基于紫外光譜的管網水水質異常檢測若干關鍵問題研究
發(fā)布時間：2018-10-09 10:22
【摘要】：水質安全問題關系國計民生,水質污染事件不僅破壞了當地的水體環(huán)境,也嚴重影響到居民的管網水飲用安全。傳統(tǒng)的化學法等水質異常檢測手段往往費時且可能會造成二次污染。利用紫外吸收光譜法具有可現場原位檢測、耗時較少、無二次污染等特性和優(yōu)點,本文開展了基于紫外吸收光譜的在線水質異常檢測方法研究,著重研究討論了應用紫外吸收全光譜分析方法如何克服噪聲干擾和基線漂移、水質背景波動、工況突變等因素的影響,從而提升對水質污染異常的檢出和判別能力。論文的主要工作和創(chuàng)新點如下:(1)開展了面向噪聲干擾和基線漂移的水質光譜異常檢測方法研究,提出了分析紫外吸收全光譜數據的水質異常檢測算法。該方法首先采用Savitzky-Golay(S-G)卷積平滑法進行管網水質紫外吸收光譜平滑濾波,并利用光譜數據均值中心化去除野值點和散射,應用非對稱最小二乘法進行了光譜基線校正;進而采用主成分分析法(PCA)對光譜數據進行降維和特征提取,對正常水質構成的訓練矩陣通過自適應優(yōu)化后建立PCA模型,然后結合殘差空間的Q統(tǒng)計量檢出水質離群點(序列),最后通過分析序列中離群點的密度等時序特征來確定水質異常概率。通過管網水污染物注入實驗對不同預處理方法的異常檢測效果進行了對比分析,驗證了所研究方法對于消除不同類型噪聲和基線漂移狀況是有效的。(2)針對長期水質背景波動的影響,對水質紫外光譜異常檢測方法進行了優(yōu)化。首先分析了長期水質背景波動的原因,討論了原水水質變化和供水管網用水量變化等因素對紫外光譜檢測數據所產生的緩慢時變波動特性;然后針對水質背景波動特點提出了滑動窗PCA法,通過注入污染物事件進行了分析和驗證,同常規(guī)PCA法相比較,滑動窗PCA法更能適應水質背景長期趨勢變化的影響,并根據不同窗口長度下的檢出效果結合目標函數不斷對"最佳"窗口長度進行優(yōu)化。(3)針對工況突變導致的水質異常誤報問題,進行了水質光譜異常分類方法的研究。分析了工況突變導致的紫外光譜數據波動特點,研究了水質異常分類的技術方案,提出建立不同工況突變導致的水質異常模式知識庫,并對檢出后的異常進行在線匹配�？紤]到紫外光譜的維數大、波長之間相關度高等特點,論文采用主成分分析和Fisher線性判別分析法來進行特征提取和異常模式分類,將污染物異常和工況突變異常加以區(qū)分。全文圍繞基于紫外吸收全光譜進行水質異常檢測過程中的各類干擾問題,對異常檢測方法進行了研究和改進完善。基于論文研究成果,開發(fā)了基于紫外光譜水質預警系統(tǒng)軟件并得到了應用。
[Abstract]:Water quality safety is related to the national economy and people's livelihood. Water pollution not only destroys the local water environment, but also seriously affects the safety of drinking water in the pipe network of residents. Traditional methods such as chemical method are time-consuming and may cause secondary pollution. Ultraviolet absorption spectrometry has the advantages of in situ detection, less time consuming and no secondary pollution. In this paper, the on-line detection method of water quality anomaly based on UV absorption spectrum is studied. How to overcome the influence of noise interference and baseline drift, water quality background fluctuation, working condition mutation and so on is discussed in this paper, so as to improve the ability of detecting and discriminating water pollution anomalies. The main work and innovations of this paper are as follows: (1) the detection method of water quality spectral anomaly based on noise interference and baseline drift is studied, and an algorithm for detecting water quality anomaly based on ultraviolet absorption data is proposed. In this method, Savitzky-Golay (S-G) convolution smoothing method is used to filter water quality UV absorption spectrum smoothing, and the outliers and scattering are removed by means of the mean value of spectral data, and the spectral baseline is corrected by asymmetric least square method. Then the principal component analysis (PCA) is used to extract the dimensionality of spectral data, and the PCA model is established by adaptive optimization of the training matrix of normal water quality. Then the outliers of water quality are detected by the Q statistics in the residual space and the probability of water quality anomalies is determined by analyzing the temporal characteristics of the outliers such as the density of the outliers in the series. The results of abnormal detection of different pretreatment methods were compared and analyzed by water pollutant injection experiment in pipe network. The results show that the proposed method is effective to eliminate different noise and baseline drift. (2) aiming at the influence of long-term water quality background fluctuation, the method of UV spectrum anomaly detection is optimized. Firstly, the reason of long-term water quality background fluctuation is analyzed, and the slow time-varying characteristics of raw water quality change and water consumption change of water supply network on UV spectrum detection data are discussed. According to the characteristics of water quality background fluctuation, a sliding window PCA method is put forward, which is analyzed and verified by injecting pollutant events. Compared with the conventional PCA method, the sliding window PCA method is more suitable for the influence of the long-term trend change of water quality background. According to the detection effect of different window length and the objective function, the optimal window length is optimized. (3) aiming at the problem of water quality anomaly misreporting caused by the sudden change of working condition, the classification method of water quality spectral anomaly is studied. In this paper, the characteristics of ultraviolet spectrum data fluctuation caused by operating condition mutation are analyzed, the technical scheme of water quality anomaly classification is studied, and the knowledge base of water quality anomaly pattern caused by water quality anomaly under different working conditions is proposed, and the anomaly after detection is matched online. Considering the characteristics of ultraviolet spectrum such as large dimension and high correlation between wavelengths, principal component analysis (PCA) and Fisher linear discriminant analysis (Fisher) are used to extract features and classify abnormal patterns. In this paper, the method of anomaly detection is studied and improved based on all kinds of interference in the process of water quality anomaly detection based on UV absorption spectrum. Based on the research results, the software of water quality early warning system based on UV spectrum is developed and applied.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X832
