Complaint warning method, device, electronic device, storage medium and product

Abstract

The present invention provides a complaint warning method, device, electronic device, storage medium, and product, including: obtaining signaling XDR data, identifying abnormal behavior events of users restarting their terminals based on the signaling XDR data; comparing the service perception indicators of users before and after the abnormal behavior event occurs, and determining that users whose service perception indicators have not improved are high-complaint risk users; performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining that candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells; and issuing complaint warnings based on the high-complaint risk users and the high-complaint risk cells. The present invention performs high-complaint risk analysis by associating the abnormal behavior of users restarting their terminals with network problems, identifying high-complaint risk users and high-complaint risk network areas, and issuing complaint warnings, thereby achieving pre-complaint analysis, which is conducive to timely and comprehensive discovery of network problems and reducing user complaint rates.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a complaint warning method, device, electronic equipment, storage medium and product. Background Art

[0002] With the continuous development of technology, users can use data and voice services through mobile networks. In order to improve user experience and reduce user complaint rates, operators usually adopt various methods such as network performance indicator early warning, indicator degradation demarcation and positioning analysis, user satisfaction surveys, user complaint diagnosis and analysis to analyze, discover and optimize network quality problems.

[0003] However, existing complaint warning schemes have difficulty identifying a variety of potential complaint risk issues, resulting in reduced user experience and a high user complaint rate. Summary of the Invention

[0004] The present invention provides a complaint warning method, device, electronic device, storage medium and product to solve the defect in the existing technology that it is difficult to identify multiple potential complaint risk issues, resulting in a reduced user experience, and to achieve the identification and timely warning of high-complaint risk users and high-complaint risk cells, thereby timely and comprehensively discovering network problems and reducing user complaint rates.

[0005] The present invention provides a complaint early warning method, comprising:

[0006] Acquire signaling XDR data, and identify an abnormal behavior event of a user restarting the terminal based on the signaling XDR data;

[0007] Comparing the user's service perception indicators before and after the abnormal behavior event occurs, and determining that the users whose service perception indicators have not improved are high-complaint risk users;

[0008] Performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high complaint risk cells;

[0009] Complaint warnings are issued based on the high complaint risk users and the high complaint risk cells.

[0010] According to a complaint warning method provided by the present invention, the identifying of an abnormal behavior event of a user restarting a terminal based on the signaling XDR data includes:

[0011] Based on the signaling XDR data, identifying a behavior event of a user restarting the terminal;

[0012] If the user's service perception index before the behavior event occurs is less than a preset perception index threshold, the behavior event is determined to be an abnormal behavior event;

[0013] If the occurrence frequency of the behavior event exceeds a preset frequency threshold, the behavior event is determined to be an abnormal behavior event.

[0014] According to a complaint warning method provided by the present invention, identifying a behavior event of a user restarting a terminal based on the signaling XDR data includes:

[0015] If the description field information of the first target event in the signaling XDR data meets the shutdown event identification condition, outputting the shutdown event information based on the basic field information of the first target event;

[0016] If the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event;

[0017] According to the information of the shutdown event and the information of the power-on event, a behavior event of the user restarting the terminal is obtained.

[0018] According to a complaint warning method provided by the present invention, the shutdown event information includes at least a user number and a shutdown time; if the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event includes:

[0019] According to the user number and shutdown time of the shutdown event, searching the signaling XDR data for a second target event of the user corresponding to the user number after the shutdown time;

[0020] If the description field information of the second target event meets the power-on event identification condition, the power-on event information is output based on the basic field information of the second target event.

[0021] According to a complaint warning method provided by the present invention, comparing the service perception indicators of users before and after the abnormal behavior event occurs and determining that users whose service perception indicators have not improved are high-complaint risk users includes:

[0022] Calculate the service perception index year-on-year based on the user service perception index within a first preset time range before the abnormal behavior event occurs and within a second preset time range after the abnormal behavior event occurs;

[0023] If the service perception indicator is within a preset range on a month-on-month basis, the user is determined to be a high-complaint risk user.

[0024] According to a complaint warning method provided by the present invention, clustering analysis is performed on the abnormal behavior events to obtain multiple candidate cells, and determining that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high complaint risk cells includes:

[0025] Based on the time dimension and the cell dimension, cluster analysis is performed on the abnormal behavior events to obtain multiple candidate cells;

[0026] For each candidate cell, associating a cell performance indicator of the candidate cell according to the number of abnormal behavior events and the cell ID of the candidate cell;

[0027] If the cell performance index of the candidate cell is less than a preset performance index threshold, the cell is determined to be a high complaint risk cell.

