Trend-based disturbance detection method, apparatus and device and control system
By using a piecewise polynomial fitting method to extract trends from time-series data and calculating the trend index and normalized average absolute error, the problem of noise interference in traditional disturbance detection methods is solved, and highly accurate disturbance identification and control strategy adjustment are achieved.
CN121637131BActive Publication Date: 2026-07-21BEIJING ZHITONG TECH CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING ZHITONG TECH CO LTD
- Filing Date
- 2026-02-03
- Publication Date
- 2026-07-21
AI Technical Summary
Technical Problem
In the existing technology, traditional disturbance detection methods based on statistical characteristics cannot effectively distinguish between environmental noise and actual disturbances, resulting in unreliable detection results.
Method used
A piecewise polynomial fitting method is used to extract trends from time series data. By calculating the trend index and the normalized mean absolute error, it is determined whether there is a disturbance.
Benefits of technology
It improves the accuracy of disturbance detection, effectively identifies minute disturbances hidden in trends, has strong noise resistance, and provides a reliable basis for adjusting control strategies.
✦ Generated by Eureka AI based on patent content.
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Figure CN121637131B_ABST
Abstract
The application discloses a disturbance detection method and device based on trend extraction, equipment and a control system, and belongs to the field of process industry control. After obtaining time series data, a segmented polynomial fitting method is used to extract the trend of the time series data to obtain a target segmentation result. Then, a trend index and a normalized mean absolute error of the time series data are calculated based on the target segmentation result. Finally, when the trend index is greater than or equal to a first threshold value and the normalized mean absolute error is less than a second threshold value, it is determined that a disturbance exists. When the trend index is greater than or equal to the first threshold value and the normalized mean absolute error is less than the second threshold value, it indicates that the time series data has a significant trend, that is, a certain disturbance exists. With the double criteria of the trend index and the normalized error, the small disturbance hidden in the trend can be effectively identified, the noise resistance is high, and the accuracy of disturbance detection is greatly improved.
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