CN115022218B - Distributed Netconf protocol subscription alarm threshold setting method - Google Patents
Distributed Netconf protocol subscription alarm threshold setting method Download PDFInfo
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- CN115022218B CN115022218B CN202210584807.9A CN202210584807A CN115022218B CN 115022218 B CN115022218 B CN 115022218B CN 202210584807 A CN202210584807 A CN 202210584807A CN 115022218 B CN115022218 B CN 115022218B
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- 238000012544 monitoring process Methods 0.000 claims abstract description 93
- 238000012549 training Methods 0.000 claims abstract description 43
- 230000004931 aggregating effect Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 6
- 238000012423 maintenance Methods 0.000 description 11
- 230000006855 networking Effects 0.000 description 4
- 238000013473 artificial intelligence Methods 0.000 description 3
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/16—Threshold monitoring
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- 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
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/26—Special purpose or proprietary protocols or architectures
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Abstract
The invention discloses a distributed Netconf protocol subscription alarm threshold setting method, which is characterized in that a central server executes central threshold model training to start and issues the central threshold model training to an ACS; after the ACS receives the training start, accessing a model training identification field flag deployed in a local historical alarm database, and judging whether to execute the following process; data in the local historical alarm database are classified and aggregated, a central threshold model is trained, the reasonable probability of the monitoring index threshold value with the optimal local corresponding type is submitted to a central server to update the central threshold model, and the local threshold database is updated; and accessing a local threshold database through program execution deployed on the local edge node server ACS to acquire a monitoring index threshold, and setting the monitoring index threshold for a Netconf event subscription message on the local threshold database. The invention solves the problem of model deviation and reduces the calculation complexity.
Description
Technical Field
The invention relates to the technical field of Netconf subscription alarm, in particular to a distributed Netconf protocol subscription alarm threshold setting method.
Background
With the gradual penetration of digital development, the operation and maintenance equipment of each unit is gradually increased, and compared with the ten-year-old equipment, the operation and maintenance equipment is increased by 10-100 times, and even if the operation and maintenance are developed from manual operation and maintenance to tool operation and maintenance platform operation and maintenance, the operation and maintenance required by the current large-scale networking to operation and maintenance monitoring still cannot be met. Under the large scale, the network equipment which is managed and monitored by manual experience and automatic operation and maintenance becomes a technical bottleneck for restricting operation and maintenance work. The threshold setting in the prior monitoring technology also mainly depends on manual experience, and cannot comprehensively reflect the actual conditions of equipment and service operation. There is a need to introduce a more intelligent and efficient method for setting the monitoring threshold of the network device to improve the monitoring operation and maintenance guaranteeing capability of the management network device, and more comprehensively understand the actual running situation of the monitored device, so as to effectively avoid the problem that important invisible problems are ignored due to excessive alarms of the monitored object.
Disclosure of Invention
Based on the method, the invention provides a distributed Netconf protocol subscription alarm threshold setting method, which highlights the rationality advantage of threshold setting of artificial intelligence in the Netconf subscription alarm event monitoring process, and simultaneously carries out comprehensive data training on the threshold of the monitoring index corresponding to the edge node managed by the Netconf protocol, thereby obtaining the most reasonable probability of the alarm threshold of the area managed by the edge node, effectively overcoming the problem of model deviation and reducing the calculation complexity.
In order to achieve the above purpose, the invention adopts the following technical scheme: a method for setting a distributed Netconf protocol subscription alarm threshold value specifically comprises the following steps:
step S1, executing a central threshold model training start instruction through a central server program deployed at a headquarter, and issuing the training start instruction to an ACS (local edge node server);
step S2, after receiving a training start instruction, the ACS accesses a model training identification field flag deployed in the local history alarm database, and judges whether to execute the step S3 according to the value of the model training identification field flag;
step S3, classifying and aggregating the data in the local historical alarm database according to the monitoring indexes, training a central threshold model, and obtaining the reasonable probability of the monitoring index threshold with the optimal corresponding type;
s4, submitting reasonable probability of the monitoring index threshold value with the optimal local corresponding type to a central server to update a central threshold value model, and updating a local threshold value database;
and S5, accessing a local threshold database through program execution deployed in an ACS (local edge node) to acquire a monitoring index threshold, and setting a monitoring index item in a [ ColumnName ] field and a threshold setting of a monitoring index in a [ ColumnValue ] field in a subscription monitoring event [ ColumnCondition ] tag in a Netconf event subscription reading message on the local threshold database to complete the monitoring and threshold setting process.
Further, the local edge node server ACS employs a network device supporting the Netconf protocol.
Further, if the value of the model training identification field flag is not 0, executing step S3; otherwise, sending a central threshold model instruction to the central server, and after receiving the instruction, the central server transmits the central threshold model to the local edge node server ACS.
