Method and device for analyzing problems of mobile communication network and electronic equipment

Abstract

The invention discloses a mobile communication network problem analysis method and apparatus, and an electronic device. The method comprises the following steps: firstly, acquiring complaint data, signaling data and network performance index data in a mobile network management system; the complaint data carries a mobile communication network problem, and the signaling data, the network performance index data and the complaint data are associated through a user identifier; determining a resident area of the user according to the signaling data; and determining a cell with poor user perception quality according to the complaint data, determining a target cell according to the resident area and the cell with poor user perception quality; screening out performance index data of the target cell from the network performance index data; identifying abnormal data from the performance index data of the target cell based on a preset threshold value; and finally, based on the abnormal data, performing root cause positioning on the mobile communication network problem so as to complete the analysis of the mobile communication network problem. According to the method, the mobile communication network problem and the root cause thereof can be accurately identified.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a method, device and electronic equipment for analyzing problems in a mobile communication network. Background Art

[0002] With the widespread adoption of 4G and 5G technologies, the mobile communications environment has become increasingly complex and volatile, presenting operators with unprecedented challenges in ensuring network quality. User experience directly impacts the operator's reputation, and when users encounter network issues, they often express their dissatisfaction through complaints. However, current technical approaches for handling user complaints mostly rely on single-source signaling or performance data for network monitoring. While these methods can detect and identify anomalies to a certain extent, they lack the ability to conduct in-depth analysis from a multi-dimensional and global perspective, often resulting in blind investigations and difficulty accurately identifying network issues and their root causes. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art and to propose a method, device, and electronic device for analyzing mobile communication network problems. The method can accurately identify mobile communication network problems and their root causes.

[0004] In a first aspect, the present invention provides a method for analyzing a mobile communication network problem, the method comprising the following steps:

[0005] Obtain complaint data, signaling data, and network performance indicator data from the mobile network management system;

[0006] Among them, complaint data carries mobile communication network issues, and signaling data, network performance indicator data, and complaint data are associated through user identification;

[0007] Determine the user's permanent location based on signaling data; and determine the cell where the user perceives poor quality based on complaint data;

[0008] Determine the target cell based on the resident area and the cell with poor user perception quality;

[0009] Filtering performance indicator data of a target cell from network performance indicator data;

[0010] Based on a preset threshold, identify abnormal data from the performance indicator data of the target cell;

[0011] Based on abnormal data, the root cause of mobile communication network problems is located, thereby completing the analysis of mobile communication network problems.

[0012] Furthermore, signaling data and network performance indicator data are obtained from the mobile network management system through the Kafka tool.

[0013] Furthermore, the user's resident area is determined based on the signaling data, specifically including the following steps:

[0014] Divide signaling data into daytime and nighttime time periods;

[0015] Based on the daytime time period, the user's connection time for each CELL_ID is counted to find the first CELL_ID with the longest residence time; and based on the nighttime time period, the user's connection time for each CELL_ID is counted to find the second CELL_ID with the longest residence time;

[0016] The geographical area corresponding to the first CELL_ID is used as the user's office area; and the geographical area corresponding to the second CELL_ID is used as the user's home area;

[0017] The user's permanent area is obtained by summarizing the office area and home area.

[0018] Furthermore, based on a preset threshold, abnormal data is identified from the performance indicator data of the target cell, specifically including:

[0019] If the overlapping coverage in the performance indicator data of the target cell is greater than a preset overlapping coverage threshold, it is determined that the overlapping coverage is abnormal; and / or,

[0020] If the MR good coverage rate in the performance indicator data of the target cell is less than a preset good coverage rate threshold, it is determined that the MR good coverage rate is abnormal; and / or,

[0021] If the uplink interference in the performance indicator data of the target cell is greater than a preset uplink interference threshold, then the uplink interference is determined to be abnormal; and / or,

[0022] If the modulo 3 or modulo 30 in the analog-to-digital interference in the performance indicator data of the target cell is greater than the preset analog-to-digital interference threshold, it is determined that the analog-to-digital interference is abnormal; and / or,

[0023] If the handover success rate in the performance indicator data of the target cell is less than a preset handover success rate threshold, it is determined that the handover success rate is abnormal; and / or,

[0024] If the PRB utilization in the performance indicator data of the target cell is greater than a preset PRB utilization threshold, it is determined that the PRB utilization is abnormal; and / or,

[0025] If there is a station disconnection alarm or a service outage alarm in the performance indicator data of the target cell, it is determined that the base station is abnormal.

[0026] Furthermore, based on abnormal data, root causes of mobile communication network problems are located, including:

[0027] Based on the abnormal overlapping coverage, it is determined that the problem of the mobile communication network is an antenna tilt problem or excessive power; and / or,

[0028] Determining that the mobile communication network problem is a weak coverage problem based on the abnormality of the MR good coverage; and / or,

[0029] According to the uplink interference anomaly, determining that the mobile communication network problem is an uplink interference problem that is too high; and / or,

[0030] Determining that the mobile communication network problem is a parameter configuration problem based on the analog-to-digital interference anomaly; and / or

[0031] Determining that the mobile communication network problem is a system interoperability problem based on the abnormal handover success rate; and / or

[0032] Determining that the problem of the mobile communication network is excessive resource load based on abnormal PRB utilization; and / or

[0033] Based on the base station abnormality, it is determined that the mobile communication network problem is a base station failure problem.

