Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

Abstract

In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in-depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm-wise sensing network for assisted wireless sensor networking to achieve crowdsourcing-assisted localization. We consider a framework for crowdsourcing-assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data-driven participant selection problem in swarm-wise sensing networks. The sprocket-chain engagement frequency has the greatest influence on the horizontal bending-vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.