Abstract: As the source of the haze data, the reliability of the haze monitoring sites is very important to the reliability of the big data. Due to the lack of effective evaluation method for the haze monitoring points, the monitoring data is not reliable enough. In order to solve the problem that the perceived data was not reliable, a kind of perceptual source trust evaluation and selection model was proposed based on the data trigger detection method. When the perceived data arrived, the K-Means clustering algorithm and the statistical results were firstly used to calculate the benchmark data, then the trust degree of data was calculated by using the current perceived data, the benchmark data and the threshold values. Secondly, according to the location of the perceptual source, neighbor relationship was determined. The current perceived data and the data of the neighbors were compared, according to the absolute value of the difference and the value of the threshold, the neighbor recommendation trust degree was calculated. Finally, the comprehensive trust degree was calculated by using the truest degree of perceived data, the historical trust degree and the recommendation trust degree of the neighbor. The initial value of the historical trust was set as the number of monitoring items, and then updated by the comprehensive trust. Theoretical analysis and simulation results prove that the proposed method can effectively evaluate the perceived source, avoid the abnormal data, and reduce post processing overhead.

Key words: trust model, haze, perception source, trust evaluation, data monitoring

摘要： 雾霾监测点作为雾霾数据感知的源头,由于缺乏有效的评价方法,导致感知的数据不可靠。针对此问题,提出一种感知源信任评价和筛选模型,该模型采用数据触发检测方式来进行。当感知源的数据到达时,首先采用K-Means聚类算法和统计结果计算感知源基准数据,根据当前感知数据、基准数据和所设定的门限值计算得到感知源的数据信任度;然后根据感知源所处地理位置确定邻居关系,将感知源当前所感知的数据和各个邻居所感知的数据进行比较,根据差值的绝对值和门限值的大小关系计算得到邻居推荐信任度;最后使用感知源的数据信任度、历史信任度和邻居推荐信任度三种信任度计算得到最终的综合信任度。其中历史信任度初始为所监测的指标数,而后使用综合信任度进行更新。从理论分析和仿真结果看,该方法可有效对感知源进行客观的评价,同时能够规避异常感知源的数据,降低后期处理开销。

关键词: 信任模型, 雾霾, 感知源, 信任评价, 数据监测