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Remote Monitoring System of Logistics Transport Vehicles Based on Internet of Things

摘要


With the development of highway transportation industry, the number of logistics transportation vehicles has greatly increased. While strengthening the carrying capacity and improving the transportation efficiency, problems such as vehicle route management, safe operation management and vehicle running state monitoring also arise. Because the dispatcher can't grasp the position of each vehicle in time and accurately, and can't mobilize the vehicles in time, the vehicles can't be used efficiently, and at the same time, the operating cost of the fleet is increased. In addition, with the increase of transport vehicles, the safety of vehicles has become the focus of fleet management, such as vehicle robbery, vehicle theft and other phenomena that affect social stability and endanger public security are increasing year by year, and vehicle accidents caused by fatigue driving, speeding and overweight driving are also increasing year by year. In view of the development of logistics and transportation industry and the development demand of intelligent transportation, this paper proposes a remote monitoring system of logistics and transportation vehicles based on the Internet of Things. The system integrates scheduling, monitoring and management, and has the characteristics of good real‐time, high transmission rate, high collection frequency, large data storage capacity and convenient use, which provides a strong technical support for the development of intelligent transportation system.

參考文獻


Kumar Akhil; Kalia Arvind; Verma Kinshuk; Sharma Akashdeep; Kahalmanisha. Scaling up face masks detection with Yoloon a novel dataset [j]. Optik-International Journal of Optical and Electron Optics. 2021, (239):
Li Hechun, Tao Shuai. Driver's dangerous behavior monitoring system based on multimodal information joint judgment [J]. Science, Technology and Engineering. 2021,21 (21): 291-298.
Zhou Huaping, Jing Wang, Sun Kelei. Research on improved YOLOv4-tiny pedestrian detection algorithm [J]. Radio communication technology .2021,47 (04): 100-106.
Wang Qi, Tang Yangshan. Driver fatigue detection method based on MTCNN [J]. Automotive technology .2021, 46 (20): 57-59.

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