The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

翻译：人工智能、信息物理系统以及跨企业数据生态系统的融合，已将工业智能推向前所未有的规模。然而，数据层、服务层和知识层之间缺乏统一的信任基础，削弱了实际部署中的可靠性、可问责性及法规遵从性。尽管现有综述分别探讨了数据治理、服务编排和知识表示等孤立方面，但尚未有研究提供一个针对工业环境定制的、跨层的整体可信性视角。为弥合这一差距，本文提出 \textsc{Trisk}（可信工业数据-服务-知识治理），这是一个面向可信工业智能的新型概念与分类学框架。基于一个五维信任模型（质量、安全、隐私、公平性和可解释性），\textsc{Trisk} 沿三个正交维度统一了 120 余项代表性研究：治理范围（数据、服务和知识）、架构范式（集中式、联邦式或边缘嵌入式）以及使能技术（知识图谱、零信任策略、因果推断等）。我们系统分析了信任如何在数字层间传递，识别了语义互操作性、运行时策略执行以及运营技术/信息技术对齐方面的关键差距，并评估了当前工业实施的成熟度。最后，我们阐述了面向工业 5.0 的前瞻性研究议程，倡导一种将可验证的信任语义嵌入工业智能栈每一层的集成化治理架构。本综述既可作为研究者的基础性参考，也可作为工程师在复杂多利益相关方环境中部署可信人工智能的实用路线图。