[0034] In a second aspect, the present invention provides a device for analyzing mobile communication network problems, the device comprising:

[0035] An acquisition unit, used to acquire complaint data, signaling data, and network performance indicator data from a mobile network management system;

[0036] Among them, complaint data carries mobile communication network issues, and signaling data, network performance indicator data, and complaint data are associated through user identification;

[0037] A determination unit, connected to the acquisition unit, configured to determine a user's resident area based on the signaling data; and to determine a cell with poor user-perceived quality based on the complaint data;

[0038] an obtaining unit connected to the determining unit, configured to determine a target cell based on the resident area and the cell with poor user-perceived quality;

[0039] a screening unit, connected to the obtaining unit, for screening the performance indicator data of the target cell from the network performance indicator data;

[0040] an identification unit, connected to the screening unit, for identifying abnormal data from the performance indicator data of the target cell based on a preset threshold;

[0041] The positioning unit is connected to the identification unit and is used to locate the root cause of the mobile communication network problem based on the abnormal data, thereby completing the analysis of the mobile communication network problem.

[0042] Furthermore, the determination unit includes:

[0043] A division module is used to divide the signaling data into daytime and nighttime time periods according to time periods;

[0044] A first processing module, connected to the partitioning module, is configured to count the user's connection duration for each CELL_ID during the daytime period and identify the first CELL_ID with the longest stay time; and further configured to use the geographical range corresponding to the first CELL_ID as the user's office area;

[0045] A second processing module, connected to the partitioning module, is configured to count the user's connection duration for each CELL_ID during the night time period and identify the second CELL_ID with the longest stay time; and further configured to use the geographical range corresponding to the second CELL_ID as the user's home area;

[0046] The aggregation module is connected to the first processing module and the second processing module respectively, and is used to aggregate according to the office area and the home area to obtain the user's permanent area.

[0047] Further, the identification unit includes a first identification unit, and / or a second identification unit, and / or a third identification unit, and / or a fourth identification unit, and / or a fifth identification unit, and / or a sixth identification unit, and / or a seventh identification unit;

[0048] The first identification unit is configured to determine that the overlapping coverage is abnormal when the overlapping coverage in the performance indicator data of the target cell is greater than a preset overlapping coverage threshold;

[0049] The second identification unit is configured to determine that the MR good coverage rate is abnormal when the MR good coverage rate in the performance indicator data of the target cell is less than a preset good coverage rate threshold;

[0050] The third identification unit is configured to determine that the uplink interference is abnormal when the uplink interference in the performance indicator data of the target cell is greater than a preset uplink interference threshold;

[0051] The fourth identification unit is configured to determine that analog-to-digital interference is abnormal when the modulo 3 or modulo 30 in the analog-to-digital interference in the performance indicator data of the target cell is greater than a preset analog-to-digital interference threshold;

[0052] The fifth identification unit is configured to determine that the handover success rate is abnormal when the handover success rate in the performance indicator data of the target cell is less than a preset handover success rate threshold;

[0053] The sixth identification unit is configured to determine that the PRB utilization is abnormal when the PRB utilization in the performance indicator data of the target cell is greater than a preset PRB utilization threshold;

[0054] The seventh identification unit is configured to determine that the base station is abnormal when a station disconnection alarm or a service withdrawal alarm exists in the performance indicator data of the target cell.

[0055] Further, the positioning unit includes a first positioning unit, and / or a second positioning unit, and / or a third positioning unit, and / or a fourth positioning unit, and / or a fifth positioning unit, and / or a sixth positioning unit, and / or a seventh positioning unit;

[0056] The first positioning unit is connected to the first identification unit and is used to determine that the problem of the mobile communication network is an antenna tilt problem or excessive power according to the abnormal overlapping coverage rate;

[0057] The second positioning unit is connected to the second identification unit and is used to determine that the mobile communication network problem is a weak coverage problem based on the abnormality of the MR good coverage;

[0058] The third positioning unit is connected to the third identification unit, and is used to determine that the problem of the mobile communication network is a problem of excessive uplink interference based on the uplink interference anomaly;

[0059] The fourth positioning unit is connected to the fourth identification unit and is used to determine that the mobile communication network problem is a parameter configuration problem based on the analog-to-digital interference anomaly;

[0060] The fifth positioning unit is connected to the fifth identification unit and is used to determine that the mobile communication network problem is a system interoperability problem based on the abnormal handover success rate;

[0061] The sixth positioning unit is connected to the sixth identification unit and is configured to determine that the problem of the mobile communication network is an excessive resource load based on the abnormal PRB utilization;

[0062] The seventh positioning unit is connected to the sixth identification unit, and is used to determine that the problem of the mobile communication network is a base station failure problem according to the base station abnormality.

[0063] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the method for analyzing mobile communication network problems according to the first aspect.

