摘 要
随着农业现代化进程的加速推进，智能农机具在农业生产中的应用日益广泛，其远程监控与故障诊断技术成为提升农机作业效率的关键环节。本研究针对当前智能农机具运行过程中存在的实时监控不足、故障诊断效率低下等问题，提出了一种基于物联网和人工智能技术的远程监控与故障诊断系统架构。研究首先构建了多源数据采集网络，通过传感器阵列实时获取农机运行状态参数；其次设计了基于深度学习的故障特征提取算法，实现了对复杂工况下农机异常状态的精准识别；最后开发了分布式故障诊断模型，结合边缘计算与云计算技术，显著提升了诊断效率和准确性。实验结果表明，该系统在典型农机作业场景下的故障识别准确率达到96.8%，平均响应时间较传统方法缩短62.3%。
关键词：智能农机具；远程监控；故障诊断；物联网
RESEARCH ON REMOTE MONITORING AND FAULT DIAGNOSIS TECHNOLOGY OF INTELLIGENT AGRICULTURAL TOOLS
ABSTRACT
With the acceleration of the process of agricultural modernization, the application of intelligent agricultural machinery and tools in agricultural production is increasingly extensive, and its remote monitoring and fault diagnosis technology has become a key link to improve the efficiency of agricultural machinery. In this paper, a remote monitoring and fault diagnosis system architecture based on the Internet of Things and artificial intelligence technology is proposed to solve the problems of insufficient real-time monitoring and low fault diagnosis efficiency during the operation of intelligent agricultural machinery. Firstly, a multi-source data acquisition network was constructed to obtain the operating parameters of farm machinery in real time through sensor array. Secondly, a fault feature extraction algorithm based on deep learning is designed to accurately identify the abnormal state of agricultural machinery under complex working conditions. Finally, a distributed fault diagnosis model is developed, which combines edge computing and cloud computing technology to significantly improve the diagnosis efficiency and accuracy. The experimental results show that the fault identification accuracy of the system is 96.8% and the average response time is reduced by 62.3% compared with the traditional method.
KEY WORDS：Intelligent agricultural machinery; Remote monitoring; Fault diagnosis; Internet of Things
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 智能农机具远程监控与故障诊断的研究意义 1
第2章 智能农机具远程监控系统架构设计 2
2.1 远程监控系统的总体框架 2
2.2 数据采集与传输模块设计 2
2.3 云平台架构与功能实现 3
第3章 智能农机具故障诊断关键技术研究 4
3.1 基于大数据的故障特征提取方法 4
3.2 多源信息融合的故障诊断模型构建 4
3.3 深度学习在故障预测中的应用 5
3.4 诊断系统的实时性与准确性优化 5
第4章 智能农机具远程监控与诊断系统实现与应用 7
4.1 系统硬件平台搭建与集成 7
4.2 软件系统开发与功能测试 7
4.3 典型应用场景分析与验证 7
4.4 系统性能评估与优化建议 8
第5章 结论 10
参考文献 11
致 谢 12
