Design of Intelligent Control System for Fire Extinguishing Equipment Based on AI Image Recognition

With the development of artificial intelligence image recognition, the limitations have been exposed on traditional fire control systems that rely on single-point fire detection, including high misjudgment rates, significant delays in the activation of firefighting facilities, and insufficient precision in intelligent control. This paper introduces an innovative intelligent fire suppression control system that combines AI-based fire detection with adaptive PID algorithms. The system employs a multi-layer convolutional neural network (CNN) visual model, trained on extensive datasets of features such as flames and smoke. It enhances fire-related feature recognition, particularly enabling long-tail effect recognition in complex scenarios. Upon effective fire detection, an innovative design introduces an adaptive PID algorithm at the control level, establishing a nonlinear mapping relationship between real-time image recognition and intelligent fire extinguishing devices. It dynamically adjusts and executes firefighting device parameters, minimizing response time and optimizing the accuracy of early-stage fire suppression. Experimental verification shows that under PID control, the system demonstrates excellent robustness in simulated fire scenarios, contributing significantly to urban development in contemporary China.