[0064] The present invention integrates multi-source data, including complaint data, signaling data, and network performance indicators, to achieve a linkage analysis of the user's actual experience and network status. First, the user's resident area is determined using signaling data, and the poor quality cells perceived by the user are identified in combination with complaint data, thereby accurately locking in the key areas that affect the user experience. Then, by screening the performance indicator data of the target cell, anomalies are automatically identified in combination with preset thresholds, and finally the root cause is located based on the anomaly data. This series of steps enables the present invention to accurately diagnose the specific reasons that affect network quality, effectively improve the efficiency and accuracy of troubleshooting, and at the same time achieve early discovery of potential network risks and prevent problems from occurring. The specific beneficial effects are as follows:

[0065] 1. Accurately locate faults and improve diagnostic efficiency: By correlating user signaling data with complaint data, the present invention can clearly identify the specific cell area where the user perceives the problem. Combined with abnormal data in performance indicators, it can quickly identify the network area that actually causes the user experience to decline and the possible root cause of the fault, significantly improving the accuracy and efficiency of fault diagnosis.

[0066] 2. Improve the proactive early warning capabilities of network operations and maintenance: Because the method introduces preset thresholds to filter abnormal performance indicators, it can proactively detect potential risks before a failure occurs, provide early warning of failures, and implement dynamic monitoring of "detection before it occurs and response before it has an impact," thereby reducing the incidence of sudden network failures and improving network stability.

[0067] 3. Enhance the accuracy of user experience perception: Complaint data and signaling data are combined to analyze users' actual perceived experience. This enables the location of network issues to no longer rely solely on overall network indicators, but to better reflect the true feelings of individual users, thereby enabling targeted network optimization and improving user satisfaction.

[0068] 4. Improve the global insight and decision-making support level of network management: This method comprehensively controls the network status and deeply explores potential problems through the integration of multi-source and multi-dimensional data, providing a refined basis for network optimization and resource scheduling, and enhancing the scientific and intelligent level of network management. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Schematic diagram of a method for analyzing mobile communication network problems in an embodiment of the present invention;

[0070] Figure 2 Schematic diagram of the analysis process of mobile communication network problems in an embodiment of the present invention;

[0071] Figure 3 Schematic diagram of a method and apparatus for analyzing mobile communication network problems according to an embodiment of the present invention;

[0072] Figure 4 2 is a diagram illustrating the architecture of an electronic device in an embodiment of the present invention.

[0073] Reference numerals: 10, acquisition unit, 20, determination unit, 30, determination unit, 40, screening unit, 50, identification unit, 60, positioning unit. DETAILED DESCRIPTION

[0074] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0075] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0076] It is understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments may be combined with each other.

[0077] It can be understood that, for the convenience of description, the drawings of the present invention only show parts related to the present invention, while parts unrelated to the present invention are not shown in the drawings.

[0078] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0079] It will be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0080] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented using a hardware-based system that implements the specified functions, or may be implemented using a combination of hardware and computer instructions.

[0081] It is understandable that the units and modules involved in the embodiments of the present invention may be implemented by software or hardware. For example, the units and modules may be located in a processor.

[0082] Example 1:

[0083] The mobile communication network problem analysis method provided by the present invention is particularly suitable for network usage scenarios in the daily lives of office workers. During the day, users are concentrated in busy business districts and around office buildings, and problems such as signal congestion, slow network speed or disconnection are prone to occur, affecting the work efficiency and communication experience of office workers; at night, they are mainly concentrated in residential communities, commercial districts or entertainment and leisure places. The needs of users and network pressures are different, which may lead to different performance bottlenecks. Through this method, operators can accurately identify affected cell areas in different time periods, combine user complaints, signaling data and performance indicators, quickly locate the root cause of network anomalies, and perform optimization and maintenance in advance to ensure that users can enjoy stable and high-quality mobile communication services on their way to and from get off work and during leisure and entertainment periods, thereby greatly improving user satisfaction and overall network operation efficiency.

[0084] like Figure 1 As shown, the mobile communication network problem analysis method includes the following steps:

[0085] Obtain complaint data, signaling data and network performance indicator data in the mobile network management system; complaint data carries mobile communication network problems, and signaling data, network performance indicator data and complaint data are associated through user identification.

[0086] Specifically, signaling data and network performance indicator data are retrieved from the mobile network management system using Kafka. This data is typically retrieved from the mobile network management system using Kafka, a crucial tool for achieving large-scale, real-time data transmission and efficient processing. In modern communications network architectures, operators collect and push various signaling information (such as control signals for user access, handovers, and disconnections) and performance indicators (such as signal strength, network throughput, latency, and connection quality) to the Kafka streaming platform in real time. Kafka's high throughput, multi-consumer subscriptions, and distributed storage enable real-time transmission, storage, and analysis of massive amounts of data, providing strong support for network fault detection, optimized scheduling, and improved user experience. This approach not only ensures data timeliness and integrity but also facilitates subsequent in-depth analysis and root cause identification, significantly enhancing the intelligence and responsiveness of network operations.

