【摘要】 在工业4.0引领产业深度变革的背景下，食品工业正由传统制造向智能制造加速转型。面对消费者对食品品质安全、营养个性化与全链条可追溯的迫切需求，以数据驱动、智能感知、智能决策、精准执行为核心技术的智能装备已成为重塑食品产业竞争力的关键引擎。系统界定了食品智能装备的定义与六大核心特征，即多维感知与数据驱动、智能决策与自主调控、精准控制与高效生产、互联互通与协同运作、全生命周期追溯与安全保障、柔性生产与个性化适配;重点介绍了智能装备在智能分选、智能切分、智能发酵、智能杀菌等食品加工关键工序以及智慧厨房、食品加工机器人等典型信息物理系统的产业化应用与技术进展;剖析了多模态传感、机器视觉、光谱分析、人工智能、数字孪生等关键技术的应用成效;指出了当前食品智能装备仍面临的食品物料异质性干扰大、传感器稳定性不足、多源异构数据融合困难、数字孪生建模精度与实时性受限等现实挑战;展望了多模态传感融合、AI与数字孪生深度集成、绿色智能装备研发及全链条可信协同等发展趋势。研究表明，食品智能装备可显著提升生产效率、保障产品质量安全、降低资源消耗并满足柔性化与个性化生产需求，为食品工业高质量、绿色化、智能化转型提供重要技术支撑。更多还原

【Abstract】 Against the background of Industry 4. 0 and the in-depth reform of industry,the food industry is accelerating its transformation from traditional manufacturing to intelligent manufacturing. Faced with the strong demand of consumers for food quality and safety, personalized nutrition and full-chain traceability, intelligent equipment characterized by data-driven, intelligent perception, intelligent decision-making and precise execution has become the key engine to reshape the competitiveness of the food industry. The definition and six core characteristics of food intelligent equipment were systematically defined, namely multi-dimensional perception and data-driven, intelligent decision-making and autonomous regulation, precise control and efficient production, interconnection and collaborative operation,full-life-cycle traceability and safety guarantee,and flexible production and personalized adaptation. The industrial application and technical progress of intelligent equipment in typical food processing scenarios such as intelligent sorting of raw materials, intelligent cutting, intelligent fermentation,intelligent sterilization,as well as typical cyber-physical systems such as smart kitchen and food processing robots were mainly reviewed,and the application effects of key technologies including multi-modal sensing,machine vision,spectral analysis,artificial intelligence and digital twin were analyzed. The current development of intelligent food equipment was pointed out to still confront with practical challenges, such as serious interference caused by the heterogeneity of food materials,insufficient stability of sensors, difficulties in multi-source heterogeneous data fusion, and limited accuracy and real-time performance of digital twin modeling. The development trends including multimodal sensing fusion,deep integration of AI and digital twin,research and development of green intelligent equipment, and full-chain trusted collaboration were prospected. Studies showed that intelligent food equipment could significantly improve production efficiency,ensure product quality and safety,reduce resource consumption,and meet the requirements of flexible and personalized production,providing important technical support for the high-quality,green and intelligent transformation of the food industry.更多还原