【摘要】：“空、天、地、底”立體監(jiān)測網(wǎng)的建立,為海洋資源開發(fā)與經(jīng)濟(jì)發(fā)展奠定了較好的海洋數(shù)據(jù)基礎(chǔ),但受海洋數(shù)據(jù)管理、數(shù)據(jù)分析以及數(shù)據(jù)應(yīng)用等軟/硬平臺的限制,海洋領(lǐng)域“大數(shù)據(jù),小知識”的現(xiàn)象日益凸顯,因此,如何從海量海洋數(shù)據(jù)中快速獲取信息為智能決策提供服務(wù)是當(dāng)前研究熱點(diǎn)之一。抽樣調(diào)查能夠快速地從海量數(shù)據(jù)中獲取有效關(guān)鍵數(shù)據(jù),適用于大規(guī)模大范圍數(shù)據(jù),其周期短,費(fèi)用低,是解決大數(shù)據(jù)快速應(yīng)用的主要途徑。而目前抽樣方法仍較多地依賴于傳統(tǒng)概率抽樣理論來建立,在面向多應(yīng)用需求的驅(qū)動下,如何抽取有效可靠的數(shù)據(jù)使之快速生成有效信息,是現(xiàn)有抽樣方法面臨的挑戰(zhàn)。海洋環(huán)境監(jiān)測數(shù)據(jù)主要指通過浮標(biāo)、調(diào)查船以及人工監(jiān)測等手段,獲取的海洋環(huán)境態(tài)勢監(jiān)測數(shù)據(jù),其數(shù)據(jù)特性及抽樣問題可歸納為:(1)海量性,海洋環(huán)境監(jiān)測數(shù)據(jù)正以空前的速度增加和加快,動態(tài)更新頻繁,多源,數(shù)據(jù)在空間尺度和時間尺度上都顯現(xiàn)出存在冗余問題;(2)空間相關(guān)性,數(shù)據(jù)具有空間屬性特征,距離近的數(shù)據(jù)相似度高,容易造成空間關(guān)聯(lián)失效或樣本重疊,抽樣精度下降;(3)空間異質(zhì)性,數(shù)據(jù)涵蓋信息復(fù)雜,空間分布不均勻,給數(shù)據(jù)復(fù)用及后期處理帶來困難。因此,考慮海洋環(huán)境監(jiān)測數(shù)據(jù)的特征,設(shè)計(jì)優(yōu)化空間抽樣方法來幫助有效使用數(shù)據(jù)是值得研究的問題。在設(shè)計(jì)抽樣和估計(jì)時,過度降低抽樣成本會產(chǎn)生估計(jì)精度出現(xiàn)偏差、抽樣結(jié)果失真等問題,而過高的樣本量會增大數(shù)據(jù)冗余,從而提高成本,因此,如何能夠平衡抽樣精度與成本是設(shè)計(jì)優(yōu)化空間抽樣方法的主旨。本文主要研究內(nèi)容如下:(1)分析海洋環(huán)境監(jiān)測數(shù)據(jù)特征,總結(jié)其呈現(xiàn)出的多模態(tài)、高維度、多屬性等特性給抽樣方法帶來的挑戰(zhàn),綜述現(xiàn)有抽樣方法的研究現(xiàn)狀,分析這些方法運(yùn)用于海洋環(huán)境監(jiān)測數(shù)據(jù)時產(chǎn)生的問題。(2)提出一種系統(tǒng)空間抽樣優(yōu)化方法,考慮數(shù)據(jù)空間相關(guān)性,將半變異函數(shù)引入空間抽樣方法設(shè)計(jì),兼顧樣本點(diǎn)在研究海域內(nèi)分布均勻的同時,又保證在其抽樣精度的前提下減少信息的冗余。(3)面向該批海洋環(huán)境監(jiān)測數(shù)據(jù)的應(yīng)用需求,考慮抽樣對象的多屬性聯(lián)系,通過計(jì)算各屬性權(quán)重對方法做進(jìn)一步擴(kuò)展,設(shè)計(jì)能夠滿足海洋環(huán)境監(jiān)測數(shù)據(jù)多屬性綜合評估需求的空間抽樣方法,用于更全面,更經(jīng)濟(jì)的抽樣估計(jì)。(4)以某海域空間數(shù)據(jù)為實(shí)驗(yàn)對象,通過方差、抽樣比率和趨勢面分析,對本文設(shè)計(jì)的復(fù)雜類型海洋環(huán)境監(jiān)測數(shù)據(jù)的空間抽樣方法與傳統(tǒng)抽樣方法進(jìn)行比較,結(jié)果表明,通過使用本方法計(jì)算得到的步長進(jìn)行抽樣能夠有效減少數(shù)據(jù)量,同時保證一定的抽樣精度,比較好的反應(yīng)總體特征趨勢。
[Abstract]:The establishment of "space, sky, earth and bottom" three-dimensional monitoring network has laid a good foundation for marine data development and economic development, but is limited by soft / hard platforms such as marine data management, data analysis and data application. The phenomenon of "big data, little knowledge" in marine field is becoming more and more prominent. Therefore, how to quickly obtain information from massive marine data to provide services for intelligent decision-making is one of the current research hotspots. Sampling survey can quickly obtain effective key data from massive data, which is suitable for large-scale and large-scale data, and its cycle is short and the cost is low, which is the main way to solve the problem of big data's rapid application. At present, sampling methods still rely on the traditional probability sampling theory. Driven by multi-application requirements, how to extract effective and reliable data to generate effective information quickly is a challenge for existing sampling methods. Marine environmental monitoring data mainly refers to the marine environmental situation monitoring data obtained by means of buoys, survey ships and manual monitoring. The data characteristics and sampling problems can be summarized as