摘 要
粮食储藏安全是保障国家粮食安全的重要环节，传统粮情监测手段存在实时性差、精度低、人工成本高等问题，难以满足现代粮食仓储管理的需求。为此，本文针对现有智能粮情监测系统存在的数据采集不全面、预警能力弱及能耗较高等问题，开展了系统优化与应用分析研究。研究旨在通过引入多源异构传感器网络、改进数据传输协议和构建动态预警模型，提升系统的实时性、准确性和能动性。采用ZigBee与LoRa混合组网技术实现粮仓内部环境参数的高效采集，并结合边缘计算架构降低通信延迟；同时，提出基于机器学习的异常识别算法，增强系统对早期粮情变化的感知能力。实验结果表明，优化后的系统在温度、湿度等关键参数的检测误差分别降低了1.2%和3.5%，预警响应时间缩短了40%，整体能耗下降18%。实际应用验证显示，该系统可有效提升粮仓管理的智能化水平，具有良好的推广价值。本研究的主要创新在于构建了多模态感知与边缘智能协同的粮情监测框架，为智慧粮食仓储提供了新的技术路径和实践支撑。
关键词：智能粮情监测；多源异构传感器；边缘计算；机器学习；能耗优化
Abstract
Food storage security is a crucial component of national food security. Traditional methods for monitoring grain conditions suffer from poor real-time performance, low accuracy, and high labor costs, making them inadequate for meeting the demands of modern grain warehouse management. To address these challenges, this study conducts systematic optimization and application analysis to overcome limitations in existing intelligent grain condition monitoring systems, including incomplete data acquisition, weak early-warning capabilities, and high energy consumption. The research aims to enhance the system’s real-time responsiveness, accuracy, and proactivity by integrating a multi-source heterogeneous sensor network, improving data transmission protocols, and constructing a dynamic early-warning model. A hybrid networking approach combining ZigBee and LoRa technologies is employed to efficiently collect environmental parameters within granaries, while an edge computing architecture is adopted to reduce communication latency. Furthermore, an anomaly detection algorithm based on machine learning is proposed to strengthen the system’s ability to perceive early changes in grain conditions. Experimental results demonstrate that the optimized system reduces detection errors in key parameters such as temperature and humidity by 1.2% and 3.5%, respectively, shortens early-warning response time by 40%, and decreases overall energy consumption by 18%. Field applications confirm that the system significantly improves the intelligence level of granary management and holds strong potential for broader implementation. The primary innovation of this study lies in the development of a grain condition monitoring fr amework that integrates multi-modal sensing with edge intelligence, providing a new technical pathway and practical support for smart grain storage solutions.
Keywords: Intelligent Grain Condition Monitoring; Multi-source Heterogeneous Sensors; Edge Computing; Machine Learning; Energy Consumption Optimization
目 录
1绪论 1
1.1研究背景与现实意义 1
1.2国内外研究现状综述 1
1.3研究内容与技术路线 1
2智能粮情监测系统架构优化分析 2
2.1系统总体架构设计改进 2
2.2数据采集模块性能提升 2
2.3通信协议的优化策略 3
2.4系统稳定性与扩展性评估 3
3智能粮情监测数据处理与建模方法 4
3.1多源异构数据融合机制 4
3.2异常数据识别与修复方法 4
3.3温湿度预测模型构建 5
3.4数据驱动的粮情状态评估 5
4智能粮情监测系统的应用实践 5
4.1典型应用场景部署方案 6
4.2实际运行效果对比分析 6
4.3用户反馈与系统适应性评价 6
4.4成本效益与推广前景探讨 7
结 论 7
参考文献 9
致 谢 10
