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  本文選題：煤礦采空區(qū) + 數(shù)據(jù)融合技術��； 參考：《西安科技大學》2013年碩士論文

【摘要】：煤炭是我國的重要資源，同時也是一個高危行業(yè)，煤礦火災事故頻發(fā)，給國民經(jīng)濟、安全都造成了重大損失。煤礦絕大多數(shù)的火災發(fā)生在采空區(qū)人們不能直視或到達的隱蔽地點，因此煤礦采空區(qū)火災預警的真實性、可靠性和及時性直接影響著企業(yè)的生產(chǎn)秩序和企業(yè)的經(jīng)濟效益。研究煤礦采空區(qū)火災預警具有重要的現(xiàn)實意義。 本文在分析煤礦采空區(qū)安全因素的基礎上，對各個安全因素與火災發(fā)生的關系進行了較全面的研究，尤其是對煤礦采空區(qū)溫度與火災發(fā)生的關系進行了深入研究�；谛畔⑷诤系拿旱V采空區(qū)火災預警主要包括信息采集、信息預處理、信息融合及決策幾部分。信息預處理基于小波變換去噪的方法，對帶噪信號進行特征提取、低通濾波、重建信號，實現(xiàn)信號的去噪。提出了基于改進的LMBP神經(jīng)網(wǎng)絡技術與D-S證據(jù)理論相結合的兩級信息融合預測火災的方法，該方法提取各安全因素的平均值、變化速率值和累積值等特征值，采用改進的LMBP神經(jīng)網(wǎng)絡進行局部信息融合，以構造獨立證據(jù)理論基本概率分配函數(shù)，和基于權值分配的D-S證據(jù)理論實現(xiàn)多特征的信息融合判決，實現(xiàn)了煤礦采空區(qū)火災預警，解決了單一安全因素對采空區(qū)環(huán)境描述較片面的缺陷。利用Matlab平臺編寫仿真軟件，仿真試驗結果表明:本文所提出的多特征兩級信息融合決策方法能較好的預測煤礦采空區(qū)的火災預警，具有較準確的預警能力和較快的預測速度。 本文研究的基于信息融合的煤礦采空區(qū)火災預警經(jīng)過兩級融合模擬仿真試驗后，，結果表明可以對采空區(qū)的自燃狀態(tài)進行實時預警，做到防災、減災，在現(xiàn)實中具有較高的可靠性和實用性。
[Abstract]:Coal is an important resource in our country, and it is also a high-risk industry. The frequent fire accidents in coal mine have caused great losses to the national economy and safety. Most of the fires in coal mines occur in hidden places where people can not directly look at or reach the goaf, so the authenticity, reliability and timeliness of fire warning in goaf have a direct impact on the production order of enterprises and the economic benefits of enterprises. It is of great practical significance to study the early warning of coal mine goaf fire. Based on the analysis of the safety factors in the goaf of coal mine, the relationship between the safety factors and the occurrence of fire is studied comprehensively, especially the relationship between the temperature of the goaf and the occurrence of fire. The coal mine goaf fire early warning based on information fusion mainly includes information collection, information preprocessing, information fusion and decision making. Based on the wavelet transform de-noising method, the information preprocessing is used to extract the feature of the noisy signal, low pass filter, reconstruct the signal, and realize the signal de-noising. Based on the improved LMBP neural network technology and D-S evidence theory, a two-level information fusion method for fire prediction is proposed. The method extracts the average value of each safety factor, the change rate value and the cumulative value, etc. Based on the improved LMBP neural network, the basic probability distribution function of independent evidence theory is constructed, and the D-S evidence theory based on weight assignment is used to realize multi-feature information fusion decision, and the early warning of coal mine goaf fire is realized. The defect of one-sided description of goaf environment by single safety factor is solved. The simulation software is compiled on the Matlab platform. The simulation results show that the multi-feature two-level information fusion decision method proposed in this paper can predict the fire early warning in the goaf of coal mine, and it has more accurate early-warning ability and faster prediction speed. In this paper, based on information fusion, the fire early warning of goaf in coal mine is studied. After two levels of fusion simulation experiment, the results show that the spontaneous combustion state of goaf can be forewarned in real time, and the disaster prevention and mitigation can be achieved. It has high reliability and practicability in reality.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TD75;TP202

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本文編號：1899104