Research on forest resources and environment monitoring system based on ant colony optimization algorithm and cloud computing technology

In view of the shortcomings of traditional forest monitoring methods in real-time, coverage and data processing efficiency, this paper proposes a forest resources and environment monitoring system that integrates improved Ant Colony Optimization (ACO) and cloud computing technology. The system adopts the "edge-cloud" collaborative architecture. By introducing environmental factors and improved ACO algorithm with regional value weight, the dynamic path optimization of UAV in complex forest environment is realized, and the data collection efficiency and coverage are improved. Edge computing layer is responsible for local data preprocessing and quick response, while cloud computing layer is responsible for large-scale data storage, path optimization calculation and disaster prediction and analysis based on LSTM (Long Short-Term Memory). The experimental results show that the system has obvious advantages in fire identification, pest warning and illegal logging monitoring. The response time of fire identification is shortened to 3.1 seconds, the accuracy rate is 96.7%, and the comprehensive cost of the system is reduced by 66.8%. The research results provide a feasible path for building an efficient, intelligent and low-cost forest monitoring system, which is of great significance for promoting "digital forestry" and ecological civilization construction.