Communication traffic fluctuation monitoring method based on data intermediate layer

Abstract

The invention relates to a communication traffic fluctuation monitoring method based on a data intermediate layer. In the method, the moving average method is adopted to predict seasonal time series so as to have traffic-level self-learning capability and alarm capability; the historical examples are filtered so as to control which examples can be involved in the prediction and calculation, thus the accuracy is higher; and traffic fluctuation graphs with different time characteristic dimensions can be generated. The method of the invention is used to predict the traffic of each day, the predicted value is obtained by averaging measured values in a recent period, the accuracy is relatively higher, the change trend of the same time characteristic in the recent period can be correctly reflected; the learning, monitoring and abnormal judgment of traffic can be automatically completed, and an alarm can be given after obtaining an abnormal result, thus the defects of the past traffic products can be solved.

Description

technical field [0001] The invention relates to a communication traffic fluctuation monitoring technology, in particular to a communication traffic fluctuation monitoring technology with traffic self-learning ability based on a data middle layer. Background technique [0002] A typical communication traffic monitoring fluctuation technology is to use the traffic volume in a certain period of time as a monitoring granularity, sample and map several consecutive time periods, and the monitoring personnel observe the peaks and valleys of the obtained waveform diagram to judge whether the traffic exists abnormal. The advantage of this method is that it can visually present traffic fluctuations in a period of time. However, since the level of communication traffic is closely related to social activities, some special social activities and periodic social activities are included. It may be a normal phenomenon that the traffic of a sampling point in a continuous period suddenly ch...

AI Technical Summary

Problems solved by technology

[0003] The problem to be solved by the present invention is: the existing communication traffic monitoring technology does not have an accurate abnormal threshold, and the purpose of automatic monitoring and alarm cannot be achieved; whether the traffic is normal is determined by the estimation of the monitoring personnel, and the accuracy of the estimation of the monitoring personnel is difficult to guarantee ; Due to some special changes and periodic changes, it is difficult to intuitively present the overall traffic development trend; for this, a set of communication traffic fluctuation monitoring technology is provided, especially the communication traffic self-learning ability based on the data middle layer Service fluctuation monitoring technology to realize automatic monitoring and alarm, multi-dimensional change trend graph and improve monitoring accuracy

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