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  本文關(guān)鍵詞： 集對原理 供水管網(wǎng) 漏損分析 健康度評價 漏損預(yù)測　出處：《湖南大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)前我國淡水資源日益短缺,水污染越來越嚴(yán)重,供水管網(wǎng)的漏損更進(jìn)一步造成了水資源的大量浪費(fèi)。這無不阻礙著我國節(jié)水型社會、水生態(tài)文明的建設(shè),因此對供水管網(wǎng)的漏損進(jìn)行控制研究已經(jīng)迫在眉睫。供水管網(wǎng)漏損的研究中存在著大量不確定性問題,集對原理是一種處理不確定性問題的新方法,它的基礎(chǔ)是集合,關(guān)鍵是兩集合間聯(lián)系度的構(gòu)建和計(jì)算。本文基于集對原理對供水管網(wǎng)的漏損控制做了如下研究:首先,對供水管網(wǎng)漏損原因進(jìn)行分析。以“管徑、壓力、埋深、管道所處道路等級”為供水管網(wǎng)漏損的自變量,“管道安全使用時間”為因變量。根據(jù)原始數(shù)據(jù),應(yīng)用集對原理得到了自變量集合與因變量集合的3元聯(lián)系度表達(dá)式,結(jié)果表明管徑、埋深、道路等級較小時供水管網(wǎng)容易發(fā)生漏損,管道運(yùn)行壓力越大容易導(dǎo)致漏損。其次,選取供水管道的“管道壓力、埋深、所在道路等級、管徑、管齡”五個指標(biāo)值,對供水管網(wǎng)健康度進(jìn)行評價。采用三標(biāo)度層次分析法確定了指標(biāo)的權(quán)重,將模糊分析與集對原理結(jié)合確定待評價樣本集合與評價指標(biāo)集合的3元聯(lián)系度,再根據(jù)置信度準(zhǔn)則得到待評價的供水管道的健康度等級。最后,對某供水管道的漏損次數(shù)進(jìn)行預(yù)測。應(yīng)用集對原理建立預(yù)測樣本和歷史樣本的影響因子集合的3元聯(lián)系度,差異不確定系數(shù)i取值后求得各集對的聯(lián)系數(shù),以此利用最相似的歷史樣本數(shù)目得到預(yù)測結(jié)果;同時對預(yù)測結(jié)果做整體評價分析,結(jié)果表明集對模型預(yù)測精度高于GM(1,1)和BP神經(jīng)網(wǎng)絡(luò)模型。上述研究表明:集對原理可以應(yīng)用到供水管網(wǎng)漏損,為供水管網(wǎng)漏損的分析、評價、預(yù)測,提供了新的方法。
[Abstract]:At present, fresh water resources in our country are increasingly short, water pollution is becoming more and more serious, and leakage loss of water supply network has further caused a large amount of waste of water resources. All of this has hindered the construction of water-saving society and water ecological civilization in our country. Therefore, it is urgent to study the leakage control of water supply network. There are a lot of uncertainty problems in the study of water supply network leakage. The set pair principle is a new method to deal with the uncertainty problem, which is based on the set. The key is to construct and calculate the connection degree between the two sets. Based on the set pair principle, the leakage control of the water supply network is studied as follows: firstly, the causes of the leakage in the water supply network are analyzed in order to "pipe diameter, pressure, buried depth," The grade of the road in which the pipeline is located is the independent variable of the leakage loss of the water supply network and the dependent variable of "the safe use time of the pipeline". According to the original data, the 3-element relation degree expression between the set of independent variables and the set of dependent variables is obtained by applying the principle of set pair. The results show that the pipe diameter, buried depth and road grade are liable to leak, and the higher the pipeline pressure is, the easier it is to cause leakage. Secondly, the pipe pressure, buried depth, road grade and pipe diameter are selected. Five index values of pipe age are used to evaluate the health of the water supply network. The weight of the index is determined by using the three-scale analytic hierarchy process (AHP), and the three-element connection degree between the sample set and the evaluation index set is determined by combining the fuzzy analysis with the set pair principle. Finally, the leakage times of a certain water supply pipeline are predicted. By applying the set pair principle, the 3-element connection degree of the set of influence factors between the predicted sample and the historical sample is established, and then the degree of health degree of the water supply pipeline to be evaluated is obtained according to the confidence criterion. The relation number of each set pair is obtained by using the most similar historical sample number after the difference uncertainty coefficient I is selected, and at the same time, the prediction result is evaluated and analyzed as a whole. The results show that the prediction accuracy of the set pair model is higher than that of the GM-1 model and the BP neural network model. The above research shows that the set pair principle can be applied to the leakage loss of the water supply network and provides a new method for the analysis, evaluation and prediction of the leakage loss of the water supply network.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TU991.33

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本文編號：1503898