candidate cell is a complained cell can be obtained. Afterwards, based on the target score of each candidate cell and the network problems of each candidate cell, the complained cell and the network problems of the complained cell are determined from multiple candidate cells. In this way, by sorting the target scores of each candidate cell and obtaining the cell with the highest probability of being complained, the source of the problem can be accurately located and the accuracy of network problem analysis can be improved. Furthermore, the troubleshooting time can be reduced and the efficiency of network problem analysis can be improved.

[0173] like Figure 8 As shown, another method for determining a mobile communication network complaint problem provided by an embodiment of the present application, in which S101 may include:

[0174] S801. Obtain at least one alarm data of a candidate cell.

[0175] Among them, one alarm data corresponds to one alarm time period.

[0176] In a possible implementation manner, at least one alarm data of the candidate cell is obtained by accessing an alarm management module.

[0177] Exemplarily, as shown in Table 6, multiple data sources are shown.

[0178] Table 6

[0179] Data Source Time granularity Object granularity Network granularity Fault / alarm data real time event Network element User trajectory data Dwell Time Slice user Community Grid cell-level MR data sky 50m grid Community Cell-level MR data Hour Network element Community Community-level KPI data Hour Network element Community Community-level engineering parameters sky Network element Community

[0180] Among them, the data sources of the embodiment of the present application include fault / alarm data, user trajectory data, grid cell-level MR data, cell-level MR data, cell-level KPI data, and cell-level engineering parameters. The time granularity of fault / alarm data is real-time, the object granularity is event, and the network granularity is network element. The time granularity of user trajectory data is the residence time slice, the object granularity is user, and the network granularity is cell.

[0181] For the introduction of other data sources in Table 6, please refer to the above introduction of data sources, which will not be repeated here.

[0182] It is understandable that grid cell-level MR data focuses mainly on the spatial dimension, and by adding grid positioning attributes, the accuracy in the spatial dimension is significantly improved. Cell-level MR data focuses more on the time dimension. Compared with grid cell-level MR data, it uses a smaller granularity to refine the data in the time dimension, thereby strengthening the close connection with the user in the time dimension. These two types of data complement each other in the time dimension and space dimension, and jointly ensure the accuracy of the analysis results.

[0183] S802: For each alarm data, determine whether the user's complaint time is within the alarm time period corresponding to the alarm data.

[0184] In some embodiments, if the user's complaint time is within the alarm time period corresponding to the alarm data, S803 is executed.

[0185] In other embodiments, if the user's complaint time is not within the alarm time period corresponding to the alarm data, S804 is executed.

[0186] S803: Use the alarm data as a network problem of the candidate cell.

[0187] In a possible implementation, if there is only one piece of alarm data, the alarm data is regarded as a network problem of the candidate cell.

[0188] In a possible design, the network problem corresponding to the alarm data is a fault alarm type problem.

[0189] In another possible implementation, if there are multiple alarm data, based on the priorities of the alarm data, the alarm data with the highest priority is used as the network problem of the candidate cell.

[0190] In a possible design, the device for determining a complaint problem in a mobile communication network pre-stores the priority of the alarm data.

[0191] In some embodiments, after the alarm data is taken as a network problem of a candidate cell, the candidate cell is taken as a complained cell.

[0192] S804: Determine the network problem of the candidate cell based on the network indicators of the candidate cell.

[0193] In a possible implementation, a network indicator of a candidate cell is obtained. Based on the network indicator of the candidate cell, a network problem of the candidate cell is determined. The network indicator also includes a grid indicator.

[0194] In one possible design, the grid-type index includes a weak coverage grid ratio index. It is determined whether the weak coverage grid ratio index is greater than zero. If the weak coverage grid ratio index is greater than zero, it is determined that the network problem of the candidate cell is a grid weak coverage problem. If the weak coverage grid ratio index is equal to zero, the network problem of the candidate cell is determined based on the weak coverage type index, the strong interference type index, and the capacity limited type index.

[0195] It should be noted that if the total number of MR sampling points in a grid of a cell is greater than 30, and the proportion of RSRP>=-110 sampling points is <90%, then the grid is a weak coverage grid.

[0196] It should be noted that based on the weak coverage indicators, the weak coverage problem of the cell can be determined. Based on the strong interference indicators, the strong interference problem of the cell can be determined. Based on the capacity limited indicators, the capacity limited problem of the cell can be determined.

[0197] In a possible design, if any indicator of the candidate cell among the weak coverage indicator, the strong interference indicator and the limited capacity indicator is abnormal, the network problem corresponding to the candidate cell can be determined.

