包加桐，錢 江，張 煒，唐鴻儒※，湯方平

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基于多通道數(shù)據(jù)流在線相關(guān)分析及聚類的閘站工程安全監(jiān)測

包加桐1，錢 江2，張 煒1，唐鴻儒1※，湯方平1

（1. 揚(yáng)州大學(xué)水利與能源動力工程學(xué)院，揚(yáng)州 225127；2. 江蘇省泰州引江河管理處，泰州 225321）

閘站工程自動安全監(jiān)測可積累大量高質(zhì)量監(jiān)測數(shù)據(jù)，然而對這些數(shù)據(jù)的在線自動分析手段較為有限。該文提出一種針對多通道實(shí)時監(jiān)測數(shù)據(jù)流的在線相關(guān)分析與聚類方法，以挖掘多個感興趣測點(diǎn)通道數(shù)據(jù)流之間的聯(lián)系。該方法能夠在線快速計算數(shù)據(jù)流的統(tǒng)計特征，在計算數(shù)據(jù)流之間相關(guān)性度量的基礎(chǔ)上，對多數(shù)據(jù)流進(jìn)行自動聚類。以泰州高港閘站工程安全監(jiān)測系統(tǒng)為例，針對揚(yáng)壓力、伸縮縫、溫度等多類型共65個通道數(shù)據(jù)流進(jìn)行在線相關(guān)分析與聚類，一次特征計算、分析與聚類總時長低于1 s，滿足在線處理的實(shí)時性要求。該文提出的方法能夠判斷閘站工程滲壓情況、伸縮縫與溫度變化特性等，可有效發(fā)現(xiàn)潛在的工程安全問題或傳感器故障。

聚類分析；在線系統(tǒng)；相關(guān)方法；閘站工程；安全監(jiān)測；多數(shù)據(jù)流

0 引 言

閘站工程通常由分布在較大范圍內(nèi)的泵站、水閘、堤壩等多座水工建筑物組成。為了保障工程安全可靠運(yùn)行，需要定期且準(zhǔn)確地觀測和分析水工建筑物的沉降、裂縫、滲壓等，以能夠及時掌握工程健康狀況與薄弱環(huán)節(jié)，為后期加固維修提供可靠資料[1-2]。傳統(tǒng)以人工定期觀測方式為主，室外觀測任務(wù)重、測量周期長、人為誤差影響大，監(jiān)測效率與精度無法保證。隨著信息化與智能化要求的提高，充分利用先進(jìn)傳感器、網(wǎng)絡(luò)、數(shù)據(jù)庫等信息技術(shù)進(jìn)行各類水工建筑物的安全自動監(jiān)測成為必然選擇[3-6]。

自動監(jiān)測在觀測頻次、精度上的顯著優(yōu)勢可以保證閘站工程安全狀況的連續(xù)準(zhǔn)確監(jiān)測要求，能夠長期記錄各類監(jiān)測數(shù)據(jù)，通過數(shù)據(jù)分析和比對，發(fā)現(xiàn)可能導(dǎo)致事故的異常參數(shù)并及時報警。在工程安全監(jiān)測數(shù)據(jù)的在線分析時，通常是設(shè)定不同告警等級及相應(yīng)的上下限閾值，當(dāng)在線測量數(shù)據(jù)超出設(shè)定閾值范圍時系統(tǒng)會執(zhí)行相應(yīng)的告警動作[3,7]。另一方面，由于各類監(jiān)測數(shù)據(jù)被長期保存至數(shù)據(jù)庫，系統(tǒng)一般會提供歷史數(shù)據(jù)的查詢與數(shù)據(jù)變化趨勢對比界面或分析工具[8]，采用的是離線查詢與分析方式。例如多利用最小二乘法或改進(jìn)的方法對工程安全監(jiān)測數(shù)據(jù)進(jìn)行建模[9-10]，剔除離群點(diǎn)，最終用于數(shù)據(jù)預(yù)測[11]等。利用模糊數(shù)學(xué)對多個監(jiān)測量的關(guān)系進(jìn)行建模，并用于評估大壩的安全程度[12-13]。使用監(jiān)測數(shù)據(jù)基于粗糙集與支持向量機(jī)、神經(jīng)網(wǎng)絡(luò)、空間相關(guān)系數(shù)等方法進(jìn)行大壩變形分析及構(gòu)建安全預(yù)警模型[14-17]。利用監(jiān)測數(shù)據(jù)與安全監(jiān)測模型進(jìn)行工程滲流安全監(jiān)測[18-19]等。可以看出，工程安全監(jiān)測系統(tǒng)雖然積累了大量數(shù)據(jù)，對數(shù)據(jù)中有效豐富信息進(jìn)行在線自動分析的手段還很有限。

