A Mobile Intelligent Mine Platform with a Hybrid Fuzzy NN and ATT-CNN Prewarning Model. (18th August 2022)

• Record Type: Journal Article
• Authors: Wu, Yuliang; Wu, Chao; Wang, Jun; Zhang, Xiangnan; Chen, Wei
• Other Names: Chen Chi-Hua Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-08-18
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/4545936
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Given the existence of coal production risk, effective prewarning is important to the reliability and safety of coal mine. So, the development of a risk prewarning system has become an important safety management tool. To improve the prediction ability and the supervision level of safety production, and handle different multidimensional (temporal and spatial) information for risk prewarning, we built a new platform based on the Internet, cloud platform, mobile communication, GIS, and artificial intelligence technology, i.e., a mobile intelligent mine platform. The terminal of the platform provides real-time queries and procedures of coal mine production and risk prewarning and provides data support and technical means for daily supervision, remote networking analysis, law enforcement inspection, and emergency rescue. The prewarning model of safety risk is an essential means to realize prewarning. The complexity of production of coal mine leads to the dynamic characteristics, fuzziness, and randomness of coal mine accidents. The complex nonlinear relationship between index and risk level leads to low accuracy of the traditional back propagation (BP) neural network prewarning method. A novel model based on a compensation fuzzy neural network (NN) and an attention mechanism-convolutional neural network (ATT-CNN) are a critical part of the new design. First, to full use of the convolutional network to get a larger receptive field, one-dimensional time series is transformed into two-dimensional matrix as the input of the CNN network by mapping. The neural network is utilized to extract the advanced features of the input signal. The results are finally output through a fully connected classifier. The model fuses multisource data at the feature level, employs the temporal and spatial relationships of monitoring data, and dynamically evaluates the risk. The experiment shows that the proposed model achieves impressive performance in both quantitative and qualitative evaluations and has improved the model generalization ability. The combination of data integration, remote examinations, and approval from existing information systems enables this platform to provide dynamic reminders of approval information, various risk prewarning, and management process automation through a mobile network.