[0198] In another possible design, if the candidate cell has at least two types of abnormal indicators among the cell weak coverage indicators, cell strong interference indicators and cell capacity limited indicators, the time consistency score corresponding to each type of indicator is summed and compared with the cell abnormality frequency score to obtain the network problem of the candidate cell.

[0199] Exemplarily, the sum of the time consistency score of the cell weak coverage index and the cell abnormal frequency score is A, the sum of the time consistency score of the cell strong interference index and the cell abnormal frequency score is B, and the sum of the time consistency score of the cell capacity limited index and the cell abnormal frequency score is C. If A is greater than B, and A is greater than C, then the network problem of the candidate cell is a cell weak coverage problem. If B is greater than A, and B is greater than C, then the network problem of the candidate cell is a cell strong interference problem.

[0200] Exemplarily, as shown in Table 7, multiple cell-level network indicators and abnormal thresholds are shown.

[0201] Table 7

[0202]

[0203] Among them, the indicator classification includes weak coverage, strong interference, and limited capacity. The indicator sources are divided into MR data and KPI data. The weak coverage indicator of the cell is determined as the sampling ratio of RSRP>=-110. The strong interference indicators of the cell include the sampling ratio of modulo 3 interference (MOD3), the sampling ratio of overlapping coverage, the sampling ratio of signal to interference plus noise ratio (SINR) greater than 0, the average interference level per physical resource block (PRB), the average CQI and the CQI excellent rate. The indicators for determining the capacity limitation of the cell include the number of RRC connection establishment failures, the number of e-Rab establishment congestion caused by limited wireless resources, the number of QoSFlow establishment failures, and the downlink PRB utilization rate of the cell. The sampling ratio of RSRP>=-110 can determine whether there is a weak coverage problem in the 4G or 5G network of the cell. If the sampling ratio of RSRP>=-110 is less than 90%, the cell has a weak coverage problem. The average CQI can determine whether there is a strong interference problem in the 4G network of the cell, but cannot determine whether there is a strong interference problem in the 5G network of the cell. If the average CQI of the cell is less than or equal to 6, there is no strong interference problem in the cell.

[0204] In some embodiments, if there are no abnormalities in the weak coverage indicators, strong interference indicators, and capacity-limited indicators, it is determined that the candidate cell has no network problems, and an expert analysis request message is sent.

[0205] It should be noted that the target scores of candidate cells without network problems are not calculated, and candidate cells without network problems are not ranked.

[0206] For the introduction of other cell-level network indicators and abnormal thresholds in Table 7, please refer to the above introduction of cell-level network indicators and abnormal thresholds, which will not be repeated here.

[0207] It should be noted that the cell unique identifier, time period or date of the candidate cell is obtained. Based on the cell unique identifier, time period or date, corresponding KPI data can be obtained in the KPI indicator database.

[0208] It should be noted that when the corresponding KPI data can be obtained in the KPI indicator database based on the cell unique identifier, time period or date, the cell-level indicators are hourly granularity, and the hourly time period needs to be consistent. The cell grid indicators are daily granularity data, and the date needs to be consistent.

[0209] Exemplarily, the unique cell identifier of a 4G network may be LAC+eNodeBID+CellID, and the unique cell identifier of a 5G network may be TAC+gNodeBID+CellID.

[0210] It should be noted that when the total number of MR samples is less than or equal to 30, no determination of MR-related indicators is performed. When the number of successful RRC connection establishments is less than or equal to 100, no determination of CQI-related indicators is performed.

[0211] It should be noted that the embodiment of the present application does not limit the timing of executing S801-S804. For example, S801-S804 may be executed before executing S101. For another example, S801-S804 may be executed after executing S104.

[0212] Exemplarily, the apparatus for determining a complaint problem in a mobile communication network may execute S801 to S804 before acquiring a target factor for each candidate cell.

[0213] Based on the above scheme, at least one alarm data of the candidate cell is obtained. For each alarm data, it is determined whether the user's complaint time is within the alarm time period corresponding to the alarm data. By associating the alarm data with the network problem complained by the user, the network fault can be quickly located, the troubleshooting time can be reduced, and the efficiency of network problem analysis can be improved.

