Multi-granularity coal mine risk conceptual prediction based on time-varying cloud model

Jin Dai; Sunling Hu; Lei Zhang

doi:10.1117/12.2681587

1 June 2023 Multi-granularity coal mine risk conceptual prediction based on time-varying cloud model

Jin Dai, Sunling Hu, Lei Zhang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 1271820 (2023) https://doi.org/10.1117/12.2681587
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy.

Citation Download Citation

Jin Dai, Sunling Hu, and Lei Zhang "Multi-granularity coal mine risk conceptual prediction based on time-varying cloud model", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 1271820 (1 June 2023); https://doi.org/10.1117/12.2681587

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