Calculation method, device, equipment and readable storage medium of shared risk link group

Abstract

The present application relates to a method, apparatus, device and readable storage medium for calculating a shared risk link group, and relates to the field of transmission network technology, including obtaining a historical alarm data set and a historical topology fiber connection data set; constructing a mapping relationship between alarms and fiber connections based on alarm location information and fiber connection port information; performing time slice-based clustering processing on the historical alarm data set based on a preset time threshold to obtain multiple alarm fine clusters; replacing and updating each alarm fine cluster based on the mapping relationship to obtain multiple fiber connection fine clusters to form a fiber connection data set; mining the fiber connection data set based on an association rule algorithm to obtain a fiber connection frequent item set, and using the fiber connection frequent item set as a shared risk link group. The present application realizes the automatic calculation of the shared risk link group, which can improve the configuration efficiency of the shared risk link group, reduce the management difficulty of the shared risk link group, and reduce the consumption of human resources and physical storage resources at the underlying physical statistics level.

Description

Technical Field

[0001] The present application relates to the field of transmission network technology, and in particular to a method, apparatus, device, and readable storage medium for calculating a shared risk link group. Background Art

[0002] As a fundamental network, the transport network carries a large number of customer services. The security of these services depends on the reliability of the transport network. To improve network reliability and ensure service security, the most reliable approach in existing networks is to establish two physically separate routes for services: working and protection. If the working route fails, services can immediately failover to the protection route, avoiding service interruption. The physical separation of working and protection routes relies on the establishment of a Shared Risk Link Group (SRLG). A Shared Risk Link Group is a group of links carried on the same physical resource (such as a multi-core optical cable or a multi-core electrical cable). If this physical resource fails, all links in the group become inoperable. Therefore, when calculating service routes, it is crucial to avoid having both the primary and backup routes in the same Shared Risk Link Group.

[0003] In related technologies, the following two methods are mainly used to avoid the main and backup routes being in the same shared risk link group: the first is that technical engineers conduct manual analysis based on alarm data to select fiber connections to set up risk link groups; the second is to count the static fiber cores, optical cables, and physical carrying facilities of optical cables (such as manholes, pipelines, poles, overhead, etc.) of external resources, and associate them with the transmission equipment ports in series throughout the process, and conduct combined detection with the transmission system based on the series results to determine the risk link group.

[0004] In the first solution, when too many alarms are generated, that is, when the amount of alarm data is large, manual analysis is not only time-consuming but also prone to errors, which makes the setting and management of risk link groups difficult and inefficient. In the second solution, since physical resources need to be managed throughout the process and a proprietary system needs to be provided for network management and control systems to collect data, not only is the workload large and difficult to manage, but also when new cables are laid or old equipment is decommissioned, physical resources need to be re-collected and associated with transmission equipment ports, which poses a problem of difficulty in maintenance. Summary of the Invention

[0005] The present application provides a calculation method, apparatus, device and readable storage medium for a shared risk link group to improve the configuration efficiency of the risk link group, reduce the management difficulty of the risk link group, and reduce the consumption of human resources and physical storage resources.

[0006] In a first aspect, a method for calculating a shared risk link group is provided, comprising the following steps:

[0007] Obtain historical alarm data sets and historical topology fiber connection data sets;

[0008] Construct a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set;

[0009] Perform time slice-based clustering on the historical alarm data set based on the preset time threshold to obtain multiple alarm clusters;

[0010] replacing and updating each alarm fine cluster based on the mapping relationship to obtain a plurality of connected fiber fine clusters to form a connected fiber data set;

[0011] The fiber-connection data set is mined based on the association rule algorithm FP-Growth to obtain at least one fiber-connection frequent item set, and the fiber-connection frequent item set is used as a shared risk link group.

[0012] In some embodiments, before the step of performing time slice-based clustering processing on the historical alarm dataset based on a preset time threshold to obtain a plurality of fine alarm clusters, the step further includes:

[0013] Each alarm data in the historical alarm data set is cleaned and feature extracted to obtain a preprocessed data set;

[0014] Sort the alarm data in the preprocessed data set according to the time sequence of the alarm occurrence to obtain a sorted data set;

[0015] The sorted data set is sliced according to the alarm hierarchy information including regional information to obtain multiple alarm coarse clusters to form a new historical alarm data set. Based on the new historical alarm data set, the step of clustering the historical alarm data set based on time slices based on a preset time threshold is executed.

