随着物联网技术的快速发展，传统的集中式云计算架构在处理海量传感器数据时面临着网络带宽压力和实时性差等问题。文章提出了一种基于边缘计算的智能物联网环境监测系统架构，通过在网络边缘层部署计算节点，实现数据的本地化处理和分析。系统采用多层级边缘计算架构，结合深度学习算法对环境数据进行实时分析和预测，显著降低了数据传输延迟和带宽占用。实验结果表明，与传统云计算架构相比，该系统在数据处理延迟上减少65%，带宽占用降低70%，同时环境参数预测准确率达到92%。该研究为物联网环境监测系统的优化提供了新的技术方案，对推动物联网技术在环境监测领域的应用具有重要意义。

With the rapid development of Internet of Things(IoT) technology traditional centralized cloud computing architecture faces problems such as bandwidth pressure and poor real-time performance when handling massive sensor data. This paper proposed an intelligent IoT environmental monitoring system framework based on edge computing,which achieved the localized processing and analysis of data by deploying computing nodes at the network edge layer. By adopting a multi-level edge computing architecture combined with deep learning algorithms, the system enabled real-time analysis and prediction of environmental data,which significantly reduced data transmission latency and bandwidth occupation. Experimental results showed that compared with traditional cloud computing architecture, the system reduced data processing latency by 65% and bandwidth occupation by 70%,while achieved the prediction accuracy rate of 92% in environmental parameter. This research provides a new technical solution for optimizing IoT environmental monitoring systems and has significant importance for promoting the application of IoT technology in the field of environmental monitoring.