【摘要】：隨著傳感器技術(shù)發(fā)展,自動(dòng)化監(jiān)測(cè)技術(shù)越來(lái)越得到廣泛的運(yùn)用。自動(dòng)化沉降監(jiān)測(cè)技術(shù)是目前建筑物沉降監(jiān)測(cè)的主要發(fā)展方向,其中的關(guān)鍵問(wèn)題就是數(shù)據(jù)的在線處理。所以本文旨在研究自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)在線處理方法,以提高獲得的沉降量的精度,為以后的工程應(yīng)用提供一定的依據(jù)。首先在自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)異常處理方面,為實(shí)現(xiàn)自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)的異常在線檢測(cè)建立滑動(dòng)窗口,并提出基于預(yù)測(cè)模型的沉降異常檢測(cè)方法,該方法降低了異常檢測(cè)的誤報(bào)率,從而提高了檢測(cè)異常的準(zhǔn)確性。然后通過(guò)模擬數(shù)據(jù)對(duì)該方法的異常處理的靈敏度進(jìn)行了測(cè)試,并確定了合適的滑動(dòng)窗口大小等參數(shù),并對(duì)檢測(cè)出的異常進(jìn)行修復(fù),保證了數(shù)據(jù)的連續(xù)性并使其滿足監(jiān)測(cè)數(shù)據(jù)精度。然后在自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)處理中,需要將自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)信號(hào)轉(zhuǎn)換為對(duì)應(yīng)的工程實(shí)體的沉降量,由于滑動(dòng)窗口內(nèi)的自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)都是不同的,使用單一閾值與擬合模型并不能很好適應(yīng)窗口內(nèi)數(shù)據(jù),為提高獲得的沉降量的精度,提出了基于小波及擬合的數(shù)據(jù)處理方法。該方法依據(jù)窗口內(nèi)數(shù)據(jù)的均方根誤差與信噪比動(dòng)態(tài)選取閾值與擬合模型。通過(guò)實(shí)驗(yàn)表明,該方法獲得的沉降量不僅可以滿足人工監(jiān)測(cè)要求而且可以提高獲得的沉降量的精度,與人工監(jiān)測(cè)得到的沉降量的差值降低了大約0.1-0.4mm。最后為了實(shí)現(xiàn)自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)的在線處理,提出了基于滑動(dòng)窗口的在線處理方案,該方案通過(guò)建立緩沖區(qū),將異常處理和基于小波及擬合的數(shù)據(jù)處理方法嵌入其中,從而對(duì)數(shù)據(jù)實(shí)現(xiàn)在線處理。通過(guò)實(shí)驗(yàn)可以得到該方案完成了自動(dòng)化沉降監(jiān)測(cè)數(shù)據(jù)的在線處理,得到沉降曲線與沉降速率。各監(jiān)測(cè)點(diǎn)人工監(jiān)測(cè)成果與在線處理成果差異值最大為0.9mm,最小為0.3 mm,獲得的沉降量與人工監(jiān)測(cè)數(shù)據(jù)基本一致。
[Abstract]:With the development of sensor technology, automatic monitoring technology is more and more widely used. Automatic settlement monitoring technology is the main development direction of building settlement monitoring at present, and the key problem is the on-line processing of data. Therefore, the purpose of this paper is to study the online processing method of automatic settlement monitoring data in order to improve the accuracy of settlement and provide a certain basis for future engineering application. Firstly, in the aspect of abnormal processing of automatic settlement monitoring data, a sliding window is established to realize the online detection of anomalies in automatic settlement monitoring data, and a settlement anomaly detection method based on prediction model is proposed, which reduces the false alarm rate of anomaly detection and improves the accuracy of anomaly detection. Then the sensitivity of the anomaly handling method is tested by the simulated data, and the appropriate sliding window size and other parameters are determined, and the detected anomalies are repaired to ensure the continuity of the data and make it meet the accuracy of the monitoring data. Then, in the automatic settlement monitoring data processing, it is necessary to convert the automatic settlement monitoring data signal into the settlement of the corresponding engineering entity. Because the automatic settlement monitoring data in the sliding window are different, the single threshold and fitting model can not adapt to the data in the window very well. In order to improve the accuracy of the settlement, a data processing method based on wavelet and fitting is proposed. In this method, the threshold and fitting model are selected dynamically according to the root mean square error and signal to noise ratio of the data in the window. The experimental results show that the settlement obtained by this method can not only meet the requirements of manual monitoring, but also improve the accuracy of the settlement, and the difference between the settlement obtained by this method and the settlement obtained by manual monitoring is reduced by about 0.1 脳 0.4 mm. Finally, in order to realize the online processing of automatic settlement monitoring data, an online processing scheme based on sliding window is proposed. By establishing buffer, the anomaly processing and the data processing method based on wavelet and fitting are embedded in the scheme, so as to realize the online processing of the data. Through experiments, the online processing of automatic settlement monitoring data can be completed, and the settlement curve and settlement rate can be obtained. The maximum difference value between the manual monitoring results and the on-line treatment results is 0.9mm, and the settlement obtained by the minimum 0.3mm mm, is basically consistent with the manual monitoring data.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TU196.2

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