[0087] In addition to Kafka, signaling data and network performance metrics can be acquired using a variety of other tools and technologies, including message queue systems like RabbitMQ and ActiveMQ, as well as APIs provided by enterprise-level monitoring platforms (such as Nagios, Zabbix, and Grafana) or dedicated network management systems (such as NetAct and NetCracker). Furthermore, data can be extracted from network devices and management systems and transferred to analysis platforms using the SNMP protocol, log collection tools, or customized ETL (extraction, transformation, loading) scripts. This allows for real-time or batch acquisition of signaling and performance metrics, meeting data collection requirements in various scenarios.

[0088] Using Kafka tools offers many distinct advantages for data collection and transmission. First, Kafka boasts high throughput and low latency, enabling it to process massive amounts of signaling data and network performance metrics, ensuring real-time performance and reliability. Second, it boasts a distributed architecture that supports horizontal scalability, adapting to the expansion needs of large-scale network environments and ensuring system stability and resilience. Furthermore, Kafka provides powerful data persistence and fault-tolerance mechanisms, ensuring data is not lost and data security is maintained even when some nodes fail. Furthermore, Kafka supports multi-consumer subscriptions, facilitating the pushing of data streams to different analysis platforms or storage systems, enhancing the flexibility and efficiency of data processing. In summary, Kafka's high performance, scalability, and robust ecosystem give it distinct advantages in massive, real-time data transmission scenarios, making it one of the preferred tools for modern large-scale network data processing.

[0089] The user's permanent area is determined based on the signaling data; and the cell with poor user-perceived quality is determined based on the complaint data.

[0090] As a specific implementation, determining the user's resident area based on signaling data specifically includes the following steps:

[0091] Divide signaling data into daytime and nighttime time periods;

[0092] Based on the daytime time period, the user's connection time for each CELL_ID is counted to find the first CELL_ID with the longest residence time; and based on the nighttime time period, the user's connection time for each CELL_ID is counted to find the second CELL_ID with the longest residence time;

[0093] The geographical area corresponding to the first CELL_ID is used as the user's office area; and the geographical area corresponding to the second CELL_ID is used as the user's home area;

[0094] The user's permanent area is obtained by summarizing the office area and home area.

[0095] Target cells are identified based on the resident area and cells with poor user-perceived quality. Specifically, the target cells are determined by performing an intersection analysis of the resident area and cells with poor user-perceived quality. In other words, all cells that are both within the resident area and rated as poor quality by user experience constitute the target cells. This approach helps pinpoint key areas requiring priority attention or optimization, effectively improving overall service quality and user experience.

[0096] Filter the performance indicator data of the target cell from the network performance indicator data.

[0097] Based on a preset threshold, abnormal data is identified from the performance indicator data of the target cell.

[0098] As a specific implementation, based on a preset threshold, identifying abnormal data from the performance indicator data of the target cell specifically includes:

[0099] If the overlapping coverage in the performance indicator data of the target cell is greater than a preset overlapping coverage threshold, it is determined that the overlapping coverage is abnormal; and / or,

[0100] If the MR good coverage rate in the performance indicator data of the target cell is less than a preset good coverage rate threshold, it is determined that the MR good coverage rate is abnormal; and / or,

[0101] If the uplink interference in the performance indicator data of the target cell is greater than a preset uplink interference threshold, then the uplink interference is determined to be abnormal; and / or,

[0102] If the modulo 3 or modulo 30 in the analog-to-digital interference in the performance indicator data of the target cell is greater than the preset analog-to-digital interference threshold, it is determined that the analog-to-digital interference is abnormal; and / or,

[0103] If the handover success rate in the performance indicator data of the target cell is less than a preset handover success rate threshold, it is determined that the handover success rate is abnormal; and / or,

[0104] If the PRB utilization in the performance indicator data of the target cell is greater than a preset PRB utilization threshold, it is determined that the PRB utilization is abnormal; and / or,

[0105] If there is a station disconnection alarm or a service outage alarm in the performance indicator data of the target cell, it is determined that the base station is abnormal.

[0106] Modulo 3 and Modulo 30 are specialized terms in the mobile communications field, primarily used to assess interference issues in wireless networks caused by improper physical cell identifier (PCI) layout. In 4G LTE networks, if the PCIs of adjacent cells have the same remainder when divided by 3 (i.e., the modulo 3 values ​​are identical), reference signal overlap can occur, causing interference and impacting signal quality and communication stability for terminal devices. In 5G NR networks, if the PCIs of adjacent cells have the same remainder when divided by 30 (i.e., the modulo 3 values ​​are identical), this can cause uplink reference signal sequence conflicts, leading to interference and degrading uplink transmission performance. When evaluating the performance metrics of a target cell, if the Modulo 3 or Modulo 30 interference intensity exceeds a preset threshold, it is identified as an Modulo 3 interference anomaly. Prompt investigation and optimization efforts are necessary to ensure normal network operation and communication quality.

[0107] Based on abnormal data, the root cause of mobile communication network problems is located, thereby completing the analysis of mobile communication network problems.

