本文選題：大氣顆粒物 + 質(zhì)譜儀��； 參考：《中國科學(xué)院研究生院（沈陽計算技術(shù)研究所）》2015年碩士論文

【摘要】：大氣顆粒物的成分對人類的身體以及生活環(huán)境、大氣的能見度、城市交通以及全球環(huán)境問題都具有很大的影響,尤其是隨著近幾年我國空氣污染情況日益嚴重,因而越來越受到人們的廣泛重視。傳統(tǒng)的大氣顆粒物分析手段主要是依靠整體顆粒物分析的技術(shù)以及人工識別顆粒物種類及來源的方法,但是這些手段耗時長、人工成本高、準確率低,沒有辦法滿足目前人們的需求。本文的目的是依托目前已有的顆粒物質(zhì)譜儀,開發(fā)出一套可以滿足應(yīng)用需求的、自動的、實時的大氣顆粒物成分分析系統(tǒng)。質(zhì)譜儀具有強大的數(shù)據(jù)收集能力,每天可以收集到海量的大氣顆粒物。通過分析和研究,我們使用了聚類算法將具有相似性質(zhì)的顆粒物聚集到一個分組中,那么后面的處理就可以以分組為單位進行,達到了系統(tǒng)實時性的要求。為了達到系統(tǒng)自動性的要求,我們使用了邏輯回歸算法對顆粒物分組進行自動命名,通過人工對訓(xùn)練樣本的命名訓(xùn)練了模型參數(shù),使得模型可以對測試樣本進行分類,減少了人工對系統(tǒng)的干預(yù)程度。顆粒物命名完成后,我們可以對大氣中的顆粒物組成成分進行分析,全面的了解監(jiān)測時間、監(jiān)測地點的空氣質(zhì)量狀況。
[Abstract]:The composition of atmospheric particulates has great influence on human body and living environment, visibility of atmosphere, urban traffic and global environmental problems, especially with the increasingly serious air pollution in China in recent years. As a result, people pay more and more attention to it. The traditional analysis methods of atmospheric particulate matter mainly rely on the technology of whole particulate matter analysis and the methods of manually identifying the kinds and sources of particulate matter, but these methods take a long time, have high labor cost, and have low accuracy. There is no way to meet the current needs of the people. The purpose of this paper is to develop an automatic, real-time analysis system of atmospheric particulate matter composition, which can meet the needs of application based on the existing particle mass spectrometer. Mass spectrometer has powerful data collection ability, can collect massive atmospheric particulate matter every day. Through analysis and research, we use clustering algorithm to cluster particles with similar properties into a group, and then the subsequent processing can be carried out in units of the group, which meets the real-time requirements of the system. In order to achieve the requirement of automatic system, we use the logical regression algorithm to name the particle groups automatically, and train the model parameters by artificial naming of the training samples, so that the model can classify the test samples. The degree of manual intervention to the system is reduced. After the designation of particulate matter, we can analyze the composition of particulate matter in the atmosphere, understand the monitoring time and monitor the air quality of the site.
【學(xué)位授予單位】：中國科學(xué)院研究生院（沈陽計算技術(shù)研究所）
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：X513

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本文編號：1917634