follows: (1) oceanography. The monitoring data of marine environment are increasing and accelerating at an unprecedented rate, and the dynamic updates are frequent and multi-source. There are redundant problems in the spatial scale and time scale of the data. (2) Spatial correlation, the data has spatial attribute characteristics, the similarity of close data is high, which is easy to cause spatial association failure or sample overlap, and the sampling accuracy decreases; (3) Spatial heterogeneity, complex data coverage and uneven spatial distribution, which bring difficulties to data reuse and post-processing. Therefore, considering the characteristics of marine environment monitoring data, the design of optimal spatial sampling method to help the effective use of data is a problem worthy of study. In the design of sampling and estimation, excessive reduction of sampling cost will lead to errors in estimation accuracy and distortion of sampling results, while excessive sample size will increase data redundancy and thus increase cost. How to balance the sampling precision with the cost is the main theme of the design optimization spatial sampling method. The main contents of this paper are as follows: (1) analyzing the characteristics of marine environmental monitoring data, summarizing the challenges brought by multi-modal, high-dimensional and multi-attribute characteristics to sampling methods, and summarizing the research status of existing sampling methods. The problems arising from the application of these methods to marine environmental monitoring data are analyzed. (2) A systematic spatial sampling optimization method is proposed, considering the spatial correlation of data, the semi-variant function is introduced into the design of spatial sampling method. At the same time, the sampling point can reduce the redundancy of information under the premise of sampling accuracy. (3) considering the application demand of the marine environmental monitoring data, considering the multi-attribute relation of sampling object, the sampling point is not only uniformly distributed in the sea area, but also guaranteed to reduce the redundancy of information under the premise of sampling accuracy. By further expanding the method by calculating the weight of each attribute, a spatial sampling method which can meet the needs of multi-attribute comprehensive assessment of marine environment monitoring data is designed for more comprehensive use. More economical sampling estimation. (4) taking the spatial data of a certain sea area as the experimental object, through the analysis of variance, sampling ratio and trend surface, The spatial sampling method of complex marine environment monitoring data designed in this paper is compared with the traditional sampling method. The results show that the amount of data can be effectively reduced by using the step size calculated by this method. At the same time, to ensure a certain sampling accuracy, a better response to the overall characteristic trend.
【學(xué)位授予單位】：上海海洋大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：P717

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