本文選題：變形預警 + 多點分析　； 參考：《西南交通大學》2017年碩士論文

【摘要】：變形監(jiān)測主要是利用監(jiān)測儀器來獲取工程體的連續(xù)變化序列,進而通過綜合評價與分析等技術對變形體的發(fā)展趨勢以及安全狀態(tài)進行評估。隨著觀測手段的不斷更新,變形監(jiān)測由傳統(tǒng)的周期性人工測量方式轉變?yōu)槲锫?lián)網(wǎng)模式下多傳感器、衛(wèi)星定位、攝影測量與遙感、移動通訊等技術相融合的現(xiàn)代化監(jiān)測方式。由單一的平面高程位移監(jiān)測系統(tǒng)轉化為溫度、氣壓、應力、位移等多角度綜合監(jiān)測系統(tǒng)。本文主要針對變形監(jiān)測系統(tǒng)在運營前期測量數(shù)據(jù)量小、信息貧乏等條件下進行多點變形分析與預報研究。在研究過程中筆者深入探討了多點預測的多項關鍵技術,主要提出了三種適用性不同的預測優(yōu)化方法:(1)、顧及傳感器數(shù)據(jù)起算誤差的多變量灰色變形分析與預報模型;(2)串聯(lián)式殘差多點灰色模型;(3)多點多尺度并聯(lián)式變形預測模型。主要工作如下:1、針對多點灰色模型五種建模方式進行了對比分析研究,結果表明五種建模方式均能較好對沉降序列數(shù)據(jù)進行模擬與預測,但動態(tài)建模方式預測精度高于靜態(tài)建模方式。2、通過仿真實驗說明了常用去噪模型在數(shù)據(jù)量較少情況下會將部分信息當作噪聲進行過濾,即過度去噪。傳感器監(jiān)測系統(tǒng)在運營前期觀測數(shù)據(jù)噪聲會對模型參數(shù)解算過程造成影響。本文提出利用最小二乘解算常數(shù)項而多元整體最小二乘解算誤差項的聯(lián)合平差方法來抑制起算數(shù)據(jù)誤差對參數(shù)解算帶來的影響,從而達到無偏最優(yōu)解以改進建模精度和預測精度。3、討論了變形監(jiān)測組合預警模型構建方式,將組合預測模型大致分為并聯(lián)式組合和串聯(lián)式組合兩種預測方式。根據(jù)灰色模型建模機理構建了串聯(lián)式殘差多點灰色模型。深度解析了常見變形監(jiān)測序列的表現(xiàn)形式,利用經(jīng)驗模態(tài)分解對變形因子序列與實際沉降序列進行提取,采用支持向量機與多點灰色模型分別對變形因子序列和實際沉降序列進行預測后重構得到最終預測序列。該組合模型彌補了多點灰色模型僅對近指數(shù)生長序列具有較好模擬效果的缺點。
[Abstract]:Deformation monitoring mainly uses the monitoring instrument to obtain the continuous change sequence of the engineering body, and then evaluates the development trend and the safety state of the deformable body by comprehensive evaluation and analysis. With the continuous renewal of observation means, deformation monitoring has changed from the traditional periodic manual measurement method to the modern monitoring method which combines multi-sensor, satellite positioning, photogrammetry and remote sensing, mobile communication and other technologies under the Internet of things mode. From the single plane elevation displacement monitoring system to the temperature, pressure, stress, displacement and other multi-angle comprehensive monitoring system. This paper mainly focuses on the multi-point deformation analysis and prediction of deformation monitoring system under the condition of small amount of measurement data and poor information in the early stage of operation. In the course of the research, the author deeply discusses several key techniques of multipoint prediction. In this paper, three kinds of prediction and optimization methods with different applicability are put forward, one is the multivariable grey deformation analysis and prediction model considering the starting error of sensor data, and the other is the series residual multi-point grey model, which is a multi-point and multi-scale parallel deformation prediction model. The main work is as follows: 1. The five modeling methods of multi-point grey model are compared and analyzed. The results show that the five modeling methods can well simulate and predict the settlement sequence data. But the prediction accuracy of dynamic modeling method is higher than that of static modeling mode. The simulation results show that some information is filtered as noise in the case of less data, that is, excessive denoising. The noise of sensor monitoring system will affect the calculation process of the model parameters. In this paper, a combined adjustment method of least square solution constant term and multivariate global least square solution error term is proposed to suppress the effect of starting data error on parameter solution. Thus the unbiased optimal solution is achieved to improve the modeling accuracy and prediction accuracy. The construction of deformation monitoring combined warning model is discussed. The combined prediction model is roughly divided into two kinds of prediction methods: parallel combination and series combination. According to the grey model modeling mechanism, the series residual multi-point grey model is constructed. The expression form of common deformation monitoring series is analyzed in depth, and the deformation factor series and the actual settlement series are extracted by empirical mode decomposition (EMD). Support vector machine (SVM) and multi-point grey model are used to predict the deformation factor series and the actual settlement series respectively. The combined model makes up for the shortcoming that the multi-point grey model only has a good simulation effect on the near-exponential growth sequence.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TU196.1

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