Edge Intelligence-Empowered Collaborative Control and Dynamic Optimization System for Computer Laboratory Equipment

Aiming at the core issues in computer laboratory equipment management, such as poor compatibility of heterogeneous protocols, insufficient real-time control accuracy, and escalating cybersecurity threats, this study proposes an intelligent IoT collaborative control system integrating edge computing, blockchain technology, and deep learning algorithms. By deploying a multimodal sensing network to collect real-time equipment status data, an improved ZigBee-Hybrid protocol is constructed to achieve dynamic frequency band switching and adaptive time slot allocation. Additionally, a composite control algorithm based on LSTM-optimized fuzzy PID is designed. Experimental results demonstrate that the system achieves a peak transmission rate of 2.18 bps under a scenario of 30 concurrent devices, reduces temperature control overshoot to 4.7%, and resists 95% of DDoS and data tampering attacks. This research provides an innovative solution for high-security and high-real-time intelligent management of laboratory equipment.