Fault location methods, devices, equipment, storage media and program products

Abstract

This disclosure provides a fault location method, apparatus, device, storage medium, and program product. The method includes: collecting multiple alarm messages from fault-related devices; filtering alarm messages corresponding to a target sub-alarm type from the alarm messages corresponding to multiple sub-alarm types based on the priority order of various sub-alarm types; performing time-series and resource-dimensional analysis on the alarm messages corresponding to the base station outage alarm type and the alarm messages corresponding to the target sub-alarm type to obtain multi-dimensional analysis information; matching the multi-dimensional analysis information with preset single-fault association rules, and obtaining the fault location result of the base station outage based on the matching result. This disclosure provides a comprehensive analysis of base station outage alarms, accurately and quickly locating the root cause of base station outage from multiple perspectives such as time series and resources, utilizing preset association rules and priority order.

Description

Technical Field

[0001] This disclosure pertains to the field of base station fault analysis in wireless communication, and particularly relates to a fault location method, apparatus, device, storage medium, and program product. Background Technology

[0002] Currently, locating wireless communication base station outages mainly relies on traditional alarm monitoring and simple correlation analysis. For example, a fixed threshold is set to initially determine the cause of the fault, or the cause is located only from a single dimension such as transmission link connectivity or environmental alarms. However, the above methods lack flexibility, are difficult to adapt to complex network environments, cannot comprehensively analyze complex outage scenarios, and have low accuracy. Summary of the Invention

[0003] This disclosure provides a fault location method, apparatus, device, storage medium, and program product that can comprehensively and accurately locate faults.

[0004] In a first aspect, embodiments of this disclosure provide a fault location method, the method comprising: Collect multiple alarm messages from fault-related devices. Each alarm message corresponds to an alarm type, which includes base station outage alarms and various sub-alarm types. Based on the priority order of multiple sub-alarm types, the alarm information corresponding to the target sub-alarm type is filtered out from the alarm information corresponding to multiple sub-alarm types; The alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type are analyzed in terms of time sequence and resource dimension to obtain multi-dimensional analysis information; The multidimensional analysis information is matched with the preset single-fault association rules, and the fault location result of the base station outage is obtained based on the matching result.

[0005] In one feasible implementation, the alarm information includes historical alarm times and network element information of the device. The alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type are analyzed in terms of time series and resource dimensions to obtain multi-dimensional analysis information, including: Based on historical alarm times, a time-series analysis is performed on the occurrence time between base station outage alarm types and target sub-alarm types to obtain time-series analysis information. Based on the network element information of each device, the first identification information of the device corresponding to the base station outage alarm type and the second identification information of the device corresponding to the target sub-alarm type are determined, and the device analysis information of the resource dimension is determined according to the first identification information and the second identification information. Based on time series analysis information and equipment analysis information, multidimensional analysis information is determined.

[0006] In one feasible implementation, based on historical alarm times, a time-series analysis is performed on the occurrence time between base station outage alarm types and target sub-alarm types to obtain time-series analysis information, including: Calculate the difference between the historical alarm time of the base station outage alarm type and the historical alarm time of the target sub-alarm type within the target historical time period to obtain multiple alarm time differences; Based on the duration of each alarm time difference, determine the dynamic attenuation weight of the corresponding alarm time difference; The cumulative weight of dynamic attenuation is obtained based on the dynamic attenuation weight within the dynamic sampling window; Among multiple dynamic sampling windows with a cumulative weight greater than a preset cumulative value, the dynamic sampling window with the smallest window length is selected to obtain the time-related window. The time series analysis information is obtained from the time correlation window.

[0007] In one feasible implementation, the sub-alarm types include power alarm types, transmission alarm types, and base station hardware alarm types. Power alarm types have a higher priority than transmission alarm types, and transmission alarm types have a higher priority than base station hardware alarm types. Based on the priority order of the various sub-alarm types, the alarm information corresponding to the target sub-alarm type is filtered from the alarm information corresponding to the various sub-alarm types, including: If there is no alarm information corresponding to the power alarm type among multiple alarm information, the alarm information corresponding to the transmission alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type. If no alarm information corresponding to the transmission alarm type is found among multiple alarm information, the alarm information corresponding to the base station hardware alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type.

[0008] In one feasible implementation, multidimensional analysis information is matched with preset single-fault association rules, and the fault location result of base station outage is obtained based on the matching result, including: The multidimensional analysis information is matched with the preset single obstacle association rules to determine the target single obstacle association rules; Determine the cause of the target failure based on the target single-fault association rules; Based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the fault information of the target fault cause is statistically analyzed.

[0009] In one feasible implementation, after statistically analyzing the fault information of the target fault cause based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the method further includes: Count the number of alarm messages corresponding to the base station outage alarm types within the target area; If the number of alarm messages corresponding to the base station outage alarm type exceeds the preset number of alarms, the number of base stations and the fault point for each target fault cause will be counted and output.

[0010] Secondly, embodiments of this disclosure provide a fault location device, the device comprising: The information acquisition module is used to collect multiple alarm messages from fault-related devices. Each alarm message corresponds to an alarm type, which includes base station outage alarm types and multiple sub-alarm types. The information filtering module is used to filter the alarm messages corresponding to the target sub-alarm type from the alarm messages corresponding to the multiple sub-alarm types based on the priority order of the multiple sub-alarm types. The correlation processing module is used to analyze the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type in terms of time sequence and resource dimensions to obtain multi-dimensional analysis information. The results output module is used to match multidimensional analysis information with preset single-fault association rules, and obtain the fault location result of base station outage based on the matching result.

[0011] Thirdly, embodiments of this disclosure provide a fault location device, the device comprising: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the above-described fault location method.

[0012] Fourthly, embodiments of this disclosure provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned fault location method.

[0013] Fifthly, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the fault location method described above.

[0014] The fault location method, apparatus, device, storage medium, and program product of this disclosure, based on comprehensive and multi-layered correlation analysis, can more comprehensively and accurately locate faults. First, this disclosure distinguishes between strongly correlated base station outage alarms and weakly correlated sub-alarms based on the correlation strength with base station outage status, and classifies alarm information according to alarm type. Then, through preset priorities, multiple weakly correlated sub-alarms are further distinguished to obtain alarm information strongly correlated with base station outage faults. Based on the filtered alarm information, analysis is performed from time-series and resource dimensions to extract multi-dimensional analysis information. Finally, the fault location result is obtained based on the matching of preset single-fault correlation rules and multi-dimensional analysis information. Throughout the entire process, base station outage alarms are comprehensively analyzed layer by layer, and from multiple perspectives such as time-series and resources, using preset correlation rules and priority order, the root cause of base station outages can be accurately and quickly located. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments of this disclosure will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a fault location method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a fault location device provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of a fault location device provided in an embodiment of this disclosure. Detailed Implementation

[0017] The features and exemplary embodiments of various aspects of this disclosure will now be described in detail. To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description, in conjunction with the accompanying drawings and specific embodiments, will provide a further detailed description. It should be understood that the specific embodiments described herein are intended only to explain this disclosure and not to limit it. For those skilled in the art, this disclosure can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this disclosure by illustrating examples.

[0018] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0019] The acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0020] In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0021] To better understand and explain the solutions provided in the embodiments of this application, some technical terms involved in the embodiments of this application will be briefly introduced below.

[0022] Fault preprocessing refers to the output information of the association rule processing module of the fault management system after completing the delimitation and business impact analysis. It mainly includes the specific professional field of the fault, the section, the cause of the fault, the business impact, and related resources.

[0023] Battery life refers to the duration that an uninterruptible power supply (UPS) in a data center can support the normal operation of the equipment in the data center during a power outage. This duration is usually calculated based on the battery capacity and equipment power when a new UPS is installed, and the data is subsequently updated by maintenance personnel through periodic discharge tests and returned to the asset management system.

[0024] A circuit name is a code used to identify a specific connection link or transmission channel in a transmission network. Circuit names often include start and end points, routing information, and protection information, which helps to outline the network topology. The stations, equipment rooms, and the order of connection mentioned in the name can help staff mentally reconstruct the approximate physical or logical structure of the network connections, either directly or using drawing tools.