[0214] The following is an introduction to the embodiments of the present application with reference to specific examples. Fig. 9 As shown, it is an example schematic diagram of a method for determining a complained cell provided by an embodiment of the present application. Based on the complaint location, cell engineering parameter data, and grid MR data, multiple first cells near the complaint location are determined. Based on the complaint number and complaint time, multiple second cells that the user passes through within the target time period are determined. Based on the first cell and the second cell, multiple candidate cells are obtained. The candidate cell is a cell with an intersection among multiple first cells and multiple second cells. At least one alarm data of the candidate cell is obtained. For each alarm data, if the user's complaint time is within the alarm time period corresponding to the alarm data, the alarm data is used as a network problem of the candidate cell. If the alarm time period corresponding to at least one alarm data does not include the complaint time, the network index of the candidate cell is obtained. Based on the network index of the candidate cell, the network problem of the candidate cell is determined. For each candidate cell, the target score of the candidate cell is determined based on the target factor of the candidate cell and the weight of the target factor. Based on the target score of each candidate cell and the network problem of each candidate cell, the complained cell (i.e., the main cause cell) and the candidate complained cell (i.e., the secondary cause cell) are determined from multiple candidate cells. Afterwards, an analysis of the complained cell in terms of maintenance and optimization is output, and suggestions are output.

[0215] The following is an introduction to the embodiments of the present application with reference to specific examples. Fig.10 As shown, it is an example schematic diagram of another method for determining a complained cell provided by an embodiment of the present application. Based on the complaint location, multiple first cells near the complaint location are determined. Based on the complaint time, multiple second cells that the user passes through within the target time period are determined. Based on the first cell and the second cell, multiple candidate cells (i.e., candidate cell A) are obtained. The candidate cell is a cell that has an intersection among multiple first cells and multiple second cells. Afterwards, it is determined whether the user's complaint time is within the alarm time period corresponding to the alarm data. If the user's complaint time is within the alarm time period corresponding to the alarm data, the alarm data is used as a network problem of the candidate cell, and a construction-type answer text is output. If the user's complaint time is not within the alarm time period corresponding to the alarm data, the network indicators of the candidate cell are obtained. Based on the network indicators of the candidate cell, the network problem of the candidate cell is determined. Determine whether the network indicators of the candidate cell are abnormal. If the network indicators are not abnormal, an expert analysis request message is issued. If the network indicators are abnormal, determine whether the weak coverage grid proportion indicator is greater than zero. If the weak coverage grid proportion indicator is greater than zero, it is determined that the network problem of the candidate cell is a grid weak coverage problem. If the weak coverage grid proportion index is equal to zero, the network problem of the candidate cell is determined based on the cell-level index, and the corresponding answer text is output. The cell-level index includes weak coverage class index, strong interference class index and capacity limited class index. Based on the weak coverage class index, strong interference class index and capacity limited class index, the network problem of the candidate cell is determined to be a weak coverage problem of the cell, a strong interference problem of the cell and a capacity limited problem of the cell. Afterwards, for each candidate cell with a network problem (i.e., candidate cell B), the target score of the candidate cell with a network problem is determined based on the target factor of the candidate cell with a network problem and the weight of the target factor. The target score includes a distance score, a residence time score, a time consistency score, a cell abnormality frequency score, a sampling point score, and a weak coverage grid score. Based on the target score of each candidate cell with a network problem, multiple candidate cells are sorted, and the complained cell (i.e., the main cause cell) and the candidate complained cell (i.e., the secondary cause cell) are determined from the multiple candidate cells with network problems.

[0216] The following is an introduction to the embodiments of the present application with reference to specific examples. Fig.11As shown, it is an example schematic diagram of another method for determining a complained cell provided by an embodiment of the present application. Determine whether the weak coverage grid ratio index is greater than zero. If the weak coverage grid ratio index is greater than zero, it is determined that the network problem of the candidate cell is a grid weak coverage problem. If the weak coverage grid ratio index is equal to zero, the network problem of the candidate cell is determined based on the weak coverage class index, the strong interference class index and the capacity limited class index. Determine whether the cell has a weak coverage problem. If the cell weak coverage class index is abnormal, the sum of the time consistency score of the cell weak coverage class index and the cell abnormal frequency score is calculated as A; if the cell weak coverage class index is normal, A is determined to be -1. If the cell strong interference class index is abnormal, the sum of the time consistency score of the cell strong interference class index and the cell abnormal frequency score is calculated as B; if the cell strong interference class index is normal, B is determined to be -1. If the cell capacity limited class index is abnormal, the sum of the time consistency score of the cell capacity limited class index and the cell abnormal frequency score is calculated as C; if the cell capacity limited class index is normal, C is determined to be -1. Determine whether A, B, and C are all -1. If A, B, and C are all -1, it is determined that the cell has no network problem; if at least two of A, B, and C are not -1, the maximum value among A, B, and C is determined. If the maximum value is A, it is determined that the network problem of the cell is a weak coverage problem. If the maximum value is B, it is determined that the network problem of the cell is a strong interference problem. If the maximum value is C, it is determined that the network problem of the cell is a limited capacity problem.

