Research on the construction and system optimization of intelligent logistics environment monitoring visualization platform based on Internet of Things data driving

This paper focuses on building a visual platform for intelligent logistics environment monitoring based on Internet of Things (IoT) data, and carries out system optimization research. The fluctuation of environmental factors in the logistics process poses a challenge to the safety, compliance and energy consumption management of goods, and the traditional monitoring system has many shortcomings. In this study, a hierarchical platform architecture of multi-source heterogeneous data fusion is designed, and Kalman filtering algorithm is used to fuse multi-type sensor data to improve data accuracy and real-time performance of the system. A hybrid model combining lightweight Long Short-Term Memory (LSTM) and attention mechanism is constructed for real-time anomaly detection, and the anomaly is determined by dynamic threshold method based on prediction residual. Design a lightweight visualization engine, use WebGL and ECharts technology to achieve cross-platform efficient rendering, and introduce dynamic update strategy to optimize performance. In the aspect of system optimization, an adaptive compression algorithm is proposed to optimize data transmission. Level of Detail (LOD) technology and quadtree spatial index structure are used to improve rendering performance, and an energy consumption optimization model is established to realize dynamic resource scheduling. The test results show that this scheme is significantly superior to the traditional scheme in data transmission delay, anomaly detection accuracy, rendering frame rate and energy consumption of edge nodes. The actual deployment in pharmaceutical cold chain enterprises has also greatly improved the operational efficiency and the level of goods safety, effectively solved the key problems in intelligent logistics environment monitoring, and promoted the intelligent upgrading of the logistics industry.