Reinforcement learning optimization for dynamic recovery of tailings resources under industrial internet of things

Aiming at the problems of low recovery rate of tailings resources and poor adaptability to dynamic working conditions, this paper proposes a reinforcement learning (RL) optimization method driven by Industrial Internet of Things (IIoT). By deploying a multi-source sensor network for real-time sensing of ore grade, flow rate and equipment status, a digital twin collaborative framework is constructed; a multi-intelligent body depth deterministic policy gradient (MADDPG) algorithm is innovatively designed to realize dynamic decision-making for agent addition and equipment control. Based on the open data set validation, the system recovery rate is increased to 92.3%, and the energy consumption per ton of ore is reduced by 18.7%, which significantly optimizes the dynamic resource recovery efficiency. The method provides key technical support for the green transformation of the mining industry.