数字孪生 边缘云协同 钻探

This study provides a technology-level reference for the future development, implementation, and validation of intelligent O&M systems for high-end drilling equipment. Intelligent operation and maintenance systems for high-end drilling equipment provide crucial technological support for addressing the challenges posed by extreme environments in deep-sea and deep-earth drilling and are intended to facilitate the transition from traditional reactive maintenance toward predictive maintenance. This study adopts a narrative technology review approach combined with a conceptual framework synthesis. The review draws on 51 sources: 36 academic publications or conference papers, 7 technical standards or official technical documentation sources, 1 institutional technical report, and 7 company or platform documents. Searches covered Web of Science, Engineering Village, and Google Scholar. Sources were selected for their direct relevance to the review scope, technical representativeness, methodological or engineering value, and ability to support specific technical claims. The reference set primarily covers 2019–2026, while earlier sources were retained when they provided foundational concepts, established standards, or directly relevant engineering evidence. The review examines user requirements, system architecture, data acquisition, application scenarios, and functional modules in deep-sea and deep-earth drilling contexts; synthesizes key technologies including data integration and transmission, edge-cloud collaboration, digital twins, data-driven and physics-informed modelling, and intelligent O&M decision support; and compares representative industrial platforms and related O&M capabilities. The review identifies distinct technical requirements and challenges for the two application contexts, particularly regarding communication constraints, environmental adaptability, data acquisition, data governance, cybersecurity, and lifecycle validation. Based on the reviewed evidence and identified engineering requirements, a conceptual reference architecture integrating edge-cloud collaboration, digital-twin-based state representation, data-driven and physics-informed modelling, and intelligent O&M decision support is synthesized. The architecture is presented as a conceptual framework rather than an implemented or field-validated system and provides a technology-level reference for the future development, implementation, and validation of intelligent O&M systems for high-end drilling equipment.