Abnormal data detection method, device, medium and equipment based on time series

Abstract

Embodiments of the present invention provide a method for detecting abnormal data based on time series. The abnormal data detection method includes: acquiring at least one monitoring data of a first monitoring quantity and monitoring data of a second monitoring quantity; Prediction; if the monitoring data of the second monitoring quantity does not correspond to the prediction, then determining that the monitoring data of the second monitoring quantity is abnormal data. The method of the invention can improve the efficiency of data abnormality detection. In addition, embodiments of the present invention provide a time-series-based abnormal data detection device, medium, and computing device.

Description

technical field [0001] Embodiments of the present invention relate to the field of big data technology, and more specifically, embodiments of the present invention relate to a time series-based abnormal data detection method and device, medium and equipment. Background technique [0002] This section is intended to provide a background or context for implementations of the invention that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] In industrial production, the operating indicators of equipment are usually monitored to determine whether the equipment is in good condition. For example, monitor the operating indicators of power generation equipment—dams (such as upstream water level, downstream water level, horizontal displacement, subsidence displacement, seepage, etc.), and then determine whether there is an operational risk in the equipment by evaluating whether these indicators are abnormal. ...

AI Technical Summary

Problems solved by technology

[0004] At present, the judgment of whether the indicators are abnormal is usually verified by manual field survey, which makes it impossible to quickly find the abnormal data when the equipment is running, which is not conducive to effective evaluation of the operation of the equipment

For example, the monitoring data of the horizontal displacement of the dam is usually acquired by preset sensors, and experts and technicians conduct regular on-site inspections based on the values ​​obtained by the sensors, and judge whether the physical quantity data detected by the sensors is abnormal based on experience. The detection method depends on the subjective experience of experts and technicians, and the judgments of different experts and technicians may be quite different, the possibility of misjudgment is relatively high, and the actual application requires a lot of manpower and material resources, and the detection efficiency is not high.

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