Further, the central threshold model is specifically:
ZY(D|+) = ZY(+|D)ZY(D)/(ZY(+|D)ZY(D)+ZY(+|N)ZY(N))
wherein ZY (D) marks the probability that the historical alarm data of the monitoring index type is close to reality under the condition of not considering the false alarm rate, ZY (D|+) represents the optimal threshold reasonable probability of the monitoring index type, ZY (+D) represents the accuracy rate of the setting of the monitoring index threshold, ZY (+N) represents the false alarm rate of the alarm data of the monitoring index of the same type, and ZY (N) represents the unreasonable probability of the setting of the monitoring index threshold of the same type.
Further, the monitoring index includes: CPU usage, memory usage, traffic size, hard disk space size.
Further, the training process of the central threshold model specifically includes: the prior probability, the conditional probability, the adjustment factor and the posterior probability are input into a central threshold model for training, the local reasonable probability of the type of monitoring index threshold is obtained through training, and the reasonable probability of the corresponding type of optimal monitoring index threshold is obtained after each monitoring index is balanced according to the adjustment factor.
Further, the prior probability is the total number of the monitoring index alarm data of the same type.
Further, the conditional probability is obtained by carrying out data statistics on the historical data of the monitoring indexes of the same type according to the alarm level, the content and the alarm duration.
Further, the adjustment factor is the ratio of the number of false alarms of historical data alarms of the monitoring index to the prior probability.
Further, the posterior probability is a product of the prior probability and the adjustment factor.
Compared with the prior art, the invention has the following beneficial effects: the scheme highlights the rationality advantage of threshold setting of artificial intelligence in the Netconf subscription alarm event monitoring process, and effectively solves the problems of irrational and special unified threshold setting caused by different businesses and monitored objects in various places of the existing networking distributed edge node Netconf protocol management and monitoring network equipment. And meanwhile, comprehensively training the threshold value of the monitoring index corresponding to the edge node managed by the Netconf protocol, thereby obtaining the most reasonable probability of the alarm threshold value of the area managed by the edge node. The method effectively solves the problem of deviation of the central threshold model, and reduces the calculation complexity.
Drawings
Fig. 1 is a flowchart of the distributed Netconf protocol subscription alert threshold setting method of the present invention.
Detailed Description
The technical scheme of the invention is further explained below with reference to the accompanying drawings.
The invention manages the CPE of non-edge node network equipment in each place through Netconf protocol, and sets subscription monitoring event; the edge node servers ACS employ network devices supporting the Netconf protocol. The threshold value of each monitoring index is obtained through the local historical threshold database, the local edge node server ACS program is used for executing the field monitoring threshold value of the [ ColumnCondition ] of each monitoring index of the subscription monitoring event to set, in the monitoring process, the monitoring data exceeds the set value of the monitoring index threshold value, the monitoring alarm event is triggered, the problem of deviation of a central threshold model can be effectively solved, and the calculation complexity is reduced.
Fig. 1 is a flowchart of the distributed Netconf protocol subscription alert threshold setting method of the present invention, and the distributed Netconf protocol subscription alert threshold setting method specifically includes the following steps:
step S1, executing a central threshold model training start instruction through a central server program deployed at a headquarter, and issuing the training start instruction to an ACS (local edge node server);
step S2, after receiving a training start instruction, the ACS accesses a model training identification field flag deployed in the local history alarm database, and judges whether to execute the step S3 according to the value of the model training identification field flag; specifically, if the value of the model training flag field is not 0, indicating that the model training flag field is not the first training central threshold model, executing step S3; otherwise, sending a central threshold model instruction to the central server, and after receiving the instruction, the central server transmits the central threshold model to the local edge node server ACS. The distributed training central threshold model effectively solves the problems of irrational and special unified threshold setting caused by different local services and monitored objects of the network equipment for managing and monitoring the Netconf protocol of the existing networking distributed edge node.
Step S3, classifying and aggregating the data in the local historical alarm database according to the monitoring indexes to form real and effective analysis data of a central threshold model, specifically, classifying all the data in the local historical alarm database according to the monitoring indexes by executing a program deployed by an ACS (auto-configuration server) of each local edge node according to the following criteria: the alarm level, content, alarm duration are classified into the following monitoring indexes: CPU usage, memory usage, traffic size, and hard disk space size. Training a central threshold model according to the monitoring index to obtain the reasonable probability of the monitoring index threshold with the optimal corresponding type.
