User generated content intelligent analysis for urban natural gas with transformer-based cyber-physical social systems

Song Wang, Zhengzhi Guo, Zhaoyang Wang, YiFan Gao and Muyi Sun

Applied Energy, 2024, vol. 374, issue C, No S0306261924013308

Abstract: Intelligent analysis of user generated content (UGC) plays an important role in ensuring urban natural gas safety and controlling process risks. However, most existing analysis methods are single-task driven and ignore the spatio-temporal information of gas sensing data. To address these problems, we propose a Transformer-based cyber-physical social security system (CPSS) for UGC analysis. Specifically, this unified system integrates multiple tasks, i.e. quality assessment and control of gas data, security factors of user consumption, and spatio-temporal abnormal gas signal detection. In the developed Transformer-based model, a time-space cross-attention module is embedded for combining the long-range spatio-temporal dependences of gas data. Moreover, a feature memory block module is introduced for abnormal feature enhancement and high-level representation of gas quality. Experimental results on related gas datasets demonstrate that this Transformer-based method achieves state-of-the-art performance, and the security system significantly improves the safety factor of natural gas use in smart cities, providing a robust framework for risk management and safety enhancement.

Keywords: CPSS; Urban natural gas; User generated content; Security system; Transformer (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123947

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