Research on Cognitive Assistance System of Augmented Reality Technology in Complex Task Execution

With the rapid development of intelligent manufacturing technology, human-computer interaction interface is undergoing revolutionary changes. Augmented reality (AR) technology, with its unique immersive experience and real-time interaction characteristics, has become one of the key technologies to improve the efficiency of complex tasks. This paper aims to explore how to use AR technology to build an efficient cognitive assistance system to support the execution of complex tasks in intelligent manufacturing environments. Firstly, this paper analyzes the challenges of human-machine interaction in the field of intelligent manufacturing, especially in tasks requiring high risk and high precision, the cognitive load of operators often becomes a bottleneck restricting efficiency. This paper proposes a framework of cognitive assistance system combining augmented reality technology and reinforcement learning. By capturing data from the operating environment in real time and combining reinforcement learning algorithms, the framework dynamically adjusts the display content of the AR interface to minimize the cognitive burden of the operator while maximizing the accuracy and safety of task execution. In terms of algorithm design, a reinforcement learning model based on Deep Q network (DQN) is adopted, which can learn and predict the optimal operation strategy in the complex task environment. Through simulation experiments, it is found that the proposed model can effectively identify and respond to the operator’s behavior pattern, so as to provide personalized AR auxiliary information. In addition, in order to ensure the safety of the system, a multi-level security strategy is designed, including emergency stop mechanism, misoperation prevention and fault self-diagnosis function. The simulation results show that the proposed cognitive assistance system can significantly improve the performance of operators in complex tasks, reduce the error rate and shorten the task completion time. Especially in the simulated high-pressure environment, the system shows good stability and adaptability. Further user studies have shown that operators have a high acceptance of the system and consider it to have significant advantages in terms of improving work efficiency and reducing work stress.