[0108] As a specific implementation method, root cause location of mobile communication network problems based on abnormal data includes:

[0109] Based on the abnormal overlapping coverage, it is determined that the problem of the mobile communication network is an antenna tilt problem or excessive power; and / or,

[0110] Determining that the mobile communication network problem is a weak coverage problem based on the abnormality of the MR good coverage; and / or,

[0111] According to the uplink interference anomaly, determining that the mobile communication network problem is an uplink interference problem that is too high; and / or,

[0112] Determining that the mobile communication network problem is a parameter configuration problem based on the analog-to-digital interference anomaly; and / or

[0113] Determining that the mobile communication network problem is a system interoperability problem based on the abnormal handover success rate; and / or

[0114] Determining that the problem of the mobile communication network is excessive resource load based on abnormal PRB utilization; and / or

[0115] Based on the base station abnormality, it is determined that the mobile communication network problem is a base station failure problem.

[0116] The specific implementation process of this embodiment is as follows Figure 2As shown, this embodiment mainly introduces a network problem root cause analysis method based on signaling data. It mainly analyzes the user's permanent location and movement trajectory through MR, XDR, and alarm data, and then locates the problem cell based on the user's service perception. The root cause of the problem is then located through network data analysis, thereby improving processing efficiency, increasing user satisfaction with complaint handling, and enhancing corporate reputation. The specific process includes the following:

[0117] Step S1: Connect to the mobile network management system through the Kafka tool to collect complaint order data, signaling data, MR data, B domain data, PM data, etc. in real time.

[0118] The specific signaling data is shown in Table 1, the B-domain order data is shown in Table 2, the PM data is shown in Table 3, and the MR data is shown in Table 4.

[0119] Table 1: 4 / 5G mobile network signaling records:

[0120] Msisdn Service start time End of service time CITY_ID NodeB CELL_ID 186XXXX2487 2020-6-1:8:00:00 2020-6-1:8:10:00 110 106544 1065441 longitude latitude Network standard IMEI Sdrtype DCNR 105.8749 25.6987 5G SA 1.23457E+12 attach UE capabilities S1APID SVRNAME Uplink service traffic Downlink service traffic Uplink delay Downlink delay 13453267 A website

[0121] 4 / 5G mobile network signaling record data records the signaling records of each interaction between mobile users and the mobile network, including user attachment, detachment, service request, service detachment, bearer resource application, bearer resource release, terminal capability reporting, tracking area update, etc.

[0122] Among them, DCNR stated:

[0123] Dual connectivity with NR(DCNR)(octet 9,bit 5)

[0124] Whether the UE supports NR dual connectivity.

[0125] This bit indicates the capability for dual connecitivity with NR.

[0126] 0dual connectivity with NR not supported

[0127] 1dual connectivity with NR supported

[0128] N1mode supported(N1mode)(octet 9,bit 6)

[0129] Whether the UE supports the 5G core network.

[0130] This bit indicates the capability for N1mode

[0131] 0N1mode not supported

[0132] 1N1mode supported.

[0133] Table 2: Domain B order data:

[0134] Msisdn Business Type Execution time Completion time User Name Package Information 186XXXX2487 5G package application 2020-6-1:8:00:00 2020-6-1:8:10:00 Zhang San 5G packages

[0135] Domain B order data records the order data related to the user's business operations, including payment, package change, new package, account cancellation, etc. Table 3: PM data:

[0136]

[0137] Performance Management (PM) data is mainly used to monitor and optimize network performance. PM data is used to evaluate the usage of existing network resources and to make reasonable resource allocation and planning based on this data.

[0138] Table 4: MR data:

[0139]

[0140] MR data records network-level signal reception strength, uplink interference, and other records, which can be used to trace the user's coverage performance when using the network.

[0141] Step S2: Analyze the user's permanent area through signaling data, including the permanent base station cell at work and the permanent base station cell at home.

[0142] The user's daily CELL_ID dwell time is grouped and sorted. The maximum dwell time between 9:00 AM and 5:00 PM is counted as CELL_ID_day, and the maximum dwell time between 9:00 PM and 5:00 AM is counted as CELL_ID_night. The user's work-resident base station cell is considered CELL_ID_day, while the user's home-resident base station cell is considered CELL_ID_night. Step S3: Analyze the user's service perception, including service rate and latency, in all cells along the user's trajectory using signaling data.

[0143] The user's service trajectory is traced and analyzed through signaling data, and the user's service volume, uplink and downlink rates, service delay, etc. in each cell are calculated.

[0144] S4: Filter out the user's resident cells and poorly perceived cells, which are areas where users are prone to complaints.

[0145] First, it is determined whether the cells in the calculation result include the user's permanent cell. If so, it is determined whether the user's downlink delay exceeds 200ms. If so, the list of these cells is entered into step S5.

[0146] S5: Analyze multi-dimensional indicators from dimensions such as coverage, interference, access performance, and alarms through MR data and PM data.

[0147] The corresponding relationship between problem classification and indicator definition is shown in Table 5:

[0148]

[0149] S6: Based on the data results from various dimensions and combined with the empirical thresholds commonly used in network optimization, network problems are located and the root cause is determined.

