本文選題：煤與瓦斯突出 + 時(shí)間序列　； 參考：《中國(guó)礦業(yè)大學(xué)》2015年碩士論文

【摘要】：論文以原始瓦斯涌出監(jiān)控?cái)?shù)據(jù)為研究對(duì)象,以數(shù)據(jù)挖掘的思想對(duì)瓦斯涌出的異常狀態(tài)曲線進(jìn)行特征分析,結(jié)合小波閾值去噪的方法進(jìn)行趨勢(shì)分析,建立了基于小波閾值去噪的分級(jí)識(shí)別預(yù)警模型,并通過(guò)軟件編程技術(shù)建立了煤與瓦斯突出識(shí)別預(yù)警系統(tǒng)。論文的主要研究?jī)?nèi)容包括以下幾個(gè)方面:(1)瓦斯涌出時(shí)間序列的建立經(jīng)過(guò)分析發(fā)現(xiàn)原始瓦斯數(shù)據(jù)時(shí)間間隔不等,存在缺失數(shù)據(jù)與異常數(shù)據(jù);以1分鐘為時(shí)間間隔、取分鐘內(nèi)瓦斯?jié)舛绕骄档姆椒ń⑼咚褂砍鰰r(shí)間序列,并通過(guò)取前后平均值的的方法進(jìn)行補(bǔ)充與清理,建立了符合可比性原則的瓦斯涌出時(shí)間序列。(2)煤與瓦斯突出的識(shí)別預(yù)警通過(guò)對(duì)瓦斯涌出時(shí)間序列數(shù)字特征的分析,得出瓦斯涌出時(shí)間序列的一般性質(zhì);通過(guò)對(duì)瓦斯涌出時(shí)間序列的不同狀態(tài)進(jìn)行對(duì)比分析,得出存在突出危險(xiǎn)性狀態(tài)的特征;在此基礎(chǔ)上結(jié)合小波閾值去噪的方法進(jìn)行瓦斯涌出時(shí)間序列的動(dòng)態(tài)趨勢(shì)分析,最終建立基于小波閾值去噪的分級(jí)識(shí)別預(yù)警模型。(3)系統(tǒng)實(shí)現(xiàn)與驗(yàn)證采用Client/Server架構(gòu)、Visual Studio開(kāi)發(fā)平臺(tái)、Microsoft SQL Server數(shù)據(jù)庫(kù),建立了基于具有可比性的瓦斯涌出時(shí)間序列、小波閾值去噪分級(jí)識(shí)別預(yù)警模型的煤與瓦斯突出識(shí)別預(yù)警系統(tǒng),并結(jié)合實(shí)際煤礦瓦斯監(jiān)控?cái)?shù)據(jù)進(jìn)行了驗(yàn)證。
[Abstract]:In this paper, the original monitoring data of gas emission is taken as the research object, the abnormal state curve of gas emission is analyzed by the idea of data mining, and the trend is analyzed by wavelet threshold de-noising method. The classification recognition and warning model based on wavelet threshold denoising is established, and the coal and gas outburst recognition and warning system is established by software programming technology. The main research contents of this paper include the following aspects: 1) the establishment of the gas emission time series. It is found that the original gas data have different time intervals, there are missing data and abnormal data, and the time interval is 1 minute. The time series of gas emission is established by taking the average value of gas concentration in minutes, and the gas emission time series is supplemented and cleaned by the method of taking the average value of gas concentration before and after taking, The identification and early warning of coal and gas outburst is established according to the principle of comparability. The general character of the time series of gas emission is obtained by analyzing the digital characteristics of the time series of gas emission. By comparing and analyzing the different states of gas emission time series, the characteristics of outburst dangerous state are obtained, and on this basis, the dynamic trend analysis of gas emission time series is carried out by combining wavelet threshold de-noising method. Finally, a hierarchical recognition and early warning model based on wavelet threshold denoising is established. The system is implemented and verified. The Client/Server framework is used to develop the Microsoft SQL Server database, and the time series of gas emission based on comparability are established. The early warning system of coal and gas outburst recognition based on wavelet threshold denoising and classifying recognition model is verified with actual coal mine gas monitoring data.
【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TD713

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