本文選題：變形監(jiān)測 + 小波分析�。� 參考：《長安大學(xué)》2014年碩士論文

【摘要】：隨著現(xiàn)代科技的快速進(jìn)步和國民經(jīng)濟(jì)的迅猛發(fā)展，現(xiàn)代各種工程建設(shè)的進(jìn)程與速度也大大加快，而且現(xiàn)在我們對(duì)工程建筑物的建設(shè)規(guī)模、精度等有了更高的要求，這樣為了保證工程建設(shè)的安全運(yùn)行，對(duì)于各類工程的變形監(jiān)測工作就顯得尤為重要，尤其是對(duì)于變形監(jiān)測數(shù)據(jù)的分析處理，更是重中之重。 目前，對(duì)于變形監(jiān)測數(shù)據(jù)的處理主要集中在分析變形原因和預(yù)報(bào)未來變形兩個(gè)方面，我們?cè)谟邢薜挠^測數(shù)據(jù)的情況下想要預(yù)測未來的變形情況，是有很大難度的，現(xiàn)在，一般是選擇有效的數(shù)學(xué)模型，根據(jù)監(jiān)測數(shù)據(jù)的時(shí)序特點(diǎn)進(jìn)行預(yù)報(bào)�，F(xiàn)在常用的變形監(jiān)測數(shù)據(jù)處理模型主要有：回歸分析模型，時(shí)間序列分析模型，灰色理論模型，人工神經(jīng)網(wǎng)絡(luò)模型，，卡爾曼濾波模型，小波分析模型等。其中各個(gè)模型都有自己的優(yōu)點(diǎn)和不足，而變形監(jiān)測又是多因素的形變因子的集合，有時(shí)候在變形數(shù)據(jù)中包含有多種因子，這樣處理數(shù)據(jù)就需要多個(gè)學(xué)科的交叉融合，所需要的處理模型也是多種多樣，單一模型的處理精度就可能會(huì)達(dá)不到要求。因此，我們現(xiàn)在一般常用組合模型的方式解決這個(gè)問題，組合模型就是利用每個(gè)模型的優(yōu)點(diǎn)，有機(jī)結(jié)合使其能夠更加有效處理各種變形監(jiān)測數(shù)據(jù)，提高分析預(yù)測的精度。 本文在查閱大量文獻(xiàn)資料以及各種工程實(shí)例基礎(chǔ)上，提出了利用小波分析和時(shí)間序列分析組合的方法來進(jìn)行各種變形監(jiān)測數(shù)據(jù)的分析與預(yù)報(bào)。時(shí)間序列分析模型是一種動(dòng)態(tài)模型，對(duì)于各類變形監(jiān)測數(shù)據(jù)有著很好的兼容性，但是在處理非平穩(wěn)的時(shí)間序列數(shù)據(jù)時(shí)，存在著差分化剔除趨勢導(dǎo)致刪除有效數(shù)據(jù)而造成預(yù)測精度降低的問題。小波分析模型中的小波變換則是一種能夠有效地從時(shí)序數(shù)據(jù)中提取誤差的方法，小波變換通過對(duì)監(jiān)測數(shù)據(jù)的分解和重構(gòu)，能夠很好地反映出監(jiān)測數(shù)據(jù)中的變形趨勢及特征，從而分離誤差。基于此，本文用兩種模型相結(jié)合，有效地解決了時(shí)間序列分析中的剔除趨勢問題，這個(gè)組合模型在實(shí)際的變形監(jiān)測數(shù)據(jù)處理中有著良好的實(shí)用價(jià)值。
[Abstract]:With the rapid progress of modern science and technology and the rapid development of the national economy, the process and speed of modern engineering construction has been greatly accelerated, and now we have higher requirements for the construction scale and precision of engineering buildings. In order to ensure the safe operation of engineering construction, it is particularly important for the deformation monitoring work of all kinds of projects, especially for the analysis and processing of deformation monitoring data, which is the most important. At present, the processing of deformation monitoring data mainly focuses on the analysis of deformation reasons and prediction of future deformation. It is very difficult for us to predict future deformation under the condition of limited observation data. Now, In general, an effective mathematical model is chosen to forecast according to the time series characteristics of monitoring data. The commonly used deformation monitoring data processing models are: regression analysis model, time series analysis model, grey theory model, artificial neural network model, Kalman filter model, wavelet analysis model and so on. Each model has its own advantages and disadvantages, and deformation monitoring is a set of multi-factor deformation factors. Sometimes there are many factors in the deformation data. The required processing models are also varied, and the processing accuracy of a single model may not meet the requirements. Therefore, we usually use the combination model to solve this problem. The combination model is to use the advantages of each model, organic combination can more effectively deal with all kinds of deformation monitoring data, improve the accuracy of analysis and prediction. On the basis of consulting a lot of documents and engineering examples, this paper puts forward a method of combining wavelet analysis and time series analysis to analyze and forecast all kinds of deformation monitoring data. Time series analysis model is a kind of dynamic model, which has good compatibility for all kinds of deformation monitoring data, but when dealing with non-stationary time series data, There exists the problem that the trend of difference differentiation and culling leads to the deletion of effective data and the decrease of prediction accuracy. Wavelet transform in wavelet analysis model is an effective method to extract errors from time series data. By decomposing and reconstructing monitoring data, wavelet transform can well reflect the trend and characteristics of deformation in monitoring data. Thus separating the error. Based on this, the problem of eliminating trend in time series analysis is effectively solved by combining the two models. The combined model has good practical value in actual deformation monitoring data processing.
【學(xué)位授予單位】：長安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU196.1

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