Comprehensive early warning method for identification of coal and gas outburst danger area based on multi-parameter data fusion

Abstract

The invention discloses a comprehensive early warning method for identification of a coal and gas outburst danger area based on multi-parameter data fusion. The method is characterized in that outburst danger evaluation is carried out on a coal seam working face by using conventional indexes capable of being quantitatively characterized, weight coefficient distribution is carried out on the conventional indexes, then basic information used for qualitative characterization is introduced as a comprehensive index coefficient, and the quantitatively characterized conventional indexes and the qualitatively characterized multi-parameter basic information are mutually fused so as to realize comprehensive early warning. The early warning method disclosed by the invention can describe the consistency of the real situation of a monitored area so as to reflect the nature of things more truly and more comprehensively, thereby making up for the defects of conventional index early warning, and providing more accurate early warning for identification of the coal and gas outburst danger area on a coal mine site.

Description

technical field [0001] The invention relates to the related technical fields of coal mine safety and coal mine gas prediction, in particular to a comprehensive early warning method for coal and gas outburst dangerous area identification based on multi-parameter data fusion. Background technique [0002] Coal and gas outbursts are the main cause of frequent gas accidents in coal mines, causing heavy casualties and property losses. Early warning and forecasting are one of the important means of disaster prevention and control. At present, domestic and foreign researches on early warning of coal and gas outburst are still in an exploratory stage, but it is obviously unrealistic to use a single parameter to early warning of outburst disasters. In fact, in addition to data that can be quantitatively characterized (conventional data, specifically such as figure 1 As shown), there are various incomplete, inaccurate, and vague qualitative information intertwined with information th...

AI Technical Summary

Problems solved by technology

At this stage, domestic and foreign researches on early warning of coal and gas outburst are still in an exploratory stage, but it is obviously unrealistic to use a single parameter to early warning of outburst disasters

In fact, in addition to data that can be quantitatively characterized (conventional data, specifically such as figure 1 As shown), there are various incomplete, inaccurate, and vague qualitative information intertwined with information that can be quantified. Due to the complexity of the underground environment, conventional data can only reflect coal and gas outburst from one aspect. Monitoring information, it is difficult to fully reflect the whole picture or authenticity of the monitoring area

As a result, a set of feasible comprehensive early warning methods has not been formed at present, so it is impossible to provide more accurate early warning for the identification of coal and gas outburst dangerous areas in coal mines

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