Wang Xiaobo

Jiangsu Yigao Tobacco Machinery Co., Ltd.

Abstract：

      Traditional logistics information systems adopt business-driven decision-making logic, simplifying logistics mechanical equipment status into binary identifiers and ignoring equipment dynamic degradation and real-time operating conditions, which leads to mismatched scheduling schemes, frequent equipment faults and low logistics operation efficiency. To address the above defects, this paper proposes and constructs a logistics information system optimization decision model based on mechanical equipment state perception. Supported by IoT sensing, edge computing and data fusion technology, the model combines ISO 13374-3 standard and non-homogeneous Poisson process to fit equipment degradation rules, and builds a layered system architecture to realize closed-loop intelligent scheduling. Practical verification shows that the model can dynamically optimize logistics tasks according to equipment health status, reduce unplanned equipment downtime and improve logistics operation efficiency and stability, providing effective technical support for intelligent logistics scheduling.

Key Words：

logistics information system; mechanical equipment; state perception; intelligent decision-making; data fusion; scheduling optimization