[0028] The present invention also provides a complaint warning device, comprising:

[0029] An acquisition module is used to acquire signaling XDR data and identify an abnormal behavior event of a user restarting the terminal based on the signaling XDR data;

[0030] A comparison module is used to compare the service perception indicators of users before and after the abnormal behavior event occurs, and determine that users whose service perception indicators have not improved are high-complaint risk users;

[0031] A clustering module is configured to perform cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determine that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high complaint risk cells;

[0032] The early warning module is used to issue complaint early warning based on the high complaint risk users and the high complaint risk cells.

[0033] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the complaint warning method described above is implemented.

[0034] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the complaint warning methods described above.

[0035] The present invention also provides a computer program product, comprising a computer program, which implements any of the above-mentioned complaint warning methods when executed by a processor.

[0036] The complaint warning method, device, electronic device, storage medium, and product provided by the present invention identify abnormal behavior events of users restarting their terminals based on signaling XDR data, compare the service perception indicators of users before and after the abnormal behavior events, determine that users whose service perception indicators have not improved are high-complaint risk users, and perform cluster analysis on abnormal behavior events to obtain multiple candidate cells, and determine that candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells. This can output two types of warnings: high-complaint risk users and high-complaint risk cells. The present invention associates the abnormal behavior of users restarting their terminals with network problems to perform high-complaint risk analysis. On the one hand, it can include users with poor service experience perception into the potential complaint analysis range and identify high-complaint risk users. On the other hand, it can discover potential problem network areas and identify network areas with high complaint risks, thereby realizing pre-complaint analysis, which is conducive to timely and comprehensive discovery of network problems and reducing user complaint rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is one of the flow charts of the complaint warning method provided by an embodiment of the present invention.

[0039] Figure 2 This is the second flow chart of the complaint warning method provided by the embodiment of the present invention.

[0040] Figure 3 The present invention provides a flowchart of an abnormal terminal restart event.

[0041] Figure 4 It is a structural diagram of the complaint warning device provided by an embodiment of the present invention.

[0042] Figure 5 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0044] In recent years, the use of DPI (Deep Packet Inspection) technology (also known as behavioral pattern recognition) to analyze user behavior has received increasing attention. Before implementing behavioral pattern recognition technology, operators typically conduct research on various end-user behaviors and build behavioral recognition models based on this research. Based on this model, they can determine the user's current or upcoming actions based on their past actions.

[0045] Currently, operators can use DPI technology to analyze user behavior characteristics to provide differentiated services. Using DPI technology and cluster crawler technology, they have achieved precise analysis of user behavior on the LTE (Long Term Evolution, or 4th generation mobile communication network) network based on protocol decoding and XDR (Extended Data Record) synthesis. The DPI-based user behavior analysis model is mainly divided into the following modules:

[0046] (1) Data acquisition and sampling module: mainly responsible for implementing the interface with the user system and collecting user data.

[0047] (2) Data deep analysis module: mainly responsible for deep analysis of the user's HTTP (Hypertext Transfer Protocol).

[0048] (3) User ID (Identity document) definition library: Mainly responsible for generating a unique ID for the user and performing hash conversion based on the user's IP (Internet Protocol) address to keep the user's identity information confidential.

[0049] (4) User behavior definition library: defines known user behaviors with obvious characteristics.

[0050] (5) User behavior classification: The data input by the deep data module can be classified using the SVM (Support Vector Machine) algorithm, and combined with the user behavior definition library, a comprehensive analysis is performed to obtain user behavior classification. Through in-depth analysis of users, strategies can be formulated to improve user perception and increase user satisfaction.

[0051] When users use data and voice services over mobile networks, to improve service perception, user experience, and user satisfaction, and to reduce user complaint rates, operators typically employ a variety of methods, including network performance indicator early warning, indicator degradation demarcation and location analysis, user satisfaction surveys, and user complaint diagnosis and analysis, to analyze, discover, and optimize network quality issues.

[0052] Among them, network performance indicator warning (hereinafter referred to as Solution A) is based on network element-level performance indicator data, such as the cell-level attachment success rate indicator, and determines network problems based on indicator thresholds. User complaint analysis and diagnosis (hereinafter referred to as Solution B) extracts the user number, problem time and location information from the complaint ticket, evaluates user service perception indicators based on historical DPI data, and analyzes it in combination with a database of low-quality network elements, terminal databases, and user contract data to identify network problems.

[0053] However, Plan A and Plan B have the following shortcomings:

[0054] (1) Shortcomings of Solution A: Since the granularity of the underlying data source - network management performance data - is only at the network element or network area level, it reflects the average service quality of all users under the entire network element. It is impossible to identify potential users with poor service quality and complain, and does not support proactive complaint prevention.

[0055] (2) Shortcomings of Solution B: It retrospectively analyzes historical data after a problem occurs, and only analyzes a portion of dissatisfied users (i.e., users who generate explicit complaints). This has limitations such as time lag, passive analysis, and inability to reduce the complaint rate. The details are as follows:

[0056] (a) Post-event analysis is not timely: There is a lag between user experience degradation and the generation of complaints. Existing solutions identify and resolve problems late, leading to increased complaints from other users in the poor network area. Furthermore, network problems are dynamic, sporadic, and volatile. Service quality is related to network capacity and load, with service quality degrading during busy hours. Since network load is dynamic, post-event analysis cannot be used for early warning analysis.

