面向智慧农场中人机协同工作的群智感知任务分配方法OA

When humans and machines collaborate to process tasks,the complex and ever-changing task environment of smart farms and the constraints of algorithm framework can easily lead to local optimal in the task allocation process,affecting the quality and efficiency of task allocation.Therefore,a swarm intelligence perception task allocation method for human-machine collaborative work in smart farms is carried out.A network model for human-machine collaborative task allocation is construc-ted using the swarm intelligence perception technology.This model aims to achieve the dual objectives of minimizing human-machine collaborative work time and maximizing the economic benefits of smart farms,while ensuring all tasks are completed within the specified time.To avoid getting stuck in local optimal solutions during task allocation,genetic algorithm and penalty coefficient are introduced into the model.Through genetic algorithm operations such as tournament selection mechanism and adaptive mutation,the model can search for a wider solution space and find the globally optimal or near globally optimal task al-location solution.The experimental results show that the proposed method can significantly improve the matching rate of task allocation and economic benefits of smart farms.