[0025] A dry contact is an electrical switch contact that is essentially a passive mechanical contact with only two stable states: open and closed. It does not carry its own power supply; its sole function is to connect or disconnect the circuit. When an external voltage is applied to the dry contact, the current is either conducted or blocked depending on its on / off state. Dry contacts are commonly used in signal transmission and control in various automated control systems, security systems, and industrial equipment. Common examples include push-button switches, relay contacts, and limit switches.

[0026] The computer room dynamic environment soft dry contact is a technical means that uses software logic to simulate the closed or open state of hardware dry contacts to transmit signals, realize equipment monitoring and linkage alarm, and has the advantages of flexible configuration, low cost and easy integration.

[0027] In base station operation and maintenance scenarios, although network management systems can collect various types of information such as base station outage alarms, power supply alarms, hardware alarms, and transmission alarms and store them in a database, operation and maintenance personnel still need to manually analyze them based on experience and basic alarm filtering rules, which is inefficient and lacks accuracy. Meanwhile, although operators have deployed resource management systems to record resource information (such as connection relationships and environmental configurations) for base stations, transmission equipment, and equipment rooms, the linkage between the resource system and the alarm analysis system is weak, failing to fully utilize resource data to assist in fault location, further limiting the efficiency of fault diagnosis.

[0028] In current wireless communication, the location of base station outage faults mainly relies on traditional alarm monitoring and simple correlation analysis techniques. Alarm correlation analysis based on simple rules uses fixed thresholds to initially determine the cause of the fault. However, this approach relies on a single threshold and lacks comprehensive analysis across multiple alarm types, making it unsuitable for complex network environments and resulting in low accuracy. For example, using a threshold for the number of times base station outage alarms overlap with transmission / environmental monitoring alarms in the same data center can determine whether the fault is due to transmission issues or power outages. However, this method is prone to failure when there is no transmission equipment in the data center or when environmental monitoring is abnormal. Furthermore, single-dimensional fault diagnosis only locates the cause from a single dimension, such as transmission link connectivity or environmental alarms, ignoring the correlation between network devices, leading to an inability to comprehensively analyze complex outage scenarios. For instance, focusing only on transmission link status or environmental power outage alarms can easily lead to misjudgments when both environmental power outages and transmission faults occur simultaneously in the same data center, simply randomly selecting one cause for judgment.

[0029] Therefore, existing technologies suffer from drawbacks such as low accuracy, low efficiency, and weak adaptability due to problems such as limited analytical dimensions, poor rule flexibility, and insufficient system linkage. They are unable to meet the need for rapid and accurate location of base station outages in complex network environments.

[0030] To address the problems of existing technologies, this disclosure provides a fault location method, apparatus, device, storage medium, and program product. Targeting the common issue of base station outages, it performs comprehensive correlation analysis from multiple dimensions, including time series and resources, using alarm information, alarm type, and priority order to comprehensively and accurately analyze and extract information. Based on this analysis, fault location is performed to improve the accuracy of fault cause identification. Furthermore, a dynamic time window algorithm is established during time series analysis to enhance scenario adaptability and reduce misjudgments in complex scenarios.

[0031] This fault location method can be applied to scenarios where base station outage faults are located. The subject executing this fault location method can be any electronic device capable of acquiring alarm information and performing correlation analysis on the information.

[0032] The fault location method provided in the embodiments of this disclosure will be introduced first below.

[0033] Figure 1 A flowchart illustrating a fault location method provided in one embodiment of this disclosure is shown. Figure 1 As shown, the method may include the following steps: S110 collects multiple alarm messages from fault-related devices. Each alarm message corresponds to an alarm type, which includes base station outage alarm types and multiple sub-alarm types. S120, based on the priority order of multiple sub-alarm types, filter out the alarm information corresponding to the target sub-alarm type from the alarm information corresponding to multiple sub-alarm types; S130, perform time-series and resource-dimensional analysis on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type to obtain multi-dimensional analysis information; S140, the multi-dimensional analysis information is matched with the preset single-fault association rules, and the fault location result of the base station outage is obtained based on the matching result.

[0034] Here, the fault-associated device can be any device in the base station operation and maintenance scenario that can issue alarm information.

[0035] It should be noted that this embodiment addresses the complexity of base station outages by implementing different fault location processes at both the single-fault and group-fault levels. Specifically, for single-fault cases, preset single-fault association rules are used for fault location; for group-fault cases, preset group-fault association rules are used. In detail, when a base station generates an outage alarm, it first determines whether it is a single-station outage or a group-fault (i.e., multiple base station outages occurring in the same city). If it is a single-station outage, the single-fault association rule processing flow is initiated; if it is a group-fault, the group-fault association rule processing flow is initiated.

[0036] For example, an alarm is triggered by a single base station outage, and the triggering conditions are determined based on the base station type (e.g., 2G, 4G, 5G). For instance, the triggering condition for a 2G base station is that a base station outage alarm occurs and the device type is a 2G base station.

[0037] Thus, this disclosure, based on comprehensive and multi-layered correlation analysis, enables more complete and accurate fault location. First, based on the strength of the correlation with base station outage status, strongly correlated base station outage alarms and weakly correlated sub-alarms are distinguished within the alarm type, and the alarm information is further divided according to alarm type. Then, through preset priorities, multiple weakly correlated sub-alarms are further distinguished to obtain alarm information strongly correlated with base station outage faults. Based on the filtered alarm information, analysis is performed from time-series and resource dimensions to extract multi-dimensional analysis information. Finally, based on the matching of preset single-fault correlation rules and multi-dimensional analysis information, the fault location result is obtained. Throughout the entire process, base station outage alarms are comprehensively analyzed layer by layer, from multiple perspectives such as time-series and resources, utilizing preset correlation rules and priority order to accurately and quickly locate the root cause of base station outages.

[0038] The above steps are explained in detail below: Regarding S110, the alarm information issued by the fault-related equipment includes an alarm title, which can be used to identify the alarm type in conjunction with other information. This disclosure classifies base station outage alarms as primary alarms and other alarm types as sub-alarms.

[0039] Table 1 illustrates the base station outage alarm types according to an embodiment of this disclosure. As shown in Table 1, the base station outage alarm type can be determined based on information such as network type, object type, alarm title, and manufacturer. The alarm title can be, for example, site ABIS control link failure, base station out of service, or 2G / 4G / 5G base station outage alarm. Here, the unmanaged situation can also be set as the base station outage alarm type.

[0040] Table 1. Illustrated Table of Base Station Outage Alarm Types The sub-alarm types include base station power supply alarms, environmental power outage alarms, transmission alarms, transmission power supply faults, and base station hardware alarms.

[0041] Table 2 shows a schematic table of base station self-power supply alarm types provided in one embodiment of this disclosure. As shown in Table 2, base station self-power supply alarms can be determined according to the alarm titles corresponding to the manufacturer. Alarm titles can include AC power outage alarm, AC OFF, power module mains power failure alarm, DC input voltage abnormality, PA power undervoltage alarm, external power supply voltage undervoltage, AC POWER OFF, base station DC power supply abnormality alarm (flashover), remote power input undervoltage alarm, equipment power failure, etc. Different manufacturers can set different alarm titles to represent base station self-power supply alarms.

[0042] Table 2. Illustrated Table of Base Station Self-Power Supply Alarm Types Table 3 shows a schematic table of environmental power outage alarm types provided in one embodiment of this disclosure. As shown in Table 3, it can be determined whether an alarm belongs to the environmental power outage category based on the alarm category and alarm title. The alarm category mainly indicates the alarm type under environmental power outage conditions, including equipment room power outage alarm, equipment room environmental power supply alarm, equipment room environmental monitoring / temperature alarm, base station soft dry contact power outage, base station hard dry contact power outage, and other base station power supply alarms. Different alarm titles can be set for different alarm types to indicate the occurrence of environmental power outage alarms. For example, when an equipment room power outage alarm occurs, and the alarm title is one of the following: mains power outage, AC input power outage alarm, input power failure alarm, AC input fault alarm, or micro-station AC input power outage alarm, it is determined that an environmental power outage alarm has occurred.

[0043] Table 3. Schematic diagram of environmental power outage alarm types Table 4 shows a schematic table of transmission alarm types provided in one embodiment of this disclosure. As shown in Table 4, transmission alarms include transmission LOS alarms, transmission disconnection alarms, transmission performance alarms, and transmission hardware alarms, which can be determined according to the corresponding manufacturer, the device type of the fault-related device, and the alarm title.