[0057] (b) Analyzing only explicitly dissatisfied users: This approach only analyzes complaining users (and the network areas where they generate traffic). However, complaining users account for a very small proportion, and existing solutions have limitations in discovering network problems.

[0058] In response to the above problems, the embodiment of the present invention performs high complaint risk analysis by associating the abnormal behavior of users restarting their terminals with network problems. On the one hand, it can include users with poor service experience perception into the potential complaint analysis scope and identify high complaint risk users. On the other hand, it can discover potential problem network areas and identify network areas with high complaint risks, thereby realizing pre-complaint analysis, which is conducive to timely and comprehensive discovery of network problems and reducing user complaint rates.

[0059] Figure 1 This is one of the flow charts of the complaint warning method provided by the embodiment of the present invention. Figure 1 The embodiment of the present invention provides a complaint warning method, which may include the following steps:

[0060] Step 110: Acquire signaling XDR data, and identify an abnormal behavior event of the user restarting the terminal based on the signaling XDR data.

[0061] It should be noted that the execution entity of the complaint warning method provided in the embodiments of the present invention can be an electronic device, a component of an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, the mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), while the non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), television, ATM, or self-service machine, etc., which are not specifically limited in the embodiments of the present invention.

[0062] The signaling XDR data may refer to an extended data record that records network signaling information in a mobile communication network.

[0063] Users who restart their terminals are likely to file complaints. In an embodiment of the present invention, signaling XDR data can be obtained and, from multiple events recorded in the signaling XDR data, abnormal behavior events caused by users restarting their terminals can be identified. This abnormal behavior event can be used as a signal of a network problem, and the abnormal behavior of users restarting their terminals can be associated with network problems for potential complaint risk analysis. This enables pre-complaint analysis, allows for more comprehensive network optimization, improves user service perception, and thus reduces user complaint rates.

[0064] Step 120 , comparing the service perception indicators of users before and after the abnormal behavior event occurs, and determining that users whose service perception indicators have not improved are high-complaint risk users.

[0065] When users perceive a deterioration in their service experience, they typically attempt to fix the problem themselves before filing a complaint. If the problem remains unresolved, they may file a complaint. This deterioration can include slow data speeds, video freezes, and dropped voice calls. Self-repair measures can include reopening the application, disconnecting and reconnecting, and even restarting the device.

[0066] Restarting a terminal may trigger a series of network signaling interactions, such as cell reselection, network re-registration, UE (User Equipment) context re-establishment, and bearer resource reallocation. If service quality improves significantly after restarting the terminal, this may indicate a network issue (including network-terminal compatibility issues).

[0067] The embodiment of the present invention compares the service perception indicators before and after the abnormal behavior event of the user restarting the terminal, and identifies users whose service perception indicators have not improved as high-complaint risk users, thereby including users with poor service experience perception in the potential complaint analysis scope, which is conducive to more comprehensive discovery of potential network problems and reducing user complaint rates.

[0068] Step 130 : performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining the candidate cells whose cell performance indicators are less than a preset performance indicator threshold as high complaint risk cells.

[0069] A cell may refer to a user distribution area where an abnormal terminal restart event occurs. In embodiments of the present invention, by clustering abnormal terminal restart events by cell, characteristics of the distribution area of ​​users experiencing abnormal terminal restart events can be effectively collected. Based on the clustering results, users with similar abnormal terminal restart events within the same cell can be identified, and user indicators for each area can be analyzed.

[0070] Specifically, each candidate cell may be analyzed to determine whether it is a cell with a high complaint risk based on its cell performance index. If the cell performance index of a candidate cell is less than a preset performance index threshold, the candidate cell may be determined to be a cell with a high complaint risk.

[0071] The embodiment of the present invention can discover potential problem network areas by clustering analysis of abnormal behavior events caused by users restarting their terminals. By further combining cell performance indicators for secondary screening, network areas with high complaint risks are determined from among the potential problem network areas, realizing pre-complaint analysis, which is conducive to timely discovery of network problems and thus reducing user complaint rates.

[0072] Step 140: Produce complaint warning based on the high complaint risk users and the high complaint risk cells.

[0073] Specifically, the embodiment of the present invention can output two types of warnings: high-complaint risk users and high-complaint risk network areas, thereby supporting complaint warnings, so that operators can proactively care for high-complaint risk users and optimize networks in high-complaint risk network areas, thereby improving network quality and enhancing user service perception, thereby reducing user complaint rates.

