Research and Analysis of UAV Fire Monitoring Index Optimization Strategy based on PSO Algorithm

Abstract

With the rapid development of artificial intelligence, drones are gradually developing in the direction of miniaturization and intelligence, and they are commonly used in emergency rescue operations due to their low cost, strong concealment as well as high flexibility. The Emergency Operations Center uses drones to monitor emergency situations to adjust personnel plans to ensure efficient and safe emergency procedures. In order to more effectively deal with an emergency, and consider the needs of the uav communication task, related to the terrain, fire frequency and size, safety, economy and so on many aspects, the influence of the optimum combination model and particle swarm optimization algorithm, the combination of Q learning algorithm supporting scheme was put forward, helping determine the unmanned aerial vehicle (uav) in the process of monitoring the most comprehensive and efficient state.This study takes fire of Australian as an example to explore the effectiveness, feasibility and optimization of UAV monitoring in many aspects. The occurrence of fire is actually affected by many aspects, such as meteorological conditions, building environment, firefighting ability, etc. Based on the randomness, suddenness and fuzziness of fire, the time series model is used to predict and analyze the possible fire situation in the next 10 years, and the influence of day and night, season, climate and human factors on fire spread is effectively analyzed, which confirms the feasibility of the research idea to some extent.