构建工程人工智能系统框架，解决领域应用中的方法缺失与落地难题
Engineering Artificial Intelligence: Framework, Challenges, and Future Direction
- 提出ABCDE五大核心要素与八层一体化框架
- 系统梳理开发瓶颈并指明八个未来研究方向
- 适合工程领域AI开发者与技术决策者参考
过去十年，人工智能与机器学习在工程领域的应用日益广泛，展现出数据驱动场景下的巨大潜力。然而，工程问题的复杂性和多样性往往需要特定领域的AI解决方案，而这些方案的开发常受限于缺乏系统化方法、可扩展性与鲁棒性。为弥补这一空白，本文提出以ABCDE作为工程AI的核心要素，并构建包含八层结构的统一、系统性工程人工智能生态系统框架，涵盖各层属性、目标与应用场景，以指导特定工程需求的AI解决方案开发与部署。同时，本文深入分析关键挑战，并明确指出八个未来研究方向。通过提供全面视角，旨在推动AI的战略化实施，促进下一代工程AI解决方案的发展。
原文摘要 · Abstract (English)
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
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