本文編號：2258989
[Abstract]:Water quality safety is related to the national economy and people's livelihood. Water pollution not only destroys the local water environment, but also seriously affects the safety of drinking water in the pipe network of residents. Traditional methods such as chemical method are time-consuming and may cause secondary pollution. Ultraviolet absorption spectrometry has the advantages of in situ detection, less time consuming and no secondary pollution. In this paper, the on-line detection method of water quality anomaly based on UV absorption spectrum is studied. How to overcome the influence of noise interference and baseline drift, water quality background fluctuation, working condition mutation and so on is discussed in this paper, so as to improve the ability of detecting and discriminating water pollution anomalies. The main work and innovations of this paper are as follows: (1) the detection method of water quality spectral anomaly based on noise interference and baseline drift is studied, and an algorithm for detecting water quality anomaly based on ultraviolet absorption data is proposed. In this method, Savitzky-Golay (S-G) convolution smoothing method is used to filter water quality UV absorption spectrum smoothing, and the outliers and scattering are removed by means of the mean value of spectral data, and the spectral baseline is corrected by asymmetric least square method. Then the principal component analysis (PCA) is used to extract the dimensionality of spectral data, and the PCA model is established by adaptive optimization of the training matrix of normal water quality. Then the outliers of water quality are detected by the Q statistics in the residual space and the probability of water quality anomalies is determined by analyzing the temporal characteristics of the outliers such as the density of the outliers in the series. The results of abnormal detection of different pretreatment methods were compared and analyzed by water pollutant injection experiment in pipe network. The results show that the proposed method is effective to eliminate different noise and baseline drift. (2) aiming at the influence of long-term water quality background fluctuation, the method of UV spectrum anomaly detection is optimized. Firstly, the reason of long-term water quality background fluctuation is analyzed, and the slow time-varying characteristics of raw water quality change and water consumption change of water supply network on UV spectrum detection data are discussed. According to the characteristics of water quality background fluctuation, a sliding window PCA method is put forward, which is analyzed and verified by injecting pollutant events. Compared with the conventional PCA method, the sliding window PCA method is more suitable for the influence of the long-term trend change of water quality background. According to the detection effect of different window length and the objective function, the optimal window length is optimized. (3) aiming at the problem of water quality anomaly misreporting caused by the sudden change of working condition, the classification method of water quality spectral anomaly is studied. In this paper, the characteristics of ultraviolet spectrum data fluctuation caused by operating condition mutation are analyzed, the technical scheme of water quality anomaly classification is studied, and the knowledge base of water quality anomaly pattern caused by water quality anomaly under different working conditions is proposed, and the anomaly after detection is matched online. Considering the characteristics of ultraviolet spectrum such as large dimension and high correlation between wavelengths, principal component analysis (PCA) and Fisher linear discriminant analysis (Fisher) are used to extract features and classify abnormal patterns. In this paper, the method of anomaly detection is studied and improved based on all kinds of interference in the process of water quality anomaly detection based on UV absorption spectrum. Based on the research results, the software of water quality early warning system based on UV spectrum is developed and applied.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X832
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