[0016] In some embodiments, the step of performing the time slice-based clustering process on the historical alarm dataset based on a preset time threshold based on the new historical alarm dataset includes:

[0017] For each alarm coarse cluster, slicing the alarm coarse cluster based on the time threshold to obtain multiple time slice coarse clusters;

[0018] For each time slice coarse cluster, cluster processing is performed on the alarm data contained in the time slice coarse cluster to obtain alarm fine clusters.

[0019] In some embodiments, each piece of alarm data in the pre-processed data set includes an alarm ID number, an alarm name, an alarm code, an alarm occurrence time, an alarm level, an alarm source, and alarm location information.

[0020] In some embodiments, the replacing and updating each alarm fine cluster based on the mapping relationship to obtain multiple fiber-connected fine clusters to form a fiber-connected data set includes:

[0021] According to the mapping relationship, the alarm in each alarm fine cluster is replaced with the corresponding fiber, generating multiple fiber-connected fine clusters;

[0022] Multiple fiber clusters are deduplicated and sorted according to the fiber ID numbers to obtain a fiber dataset.

[0023] In some embodiments, the mining of the fiber-connected dataset based on the association rule algorithm FP-Growth to obtain at least one fiber-connected frequent itemset includes:

[0024] Sort the target fiber connections in the fiber connection data set in descending order of support, and construct a support item header table, wherein the support of the target fiber connection is greater than a preset minimum support;

[0025] Constructing a frequent pattern FP tree based on the fiber data set;

[0026] Determine the frequent itemsets of each target fiber through the support item header table and the FP tree;

[0027] De-duplicate all target fiber-linked frequent item sets to obtain at least one fiber-linked frequent item set.

[0028] In some embodiments, taking the fiber-connected frequent itemsets as a shared risk link group includes:

[0029] The same group ID number is assigned to the links belonging to the same link frequent itemset to establish a shared risk link group.

[0030] In a second aspect, a computing device for a shared risk link group is provided, comprising:

[0031] An acquisition unit, which is used to acquire a historical alarm data set and a historical topology fiber connection data set;

[0032] A construction unit, which is used to construct a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set;

[0033] A clustering unit, configured to perform time slice-based clustering processing on the historical alarm data set based on a preset time threshold to obtain a plurality of fine alarm clusters; and to replace and update each fine alarm cluster based on the mapping relationship to obtain a plurality of continuous fine clusters to form a continuous data set;

[0034] A computing unit is configured to mine the fiber-connected data set based on an association rule algorithm FP-Growth to obtain at least one fiber-connected frequent item set, and use the fiber-connected frequent item set as a shared risk link group.

[0035] In a third aspect, a computing device for a shared risk link group is provided, comprising: a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned method for computing the shared risk link group.

[0036] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed by a computer, the computer executes the aforementioned method for calculating a shared risk link group.

[0037] The present application provides a method, apparatus, device and readable storage medium for calculating a shared risk link group, including obtaining a historical alarm data set and a historical topology fiber connection data set; constructing a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set; performing time slice-based clustering processing on the historical alarm data set based on a preset time threshold to obtain multiple alarm fine clusters; replacing and updating each alarm fine cluster based on the mapping relationship to obtain multiple fiber connection fine clusters to form a fiber connection data set; mining the fiber connection data set based on the association rule algorithm FP-Growth to obtain at least one fiber connection frequent item set, and using the fiber connection frequent item set as a shared risk link group. The present application can improve the configuration efficiency of the shared risk link group by automatically establishing an alarm fiber connection mapping relationship, and at the same time realizes the automatic calculation of the shared risk link group through the analysis of the alarm big data association rule algorithm. It does not need to establish a mapping relationship between the underlying physical resources and the upper-level physical topology, which can not only effectively reduce the management difficulty of the shared risk link group, but also reduce the consumption of human resources and physical storage resources at the underlying physical statistics level. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 A flowchart of a method for calculating a shared risk link group provided in an embodiment of the present application;

[0040] Figure 2A schematic diagram of the frequent itemset mining process provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of the FP tree corresponding to inserting the first record provided in an embodiment of the present application;

[0042] Figure 4 A schematic diagram of the FP tree corresponding to inserting the second record provided in an embodiment of the present application;

[0043] Figure 5 A schematic diagram of the FP tree corresponding to inserting the third record provided in an embodiment of the present application;

[0044] Figure 6 A schematic diagram of the FP tree corresponding to inserting the fourth record provided in an embodiment of the present application;

[0045] Figure 7 A schematic diagram of the FP tree corresponding to inserting the fifth record provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of the FP tree corresponding to inserting the sixth record provided in an embodiment of the present application;

[0047] Figure 9 A schematic diagram of the final FP tree generated according to an embodiment of the present application;

[0048] Figure 10 A schematic diagram of the structure of a computing device for sharing a risk link group provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] The embodiments of the present application provide a calculation method, apparatus, device, and readable storage medium for a shared risk link group to improve the configuration efficiency of the risk link group, reduce the difficulty of managing the risk link group, and reduce the consumption of human resources and physical storage resources.

