Data prediction and completion method and device based on time sequence, medium and equipment

Abstract

The embodiment of the invention provides a data prediction and completion method and device based on a time sequence, a medium and computing equipment. The data prediction method based on the time sequence comprises the steps of obtaining monitoring data of at least one first monitoring quantity; based on a specific time series data prediction model and the monitoring data of the at least one first monitoring quantity, obtaining prediction of a second monitoring quantity, wherein the monitoring data of the first monitoring quantity corresponds to the monitoring data of the second monitoring quantity in time sequence, and the time sequence data prediction model comprises at least one kernel function for fitting feature information of the data of the first monitoring quantity and the secondmonitoring quantity. According to the method, data prediction can be carried out on the monitoring quantity of the monitoring object, the problem of data missing is avoided, the safety of the monitoring object can be evaluated, and the safety risk of the monitoring object during operation is reduced.

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 methods, devices, media, and equipment for predicting and completing data based on time series. 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 the power generation equipment-dam (such as upstream water level, downstream water level, horizontal displacement, subsidence displacement, seepage, etc.), and then use these indicators to evaluate whether there is a safety risk in the operation of the equipment. [000...

AI Technical Summary

Problems solved by technology

[0004] Usually when the equipment is running, the monitoring data of some indicators is easy to obtain, while the monitoring data of some indicators is not easy to obtain or can only be obtained manually (manual acquisition efficiency is low and the error rate is high), and the monitoring data of some indicators is prone to be missing. This is not only not conducive to assessing whether there is a security risk in the operation of the equipment, but also unable to detect possible security risks in the equipment in time

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