發(fā)布時(shí)間：2018-04-15 21:38

  本文選題：變形監(jiān)測(cè) + 自動(dòng)化�。� 參考：《沈陽(yáng)建筑大學(xué)》2014年碩士論文

【摘要】：本文以沈陽(yáng)中街人防工程為背景,對(duì)人防工程與地鐵重疊段進(jìn)行變形監(jiān)測(cè)。因地鐵處于運(yùn)營(yíng)階段,采用自動(dòng)化監(jiān)測(cè)系統(tǒng)——靜力水準(zhǔn)監(jiān)測(cè)系統(tǒng)進(jìn)行實(shí)時(shí)監(jiān)測(cè)。但是該系統(tǒng)輸出的數(shù)據(jù),與人工二等水準(zhǔn)測(cè)量結(jié)果相比,數(shù)據(jù)跳動(dòng)較大,存在17%——26%的誤差,需對(duì)監(jiān)測(cè)數(shù)據(jù)進(jìn)行去噪處理。本文在VS2010下編寫(xiě)濾波軟件,首先采用經(jīng)典卡爾曼濾波、自適應(yīng)卡爾曼濾波和小波濾波三種方法分別對(duì)監(jiān)測(cè)數(shù)據(jù)進(jìn)行去噪處理,通過(guò)實(shí)驗(yàn)結(jié)果發(fā)現(xiàn)經(jīng)典卡爾曼濾波能夠有效剔除掉的誤差為38%,自適應(yīng)卡爾曼濾波能夠有效剔除掉的誤差為55%,小波濾波能夠有效剔除掉的誤差為52%,三種方法單獨(dú)使用時(shí)都不能滿足剔除60%以上誤差的要求。針對(duì)這一問(wèn)題,本文提出使用濾波模型組合技術(shù),根據(jù)單獨(dú)使用一種濾波方法時(shí)發(fā)現(xiàn)的特點(diǎn),即：1、經(jīng)典卡爾曼濾波不適合長(zhǎng)期使用；2、自適應(yīng)卡爾曼濾波在低頻部分有明顯優(yōu)勢(shì)；3、小波濾波在高頻部分有降噪效果明顯。進(jìn)行濾波模型組合。根據(jù)濾波先后順序確定了四種濾波模型組合BCa1、BCa2、CalB和Ca2B,并由理論知識(shí)推測(cè)BCal降噪效果最好。通過(guò)工程實(shí)例的驗(yàn)證,濾波模型組合方法在降噪效果方面優(yōu)于單獨(dú)使用一種濾波技術(shù),濾波模型組合方法能夠有效剔除掉65%——75%的誤差。其中濾波模型組合BCal降噪效果最好,能夠有效剔除掉75%的誤差,大大提高了自動(dòng)化監(jiān)測(cè)數(shù)據(jù)的準(zhǔn)確性和可靠性,達(dá)到了預(yù)期有效剔除60%的誤差的目的,得出處理中街人防工程地鐵自動(dòng)化監(jiān)測(cè)數(shù)據(jù)的最優(yōu)方法是濾波模型組合BCal。
[Abstract]:Based on the civil air defense engineering of Shenyang Central Street, deformation monitoring of overlapped section between civil air defense engineering and subway is carried out.Because the subway is in operation stage, the automatic monitoring system-static leveling monitoring system is used for real-time monitoring.However, the output data of the system is larger than the result of manual secondary leveling, and the error of 17% to 26% exists, so it is necessary to Denoise the monitoring data.In this paper, the filtering software is written under VS2010. Firstly, three methods of classical Kalman filter, adaptive Kalman filter and wavelet filter are used to Denoise the monitoring data.The experimental results show that the classical Kalman filter can effectively eliminate the error of 38 parts, the adaptive Kalman filter can effectively eliminate the error of 55 steps, the wavelet filter can effectively eliminate the error of 52 parts.The time can not meet the requirement of eliminating more than 60% error.In order to solve this problem, this paper proposes to use filter model combination technique, according to the characteristics found when a single filtering method is used.That is, the classical Kalman filter is not suitable for long-term use, adaptive Kalman filter has obvious advantages in the low frequency part and wavelet filter has obvious noise reduction effect in the high frequency part.Filter model combination is carried out.According to the sequence of filtering, four filtering models, BCA _ 1C _ (2) Ca _ (2) CalB and Ca _ (2) B _ (2) B, are determined, and it is inferred from the theoretical knowledge that BCal has the best effect on noise reduction.Through the verification of engineering examples, the filter model combination method is superior to one kind of filtering technique in noise reduction effect, and the filter model combination method can effectively eliminate the error of 65% to 75%.The filter model combined with BCal has the best denoising effect and can eliminate 75% of the error effectively, which greatly improves the accuracy and reliability of the automatic monitoring data, and achieves the goal of eliminating 60% of the expected error effectively.It is concluded that the optimal method to deal with the subway automatic monitoring data of civil air defense engineering in the middle street is the combination of filter model and BCal.
【學(xué)位授予單位】：沈陽(yáng)建筑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U456.3;U231.3

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