Synergistic Optimization of UAV Swarm Path Planning for Proactive Forest Disturbance Intervention and Ecological Efficacy Quantification

Traditional forest fire monitoring methods such as satellite remote sensing and ground patrols are prone to response delays and limited coverage, while traditional path planning algorithms such as the A* algorithm cannot adapt to the dynamic spread of wildfires. Additionally, current systems lack real-time closed-loop control and ecological efficacy quantification. Therefore, this study proposes a comprehensive system for early precise intervention, featuring a collaborative Information Acquisition Optimizer–Catch Fish Optimization Algorithm (IAO–CFOA) core. The IAO dynamically fuses multi-sensor inputs, while the CFOA calculates compensation paths, thereby reducing transmission errors. An ecological efficacy model centered on prevention is established, and a Monte Carlo simulation is used to quantify the net carbon reduction of forest ecosystems in northern and southern China. The results show that the IAO–CFOA coupling strategy outperforms traditional algorithms in path planning, increasing the response speed and flight stability with high fire source coverage in dynamic scenarios. The prevention efficiency of single-UAV patrol reaches 78%—an 11–30% improvement over existing UAV systems. This study fills the gap in ecological efficacy quantification, although limitations include simulated environment constraints, the parameter accuracy relying on existing data, and the underexplored synergy with ground firefighting forces.