Multi-unmanned aerial vehicle gas leakage source positioning method based on improved particle swarm optimization

Abstract

The invention discloses a multi-unmanned aerial vehicle gas leakage source positioning method based on an improved particle swarm algorithm. The multi-unmanned aerial vehicle gas leakage source positioning method is mainly used for efficiently and accurately positioning a gas leakage source. The method is combined with human psychology, so that the method is more intelligent. People become more careful when approaching success. The mind of a person is simulated, particles become careful when being closer to a pollution source, the speed of the particles is slowed down, and the particles move carefully. According to the method, a basic particle swarm algorithm is adopted in the initial stage of iteration, so that a particle swarm searches in a large range in a gas leakage area. When particles exceed a threshold value, layered iteration is started. The residual electric quantity of the unmanned aerial vehicle and the influence of the searched gas leakage source are taken as measurement indexes of a prudent factor, the prudent factor is added when the speed of a positive particle layer is updated, so that particles are subjected to more detailed local search, and the driving effect of the positive particle layer is added when the speed of a negative particle layer is updated, so that the search efficiency is improved, and efficient positioning of a gas leakage source is realized.

Description

technical field [0001] The invention relates to a leakage source location method, in particular to a multi-UAV gas leakage source location method based on an improved particle swarm algorithm. Background technique [0002] Dangerous gases often appear in people's daily life. For example, natural gas, industrial gas for industrial use, toxic gas produced after natural disasters or safety accidents, etc., the leakage of these gases will cause great harm to the environment and people's lives. At the same time, after a gas leak, if the detected area is large, it is often difficult to determine the location of the gas source. Traditional pollution source location is generally based on fixed detection stations, vehicle-mounted monitoring stations, and wireless sensor networks, and the location of pollution sources is estimated by combining their location with pollution and pollutant concentration information. However, due to the limitation of ground conditions, the distribution ...

AI Technical Summary

Problems solved by technology

In the standard particle swarm optimization algorithm, the convergence accuracy of the algorithm is not high enough, the algorithm converges quickly at the initial stage of iteration, and when searching the local area later, the convergence speed is slow and the algorithm is difficult to jump out after falling into the local optimum

Images