Abstract: With the deep integration of information technology and manufacturing technology,cloud manufacturing industrial production has become a key part of the manufacturing industry.Due to the dynamics of the cloud manufacturing environment and the interdependencies between service resources,it isn’t easy to select the best industrial resource services.Most of the existing selection optimization methods are based on heuristic algorithms,but these algorithms cannot often adapt to the cloud manufacturing environment.Therefore,this paper constructs a service selection model in the cloud manufacturing environment,proposes a service selection algorithm based on deep learning and generative adversarial network ideas,which can flexibly adapt to environmental changes,uses the graph representation learning method to construct a task service constraint graph,and then learns the characteristics of resource services according to the intrinsic relationship between tasks,services,and industrial production constraints,and introduces gradient optimization and loss function strategies in the algorithm improvement stag and select the best industrial resource services.Experiments show that the proposed algorithm has stronger performance advantages than other comparison algorithms.

Key words: Cloud manufacturing, Industrial production constraints, Graph represents learning, Generate adversarial networks, Gradient loss function