工程安全監(jiān)測系統(tǒng)從各類通道在線定時采集數(shù)據(jù)，產(chǎn)生了時間序列上的具有不同類別的多數(shù)據(jù)流。直接對多數(shù)據(jù)流進(jìn)行分析可有效挖掘數(shù)據(jù)的特性。采用基于聚類的無監(jiān)督學(xué)習(xí)方法[20-23]來分析多數(shù)據(jù)流是常用技術(shù)手段。例如文獻(xiàn)[24]在對數(shù)據(jù)流聚類的基礎(chǔ)上計算數(shù)據(jù)流全局演化屬性并用于云虛擬主機(jī)的在線異常檢測。文獻(xiàn)[25]和文獻(xiàn)[26]則分別采用了在線聚類方法來檢測社交媒體數(shù)據(jù)流的主題和檢測網(wǎng)絡(luò)入侵行為。其他的應(yīng)用領(lǐng)域包括圖像分類[27]，生物醫(yī)學(xué)數(shù)據(jù)分析[28]等，然而相關(guān)方法在水利工程領(lǐng)域的應(yīng)用卻非常少見。因而，本文將多通道數(shù)據(jù)流的在線相關(guān)分析與聚類方法應(yīng)用于閘站工程安全監(jiān)測領(lǐng)域，通過在線挖掘多個感興趣測點(diǎn)通道數(shù)據(jù)流之間的聯(lián)系來發(fā)現(xiàn)潛在的工程安全問題或傳感器故障，以期豐富基于閾值判斷告警等常用的在線安全監(jiān)測手段。

1 系統(tǒng)結(jié)構(gòu)

閘站工程常態(tài)觀測項目一般包括垂直位移、揚(yáng)壓力、引河河床變形、伸縮縫、水位以及流量等，觀測工作應(yīng)按照規(guī)定的項目、測次、順序和時間進(jìn)行現(xiàn)場觀測。為了改進(jìn)以人工定期觀測為主的閘站工程安全監(jiān)測工作，前期針對某閘站工程，研究開發(fā)了基于網(wǎng)絡(luò)的安全監(jiān)測系統(tǒng)[3]。從數(shù)據(jù)層面對系統(tǒng)結(jié)構(gòu)進(jìn)行了劃分，如圖1所示。數(shù)據(jù)采集層主要從工程安全監(jiān)測數(shù)據(jù)采集箱和計算機(jī)監(jiān)控系統(tǒng)中，匯集相關(guān)測點(diǎn)的實(shí)時數(shù)據(jù)，并通過數(shù)據(jù)發(fā)布接口提供給上層數(shù)據(jù)分析層調(diào)用和處理。數(shù)據(jù)服務(wù)層則通過開發(fā)功能服務(wù)及人機(jī)界面，供用戶來觀測系統(tǒng)中相關(guān)數(shù)據(jù)及分析結(jié)果。如圖2所示，該系統(tǒng)已實(shí)現(xiàn)定時采集揚(yáng)壓力測管水位、伸縮縫、溫度等各類數(shù)據(jù)，能夠通過人機(jī)界面觀測任意時間段內(nèi)各個測點(diǎn)數(shù)據(jù)的歷史變化曲線。且能夠在發(fā)生測點(diǎn)數(shù)據(jù)越限或者指定時段內(nèi)變化值越限時自動通過短信進(jìn)行報警。

圖1 閘站工程安全監(jiān)測系統(tǒng)結(jié)構(gòu)

圖2 安全監(jiān)測系統(tǒng)人機(jī)界面

為了能夠進(jìn)一步挖掘感興趣測點(diǎn)通道數(shù)據(jù)流之間的聯(lián)系，自動發(fā)現(xiàn)潛在的工程安全問題或傳感器故障，本文重點(diǎn)研究多數(shù)據(jù)流的在線相關(guān)分析與聚類方法。研究內(nèi)容處于系統(tǒng)的數(shù)據(jù)分析層，主要包括多數(shù)據(jù)流獲取、數(shù)據(jù)流統(tǒng)計特征計算、在線相關(guān)分析與聚類3個過程。多數(shù)據(jù)流的分析結(jié)果可進(jìn)一步交由監(jiān)測預(yù)警模塊進(jìn)行推理及執(zhí)行預(yù)警動作。

