Dynamic Intelligent Supply-Demand Adaptation Model Towards Intelligent Cloud Manufacturing

Abstract

As a new mode and means of smart manufacturing, smart cloud manufacturing (SCM) faces great challenges in massive supply and demand, dynamic resource collaboration and intelligent adaptation. To address the problem, this paper proposes an SCM-oriented dynamic supply-demand (S-D) intelligent adaptation model for massive manufacturing services. In this model, a collaborative network model is established based on the properties of both the supply-demand and their relationships; in addition, an algorithm based on deep graph clustering (DGC) and aligned sampling (AS) is used to divide and conquer the large adaptation domain to solve the problem of the slow computational speed caused by the high complexity of spatiotemporal search in the collaborative network model. At the same time, an intelligent supply-demand adaptation method driven by the quality of service (QoS) is established, in which the experiences of adaptation are shared among adaptation subdomains through deep reinforcement learning (DRL) powered by a transfer mechanism to improve the poor adaptation results caused by dynamic uncertainty. The results show that the model and the solution proposed in this paper can perform collaborative and intelligent supply-demand adaptation for the massive and dynamic resources in SCM through autonomous learning and can effectively perform global supply-demand matching and optimal resource allocation.

An Intrusion Detection Method Based on a Universal Gravitation Clustering Algorithm

“…Cluster analysis is a highly versatile, multivariable statistical method that exhibits a wealth of information and diverse applications. Commonly employed techniques include dynamic clustering [10], ordered sample clustering, fuzzy clustering [11], clustering forecasting methods, and graph clustering methods [12,13]. Moreover, clustering plays a crucial role in the detection of anomalous behaviors.…”