發(fā)布時間：2019-05-28 15:39

【摘要】：露天煤礦的開采是一種極其危險的工程,因為露天礦中高邊坡會隨著施工的進行越來越高,內(nèi)部受力越來越不平衡,這樣就造成了高邊坡處于極不穩(wěn)定狀態(tài)。再經(jīng)過降雨,暴曬,風化等不利因素,在某一時刻就有可能會發(fā)生滑坡。為了對滑坡進行預測,有很多學者對高邊坡監(jiān)測進行了研究,也產(chǎn)生了很多預測模型。但是如果預測模型的輸入值誤差較大,這樣就會使得預測的效果不太好。為了解決此類問題,需要對數(shù)據(jù)進行濾波操作。由于卡爾曼濾波對于統(tǒng)計特征有著不穩(wěn)定性,可能會導致離散現(xiàn)象。為了解決這個問題本文提出了使用BP神經(jīng)網(wǎng)絡修正卡爾曼濾波的改進算法BPKF對數(shù)據(jù)進行濾波處理。將訓練好的BP神經(jīng)網(wǎng)絡運用到卡爾曼濾波中對數(shù)據(jù)進行平滑處理,最后進行預測。針對山西某礦邊坡監(jiān)測項目的特殊地理環(huán)境,本文進行了針對該礦高邊坡和其余相似環(huán)境的施工組織方案的設計。并且對系統(tǒng)的運行、監(jiān)測點的埋設,數(shù)據(jù)的報送等相關內(nèi)容做了介紹。最后通過測試數(shù)據(jù),運用均方根對BPKF算法和標準卡爾曼濾波進行了對比評估。結果顯示BPKF算法的濾波結果更平滑,更有利于預測。同時通過結合設計好的監(jiān)測方案、BPKF和預測模型,在山西某礦監(jiān)測中一共成功預測了5次滑坡,其中較大滑坡1次,小范圍滑坡4次。
[Abstract]:The mining of open pit coal mine is an extremely dangerous project, because the middle and high slope of open pit mine will become higher and higher with the construction, and the internal force will become more and more unbalanced, which results in the high slope in a very unstable state. After rainfall, sun exposure, weathering and other adverse factors, there may be landslides at some point. In order to predict landslide, many scholars have studied the monitoring of high slope and produced a lot of prediction models. However, if the input error of the prediction model is large, the prediction effect will not be very good. In order to solve this kind of problem, it is necessary to filter the data. Because Kalman filter is unstable to statistical characteristics, it may lead to discrete phenomenon. In order to solve this problem, an improved algorithm BPKF, which uses BP neural network to modify Kalman filter, is proposed to filter the data. The trained BP neural network is applied to Kalman filter to smooth the data, and finally, the prediction is carried out. In view of the special geographical environment of a mine slope monitoring project in Shanxi Province, this paper designs the construction organization scheme for the high slope and other similar environments of the mine. And the operation of the system, the embedding of monitoring points, the submission of data and other related contents are introduced. Finally, the BPKF algorithm and the standard Kalman filter are compared and evaluated by root mean square (root mean square) through the test data. The results show that the filtering results of BPKF algorithm are smoother and more conducive to prediction. At the same time, through the combination of the designed monitoring scheme, BPKF and prediction model, a total of five landslides have been successfully predicted in a mine monitoring in Shanxi Province, including 1 large landslide and 4 small scale landslides.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TD824.7;TP183

【參考文獻】

相關期刊論文 前4條

1 胡叢瑋,劉大杰;基于方差分量估計原理的自適應卡爾曼濾波及其應用[J];測繪學院學報;2002年01期

2 楊茂興;小樣本容量測量數(shù)據(jù)中粗差的剔除[J];計量與測試技術;2005年01期

3 趙琳;王小旭;孫明;丁繼成;閆超;;基于極大后驗估計和指數(shù)加權的自適應UKF濾波算法[J];自動化學報;2010年07期

4 胡大賀;吳侃;陳冉麗;;三維激光掃描用于開采沉陷監(jiān)測研究[J];煤礦開采;2013年01期

相關博士學位論文 前1條

1 劉超;礦區(qū)GPS變形監(jiān)測及其偽衛(wèi)星增強技術[D];中國礦業(yè)大學;2011年

，

本文編號：2487170