[0044] Table 4. Illustration of Transmission-Related Alarm Types Table 5 shows a schematic table of power supply alarm types provided in one embodiment of this disclosure. As shown in Table 5, the types can be determined according to the manufacturer's specifications. Of course, Table 5 is only a few examples, and other situations may also be included.

[0045] Table 5. Illustrated Table of Power Transmission Alarm Types Table 6 illustrates a base station hardware alarm type diagram according to an embodiment of this disclosure. As shown in Table 6, the alarm type can be determined based on the manufacturer, alarm title, alarm object type, and alarm category. It should be noted that real-world situations are complex, and Tables 1 to 6 cannot list all of them; only some examples are shown here.

[0046] Table 6. Illustrated list of base station hardware alarm types The alarm information includes the information in Tables 7-10, the alarm text, etc., and can be obtained by executing SQL queries on a series of resource tables in the asset management system. Similar to Tables 1-6, this disclosure cannot fully list all real-world situations; only a few examples are given here. The tables may also include other information. A brief explanation of each table follows.

[0047] Table 7 shows a schematic table of base station circuit information provided in one embodiment of this disclosure, used to record the transmission circuit of the base station outage alarm. Table 7 may include circuit name, service bearer equipment type, service origin city, service origin district / county, service origin protection method, service origin access port type, service destination access port type, service destination protection method, service destination province, service destination city, and service destination district / county, etc.

[0048] Table 7. Base Station Circuit Information Diagram Table 8 shows a schematic table of transmission network element information provided in one embodiment of this disclosure, used to record the transmission network elements of the transmission circuit for base station outage alarms. Here, the routing port can be in the form of 963-×××XYD62-1-1-3(B4EGC)-ETM_1.

[0049] Table 8. Schematic diagram of transmission network element information Table 9 shows a schematic table of equipment room information provided in one embodiment of this disclosure, used to record the equipment room to which the base station belongs. It includes the equipment room name, base station name, city / prefecture / county, and base station coverage type. The base station coverage type includes indoor distributed antenna systems (DAS), macro base stations, and repeater base stations.

[0050] Table 9. Schematic Diagram of Computer Room Information Table 10 shows a schematic table of transmission equipment information provided in an embodiment of the present disclosure, used to record the transmission equipment in the computer room.

[0051] Table 10 Schematic diagram of transmission equipment information In addition, exemplarily, the alarm text can be {"addInfo": "Disconnected port: 6007; Disconnection error code: N / A; Disconnection information: Port handshake timeout; Adaptation layer partition: MediationService0305; OMC IP: 10.133.246.9; Reason: The network interruption between MAE and the network element exceeds 3 minutes; Suggestion: Please click on the \"Cause and Repair Suggestion\" hyperlink field of this alarm; Open the help materials and handle according to the suggestions; eNodeBId: 736562; deployment: NSA", "alarmId": "88862422", "alarmSeq": "41366105", "alarmStatus": 1, "alarmTitle": "Network element connection interruption", "alarmType": "Internal to network management", "eventTime": "2025-01-01 00:29:43", "locationInfo": "Network element name: × County × Town × Hospital - HLH - HZKO145TL; Network element IP address: 100.105.207.161; Network element IP address (IPMode): NULL; Disconnected port: 6007; Disconnection error code: N / A; Disconnection information: Power supply status may be abnormal; Adaptation layer partition: MediationService0305; OMC IP: 10.133.246.9", "neName": "× Hospital - HLH - HZKO145TL", "neType": "ENB", "neUID": "6101HWWXOMNE3221287802", "objectType": "EnbFunction", "objectUID": "6101HWWXOENB1003,225846470", "origSeverity": 1, "rNeName": "", "rNeType": "", "rNeUID": "", "specificProblem": "718", "specificProblemID": "40012"}.

[0052] In order to accurately and quickly obtain alarm information and alarm types, an alarm collection module can also be designed to collect alarm information from network devices such as base station devices, dynamic environment monitoring devices, and transmission devices, including various alarms such as base station out - of - service alarms, power supply alarms, hardware alarms, and transmission alarms, and transmit them to the subsequent processing module.

[0053] The alarm acquisition module establishes connections with network devices such as base station equipment, environmental monitoring equipment, and transmission equipment through dedicated acquisition interfaces. The acquired alarm information covers base station outage alarms, power supply alarms, hardware alarms, and transmission alarms. Corresponding alarm information acquisition rules are set for different types of fault-related devices, such as active polling and passive reception. Active polling involves actively polling for alarm status every minute. Passive reception uses an event-triggered mechanism, immediately pushing alarm information to the alarm acquisition module when an anomaly is detected. Furthermore, standardized protocols are used for interfaces with different types of devices to ensure accurate alarm information acquisition and transmission. For example, Kafka, Common Object Request Broker Architecture (CORBA), and Simple Network Management Protocol (SNMP) are used to communicate with base station equipment to obtain base station alarm information.

[0054] Next, the collected alarm information undergoes preliminary format conversion and encapsulation locally, unifying alarm data acquired through different protocols into JSON format for easier parsing by subsequent processing modules. Then, the encapsulated alarm information is transmitted to the subsequent processing modules via a network transport layer protocol. During transmission, a verification mechanism is employed to ensure data reliability, such as calculating the MD5 value of the alarm information. The receiving end verifies the MD5 value to determine data integrity. If a network failure occurs during transmission, a retransmission mechanism is set up to automatically retransmit the failed alarm information after network recovery. The retransmission count is set to 3 times, with a 5-second interval between each retransmission.

[0055] Furthermore, to adapt to the interface characteristics of different types of devices, an interface adaptation layer is designed. This adaptation layer is based on the factory pattern, dynamically creating corresponding protocol processing objects according to the device type. For example, when a device is detected to support the KAFKA protocol, a KAFKA protocol processing object is created, which is responsible for communicating with the device and collecting alarm information; if the device supports the SNMP protocol, an SNMP protocol processing object is created. Simultaneously, the interface adaptation layer reserves extension interfaces to facilitate rapid expansion when adding new device types or protocols in the future.

[0056] In addition, a resource management module was designed to better capture the correlation between alarm information.

[0057] The resource management module interfaces with the resource management system to obtain relevant information about network resources such as base stations, transmission equipment, and equipment rooms, including the connection relationships between base stations and transmission equipment, the configuration of environmental monitoring equipment in the equipment room, and the geographical location of the equipment. The resource management system, on the other hand, is a network resource management and operation system. Through manual input or automatic system data collection, it achieves comprehensive resource integration and intelligent scheduling, constructing a standardized management system covering spatial resources such as equipment rooms and base stations, physical resources such as equipment and cables, logical resources such as IP addresses, spectrum, and transmission circuits, and business resources such as leased lines and cloud services.

[0058] The resource management module provides resource information to assist in determining the cause of a fault. For example, when determining the cause of a transmission failure, it queries the transmission equipment information in the equipment room to which the base station belongs to determine whether a transmission equipment failure is causing the base station to go out of service. The module associates alarms with resources through information such as device name, port, IP address, and unique identifier in the alarm message, thus providing an informational basis for determining the cause of base station outages. For example, a base station outage alarm can be associated with the equipment room and transmission equipment through the device name, and further associated with other resources through the equipment room and transmission equipment. For instance, it can locate environmental protection power outage and high temperature alarms belonging to the equipment room through the equipment room information.

[0059] Regarding S120, this disclosure sets priorities for sub-alarm types based on their specific circumstances, in order to distinguish the alarm information corresponding to the sub-alarm types and filter out the alarm information that is more relevant to the main alarm type, so as to quickly obtain more accurate fault location results.

[0060] In one optional implementation, the sub-alarm types include power alarm types, transmission alarm types, and base station hardware alarm types. Power alarm types have a higher priority than transmission alarm types, and transmission alarm types have a higher priority than base station hardware alarm types. Based on the priority order of the various sub-alarm types, alarm information corresponding to the target sub-alarm type is filtered from the alarm information corresponding to the various sub-alarm types, including: If there is no alarm information corresponding to the power alarm type among multiple alarm information, the alarm information corresponding to the transmission alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type. If no alarm information corresponding to the transmission alarm type is found among multiple alarm information, the alarm information corresponding to the base station hardware alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type.

