Single-index prediction and early warning method based on artificial intelligent big data platform

Abstract

The invention discloses a single-index prediction and early warning method based on an artificial intelligent big data platform. The method includes the steps: (1) single-index prediction based on theartificial intelligent big data platform; (2) single-index warning based on the artificial intelligent big data platform. Based on the range interval of historical upper and lower bound predictive index values, warning is performed once historical upper and lower bound threshold ranges are continuously and repeatedly broken through within a period of time, namely, abnormity exists within the period of time. Intelligent analysis is performed based on historical index data, index abnormity trend can be judged in real time according to corresponding early warning strategies by the aid of index data of indexes monitored by the artificial intelligent big data platform in real time, potential risks are discovered before a monitoring system, early warning for a production system is realized, enough time is reserved for operation and maintenance experts to remove system faults, and production system risks are avoided as far as possible.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a single-index prediction and early warning method based on an artificial intelligence big data platform. Background technique [0002] The current traditional monitoring alarm has the following disadvantages: the response is not fast. The monitoring threshold is fixed and does not change; the timeliness is poor. When the alarm is issued, the system performance has seriously deteriorated, or external services have been affected; the recognition rate is poor. When an alarm occurs in the system, a message storm is often generated, and it is difficult for traditional ITOM tools to achieve alarm event correlation; it is of no value. Traditional data center operation and maintenance only focuses on the host performance data collected by ITOM. For the data association between host performance data and business system performance, traditional ITOM cannot realize data v...

AI Technical Summary

Problems solved by technology

[0002] The current traditional monitoring alarm has the following disadvantages: the response is not fast

The monitoring threshold is fixed and does not change; the timeliness is poor

When the alarm is issued, the system performance has seriously deteriorated, or external services have been affected; the recognition rate is poor

When an alarm occurs in the system, a message storm is often generated, and it is difficult for traditional ITOM tools to achieve alarm event correlation; it is of no value

Traditional data center operation and maintenance only focuses on the host performance data collected by ITOM. For the data association between host performance data and business system performance, traditional ITOM cannot realize data value mining; lack of technical support

After the alarm is generated, before the operation and maintenance experts arrive at the scene, it is often impossible to start the emergency repair work normally, lacking the guidance of the operation and maintenance expert knowledge base

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