【摘要】：煤礦地表變形是一種普遍的災(zāi)害現(xiàn)象,其影響因素非常復(fù)雜,不僅包括煤炭的地下開采,煤礦的地質(zhì)條件以及地下水的分布情況也都會(huì)對煤礦的地表變形產(chǎn)生一定的影響,準(zhǔn)確地預(yù)測煤礦地表變形可以有效避免災(zāi)害的發(fā)生。目前傳統(tǒng)的預(yù)測方法都存在預(yù)測精度低的問題。因此,如何有效地預(yù)測煤礦地表變形對避免災(zāi)害的發(fā)生有重大的意義。本文首先介紹了煤礦地表變形預(yù)測及基因表達(dá)式編程的國內(nèi)外研究現(xiàn)狀,闡述了煤礦地表變形特點(diǎn)及變形監(jiān)測相關(guān)理論;然后,針對煤礦地表變形監(jiān)測數(shù)據(jù)含有高頻噪聲的特點(diǎn),設(shè)計(jì)了基于Fibonacci數(shù)列的加權(quán)預(yù)處理程序?qū)γ旱V地表變形監(jiān)測原始數(shù)據(jù)進(jìn)行了平滑預(yù)處理;另外,根據(jù)基因表達(dá)式編程原理,在Visual Studio編程環(huán)境下,利用C#編程語言對基于基因表達(dá)式編程的煤礦地表變形預(yù)測模型進(jìn)行一系列程序設(shè)計(jì);最后,利用某煤礦前20期的地表變形原始數(shù)據(jù)作為訓(xùn)練數(shù)據(jù),通過設(shè)定一定參數(shù),構(gòu)建了基于基因表達(dá)式編程的煤礦地表預(yù)測模型,并對后5期的變形數(shù)據(jù)進(jìn)行了預(yù)測分析;同時(shí)把該預(yù)測模型與另外建立的基于灰色GM(1,1)預(yù)測模型進(jìn)行精度的對比分析。計(jì)算結(jié)果表明,利用GEP得到的預(yù)測值和實(shí)際值相差在4mm到9mm之間,而利用GM(1,1)得到的預(yù)測值和實(shí)際值相差都在10mm左右,由此可以得出基于基因表達(dá)式編程的煤礦地表預(yù)測模型能夠有效地對煤礦地表變形進(jìn)行預(yù)測,為煤礦地表變形預(yù)測提供了一種新的方法。
[Abstract]:The surface deformation of coal mine is a kind of universal disaster phenomenon, and its influencing factors are very complex. Not only the underground mining of coal, but also the geological conditions and the distribution of underground water of coal mine will have certain influence on the surface deformation of coal mine. Accurate prediction of coal mine surface deformation can effectively avoid the occurrence of disasters. At present, the traditional forecasting methods all have the problem of low prediction precision. Therefore, how to effectively predict the surface deformation of coal mines is of great significance to avoid disasters. This paper first introduces the research status of coal mine surface deformation prediction and gene expression programming at home and abroad, expounds the characteristics of coal mine surface deformation and the relevant theory of deformation monitoring. Aiming at the feature of high frequency noise in surface deformation monitoring data of coal mine, a weighted preprocessing program based on Fibonacci sequence is designed for smoothing the original data of surface deformation monitoring in coal mine, in addition, according to the principle of gene expression programming, In the Visual Studio programming environment, a series of programs are designed to predict the surface deformation of coal mine based on genetic expression programming language, and finally, the original data of surface deformation in the first 20 periods of a coal mine are used as the training data. The prediction model of coal mine surface based on gene expression programming is constructed by setting certain parameters, and the deformation data of the latter five periods are predicted and analyzed. At the same time, the accuracy of the prediction model is compared with that based on the grey GM (1 ~ 1) prediction model. The results show that the difference between the predicted value and the actual value obtained by GEP is between 4mm and 9mm, while the difference between the predicted value and the actual value by using GM (1 / 1) is about 10mm. It can be concluded that the prediction model of coal mine surface deformation based on genetic expression programming can effectively predict the surface deformation of coal mine, which provides a new method for the prediction of coal mine surface deformation.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TD325

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本文編號(hào)：2190944