[0074] Figure 2 This is the second flow chart of the complaint warning method provided by the embodiment of the present invention. Figure 2 In an embodiment of the present invention, after identifying a terminal restart abnormal event, user analysis and cluster analysis can be performed based on the terminal restart abnormal event.

[0075] When conducting user analysis, you can compare the user's business perception indicators before and after the abnormal behavior event occurs. If the business perception indicators improve (indicating an improvement in user experience), the complaint warning analysis ends; if the business perception indicators do not improve (indicating no improvement in user experience), the users whose business perception indicators have not improved are identified as high-complaint risk users, and a high-complaint risk user warning is issued, and proactive care is provided to high-complaint risk users.

[0076] When performing cluster analysis, abnormal behavior events can be clustered based on time and regional dimensions, and secondary screening can be performed by associating cell-level performance indicators to identify high-complaint risk cells, issue early warnings for high-complaint risk areas, and optimize the network in high-complaint risk areas.

[0077] The embodiment of the present invention identifies abnormal behavior events of users restarting their terminals based on signaling XDR data, compares the service perception indicators of users before and after the abnormal behavior events, determines that users whose service perception indicators have not improved are high-complaint risk users, and performs cluster analysis on abnormal behavior events to obtain multiple candidate cells, and determines that candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells. This can output two types of early warnings: high-complaint risk users and high-complaint risk cells. The embodiment of the present invention associates the abnormal behavior of users restarting their terminals with network problems to perform high-complaint risk analysis. On the one hand, it can include users with poor service experience perception into the potential complaint analysis range and identify high-complaint risk users. On the other hand, it can discover potential problem network areas and identify network areas with high complaint risks, thereby realizing pre-complaint analysis, which is conducive to timely and comprehensive discovery of network problems and reducing user complaint rates.

[0078] Based on any of the foregoing embodiments, identifying an abnormal behavior event of a user restarting a terminal based on the signaling XDR data may specifically include:

[0079] Step 111: Identify a user restarting terminal behavior event based on the signaling XDR data.

[0080] In the embodiment of the present invention, the behavior event of the user restarting the terminal can be identified based on the signaling XDR data, and then the abnormal behavior event can be determined according to the user service experience and the terminal restart frequency before the terminal restart behavior event.

[0081] Step 112: If the user's service perception index before the behavior event occurs is less than a preset perception index threshold, the behavior event is determined to be an abnormal behavior event.

[0082] In the embodiment of the present invention, the user service perception in a period before the terminal restart event occurs may be evaluated based on the user plane XDR data.

[0083] Specifically, at least one service perception indicator of the user before the behavior event occurs can be compared with the corresponding perception indicator threshold. If at least one service perception indicator is less than the corresponding perception indicator threshold, that is, if any service perception indicator is less than the corresponding perception indicator threshold, it indicates that the user's service experience was poor before the restart. It can be determined that the terminal restart behavior event was caused by the user's service perception degradation, and the terminal restart behavior event is marked as a type 1 terminal restart abnormal behavior event.

[0084] Among them, service perception indicators may include large packet download rate, small packet delay, wireless side TCP handshake delay, etc.

[0085] Step 113: If the occurrence frequency of the behavior event exceeds a preset frequency threshold, the behavior event is determined to be an abnormal behavior event.

[0086] In an embodiment of the present invention, regardless of whether user plane XDR data is generated before the restart and whether the service perception is degraded, if the frequency of the terminal restart behavior event exceeds the preset frequency threshold, it means that the user terminal is frequently restarted, then the terminal restart behavior event can be marked as a type 2 terminal restart abnormal behavior event.

[0087] The preset frequency threshold can be set according to actual needs, for example, 5 times / hour, and the present invention is not limited thereto.

[0088] Figure 3 This is a flow chart of identifying abnormal terminal restart events provided by an embodiment of the present invention. Figure 3 After identifying a shutdown event, it is possible to determine whether there is a power-on event within the window time. If there is a power-on event within the window time, the user service perception in the period before the terminal restart event occurs can be evaluated.

[0089] If the user's service perception is poor before the restart, it can be determined that it is an abnormal behavior event of the terminal restart; if the user's service perception is good before the restart, it can be determined whether the user terminal restarts frequently. If the user terminal restarts frequently, it can be determined that it is an abnormal behavior event of the terminal restart.

[0090] The embodiment of the present invention identifies terminal restart behavior events based on signaling XDR data, and then determines abnormal behavior events based on user service perception and terminal restart frequency before the terminal restart behavior event, so as to associate the abnormal behavior events of the user restarting the terminal with network problems for high complaint risk analysis, thereby realizing pre-complaint analysis and network optimization, which is conducive to reducing user complaint rates.

[0091] Based on any of the foregoing embodiments, identifying a behavior event of a user restarting a terminal based on the signaling XDR data may specifically include:

[0092] Step 1111: If the description field information of the first target event in the signaling XDR data meets the shutdown event identification condition, output the shutdown event information based on the basic field information of the first target event.

