Mine fire risk factor weight determining method

Abstract

The invention discloses a mine fire risk factor weight determining method. The mine fire risk factor weight determining method is characterized in that a weight determining method based on a Skowron difference matrix is provided to get the relationship between a mine fire influence factor and a fire risk. By adopting the method, fuzzy data in expert information U can be processed, and a logical inference capability is achieved; weights influencing mine fire risk factors can be updated according to the accumulated feature of U; condition factor weights and the sequence thereof under U can be obtained directly without giving consideration to the compatibility of the difference matrix, and a mine fire rating system and standard are established; the method can be applied to determination of mine fire factor weights.

Description

technical field [0001] The invention relates to mine safety engineering, in particular to analyzing the weight determination of mine fire risk factors. Background technique [0002] Mine fire evaluation is a problem that has always existed in mining engineering but is difficult to solve. Mine fires occur underground and involve many related possible factors, which brings great difficulties to monitoring and prediction. On the other hand, although mine fires have been extensively studied , but it is still difficult to form a unified understanding of the specific mechanism. In this case, experience is particularly important. Professional and technical personnel can analyze the fire risk of a specific mine based on their own knowledge and experience, and give the fire level they think , but this is usually mixed with more personal factors. Different experts have different understandings of the degree of fire caused by different factors, resulting in different evaluation results...

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Problems solved by technology

[0003] At present, the research on mine fire risk assessment and analysis mainly includes: Du Zhenyu et al. studied mine fire safety assessment method based on extension goodness; Sun Yong et al. studied mine fire safety assessment method based on BP neural network; Li Tongsuo et al. used set pair analysis Methods The risk evaluation of coal spontaneous combustion was studied; Tan Yanchun et al. used fuzzy comprehensive evaluation to study the fire evaluation method in mines; Liu Zhiqiang studied the prediction index and early warning value of coal spontaneous combustion in goaf; Song Wanxin et al. The risk of coal spontaneous combustion in high-gassy goaf is predicted below; Jin Yuping et al. used spherical support vector machine to predict coal spontaneous combustion. analysis methods; expert information is fuzzy, and information mining requires logical reasoning, so the above research results do not have such capabilities

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