[0150] If the overlap coverage is greater than 40%, it is judged that there is a problem with the antenna tilt angle or the power is too high;

[0151] If the MR good coverage is less than 50%, it is judged as a weak coverage problem;

[0152] If the uplink interference is greater than -90dBM, it is judged that the uplink interference is too high;

[0153] If the modulo 3 or modulo 30 is greater than 20%, it is judged to be a parameter configuration problem;

[0154] If the handover success rate is less than 90%, it is considered a system interoperability problem;

[0155] If the PRB utilization rate is greater than 70% during busy hours, it is judged that the resource load is too high and capacity expansion is required;

[0156] If there is a site disconnection alarm, it is considered a fault problem.

[0157] This embodiment emphasizes analyzing network issues from the user's perspective, rather than relying solely on traditional network indicator testing. Traditional methods primarily define the scope of faults by analyzing various network indicators, but these methods may not directly reflect the user's service experience, thus affecting the efficiency of problem location and resolution. To improve the overall effectiveness of network optimization, data analysis should be combined to identify network issues that truly impact user perception, and expert experience should be used to diagnose the root causes. Specific methods include using MR and PM data to conduct multi-level, multi-angle analysis across multiple dimensions, including coverage, interference, access performance, and alarm information, to accurately locate network root causes, helping operators implement more targeted optimizations and effectively improve work efficiency and overall service quality.

[0158] This embodiment proposes a mobile communication network problem analysis method with user perception as the core, emphasizing the use of multi-dimensional information such as signaling data, MR data, and PM data to conduct a comprehensive analysis from multiple aspects such as coverage, interference, access performance, and alarms, and accurately identify key network problems that affect user experience. This method uses tools such as Kafka to achieve real-time transmission and efficient processing of massive data, thereby achieving rapid positioning of user resident areas, service perception conditions, and potential fault points. It combines expert experience and preset thresholds to determine the root cause, helping operators to optimize the network in a targeted manner. This user experience-oriented analysis system can not only improve the accuracy and response speed of problem location, but also effectively improve user satisfaction, improve the overall network operation efficiency and service quality, and meet the needs of modern large-scale, intelligent network management.

[0159] Example 2:

[0160] like Figure 3 As shown, this embodiment provides a device for analyzing mobile communication network problems, the device comprising:

[0161] An acquisition unit 10 is configured to acquire complaint data, signaling data, and network performance indicator data from a mobile network management system;

[0162] Among them, complaint data carries mobile communication network issues, and signaling data, network performance indicator data, and complaint data are associated through user identification;

[0163] The determination unit 20 is connected to the acquisition unit 10 and is used to determine the user's permanent area based on the signaling data; and to determine the user's perceived poor quality cell based on the complaint data;

[0164] The obtaining unit 30 is connected to the determining unit 20 and is used to obtain the target cell according to the resident area and the cell with poor user perceived quality;

[0165] The screening unit 40 is connected to the obtaining unit 30 and is used to screen the performance indicator data of the target cell from the network performance indicator data;

[0166] an identification unit 50 connected to the screening unit 40 and configured to identify abnormal data from the performance indicator data of the target cell based on a preset threshold;

[0167] The positioning unit 60 is connected to the identification unit 50 and is used to locate the root cause of the mobile communication network problem based on the abnormal data, thereby completing the analysis of the mobile communication network problem.

[0168] As a specific implementation, the determination unit 20 includes:

[0169] A division module is used to divide the signaling data into daytime and nighttime time periods according to time periods;

[0170] A first processing module, connected to the partitioning module, is configured to count the user's connection duration for each CELL_ID during the daytime period and identify the first CELL_ID with the longest stay time; and further configured to use the geographical range corresponding to the first CELL_ID as the user's office area;

[0171] A second processing module, connected to the partitioning module, is configured to count the user's connection duration for each CELL_ID during the night time period and identify the second CELL_ID with the longest stay time; and further configured to use the geographical range corresponding to the second CELL_ID as the user's home area;

[0172] The aggregation module is connected to the first processing module and the second processing module respectively, and is used to aggregate according to the office area and the home area to obtain the user's permanent area.

[0173] As a specific implementation, the identification unit 50 includes a first identification unit, and / or a second identification unit, and / or a third identification unit, and / or a fourth identification unit, and / or a fifth identification unit, and / or a sixth identification unit, and / or a seventh identification unit;

[0174] The first identification unit is configured to determine that the overlapping coverage is abnormal when the overlapping coverage in the performance indicator data of the target cell is greater than a preset overlapping coverage threshold;

[0175] The second identification unit is configured to determine that the MR good coverage rate is abnormal when the MR good coverage rate in the performance indicator data of the target cell is less than a preset good coverage rate threshold;

[0176] The third identification unit is configured to determine that the uplink interference is abnormal when the uplink interference in the performance indicator data of the target cell is greater than a preset uplink interference threshold;

[0177] The fourth identification unit is configured to determine that analog-to-digital interference is abnormal when the modulo 3 or modulo 30 in the analog-to-digital interference in the performance indicator data of the target cell is greater than a preset analog-to-digital interference threshold;

[0178] The fifth identification unit is configured to determine that the handover success rate is abnormal when the handover success rate in the performance indicator data of the target cell is less than a preset handover success rate threshold;

[0179] The sixth identification unit is configured to determine that the PRB utilization is abnormal when the PRB utilization in the performance indicator data of the target cell is greater than a preset PRB utilization threshold;

[0180] The seventh identification unit is configured to determine that the base station is abnormal when a station disconnection alarm or a service withdrawal alarm exists in the performance indicator data of the target cell.