[0061] In a specific example, the power alarm type is used to describe environmental alarms, including the aforementioned base station power supply alarms, environmental power outage alarms, and transmission power supply alarms. The transmission alarm type is a transmission alarm, and the base station hardware alarm type is the aforementioned base station hardware alarm. Furthermore, alarm information is filtered according to the priority order: environmental alarms > transmission alarms, and transmission alarms > base station alarms.

[0062] Furthermore, to further improve the processing efficiency, after filtering the alarm information corresponding to the target sub-alarm type according to the current priority, steps S130-S140 are executed. If no fault location result is obtained, steps S120-S140 are repeated, using the next lower priority as the current priority, until a fault location result is obtained. Specifically, fault location is first performed based on the alarm information filtered by the environmental monitoring system. If no fault location result is obtained, fault location is then performed based on the alarm information filtered by the transmission system. If no fault location result is obtained, fault location is then performed based on the alarm information filtered by the base station hardware.

[0063] In this way, by pre-setting the priority order of power alarms > transmission alarms > base station hardware alarms, the most urgent task can be quickly identified when multiple alarms occur, avoiding delays in handling critical faults due to mixed alarm types. When high-priority alarms are not available, the system can automatically filter out the highest-level, most relevant, and effective alarms according to priority, ensuring that there is always a clear and effective handling target. In addition, converging multiple types and multiple alarms into target sub-alarm types reduces the information sifting burden on maintenance personnel from massive alarms, helping to quickly locate core issues and initiate corresponding handling procedures.

[0064] Regarding S130, for the selected alarm information, the effective information of the alarm information is extracted from all aspects and multiple angles, so as to quickly and accurately match it with the preset single fault association rules.

[0065] In one optional implementation, the alarm information includes historical alarm times and network element information of the device. The alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type are analyzed in terms of time series and resource dimensions to obtain multi-dimensional analysis information, including: Based on historical alarm times, a time-series analysis is performed on the occurrence time between base station outage alarm types and target sub-alarm types to obtain time-series analysis information. Based on the network element information of each device, the first identification information of the device corresponding to the base station outage alarm type and the second identification information of the device corresponding to the target sub-alarm type are determined, and the device analysis information of the resource dimension is determined according to the first identification information and the second identification information. Based on time series analysis information and equipment analysis information, multidimensional analysis information is determined.

[0066] In a specific example, when extracting time-series analysis information, historical occurrence times within a specific historical period can be summarized and statistically analyzed. When the target sub-alarm type is a power alarm type, the name of the equipment room corresponding to the network element device that issued the main alarm can be extracted as the first identification information; the name of the equipment room corresponding to the network element device that issued the power alarm type can be extracted as the second identification information. When the target sub-alarm type is a transmission alarm type, the name of the base station and the name of the equipment room connected to the fault-associated device that issued the main alarm can be extracted as the first identification information; the name of the base station and the name of the equipment room connected to the fault-associated device that issued the transmission alarm type can be extracted as the second identification information.

[0067] Thus, through time-series analysis, the chronological order and temporal correlation of target sub-alarms and base station outage alarms can be determined, helping to identify the causal chain between alarms and quickly pinpoint the root cause of base station outages. Meanwhile, through resource-dimensional analysis, by comparing the first and second identifier information based on the network element information of the device, it is possible to determine whether the two types of alarms occur on the same or related devices, thereby accurately defining the scope of the fault's impact and the device to which it belongs. Integrating the analysis results from both time-series and resource dimensions into multi-dimensional analysis information avoids misjudgments that may result from relying on a single dimension, providing a more comprehensive and reliable basis for decision-making.

[0068] Furthermore, given the large number of alarm messages and the different characteristics of alarm occurrence times at different time stages, this disclosure also designs a dynamic time window to enhance the accuracy and adaptability of time series analysis.

[0069] In one feasible implementation, based on historical alarm times, a time-series analysis is performed on the occurrence time between base station outage alarm types and target sub-alarm types to obtain time-series analysis information, including: Calculate the difference between the historical alarm time of the base station outage alarm type and the historical alarm time of the target sub-alarm type within the target historical time period to obtain multiple alarm time differences; Based on the duration of each alarm time difference, determine the dynamic attenuation weight of the corresponding alarm time difference; The cumulative weight of dynamic attenuation is obtained based on the dynamic attenuation weight within the dynamic sampling window; Among multiple dynamic sampling windows with a cumulative weight greater than a preset cumulative value, the dynamic sampling window with the smallest window length is selected to obtain the time-related window. The time series analysis information is obtained from the time correlation window.

[0070] In a specific example, based on the time-series correlation dynamic window algorithm, the occurrence time of primary alarms and sub-alarms in the full alarm data of the past year is dynamically analyzed. The time correlation window is determined with the time window of primary alarm type and sub-alarm type covering the preset coverage as the calculation target.

[0071] First, we define the alarm time difference. Let the time of occurrence of the main alarm be... The alarm occurred at the time of The alarm time difference is .when When, it indicates that the child alarm occurs after the main alarm; and This indicates that the child alarm occurred before the main alarm.

[0072] Next, the dynamic decay weight is calculated using the median of the absolute values ​​of multiple alarm time differences. Based on the standard, the dynamic decay weight is defined by equation (1). The dynamic decay weight decays rapidly in a quadratic manner. Therefore, the larger the absolute value of the alarm time difference, the faster the dynamic decay weight decays. A larger weight is set for the occurrence time of the sub-alarm that is close to the current main alarm, so as to highlight the time point close to the main alarm.

[0073] (1) in, This represents the dynamic decay weight.

[0074] Then, the dynamic time-related window boundary is solved. After arranging multiple alarm time differences in descending order of dynamic attenuation weight, the cumulative weight sum of the dynamic attenuation weights is calculated using equation (2). When the value exceeds the preset cumulative coverage, the corresponding minimum value will be used. Define the window boundaries. The forward window is... The back window is Among them, preset cumulative values ​​are set according to the coverage of the occurrence time of the main alarm type and the sub-alarm type.

[0075] (2) in, This represents the cumulative weight sum; This indicates the total number of alarm time differences included in the calculation; This represents the time window threshold variable.

[0076] For example, the accuracy of the method described above is verified by using the period within 24 hours before and 8 minutes after the occurrence of the main alarm type as the target historical period. Based on the historical alarm times within this period, a time correlation window is calculated using the above process with a preset cumulative value of 99% coverage. The final time correlation window is -1438.5 minutes to 7.9 minutes, which is close to the period within 24 hours before and 8 minutes after the main alarm time, thus demonstrating high accuracy.

[0077] Thus, by establishing a dynamic attenuation weight based on the time difference between the main alarm and sub-alarms, higher weights are given to closer time differences, enabling more sensitive capture of the time-related correlations between alarms and avoiding misjudging distant, random events as causal relationships. Furthermore, selecting the smallest window with a cumulative weight greater than a preset threshold within the dynamic sampling window adaptively determines the length of the window most statistically significant in determining the time correlation between two types of alarms, ensuring the significance of the correlation while avoiding excessively large windows that introduce noise. Through the dynamic attenuation of cumulative weights and the threshold judgment mechanism, time-series analysis is elevated from a simple determination of time sequence to a statistical analysis method that quantifies the strength of correlations, providing a more reliable time-series dimension for subsequent root cause localization and reducing false alarms and missed alarms.

[0078] Regarding S140, this disclosure determines the fault location result based on the aforementioned extracted effective and concise multidimensional analysis information, combined with preset single-fault association rules.

[0079] In one optional implementation, multidimensional analysis information is matched with preset single-fault association rules, and the fault location result of base station outage is obtained based on the matching result, including: The multidimensional analysis information is matched with the preset single obstacle association rules to determine the target single obstacle association rules; Determine the cause of the target failure based on the target single-fault association rules; Based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the fault information of the target fault cause is statistically analyzed.

[0080] Here, given the diverse and complex nature of fault conditions, this disclosure establishes multiple preset single-fault association rules. Based on the matching between multi-dimensional analysis information and these preset single-fault association rules, the corresponding target fault cause is determined under the matching conditions, thereby enabling timely and accurate fault location. Furthermore, fault information may also include the number of base stations affected by the fault cause.

