Analysis and Application of Sensor Alarm Threshold Adaptive Technology Based on Big Data Analysis
- DOI
- 10.2991/iceep-18.2018.107How to use a DOI?
- Keywords
- Gas Sensor; Fast Big Data Analysis; Data Hierarchy and Knowledge Map Model; Adaptive Technology
- Abstract
In China, a multi-sensor intelligent alarm system based on the sensor identification technology has been widely used across the country and produces a wealth of information data. In the process of actual application, however, the alarm threshold of a traditional gas sensor is set at a fixed value, which often leads to false alarm. In some big cities, such false alarm has reduced the work efficiency of the danger-removal personnel, and affected the further promotion and application of the multi-sensor intelligent alarm system. The new data acquired by tens of millions of sensors are added every day and stored for a long time, so it is difficult to perform the analysis and judgment on the data with manual methods, and intelligent analysis and processing are urgently needed on this aspect. With the "Sensor Alarm Threshold Adaptive Technology Based on Big Data Analysis" presented in this paper, the prediction and judgment of the gas sensor threshold can be implemented through the "fast big data analysis", thereby realizing the self-adaption of the sensor alarm threshold. The actual application effects prove that it can effectively improve the efficiency of the multi-sensor intelligent alarm system.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Tong Zhu AU - Zhanbin Wang AU - Ke Shao PY - 2018/09 DA - 2018/09 TI - Analysis and Application of Sensor Alarm Threshold Adaptive Technology Based on Big Data Analysis BT - Proceedings of the 2018 7th International Conference on Energy and Environmental Protection (ICEEP 2018) PB - Atlantis Press SP - 609 EP - 615 SN - 2352-5401 UR - https://doi.org/10.2991/iceep-18.2018.107 DO - 10.2991/iceep-18.2018.107 ID - Zhu2018/09 ER -