[0093] In an embodiment of the present invention, it is possible to determine whether the description field information of the first target event in the signaling XDR data meets the shutdown event identification condition. The description field information may include at least an interface type field, a process type field, a detach type keyword field, and a trigger mode keyword field.

[0094] According to 3GPP (3rd Generation Partnership Project) specifications, the fourth bit of the first octet of the detach type information field (Detach Type IE) in the detach request message from the user terminal to the network indicates the detach type. A 1 indicates a power-off condition. Therefore, a detach request message initiated by the UE due to power-off must follow the above message encoding.

[0095] Specifically, the shutdown event identification conditions can be as follows: interface type = 5 (i.e. S1-MME, Mobility Management Entity, mobility management entity) and process type = 6 (i.e. detachment) and bit 4 of keyword 1 (i.e. detachment type) is 1 (i.e. switch off) and keyword 2 (i.e. trigger mode) = 1 (i.e. initiated by the user terminal device).

[0096] In this embodiment of the present invention, if the description field information of the first target event in the signaling XDR data meets the shutdown event identification conditions, the shutdown event information can be output based on the basic field information of the first target event. The basic field information can include at least a time field, a user number field, and a cell field, and the shutdown event information can include at least the shutdown time, user number, and shutdown cell.

[0097] Specifically, for an XDR that matches the shutdown event identification condition, the following shutdown event information may be output, as shown in Table 1:

[0098] Table 1

[0099]

[0100] Step 1112: If the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, output the power-on event information based on the basic field information of the second target event.

[0101] In an embodiment of the present invention, it is possible to determine whether the description field information of the second target event in the signaling XDR data meets the shutdown event identification condition. The description field information may include at least an interface type field, a process type field, a user number field, and a time field.

[0102] Specifically, the power-on event identification condition may be as follows: interface type=5 (S1-MME) and procedure type=1 (attach) and msisdn=power-off event user number and (start_time-power-off time)<60 seconds.

[0103] In an embodiment of the present invention, if the description field information of the second target event in the signaling XDR data meets the power-on event identification conditions, power-on event information can be output based on the basic field information of the second target event. The basic field information may include a time field, a user number field, and a cell field, and the power-on event information may include at least the power-on event, the user number, and the power-on cell.

[0104] Specifically, for an XDR that matches the power-on event identification conditions, the following power-on event information can be output, as shown in Table 2:

[0105] Table 2

[0106]

[0107] Step 1113: Obtain a user restarting terminal behavior event based on the shutdown event information and the startup event information.

[0108] The information of the behavior event of the user restarting the terminal may include at least the user number, the shutdown time, the startup event, the shutdown cell and the startup cell.

[0109] In an embodiment of the present invention, a terminal restart behavior event may be formed based on information of a pair of mutually related shutdown events and power-on events. The following information of the user restarting the terminal behavior event may be output, as shown in Table 3:

[0110] Table 3

[0111]

[0112] In an embodiment of the present invention, after identifying a terminal restart abnormal event of type 1 and a terminal restart abnormal event of type 2, the terminal restart abnormal events of type 1 and type 2 may be merged to output abnormal behavior event information of the terminal restart, as shown in Table 4:

[0113] Table 4

[0114]

[0115] The embodiment of the present invention identifies terminal restart behavior events by utilizing the detachment request message initiated by the user terminal device due to shutdown and the message coding of the startup, realizes the identification of terminal restart behavior events based on XDR data and then associates them with network problems to perform high complaint risk analysis, thereby realizing pre-complaint analysis, which is conducive to reducing user complaint rates.

[0116] Based on any of the above embodiments, the shutdown event information includes at least a user number and a shutdown time; if the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event may specifically include:

[0117] Step 11121: searching the signaling XDR data for a second target event corresponding to the user with the user number after the shutdown time, based on the user number and the shutdown time of the shutdown event;

[0118] Step 11122: If the description field information of the second target event meets the power-on event identification condition, output the power-on event information based on the basic field information of the second target event.

[0119] In this embodiment of the present invention, after outputting the shutdown event information, the user ID and shutdown time of the shutdown event can be used to determine whether the user corresponding to the user ID has had an attach process within a certain period of time after the shutdown time, for example, within one minute. The first attach process that matches is the power-on event.

[0120] Specifically, the power-on event within a certain period of time after the shutdown time can be searched. If the description field information of the second target event meets the power-on event identification conditions, the second target event can be determined to be a power-on event associated with the shutdown event, and the power-on event information can be output based on the basic field information of the second target event.

[0121] The embodiment of the present invention searches for the power-on event associated with the shutdown event based on the user number and shutdown time of the shutdown event, and then determines the terminal restart event, thereby performing complaint risk analysis based on the terminal restart behavior event and associating it with network problems, thereby realizing pre-complaint analysis and helping to reduce user complaint rates.