[0081] In a specific example, the multidimensional analysis information is obtained by filtering according to the priority order of sub-alarm types. Therefore, in order to improve the efficiency of fault location, this embodiment still performs fault location according to the priority order. Specifically, fault location is first performed based on the multidimensional analysis information corresponding to the power alarm type, and the fault location ends when a fault location result is obtained. If no fault location result is obtained, fault location is then performed based on the multidimensional analysis information corresponding to the transmission alarm type. If no fault location result is obtained, fault location is then performed based on the multidimensional analysis information corresponding to the base station hardware alarm type, until a fault location result is obtained.

[0082] For example, it is determined whether the sub-alarm type meets the current requirements. If the requirements are met, it is matched with the multi-dimensional analysis information according to the corresponding preset single fault association rules. After successful matching, the station failure alarm cause, cause subdivision and fault preprocessing information are statistically analyzed and updated.

[0083] More specifically, the process of fault location based on multi-dimensional analysis information corresponding to power alarm types is as follows.

[0084] When the primary alarm is a base station outage alarm and the secondary alarm is a data center environmental monitoring outage alarm, it is matched with the first preset single-fault association rule. The first preset single-fault association rule is: the secondary alarm occurs within the preset time association window of the primary alarm (e.g., within 8 minutes after the previous 24 hours), and the data center name corresponding to the network element device of the primary alarm is equal to the data center name corresponding to the environmental monitoring device of the secondary alarm. When the match is successful, the alarm cause of the primary alarm is determined to be a power outage, and the cause is further subdivided into accurate power outage and FSU power outage. Afterwards, fault information is statistically analyzed: the logic for locating the outage cause in the fault preprocessing content is output, and information such as data center name, power outage time, and battery life is recorded in the alarm preprocessing fields. For example, the relevant information of the primary alarm is updated, including the cause of the base station outage alarm, the cause subdivision, and the fault preprocessing information. It can also present the three most recent fault points according to the time when the data center power outage alarm occurred.

[0085] When the primary alarm is a base station outage alarm and the secondary alarm is a data center environmental power supply alarm, it is matched with the second preset single-fault association rule. The second preset single-fault association rule is similar to the first preset single-fault association rule, and different thresholds or attributes can be set. After successful association, the alarm cause of the primary alarm is determined to be a suspected power outage, further subdivided into suspected power outage - environmental power supply alarm. Data center name, power outage time, and battery life are recorded as fault information, and the three most recent fault points can be presented chronologically.

[0086] When the primary alarm is a base station outage alarm and the secondary alarm is a soft dry contact alarm in the equipment room environmental monitoring system, the preset single-fault association rules and information update methods are similar to those described above. After successful matching, the cause of the fault is determined to be a suspected power outage - dry contact power outage - soft dry contact power outage. Fault information such as network element name, occurrence time, and battery life are recorded. The system can display the three most recent fault points based on the time the soft dry contact occurred in the equipment room.

[0087] When the primary alarm is a base station outage alarm and the secondary alarm is a hardware room environmental hard dry contact alarm, the preset single-fault association rules and update operations are similar to those before. After successful matching, the cause of the fault is determined to be suspected power outage - dry contact power outage - hard dry contact power outage. The network element name, occurrence time, and battery life are recorded as fault information. The three most recent fault points can be presented according to the time when the hard dry contact occurred in the hardware room.

[0088] When the primary alarm is a base station outage alarm and the secondary alarm is a power supply / environmental alarm related to the equipment room, the preset single-fault association rules and information update method remain unchanged. After successful matching, the cause of the fault is determined to be a suspected power outage - other power supply alarms of the base station. The fault information is recorded, including the network element name, occurrence time, and battery life. The three most recent fault points can be presented according to the time when the power supply alarm occurred in the equipment room.

[0089] The process of fault location based on multi-dimensional analysis information corresponding to transmission alarm types is as follows.

[0090] When a base station experiences a service outage alarm, and the corresponding transmission equipment of the base station experiences a transmission-related alarm, and there is no dynamic environment fault in the equipment room to which the transmission equipment connected to the base station belongs (i.e., no dynamic environment power outage alarm or suspected dynamic environment power outage alarm), and the transmission equipment connected to the base station does not report a transmission power supply alarm, and the associated time window of the sub-alarm occurrence time is a target preset value, such as within 8 minutes after 6 hours before the main alarm, the fault is determined to be due to transmission.

[0091] More specifically, if there are transmission-related faults in the transmission network elements listed in Table 8, no transmission power-related faults in Table 10, and no environmental power outage alarms or suspected environmental power outage alarms in the equipment room listed in Table 9, then the cause is determined to be transmission-related. The main alarm information is then updated, and the transmission fault point is recorded. For example, if the link between transmission network element 1 and transmission network element 2 is interrupted, the top three fault location results are presented based on the number of transmission interruptions or faulty network elements.

[0092] The process of fault location based on multi-dimensional analysis information corresponding to base station hardware alarm types is as follows.

[0093] When the primary alarm is a base station outage alarm and the secondary alarm is a base station hardware alarm, the preset single-fault association rule is that a secondary alarm occurred within 8 minutes after 6 hours prior to the primary alarm, and the alarm objects of the primary alarm and the secondary alarm are the same. After successful association, the cause of the primary alarm's outage is updated to "equipment-related," further subdivided into "wireless device-related," and the base station name is recorded.

[0094] In another scenario, if only the base station outage alarm type is detected, but no sub-alarm type is clearly detected, the fault location can be performed according to the following procedure.

[0095] First, determine if the corresponding fields in the original alarm content of the base station outage alarm text meet the following criteria: manufacturer is ××, alarm title is "Network Element Connection Interruption," and alarm text includes "Power Supply Status May Be Abnormal." If these criteria are met, update the alarm reason to "Power Outage Reason," further refine the reason to "Suspected Power Outage - Base Station Outage Alarm Text Extraction," and record environmental information in the preprocessing logic. Some base station models from manufacturer ×× can detect abnormal power supply information before their own power failure and assign the "Power Supply Status May Be Abnormal" value to the disconnection information field in the alarm text message. If this field has a different value, it indicates a normal base station outage.

[0096] Secondly, it can also determine whether the network element name in the base station outage alarm contains the keyword "remote". If so, the cause of the outage alarm is updated to "power outage", and the cause is further subdivided and updated to "suspected power outage - remote station". Environmental information is recorded in the preprocessing logic. For example, for a remote station, if there is no power outage alarm in the equipment room, it is determined to be affected by a suspected power outage.

[0097] Among them, remote base station stations, also known as remote radio unit (RRU) stations, are a special type of base station deployment in mobile communication networks. They separate the traditional baseband unit (BBU) from the radio frequency (RF) processing unit. The BBU is typically located in a central equipment room, centrally processing baseband signals; the RRU is installed at a site far from the central equipment room, responsible for transmitting and receiving RF signals. The two are connected by optical fiber, which transmits baseband signals, clock signals, and power, allowing the RRU to operate remotely. They are widely used in scenarios such as deep coverage patching in cities, network extension in remote rural areas, continuous signal assurance along high-speed rail lines, and temporary network expansion for large venue events. Therefore, without the protection of environmental monitoring equipment in the equipment room, they are susceptible to power outages at venues or in rural areas.

[0098] For example, the network element name can be × County × Township Zigeyu Jianchaya (Large Distance) - ZLH-XAJO011TL-Network Element Link Disconnection.

[0099] It can also determine whether the coverage type in the base station outage alarm is indoor. If so, the cause of the outage alarm is updated to power outage, and the cause is further subdivided into suspected power outage - indoor distribution, and environmental information is recorded in the preprocessing logic. For example, if the network element coverage type is equal to indoor distribution and there is no power outage alarm, it is determined to be affected by a suspected power outage.

[0100] For situations where high-temperature faults cause base station hardware downtime and service outages, the main alarm is a base station outage alarm, sub-alarm A is a high-temperature alarm within the environmental power outage alarm category, and sub-alarm B is a power outage fault within the environmental power outage alarm category. The preset single-fault association rule is that sub-alarm A occurred within 8 minutes after 24 hours prior to the main alarm, but sub-alarm B did not. This means there is a high-temperature alarm but no power outage alarm, and the alarm target room name of the main alarm matches the alarm target room name of the sub-alarm. After successful association, the relevant information of the main alarm is updated, changing the base station outage alarm reason to a power outage reason, further subdividing the reason to a high-temperature fault, and recording the room name, high-temperature time, etc., as fault information. The system can present the three most recent fault points based on the time the high-temperature alarm occurred in the room.

