关键词: 移动回归法, 线性回归法, 判识因子, 自适应变换, 实时预警

Abstract:

Affected by factors such as external environment of collector deployment and human interference, streaming data has drift characteristics. Simultaneously, the occurrence of streaming data events has no fixed rule and random characteristics, which leads to the low accuracy of existing methods for identifying data stream events, and the result of identifying cannot be obtained before the event is completely completed, the identification is lag behind. In order to solve these problems, this paper proposes an adaptive two-stage regression method for real-time event identification of large data streams. Firstly, based on massive historical disaster events, this method introduces the first-order moving regression method to establish weight support region and extract event data feature points. Secondly, the second-order linear regression method is used to set up event model, the least square error analysis is performed on the model, and then constructs the event identification domain. Finally, this paper proposes a step-by-step real-time identification method for streaming data events, introduces confidence factor concept based on event identification domain, estimates the future development trend of events through self-adaptive transformation strategy of confidence factor and realizes real-time event identification. Experiments show that the proposed method has great advantages in the efficiency and accuracy of event identification.

Key words: moving regression method, linear regression method, confidence factor, adaptive transformation, real-time early warning