【摘要】 随着人工智能技术的快速发展，培养适应数字化时代需求的高素质人才已成为新工科教育改革的核心任务。为推进“人工智能+高等教育”的深度融合与实践探索，设计了球磨机负荷识别的创新型教学实验，内容涵盖实验平台搭建、信号采集与处理以及深度学习算法应用。实验采用项目驱动式教学模式与分层递进的教学策略，通过部署和优化人工智能算法，整合线上线下资源，并设置个性化、差异化的选择学习路径，以满足不同学生的学习需求。该实验有助于学生深入理解人工智能技术在矿冶装备中的具体应用，促进跨学科知识融合，提升了人才培养与矿冶企业人才需求之间的契合度，为地方行业高校在新工科背景下实施人工智能赋能高等教育提供了可行路径。更多还原

【Abstract】 With the development of artificial intelligence technology, cultivating high-level talents that meet the needs of the digital age becomes the core task of the new engineering education reform. To further deepen the exploration and practice of “artificial intelligence + higher education”, an innovative teaching experiment for ball mill load identification was designed, including the construction of an experimental platform, signal acquisition and processing, and the application of deep learning algorithms. In the experiment, artificial intelligence algorithms were deployed and optimized, and a project-driven teaching model and a hierarchical progressive teaching strategy were adopted. Online and offline resources were integrated, and personalized and differentiated learning paths were set up to meet the learning needs of different students. This enables students to deeply understand the application of artificial intelligence technology in mining and metallurgical equipment, promote the integration of interdisciplinary knowledge, and improve the match between talent cultivation and the talent demands of mining and metallurgical enterprises. It provides an effective path for local industry universities to explore the empowerment of higher education by artificial intelligence in the context of new engineering.更多还原