[0051] Figure 1 A method for calculating a shared risk link group provided in an embodiment of the present application includes the following steps:

[0052] Step S10: Obtain a historical alarm data set and a historical topology fiber connection data set, wherein the historical topology fiber connection data set includes multiple fiber connections and their corresponding fiber connection port information, and the fiber connection port information includes a port unique identification value, a fiber connection ID, a source network element, a source network element disk, a source network element port, a sink network element, a sink network element disk, and a sink network element port;

[0053] For example, it should be understood that the operation and maintenance personnel of the existing network often locate the fiber optic links through alarms, and then set the fiber optic links of the same alarm group as a risk link group. However, when the alarms are generated frequently and in large numbers, screening the alarms and locating the alarms to the fiber optic links, and then adding each fiber optic link to a risk link group is undoubtedly a complex, time-consuming and error-prone process; alternatively, by counting the physical carrying facilities of the underlying physical laying of optical cables and providing a proprietary system for the management and control system to collect and calculate the risk link group, but the statistics of the underlying physical resources require the coordination of multiple resources, and the proprietary collection system requires personnel to maintain. In addition, the existing network often has new equipment entering the network and old equipment leaving the network, which will cause changes in physical resources and proprietary collection systems, making maintenance difficult.

[0054] Therefore, this embodiment provides a method for calculating shared risk link groups that meets operators' needs for automated configuration and management of risk link groups, while avoiding the need to count underlying physical resources to achieve resource conservation. Specifically, the management and control system first collects raw historical alarm data and raw historical topology fiber connection data. This data is then stored in a cache to form the historical alarm dataset vecAlarmInfo and the historical topology fiber connection dataset mapPort2LinkInfo.

[0055] Among them, vecAlarmInfo stores a vector (a serial container) sequence composed of alarm information AlarmInfo corresponding to each alarm data. The alarm information AlarmInfo includes alarm ID, alarm name, alarm level, alarm source, alarm location information, alarm occurrence time, alarm end time, alarm duration, alarm type, alarm code and alarm level; mapPort2LinkInfo stores the fiber connection port information corresponding to each fiber connection. The fiber connection port information includes a key-value pair (Port, LinkInfo) composed of port information Port and fiber connection information LinkInfo. Port is a unique identification value (i.e., key value) in the form of a string composed of network element ID + single disk ID + port key. LinkInfo fiber connection information includes fiber connection ID, source network element, source network element single disk, source network element port, sink network element, sink network element single disk and sink network element port.

[0056] Step S20: constructing a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set;

[0057] Exemplarily, in this embodiment, the alarm location information and fiber connection port information are mapped to automatically establish a mapping relationship between alarms and fiber connections. Specifically, the alarm location information is extracted from the alarm feature information, and the fiber connection port information, including source and sink network element and port information, is extracted from the topology feature information. The alarm location information and fiber connection port information are then matched to establish a mapping relationship between alarms and fiber connections. This avoids the time-consuming manual alarm location and fiber connection, effectively improving the configuration efficiency of risk link groups.

[0058] For example, assuming that the port in the alarm location information corresponding to alarm 001 is port 1, and the port in the fiber port information corresponding to fiber connection 001 is Port1, since port 1 and Port1 are the same, a mapping relationship between alarm 001 and fiber connection 001 is established.

[0059] Step S30: performing time slice-based clustering processing on the historical alarm data set based on a preset time threshold to obtain multiple alarm fine clusters;

[0060] Before the step of performing time slice-based clustering processing on the historical alarm data set based on the preset time threshold to obtain multiple alarm fine clusters, the method further includes:

[0061] Each alarm data in the historical alarm data set is cleaned and feature extracted to obtain a preprocessed data set; wherein each alarm data in the preprocessed data set includes an alarm ID number, an alarm name, an alarm code, an alarm occurrence time, an alarm level, an alarm source, and an alarm location information;

[0062] Sort the alarm data in the preprocessed data set according to the time sequence of the alarm occurrence to obtain a sorted data set;

[0063] The sorted data set is sliced according to the alarm hierarchy information including regional information to obtain multiple alarm coarse clusters to form a new historical alarm data set. Based on the new historical alarm data set, the step of clustering the historical alarm data set based on time slices based on a preset time threshold is executed.