[0181] As a specific embodiment, the positioning unit 60 includes a first positioning unit, and / or a second positioning unit, and / or a third positioning unit, and / or a fourth positioning unit, and / or a fifth positioning unit, and / or a sixth positioning unit, and / or a seventh positioning unit;

[0182] The first positioning unit is connected to the first identification unit and is used to determine that the problem of the mobile communication network is an antenna tilt problem or excessive power according to the abnormal overlapping coverage rate;

[0183] The second positioning unit is connected to the second identification unit and is used to determine that the mobile communication network problem is a weak coverage problem based on the abnormality of the MR good coverage;

[0184] The third positioning unit is connected to the third identification unit, and is used to determine that the problem of the mobile communication network is a problem of excessive uplink interference based on the uplink interference anomaly;

[0185] The fourth positioning unit is connected to the fourth identification unit and is used to determine that the mobile communication network problem is a parameter configuration problem based on the analog-to-digital interference anomaly;

[0186] The fifth positioning unit is connected to the fifth identification unit and is used to determine that the mobile communication network problem is a system interoperability problem based on the abnormal handover success rate;

[0187] The sixth positioning unit is connected to the sixth identification unit and is configured to determine that the problem of the mobile communication network is an excessive resource load based on the abnormal PRB utilization;

[0188] The seventh positioning unit is connected to the sixth identification unit, and is used to determine that the problem of the mobile communication network is a base station failure problem according to the base station abnormality.

[0189] The device in this embodiment can execute the method in embodiment 1.

[0190] Example 3:

[0191] like Figure 4 As shown, this embodiment provides an electronic device, which includes a memory 200 and a processor 100, wherein a computer program is stored in the memory 200. When the processor 100 runs the computer program stored in the memory 200, the processor 100 executes the analysis method of the mobile communication network problem according to Example 1.

[0192] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for analyzing mobile communication network problems, characterized in that: The method comprises the following steps: Obtain complaint data, signaling data, and network performance indicator data from the mobile network management system; Wherein, the complaint data carries mobile communication network problems, and the signaling data, the network performance indicator data and the complaint data are associated through a user identifier; determining a user's resident area based on the signaling data; and determining a cell where the user perceives poor quality based on the complaint data; Determining a target cell according to the resident area and the cell with poor user perceived quality; Filtering the performance indicator data of the target cell from the network performance indicator data; Based on a preset threshold, identifying abnormal data from the performance indicator data of the target cell; Based on the abnormal data, the root cause of the mobile communication network problem is located, thereby completing the analysis of the mobile communication network problem.

2. The method for analyzing mobile communication network problems according to claim 1, characterized in that: The signaling data and the network performance indicator data are obtained from the mobile network management system through the Kafka tool.

3. The method for analyzing mobile communication network problems according to claim 1, characterized in that: Determining the user's resident area based on the signaling data specifically includes the following steps: Divide signaling data into daytime and nighttime time periods; Based on the daytime period, count the user's connection time on each CELL_ID and find the first CELL_ID with the longest residence time; and, based on the night time period, counting the user's connection time on each CELL_ID, and finding the second CELL_ID with the longest residence time; The geographical area corresponding to the first CELL_ID is used as the user's office area; and, using the geographical range corresponding to the second CELL_ID as the user's home area; The user's permanent area is obtained by summarizing the office area and the home area.

4. The method for analyzing mobile communication network problems according to any one of claims 1 to 3, characterized in that: The identifying abnormal data from the performance indicator data of the target cell based on a preset threshold specifically includes: If the overlapping coverage in the performance indicator data of the target cell is greater than a preset overlapping coverage threshold, it is determined that the overlapping coverage is abnormal; and / or, If the MR good coverage rate in the performance indicator data of the target cell is less than a preset good coverage rate threshold, it is determined that the MR good coverage rate is abnormal; and / or, If the uplink interference in the performance indicator data of the target cell is greater than a preset uplink interference threshold, determining that the uplink interference is abnormal; and / or, If the modulo 3 or modulo 30 in the analog-to-digital interference in the performance indicator data of the target cell is greater than a preset analog-to-digital interference threshold, it is determined that the analog-to-digital interference is abnormal; and / or, If the handover success rate in the performance indicator data of the target cell is less than a preset handover success rate threshold, determining that the handover success rate is abnormal; and / or, If the PRB utilization in the performance indicator data of the target cell is greater than a preset PRB utilization threshold, determining that the PRB utilization is abnormal; and / or, If there is a station disconnection alarm or a service outage alarm in the performance indicator data of the target cell, it is determined that the base station is abnormal.

