冠心病患者血运重建术后规范化地进行康复训练，是改善患者心功能、提高运动耐力、改善预后的重要治疗方式。然而目前的心脏康复方式存在着训练场景单一、个体化方案把控不准、居家锻炼执行力缺乏等问题。近年来，随着人工智能技术的不断创新与成熟，具备中山特色的元宇宙虚实融合平台为打破康复训练瓶颈提供了新思路。本文依托元宇宙沉浸式交互技术与穿戴式动态心电监测技术，拟构建适配冠心病患者血运重建术后规范化心脏康复的一体化干预应用体系，并明确该应用体系的实施流程、风险管控标准与临床适配规范。通过“体系构建、场景模块化设计、循证指南对标、教学分层建模”四法并驱的方式进行探讨。整体冠心病术后康复构架设计将分为虚拟现实（virtual reality，VR）沉浸式运动训练模块、穿戴式动态心电实时采集模块、人工智能（artificial intelligence，AI）心电风险预警模块三大核心单元。根据冠心病患者术后功能状态划分康复分期，并以此建立个体化康复场景、分级运动干预标准、心电异常分级判定体系及应急处置流程。同时结合康复科临床教学需求，依托“复旦中山惠生智育”探讨可落地的专科教学转化模式，为后续临床正式开展该新型康复方案提供完整理论框架、技术路径与实践依据。

Standardized rehabilitation training for patients with coronary artery disease following coronary revascularization surgery is a core therapeutic strategy to improve postoperative cardiac function, boost exercise tolerance and optimize long-term prognosis. However, mainstream cardiac rehabilitation methods currently suffer from three prominent limitations: monotonous training scenarios, poorly individualized regimen titration, and poor adherence to home-based exercise regimens. In recent years, alongside iterative advances and technical maturation in artificial intelligence (AI), the Zhongshan-specific metaverse platform featuring virtual-physical integration has offered novel insights into overcoming bottlenecks in cardiac rehabilitation training. Leveraging metaverse-based immersive interactive technology and wearable dynamic electrocardiogram (ECG) monitoring technology, this study aims to develop an integrated intervention system tailored to standardized cardiac rehabilitation for coronary artery disease patients post coronary revascularization. It further defines the system's implementation workflow, risk control protocols and clinical eligibility criteria. The research adopts four methodological approaches: system architecture development, scenario-based modular design, alignment with evidence-based clinical guidelines, and hierarchical competency-based teaching modeling. The postoperative cardiac rehabilitation framework consists of three core functional modules: virtual reality (VR) immersive exercise training, wearable real-time dynamic ECG data acquisition, and AI-powered ECG risk early warning. Rehabilitation phases are stratified according to patients'postoperative functional capacity. On this basis, we established individualized immersive rehabilitation scenarios, graded exercise intervention thresholds, a tiered judgment system for ECG abnormalities, and standardized emergency response workflows. Meanwhile, to meet clinical teaching demands in cardiac rehabilitation departments, this study explores a sustainable specialty teaching transformation model supported by the Fudan Zhongshan Huisheng Intelligent Education platform. The findings will provide a comprehensive theoretical framework, technical roadmap and empirical evidence for the large-scale clinical translation of this innovative cardiac rehabilitation protocol.