CN114666686A - Optical network alarm processing priority automatic judging method based on dynamic assignment calculation - Google Patents
Optical network alarm processing priority automatic judging method based on dynamic assignment calculation Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q11/0067—Provisions for optical access or distribution networks, e.g. Gigabit Ethernet Passive Optical Network (GE-PON), ATM-based Passive Optical Network (A-PON), PON-Ring
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q2011/0079—Operation or maintenance aspects
Abstract
The invention discloses an automatic judging method for optical network alarm processing priority based on dynamic assignment calculation, which belongs to the field of optical network communication and specifically comprises the following steps: firstly, acquiring historical alarm data of a northbound network manager, and dividing all the alarm data into four groups according to the relation between alarms and network elements; reserving the alarm group with the largest dimension aiming at the same service in the same time period, abandoning the rest alarm data, and carrying out normalization processing; then, setting a confidence threshold value, and carrying out relevance analysis on the alarm group subjected to normalization processing; respectively calculating the weight factor W of each alarm in each group aiming at each alarm combination with strong relevance; weighting each weight factor to the corresponding strong correlation alarm combination, performing descending order, and selecting the alarm combination with the maximum weight for priority treatment; finally, in the alarm combination, the alarm data with the maximum weight is selected again for priority processing. The invention solves the problem that the alarm processing sequence of the MESH network is difficult to determine, and simplifies the alarm processing flow.
Description
Technical Field
The invention belongs to the field of optical network communication, relates to alarm fault analysis in an optical network, and particularly relates to an automatic judgment method for alarm processing priority of the optical network based on dynamic assignment calculation.
Background
With the continuous development of network technology and the continuous increase of traffic, optical networks as a bearer support are also developing forward. In order to meet the requirements of upper-layer services, the flexible configuration and the optimized management of an optical network are of great importance, and an excellent network management technology is provided, so that more reliable and stable support can be provided for the upper-layer services. The fault management of the optical network is very important, along with the enlargement of the scale of the optical network and the improvement of the complexity, the fault of the network equipment is quickly discovered and timely maintained, and the loss of service data and the economic loss caused by the fault can be reduced to a certain extent.
The existing alarm processing method adopts manual processing after preliminary filtering, and long-term operation and maintenance experience is needed for accumulation of sequential selection of alarm processing, so that the method has high labor cost and poor robustness; the existing network management system mostly adopts alarm priority to divide the sequence, but is too general, once too many alarms are given, the alarms of the same level still cannot be sequenced.
Disclosure of Invention
In order to solve the problem that the MESH network alarm processing sequence is difficult to determine and simplify the alarm processing flow, the invention provides an automatic judgment method for the priority of the alarm processing of the optical network based on dynamic assignment calculation.
The method for automatically judging the priority of the optical network alarm processing based on the dynamic assignment calculation comprises the following specific steps:
step one, obtaining historical alarm data of a northbound network management interface, and dividing all alarm data into four groups according to the relation between alarms and network elements;
dividing alarm data belonging to the same type into a group according to the single network element internal type, the upstream and downstream network element type, the upstream and downstream inter-multiplexing station type and the service level type;
aiming at a service in a certain time period, when the service has only one network element with alarm, taking the alarm data as an alarm group of a single network element type;
the network element comprises a multiplexing station network element and an amplification station network element; the amplification station network elements are located between the multiplexing station network elements.
When the service only has alarm between any two adjacent network elements, the two alarm data are used as the alarm group of the relation between the upstream network element and the downstream network element.
The two adjacent network elements comprise a multiplexing station network element and an amplification station network element adjacent to the multiplexing station network element; or two adjacent amplification station network elements;
when alarm data exists between two adjacent multiplexing station network elements in the service, or alarm data exists between one multiplexing station network element and at least two amplification station network elements adjacent to the multiplexing station network element, all the alarm data form an alarm group of the type between the upstream and downstream multiplexing stations.
