基于大模型与仪器仿真的网络数字孪生系统构建

With the rapid expansion of carrier network scales, the complexity and risks of network operations continue to escalate, making traditional operation models inadequate for meeting the demands of efficient and low-risk network changes. While digital twin technology has emerged as a promising solution through virtual-physical network mapping, extant implementations still face fundamental limitations: the fidelity-scale tradeoff in simulations and latency in dynamic responsiveness. This paper introduces an instrumentation-enhanced large model (IELM) framework, integrating data-driven modeling, instrumented simulation, and large-model verification. The proposed approach leverages network instrumentation to ensure scalable high-fidelity emulation and observability of digital twin simulations. Meanwhile, large language models (LLMs) power a closed-loop simulation-measurement-optimization cycle—enabling autonomous configuration generation and real-time policy refinement. Validated in China Mobile′s network digital twin system, IELM achieved largescale network twin pre-verification. It reduced validation cycles for configuration changes and service deployments from weeks to hours, improving network operation efficiency by approximately 40%. This research establishes a new paradigm for intelligent network assurance in hyper-scale carrier environments.