煤炭安全管理时序数据可视化方式研究
Study on visualization mode of time series data of coal safety management
-
摘要: 为探究煤炭安全管理时序数据的最优可视化展示方式，基于时序数据不同维度和管理任务类型层面下选取7幅可视化图形开展眼动实验，并记录被试者的3种眼动指标作为实验数据，运用SPSS19.0软件进行数据分析。结果表明：展示多维数据的趋势任务时，折线图展示效果最好；展示二维数据的循环与周期任务时，螺旋图展示效果最好；展示二维数据的数值点识别任务时，散点图展示效果最好。Abstract: In order to explore the optimal visualization display mode of coal safety management time series data, this paper selects 7 visualization graphics to carry out eye movement experiment based on different data dimensions and management task types of time series data, and records 3 eye movement indexes of subjects as experimental data, and uses spss19.0 software for data analysis. The results show that: the line chart shows the best effect during displaying the trend task of multi-dimensional data; the spiral chart shows the best effect during displaying the cycle and cycle task of two-dimensional data; the scatter chart shows the best effect during displaying the numerical point recognition task of two-dimensional data.
-
Keywords:
- coal safety management /
- time series data /
- visual display mode /
- data dimension /
- management tasks
-
-
[1] 王立杰，刘志东．经济时间序列分析技术在煤炭价格预测中的应用[J]．煤炭学报，2001，26（1）：109－112. WANG Lijie, LIU Zhidong. The application of economy time series analysis technique in coal price forecast[J]. Journal of China Coal Society, 2001, 26（1）: 109-112.
[2] 李润求，吴莹莹，施式亮，等．煤矿瓦斯涌出时序预测的自组织数据挖掘方法[J]．中国安全生产科学技术，2017，13（7）：18－23. LI Runqiu, WU Yingying, SHI Shiliang, et al. Research on self-organizing data mining method for time series prediction of gas emission in coal mine[J]. Journal of Safety Science and Technology, 2017, 13（7）: 18-23.
[3] 穆文瑜，李茹，阴志洲，等．煤矿多传感器混沌时序数据融合预测[J]．计算机应用，2012，32（6）：1769. MU Wenyu, LI Ru, YIN Zhizhou, et al. Fusion prediction of mine multi-sensor chaotic time series data[J]. Journal of Computer Applications, 2012, 32（6）: 1769.
[4] 马豪．高维数据可视化方法研究[D]．长沙：国防科学技术大学，2016. [5] 宁小亮，蒲阳．基于矿压监测动态特征的瓦斯突出预警系统设计[J]．工矿自动化，2015，41（3）：10－13. NING Xiaoliang, PU Yang. Design of early-warning system of gas outburst based on dynamic characteristics of pressure monitoring[J]. Industry and Automation, 2015, 41（3）: 10-13.
[6] Ｃ 查特菲尔德．时间序列分析导论[Ｍ]．方再根，译．北京：宇航出版社，1986. [7] 席茂．矿山地面灾害监测数据可视化表达和实现技术[D]．太原：太原理工大学，2015. [8] 陈晓皎，薛澄岐，陈默，等．基于眼动追踪实验的数字界面质量评估模型[J]．东南大学学报（自然科学版）， 2017，47（1）：38－42. CHEN Xiaojiao, XUE Chengqi, CHEN Mo, et al. Quality assessment model of digital interface based on eye-tracking experiments[J]. Journal of Southeast University（Natural Science Edition）, 2017, 47（1）: 38-42.
[9] 田芸，于赛克，周前祥，等．眼动指标在脑力疲劳研究中的应用分析[J]．人类工效学，2015，21（4）：69－73. [10] Subramanian S.CG&A with eye tracking for cognitive behavior analysis and psychoanalysis[C]//2013 International Conference on Developments in Esystems Engineering. IEEE, 2013: 132-137.
计量
- 文章访问数: 35
- HTML全文浏览量: 0
- PDF下载量: 18
下载:
