系统仿真学报 ›› 2016, Vol. 28 ›› Issue (2): 354-360.

• 仿真应用工程 • 上一篇    下一篇

一种农残检测数据的融合对比可视化方法

陈红倩1, 李慧2, 方艺1, 刘启贤1, 陈谊1   

  1. 1.北京工商大学计算机与信息工程学院食品安全大数据技术北京市重点实验室,北京 100048;
    2.北京联合大学管理学院,北京 100101
  • 收稿日期:2015-07-24 修回日期:2016-01-12 出版日期:2016-02-08 发布日期:2020-08-17
  • 作者简介:陈红倩(1982-), 男, 山东, 博士, 副教授, 研究方向为虚拟现实与可视分析; 李慧(通讯作者1983-), 女, 博士, 讲师, 研究方向为数据挖掘与知识发现。
  • 基金资助:
    十二五国家科技支撑项目(2012BAD29B01-2); 北京市自然基金资助项目(4154066)

Fusion Comparing Visualization Method for Pesticide Residue Detection Data

Chen Hongqian1, Li Hui2, Fang Yi1, Liu Qixian1, Chen Yi1   

  1. 1. Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering,Beijing Technology and Business University, Beijing 100048, China;
    2. College of Management, Beijing Union University, Beijing 100101, China
  • Received:2015-07-24 Revised:2016-01-12 Online:2016-02-08 Published:2020-08-17

摘要: 针对农残检测数据分析过程中,多城市、多类别、多标准间的高效对比与数据筛选,提出了一种基于OpenGL图形库并结合数据统计的融合可视化方法。该方法基于农残检测数据库,对检测结果进行分类汇总;将汇总后的数据融合显示在一个可视化界面中,实现数据的快速对比与直观初步分析;将专家交互选定的数据集,依据多国/地区MRL(Maximum Residue Limits)标准进行衡量,将衡量结果再次融合显示至一个可视化界面中,实现检测结果在多标准条件下的快速评价与表达。实验结果表明,方法能够对一定规模的农残检测数据进行快速展示,并能从可视化结果中高效的进行数据概览与预判,算法处理效率能满足实时交互需求。

关键词: 数据可视化, 数据对比, 融合可视化, 农残检测数据

Abstract: To express efficiently the association and difference among cities, categories and standards in pesticide residue detection data analysis, a fusion visualization method based on data statistic and OpenGL graphics library was proposed. The method can accelerate the dataset comparing and selecting for the expert interaction and data analysis. The method firstly summarizes the detection data in pesticide residue detection database according to its various category and belonging region. All the data are shown in one fusion visualization result. The result can assist expert to compare efficiently and analysis intuitionally the dataset. The data of the selected dataset in expert interaction are measured based on multiple regional MRL standard. The measured results based multiple standards are shown in one fusion visualization interface. The fusion visualization can achieve to support the fast evaluation and representation for the detection data. The experimental results denoted the method can achieve the intuitive and accurate global data overview and prognostication. The method can support the expert interaction in real time.

Key words: data visualization, data comparing, fusion visualization, pesticide residue detection data

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