Design of labor education task allocation and automatic execution system based on deep learning

In this paper, a task allocation and automatic execution system of labor education based on deep learning is proposed, aiming at realizing the intelligent upgrade of the whole process of labor education through AI technology, and solving the problem of insufficient dynamic adaptation to students' ability differences, task complexity and environmental conditions in traditional labor education task allocation. The system adopts a three-tier architecture of data perception-intelligent decision-making-execution monitoring. Through multi-modal data fusion modeling, combined with natural language processing (NLP) and LSTM network, the matching degree between students' abilities and tasks is dynamically predicted, and the task allocation strategy is optimized by TD3 reinforcement learning algorithm. The experimental results show that the system is significantly superior to the traditional manual distribution method in terms of task matching degree, task completion time deviation, high-order task completion rate and safety risk incident rate, and it also performs well in multimodal data fusion, dynamic difficulty adjustment mechanism and robustness test, which can effectively improve students' comprehensive practical ability and provide new ideas and methods for the intelligent development of labor education.