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Design Development and Application of Mobile Epidemic Prevention and Isolation Treatment System

Published: 31 May 2023 Publication History

Abstract

The rapid spread of the virus in the outbreak is alarming. Epidemic prevention is the first front of life safety. Especially, the establishment and improvement of epidemic prevention workstations and facilities such as temporary isolation points can improve the efficiency of epidemic prevention and reduce the risk of infection. In order to solve the problems of strong randomness of parameter selection process, dependence on empirical value, slow convergence speed, and different parameter combinations affecting the convergence speed of the algorithm in the process of searching the optimal path of the mobile isolation treatment system. An ant colony algorithm based on reinforcement learning and artificial potential field is proposed. First, the parameters of ant colony algorithm are configured intelligently by reinforcement learning, that is, reinforcement ant colony algorithm. Secondly, based on the enhanced ant colony algorithm, the local optimization mechanism of artificial potential field algorithm is introduced, and the local path re-planning is carried out for grid maps with different dimensions. The results show that the improved ant colony algorithm can search the optimal path with faster convergence speed and fewer iterations in different dimensional obstacle environments. For the path planning problem in high-dimensional complex obstacle environments, the improved algorithm shows more obvious advantages in convergence speed, iterations, obstacle avoidance ability and path smoothness, providing a new technical route for epidemic prevention.

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BIC '23: Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing
February 2023
398 pages
ISBN:9798400700200
DOI:10.1145/3592686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 31 May 2023

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