[0217] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. It can be understood that in order to realize the above functions, the device for determining mobile communication network complaint problems includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the steps of the method for determining mobile communication network complaint problems in each example described in the embodiment disclosed in this application, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0218] The embodiment of the present application also provides a device for determining a mobile communication network complaint problem. The device for determining a mobile communication network complaint problem can be a server, a CPU in the server, a determination module in the server for determining a mobile communication network complaint problem, or a client in the server for determining a network problem.

[0219] The embodiment of the present application can divide the functional modules or functional units of the determination device of the mobile communication network complaint problem according to the above method example. For example, each functional module or functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules or functional units. Among them, the division of modules or units in the embodiment of the present application is schematic, which is only a logical functional division. There may be other division methods in actual implementation.

[0220] The present application embodiment provides a device for determining a mobile communication network complaint problem. Fig.12 As shown, the device for determining the mobile communication network complaint problem may include: an acquisition module 1201 and a processing module 1202.

[0221] The acquisition module 1201 is used to acquire network problems of multiple candidate cells and a target factor of each candidate cell, where the target factor is used to reflect the degree of correlation between the candidate cell and user complaints.

[0222] The processing module 1202 is used to determine, for each candidate cell, a target score of the candidate cell based on the target factor of the candidate cell to obtain a target score of each candidate cell, where the target score is used to indicate the probability that the candidate cell is a complained cell. The processing module 1202 is also used to determine a complained cell and a network problem of the complained cell from multiple candidate cells based on the target score of each candidate cell and the network problem of each candidate cell.

[0223] Fig.13 1 is a schematic diagram of a structure of a device for determining a mobile communication network complaint problem according to an exemplary embodiment. The device for determining a mobile communication network complaint problem may include a processor 1302, and the processor 1302 is used to execute application code to implement the method for determining a mobile communication network complaint problem in the present application.

[0224] The processor 1302 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

[0225] like Fig.13 As shown, the apparatus for determining a complaint problem in a mobile communication network may further include a memory 1303. The memory 1303 is used to store application program codes for executing the solution of the present application, and the execution is controlled by the processor 1302.

[0226] The memory 1303 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 1303 may exist independently and be connected to the processor 1302 via the bus 1304. The memory 1303 may also be integrated with the processor 1302.

[0227] like Fig.13 As shown, the apparatus for determining a mobile communication network complaint problem may further include a communication interface 1301, wherein the communication interface 1301, the processor 1302, and the memory 1303 may be coupled to each other, for example, via a bus 1304. The communication interface 1301 is used to perform information interaction with other devices, for example, to support information interaction between the apparatus for determining a mobile communication network complaint problem and other devices.

[0228] It should be pointed out that Fig.13 The device structure shown in the figure does not constitute a limitation on the determination device of the mobile communication network complaint problem, except Fig.13 In addition to the components shown, the device for determining mobile communication network complaint problems may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0229] In actual implementation, the functions implemented by the processing unit can be Fig.13 The processor 1302 shown calls the program code in the memory 1303 to implement.

[0230] The present application also provides a computer-readable storage medium, on which instructions are stored. When the instructions in the computer-readable storage medium are executed by a processor of a computer device, the computer can execute the method for determining mobile communication network complaint problems provided in the above-mentioned embodiment. For example, the computer-readable storage medium may be a memory 1303 including instructions, and the above instructions may be executed by a processor 1302 of a computer device to complete the above method. Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, a non-temporary computer-readable storage medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

[0231] Fig.14 A conceptual partial view of a computer program product provided by an embodiment of the present application is schematically shown, where the computer program product includes a computer program for executing a computer process on a computing device.

[0232] In one embodiment, the computer program product is provided using a signal bearing medium 1400. The signal bearing medium 1400 may include one or more program instructions that, when executed by one or more processors, may provide the above-described Figure 1 , Figure 2 Thus, for example, reference to Figure 1 In the embodiment shown in , one or more features of S101 to S104 may be undertaken by one or more instructions associated with the signal bearing medium 1400. In addition, Fig.14 The program instructions in also describe example instructions.

[0233] In some examples, the signal bearing medium 1400 may include a computer readable medium 1401, such as, but not limited to, a hard drive, a compact disk (CD), a digital video disk (DVD), a digital tape, a memory, a read-only memory (ROM) or a random access memory (RAM), and the like.

