溫玉波
摘 要: 傳統(tǒng)海洋環(huán)境污染監(jiān)測(cè)技術(shù)雖然可以進(jìn)行海洋污染檢測(cè),但存在監(jiān)測(cè)范圍短、監(jiān)測(cè)精度低以及監(jiān)測(cè)時(shí)間長(zhǎng)等不足,針對(duì)此問(wèn)題提出基于數(shù)字遙感多層分析的海洋環(huán)境污染信息智能圖像監(jiān)測(cè)技術(shù)。通過(guò)引入智能數(shù)字遙感多層分析技術(shù),對(duì)海洋水質(zhì)進(jìn)行多維度監(jiān)測(cè);采用大數(shù)據(jù)分析法對(duì)采集數(shù)據(jù)進(jìn)行計(jì)算,完成海洋環(huán)境污染監(jiān)測(cè)分析。實(shí)驗(yàn)結(jié)果表明,改進(jìn)圖像監(jiān)測(cè)技術(shù)能夠?qū)崿F(xiàn)全覆蓋的高精度海洋污染監(jiān)測(cè),并可以提取海洋環(huán)境污染樣品信息。
關(guān)鍵詞: 海洋污染; 智能監(jiān)測(cè); 圖像監(jiān)測(cè); 多層分析技術(shù); 大數(shù)據(jù)分析; 數(shù)字遙感
中圖分類號(hào): TN911.73?34; TN913 文獻(xiàn)標(biāo)識(shí)碼: A 文章編號(hào): 1004?373X(2018)06?0109?03
Abstract: Although the traditional marine environment pollution monitoring technology can be used in marine pollution detection, there exist problems like short monitoring range, low monitoring precision and long monitoring time. In allusion to the above problems, an intelligent image monitoring technology based on multilevel analysis of digital remote sensing for marine environment pollution information is proposed. The intelligent digital remote sensing multilevel analysis technology is introduced to conduct multi?dimensional marine water quality monitoring. The big data analysis method is used to calculate the collected data and complete the monitoring and analysis of marine environment pollution. The experimental results show that the improved image monitoring technology can achieve full?coverage and high precision marine pollution monitoring, and extract information of marine environment pollution samples.
Keywords: marine pollution; intelligent monitoring; image monitoring; multilevel analysis technology; big data analysis; digital remote sensing
隨著科學(xué)技術(shù)的發(fā)展,人類與海洋的關(guān)系越來(lái)越密切,對(duì)海洋環(huán)境的保護(hù)也越發(fā)重要。傳統(tǒng)海洋監(jiān)測(cè)技術(shù)主要適應(yīng)近海環(huán)境監(jiān)測(cè),監(jiān)測(cè)范圍短,采用低維度監(jiān)測(cè)技術(shù),監(jiān)測(cè)精度低,對(duì)污染源監(jiān)測(cè)時(shí)間長(zhǎng),發(fā)現(xiàn)某處存在污染源后由于監(jiān)測(cè)時(shí)間較長(zhǎng),污染源可能已漂到另一區(qū)域[1],對(duì)此,提出海洋環(huán)境污染信息智能圖像監(jiān)測(cè)技術(shù)。實(shí)驗(yàn)結(jié)果表明,提出的海洋環(huán)境污染信息智能圖像監(jiān)測(cè)技術(shù),能夠?qū)崿F(xiàn)全覆蓋的高精度海洋污染監(jiān)測(cè),并可以提取海洋環(huán)境污染樣品信息。
1 海洋環(huán)境污染信息智能圖像監(jiān)測(cè)技術(shù)