[0122] Based on any of the above embodiments, comparing the service perception indicators of users before and after the abnormal behavior event occurs and determining that users whose service perception indicators have not improved are high-complaint risk users may specifically include:

[0123] Step 121, calculating a year-on-year comparison of service perception indicators based on user service perception indicators within a first preset time range before and a second preset time range after the abnormal behavior event occurs;

[0124] Step 122: If the service perception indicator is within a preset range on a month-on-month basis, the user is determined to be a high-complaint risk user.

[0125] In an embodiment of the present invention, for type 1 terminal restart abnormal events, it can be determined whether the user service perception index after the restart is significantly improved compared to before the restart. If there is no obvious improvement, the user can be determined as a high complaint risk user.

[0126] Specifically, based on user-plane XDR data, the user's service perception can be evaluated within a first preset time range before and a second preset time range after the abnormal terminal restart event, that is, within a certain period before and after the abnormal terminal restart event, and the year-on-year comparison of various service perception indicators can be calculated. If at least one service perception indicator is within the corresponding preset year-on-year range, it indicates that at least one service perception indicator has not significantly improved, and the user can be determined to be a high-complaint risk user.

[0127] The service perception indicators may include at least the large packet download rate, small packet latency, and wireless side TCP handshake latency for each service. The preset year-over-year range may be set to 90%-110%. The first preset time range and the second preset time range may be the same or different, for example, both may be set to 1 hour.

[0128] In the embodiment of the present invention, for type 2 terminal restart abnormal events, that is, terminals that restart frequently, users can be directly identified as high-complaint risk users.

[0129] The related technical solution is to analyze complaints based on users with poor experience, unresolved problems, and complaints, that is, users with explicit poor experience, which makes it difficult to discover potential complaint risks. The embodiment of the present invention uses user service perception indicators to identify users with poor experience, self-service solutions, and no complaints, that is, users with implicit poor experience. By incorporating users with implicit poor experience into the complaint risk analysis, the scope of analyzed users can be expanded, which is conducive to more comprehensive discovery of potential network problems.

[0130] Based on any of the above embodiments, performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining that a candidate cell having a cell performance index less than a preset performance index threshold is a high complaint risk cell may specifically include:

[0131] Step 131: performing cluster analysis on the abnormal behavior events based on the time dimension and the cell dimension to obtain multiple candidate cells;

[0132] Step 132: for each candidate cell, correlating the cell performance index of the candidate cell according to the number of abnormal behavior events and the cell ID of the candidate cell;

[0133] Step 133: If the cell performance index of the candidate cell is less than a preset performance index threshold, the cell is determined to be a high complaint risk cell.

[0134] Specifically, a two-dimensional coordinate system can be constructed based on the time dimension and the cell dimension, abnormal behavior events can be mapped into the two-dimensional coordinate system, and cluster analysis can be performed on the abnormal behavior events in the two-dimensional coordinate system to obtain multiple candidate cells with complaint risks.

[0135] Specifically, the number of abnormal behavior events and the cell ID of the candidate cell can be correlated with cell-level indicators such as coverage, interference, and capacity load. If at least one cell performance indicator is below the corresponding performance indicator threshold, it indicates that the cell not only has a high incidence of abnormal terminal restart events but also has degraded cell performance. The cell can be judged as a high-complaint risk cell.

[0136] Clustering cells based on abnormal terminal reboot events can be influenced by user behavior, while judging based on network element performance indicator degradation thresholds also struggles to fully and accurately reflect user perceptions. This embodiment combines the results of these two analyses for secondary screening, ultimately identifying cells and network areas with high complaint risks. This helps comprehensively identify network issues and reduce user complaint rates.

[0137] Existing technical solutions usually conduct complaint risk analysis based on traditional user perception indicators such as speed, latency, success rate, etc. When the user service experience deteriorates, such as slow Internet access, video freezing, and dropped calls, users usually perform simple self-repairs such as restarting the terminal. The embodiments of the present invention incorporate abnormal user behavior events into network quality analysis, associate user self-troubleshooting behavior with network problems, and use XDR to compare service quality indicators before and after shutdown and after startup for a certain period of time, or the frequency of abnormal events within a certain time sliding window, to identify high-complaint risk users, and identify high-complaint risk areas based on time and cell clustering of terminal restart abnormal events. This can achieve pre-complaint analysis, facilitate timely and comprehensive discovery of network problems, and reduce user complaint rates.

[0138] The complaint warning device provided by the present invention is described below. The complaint warning device described below and the complaint warning method described above can be referenced to each other.