The central threshold model in the invention is specifically as follows:
ZY(D|+) = ZY(+|D)ZY(D)/(ZY(+|D)ZY(D)+ZY(+|N)ZY(N))
wherein, ZY (D) marks the probability that the historical alarm data of the monitoring index type is close to reality under the condition of not considering the false alarm rate; ZY (D|+) represents the optimal threshold reasonable probability of the monitoring index type; ZY (+|d) represents the accuracy of the monitoring index threshold setting, i.e. 1-false alarm rate; ZY (+I N) represents the false alarm rate of the alarm data of the same type of monitoring index, namely the ratio of the number of false alarm times of the same type of monitoring index to the total number of alarms of the same type; ZY (N) represents the same type of monitoring indicator threshold setting unreasonable probability, i.e. 1-ZY (D).
The training process of the central threshold model in the invention specifically comprises the following steps: the prior probability, the conditional probability, the adjustment factor and the posterior probability are input into a central threshold model for training, the local reasonable probability of the type of monitoring index threshold is obtained through training, and the reasonable probability of the corresponding type of optimal monitoring index threshold is obtained after each monitoring index is balanced according to the adjustment factor.
The prior probability is the total number of the alarm data of the same type of monitoring index; the conditional probability is that the historical data of the same type of monitoring index is subjected to data statistics according to the alarm level, the content and the alarm duration to obtain the conditional probability of the training; the adjustment factor is the ratio of the number of false alarms of historical data alarms of the monitoring index to the prior probability; the posterior probability in the invention is the product of the prior probability and the adjustment factor.
And S4, submitting the reasonable probability of the local corresponding type optimal monitoring index threshold value to a central server to update a central threshold value model, updating a local threshold value database, and re-executing a new round of central threshold value model training by the central server program after the updated monitoring index after each training is stored.
And S5, accessing a local threshold database through program execution deployed in an ACS (local edge node) to acquire a monitoring index threshold, and setting a monitoring index item in a [ ColumnName ] field and a threshold setting of a monitoring index in a [ ColumnValue ] field in a subscription monitoring event [ ColumnCondition ] tag in a Netconf event subscription reading message on the local threshold database to complete the monitoring and threshold setting process. In the monitoring process, the monitoring data exceeds the set value of the monitoring index threshold value, and the monitoring alarm event is triggered, so that the problem of deviation of a central threshold model can be effectively solved, and the calculation complexity is reduced.
The distributed Netconf protocol subscription alarm threshold setting method highlights the rationality advantage of threshold setting of artificial intelligence in the Netconf subscription alarm event monitoring process, and effectively solves the problems of irrational and special unified threshold setting caused by different services and monitored objects in various places of the existing networking distributed edge node Netconf protocol management and monitoring network equipment. Meanwhile, comprehensive data training is carried out on the threshold value of the monitoring index corresponding to the local edge node managed by the Netconf protocol, so that the most reasonable probability of the alarm threshold value of the area managed by the edge node is obtained, the problem of deviation of a central threshold model is effectively solved, and the calculation complexity is reduced.
The above is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, and all technical solutions belonging to the concept of the present invention are within the scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (9)
1. A method for setting a distributed Netconf protocol subscription alarm threshold is characterized by comprising the following steps:
step S1, executing a central threshold model training start instruction through a central server program deployed at a headquarter, and issuing the training start instruction to an ACS (local edge node server);
step S2, after receiving a training start instruction, the ACS accesses a model training identification field flag deployed in the local history alarm database, and judges whether to execute the step S3 according to the value of the model training identification field flag; specifically, if the value of the model training identification field flag is not 0, executing step S3; otherwise, sending a central threshold model instruction to a central server, and after receiving the instruction, the central server transmits the central threshold model to an ACS (local edge node server);
step S3, classifying and aggregating the data in the local historical alarm database according to the monitoring indexes, training a central threshold model, and obtaining the reasonable probability of the monitoring index threshold with the optimal corresponding type;
s4, submitting reasonable probability of the monitoring index threshold value with the optimal local corresponding type to a central server to update a central threshold value model, and updating a local threshold value database;
and S5, accessing a local threshold database through program execution deployed in an ACS (local edge node) to acquire a monitoring index threshold, and setting a monitoring index item in a [ ColumnName ] field and a threshold setting of a monitoring index in a [ ColumnValue ] field in a subscription monitoring event [ ColumnCondition ] tag in a Netconf event subscription reading message on the local threshold database to complete the monitoring and threshold setting process.
2. The method for setting the alarm threshold of the distributed Netconf protocol subscription according to claim 1, wherein the local edge node server ACS adopts a network device supporting the Netconf protocol.
3. The distributed Netconf protocol subscription alert threshold setting method according to claim 1, wherein the central threshold model is specifically:
ZY(D|+)＝ZY(+|D)ZY(D)/(ZY(+|D)ZY(D)+ZY(+|N)ZY(N))
wherein ZY (D) marks the probability that the historical alarm data of the monitoring index type is close to reality under the condition of not considering the false alarm rate,
ZY (d|+) represents the optimal threshold reasonable probability of the monitoring index type, ZY (|d) represents the accuracy of the monitoring index threshold setting, ZY (|n) represents the alarm data false alarm rate of the same type of monitoring index, and ZY (N) represents the unreasonable probability of the same type of monitoring index threshold setting.