[0234] In some implementations, the signal bearing medium 1400 may include a computer recordable medium 1402 such as, but not limited to, a memory, a read / write (R / W) CD, a R / W DVD, or the like.

[0235] In some implementations, signal bearing medium 1400 may include communication medium 1403 such as, but not limited to, digital and / or analog communication media (eg, fiber optic cables, waveguides, wired communication links, wireless communication links, etc.).

[0236] The signal bearing medium 1400 may be communicated by a wireless form of communication medium 1403. The one or more program instructions may be, for example, computer executable instructions or logic implemented instructions.

[0237] In some examples, the apparatus for determining mobile communication network complaint problems may be configured to provide various operations, functions, or actions in response to one or more program instructions in computer-readable media 1401 , computer-recordable media 1402 , and / or communication media 1403 .

[0238] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0239] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0240] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0241] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0242] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes various media that can store program codes, such as-U disk, mobile hard disk, ROM, RAM, disk or CD.

[0243] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining a complaint problem in a mobile communication network, characterized in that: The method comprises: Acquire network problems of multiple candidate cells and a target factor of each candidate cell, where the target factor is used to reflect the degree of correlation between the candidate cell and user complaints; For each candidate cell, determining a target score of the candidate cell based on a target factor of the candidate cell to obtain a target score of each candidate cell, wherein the target score is used to indicate a probability that the candidate cell is a complained cell; Based on the target score of each candidate cell and the network problem of each candidate cell, a complained cell and the network problem of the complained cell are determined from the multiple candidate cells.

2. The method according to claim 1, characterized in that: The target factor includes at least one of the following: a distance factor, a dwell time factor, a time consistency factor, a cell abnormal frequency factor, a sampling point factor, and a weak coverage grid factor; The determining a target score of the candidate cell based on the target factor of the candidate cell includes: Based on each target factor of the candidate cell, determining a score corresponding to each target factor; The target score of the candidate cell is determined based on the score corresponding to each target factor and the weight value of each target factor.

3. The method according to claim 1, characterized in that Before obtaining the network problems of the plurality of candidate cells, the method further includes: Obtain the user's complaint time and location; Based on the complaint time and the complaint location, a plurality of candidate cells are determined.

4. The method according to claim 3, characterized in that The determining of a plurality of candidate cells based on the complaint time and the complaint location comprises: Based on the complaint time, determining a plurality of first cells, where the first cells are cells that the user passes through within a target time period, and the target time period is related to the complaint time; Based on the complaint location, determining a plurality of second cells, where the second cells are cells within a preset distance threshold from the complaint location; The multiple candidate cells are determined, where the candidate cells are cells that have intersections among the multiple first cells and the multiple second cells.

5. The method according to claim 1, characterized in that Obtain network problems of candidate cells, including: Acquire at least one alarm data of the candidate cell, one alarm data corresponds to one alarm time period; For each alarm data, if the user's complaint time is within the alarm time period corresponding to the alarm data, the alarm data is regarded as a network problem of the candidate cell.

6. The method according to claim 5, characterized in that The method further comprises: If the alarm time period corresponding to the at least one alarm data does not include the complaint time, obtaining the network indicator of the candidate cell; Based on the network indicators of the candidate cells, a network problem of the candidate cells is determined.

7. A device for determining a complaint problem in a mobile communication network, characterized in that: The device comprises an acquisition module and a processing module: The acquisition module is used to acquire network problems of multiple candidate cells and a target factor of each candidate cell, where the target factor is used to reflect the degree of correlation between the candidate cell and user complaints; The processing module is used to determine, for each candidate cell, a target score of the candidate cell based on the target factor of the candidate cell, so as to obtain the target score of each candidate cell, wherein the target score is used to indicate the probability that the candidate cell is a complained cell; The processing module is further configured to determine a complained cell and the network problem of the complained cell from the multiple candidate cells based on the target score of each candidate cell and the network problem of each candidate cell.

8. A device for determining a complaint problem in a mobile communication network, characterized in that: include: Processor and memory; The processor is coupled to the memory; The memory is used to store one or more programs, and one or more of the programs include computer execution instructions. When the device for determining mobile communication network complaint problems is running, the processor executes the computer execution instructions stored in the memory to enable the device for determining mobile communication network complaint problems to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, characterized in that: When a computer executes the instruction, the computer performs the method according to any one of claims 1 to 6.

10. A computer program product comprising instructions, characterized in that When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 6.

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