2 多通道數(shù)據(jù)流在線分析

2.1 基本概念及數(shù)學(xué)描述

因此，為提高計算速度與節(jié)省存儲資源，只需計算和存儲數(shù)據(jù)流的統(tǒng)計特征。

2.2 數(shù)據(jù)流相關(guān)分析

2.3 數(shù)據(jù)流聚類

閘站工程安全監(jiān)測會涉及眾多不同類型測點(diǎn)的數(shù)據(jù)流。為在線將相關(guān)度高的數(shù)據(jù)流自動分組，以發(fā)現(xiàn)可能存在的工程安全問題或傳感器故障，采用基于密度聚類的DBSCAN算法[30]對多數(shù)據(jù)流進(jìn)行聚類，算法偽代碼如下：

begin

end while

end if

end if

end for

end

3 案例及結(jié)果分析

3.1 案例簡介

圖3 泰州高港閘站工程安全監(jiān)測測點(diǎn)布置

試驗(yàn)中選擇2015年4月29日—2015年11月23日共209 d內(nèi)存儲于數(shù)據(jù)庫的65個通道數(shù)據(jù)流進(jìn)行在線回放分析。數(shù)據(jù)存儲的頻度是每個通道每小時記錄1個數(shù)據(jù)點(diǎn)，因此待分析的每個通道的數(shù)據(jù)流的總長度為5 016。通過常規(guī)上下限值比較手段判斷出YYL_022、YYL_041、YYL_042、WD_YA1_SS通道數(shù)據(jù)存在大量異常數(shù)據(jù)，因此不參與數(shù)據(jù)流的相關(guān)分析與聚類。試驗(yàn)中主要進(jìn)行2類多數(shù)據(jù)流的相關(guān)分析與聚類：水位數(shù)據(jù)流（包含揚(yáng)壓力測管水位與上下游水位）與伸縮縫數(shù)據(jù)流（包含伸縮縫測點(diǎn)溫度與縫隙大?。?shù)據(jù)流統(tǒng)計特征計算公式中，衰減系數(shù)取0.99。聚類算法中閾值取1，鄰域半徑取±0.9。當(dāng)數(shù)據(jù)流相關(guān)系數(shù)>0.9時，稱數(shù)據(jù)流之間具有強(qiáng)正相關(guān)性，相關(guān)系數(shù)<-0.9時稱數(shù)據(jù)流之間具有強(qiáng)負(fù)相關(guān)性。在配置為Intel Core i5 @ 2.3 GHz CPU，4 GB內(nèi)存的計算機(jī)上利用Visual C++ 6.0編程實(shí)現(xiàn)在線分析與聚類功能，平均處理1次多數(shù)據(jù)流的總時長低于1 s，滿足實(shí)時性處理要求。

3.2 結(jié)果與分析

在線檢測結(jié)果如圖4、圖5、表1和表2所示。表1與圖4分別顯示了回放至數(shù)據(jù)流最后1個數(shù)據(jù)點(diǎn)對應(yīng)的離散時間點(diǎn)時，水位數(shù)據(jù)流的在線相關(guān)分析與聚類結(jié)果。表2與圖5分別顯示了伸縮縫數(shù)據(jù)流的相關(guān)分析與聚類結(jié)果。

表1 水位數(shù)據(jù)流相關(guān)系數(shù)矩陣

注：YYL表示揚(yáng)壓力，SW代表水位，011～XY為測點(diǎn)，見圖3。

Note: YYL and SW represent uplift pressure and water level, respectively, 011-XY is measuring point, see in Fig.3.

