With the advancement of Industry 4.0

intelligent manufacturing systems are more and more widely used in industrial production. At the same time

as the core equipment of intelligent manufacturing

the frequent replacement and maintenance of industrial robots have become an important factor restricting production efficiency. However

the traditional manual replacement and maintenance methods are not only inefficient

but also prone to misjudgment and delay due to human factors

which in turn affects the stability and reliability of the entire production system. In order to improve the intelligent level of claw replacement and maintenance

this study proposes an industrial robot claw replacement and maintenance system based on Internet of Things and Deep Reinforcement Learning (DRL). The system integrates deep Q network (DQN) and multi-sensor data fusion technology to realize real-time monitoring of the status of the claw and intelligent maintenance decision-making. The results of this experiment show that the average accuracy of the system in the claw state recognition and maintenance decision-making reaches 86.2%

which is significantly improved compared with the traditional method

and provides reliable technical support for the efficient operation of the intelligent manufacturing system.