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  本文選題：變形監(jiān)測　切入點：數(shù)據(jù)處理　出處：《東華理工大學》2015年碩士論文

【摘要】：隨著我國新型城鎮(zhèn)化建設的不斷推進,大城市在資源和環(huán)境方面的承載力趨于飽和,不斷增多的人口加重了交通擁擠和環(huán)境污染的程度,人們的生活質量嚴重下降,尤其是出行不便帶來的困擾。大力發(fā)展城市地下軌道交通成了大城市解決問題的必由之路,這也給地鐵變形監(jiān)測領域帶來了重大發(fā)展機遇,各種監(jiān)測設備和技術相繼問世,但是當前的技術仍無法代替人工測量。地鐵變形監(jiān)測要求數(shù)據(jù)具有準確性和及時性,與時間關聯(lián)性很強,但是受地鐵施工環(huán)境的影響,監(jiān)測數(shù)據(jù)有時會出現(xiàn)差錯、漏測等問題,這些問題深深困擾著一線的測量工作者們。本文的研究就是以解決這種情況為出發(fā)點,為一線的測量工作者提供多種解決問題的方法。變形監(jiān)測的數(shù)據(jù)處理主要分為兩大部分:數(shù)據(jù)的預處理階段和數(shù)據(jù)的分析預測階段。預處理階段包括數(shù)據(jù)的粗差探測處理和缺失數(shù)據(jù)的插補處理,在介紹相應理論的同時,利用實測數(shù)據(jù)進行對比分析并對一些算法的缺陷做出改進,提高其準確性和可靠性。在數(shù)據(jù)分析建模階段包括數(shù)據(jù)的拐點探測,時間序列的模型檢驗,建模過程等內容。根據(jù)數(shù)據(jù)的特點,采用了經(jīng)過差分處理的ARIMA模型,在建模的過程中,大量采用圖像和數(shù)據(jù)表格相結合的方式,使得建模過程更簡便直觀。本論文的實驗數(shù)據(jù)來自青島地鐵二號線工程施工方變形監(jiān)測實測的數(shù)據(jù),其數(shù)據(jù)真實可靠,這對于相關領域的其他監(jiān)測項目,有著一定的參考意義。
[Abstract]:With the development of new urbanization in China, the carrying capacity of large cities in resources and environment tends to saturation, the increasing population increases the degree of traffic congestion and environmental pollution, and the quality of life of people drops seriously. Especially the trouble caused by the inconvenience of travel. Vigorously developing urban underground rail transit has become the only way to solve the problem in large cities, which has also brought great development opportunities to the field of subway deformation monitoring, and various monitoring equipment and technologies have emerged one after another. However, the current technology can not replace manual measurement. Subway deformation monitoring requires that the data be accurate and timely, and have strong correlation with time. However, due to the influence of subway construction environment, the monitoring data will sometimes appear some problems, such as errors, missing measurements, etc. These problems are deeply perplexing the front-line surveyors. The purpose of this paper is to solve this problem. The data processing of deformation monitoring is divided into two parts: the data preprocessing stage and the data analysis and prediction stage. The preprocessing stage includes the gross error of the data. Detection processing and interpolation of missing data, At the same time of introducing the corresponding theory, we use the measured data to carry on the contrast analysis and make the improvement to some algorithm's defect, improve its accuracy and reliability. In the stage of the data analysis and modeling, it includes the data inflection point detection, the time series model checking, According to the characteristics of the data, the ARIMA model which is processed by difference is adopted. In the process of modeling, a large number of images and data tables are combined. The experimental data in this paper come from the actual data of deformation monitoring of the construction side of Qingdao Metro Line 2, and the data are true and reliable, which has certain reference significance for other monitoring projects in related fields.
【學位授予單位】：東華理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P642.26;U231.1

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本文編號：1666977