圖4 水位數(shù)據(jù)流聚類結(jié)果

注：WD表示溫度，F(xiàn)X表示伸縮縫，SS表示水平東西向，CD表示水平南北向，下同。

從表1可以查看到任意2個水位數(shù)據(jù)流的相關(guān)系數(shù)。圖4a中各水位數(shù)據(jù)流被聚類為強(qiáng)相關(guān)的2類，除YYL_023、YYL_043測點(diǎn)外，布置于泵站工程5個斷面上的揚(yáng)壓力測管的水位，表現(xiàn)出較強(qiáng)的相關(guān)性，屬正常地下水滲透現(xiàn)象，并且與上下游水位SW_SY和SW_XY均不相關(guān)，表明閘站地基滲壓大小與上下游水位無直接關(guān)系。圖4b中的水位數(shù)據(jù)流被歸為噪聲點(diǎn)，YYL_YA1測點(diǎn)處揚(yáng)壓力測管安裝于泵站工程右岸，與上游的內(nèi)河和下游的長江相距較遠(yuǎn)，表現(xiàn)出非相關(guān)性；YYL_023測點(diǎn)處與下游側(cè)長江距離較近，雖未達(dá)到強(qiáng)相關(guān)，相關(guān)系數(shù)值也達(dá)到0.81；且通過圖4b中所示的波形可以看出，YYL_023測點(diǎn)處揚(yáng)壓力測管水位波動受長江水位波動影響較大，表明該測點(diǎn)處閘站地基可能出現(xiàn)滲漏，應(yīng)加強(qiáng)觀測。此外，YYL_043測點(diǎn)處的水位數(shù)據(jù)變化趨勢較為異常，較大可能性是傳感器測量故障導(dǎo)致，需進(jìn)一步排查。可以看出，對揚(yáng)壓力測管水位與上下游水位數(shù)據(jù)流進(jìn)行在線相關(guān)分析與聚類，可以有效判斷閘站工程滲壓情況及發(fā)現(xiàn)傳感器故障。

表2顯示了溫度與各伸縮縫大小數(shù)據(jù)流的相關(guān)系數(shù)矩陣。經(jīng)聚類后，如圖5a所示各測點(diǎn)的溫度數(shù)據(jù)流均表現(xiàn)為強(qiáng)相關(guān)性，圖5b顯示了與溫度表現(xiàn)出強(qiáng)負(fù)相關(guān)的測點(diǎn)處伸縮縫大小數(shù)據(jù)流。其中，閘站工程各個斷面連接處的底板向的水平伸縮縫隙大小與溫度多表現(xiàn)出強(qiáng)負(fù)相關(guān)特性，其余測點(diǎn)處水平伸縮縫隙大小表現(xiàn)為弱負(fù)相關(guān)特性，相關(guān)系數(shù)取值均落在(-0.9，-0.8)；除FX_DB2XY_CD和FX_DB4XY_CD外，向的水平錯動縫隙大小與溫度均未表現(xiàn)出強(qiáng)負(fù)相關(guān)特性。此外，試驗(yàn)中發(fā)現(xiàn)測點(diǎn)FX_XYZY_CD與FX_YA2_CD處縫隙大小變化與溫度變化卻表現(xiàn)出正相關(guān)，存在異常，需進(jìn)一步排查原因。因此，對伸縮縫與溫度數(shù)據(jù)流進(jìn)行在線相關(guān)分析與聚類，可以挖掘出伸縮縫與溫度的變化特性。對于所有被歸類為噪聲點(diǎn)的數(shù)據(jù)流，可被直接用于發(fā)現(xiàn)各類工程安全監(jiān)測傳感器的異常情況。

4 結(jié) 論

本文提出了一種對閘站工程自動安全監(jiān)測系統(tǒng)中產(chǎn)生的多數(shù)據(jù)流進(jìn)行在線相關(guān)分析與聚類的方法，詳細(xì)給出了多數(shù)據(jù)流統(tǒng)計特征快速計算，基于統(tǒng)計特征的相關(guān)系數(shù)計算以及基于相關(guān)系數(shù)密度的聚類過程。在泰州高港閘站工程應(yīng)用與試驗(yàn)，發(fā)現(xiàn)了工程5個斷面上各揚(yáng)壓力測管水位表現(xiàn)出強(qiáng)正相關(guān)，反映出正常地下水滲透現(xiàn)象，其中1個揚(yáng)壓力測點(diǎn)處位置出現(xiàn)滲漏，1個揚(yáng)壓力測點(diǎn)處傳感器出現(xiàn)了故障；發(fā)現(xiàn)各伸縮縫測點(diǎn)處溫度表現(xiàn)出強(qiáng)正相關(guān)，水平伸縮縫隙大小與溫度表現(xiàn)出強(qiáng)負(fù)相關(guān)，受溫度變化影響明顯，水平錯動縫隙大小則受溫度影響較小。表明了提出的多數(shù)據(jù)流在線相關(guān)分析與聚類方法可以有效挖掘多個感興趣測點(diǎn)通道數(shù)據(jù)流之間的聯(lián)系，自動發(fā)現(xiàn)潛在的工程安全問題或傳感器故障，豐富了閘站工程安全監(jiān)測數(shù)據(jù)的在線自動分析手段。該方法以數(shù)據(jù)為驅(qū)動，將多數(shù)據(jù)流進(jìn)行在線自動分組，用戶無需手動從大量測點(diǎn)列表中選取待分析對比的數(shù)據(jù)流，即可高效、全面且有針對性地查看異常數(shù)據(jù)流。數(shù)據(jù)流的自動分組結(jié)果，可直接用于分析得出工程相關(guān)特性或客觀規(guī)律，以及發(fā)現(xiàn)存在的工程安全隱患。多數(shù)據(jù)流的聚類結(jié)果可利用規(guī)則庫進(jìn)行自動推理及執(zhí)行預(yù)警動作，值得進(jìn)一步研究。