[0064] For example, in this embodiment, the original historical alarm data is cleaned and irrelevant alarms are filtered to reduce the amount of data. Feature extraction is performed on each piece of original historical alarm data to obtain the alarm information PreAlarmInfo corresponding to each piece of original alarm data, thereby forming a preprocessing data set vecPreAlarmInfo. For each piece of original historical alarm data, the extracted features preferably include the alarm ID, alarm name, alarm code, alarm occurrence time, alarm level, alarm source, and alarm location information; vecPreAlarmInfo is traversed and the alarm data with extracted features are fully sorted in chronological order of occurrence time to obtain the sorted data set multimapOccurTime2PreAlarmInfo; it should be understood that multimapOccurTime2PreAlarmInfo stores a repeatable key-value pair (OccurTime, PreAlarmInfo) consisting of the alarm occurrence time OccurTime and the alarm information PreAlarmInfo obtained by preprocessing.

[0065] Then, the sorted data set multimapOccurTime2PreAlarmInfo is traversed, and the sorted alarms are sliced according to the alarm hierarchy information and regional information such as logical domain and network block to achieve the first coarse clustering of the alarms, and obtain multiple alarm coarse clusters (i.e., coarse clustered alarm clusters) sliced by the alarm hierarchy, such as alarm coarse cluster 1-multimapOccurTime2PreAlarmInfo1 and alarm coarse cluster 2-multimapOccurTime2PreAlarmInfo2.

[0066] Then, each alarm coarse cluster is traversed separately (for example, alarm coarse cluster 1 and alarm coarse cluster 2 are traversed separately). For each alarm coarse cluster, fine-grained clustering based on time slices is performed according to the set time threshold (this time threshold indicates that the alarms occur simultaneously within the time interval, and its specific value can be set according to actual needs) to achieve secondary clustering of the alarms, and then generate multiple fine-grained clusters sliced by time slices, that is, multiple alarm fine clusters vecTimePreAlarmInfo.

[0067] Furthermore, the step of performing the time slice-based clustering processing on the historical alarm dataset based on the preset time threshold based on the new historical alarm dataset includes:

[0068] For each alarm coarse cluster, slicing the alarm coarse cluster based on the time threshold to obtain multiple time slice coarse clusters;

[0069] For each time slice coarse cluster, cluster processing is performed on the alarm data contained in the time slice coarse cluster to obtain alarm fine clusters.

[0070] Specifically, take the time threshold set to 2 minutes and the time length of a certain alarm coarse cluster is from 10:00 am to 10:14 am as an example: clustering is performed on the alarm coarse cluster every 2 minutes, that is, the alarm coarse cluster is divided into 7 time slice coarse clusters, namely, the time slice coarse cluster corresponding to 10:00 am to 10:02 am, the time slice coarse cluster corresponding to 10:02 am to 10:04 am, and so on;

[0071] Then cluster the alarms that occur in each time slice coarse cluster. For example, the alarms that occur in the time period from 10:00 am to 10:02 am include alarm 001, alarm 002, and alarm 002, forming alarm fine cluster 1; the alarms that occur in the time period from 10:02 am to 10:04 am include alarm 001, alarm 002, and alarm 003, forming alarm fine cluster 2. Similarly, the alarms that occur in other time periods are determined, and the alarm fine cluster 3 is obtained as [alarm 003, alarm 004]; the alarm fine cluster 4 is [alarm 004, alarm 001, alarm 003, alarm 004]; Alarm 004]; alarm fine cluster 5 is [alarm 001, alarm 002, alarm 004]; alarm fine cluster 6 is [alarm 001, alarm 002]; alarm fine cluster 7 is [alarm 005], and then the alarm fine cluster set is obtained: {[alarm 001, alarm 002, alarm 002], [alarm 001, alarm 002, alarm 003], [alarm 003, alarm 004], [alarm 004, alarm 001, alarm 00, alarm 004], [alarm 001, alarm 002, alarm 004], [alarm 001, alarm 002], [alarm 005]}.

