Automated Warehouse Environment Monitoring and Intelligent Control Strategy Based on IoT Sensors

With the widespread application of automated warehouses in the logistics industry, the demand for intelligent environmental monitoring and control systems has gradually increased. Traditional environmental monitoring and control methods rely on manual inspections or simple threshold adjustments, which are difficult to meet the dynamic management needs of complex environments. The introduction of Internet of Things (IoT) technology provides a new solution for accurate monitoring and intelligent control of warehouse environments. This study proposes an automated warehouse environmental monitoring and intelligent control strategy based on the Internet of Things, aiming to improve the efficiency of warehouse environmental management, optimize energy consumption, and ensure the quality of cargo storage. This study first designed an environmental monitoring system based on a multi-sensor network, which can collect environmental parameters such as temperature, humidity, and gas concentration in the warehouse in real time. The accuracy and reliability of the monitoring data are improved through the reasonable arrangement of sensors and data preprocessing methods (such as filtering, outlier detection, and dimensionality reduction). Secondly, a dynamic control strategy is proposed by combining fuzzy logic control and deep learning algorithms to predict environmental changes based on real-time data and historical trends, and automatically adjust environmental parameters to achieve more accurate control effects. The experimental results show that the system has achieved significant improvements in the real-time and accuracy of environmental monitoring, and can dynamically adjust the temperature, humidity, and gas concentration in the warehouse to reduce energy consumption and operating costs. By comparing with traditional methods, the intelligent control system based on the Internet of Things performs well in optimizing energy management, can effectively reduce operating costs, and ensure the stability of the cargo storage environment. The results of this study provide technical support for the intelligent environmental management of automated warehouses, which can not only improve the efficiency and economic benefits of warehouse management, but also have broad application prospects and can be extended to other fields with high environmental requirements, such as smart factories, cold chain logistics and medical storage.