1.1 引入智能數(shù)字遙感技術(shù)
引入智能數(shù)字遙感技術(shù)是依托遙感衛(wèi)星,構(gòu)建智慧型數(shù)據(jù)模塊實(shí)現(xiàn)海洋環(huán)境污染的監(jiān)測(cè),通過(guò)多層數(shù)據(jù)庫(kù)進(jìn)行篩選分析獲得可用信息[2]。智能數(shù)字遙感技術(shù)結(jié)構(gòu)包括圖像層、海洋信息表示層及海洋分析顯示層。圖像層作用是利用遙感衛(wèi)星對(duì)海洋環(huán)境進(jìn)行遙感識(shí)別,將拍攝圖片進(jìn)行簡(jiǎn)單處理打包,通過(guò)無(wú)線傳輸送到海洋信息處理界面。海洋信息處理層將圖像層所監(jiān)測(cè)信息進(jìn)行數(shù)字化表示[3],依托海洋對(duì)象數(shù)據(jù)庫(kù)、海洋環(huán)境物理場(chǎng)分析數(shù)據(jù)庫(kù)進(jìn)行處理。數(shù)據(jù)傳遞到分析顯示層,進(jìn)行數(shù)據(jù)分析。分析顯示層對(duì)海洋信息表示層的數(shù)據(jù)信息進(jìn)行接納,利用數(shù)據(jù)鏈路模式、圖像數(shù)據(jù)模塊以及數(shù)據(jù)挖掘技術(shù)等進(jìn)行圖像處理[4],智能數(shù)據(jù)遙感數(shù)據(jù)庫(kù)結(jié)構(gòu)流程如圖1所示。
海洋衛(wèi)星圖像數(shù)據(jù)庫(kù)主要作用是對(duì)衛(wèi)星圖像處理分割、對(duì)象識(shí)別,構(gòu)建多維圖像組織,計(jì)算圖像物理間距[5]。海洋對(duì)象數(shù)據(jù)庫(kù)主要作用是對(duì)海洋環(huán)境物理場(chǎng)流形,海洋物理場(chǎng)流形內(nèi)嵌維度,低微分布分析,海洋對(duì)象特征提取[6]。
1.2 利用水質(zhì)傳感器采集污染源數(shù)據(jù)
水質(zhì)傳感器作為海洋環(huán)境污染監(jiān)測(cè)技術(shù)重要手段之一,本文利用水質(zhì)傳感器進(jìn)行海水pH值監(jiān)測(cè)、海水溶解氧監(jiān)測(cè)、海水電導(dǎo)率監(jiān)測(cè)、海水溫度監(jiān)測(cè)。水質(zhì)傳感器包含傳感器及對(duì)應(yīng)作用如表1所示。水質(zhì)傳感器采用特種立體水質(zhì)傳感裝置,通過(guò)計(jì)算機(jī)圖像顯示系統(tǒng)完成監(jiān)測(cè)圖像處理。
水質(zhì)立體傳感裝置包含立體感應(yīng)原件,能夠同時(shí)對(duì)多個(gè)監(jiān)測(cè)源進(jìn)行監(jiān)測(cè)。對(duì)集成pH值監(jiān)測(cè)裝置、海水溶解氧監(jiān)測(cè)裝置、海水電導(dǎo)率監(jiān)測(cè)裝置以及海水溫度監(jiān)測(cè)裝置進(jìn)行綜合信號(hào)的采集。傳輸裝置將感應(yīng)信號(hào),以脈沖電流形式進(jìn)行無(wú)線傳輸,顯示在計(jì)算機(jī)圖像系統(tǒng)中[7]。
1.3 優(yōu)化大數(shù)據(jù)對(duì)比分析法
依托智能數(shù)字遙感技術(shù)和水質(zhì)傳感器監(jiān)測(cè)技術(shù)的綜合數(shù)據(jù)采集,對(duì)污染信息充分提取,由于提取數(shù)據(jù)量巨大,對(duì)比分析難度大。針對(duì)以上問(wèn)題,優(yōu)化大數(shù)據(jù)對(duì)比分析法對(duì)采集數(shù)據(jù)進(jìn)行處理[8]。首先將采集的數(shù)據(jù)源類型進(jìn)行重新定義,通過(guò)對(duì)圖像信息以及數(shù)據(jù)源的提取,與標(biāo)準(zhǔn)污染圖像以及污染參數(shù)進(jìn)行大數(shù)據(jù)對(duì)比,得出綜合監(jiān)測(cè)結(jié)果[9]?;诤Q髮?shí)際情況,存在逆流、順流、海風(fēng)、能見(jiàn)度低等因素,對(duì)采集的大數(shù)據(jù)會(huì)產(chǎn)生“合理量化”影響[10],當(dāng)產(chǎn)生數(shù)據(jù)渦流時(shí),監(jiān)測(cè)數(shù)據(jù)分析量化會(huì)有所變化,其變化規(guī)律如圖2所示。endprint
數(shù)據(jù)渦流對(duì)污染數(shù)據(jù)分析造成很大的難度。因此需要對(duì)不同數(shù)據(jù)變相、數(shù)據(jù)矢量以及數(shù)據(jù)失衡度進(jìn)行統(tǒng)一量化。
當(dāng)數(shù)據(jù)渦流變換時(shí),其監(jiān)測(cè)數(shù)據(jù)分析量化變化規(guī)律如圖3所示。
