Design of an Intelligent Diagnosis and Self-Healing System for ZigBee Networks Based on Machine Learning

Aiming at the issues of signal attenuation, electromagnetic interference, and low maintenance efficiency in ZigBee networks in complex indoor scenarios, this paper proposes an intelligent diagnosis and self-healing system based on machine learning. By innovatively integrating network-layer feature aggregation technology, lightweight machine learning algorithms, and topology visualization diagnosis systems, the system constructs a spatiotemporal analysis model using the inherent data of the ZigBee protocol stack. It employs a dual-model architecture of Random Forest and 1D-CNN to classify faults and provides a visual diagnosis interface through dynamic topology mapping technology. Experiments show that the system can achieve a fault location accuracy rate of 100% and reduce maintenance costs by 42%. It supports device migration across gateways (with a success rate of ≥99.5%) and dynamic optimization of network topology, with a network recovery time of <3 minutes. This system offers a non-contact maintenance solution for smart home and industrial Internet of Things scenarios, significantly enhancing the robustness and maintainability of large-scale ZigBee networks.