[0072] Step S40: replacing and updating each alarm fine cluster based on the mapping relationship to obtain multiple connected fiber fine clusters to form a connected fiber data set;

[0073] For example, in this embodiment, fiber connections are associated with alarm location information to generate fine-grained fiber connection clusters. Specifically, the alarms in each fine alarm cluster are replaced based on the mapping relationship between alarms and fiber connections, constructed using alarm location information and fiber connection port information. This results in multiple fine fiber connection clusters, which in turn form a fiber connection dataset. For example, assuming a mapping relationship exists between alarm 001 and fiber connection 001, alarm 001 in fine alarm cluster 1 is replaced with fiber connection 001.

[0074] Furthermore, the replacing and updating of each alarm fine cluster based on the mapping relationship to obtain multiple fiber-connected fine clusters to form a fiber-connected data set includes:

[0075] According to the mapping relationship, the alarm in each alarm fine cluster is replaced with the corresponding fiber, generating multiple fiber-connected fine clusters;

[0076] Multiple fiber clusters are deduplicated and sorted according to the fiber ID numbers to obtain a fiber dataset.

[0077] Exemplarily, in this embodiment, fine-grained clustering of connected fibers will be created based on the aforementioned mapping relationship, and deduplication and sorting will be performed based on the connected fiber ID to generate fine-grained clustering of connected fibers, thereby obtaining fine-grained clustering of connected fibers corresponding to each alarm fine cluster. Specifically, assuming that there is a mapping relationship between alarm 001 and connected fiber 001, alarm 002 and connected fiber 002, alarm 003 and connected fiber 003, alarm 004 and connected fiber 004, and alarm 005 and connected fiber 005, then after replacement, deduplication and sorting, fine-grained clustering 1 corresponding to alarm fine cluster 1 can be obtained as [connected fiber 001, connected fiber 002], and fine-grained clustering 2 corresponding to alarm fine cluster 2 can be obtained as [connected fiber 001, connected fiber 002, connected fiber 003]. 03], the fiber-connected cluster 3 corresponding to alarm cluster 3 is [fiber-connected cluster 003, fiber-connected cluster 004], the fiber-connected cluster 4 corresponding to alarm cluster 4 is [fiber-connected cluster 001, fiber-connected cluster 003, fiber-connected cluster 004], the fiber-connected cluster 5 corresponding to alarm cluster 5 is [fiber-connected cluster 001, fiber-connected cluster 002, fiber-connected cluster 004], the fiber-connected cluster 6 corresponding to alarm cluster 6 is [fiber-connected cluster 001, fiber-connected cluster 002], and the fiber-connected cluster 7 corresponding to alarm cluster 7 is [fiber-connected cluster 005].

[0078] Taking the fine clustering of each connected fiber obtained above as a data set, we can get the connected fiber data set {[connected fiber 001, connected fiber 002], [connected fiber 001, connected fiber 002, connected fiber 003], [connected fiber 003, connected fiber 004], [connected fiber 001, connected fiber 003, connected fiber 004], [connected fiber 001, connected fiber 002, connected fiber 004], [connected fiber 001, connected fiber 002], [connected fiber 005]}.

[0079] Step S50: mining the fiber-connection data set based on the association rule algorithm FP-Growth to obtain at least one fiber-connection frequent item set, and using the fiber-connection frequent item set as a shared risk link group.

[0080] The method of mining the fiber-connected data set based on the association rule algorithm FP-Growth to obtain at least one fiber-connected frequent item set includes:

[0081] Sort the target fiber connections in the fiber connection data set in descending order of support, and construct a support item header table, wherein the support of the target fiber connection is greater than a preset minimum support;

[0082] Constructing a frequent pattern FP tree based on the fiber data set;

[0083] Determine the frequent itemsets of each target fiber through the support item header table and the FP tree;

[0084] De-duplicate all target fiber-linked frequent item sets to obtain at least one fiber-linked frequent item set.

[0085] For example, in this embodiment, the fiber-linked data set will be traversed and mined using the FP-Growth prior algorithm to obtain the final fiber-linked frequent item set, and the fiber-linked frequent item set will be used as the shared risk link group to complete the calculation of the risk link group. The entire calculation process does not require human intervention.

[0086] The following combination Figure 2 The calculation process and principle of continuous fiber frequent itemsets are explained using the above continuous fiber data set as an example.