5. The method for analyzing mobile communication network problems according to claim 4, characterized in that: The root cause location of the mobile communication network problem based on the abnormal data specifically includes: Based on the abnormal overlapping coverage, it is determined that the problem of the mobile communication network is an antenna tilt problem or excessive power; and / or, According to the abnormality of the MR good coverage, determining that the mobile communication network problem is a weak coverage problem; and / or, According to the uplink interference anomaly, determining that the mobile communication network problem is an uplink interference problem that is too high; and / or, Determining, based on the analog-to-digital interference anomaly, that the mobile communication network problem is a parameter configuration problem; and / or, According to the abnormal handover success rate, determining that the mobile communication network problem is a system interoperability problem; and / or, According to the abnormal PRB utilization, determining that the problem of the mobile communication network is excessive resource load; and / or, According to the base station abnormality, it is determined that the mobile communication network problem is a base station failure problem.

6. A device for analyzing mobile communication network problems, characterized in that: include: An acquisition unit, used to acquire complaint data, signaling data, and network performance indicator data from a mobile network management system; Wherein, the complaint data carries mobile communication network problems, and the signaling data, the network performance indicator data and the complaint data are associated through a user identifier; a determination unit connected to the acquisition unit, configured to determine a user's resident area based on the signaling data; and to determine a cell with poor user-perceived quality based on the complaint data; an obtaining unit, connected to the determining unit, configured to determine a target cell according to the resident area and the cell with poor user perceived quality; a screening unit, connected to the obtaining unit, configured to screen the performance indicator data of the target cell from the network performance indicator data; an identification unit, connected to the screening unit, for identifying abnormal data from the performance indicator data of the target cell based on a preset threshold; A positioning unit is connected to the identification unit and is used to locate the root cause of the mobile communication network problem based on the abnormal data, thereby completing the analysis of the mobile communication network problem.

7. The device for analyzing mobile communication network problems according to claim 6, characterized in that: The determination unit includes: A division module is used to divide the signaling data into daytime and nighttime time periods according to time periods; A first processing module, connected to the partitioning module, is configured to count the user's connection duration for each CELL_ID during the daytime period to find the first CELL_ID with the longest stay time; and further configured to use the geographical range corresponding to the first CELL_ID as the user's office area; a second processing module, connected to the partitioning module, configured to count the user's connection duration for each CELL_ID during the night time period and find the second CELL_ID with the longest stay time; and further configured to use the geographical range corresponding to the second CELL_ID as the user's home area; The aggregation module is connected to the first processing module and the second processing module respectively, and is used to aggregate the user's permanent area according to the office area and the home area.

8. The mobile communication network problem analysis device according to claim 6 or 7, characterized in that: The identification unit includes a first identification unit, and / or a second identification unit, and / or a third identification unit, and / or a fourth identification unit, and / or a fifth identification unit, and / or a sixth identification unit, and / or a seventh identification unit; The first identification unit is configured to determine that the overlapping coverage is abnormal when the overlapping coverage in the performance indicator data of the target cell is greater than a preset overlapping coverage threshold; The second identification unit is configured to determine that the MR good coverage rate is abnormal when the MR good coverage rate in the performance indicator data of the target cell is less than a preset good coverage rate threshold; The third identification unit is configured to determine that the uplink interference is abnormal when the uplink interference in the performance indicator data of the target cell is greater than a preset uplink interference threshold; The fourth identification unit is configured to determine that analog-to-digital interference is abnormal when the modulo 3 or modulo 30 in the analog-to-digital interference in the performance indicator data of the target cell is greater than a preset analog-to-digital interference threshold; The fifth identification unit is configured to determine that the handover success rate is abnormal when the handover success rate in the performance indicator data of the target cell is less than a preset handover success rate threshold; The sixth identification unit is configured to determine that the PRB utilization is abnormal when the PRB utilization in the performance indicator data of the target cell is greater than a preset PRB utilization threshold; The seventh identification unit is configured to determine that a base station is abnormal when a station disconnection alarm or a service withdrawal alarm exists in the performance indicator data of the target cell.

9. The device for analyzing mobile communication network problems according to claim 8, characterized in that: The positioning unit includes a first positioning unit, and / or a second positioning unit, and / or a third positioning unit, and / or a fourth positioning unit, and / or a fifth positioning unit, and / or a sixth positioning unit, and / or a seventh positioning unit; The first positioning unit is connected to the first identification unit and is configured to determine, based on the abnormal overlapping coverage, that the problem of the mobile communication network is an antenna tilt problem or excessive power; The second positioning unit is connected to the second identification unit and is configured to determine that the mobile communication network problem is a weak coverage problem based on the abnormality of the MR good coverage rate; The third positioning unit is connected to the third identification unit, and is configured to determine, based on the uplink interference anomaly, that the mobile communication network problem is an uplink interference excessive problem; The fourth positioning unit is connected to the fourth identification unit and is used to determine that the mobile communication network problem is a parameter configuration problem based on the analog-to-digital interference anomaly; The fifth positioning unit is connected to the fifth identification unit and is configured to determine, based on the abnormal handover success rate, that the mobile communication network problem is a system interoperability problem; The sixth positioning unit is connected to the sixth identification unit, and is configured to determine that the problem of the mobile communication network is an excessive resource load based on the abnormal PRB utilization; The seventh positioning unit is connected to the sixth identification unit, and is configured to determine, based on the base station abnormality, that the mobile communication network problem is a base station failure problem.

10. An electronic device, characterized in that: The apparatus comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the method for analyzing mobile communication network problems according to any one of claims 1 to 5.

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