Intelligent Garbage Collection Path Planning Method Based on Quantum Cuckoo Search Algorithm

Abstract

The invention discloses an intelligent garbage collection path planning method based on a quantum cuckoo search algorithm. The method comprises the following steps: S1, the state of a garbage can is detected, whether the garbage can is full is determined, and the full garbage can data are obtained; S2, the transmitted data of each garbage can are subjected to path planning through the quantum cuckoo search algorithm; and S3, according to path planning, a garbage collection navigation map is drawn. The quantum cuckoo search algorithm is designed for planning the collection path, a quantum double chain encoding mode is adopted to improve the cuckoo search algorithm to plan the best collection path, and the garbage collection navigation map is finally made according to the collection path. The method has the advantages of good intelligence and high efficiency, the collection path accuracy is improved, the cost manpower and mateiral resurces for garbage collection management in a certain range can be effectively saved, and the operation cost is greatly reduced.

Description

technical field [0001] The invention relates to the field of intelligent garbage recovery path planning, in particular to a garbage recovery path planning method based on the combination of an intelligent optimization algorithm and an application of the Internet of Things, belonging to the practical application field of intelligent optimization. Background technique [0002] Due to the high population density in China today, a large amount of domestic garbage is produced in cities every day, and the cleaning of garbage has become an important task. At present, it is difficult to ensure that the garbage cans in the city are in the state of needing to be cleaned. The garbage cans in some areas are more likely to be full of garbage and need to be cleaned frequently, otherwise it will affect people's physical and mental health; while the garbage cans in some areas are often vacant. , there is no need to clean up frequently, so how to efficiently recycle garbage is an urgent prob...

AI Technical Summary

Problems solved by technology

The genetic algorithm has a good global search ability, but it has the disadvantages of slow operation speed, large storage space, and easy to fall into premature maturity; the neural network method has a good learning ability, but when the number of garbage bins is large and the environment is dynamic, the network The structure is huge and the threshold of neurons needs to be constantly changed with time; the convergence speed of ant colony algorithm is slow, and it is easy to fall into local optimum

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