基于聚类的农产品流通物联网感知数据时空可视化技术

Using clustering algorithm to visualize spatial-temporal internet of things data in process of agricultural product circulation

农产品在流通过程中对运输环境的要求相对严格,借助物联网技术可以实时感知农产品流通过程中的环境数据以及车辆位置数据,通过对这些数据进行有效地可视化,能够实现对整个运输环节的环境监控和位置追踪。但是,流通过程中往往会产生大规模的环境感知数据和车辆位置数据。直接将这些数据进行可视化映射将面临如下挑战:感知点会显得很繁杂错乱,容易出现视觉混淆问题;关键位置点会被湮没在密集的点群之中,难以发现数据中蕴含的有价值的信息;大规模数据的渲染会占用大量的系统计算资源,导致浏览器卡顿等现象,影响用户的使用体验。针对这些难题,该文提出了一种基于聚类的农产品流通过程感知的时空数据可视化技术,该技术首先综合考虑地理空间分布、时间连续性、语义特性对采集到的大规模数据进行聚类分析,挖掘出流通过程的关键位置;然后基于这些关键位置绘制运输轨迹,以实现对流通过程感知数据的时空可视化;最后,将此技术应用到农业物联网地理空间分析与可视化系统中,该系统成功应用到浙江省多个农业基地,针对农业物流环节进行应用示范,应用表明该技术方便了对农产品流通过程进行直观地时空可视化分析。

Stable environment is of vital importance in the process of long-distance circulation for agricultural products. With the development of Internet of Things（Io T） technique, it is relatively convenient to acquire the real-time data about the agricultural environment and the location data of the transport vehicle during the whole circulation process. In order to better perform the environment monitoring and position tracing, one can supervise the trajectory based on the collected Io T data through some current visualization approaches. However, the data collection of one specific trajectory on circulation for agricultural products is usually extremely large because the real-time data is usually required in real-world applications. Therefore, there still exist several open challenges to effectively and efficiently visualize the trajectory by large data collected on circulation for agricultural products. Firstly, there will be too many markers on the limited map once the visualization mapping is made directly from the original collected data set. Then the visual overload problem may occur when loading all the markers on the screen. Meanwhile, it also wastes computational resources to be rendered for the large scale data set, and it will decay the satisfaction of the end users. In addition, it is not conducive to grab the valuable information, which is typically usable for decision-making but hidden in the large raw data set. In order to effectively address these problems, a novel spatial-temporal visualization technique based on clustering the original data points is proposed in this paper. The clustering algorithm considers both the spatial-temporal characteristics and the semantic features of the data collected from the transport vehicle during the circulation process. For the spatial aspect, the trajectory is consecutive in nature, and the curves of the trajectory are well guaranteed with the constraint of the temporal factor. As for the spatial perspective, the nearby points are surely clustered together. Besides, the semantic features are taken into account, and then the points with abnormal Io T sensing values are detected in time, which is demanded by the manager of the circulation. In this approach, the original data will be firstly clustered by the proposed clustering algorithm and reduced to relatively fewer points, which are deemed to be critical positions in a specific trajectory on circulation for agricultural products. Then the real-time trajectory of this transport vehicle can be drawn with these critical positions to monitor the environment and trace its position during the circulation process. Furthermore, the proposed spatial-temporal visualization method is applied to the project that focuses on position-based analysis and visualization of agricultural Io T data based on the geographic information system. Finally, the system is successfully applied to several agricultural companies, and the intuitive visualization of the entire trajectory on the circulation process of agricultural products is effectively achieved.