[0101] If no root cause alarm is found using the above process, query the 3-month historical alarm history of the out-of-service base station. If the number of out-of-service alarms per month exceeds 7, update the content of the base station outage alarms, changing the cause of the outage alarm to a power outage, and further subdividing the cause into suspected power outage - possible environmental factors. Record environmental factors related to the outage in the preprocessing logic. For example, if no root cause alarm is found to be associated with the outage, it is determined to be due to a suspected power outage.

[0102] In this way, by automatically matching multidimensional analysis information with preset single-fault association rules, the cause of the fault can be quickly determined, avoiding the difficulty in drawing clear conclusions due to scattered analysis information. Based on locating the cause of the fault, further statistical analysis of the fault information corresponding to that cause provides data support for subsequent fault review, trend analysis, and preventive measures, which helps to optimize operation and maintenance strategies.

[0103] However, when a large number of alarm messages occur in a certain period of time, if the fault location results are reported sequentially, the information becomes redundant and the correlation between the fault location results is easily overlooked, thus making it impossible to use effective information for fault location. Therefore, this disclosure sets up a corresponding solution for the situation of multiple faults.

[0104] In an optional implementation, after statistically analyzing the fault information of the target fault cause based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the method further includes: Count the number of alarm messages corresponding to the base station outage alarm types within the target area; If the number of alarm messages corresponding to the base station outage alarm type exceeds the preset number of alarms, the number of base stations and the fault point for each target fault cause will be counted and output.

[0105] For example, based on the definition of a major network fault, when the number of base stations out of service for different alarm objects in the same city is greater than or equal to 60, the number of base stations and the fault point for each target fault cause are counted and output. Of course, the preset number of alarms can also be modified according to actual needs.

[0106] In a specific example, firstly, when calculating the number of different alarm objects or network element names for base station outage alarms, it is necessary to deduplicate the alarm objects or network element names. The deduplication method is to first deduplicate the alarms by the device name of the out-of-service base station when counting base station outage alarms in the active alarm table, and then perform alarm statistics. The threshold can be configured according to the actual situation on site. Secondly, a preliminary judgment of the fault cause is performed. Based on the preliminary judgment of the cause of each single fault, the number of base stations is calculated, and the base station situation affected by different causes is summarized. Specifically, for different fault cause categories, the alarms with determined causes are traversed, and the number of base stations involved in each category is counted. Through database query statements, the base station fault information is grouped and counted according to the fault cause field from the table storing base station fault information, thereby obtaining the number of base stations affected by each fault cause.

[0107] When analyzing power-related issues, record the total number of base stations affected and the main fault locations. Present the top three fault locations based on the number of base stations affected by the fault location (e.g., equipment room or site), and obtain the names of their respective equipment rooms.

[0108] Specifically, the activity alarm table counts the number of alarms containing "power outage" or "high temperature" as the reason for base station outage in each base station outage alarm. The number of affected base stations is then sorted according to the data center or site field. Using the database's sorting and filtering functions, the top three data centers or sites with the most affected base stations are selected as the primary fault points. Simultaneously, the data center names of these three primary fault points are retrieved from the corresponding data center information table in the database. The number of affected base stations, the primary fault points, and their data center names are recorded and summarized. When the number of affected base stations exceeds the target number, the time of each fault, data center name, alarm type, and number of affected base stations are recorded. Specifically, transmission power supply alarms are queried from the data center where the transmission network element is located, and base station self-power supply alarms are queried from the data center where the base station itself is located, with the alarm time being the most recent.

[0109] When analyzing transmission causes, record the number of base stations affected and the main fault points. For example, if the link between network element 1 and network element 2 is interrupted, link interruptions and performance-related alarms are identified by querying the circuit table to determine the port and name of the peer network element. The top three fault point location results are presented based on the network element with the most transmission interruptions or transmission faults.

[0110] Specifically, in the activity alarm table, the number of alarms whose base station outage reason includes the character for "transmission reason" is counted. These alarms are then sorted based on the number of faults or the scope of impact involved in each transmission interruption or faulty network element. Using database functions, the top three fault points with the highest frequency or largest impact are selected as primary fault points, and their information, along with the total number of affected base stations, is recorded. When the number of affected base stations exceeds the target number (e.g., 2), the occurrence time, involved network element or equipment room, fault type, and number of affected base stations for each transmission fault are recorded. Fault types can include interruption, performance degradation, disconnection, and hardware failure.

[0111] When analyzing base station outages caused by their own reasons, record the total number of base stations affected. In the activity alarm table, record the number of base station outage alarms for each base station outage where the reason for outage includes "radio device reason" or "unknown reason." This number represents the number of outages caused by the base station itself.

[0112] Thus, in addition to single-fault location, a group-fault location method was also established. By statistically analyzing the total number of base station outage alarms within the target area, the overall alarm severity of the area can be grasped, avoiding the situation of focusing only on single-point faults while ignoring regional alarm outbreaks, and also avoiding redundant output when the number of alarms is small. When a regional alarm outbreak occurs, the number of base stations and fault points corresponding to each target fault cause are statistically analyzed and output, which can intuitively present the impact range and severity of each fault cause, helping maintenance personnel to quickly identify the main fault causes, prioritize the issues with the largest impact, and improve the efficiency of regional fault handling.

[0113] In addition, to facilitate subsequent statistics on group failure information, a group failure alarm signal can be generated when the number of alarm messages corresponding to the base station outage alarm type exceeds the preset alarm quantity.

[0114] In a specific example, when a base station outage alarm message is collected, all active base station outage alarms are counted to determine the number of base station outage alarms within city XX. If the result is greater than 60, a group fault alarm signal with the title "Sudden City Base Station Outage Count Exceeds Threshold" is generated, and the occurrence time is the time of the base station outage alarm. When a message to clear a base station outage alarm is collected, all active base station outage alarms in the active alarm database are counted to determine the number of base station outage alarms within city XX. If the result is less than or equal to 60, the current group fault alarm signal with the "Sudden City Base Station Outage Count Exceeds Threshold" is cleared, and the clearing time is the clearing time of the base station outage alarm.

[0115] Furthermore, this disclosure also includes an association rule processing module to implement the processing logic for single-fault association rules and group-fault association rules. Based on preset association rules and priorities, the collected alarm information is analyzed and processed. A group of related alarms is associated together within a window time, thereby supporting alarm location analysis. For associated alarms, preprocessing and compressed dispatching can be performed.

[0116] Specifically, a primary and secondary alarm relationship is set up. When a batch of alarms occurs within a certain time period, one is selected as the primary alarm, and the others are designated as secondary alarms under that primary alarm. Base station outage alarms are designated as primary alarms, and environmental alarms, etc., are designated as secondary alarms. The settings for primary and secondary alarms can also be cleared according to their respective alarm clearing logic. Correspondingly, fields for the primary and secondary alarm association rules are set, including rule name, specialty, equipment type, data center, city / district / county, equipment name, manufacturer, network management alarm ID, alarm title, associated time window, alarm preprocessing information, and activation status.

[0117] In addition, a derivative association system is set up, which generates a new alarm based on a set of alarms. The new alarm is cleared when the derivative conditions are not met. Correspondingly, the fields of the derivative alarm association rule include: rule name, derivative alarm title, derivative alarm level, specialty, device type, manufacturer, network management alarm ID, alarm title, frequency threshold, association time window, and enabled status.

[0118] It also establishes alarm associations within network elements, between network elements within a specific specialty, and across specialties. This enables alarm associations between various components within the equipment, such as relays and relay groups, signaling and signaling link groups, and base station outages and cell outages. It also enables alarm associations within specialties such as wireless networks, transmission networks, core networks, data networks, environmental monitoring, and customer access. For example, alarm associations between transmission network elements are established using transmission circuit numbers as indexes. It also enables alarm associations between base station wireless, environmental monitoring, and transmission alarms; between the core network and the IP bearer network; and between the IP bearer network and the transmission network. For example, alarm associations between switching and transmission are established using physical ports as indexes. Correspondingly, a rule base is established to store various association rules for locating the causes of base station outages, such as the aforementioned association rules between environmental monitoring and base station outage alarms, and between transmission and base station outage alarms. The rule base can be updated and optimized based on actual network operation and experience. The association and preprocessing rules are essentially SQL scripts or SQL scripts processed based on JS / PYTHON logic.

