摘 要
随着工业化的快速发展，工厂噪声污染问题日益严重，不仅对周边环境造成影响，还可能威胁到作业人员的健康。为此，本研究旨在设计一种基于人工智能技术的工厂噪声实时监测与预警系统，以实现对噪声数据的高效采集、智能分析和及时预警。该系统结合了深度学习算法和物联网技术，通过部署高灵敏度的声学传感器网络，能够对工厂内部及周边区域的噪声进行全天候监测。在数据处理方面，采用卷积神经网络（CNN）模型对噪声信号进行特征提取与分类，同时引入长短期记忆网络（LSTM）对噪声趋势进行预测。实验结果表明，该系统能够在复杂噪声环境下准确识别异常声音源，并提前发出预警信号，其分类准确率超过95%，预测误差低于5%。此外，系统还具备自适应学习能力，可随着数据积累不断优化性能。本研究的主要创新点在于将AI技术与噪声监测深度融合，突破了传统方法在精度和时效性方面的局限，为工厂噪声管理提供了智能化解决方案，具有重要的实际应用价值和推广前景。
关键词：工厂噪声监测；人工智能技术；卷积神经网络；长短期记忆网络；实时预警系统
ABSTRACT
With the rapid development of industrialization, factory noise pollution has become increasingly severe, not only affecting the surrounding environment but also potentially threatening the health of workers. To address this issue, this study aims to design a real-time monitoring and early warning system for factory noise based on artificial intelligence technology, enabling efficient data collection, intelligent analysis, and timely warnings. By integrating deep learning algorithms with Internet of Things (IoT) technology, the system deploys a network of high-sensitivity acoustic sensors for 24/7 monitoring of noise levels both inside and around factories. In terms of data processing, a Convolutional Neural Network (CNN) model is employed for feature extraction and classification of noise signals, while a Long Short-Term Memory (LSTM) network is introduced to predict noise trends. Experimental results demonstrate that the system can accurately identify abnormal sound sources in complex noise environments and issue early warning signals, achieving a classification accuracy exceeding 95% and a prediction error below 5%. Moreover, the system possesses adaptive learning capabilities, continuously optimizing its performance as more data is accumulated. The primary innovation of this research lies in the deep integration of AI technology with noise monitoring, overcoming the limitations of traditional methods in terms of precision and timeliness, and providing an intelligent solution for factory noise management with significant practical application value and broad prospects for promotion.
Keywords: Factory Noise Monitoring; Artificial Intelligence Technology; Convolutional Neural Network; Long Short-Term Memory Network; Real-Time Early Warning System
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 工厂噪声监测的研究背景与意义 1
1.2 基于AI的噪声监测系统研究现状 1
1.3 本文研究方法与技术路线 2
第2章 系统需求分析与设计框架 3
2.1 工厂噪声监测的需求分析 3
2.2 AI技术在噪声监测中的适用性评估 3
2.3 系统功能架构设计 4
2.4 数据采集与处理流程设计 4
第3章 核心算法与模型构建 6
3.1 噪声数据特征提取方法 6
3.2 AI模型选择与优化策略 6
3.3 实时预警算法的设计与实现 7
3.4 模型训练与验证过程 7
第4章 系统实现与性能评估 9
4.1 系统开发环境与工具选型 9
4.2 系统模块实现与集成测试 9
4.3 性能评估指标体系设计 10
4.4 实验结果分析与改进方向 10
结论 12
参考文献 13
致 谢 14
