本文選題：常規(guī)水質(zhì)參數(shù) + 特征污染物��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著城市供水安全受到越來(lái)越嚴(yán)峻的挑戰(zhàn),構(gòu)建能夠?qū)Τ鞘泄┧芫W(wǎng)水質(zhì)進(jìn)行持續(xù)在線監(jiān)控的預(yù)警系統(tǒng)意義重大。在檢測(cè)出水污染事件之后,為了更好地提供污染物特性等應(yīng)急信息,需要進(jìn)一步識(shí)別污染物的具體類(lèi)別�？赡芤鹚w污染的物質(zhì)種類(lèi)繁多,且很多沒(méi)有針對(duì)性的檢測(cè)儀器。面對(duì)這一狀況,本文研究了污染物與常規(guī)水質(zhì)參數(shù)響應(yīng)之間的關(guān)系,并基于此開(kāi)展了污染物分類(lèi)識(shí)別研究。論文主要工作和創(chuàng)新點(diǎn)如下:(1)研究了常規(guī)水質(zhì)參數(shù)與某些重金屬鹽、有機(jī)鹽和無(wú)機(jī)鹽污染物之間的相關(guān)響應(yīng)規(guī)律,分析了不同監(jiān)測(cè)數(shù)據(jù)時(shí)間序列幅值變化特性,提出了利用這些因不同污染物而不同的變化特性及其組合信息,進(jìn)行不同污染物的分類(lèi)與識(shí)別的技術(shù)架構(gòu)。(2)研究了通過(guò)度量常規(guī)水質(zhì)參數(shù)組合信息之間的相似性判別污染物類(lèi)型的技術(shù)方法。該方法首先采用自回歸模型進(jìn)行水質(zhì)背景信號(hào)估計(jì),再利用K均值聚類(lèi)算法融合多個(gè)指標(biāo)的預(yù)測(cè)殘差獲取污染物引起的水質(zhì)參數(shù)響應(yīng)類(lèi)別中心,最后采用相似性度量方法進(jìn)行污染物識(shí)別。其中重點(diǎn)針對(duì)污染物識(shí)別過(guò)程中,常規(guī)水質(zhì)參數(shù)響應(yīng)幅值受污染物濃度影響的問(wèn)題,從理論上分析了余弦距離的特性,其主要度量的是水質(zhì)參數(shù)向量之間的夾角,因此受幅值改變的影響較小,在污染物分類(lèi)識(shí)別中具有較好的效果。通過(guò)污染物注入實(shí)驗(yàn)比較了歐式距離,馬氏距離,余弦距離等不同相似性度量方法在五種特征污染物上的識(shí)別效果,驗(yàn)證了理論分析的正確性。(3)針對(duì)常規(guī)水質(zhì)參數(shù)與污染物濃度變化之間的非線性、各參數(shù)之間變化趨勢(shì)不一致以及訓(xùn)練樣本不足等問(wèn)題,提出基于SVM多分類(lèi)模型進(jìn)行污染物分類(lèi)的方法�？紤]到污染物注入初始階段錯(cuò)分率高,論文引入分類(lèi)概率,通過(guò)研究最大分類(lèi)概率以及分類(lèi)概率標(biāo)準(zhǔn)差,對(duì)樣本進(jìn)行區(qū)分,避免在水質(zhì)參數(shù)波動(dòng)信息不顯著情況下做出錯(cuò)誤的單一決策。最后對(duì)相似性度量方法和SVM多分類(lèi)模型在不同情況下的性能進(jìn)行了詳細(xì)對(duì)比分析,明確了各自的性能優(yōu)勢(shì)和適用場(chǎng)合。(4)利用所研究的基于相似性度量的分類(lèi)方法和基于SVM多分類(lèi)模型的分類(lèi)方法結(jié)合C#與MATLAB混合編程技術(shù),在實(shí)驗(yàn)室模擬水質(zhì)監(jiān)測(cè)系統(tǒng)基礎(chǔ)上設(shè)計(jì)開(kāi)發(fā)了管網(wǎng)水質(zhì)污染物分類(lèi)軟件。該軟件具有特征污染物分類(lèi)判別,特征庫(kù)動(dòng)態(tài)更新,分類(lèi)算法管理,分類(lèi)結(jié)果展示等功能。
[Abstract]:As the security of urban water supply is facing more and more serious challenges, it is of great significance to construct an early warning system which can continuously monitor the water quality of urban water supply network. After the detection of water pollution events, in order to provide better emergency information such as pollutant characteristics, it is necessary to further identify the specific types of pollutants. There are many kinds of substances which may cause water pollution, and many untargeted detection instruments. In this paper, the relationship between pollutants and the response of conventional water quality parameters is studied, and the classification and identification of pollutants are carried out. The main work and innovation of this paper are as follows: (1) the correlation response between conventional water quality parameters and some heavy metal, organic and inorganic salt pollutants is studied, and the variation characteristics of time series amplitudes of different monitoring data are analyzed. It is proposed to use these information, which vary from pollutant to pollutant, and their combinations, The technical framework for classification and identification of different pollutants. Firstly, the autoregressive model is used to estimate the background signal of water quality, and then K-means clustering algorithm is used to fuse the prediction residuals of multiple indexes to obtain the response class center of water quality parameters caused by pollutants. Finally, the similarity measurement method is used to identify pollutants. Aiming at the problem that the response amplitude of conventional water quality parameters is affected by pollutant concentration in the process of pollutant identification, the characteristics of cosine distance are analyzed theoretically. The main measure is the angle between water quality parameter vectors. Therefore, the effect of amplitude change is relatively small, and it has better effect in pollutant classification and identification. The effects of different similarity measures, such as Euclidean distance, Markov distance and cosine distance, on the recognition of five characteristic pollutants were compared by pollutant injection experiments. The correctness of the theoretical analysis is verified. (3) aiming at the nonlinearity between the conventional water quality parameters and the change of pollutant concentration, the variation trend between the parameters and the shortage of training samples, etc. A method of pollutant classification based on SVM multi-classification model is proposed. Considering the high misclassification rate in the initial stage of pollutant injection, the classification probability is introduced, and the sample is distinguished by studying the maximum classification probability and the classification probability standard deviation. Avoid making a single wrong decision when the fluctuation information of water quality parameters is not significant. Finally, the performance of similarity measurement method and SVM multi-classification model in different cases are compared and analyzed in detail. It is clear that their respective performance advantages and applicable situation. (4) the classification method based on similarity measure and the classification method based on SVM multi-classification model are used to combine C # and MATLAB hybrid programming technology. Based on the laboratory simulation water quality monitoring system, the classification software of water pollution in pipe network is designed and developed. The software has the functions of distinguishing the characteristic pollutants, updating the feature database dynamically, managing the classification algorithm and displaying the classification results.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TU991.2

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