[0119] It should be noted that when handling single-fault association, the priority order is: environmental factors > transmission > base station itself. The corresponding judgment modules are called sequentially to perform association analysis on the alarms and determine the cause of base station outage. For group fault association, the system is responsible for calculating the threshold for base station outage alarms, determining whether the group fault triggering conditions are met, and performing preliminary cause judgment, fault detail analysis, and generation and processing of derived alarm information.

[0120] In addition, this disclosure also sets out the corresponding software implementation method.

[0121] The software implementation adopts a layered architecture design, employing modular development, multi-threaded parallel processing, interface adaptation layer protocol compatibility, and database optimization to achieve efficient and accurate location of base station outage faults. Specific implementation details are as follows: 1. System Architecture and Development Environment The system adopts a three-tier architecture of data acquisition, processing, and storage. Core modules include an alarm acquisition module, a correlation rule processing module, a resource management module, and a database management system. Java is used for core business logic, and Python is used for rule script parsing. The development tools are IntelliJ IDEA 2024 and PyCharm 2024, ensuring code maintainability and cross-platform compatibility.

[0122] 2. Key technologies and implementation methods (1) Multi-threaded parallel processing mechanism To address the issues of alarm collection delays and correlation analysis blockages, the system employs a multi-threaded task scheduling mechanism, with the specific thread division of labor as follows: Alarm collection thread: An independent thread group acquires alarm data from base stations, environmental monitoring equipment, and transmission equipment in real time through polling or event triggering. Each thread is bound to a specific device type, such as base station equipment, environmental monitoring equipment, or transmission equipment, to avoid conflicts in the interaction of different device protocols.

[0123] Association rule processing threads: Two high-priority threads are responsible for parsing the collected alarm data and executing single-fault / group-fault association rules, such as the priority logic of environmental monitoring > transmission > base station itself. Alarms to be processed are passed between threads via a queue to ensure decoupling between rule processing and data collection.

[0124] Resource Query Thread: A single, dedicated thread for calling the resource management system interface to query information such as the base station's equipment room and transmission circuit, preventing resource queries from blocking the main processing flow. The thread manages HTTP requests to the resource system through a connection pool, supporting a maximum of 10 concurrent queries.

[0125] The inter-thread communication mechanism involves the alarm collection thread writing converted JSON-formatted alarm data to the Kafka topic `alarm_raw_data`, and the association rule processing thread consuming data from this topic. The query results from the resource query thread are returned to the processing thread through another Kafka topic, `resource_query_result`. In the event of a thread failure, an alarm is triggered by the monitoring component, and the faulty thread is automatically restarted.

[0126] The multi-threading mechanism is implemented through Java ThreadPoolExecutor. Thread priority, queue capacity (default 1000 queues), and timeout (30 seconds) can be dynamically configured to ensure that the system can still run stably in high-concurrency scenarios (such as when more than 60 base stations go out of service when a group failure is triggered).

[0127] (2) Interface adaptation layer and protocol compatibility To address the protocol differences among various devices (base stations, environmental monitoring systems, and transmission systems), the system is designed with an interface adaptation layer that supports multiple protocols based on the factory pattern. Dynamic creation of protocol processing objects: Based on the device type (such as a × base station that supports the Kafka protocol, or a × × transmission device that supports the SNMP protocol), the ProtocolFactory factory class dynamically generates KafkaProtocolHandler or SNMPProtocolHandler instances, which are responsible for protocol parsing and alarm data extraction.

[0128] Protocol extension support: The adapter layer reserves the CustomProtocolHandler interface. When adding a new device type, you only need to implement this interface and register it to the factory class to support alarm collection for the new protocol without modifying the existing code.

[0129] (3) Data transmission and verification mechanism The collected alarm data needs to be formatted and reliably transmitted before entering the processing module: Standardized format: Raw alarm data from different protocols is converted into a unified JSON format (e.g., {"alarmId":"88862422","alarmTitle":"Network element connection interrupted","eventTime":"2025-01-0100:29:43"}) by the DataConverter component, facilitating parsing by subsequent modules.

[0130] Reliable transmission: JSON data is transmitted using the TCP / IP protocol. The sending end calculates the MD5 checksum of the data, such as MD5("alarmId=88862422")=a1b2c3d4, and sends it synchronously with the data. The receiving end verifies the data integrity through MD5Util. If the verification fails, a retransmission mechanism is triggered.

[0131] (4) Database design and query optimization The system uses Oracle 19c as the core database (MySQL 8.0 as an alternative) to store alarm information, resource data, and correlation rules. Key optimization measures are as follows: Index optimization: Composite indexes were created for frequently queried fields, reducing the time spent querying environmental alarms in the same data center from an average of 500ms to 80ms in single-fault association rules.

[0132] Dynamic storage of rule base: Association rules are stored in the rule_library table in the form of SQL scripts or Python / JS logic scripts, and can be dynamically updated through the management interface, such as adjusting the group failure threshold to 50 base stations to avoid system restart.

[0133] Partitioned storage: Alarm history data is partitioned by eventTime and month, reducing the amount of data in a single table and improving the efficiency of historical alarm query.

[0134] In summary, based on a three-layer architecture of data acquisition, processing, and storage, and combined with technologies such as multi-threaded parallel processing, cross-protocol adaptation, dynamic rule engine, and database optimization, the core operation process covers four major stages: alarm acquisition, association rule processing, resource coordination, and result output and verification. Association rules are stored in script form, supporting hot updates and allowing threshold adjustments or rule additions without downtime, adapting to network evolution needs. The multi-threaded task scheduling algorithm, based on a priority queue and dynamic scaling mechanism of ThreadPoolExecutor, automatically increases the number of association rule processing threads in group failure scenarios, reducing the processing time for base station outage alarms. The cross-protocol adaptation framework, with its factory pattern and interface extension design, supports over 10 device protocols, is compatible with multiple manufacturers' devices, and solves the problem of difficult rule representation for device manufacturers in existing technologies. Through the optimized association rule processing flow and efficient system architecture, accurate fault location results can be obtained in a short time, significantly shortening network fault recovery time. In conclusion, through the above-mentioned end-to-end coordination, efficient and accurate location of base station outage faults is achieved, meeting the operational and maintenance needs for rapid network recovery in complex network environments.

[0135] This disclosure proposes a multi-dimensional comprehensive correlation analysis method. For base station outage faults, it constructs a priority analysis logic and combines multi-dimensional information such as alarm time windows, data center / transmission equipment relationships, and resource data to achieve hierarchical and progressive fault location. Unlike existing technologies that rely on single dimensions or simple thresholds, this method, through multi-dimensional comprehensive analysis of alarm correlation rules and combined with resource correlation information, can more accurately determine the true cause of base station outages, improving location accuracy. Furthermore, based on historical alarm data, it calculates attenuation weights using the median of the absolute value of the time difference to dynamically determine the master-sub-alarm correlation time window. This method is adaptable to different network environments, solving the problem of traditional fixed time windows being susceptible to alarm storm interference. Moreover, existing technologies use fixed thresholds and analysis methods, making it difficult to adapt to network changes. The configurable thresholds and updatable rule base of this disclosure can be adjusted according to different network environments and service requirements. For example, during network expansion or service adjustments, it is convenient to modify the base station outage threshold in the group fault correlation rules or add new correlation rules, improving the system's adaptability to network changes.

[0136] Figure 2 This is a schematic diagram of a fault location device provided in an embodiment of this disclosure. Figure 2 As shown, the fault location device 200 may include an information acquisition module 210, an information filtering module 220, an association processing module 230, and a result output module 240.

