Human-factor-state-aware Lightweight and Energy-efficient Real-time Task Scheduling Strategy for MEC-Empowered AI Quality Inspection Systems

Abstract

AI quality inspection is a key enabler of intelligent manufacturing, generating computation-intensive and latency-sensitive tasks that may overload resource-constrained inspection devices and affect production efficiency. Although Multi-Access Edge Computing (MEC) can reduce task execution delay by offloading workloads to nearby servers, dynamic wireless conditions, stochastic task arrivals, and human-in-the-loop intervention requirements make real-time scheduling highly challenging. To address this issue, this paper investigates a human-factor-state-aware MEC-enabled AI quality inspection system, where task offloading and computing resource allocation are jointly optimized with consideration of both system states and operator-related collaboration states. Specifically, the task scheduling problem is formulated as a Markov Decision Process (MDP), in which queue states, channel conditions, computing resource states, and human-factor indicators such as manual re-inspection pressure and collaboration urgency are jointly incorporated into the state space. To solve the resulting mixed discrete-continuous optimization problem, a tailored Deep Deterministic Policy Gradient (DDPG) algorithm is developed to generate real-time scheduling decisions for lightweight AI inspection tasks. In addition to minimizing long-term task latency and system energy consumption, the proposed method further suppresses collaboration delay and high-risk task backlog under dynamic industrial environments. Simulation results show that the proposed strategy converges rapidly and consistently outperforms benchmark schemes in latency and energy efficiency, while providing stronger adaptability to human-machine collaborative quality inspection scenarios.