亚洲一区欧美在线,日韩欧美视频免费观看,色戒的三场床戏分别是在几段,欧美日韩国产在线人成

水產(chǎn)養(yǎng)殖大數(shù)據(jù)技術(shù)研究進(jìn)展與發(fā)展趨勢(shì)分析
作者:
作者單位:

作者簡(jiǎn)介:

通訊作者:

中圖分類號(hào):

基金項(xiàng)目:

北京市科技計(jì)劃項(xiàng)目(Z171100001517016)和公益性行業(yè)(農(nóng)業(yè))科研專項(xiàng)(201203017)


State-of-the-art Review for Application of Big Data Technology in Aquaculture
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問(wèn)統(tǒng)計(jì)
  • |
  • 參考文獻(xiàn)
  • |
  • 相似文獻(xiàn)
  • |
  • 引證文獻(xiàn)
  • |
  • 資源附件
  • |
  • 文章評(píng)論
    摘要:

    水產(chǎn)養(yǎng)殖對(duì)象特殊、環(huán)境復(fù)雜、影響因素眾多,精準(zhǔn)地監(jiān)測(cè)、檢測(cè)和優(yōu)化控制極其困難。大數(shù)據(jù)技術(shù)結(jié)合數(shù)學(xué)模型,把水產(chǎn)養(yǎng)殖產(chǎn)生的大量數(shù)據(jù)加以處理和分析,并將有用的結(jié)果以直觀的形式呈現(xiàn)給生產(chǎn)者與決策者,是解決上述難題的根本途徑。本文主要對(duì)水產(chǎn)養(yǎng)殖大數(shù)據(jù)技術(shù)研究進(jìn)展與發(fā)展趨勢(shì)進(jìn)行了深入剖析,提出了水產(chǎn)養(yǎng)殖業(yè)大數(shù)據(jù)技術(shù)的總體架構(gòu);分析了水產(chǎn)養(yǎng)殖大數(shù)據(jù)的來(lái)源和獲取手段,重點(diǎn)總結(jié)了幾種水產(chǎn)養(yǎng)殖大數(shù)據(jù)分析技術(shù)的研究進(jìn)展和現(xiàn)有水產(chǎn)養(yǎng)殖大數(shù)據(jù)平臺(tái)及其提供的應(yīng)用服務(wù);最后針對(duì)水產(chǎn)養(yǎng)殖與大數(shù)據(jù)技術(shù)結(jié)合過(guò)程所面臨的困難與挑戰(zhàn),從實(shí)現(xiàn)全面感知、全產(chǎn)業(yè)鏈數(shù)據(jù)智能分析與自動(dòng)決策、水產(chǎn)養(yǎng)殖大數(shù)據(jù)標(biāo)準(zhǔn)體系建設(shè)等方面提出水產(chǎn)養(yǎng)殖大數(shù)據(jù)技術(shù)的發(fā)展方向。數(shù)據(jù)是根本,分析是核心,利用大數(shù)據(jù)技術(shù)提高水產(chǎn)養(yǎng)殖綜合生產(chǎn)力和效益是最終目的,應(yīng)深度挖掘現(xiàn)實(shí)需求,整合水產(chǎn)養(yǎng)殖全產(chǎn)業(yè)鏈數(shù)據(jù),加強(qiáng)基礎(chǔ)理論和核心關(guān)鍵技術(shù)研究,從而推進(jìn)大數(shù)據(jù)技術(shù)與水產(chǎn)養(yǎng)殖產(chǎn)業(yè)的深度融合,支撐我國(guó)水產(chǎn)養(yǎng)殖業(yè)徹底轉(zhuǎn)型升級(jí)。

    Abstract:

    It has many difficulties in monitoring and detection accurately and optimal control in aquaculture because the targets are so special and environment is so sophisticated that contributes too many impact factors. Big data technology, as well as mathematical models are used to process and analyze the large scale of data producing in aquaculture industry and the useful results are presented to producers and decision makers in intuitive form, which is the fundamental way to solve the above problems. The research progress and development trend of the applications of big data technology in aquaculture were deeply discussed. Firstly, the overall architecture of applying big data technology in aquaculture was proposed and the data sources and data acquisition tools were listed. Then, several kinds of analysis techniques, which had been well applied to deal with the existing problems in aquaculture, were mainly summarized and the several current big data platforms and the services they provided for aquaculture were introduced. Finally, in view of solving the difficulties and challenges faced in the process of applying big data technologies in aquaculture, the research future in this field was proposed form the aspects of comprehensive awareness, intelligent analysis, automatic decision-making, and big data standard system construction of aquaculture. In the applications of big data technology in aquaculture, data is the basis and analysis is the core. The ultimate goal is to take advantage of big data technology to improve the comprehensive productivity and efficiency of aquaculture. In order to achieve it, the actual demands in aquaculture should be greatly concerned. In addition, data of the whole industry chain in aquaculture should be integrated and the basic theories and core key technologies should be studied intensively and thoroughly. In this way, the application of big data technology in aquaculture will be deeper and the integration of the two will be closer, which will support the complete transformation and upgrading of China aquaculture industry.

    參考文獻(xiàn)
    相似文獻(xiàn)
    引證文獻(xiàn)
引用本文

段青玲,劉怡然,張璐,李道亮.水產(chǎn)養(yǎng)殖大數(shù)據(jù)技術(shù)研究進(jìn)展與發(fā)展趨勢(shì)分析[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2018,49(6):1-16. DUAN Qingling, LIU Yiran, ZHANG Lu, LI Daoliang. State-of-the-art Review for Application of Big Data Technology in Aquaculture[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(6):1-16.

復(fù)制
分享
文章指標(biāo)
  • 點(diǎn)擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:2018-05-03
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期: 2018-06-10
  • 出版日期: