The study of intelligent scheduling technologies for high-end manufacturing projects based on real-time perception is presented in this paper. We provide a generative scheduling model for the Internet of Things (IoT), demonstrating how IoT sensor systems may be configured for unique shop floor situations. We also present an intelligent scheduling system concept and scheduling method. Finally, we assess and compare the algorithms using computational heuristics refined by a mix of Tabu search, simulated annealing, genetic algorithms, and other approaches. The findings of numerical comparison reveal that the convergence of different methods is strongly related to the size and complexity of the issue.