[0087] Step N10: Traverse the fiber-link data set, sort the individual items that meet the minimum support (i.e., K=1 item sets, each fiber-link in the fiber-link data set) from high to low according to support, and delete the K=1 item sets with lower support to obtain a support item header table. It can be understood that this embodiment will sort the fibers whose support is greater than the preset minimum support, that is, delete the fibers whose support is less than the minimum support. It should be noted that the specific value of the minimum support can be determined according to actual needs and is not limited here. For example, the minimum support is set to 15%. Since only fiber-link 005 has a support less than 15%, fiber-link 005 is deleted, and the remaining fiber-links 001, fiber-link 002, fiber-link 003, and fiber-link 004 are statistically sorted from high to low according to support, and the support item header table shown in Table 1 can be obtained (the linked list is empty initially).

[0088] Table 1 Support item header table

[0089] item Support Linked List Continuous Fiber 001 5 Continuous Fiber 002 4 Continuous Fiber 003 3 Continuous Fiber 004 3

[0090] Step N20: Construct a FP (Frequent Pattern) tree and mark the root node of the FP tree as a NULL node. Then traverse the continuous fiber data set again and insert each record (i.e., each continuous fiber cluster in the continuous fiber data set) into the FP tree. If the corresponding K=1 set already exists, then add 1 to its count to obtain the final FP tree. Specifically, insert the first record (i.e., continuous fiber cluster 1) and obtain Figure 3 The FP tree shown in Figure 2; Similarly, insert the second record (i.e., thin cluster 2) and get Figure 4 The FP tree shown in Figure 1 is inserted into the third record (i.e., thin cluster 3), and we get Figure 5 The FP tree shown in Figure 1 is inserted into the fourth record (i.e., the thin cluster 4), and the result is Figure 6The FP tree shown in Figure 1 is inserted into the fifth record (i.e., the thin cluster 5), and the result is Figure 7 The FP tree shown in FIG5; insert the sixth record (ie, the thin cluster 6), and get Figure 8 The FP tree shown in Figure 2; since the support of the last record (i.e., thin cluster 7) is too low, it is not inserted, and the final result is Figure 9 The FP tree shown.

[0091] Step N30: Starting from the last item in the support header table, find all related paths in the FP tree. For example, starting from fiber 004, all the paths can be found upwards as [fiber 001, fiber 002, fiber 004], [fiber 001, fiber 003, fiber 004], and [fiber 003, fiber 004].

[0092] Step N40: Remove the K=1 item itself (ie, fiber 004) in the path, and the conditional pattern base of fiber 004 is obtained as [fiber 001, fiber 002], [fiber 001, fiber 003], [fiber 003].

[0093] Step N50: Count all K=1 item sets in the conditional pattern base, that is, fiber 001 appears twice, fiber 002 appears once, and fiber 003 appears twice. Therefore, the frequent item sets of fiber 004 are [fiber 001, fiber 003, fiber 004] and [fiber 003, fiber 004].

[0094] Step N60: Repeating steps N30 to N50, it can be concluded that the frequent itemset of fiber 003 is [fiber 001, fiber 003], and the frequent itemset of fiber 002 is [fiber 001, fiber 002]. Since fiber 001 is a vertex node, it does not have a corresponding frequent itemset.

[0095] Step N70: de-duplicate and merge all frequent item sets corresponding to fiber-link 002 to fiber-link 004, and then obtain the final fiber-link frequent item sets as [fiber-link 001, fiber-link 003, fiber-link 004] and [fiber-link 001, fiber-link 002].

[0096] Step N80: The fiber-link frequent item sets [fiber-link 001, fiber-link 003, fiber-link 004] and [fiber-link 001, fiber-link 002] are respectively used as shared risk link groups.

[0097] As can be seen, this embodiment achieves decoupling of physical resources through big data association rule mining. Based on the fact that connections to the same physical resource manifest as simultaneous failures, association rule mining is performed on all historical alarm big data or for specific time periods, thereby generating risky fiber connection groups with strong association rules. This embodiment calculates risky link groups based on big data association rule analysis, eliminating the need to count physical resources, saving manpower while dynamically adapting to changes in physical resources.

[0098] Furthermore, taking the fiber-connected frequent itemsets as a shared risk link group includes:

[0099] The same group ID number is assigned to the links belonging to the same link frequent itemset to establish a shared risk link group.

[0100] For example, in this embodiment, each frequent item set is traversed, and the same group ID is assigned to the links belonging to the same frequent item set to establish a temporary risk link group. This group is then stored for persistence and displayed in a visual interface. Users can confirm the temporary risk link group through the visual interface, and if confirmed, a formal risk link group is created and stored for persistence. For example, for the temporary risk link group [link 001, link 003, link 004], if the user confirms the temporary risk link group as a configuration object, [link 001, link 003, link 004] is created as the formal risk link group.

