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针对农残检测数据的多MRL分析系统 被引量:2

MULTI-MRL ANALYSIS SYSTEM FOR PESTICIDE RESIDUE DETECTION DATA
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摘要 针对食品安全领域农残检测数据的可视分析需求,提出一种多重放射环的单区域多MRL(Maximum Residue Limits)数据可视化方法,并设计开发多MRL共现的农残检测数据分析系统。提出的多重放射环方法,结合饼图、玫瑰图、柱状图、颜色映射等多种可视化元素,能高效地展示和分析单个区域内多种MRL标准下的数据判定结果。分析系统结合选择、过滤、导航、概览+细节等一系列交互手段,能便捷地支持农产品样品检测状态的综合分析。分析系统支持多视图联动,可实现地理位置、农药等多维度的对比分析。食品安全领域专家和测试用户表明,该方法能够有效地提高农残检测数据的综合分析效率,可以高效地获知数据所表征的规律和内涵。 Aiming at the demand of visual analysis of pesticide residue detection data in the field of food safety, this paper proposes a multi-MRL (Maximum Residue Limits) data visualization method, and designs a multi-MRL cooccurrence pesticide residue detection data analysis system. The multi-standard contrast method proposed in this paper is combined with a variety of visualization elements such as pie charts, rose charts, bar charts, and color mapping. It can efficiently display and analyze the results of data determination under multiple MRL standards in a single area. The analysis system combined a series of interactive means such as selection, filtering, navigation, overview and details, which could easily support the comprehensive analysis for agricultural product sample detection status. The analysis system supported multi-view linkage and enabled multi-dimensional comparative analysis of geographic locations and pesticides. The experimental result of experts in the food safety field and the test users shows that the method can improve effectively the comprehensive analysis of pesticide residue detection data.
作者 陈红倩 温玉琳 杨倩玉 李慧 Chen Hongqian;Wen Yulin;Yang Qianyu;Li Hui(Beijing Key Laboratory of Big Data Technology for Food Safety,School of Computer and Information Engineering,Beijing Technology and Business University,Beijing 100048,China;College of Management,Beijing Union University,Beijing 100101,China)
出处 《计算机应用与软件》 北大核心 2018年第9期79-84,共6页 Computer Applications and Software
基金 国家自然科学基金项目(31701517) 北京市社会科学基金项目(17GLC060) "十三五"时期北京市属高校高水平教师队伍建设支持计划(CIT&TCD201704039)
关键词 农残检测数据 多重放射环 对比分析 多视图联动 数据可视化 Pesticide residue detection data Multi-standard contrast Comparative analysis Multi-view linkage Data visualization
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  • 1Nature. Big Data [EB/OL]. [2012-10-02]. http,//www. nature, com/news/specials/bigdata/index, html.
  • 2Bryant R E, Katz R H, Lazowska E D. Big-Data computing : Creating revolutionary breakthroughs in commerce, science, and society [R]. [2012-10-02]. http:// www. cra. org/ccc/docs/init/Big_Data, pdf.
  • 3Science. Special online collection: Dealing with data [EB/OL]. [2012-10-02]. http://www, sciencemag, org/site/ special/data/, 2011.
  • 4Agrawal D, Bernstein P, Bertino E, et al. Challenges and opportunities with big data A community white paper developed by leading researchers across the United States [R/OL]. [2012-10-02]. http://cra, org/ccc/docs/init/bigdata whitepaper, pdf.
  • 5Manyika J, Chui M, Brown B, et al. Big data: The next frontier for innovation, competition, and productivity [R/OL]. [ 2012-10-02 ]. http://www, mekinsey, corn/ Insights]MGI[Research/Teehnology _ and _ Innovation]Big _ data The next frontier for innovation.
  • 6World Economic Forum. Big data, big impact: New possibilities for international development [R/OL]. [2012- 10-02]. http://www3, weforum, org/docs/WEF TC MFS BigDataBigImpact_Briefing 2012. pdf.
  • 7Big Data Across the Federal Government [EB/OL]. [2012-10-02]. http://www, whitehouse, gov/sites/default/ files/microsites/ostp/big_data fact sheet_final_ 1. pdf.
  • 8UN Global Pulse. Big Data for Development:Challenges Opportunities [R/OL]. [ 2012-10-02 ]. http://www. unglobalpulse, org/proj ects/BigDataforDevelopment.
  • 9Times N Y. The age of big data fEB/OLd. [2012-10 -02]. http://www, nytimes, com/2012/02/12/sunday review/big- datas-impact in-the-world, html?pagewanted=all.
  • 10Grobelnik M. Big-data computing: Creating revolutionary breakthroughs in commerce, science, and society [R/OL]. [2012-10 -02]. http://videolectures, net/cswc2012_grobelnik_ big_data/.

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