When all network elements of the service have alarms or exceed alarm data of the type between upstream and downstream multiplexing stations, an alarm group of the service level type is formed;
step two, in all alarm group data in the same time period, the alarm group with the largest dimension is reserved for the same service, the rest alarm data are abandoned, and the reserved alarm data of each service are respectively subjected to normalization processing;
the dimensionality of the alarm group is as follows from big to small in sequence: service level, upstream and downstream multiplexing stations, upstream and downstream network elements and in single network element;
alarm groups in the same service may occupy several different types at the same time, the alarm groups are arranged according to descending order of dimensionality, the types with smaller dimensionality are inevitably contained in the types with larger dimensionality, the types with smaller dimensionality are abandoned, and each service only retains the alarm group with the largest dimensionality.
Setting a confidence coefficient threshold value confth, and performing relevance analysis on all the reserved alarm groups subjected to normalization processing;
counting all alarm names in an alarm group reserved in all unified services, randomly combining at least two names, traversing each combination one by one, and calculating the confidence of each combination;
the confidence coefficient calculation formula is as follows:
the confidence coefficient is the frequency of simultaneous occurrence of (alarm name ax, alarm name ay, … … alarm name an)/(alarm name ax);
n is the number of alarm names.
If the confidence of the current combination is greater than the threshold, strong correlation exists among the alarm name ax, the alarm name, y, … … and the alarm name an, all alarm combinations with the confidence greater than the threshold are selected, and the proper subset is cut off according to the inclusion relation;
respectively calculating a weight factor W corresponding to each alarm name in each group aiming at each alarm combination with strong relevance;
the calculation formula is as follows:
Wi＝Wlevel*Waddr*[deg(vi)+N(vi)]
Withe data weight value corresponding to the ith alarm name; wlevelThe data grade corresponding to the ith alarm name; waddrThe topological geographic position of the data corresponding to the ith alarm name; deg (v)i) The network element topology position of the data corresponding to the ith alarm name; n (v)i) The service flow condition of the data corresponding to the ith alarm name is obtained;
assigning each weight factor to a corresponding alarm combination, performing descending order arrangement on the weighted strong association alarm combinations, and selecting the alarm combination with the largest weight for priority processing;
and step six, in the same alarm combination of the strong association rule, selecting the alarm data with the maximum weight value again for priority processing.
The invention has the advantages that:
1) the method for automatically judging the priority of the alarm processing of the optical network based on dynamic assignment calculation combines the relevance algorithm with the alarm processing, and the principle of the relevance algorithm is simple, so that the subsequent analysis of the alarm processing is easier.
2) The optical network alarm processing priority automatic judgment method based on dynamic assignment calculation performs formula-unified weighting processing on the alarms, so that the sorted alarm groups are more convenient to process and can be used for alarm tracing or unknown alarm classification guidance.
Drawings
FIG. 1 is a flowchart illustrating steps of a method for automatically determining priority of alarm handling in an optical network based on dynamic assignment calculation according to the present invention;
FIG. 2 is an exemplary diagram of the present invention classifying historical alarm data according to the relationship between alarms and network elements;
fig. 3 is a network element of two services and a corresponding alarm information topology diagram in the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
The invention has proposed the automatic judgement method of priority of alarm processing of the optical network based on that the dynamic assignment calculates, has adopted the analysis algorithm of the associativity, such as Apriori algorithm, through obtaining the historical alarm information of the north network management, can process the grouping to the historical alarm, and set up the rational degree of confidence, choose all item sets with strong association rule, and judge the item set chosen, if there is inclusion relation, abandon the proper subset comprising the relation; finally, weighting the strong association alarm, and sequencing the alarms in the strong association rule in a descending order to obtain the alarm with the maximum weight value as a priority processing alarm; compared with manual processing, the method has the advantages of higher speed, analysis of potential association of the alarm and guidance of the significance of root-cause alarm.
As shown in fig. 1, the method for automatically determining the priority of alarm processing in an optical network based on dynamic assignment calculation specifically includes the following steps:
step one, obtaining historical alarm data of a northbound network management interface, and dividing all alarm data into four groups according to the relation between alarms and network elements;
the historical alarm data comprises parameters such as alarm names, network element ids, alarm levels and the like.
Dividing alarm data belonging to the same type into a group according to the single network element internal type, the upstream and downstream network element type, the upstream and downstream inter-multiplexing station type and the service level type;
aiming at a service in a certain time period, when the service has only one network element with alarm, taking the alarm data as an alarm group of a single network element type;
the network element comprises a multiplexing station network element and an amplification station network element; the amplification station network elements are located between the multiplexing station network elements.
When the service only has alarm between any two adjacent network elements, the two alarm data are used as the alarm group of the relation between the upstream network element and the downstream network element.
The two adjacent network elements comprise a multiplexing station network element and an amplification station network element adjacent to the multiplexing station network element; or two adjacent amplification station network elements;
when alarm data exists between two adjacent multiplexing station network elements in the service, or alarm data exists between one multiplexing station network element and at least two amplification station network elements adjacent to the multiplexing station network element, all the alarm data form an alarm group of the type between the upstream and downstream multiplexing stations.
When all network elements of the service have alarms or exceed alarm data of the type between upstream and downstream multiplexing stations, an alarm group of the service level type is formed;
as shown in fig. 2, the service flows from ROADM a to ROADM C are multiplex station network elements, OA1 to OA3 are amplifier station network elements, and there are five alarms in the whole service flow: wherein, Alarm 1 and Alarm 2 are single network element inner types, Alarm 1 and Alarm 3 are upstream and downstream network element types, Alarm 1 and Alarm 4 are upstream and downstream inter-multiplexing station types, and Alarm 1 and Alarm 5 are service level types.
As shown in fig. 3, for service 1, there are alarms between network element a and network element a at the same time, and then the two alarms are used as an alarm group of the relationship between the upstream and downstream network elements. And if the alarms exist between the network element A and the network element B at the same time, the two alarms form an alarm group of the type between the upstream and downstream multiplexing stations. And if the alarm exists between the network element A and the network element D at the same time, the two alarms form an alarm group of the service level type.
Step two, in all alarm group data in the same time period, the alarm group with the largest dimension is reserved for the same service, the rest alarm data are abandoned, and the reserved alarm data of each service are respectively subjected to normalization processing;
the dimensionality of the alarm group is as follows from big to small in sequence: service level, upstream and downstream multiplexing stations, upstream and downstream network elements and in single network element;
alarm groups in the same service may occupy several different types at the same time, the alarm groups are arranged according to descending order of dimensionality, the types with smaller dimensionality are inevitably contained in the types with larger dimensionality, the types with smaller dimensionality are abandoned, and each service only retains the alarm group with the largest dimensionality.
As shown in fig. 2, there are a service level Alarm group S1 ═ { Alarm 1, Alarm 2, Alarm 3, Alarm 4, Alarm 5} and a single cell level Alarm group S2 ═ { Alarm 1, Alarm 2} etc., and since S1 contains S2, the proper subset S2 is discarded.
As shown in fig. 3, when there are alarms from network element a, network element B, and network element D in service 1 at the same time, the service includes an alarm group S1 of service level type { a, B, D }; and the intra-network-element type alarm set S2 ═ a }, the alarm set S3 of the relationship between the upstream and downstream network elements ═ a, a }, the alarm set S4 of the type between the upstream and downstream multiplexing stations ═ a, B }, and the alarm set S5 of the service level type ═ a, D };
after discarding the proper subset according to the dimension of the alarm group, only S1 remains.
For example {10:00, A-a1, a2} is the set of alarms within the network element, and is contained by the set of alarms at the traffic level {10:00, A-a1, a2, C-a3, a5, D-a6, a7}, then the former is discarded.