[0101] In summary, this embodiment can automatically calculate the shared risk link group through historical alarm and association rule analysis, and provide users with selection and setting, thereby realizing automatic configuration management of the shared risk link group. The entire calculation process does not require human intervention, which not only reduces the management difficulty of the risk link group, but also eliminates the need to establish a mapping relationship between the underlying physical resources and the upper-level physical topology, thereby reducing the consumption of human resources and physical storage resources at the underlying physical statistical level. At the same time, with the support of big data analysis and calculation methods, the rationality, effectiveness and accuracy of the calculation results are guaranteed.

[0102] It should be noted that the step numbers of the steps in the embodiments of the present application do not limit the order of the operations in the technical solution of the present application.

[0103] The present application also provides a computing device for a shared risk link group, including:

[0104] An acquisition unit, which is used to acquire a historical alarm data set and a historical topology fiber connection data set;

[0105] A construction unit, which is used to construct a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set;

[0106] A clustering unit, configured to perform time slice-based clustering processing on the historical alarm data set based on a preset time threshold to obtain a plurality of fine alarm clusters; and to replace and update each fine alarm cluster based on the mapping relationship to obtain a plurality of continuous fine clusters to form a continuous data set;

[0107] A computing unit is configured to mine the fiber-connected data set based on an association rule algorithm FP-Growth to obtain at least one fiber-connected frequent item set, and use the fiber-connected frequent item set as a shared risk link group.

[0108] Furthermore, the clustering unit is further configured to:

[0109] Each alarm data in the historical alarm data set is cleaned and feature extracted to obtain a preprocessed data set;

[0110] Sort the alarm data in the preprocessed data set according to the time sequence of the alarm occurrence to obtain a sorted data set;

[0111] The sorted data set is sliced according to the alarm hierarchy information including regional information to obtain multiple alarm coarse clusters to form a new historical alarm data set. Based on the new historical alarm data set, the step of clustering the historical alarm data set based on time slices based on a preset time threshold is executed.

[0112] Furthermore, the clustering unit is specifically used for:

[0113] For each alarm coarse cluster, slicing the alarm coarse cluster based on the time threshold to obtain multiple time slice coarse clusters;

[0114] For each time slice coarse cluster, cluster processing is performed on the alarm data contained in the time slice coarse cluster to obtain alarm fine clusters.

[0115] Furthermore, each piece of alarm data in the pre-processed data set includes an alarm ID number, an alarm name, an alarm code, an alarm occurrence time, an alarm level, an alarm source, and alarm location information.

[0116] Furthermore, the clustering unit is further configured to:

[0117] According to the mapping relationship, the alarm in each alarm fine cluster is replaced with the corresponding fiber, generating multiple fiber-connected fine clusters;

[0118] Multiple fiber clusters are deduplicated and sorted according to the fiber ID numbers to obtain a fiber dataset.

[0119] Furthermore, the computing unit is specifically configured to:

[0120] Sort the target fiber connections in the fiber connection data set in descending order of support, and construct a support item header table, wherein the support of the target fiber connection is greater than a preset minimum support;

[0121] Constructing a frequent pattern FP tree based on the fiber data set;

[0122] Determine the frequent itemsets of each target fiber through the support item header table and the FP tree;

[0123] De-duplicate all target fiber-linked frequent item sets to obtain at least one fiber-linked frequent item set.

[0124] Furthermore, the computing unit is further configured to:

[0125] The same group ID number is assigned to the links belonging to the same link frequent itemset to establish a shared risk link group.

[0126] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described device and each unit can refer to the corresponding process in the aforementioned embodiment of the calculation method of the shared risk link group, and will not be repeated here.

[0127] The apparatus provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 10 The shared risk link shown is run on a group of computing devices.

[0128] An embodiment of the present application also provides a computing device for a shared risk link group, comprising: a memory, a processor, and a network interface connected via a system bus, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement all or part of the steps of the aforementioned method for calculating the shared risk link group.

[0129] Among them, the network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0130] The processor may be a CPU, other general-purpose processors, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor, or any conventional processor. The processor is the control center of a computer device, connecting various parts of the entire computer device using various interfaces and lines.

[0131] The memory can be used to store computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application required for a function (such as a video playback function, an image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as video data, image data, etc.). In addition, the memory can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, an SMC (SmartMediaCard, smart memory card), an SD (Secure Digital) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0132] The embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the steps of the aforementioned method for calculating the shared risk link group are implemented.