[0137] The information acquisition module 210 is used to collect multiple alarm messages from fault-related devices. Each alarm message corresponds to an alarm type, including base station outage alarm types and multiple sub-alarm types. The information filtering module 220 is used to filter out the alarm information corresponding to the target sub-alarm type from the alarm information corresponding to multiple sub-alarm types based on the priority order of multiple sub-alarm types; The association processing module 230 is used to analyze the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type in terms of time sequence and resource dimension to obtain multi-dimensional analysis information. The result output module 240 is used to match multidimensional analysis information with preset single-fault association rules, and obtain the fault location result of base station outage based on the matching result.

[0138] Optionally, the alarm information includes historical alarm time and network element information of the device. The association processing module 230 is also used for: Based on historical alarm times, a time-series analysis is performed on the occurrence time between base station outage alarm types and target sub-alarm types to obtain time-series analysis information. Based on the network element information of each device, the first identification information of the device corresponding to the base station outage alarm type and the second identification information of the device corresponding to the target sub-alarm type are determined, and the device analysis information of the resource dimension is determined according to the first identification information and the second identification information. Based on time series analysis information and equipment analysis information, multidimensional analysis information is determined.

[0139] Optionally, the association processing module 230 is also used for: Calculate the difference between the historical alarm time of the base station outage alarm type and the historical alarm time of the target sub-alarm type within the target historical time period to obtain multiple alarm time differences; Based on the duration of each alarm time difference, determine the dynamic attenuation weight of the corresponding alarm time difference; The cumulative weight of dynamic attenuation is obtained based on the dynamic attenuation weight within the dynamic sampling window; Among multiple dynamic sampling windows with a cumulative weight greater than a preset cumulative value, the dynamic sampling window with the smallest window length is selected to obtain the time-related window. The time series analysis information is obtained from the time correlation window.

[0140] Optionally, the sub-alarm types include power alarm types, transmission alarm types, and base station hardware alarm types. The priority of power alarm types is higher than that of transmission alarm types, and the priority of transmission alarm types is higher than that of base station hardware alarm types. The information filtering module 220 is also used for: If there is no alarm information corresponding to the power alarm type among multiple alarm information, the alarm information corresponding to the transmission alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type. If no alarm information corresponding to the transmission alarm type is found among multiple alarm information, the alarm information corresponding to the base station hardware alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type.

[0141] Optionally, the result output module 240 is also used for: The multidimensional analysis information is matched with the preset single obstacle association rules to determine the target single obstacle association rules; Determine the cause of the target failure based on the target single-fault association rules; Based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the fault information of the target fault cause is statistically analyzed.

[0142] Optionally, the result output module 240 is also used for: Count the number of alarm messages corresponding to the base station outage alarm types within the target area; If the number of alarm messages corresponding to the base station outage alarm type exceeds the preset number of alarms, the number of base stations and the fault point for each target fault cause will be counted and output.

[0143] Figure 3 A schematic diagram of the hardware structure of the fault location device provided in an embodiment of this disclosure is shown.

[0144] The fault location device may include a processor 301 and a memory 302 storing computer program instructions.

[0145] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this disclosure.

[0146] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be non-volatile solid-state memory. Memory 302 may be internal or external to the integrated gateway disaster recovery device.

[0147] In one instance, memory 302 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0148] Memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0149] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 The fault location method in the illustrated embodiment.

[0150] In one example, the fault location device may further include a communication interface 303 and a bus 304. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0151] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this disclosure.

[0152] Bus 304 includes hardware, software, or both, that couples components of a fault location device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in embodiments of this disclosure, this disclosure contemplates any suitable bus or interconnect.

[0153] Furthermore, in conjunction with the fault location methods in the above embodiments, this disclosure can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the fault location methods in the above embodiments.

[0154] This application also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the fault location methods described in the above embodiments.

[0155] It should be clarified that this disclosure is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this disclosure is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this disclosure.

[0156] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this disclosure are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0157] It should also be noted that the exemplary embodiments mentioned in this disclosure describe methods or systems based on a series of steps or apparatus. However, this disclosure is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0158] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0159] The above description is merely a specific embodiment of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this disclosure, and these modifications or substitutions should all be covered within the protection scope of this disclosure.

Claims

1. A fault location method characterized by, include: Collect multiple alarm messages from fault-related devices, where each alarm message corresponds to an alarm type, including base station outage alarm types and multiple sub-alarm types; Based on the priority order of the various sub-alarm types, the alarm information corresponding to the target sub-alarm type is filtered out from the alarm information corresponding to the various sub-alarm types; The alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type are analyzed in terms of time sequence and resource dimension to obtain multi-dimensional analysis information; The multidimensional analysis information is matched with preset single-fault association rules, and the fault location result of the base station outage is obtained based on the matching result.

2. The fault location method according to claim 1, characterized in that, The alarm information includes historical alarm time and network element information of the device. The alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type are analyzed in terms of time series and resource dimensions to obtain multi-dimensional analysis information, including: Based on the historical alarm times, a time-series analysis is performed on the occurrence time between the base station outage alarm type and the target sub-alarm type to obtain the time-series analysis information. Based on the network element information of each device, the first identification information of the device corresponding to the base station outage alarm type and the second identification information of the device corresponding to the target sub-alarm type are determined, and the device analysis information of the resource dimension is determined according to the first identification information and the second identification information. Based on the time series analysis information and the device analysis information, multidimensional analysis information is determined.

3. The fault location method according to claim 2, characterized in that, The step involves performing a time-series analysis on the occurrence time between the base station outage alarm type and the target sub-alarm type based on the historical alarm time, to obtain time-series analysis information, including: Calculate the difference between the historical alarm time of the base station outage alarm type and the historical alarm time of the target sub-alarm type within the target historical time period to obtain multiple alarm time differences; Based on the duration of each alarm time difference, determine the dynamic attenuation weight of the corresponding alarm time difference; The cumulative weight of dynamic attenuation is obtained based on the dynamic attenuation weight within the dynamic sampling window; Among multiple dynamic sampling windows where the cumulative weight of dynamic attenuation is greater than a preset cumulative value, the dynamic sampling window with the smallest window length is selected to obtain the time-related window. The time-series analysis information is obtained based on the time-related window.

4. The fault location method according to claim 1, characterized in that, The sub-alarm types include power alarm types, transmission alarm types, and base station hardware alarm types. The power alarm type has a higher priority than the transmission alarm type, and the transmission alarm type has a higher priority than the base station hardware alarm type. The step of filtering the alarm information corresponding to the target sub-alarm type from the alarm information corresponding to the various sub-alarm types based on their priority order includes: If no alarm information corresponding to the power alarm type is found among the multiple alarm information, the alarm information corresponding to the transmission alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type. If no alarm information corresponding to the transmission alarm type is found among the multiple alarm information, the alarm information corresponding to the base station hardware alarm type is selected from the multiple alarm information and used as the alarm information corresponding to the target sub-alarm type.

5. The fault location method according to claim 1, characterized in that, The step of matching the multidimensional analysis information with preset single-fault association rules and obtaining the fault location result of the base station outage based on the matching result includes: The multidimensional analysis information is matched with preset single-obstacle association rules to determine the target single-obstacle association rule; Based on the aforementioned target single-fault association rules, determine the cause of the target fault; Based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the fault information of the target fault cause is statistically analyzed.

6. The fault location method according to claim 5, characterized in that, After calculating the fault information of the target fault cause based on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type, the method further includes: The number of alarm messages corresponding to the base station outage alarm types within the target area is counted; If the number of alarm messages corresponding to the base station outage alarm type exceeds the preset alarm number, the number of base stations and the fault point for each target fault cause will be counted and output.

7. A fault location device, characterized in that, The device includes: The information acquisition module is used to collect multiple alarm messages from fault-related devices, wherein each alarm message corresponds to an alarm type, and the alarm type includes a base station outage alarm type and multiple sub-alarm types; the information filtering module is used to filter the alarm message corresponding to the target sub-alarm type from the alarm messages corresponding to the multiple sub-alarm types based on the priority order of the multiple sub-alarm types. The association processing module is used to perform time-series and resource-dimensional analysis on the alarm information corresponding to the base station outage alarm type and the alarm information corresponding to the target sub-alarm type to obtain multi-dimensional analysis information. The result output module is used to match the multidimensional analysis information with preset single-fault association rules, and obtain the fault location result of the base station outage based on the matching result.

8. A fault location device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the fault location method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the fault location method as described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the fault location method as described in any one of claims 1-6.

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