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Coal Geology & Exploration

Abstract

Coal floor water inrush is a common type of water disaster in the production process of coal mines in North China coalfield. In order to solve the scientific decision making problem of water hazard prevention and control engineering and further improve the reliability of the relevant projects, this paper puts forward a technical idea of establishing an intelligent decision support system(IDSS). The IDSS is a combination of the traditional decision support system(DSS) and artificial intelligence. On the basis of analyzing the functional requirements of DSS for water hazard prevention and control, an integrated design process of "data-model-solution" is proposed, and the basic framework of IDSS for coal floor water hazard is established from three aspects, data import, model-driven and intelligent decision making. Model-driven layer is composed of three specialized services:method base, model base and knowledge map. Meanwhile, the model base consists of spatial prediction model of floor water inrush, numerical simulation model of draining depressurization, reliability analysis model of grouting reconstruction project, design model of isolation project and monitoring and warning model of floor water disaster. The final output decision scheme of the system includes risk zoning of floor water inrush, the Q-t-s scheme of draining depressurization, regional grouting reconstruction design and engineering reliability evaluation, isolation engineering design, and floor water inrush monitoring and early warning information release. The system realizes its intelligent decision by feedback control of grouting process, deep learning algorithm of water inrush monitoring and early warning, dynamic optimization of drainage and depressurization scheme, etc. Intelligent decision support system will provide new technical support in the reliability guarantee of coal floor water disaster prevention and control.

Keywords

floor water hazard, IDSS(Intelligent Decision Suppport System), data-model-solution, model-driven, decision making solution

DOI

10.3969/j.issn.1001-1986.2021.01.017

Reference

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