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环境监测有害成分的数据融合及其水质状况评价

陈敏欣%谢冬青%黄海   

  • 基金资助:
    广东省高等学校科技创新重点项目[cxzd1144]、广州市属高校“羊城学者”科研计划(10A033D)

Data Fusion of Harmful Components of Environmental Monitoring and Evaluation of Water Quality Condition

CHEN Min-xin%XIE Dong-qing%HUANG Hai   

  • About author:广州大学数学与信息科学学院,广东广州,510006%广州大学计算机科学与教育软件学院,广东广州,510006

摘要: 文章首先提出安全态势评估模型,实现网络安全趋势的预测,使传感器采集的数据信息的完整性和机密性得到保证。然后采用数据融合与数据挖掘相结合的方法,对水质监控传感器采集的数据信息进行融合并依据D-S证据结构进行决策,提出一种用于监测水质的D-S证据决策方法。该方法分析水质环境的变化,克服了对每个传感器采集的信息分别进行处理时的不确定性和不稳定性。实验表明,该方法有效。

Abstract: This paper ifrstly proposes a network security situation awareness model to forecast the future trend in network security, while the integrity and the conifdentiality of the data information collected by the sensors are guaranteed. By using the method of data fusion combining with the data mining, the system implements the water quality monitoring data fusion and decision based on D-S evidence structure. The paper then proposes a decision method of D-S evidence of monitoring water quality, which can analyze the changes of water quality environment, overcome the uncertainty and instability while processing the data information collected by each sensor. Experiments show that the method is effective.