Time series data processing method based on variable window mode recognition

Abstract

The invention discloses a time sequence missing value filling method based on variable window pattern recognition. The method comprises the following steps: selecting an active power correlation variable of a wind turbine generator based on an operation mechanism; selecting a wind turbine generator active power correlation variable based on data feature selection; carrying out variable window mode matching on the multi-dimensional correlation variable of the active power of the wind turbine generator; performing wind turbine generator active power continuous missing data block multi-filling based on a similar mode; and evaluating and confirming multiple filling results. Aiming at wide existence of the industrial Internet of Things and common data continuous missing conditions, high-proportion missing data can be efficiently and accurately filled, the effective data volume is greatly increased, and an important data foundation is laid for implementation and application of data driving algorithms such as machine learning and artificial intelligence.

Description

technical field [0001] The invention belongs to the field of data processing, in particular to a time series data processing method based on variable window pattern recognition. Background technique [0002] With the advent of the Internet of Things, time-series data is widely collected and stored by sensors. However, due to factors such as power outages, communication or storage, it is easy to cause continuous data loss and form missing data blocks, which reduces data quality, not only affects real-time monitoring performance, but also jeopardizes subsequent offline data analysis and processing. [0003] In addition, affected by communication interference, sensor failure and other factors, the time series collected by the Internet of Things also contains a large amount of abnormal data. After the data preprocessing link, a large amount of abnormal data is cleaned, further exacerbating the degree of data loss. In particular, the further increase in the size of consecutive m...

AI Technical Summary

Problems solved by technology

However, affected by factors such as power failure, communication or storage, it is easy to cause continuous data loss and form missing data blocks, which reduces data quality, not only affects real-time monitoring performance, but also endangers subsequent offline data analysis and processing.

[0003] In addition, due to communication interference, sensor failure and other factors, the time series collected by the Internet of Things also contains a large amount of abnormal data. After the data preprocessing link, a large amount of abnormal data is cleaned, further exacerbating the degree of data loss.

In particular, the scale of consecutive missing data blocks is further increased, which greatly increases the difficulty of missing data filling

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