系统阐述了AI for Engineering（AI4E）驱动数字生态系统网络发展范式的转型动因、机理与实践路径，指出传统数字生态系统网络发展范式面临“刚性架构与场景多样化”的根本矛盾，亟需以“超融合、高可信、一体化”为目标进行重构。介绍了AI4E驱动数字生态系统网络发展范式转型的重要基础、技术支撑、运作方式，从思维视角、方法论、实践规范、发展路径等方面阐述了新范式的主要特征；同时，介绍了AI4E赋能转型的实践探索，提出基于生成式AI的多模态网络环境（PINE），开辟网络技术体制“第二曲线”；提出晶上生成式变结构计算，打造智能算力“芯物种”；推动内生安全赋能数字系统网络弹性工程，提升人工智能应用系统内生安全能力；呼吁建设"超融合网络与智能计算实验床"大科学装置，验证“结构决定效能/安全/多样性”的科学猜想，为构建自主知识体系、推动科技自主创新、深化人才自主培养改革提供支撑。
Artificial Intelligence (AI), as a core driver propelling socioeconomic development, is triggering a dual paradigm shift in scientific research (AI for Science, AI4S) and engineering technology (AI for Engineering, AI4E). This paper systematically elaborates on the driving forces, mechanisms, and practical pathways for the paradigm shift in digital ecosystem network development driven by AI4E. It points out that the traditional development paradigm of digital ecosystem networks faces a fundamental conflict between "rigid architectures and diversified scenarios", necessitating reconstruction with the goals of being "hyper-converged, highly trustworthy, and integrated". The paper introduces the critical foundations, technological underpinnings, and operational mechanisms for this AI4E-driven paradigm shift in digital ecosystem networks. It delineates the main characteristics of the new paradigm from perspectives including mindset, methodology, practical norms, and developmental pathways. Furthermore, it presents practical explorations of AI4E-empowered transformation: proposing the Polymorphic Intelligent Network Environment (PINE) based on Generative AI to forge the "second curve" of network technology systems; introducing On-Wafer Generative Vari-Structure Computing to foster new "chip species" of intelligent computing power; promoting endogenous safety and security (ESS) to empower the resilience engineering of digital system networks, thereby enhancing the endogenous security of AI application systems; and advocating for the construction of the "Hyper-Converged Networks and Intelligent Computing Testbed" as a major scientific facility. This testbed will validate the scientific conjecture that "structure determines efficiency/security/diversity", providing support for building an independent knowledge system, advancing independent sci-tech innovation, and deepening reforms in self-reliant talent training. The study provides both a theoretical framework and technological pathways for the paradigm evolution of digital ecosystem networks in the AI era.
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