CAJ | 학술논문

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。
농산품재류통과정중대운수배경적요구상대엄격，차조물련망기술가이실시감지농산품류통과정중적배경수거이급차량위치수거，통과대저사수거진행유효지가시화，능구실현대정개운수배절적배경감공화위치추종。단시，류통과정중왕왕회산생대규모적배경감지수거화차량위치수거。직접장저사수거진행가시화영사장면림여하도전：감지점회현득흔번잡착란，용역출현시각혼효문제；관건위치점회피인몰재밀집적점군지중，난이발현수거중온함적유개치적신식；대규모수거적선염회점용대량적계통계산자원，도치류람기잡돈등현상，영향용호적사용체험。침대저사난제，해문제출료일충기우취류적농산품류통과정감지적시공수거가시화기술，해기술수선종합고필지리공간분포、시간련속성、어의특성대채집도적대규모수거진행취류분석，알굴출류통과정적관건위치；연후기우저사관건위치회제운수궤적，이실현대류통과정감지수거적시공가시화；최후，장차기술응용도농업물련망지리공간분석여가시화계통중，해계통성공응용도절강성다개농업기지，침대농업물류배절진행응용시범，응용표명해기술방편료대농산품류통과정진행직관지시공가시화분석。
Stable environment is of vital importance in the process of long-distance circulation for agricultural products. With the development of Internet of Things (IoT) 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 IoT 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 IoT 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 IoT 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.