數(shù)據(jù)渦流變換時(shí),大數(shù)據(jù)對(duì)比分析難度會(huì)適當(dāng)?shù)慕档停?因?yàn)槠渥兓瘮?shù)據(jù)不考慮影響程度,因此將量化值與大數(shù)據(jù)監(jiān)測(cè)參數(shù)進(jìn)行平均修正。從而實(shí)現(xiàn)大數(shù)據(jù)對(duì)比分析。
2 測(cè)試與仿真實(shí)驗(yàn)
2.1 測(cè)試準(zhǔn)備
為了驗(yàn)證智能圖像監(jiān)測(cè)技術(shù)的可靠性,選擇不同海域、不同海洋環(huán)境下、不同仿真污染源的大小、不同監(jiān)測(cè)范圍進(jìn)行對(duì)比實(shí)驗(yàn),具體數(shù)據(jù)如表2所示。為了保護(hù)海洋環(huán)境,本次實(shí)驗(yàn)采用的均為仿真污染源,通過(guò)測(cè)量監(jiān)測(cè)辨識(shí)度進(jìn)行對(duì)比分析,其監(jiān)測(cè)辨識(shí)度等于發(fā)現(xiàn)污染源實(shí)驗(yàn)次數(shù)與總實(shí)驗(yàn)次數(shù)之比。
2.2 監(jiān)測(cè)分辨率測(cè)試
在某固定海域,選擇1 000 km2監(jiān)測(cè)區(qū)域,通過(guò)傳統(tǒng)海洋監(jiān)測(cè)技術(shù)和智能圖像監(jiān)測(cè)技術(shù)同時(shí)進(jìn)行不同仿真污染源監(jiān)測(cè)。記錄其監(jiān)測(cè)結(jié)果后,更換另一固定海域,選擇不同流速,不同對(duì)比度環(huán)境下,再進(jìn)行不同仿真污染源監(jiān)測(cè)測(cè)試,記錄其監(jiān)測(cè)結(jié)果。對(duì)監(jiān)測(cè)結(jié)果進(jìn)行檢測(cè)辨識(shí)度計(jì)算,得出辨識(shí)度?仿真污染源面積曲線如圖4所示。
通過(guò)辨識(shí)度?監(jiān)測(cè)范圍曲線可得出,傳統(tǒng)海洋污染監(jiān)測(cè)系統(tǒng)不適合對(duì)小污染源進(jìn)行監(jiān)測(cè),當(dāng)監(jiān)測(cè)污染源小于300 m2時(shí),其辨識(shí)度低于85%,當(dāng)監(jiān)測(cè)污染源小于200 m2時(shí),其辨識(shí)度低于80%,辨識(shí)度受監(jiān)測(cè)污染源大小限制程度大。智能圖像監(jiān)測(cè)技術(shù),監(jiān)測(cè)污染源等于75 m2時(shí),其辨識(shí)度高于90%,隨著污染源面積的增大,監(jiān)測(cè)辨識(shí)率保持平衡。
2.3 監(jiān)測(cè)時(shí)效性測(cè)試
在某固定海域,選擇1 000 km2仿真污染源,利用傳統(tǒng)海洋監(jiān)測(cè)技術(shù)和智能圖像監(jiān)測(cè)技術(shù)進(jìn)行不同仿真污染源的監(jiān)測(cè)時(shí)間測(cè)試。對(duì)各系統(tǒng)發(fā)現(xiàn)污染源時(shí)間進(jìn)行記錄其監(jiān)測(cè)結(jié)果,如圖5所示。
通過(guò)仿真污染源?監(jiān)測(cè)時(shí)間曲線可得出,傳統(tǒng)海洋污染監(jiān)測(cè)系統(tǒng)隨著污染源的減小,監(jiān)測(cè)發(fā)現(xiàn)時(shí)間增大,當(dāng)監(jiān)測(cè)污染源小于300 m2時(shí),監(jiān)測(cè)時(shí)間達(dá)到10 min,當(dāng)監(jiān)測(cè)污染源小于150 m2時(shí),監(jiān)測(cè)時(shí)間達(dá)到15 min,屬于低效運(yùn)行。智能圖像監(jiān)測(cè)技術(shù),監(jiān)測(cè)污染源隨著污染源面積的減小,監(jiān)測(cè)時(shí)間保持穩(wěn)定,可在5 min之內(nèi)發(fā)現(xiàn)污染目標(biāo),并進(jìn)行及時(shí)通報(bào)。
3 結(jié) 語(yǔ)
本文提出的海洋環(huán)境污染信息智能圖像監(jiān)測(cè)技術(shù),采用智能數(shù)字遙感技術(shù)與水質(zhì)傳感器技術(shù)進(jìn)行污染數(shù)據(jù)采集,利用大數(shù)據(jù)對(duì)比分析法進(jìn)行數(shù)據(jù)計(jì)算,實(shí)現(xiàn)海洋污染信息智能圖像監(jiān)測(cè)技術(shù)。通過(guò)監(jiān)測(cè)范圍測(cè)試、監(jiān)測(cè)分辨率測(cè)試和監(jiān)測(cè)時(shí)效性測(cè)試表明,提出的海洋環(huán)境污染信息智能圖像監(jiān)測(cè)技術(shù),能夠進(jìn)行大范圍的海洋環(huán)境污染監(jiān)測(cè)。
參考文獻(xiàn)
[1] 欒坤祥,馬秀冬.智能水下航行器對(duì)海洋重金屬污染狀況的檢測(cè)研究[J].艦船科學(xué)技術(shù),2017,39(16):187?189.