In order to facilitate the correlation analysis, the data is normalized, and most importantly, the network element id of the alarm group is subjected to certain processing. The network element id is omitted from the group in the network element, and only the alarm name and level are reserved; for the group of types between the upstream network element and the downstream network element, according to the flow direction of the service where the alarm is positioned, the id of the upstream network element is set to be 01, and the id of the downstream network element is set to be 02; for the group of types between upstream and downstream multiplexing stations, according to the flow direction of the service where the alarm is located, the network element id of the upstream multiplexing station is set to be 01, the network element id of the downstream multiplexing station is set to be 02, and the network element ids of the intermediate regeneration stations are uniformly set to be 03; for the group of service class types, according to the flow direction of the service where the alarm is located, the network element id of the upstream multiplexing station is set to be 01, the network element id of the downstream multiplexing station is set to be 02, the network elements id of the intermediate regeneration stations are uniformly set to be 03, and the network elements id of the intermediate multiplexing stations are uniformly set to be 04.
Normalization means that a transmitting station (regarded as 01), a receiving station (regarded as 02), a relay station (regarded as 03) and an amplifying station (regarded as 04) are normalized and are not regarded as an ABCD multiplexing station network element or an abcde amplifying station any more, and the alarm groups after normalization are as follows:
service level:
{10:00,01-a1,a2,02-a6,a7,03-a3,a4}
{10:00,01-a1,a2,02-a6,a7,03-a3,a5}
{12:00,01-a1,02-a6,a8,03-a3}
{12:00,01-a1,02-a6,a8,04-a11}
single-cell intra-level:
{13:00,01-a9,a10}。
step three, setting confidence coefficient threshold conf through actual testthPerforming relevance analysis on all the reserved alarm groups subjected to normalization processing;
selecting the alarm level in the transaction as the emergency alarm, calculating the confidence degree of other item sets in the transaction relative to the emergency alarm, and meeting the confthNamely the alarm group with strong association rules.
For alarm groups with strong association rules, the alarm groups can be recorded in a database as a reference library when new alarm streams appear.
Counting all alarm names in an alarm group reserved in all unified services, randomly combining at least two names, traversing each combination one by one, and calculating the confidence of each combination;
the confidence coefficient calculation formula is as follows:
the confidence coefficient is the frequency of simultaneous occurrence of (alarm name ax, alarm name ay, … … alarm name an)/(alarm name ax);
n is the number of alarm names.
Such as confidence (frequency of simultaneous occurrence of 01-a1, 01-a2, 03-a3, 02-a 6)/(frequency of occurrence of 01-a 1);
if the confidence is greater than the threshold, the rules 01-a1, 01-a2, 03-a3 and 02-a6 are strong association rules.
In this step, the confidence may be (the frequency of simultaneous occurrence of 01-a1, 01-a2, 03-a 3)/(the frequency of occurrence of a 1), and 01-a1, 01-a2, 03-a3 are also strong association rules, but are included in the above strong association rules, so that the confidence is discarded.
If the confidence coefficient of the current combination is larger than the threshold (generally 0.95), strong correlation exists among the alarm name ax, the alarm name, y, … … and the alarm name an, all alarm combinations with the confidence coefficient larger than the threshold are selected, and the proper subset is cut off according to the inclusion relation;
the de-duplication rule is as follows: if the two groups of strong association rules have the same numerator and different denominators, taking any one group of the two groups of strong association rules and leaving the other group of the two groups of strong association rules; if the denominators of the two groups of strong association rules are the same and the numerator has an inclusion relationship, the included groups are discarded.
For example: there are four sets of strongly associated rule data sets:
eventually only P1 needs to be retained.
