Alarm association rule mining method and device

Abstract

The invention discloses an alarm association rule mining method. The method comprises the following steps: de-noising the current network alarm according to set field information and obtaining a weight parameter of the de-noised alarm according to an alarm characteristic attribute; carrying out window splitting on the de-noised alarm; constructing a transaction database, and constructing a weighted frequent mode tree by taking the de-noised alarm as a project, taking an alarm window as a transaction and combining the weight parameter of the de-noised alarm; obtaining a weighted frequent mode set according to the weighted frequent mode tree; and obtaining a weighted association rule for alarms in the weighted frequent mode set according to a relationship between the weighted frequent mode set and all the sub-sets thereof. According to the method disclosed in the invention, the efficient and correct mining of the alarm association rule can be realized. The invention furthermore discloses an alarm association rule mining device.

Description

technical field [0001] The present invention relates to the field of communication technology, in particular to alarm association rule mining technology. Background technique [0002] After years of network management system construction and continuous improvement, telecom operators have established a relatively mature monitoring management model and alarm data analysis and processing process in terms of alarm management. The alarm monitoring and management module of the telecom network management system plays an important role in daily operation and maintenance, and has become an essential tool for operators' alarm monitoring, management and business analysis. [0003] In recent years, data mining technology has achieved remarkable application results in various industries, and the same is true in the communication industry, especially in the field of business support. There have been many application cases. In terms of alarm applications, operators have accumulated a larg...

AI Technical Summary

Problems solved by technology

This method of manually obtaining effective association rules on the live network is inherently inefficient and relies on specific expert experience, making the acquisition of effective alarm association rules expensive and difficult to adapt to emerging new business scenarios

Since the new business network is based on a variety of new technologies, and the network structure is more complex, and there is a trend that the network structure changes more and more frequently, the original method of relying on expert experience is becoming more and more difficult to work, and the acquisition of new effective rules becomes more and more difficult. more and more difficult

Different types of network elements, different levels of alarms, or other factors may lead to differences in the distribution of the number of alarms. This difference makes the results of alarm rule mining not fully meet expectations.

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