4. The distributed Netconf protocol subscription alert threshold setting method of claim 1, wherein the monitoring metrics comprise: CPU usage, memory usage, traffic size, hard disk space size.
5. The method for setting the alarm threshold of the distributed Netconf protocol subscription according to claim 1, wherein the training process of the central threshold model is specifically: the prior probability, the conditional probability, the adjustment factor and the posterior probability are input into a central threshold model for training, the local reasonable probability of the type of monitoring index threshold is obtained through training, and the reasonable probability of the corresponding type of optimal monitoring index threshold is obtained after each monitoring index is balanced according to the adjustment factor.
6. The method for setting a subscription alarm threshold of a distributed Netconf protocol according to claim 5, wherein the prior probability is a total number of monitoring index alarm data of the same type.
7. The method for setting the alarm threshold of the distributed Netconf protocol subscription according to claim 5, wherein the conditional probability is obtained by carrying out data statistics on historical data of the same type of monitoring indexes according to alarm levels, contents and alarm duration.
8. The method for setting a subscription alarm threshold of a distributed Netconf protocol according to claim 5, wherein the adjustment factor is a ratio of a number of false alarms of historical data alarms of a monitoring index to a priori probability.
9. The method for setting a distributed Netconf protocol subscription alert threshold according to claim 5, wherein the posterior probability is a product of a priori probability and an adjustment factor.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109582529A (en) * | 2018-09-29 | 2019-04-05 | 阿里巴巴集团控股有限公司 | A kind of setting method and device of alarm threshold value |
| CN110489306A (en) * | 2019-08-26 | 2019-11-22 | 北京博睿宏远数据科技股份有限公司 | A kind of alarm threshold value determines method, apparatus, computer equipment and storage medium |
| WO2020244336A1 (en) * | 2019-06-04 | 2020-12-10 | 深圳前海微众银行股份有限公司 | Alarm classification method and device, electronic device, and storage medium |
| CN113886173A (en) * | 2021-08-29 | 2022-01-04 | 苏州浪潮智能科技有限公司 | Method, device and equipment for monitoring multi-node distributed cluster and readable medium |
| CN114172786A (en) * | 2021-12-03 | 2022-03-11 | 中国电信集团系统集成有限责任公司 | Method and device for dynamically adjusting network monitoring threshold based on TR069 protocol and NETCONF protocol |
| CN114167181A (en) * | 2021-12-03 | 2022-03-11 | 中国电信集团系统集成有限责任公司 | Method and system for monitoring local and allopatric line fault tracing |
| CN114301817A (en) * | 2021-12-17 | 2022-04-08 | 中电信数智科技有限公司 | Equipment monitoring threshold setting method and system based on Netconf protocol |
| CN114358312A (en) * | 2021-12-31 | 2022-04-15 | 中国联合网络通信集团有限公司 | Training method, equipment and storage medium of network alarm event recognition model |
-
2022
- 2022-05-27 CN CN202210584807.9A patent/CN115022218B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109582529A (en) * | 2018-09-29 | 2019-04-05 | 阿里巴巴集团控股有限公司 | A kind of setting method and device of alarm threshold value |
| WO2020244336A1 (en) * | 2019-06-04 | 2020-12-10 | 深圳前海微众银行股份有限公司 | Alarm classification method and device, electronic device, and storage medium |
| CN110489306A (en) * | 2019-08-26 | 2019-11-22 | 北京博睿宏远数据科技股份有限公司 | A kind of alarm threshold value determines method, apparatus, computer equipment and storage medium |
| CN113886173A (en) * | 2021-08-29 | 2022-01-04 | 苏州浪潮智能科技有限公司 | Method, device and equipment for monitoring multi-node distributed cluster and readable medium |
| CN114172786A (en) * | 2021-12-03 | 2022-03-11 | 中国电信集团系统集成有限责任公司 | Method and device for dynamically adjusting network monitoring threshold based on TR069 protocol and NETCONF protocol |
| CN114167181A (en) * | 2021-12-03 | 2022-03-11 | 中国电信集团系统集成有限责任公司 | Method and system for monitoring local and allopatric line fault tracing |
| CN114301817A (en) * | 2021-12-17 | 2022-04-08 | 中电信数智科技有限公司 | Equipment monitoring threshold setting method and system based on Netconf protocol |
| CN114358312A (en) * | 2021-12-31 | 2022-04-15 | 中国联合网络通信集团有限公司 | Training method, equipment and storage medium of network alarm event recognition model |
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