This paper aims at the problems of unclear scheduling model and low intelligence in the scheduling technology of bulk cargo terminals, analyzes the business process and characteristics of bulk cargo terminals based on the current situation and demand of the scheduling business of bulk cargo terminals, according to the actual scheduling characteristics and use rules of the berths and yards of bulk cargo terminals, and combined with the characteristics of complex coordination factors between the berths and yards of bulk cargo terminals, A multi resource collaborative scheduling model of bulk cargo terminal based on particle swarm optimization algorithm is proposed, and the algorithm is analyzed. This paper also proposes an optimization scheme for bulk cargo terminal scheduling.
In this paper, taking the conveying equipment as the research object, aiming at the main characteristics and intelligent requirements of the current dry bulk terminal conveying system, a monitoring and diagnosis model of the dry bulk terminal conveying equipment based on the edge-cloud collaborative technology is proposed. The technical advantages of the company can jointly realize the tasks of perception, integration, monitoring and diagnosis of the conveying equipment, improve the monitoring and diagnosis function of the conveying equipment in the dry bulk terminal, and improve the safety supervision ability of the dry bulk terminal.
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