[0139] Figure 4 This is a schematic diagram of the structure of the complaint warning device provided by the embodiment of the present invention. Figure 3 The embodiment of the present invention provides a complaint warning device, which may include the following modules:

[0140] An acquisition module 410 is configured to acquire signaling XDR data and identify an abnormal behavior event of a user restarting a terminal based on the signaling XDR data;

[0141] Comparison module 420, configured to compare the service perception indicators of users before and after the abnormal behavior event occurs, and determine that users whose service perception indicators have not improved are high-complaint risk users;

[0142] A clustering module 430 is configured to perform cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determine that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high complaint risk cells;

[0143] The early warning module 440 is configured to generate complaint early warnings based on the high complaint risk users and the high complaint risk cells.

[0144] In an optional embodiment, the acquisition module 410 is specifically configured to:

[0145] Based on the signaling XDR data, identifying a behavior event of a user restarting the terminal;

[0146] If the user's service perception index before the behavior event occurs is less than a preset perception index threshold, the behavior event is determined to be an abnormal behavior event;

[0147] If the occurrence frequency of the behavior event exceeds a preset frequency threshold, the behavior event is determined to be an abnormal behavior event.

[0148] In an optional embodiment, the acquisition module 410 is specifically configured to:

[0149] If the description field information of the first target event in the signaling XDR data meets the shutdown event identification condition, outputting the shutdown event information based on the basic field information of the first target event;

[0150] If the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event;

[0151] According to the information of the shutdown event and the information of the power-on event, a behavior event of the user restarting the terminal is obtained.

[0152] In an optional embodiment, the shutdown event information includes at least a user number and a shutdown time; the acquisition module 410 is specifically configured to:

[0153] According to the user number and shutdown time of the shutdown event, searching the signaling XDR data for a second target event of the user corresponding to the user number after the shutdown time;

[0154] If the description field information of the second target event meets the power-on event identification condition, the power-on event information is output based on the basic field information of the second target event.

[0155] In an optional embodiment, the comparison module 420 is specifically configured to:

[0156] Calculate the service perception index year-on-year based on the user service perception index within a first preset time range before the abnormal behavior event occurs and within a second preset time range after the abnormal behavior event occurs;

[0157] If the service perception indicator is within a preset range on a month-on-month basis, the user is determined to be a high-complaint risk user.

[0158] In an optional embodiment, the clustering module 430 is specifically configured to:

[0159] Based on the time dimension and the cell dimension, cluster analysis is performed on the abnormal behavior events to obtain multiple candidate cells;

[0160] For each candidate cell, associating a cell performance indicator of the candidate cell according to the number of abnormal behavior events and the cell ID of the candidate cell;

[0161] If the cell performance index of the candidate cell is less than a preset performance index threshold, the cell is determined to be a high complaint risk cell.

[0162] The embodiment of the present invention identifies abnormal behavior events of users restarting their terminals based on signaling XDR data, compares the service perception indicators of users before and after the abnormal behavior events, determines that users whose service perception indicators have not improved are high-complaint risk users, and performs cluster analysis on abnormal behavior events to obtain multiple candidate cells, and determines that candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells. This can output two types of early warnings: high-complaint risk users and high-complaint risk cells. The embodiment of the present invention associates the abnormal behavior of users restarting their terminals with network problems to perform high-complaint risk analysis. On the one hand, it can include users with poor service experience perception into the potential complaint analysis range and identify high-complaint risk users. On the other hand, it can discover potential problem network areas and identify network areas with high complaint risks, thereby realizing pre-complaint analysis, which is conducive to timely and comprehensive discovery of network problems and reducing user complaint rates.

[0163] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communications bus 540. The processor 510 may call logic instructions in the memory 530 to execute a complaint warning method, which includes: obtaining signaling XDR data, and identifying an abnormal behavior event in which a user restarts a terminal based on the signaling XDR data; comparing the user's service perception indicators before and after the abnormal behavior event, and determining that users whose service perception indicators have not improved are high-complaint risk users; performing cluster analysis on the abnormal behavior event to obtain multiple candidate cells, and determining that candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells; and issuing complaint warnings based on the high-complaint risk users and the high-complaint risk cells.

[0164] Furthermore, the logic instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0165] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the complaint warning method provided by the above methods, which includes: obtaining signaling XDR data, and identifying abnormal behavior events of users restarting the terminal based on the signaling XDR data; comparing the service perception indicators of users before and after the abnormal behavior event occurs, and determining that users whose service perception indicators have not improved are high-complaint risk users; performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells; and performing complaint warnings based on the high-complaint risk users and the high-complaint risk cells.

[0166] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the complaint warning method provided by the above-mentioned methods, the method comprising: obtaining signaling XDR data, and identifying abnormal behavior events of users restarting their terminals based on the signaling XDR data; comparing the service perception indicators of users before and after the abnormal behavior event occurs, and determining that users whose service perception indicators have not improved are high-complaint risk users; performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high-complaint risk cells; and performing complaint warnings based on the high-complaint risk users and the high-complaint risk cells.