LUAN Kunxiang, MA Xiudong. Study on the detection of heavy metal pollution in ocean by intelligent underwater vehicle [J]. Ship science and technology, 2017, 39(16): 187?189.
[2] 張美枝,李建榮.Android環(huán)境下的海洋環(huán)境監(jiān)測(cè)數(shù)據(jù)智能處理系統(tǒng)設(shè)計(jì)[J].艦船科學(xué)技術(shù),2016,38(20):136?138.
ZHANG Meizhi, LI Jianrong. Design of intelligent processing system for monitoring data of marine environment under Android environment [J]. Ship science and technology, 2016, 38(20): 136?138.
[3] 韋楊.基于TCS230的船舶智能化污染監(jiān)測(cè)技術(shù)研究[J].艦船科學(xué)技術(shù),2017,39(8):145?147.
WEI Yang. Research on intelligent monitoring technology of ship pollution based on TCS230 [J]. Ship science and technology, 2017, 39(8): 145?147.
[4] 于洋.視覺(jué)圖像與檢測(cè)技術(shù)相結(jié)合共創(chuàng)人工智能發(fā)展新視角[J].電子測(cè)量與儀器學(xué)報(bào),2017,31(6):819.
YU Yang. Visual image and detection technology combined to create a new perspective of the development of artificial intelligence [J]. Journal of electronic measurement and instrumentation, 2017, 31(6): 819.
[5] 胡利永,魏玉艷,鄭堤,等.基于機(jī)器視覺(jué)技術(shù)的智能投餌方法研究[J].熱帶海洋學(xué)報(bào),2015,34(4):90?95.
HU Liyong, WEI Yuyan, ZHENG Di, et al. Research on intelligent bait casting method based on machine vision technology [J]. Journal of tropical oceanography, 2015, 34(4): 90?95.endprint
[6] 王軍鋒,鄧豪,魏育成,等.無(wú)人機(jī)海洋觀測(cè)系統(tǒng)集成技術(shù)研究[J].艦船科學(xué)技術(shù),2017,39(9):157?162.
WANG Junfeng, DENG Hao, WEI Yucheng, et al. UAV onboard ocean observing system [J]. Ship science and technology, 2017, 39(9): 157?162.
[7] 馮翔宇.面向云計(jì)算的海洋環(huán)境監(jiān)測(cè)系統(tǒng)訪問(wèn)控制方法研究[J].艦船科學(xué)技術(shù),2016,38(16):136?138.
FENG Xiangyu. Research on access control method for cloud computing marine environment monitoring system [J]. Ship science and technology, 2016, 38(16): 136?138.
[8] 楊秀芳.一種基于無(wú)線傳感器網(wǎng)絡(luò)的海洋信息智能采集方法[J].艦船科學(xué)技術(shù),2016,38(24):145?147.
YANG Xiufang. The method for marine information intelligent acquisition based on wireless sensor network [J]. Ship science and technology, 2016, 38(24): 145?147.
[9] 蔡紅梅,張轉(zhuǎn)芳,張光利.基于圖像處理的螺栓松動(dòng)智能監(jiān)測(cè)方法研究[J].計(jì)算機(jī)仿真,2015,32(10):94?96.
CAI Hongmei, ZHANG Zhuanfang, ZHANG Guangli. Unmanned aerial vehicle (UAV) is loose monitoring method based on image monitoring research [J]. Computer simulation, 2015, 32(10): 94?96.
[10] 楊禎明.基于海洋環(huán)境數(shù)據(jù)的物聯(lián)網(wǎng)動(dòng)態(tài)監(jiān)測(cè)系統(tǒng)設(shè)計(jì)[J].艦船科學(xué)技術(shù),2017,39(6):153?155.
YANG Zhenming. Design of dynamic monitoring system for Internet of Things based on marine environment data [J]. Ship science and technology, 2017, 39(6): 153?155.endprint