檀巧斌,王芹,苏澈,肖瑶,庞杰.无人机食品物流的未来:机器学习与传感监测的数据融合[J].包装工程,2025,(7):93-106.
TAN Qiaobin,WANG Qin,SU Che,XIAO Yao,PANG Jie.Future of Food Logistics by Unmanned Aerial Vehicle:Data Fusion of Machine Learning and Sensor Monitoring[J].Packaging Engineering,2025,(7):93-106. |
| 无人机食品物流的未来:机器学习与传感监测的数据融合 |
| Future of Food Logistics by Unmanned Aerial Vehicle:Data Fusion of Machine Learning and Sensor Monitoring |
| 投稿时间：2024-12-13 |
| DOI：10.19554/j.cnki.1001-3563.2025.07.012 |
| 中文关键词: 无人机 食品物流 机器学习算法 传感监测 |
| 英文关键词:unmanned aerial vehicle food logistics machine learning algorithm sensor monitoring |
| 基金项目:“十四五”国家重点研发计划子课题(2023YFD2100601) |
| 作者 | 单位 |
| 檀巧斌 | 福建省食品工业协会，福州 350028 |
| 王芹 | 沧州市物资储备和粮油质检中心，河北 沧州 061000 |
| 苏澈 | 福建农林大学 食品科学学院，福州 350002 |
| 肖瑶 | 福建江夏学院 公共事务学院，福州 350002 |
| 庞杰 | 福建农林大学 食品科学学院，福州 350002 |
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| Author | Institution |
| TAN Qiaobin | Fujian Food Industry Association, Fuzhou 350028, China |
| WANG Qin | Cangzhou Strategic Reserves and Grain and Oil Quality Inspection Center, Hebei Cangzhou 061000, China |
| SU Che | College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China |
| XIAO Yao | College of Public Affairs, Fujian Jiangxia University, Fuzhou 350002, China |
| PANG Jie | College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China |
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| 中文摘要: |
| 目的 低空经济迅速崛起，无人机食品物流展现出广阔的发展前景，国家和地方政府纷纷为无人机配送提供有力的政策保障，旨在为培育低空经济新增长提供技术参考。方法 无人机食品物流通过集成先进的传感器和机器学习算法，实时监测飞行过程中的各种参数和周围环境的变化，确保安全性和可靠性。结果 本文聚焦于机器学习算法与传感监测数据融合技术的应用，通过深度整合这2种技术，无人机将能更全面地感知食品品质并做出智能决策，优化飞行路线，应对突发情况。结论 机器学习与传感监测数据融合显著提升无人机的配送效率，降低成本，为更多消费者提供不受地面交通限制的便捷配送服务。 |
| 英文摘要: |
| With the rapid rise of low-altitude economy, the food logistics by Unmanned Aerial Vehicles (UAVs) has shown broad prospects for development, and national and local governments have provided strong policy guarantees for delivery by UAVs. The work aims to provide technical reference for cultivating new growth of low-altitude economy. By integrating advanced sensors and machine learning algorithms, the food logistics by UAVs could monitor the changes of various parameters and surrounding environment in flight in real time to ensure safety and reliability. The focus was placed on the application of machine learning algorithm and sensor monitoring data fusion technology. By deeply integrating these two technologies, the UAVs would be able to perceive food quality more comprehensively, make intelligent decisions, optimize flight routes, and cope with emergencies. The data fusion of machine learning and sensor monitoring significantly improves the delivery efficiency of UAVs, reduces the cost, and provides more consumers with convenient delivery services that are not restricted by ground traffic. |
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