Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture, for learning-informed and imagination-driven network control. Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout, enabling the scheduler to anticipate future network states and adjust transmission decisions accordingly. We implement the framework in a configurable simulation testbed and benchmark its performance against traditional heuristics and reinforcement learning baselines under varied traffic conditions. Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments, while maintaining interpretability and sample efficiency. The proposed design bridges the gap between network-level reasoning and low-overhead learning, marking a step toward scalable and adaptive NDT-based network optimization.

翻译：工业物联网与实时信息物理系统等新兴网络系统亟需能够适应动态流量、截止时间与干扰约束的智能调度策略。本研究提出一种受双心智世界模型架构启发的新型数字孪生调度框架，实现基于学习认知与想象驱动的网络控制。与传统基于规则的策略或纯数据驱动策略不同，所提出的DMWM架构将短时域预测规划与基于符号模型的推演相结合，使调度器能够预判未来网络状态并相应调整传输决策。我们在可配置仿真测试平台中实现了该框架，并在多样化流量条件下与传统启发式方法及强化学习基线进行性能对比。实验结果表明，DMWM在突发流量、干扰受限及截止时间敏感的环境中均表现出优越性能，同时保持可解释性与样本效率。该设计弥合了网络级推理与低开销学习之间的鸿沟，标志着向可扩展自适应网络数字孪生优化迈出了重要一步。