Respectively calculating a weight factor W corresponding to each alarm name with strong relevance;
the W factor is calculated by parameters such as alarm level, topological geographical position, network element topological position, service flow condition and the like, and the calculation formula is as follows:
Wi＝Wlevel*Waddr*[deg(vi)+N(vi)]
Withe data weight value corresponding to the ith alarm name; wlevelThe data level weight corresponding to the ith alarm name; waddrThe topological geographical position weight of the network element where the alarm data corresponding to the ith alarm name is located; the setting range is 0.9-1.1, and the specific setting should be determined according to the actual network, for example, in the Kyoto Ji backbone network, the weight of Beijing should be set to 1.1, in the Guangdong province backbone network, the weight of Jinan City should be set to 1.1, and so on; deg (v)i) The degree of a network element node where data corresponding to the ith alarm name is located, namely the number of links directly connected with the network element; n (v)i) And the service number of the network element where the data corresponding to the ith alarm name is located.
The corresponding relationship of the alarm level weight is shown in the following table (the following weight is an example, and the specific case may change the weight):
TABLE 1
| Alarm level | Emergency system | Of importance | Of secondary | Prompting |
| W | ||||
| level | 1 | 0.8 | 0.25 | 0.1 |
Assigning each weight factor to a corresponding alarm combination, performing descending order arrangement on the weighted strong association alarm combinations according to the weight, and selecting the alarm combination with the largest weight for priority treatment;
because the same alarm is located in different network elements and has different meanings, and because the positions in the topology are different, the same alarm may have different sizes in different strong association rules when the weight factor W is calculated, the alarm combinations of the strong association rules need to be sorted first, and then the alarms in each strong association rule alarm combination need to be sorted.
Accumulating each alarm in each weighted alarm group to obtain the weighted value of the weighted strong association alarm combination:
the obtained weight values of each alarm group are sorted, and the larger the weight value of the alarm group is, the higher the processing priority is; the larger the alarm weight value in each alarm group, the higher the processing priority, and the higher the probability that the alarm with the high priority is the root alarm.
And step six, in the same alarm combination of the strong association rule, selecting the alarm data with the maximum weight value again for priority processing.
The association analysis is a very useful data mining method, and can mine potential association relations from data; the association rule may be described as: item set → item set.
The number of transactions (also called support count) that an item set X occurs is defined as:
wherein, tiRepresenting a certain Transaction (TID) and T representing a set of transactions.
Support of association rule X → Y (support):
the support degree describes the occurrence frequency of an item set X U Y;
the confidence (confidence) is defined as follows:
confidence may be understood as the conditional probability p (YX), which measures the probability that X and Y are both included in a known transaction.
For the credible association rule, the support degree and the confidence degree are both larger than the set threshold value. Then, the correlation analysis problem is equivalent to: for a given support threshold supthConfidence threshold confthFinding all association rules that satisfy the following conditions: the support degree is more than or equal to supthConfidence coefficient is not less than confth(ii) a The term set with a support greater than the threshold is called a frequent term set (frequentitemset). Therefore, the association rule analysis can be divided into the following two steps:
generating a frequent item set F which is X, U and Y, and finding out all association rules X → Y with confidence degrees larger than the minimum confidence degree in the frequent item set F; for the alarm problem, because the alarm source does not appear on all network elements or links, and all or some alarms do not exist in the alarms reported by all network elements, the analysis of the support degree is not significant for the alarm tracing, or a lower support degree is set to find out the more common alarms.
Claims (5)
1. The method for automatically judging the priority of the optical network alarm processing based on the dynamic assignment calculation is characterized by comprising the following specific steps of: firstly, acquiring historical alarm data of a northbound network management interface, and aiming at each service in a certain time period, according to whether an alarm exists in a network element through which each service passes, and according to the following four types of alarm data: grouping according to the single network element internal type, the upstream and downstream network element type, the upstream and downstream multiplex station type and the service level type;
then, only the alarm group with the largest dimension in each service is reserved, and the rest alarm data is discarded and normalized;
setting a confidence threshold value confth, performing relevance analysis on the normalized alarm groups corresponding to all the reserved services, selecting alarm combinations with strong relevance, and further calculating a weight factor W corresponding to each alarm in each group;
and finally, weighting each weight factor into the corresponding alarm combination, arranging each strong association alarm combination according to the descending order of the weight, selecting the alarm combination with the maximum weight for priority processing, and selecting the alarm data with the maximum weight for priority processing again in the alarm combination of the same strong association rule.
