本文選題：供水管網(wǎng)　切入點：水質(zhì)時間序列　出處：《浙江大學》2015年碩士論文　論文類型：學位論文

【摘要】：水質(zhì)安全問題事關(guān)民生,在全球范圍內(nèi)都受到了高度的重視。水質(zhì)污染異常事件往往發(fā)生突然,而且易于在短時間內(nèi)帶給人們巨大危害。因此,準確、快速地檢測出水體中潛在的水質(zhì)異常,實現(xiàn)及早預警是保障民生的前提,也是當前大家共同關(guān)注的重大課題。由于供水管網(wǎng)中的水質(zhì)異常發(fā)生時會在水質(zhì)指標時間序列上有所反映,為此本文主要基于小波分解和經(jīng)驗模態(tài)分解方法,在時域和頻域上對水質(zhì)時間序列進行分析,提取出水質(zhì)異常發(fā)生時水質(zhì)指標時間序列在時頻域上的特征,采用閾值法、能量譜分析法等進行特征判別,從而實現(xiàn)更為有效的水質(zhì)異常檢測。本文的主要研究工作和成果如下：(1)針對水質(zhì)時間序列中所隱含的不同時頻特性,引入并研究了基于小波分析的水質(zhì)監(jiān)測數(shù)據(jù)預處理和閾值超標異常檢測方法,進行了水質(zhì)單指標和多常規(guī)指標融合的異常事件判斷,并用受試者工作特征曲線(ROC)對其檢測效果進行評估。論文首先通過小波變換對水質(zhì)信號中存在的離群點與基線漂移等進行預處理,然后利用小波包分析方法分解水質(zhì)單指標和多指標時間序列,再根據(jù)各頻段上水質(zhì)信號的強度分布進行信號異常與否的判斷。此后,論文利用美國國家環(huán)保署Canary軟件模擬的管網(wǎng)水質(zhì)數(shù)據(jù),進行了單指標和多指標異常檢測仿真分析,以驗證該方法對水質(zhì)異常檢測的有效性。(2)為了充分利用水質(zhì)信號在不同頻段上體現(xiàn)出的能量特征,在進行水質(zhì)數(shù)據(jù)小波分析預處理的基礎(chǔ)上,研究了基于小波包能量譜(能量特征向量)的水質(zhì)時間序列分析方法。論文將水質(zhì)監(jiān)測數(shù)據(jù)和背景數(shù)據(jù)(正常數(shù)據(jù))在不同時頻段內(nèi)的能量譜進行比較,以統(tǒng)計分布標準差大小為判斷準則來綜合判斷水質(zhì)波動是否隱含異常事件。論文利用多指標水質(zhì)數(shù)據(jù)對算法準確率等性能進行了討論。(3)針對水質(zhì)信號常常表現(xiàn)出的周期性特征,研究了面向周期性水質(zhì)波動和異常的分析和檢測方法。論文首先研究了基于傅里葉頻譜分析的水質(zhì)監(jiān)測數(shù)據(jù)周期性波動判定方法,利用基于經(jīng)驗模態(tài)分解的水質(zhì)信號周期性分量提取和周期模式判斷技術(shù),以統(tǒng)計分布標準差大小為判斷準則,實現(xiàn)了周期模式的匹配與判斷。最后采用水質(zhì)時間序列數(shù)據(jù),展開了水質(zhì)周期性波動異常事件檢測實驗,并與時間序列遞增、線性濾波等算法的檢測結(jié)果進行了對比分析。論文工作分別從水質(zhì)時間序列不同時頻段特征分析、不同時頻段能量譜分析以及不同時域周期性特征分析的視角,研究了相應的水質(zhì)異常檢測方法,并進行了驗證實驗,為各型水質(zhì)異常波動的有效檢出提供了技術(shù)積累。
[Abstract]:Water quality and safety issues related to people's livelihood, in the global scope is highly emphasized. Water pollution incident often occurs suddenly, in a short time and easy to bring people great harm. Therefore, accurate and rapid detection of water quality in water potential abnormal, and early warning is a prerequisite for the protection of people's livelihood, but also a major issue the current common concern. Due to the water quality of the water supply network system exception occurs will be reflected in the water quality index time series, this paper mainly based on wavelet decomposition and empirical mode decomposition method, time domain and frequency domain on time series of water quality analysis, water extract abnormal frequency characteristics on the time when water quality index sequence, using the threshold method for the discriminant feature energy spectrum analysis, in order to achieve a more effective water anomaly detection. The main research work and The results are as follows: (1) according to the frequency characteristics of the implied time series of water quality in different time, we introduce and study the wavelet analysis of water quality monitoring data preprocessing and threshold exceed the standard anomaly detection method based on the integration of water quality incident single index and multi routine index of judgment, and the receiver operating characteristic curve (ROC) to evaluate its detection effect. Firstly, using wavelet transform to preprocess the existing water quality signal outliers and baseline drift, and then use the wavelet packet decomposition quality of single index and multi index time series analysis method, according to the water quality signals of the frequency band of the intensity distribution of abnormal signal judgment. Then the water quality simulation data, the national environmental protection agency of Canary software, the single and multi indicator anomaly detection simulation analysis to verify the method of water quality anomaly detection The validity of (2). In order to fully reflect the characteristics of the energy in different frequency band signals in water use, water quality data preprocessing based on wavelet analysis, the wavelet packet energy spectrum based on (energy eigenvector) water quality time series analysis method. The water quality monitoring data and background data (normal the data were compared in different time) frequency band energy spectrum, the statistical distribution of standard deviation as the criterion to judge whether the implied fluctuation of water quality anomalies. Multi index data of water quality of accuracy using the algorithm performance are discussed. (3) the periodic characteristics for water quality signals often exhibit, study the periodic fluctuation of water quality and anomaly analysis and detection methods. The thesis firstly studies the method to determine the Fourier spectrum analysis based on the monitoring data of water quality fluctuation, based on empirical mode The water quality state decomposition periodic signal component extraction and periodic pattern prediction technology to the statistical distribution of standard deviation as the criterion, to achieve matching cycle model and judgment. Finally, the water quality time series data, the quality of the periodic fluctuation of abnormal event detection experiments, and increasing with time series, linear filtering detection results methods are compared and analyzed. This paper respectively from the water quality time series and frequency characteristic analysis, time frequency energy spectrum analysis and time-domain periodic characteristics of the perspective of water by the method of anomaly detection, and the results were validated, provides technical accumulation for effective detection of abnormal fluctuations of various types of water quality.

【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：X832

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本文編號：1602516