[0133] The embodiments of the present application implement all or part of the aforementioned processes, and may also be completed by instructing related hardware through a computer program. The computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above methods may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, ROM (Read-Only memory), RAM (Random Access memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0134] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, servers, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0135] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0137] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A method for calculating a shared risk link group, characterized in that: The following steps are involved: Obtain historical alarm data sets and historical topology fiber connection data sets; Construct a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set; Perform time slice-based clustering on the historical alarm data set based on the preset time threshold to obtain multiple alarm clusters; replacing and updating each alarm fine cluster based on the mapping relationship to obtain a plurality of connected fiber fine clusters to form a connected fiber data set; The fiber-connection data set is mined based on the association rule algorithm FP-Growth to obtain at least one fiber-connection frequent item set, and the fiber-connection frequent item set is used as a shared risk link group.

2. The method for calculating a shared risk link group according to claim 1, wherein: Before the step of performing time slice-based clustering processing on the historical alarm data set based on the preset time threshold to obtain multiple alarm fine clusters, the method further includes: Each alarm data in the historical alarm data set is cleaned and feature extracted to obtain a preprocessed data set; Sort the alarm data in the preprocessed data set according to the time sequence of the alarm occurrence to obtain a sorted data set; The sorted data set is sliced according to the alarm hierarchy information including regional information to obtain multiple alarm coarse clusters to form a new historical alarm data set. Based on the new historical alarm data set, the step of clustering the historical alarm data set based on time slices based on a preset time threshold is executed.

3. The method for calculating a shared risk link group according to claim 2, wherein: The step of performing the time slice-based clustering process on the historical alarm dataset based on the preset time threshold based on the new historical alarm dataset includes: For each alarm coarse cluster, slicing the alarm coarse cluster based on the time threshold to obtain multiple time slice coarse clusters; For each time slice coarse cluster, cluster processing is performed on the alarm data contained in the time slice coarse cluster to obtain alarm fine clusters.

4. The method for calculating a shared risk link group according to claim 2, wherein: Each piece of alarm data in the pre-processed data set includes an alarm ID number, an alarm name, an alarm code, an alarm occurrence time, an alarm level, an alarm source, and alarm location information.

5. The method for calculating a shared risk link group according to claim 1, wherein: The replacing and updating each alarm fine cluster based on the mapping relationship to obtain multiple fiber-connected fine clusters to form a fiber-connected data set includes: According to the mapping relationship, the alarm in each alarm fine cluster is replaced with the corresponding fiber, generating multiple fiber-connected fine clusters; Multiple fiber clusters are deduplicated and sorted according to the fiber ID numbers to obtain a fiber dataset.

6. The method for calculating a shared risk link group according to claim 1, wherein: The FP-Growth-based association rule algorithm is used to mine the fiber-connected data set to obtain at least one fiber-connected frequent item set, including: Sort the target fiber connections in the fiber connection data set in descending order of support, and construct a support item header table, wherein the support of the target fiber connection is greater than a preset minimum support; Constructing a frequent pattern FP tree based on the fiber data set; Determine the frequent itemsets of each target fiber through the support item header table and the FP tree; De-duplicate all target fiber-linked frequent item sets to obtain at least one fiber-linked frequent item set.

7. The method for calculating a shared risk link group according to claim 1, wherein: The step of using the connected fiber frequent itemsets as a shared risk link group includes: The same group ID number is assigned to the links belonging to the same link frequent itemset to establish a shared risk link group.

8. A computing device for a shared risk link group, characterized in that: include: An acquisition unit, which is used to acquire a historical alarm data set and a historical topology fiber connection data set; A construction unit, which is used to construct a mapping relationship between alarms and fiber connections based on the alarm location information in the historical alarm data set and the fiber connection port information in the historical topology fiber connection data set; A clustering unit, which is used to perform time slice-based clustering processing on the historical alarm data set based on a preset time threshold to obtain multiple alarm fine clusters; replacing and updating each alarm fine cluster based on the mapping relationship to obtain a plurality of connected fiber fine clusters to form a connected fiber data set; A computing unit is configured to mine the fiber-connected data set based on an association rule algorithm FP-Growth to obtain at least one fiber-connected frequent item set, and use the fiber-connected frequent item set as a shared risk link group.

9. A computing device for sharing a risk link group, characterized in that: include: A memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the calculation method of the shared risk link group according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a computer, the computer is caused to execute the method for calculating a shared risk link group according to any one of claims 1 to 7.

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