2. The method for automatically determining optical network alarm processing priority based on dynamic assignment calculation as claimed in claim 1, wherein the process of grouping the single service according to whether there is an alarm in the passed network element is as follows:
the network element comprises a multiplexing station network element and an amplification station network element; the amplification station network elements are positioned between the multiplexing station network elements;
when the service has only one network element with alarm, the alarm data is used as an alarm group of the type in a single network element;
when the service only has an alarm between any two adjacent network elements, the two alarm data are used as an alarm group of the relationship between the upstream network element and the downstream network element;
the two adjacent network elements comprise a multiplexing station network element and an amplification station network element adjacent to the multiplexing station network element; or two adjacent amplification station network elements;
when alarm data exist between two adjacent multiplexing station network elements in the service, or alarm data exist in one multiplexing station network element and at least two amplification station network elements adjacent to the multiplexing station network element, all the alarm data form an alarm group of the type between the upstream and downstream multiplexing stations;
and when all network elements of the service have alarms or exceed the alarm data of the type between the upstream multiplexing station and the downstream multiplexing station, forming an alarm group of the service level type.
3. The method according to claim 1, wherein the dimension of the alarm group is, in order from large to small: service level, upstream and downstream multiplexing stations, upstream and downstream network elements and in single network element;
alarm groups in the same service may occupy several different types at the same time, the alarm groups are arranged according to descending order of dimensionality, the types with smaller dimensionality are inevitably contained in the types with larger dimensionality, the types with smaller dimensionality are abandoned, and each service only retains the alarm group with the largest dimensionality.
4. The method according to claim 1, wherein the association analysis is performed on all the remaining alarm groups subjected to normalization processing, and the specific process is as follows:
counting all alarms in the alarm group subjected to normalization processing according to names, randomly combining at least two names, traversing each combination one by one, and calculating the confidence coefficient of each combination;
the confidence coefficient calculation formula is as follows:
the confidence coefficient is the frequency of simultaneous occurrence of (alarm name ax, alarm name ay, … … alarm name an)/(alarm name ax);
n is the number of alarm names;
if the confidence of the current combination is larger than the threshold value, the alarm name ax, the alarm name, y, … … and the alarm name an have strong correlation, all alarm combinations with the confidence larger than the threshold value are selected, and the proper subset is cut off according to the inclusion relation.
5. The method according to claim 1, wherein the calculating comprises calculating a weight factor W corresponding to each alarm in each alarm group, and the calculation formula is:
Wi＝Wlevel*Waddr*[deg(vi)+N(vi)]
Withe data weight value corresponding to the ith alarm name; w is a group oflevelThe data grade corresponding to the ith alarm name; waddrThe topological geographic position of the data corresponding to the ith alarm name; deg (v)i) The network element topology position of the data corresponding to the ith alarm name; n (v)i) And the service flow condition of the data corresponding to the ith alarm name is obtained.
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| CN117193232A (en) * | 2023-07-26 | 2023-12-08 | 珠海金智维信息科技有限公司 | RPA-based flow node fault processing method, system, device and medium |
| WO2024001666A1 (en) * | 2022-06-29 | 2024-01-04 | 华为技术有限公司 | Network risk assessment method and related apparatus |
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| WO2024001666A1 (en) * | 2022-06-29 | 2024-01-04 | 华为技术有限公司 | Network risk assessment method and related apparatus |
| CN117193232A (en) * | 2023-07-26 | 2023-12-08 | 珠海金智维信息科技有限公司 | RPA-based flow node fault processing method, system, device and medium |
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