[0167] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0168] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A complaint warning method, characterized in that: include: Acquire signaling XDR data, and identify an abnormal behavior event of a user restarting the terminal based on the signaling XDR data; The signaling XDR data refers to an extended data record that records network signaling information in a mobile communication network; Comparing the user's service perception indicators before and after the abnormal behavior event occurs, and determining that the users whose service perception indicators have not improved are high-complaint risk users; Performing cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determining that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high complaint risk cells; Providing complaint warning based on the high complaint risk users and the high complaint risk cells; The identifying, based on the signaling XDR data, an abnormal behavior event of a user restarting a terminal includes: Based on the signaling XDR data, identifying a behavior event of a user restarting the terminal; If the user's service perception index before the behavior event occurs is less than a preset perception index threshold, the behavior event is determined to be an abnormal behavior event; If the occurrence frequency of the behavior event exceeds a preset frequency threshold, the behavior event is determined to be an abnormal behavior event; The identifying, based on the signaling XDR data, a behavior event of a user restarting the terminal includes: If the description field information of the first target event in the signaling XDR data meets the shutdown event identification condition, outputting the shutdown event information based on the basic field information of the first target event; If the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event; Obtaining a user restarting terminal behavior event based on the shutdown event information and the startup event information; The cluster analysis of the abnormal behavior events to obtain multiple candidate cells, and determining a candidate cell whose cell performance index is less than a preset performance index threshold as a high complaint risk cell, includes: Based on the time dimension and the cell dimension, cluster analysis is performed on the abnormal behavior events to obtain multiple candidate cells; For each candidate cell, associating a cell performance indicator of the candidate cell according to the number of abnormal behavior events and the cell ID of the candidate cell; If the cell performance index of the candidate cell is less than a preset performance index threshold, the cell is determined to be a high complaint risk cell.

2. The complaint warning method according to claim 1, characterized in that: The information of the shutdown event includes at least a user number and a shutdown time; if the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event, including: According to the user number and shutdown time of the shutdown event, searching the signaling XDR data for a second target event of the user corresponding to the user number after the shutdown time; If the description field information of the second target event meets the power-on event identification condition, the power-on event information is output based on the basic field information of the second target event.

3. The complaint warning method according to claim 1, characterized in that: The comparing the service perception indicators of users before and after the abnormal behavior event occurs and determining that users whose service perception indicators have not improved are high-complaint risk users includes: Calculate the service perception index year-on-year based on the user service perception index within a first preset time range before the abnormal behavior event occurs and within a second preset time range after the abnormal behavior event occurs; If the service perception indicator is within a preset range on a month-on-month basis, the user is determined to be a high-complaint risk user.

4. A complaint warning device, characterized in that: include: An acquisition module is used to acquire signaling XDR data and identify an abnormal behavior event of a user restarting the terminal based on the signaling XDR data; The signaling XDR data refers to an extended data record that records network signaling information in a mobile communication network; A comparison module is used to compare the service perception indicators of users before and after the abnormal behavior event occurs, and determine that users whose service perception indicators have not improved are high-complaint risk users; A clustering module is configured to perform cluster analysis on the abnormal behavior events to obtain multiple candidate cells, and determine that the candidate cells whose cell performance indicators are less than a preset performance indicator threshold are high complaint risk cells; An early warning module is used to issue complaint early warnings based on the high complaint risk users and the high complaint risk cells; The identifying, based on the signaling XDR data, an abnormal behavior event of a user restarting a terminal includes: Based on the signaling XDR data, identifying a behavior event of a user restarting the terminal; If the user's service perception index before the behavior event occurs is less than a preset perception index threshold, the behavior event is determined to be an abnormal behavior event; If the occurrence frequency of the behavior event exceeds a preset frequency threshold, the behavior event is determined to be an abnormal behavior event; The identifying, based on the signaling XDR data, a behavior event of a user restarting the terminal includes: If the description field information of the first target event in the signaling XDR data meets the shutdown event identification condition, outputting the shutdown event information based on the basic field information of the first target event; If the description field information of the second target event in the signaling XDR data meets the power-on event identification condition, outputting the power-on event information based on the basic field information of the second target event; Obtaining a user restarting terminal behavior event based on the shutdown event information and the startup event information; The cluster analysis of the abnormal behavior events to obtain multiple candidate cells, and determining a candidate cell whose cell performance index is less than a preset performance index threshold as a high complaint risk cell, includes: Based on the time dimension and the cell dimension, cluster analysis is performed on the abnormal behavior events to obtain multiple candidate cells; For each candidate cell, associating a cell performance indicator of the candidate cell according to the number of abnormal behavior events and the cell ID of the candidate cell; If the cell performance index of the candidate cell is less than a preset performance index threshold, the cell is determined to be a high complaint risk cell.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the complaint warning method according to any one of claims 1 to 4 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the complaint warning method according to any one of claims 1 to 4 is implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the complaint warning method according to any one of claims 1 to 4 is implemented.

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