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Safety monitoring of sluice-pump station project based on online correlation analysis and clustering of multichannel data streams

Bao Jiatong1, Qian Jiang2, Zhang Wei1, Tang Hongru1※, Tang Fangping1

(1.225127,2225321,)

Sluice-pump station projects usually consist of many widely distributed hydraulic structures, such as pumping stations, sluices and dams. In order to ensure the safe and reliable operation of the project, it is necessary to observe and measure the settlement, expansion joints and seepage flow of hydraulic structures regularly and accurately. In this paper, an online correlation analysis and clustering method for multichannel real-time monitoring data streams was proposed. It aimed at finding the connections between data streams from multiple interested measuring channels, and automatically discovering potential project security problems and sensor failures. Firstly, the real-time data streams were continuously collected by recording sensor data from multiple measuring channels with the same frequency and aligning them on the time axis. Secondly, 3 statistical features of the data streams were incrementally calculated. By employing the statistical features, the calculation of correlation coefficients of any 2 data streams could only run in 0(1) time. Thirdly, the clustering algorithm of density-based spatial clustering of applications with noise was used in order to automatically find grouped data streams with strong correlations and noised data streams with weak or without correlations. By analyzing the clustering results according to project related characteristics and objective laws, potential project safety risks as well as sensor failures could be identified. Based on an earlier developed safety monitoring system for Taizhou Gaogang sluice-pump station project, the experiments were carried out to analyze and cluster multichannel data streams of uplift pressure, expansion joint and temperature online. It took less than 1 s to process multiple data streams for one time. The clustering results of the water level data streams revealed that the water levels in the uplift pressure tubes installed in 5 sections of the project had strong positive relations owing to the normal action of ground water penetration. Exceptionally, the variation of water level in 1 tube was highly affected by water level change of the Yangtze River, which means there existed an abnormal seepage in that position. The failure of 1 uplift pressure sensor was also found according to the clustering results. Besides, the clustering results of the data streams of expansion joint size and temperature could be explained by thermal expansion and contraction. Especially, the expansion joint sizes of most places in the east-west direction of the horizontal plane had strong negative correlations to the environment temperature while the ones in the other directions were less affected. All the data streams classified as the noises could be directly used to discover the abnormal situations of the corresponding sensors. In conclusion, the proposed method could effectively find the connections between the online data streams from multiple interested measuring channels, and discover potential project safety problems and sensor failures. It showed to be an effective way to supplement the online data analysis methods in the hydraulic area.

clustering analysis;online systems; correlation methods; sluice-pump station project; safety monitoring; multiple data streams

包加桐，錢 江，張 煒，唐鴻儒，湯方平. 基于多通道數(shù)據(jù)流在線相關(guān)分析及聚類的閘站工程安全監(jiān)測[J]. 農(nóng)業(yè)工程學(xué)報，2019，35(3)：101－108. doi：10.11975/j.issn.1002-6819.2019.03.013 http://www.tcsae.org

Bao Jiatong, Qian Jiang, Zhang Wei, Tang Hongru, Tang Fangping. Safety monitoring of sluice-pump station project based on online correlation analysis and clustering of multichannel data streams [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(3): 101－108. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2019.03.013 http://www.tcsae.org

10.11975/j.issn.1002-6819.2019.03.013

TL364+.1;S277

A

1002-6819(2019)-03-0101-08

2018-05-12

2019-01-01

國家自然科學(xué)基金項目（51376155）；江蘇省重點(diǎn)研發(fā)計劃項目（BE2015734）；江蘇省水利科技項目（2015050）

包加桐，副教授，博士，主要從事水利信息化、測控技術(shù)與智能系統(tǒng)研究工作。Email：jtbao@yzu.edu.cn

唐鴻儒，教授，博士，主要從事水利信息化、測控技術(shù)與智能系統(tǒng)研究工作。Email：hrtang@yzu.edu.cn