本文選題：煤與瓦斯突出 + 時間序列�。� 參考：《中國礦業(yè)大學》2015年碩士論文

【摘要】：論文以原始瓦斯涌出監(jiān)控數(shù)據(jù)為研究對象,以數(shù)據(jù)挖掘的思想對瓦斯涌出的異常狀態(tài)曲線進行特征分析,結(jié)合小波閾值去噪的方法進行趨勢分析,建立了基于小波閾值去噪的分級識別預警模型,并通過軟件編程技術(shù)建立了煤與瓦斯突出識別預警系統(tǒng)。論文的主要研究內(nèi)容包括以下幾個方面:(1)瓦斯涌出時間序列的建立經(jīng)過分析發(fā)現(xiàn)原始瓦斯數(shù)據(jù)時間間隔不等,存在缺失數(shù)據(jù)與異常數(shù)據(jù);以1分鐘為時間間隔、取分鐘內(nèi)瓦斯?jié)舛绕骄档姆椒ń⑼咚褂砍鰰r間序列,并通過取前后平均值的的方法進行補充與清理,建立了符合可比性原則的瓦斯涌出時間序列。(2)煤與瓦斯突出的識別預警通過對瓦斯涌出時間序列數(shù)字特征的分析,得出瓦斯涌出時間序列的一般性質(zhì);通過對瓦斯涌出時間序列的不同狀態(tài)進行對比分析,得出存在突出危險性狀態(tài)的特征;在此基礎(chǔ)上結(jié)合小波閾值去噪的方法進行瓦斯涌出時間序列的動態(tài)趨勢分析,最終建立基于小波閾值去噪的分級識別預警模型。(3)系統(tǒng)實現(xiàn)與驗證采用Client/Server架構(gòu)、Visual Studio開發(fā)平臺、Microsoft SQL Server數(shù)據(jù)庫,建立了基于具有可比性的瓦斯涌出時間序列、小波閾值去噪分級識別預警模型的煤與瓦斯突出識別預警系統(tǒng),并結(jié)合實際煤礦瓦斯監(jiān)控數(shù)據(jù)進行了驗證。
[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.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TD713

【參考文獻】

相關(guān)期刊論文 前7條

1 付華;王雨虹;;基于數(shù)據(jù)挖掘的瓦斯災害信息融合模型的研究[J];傳感器與微系統(tǒng);2008年01期

2 樊栓保;國內(nèi)外煤與瓦斯突出預測的新方法[J];礦業(yè)安全與環(huán)保;2000年05期

3 趙旭生;鄒云龍;;近兩年我國煤與瓦斯突出事故原因分析及對策[J];礦業(yè)安全與環(huán)保;2010年01期

4 石顯鑫,蔡栓榮,馮宏,李華;利用聲發(fā)射技術(shù)預測預報煤與瓦斯突出[J];煤田地質(zhì)與勘探;1998年03期

5 何滿潮,謝和平,彭蘇萍,姜耀東;深部開采巖體力學研究[J];巖石力學與工程學報;2005年16期

6 肖紅飛;彭斌;;基于自記憶模型的煤與瓦斯突出電磁輻射預測研究[J];中國安全科學學報;2009年10期

7 程健;白靜宜;錢建生;李世銀;;基于混沌時間序列的煤礦瓦斯?jié)舛榷唐陬A測[J];中國礦業(yè)大學學報;2008年02期

相關(guān)博士學位論文 前1條

1 張豫生;基于地質(zhì)構(gòu)造的煤與瓦斯突出預測研究[D];遼寧工程技術(shù)大學;2006年

，

本文編號：1787087