摘 要
随着现代工业自动化和智能化的发展，机械电子系统对信息感知与处理能力提出了更高要求。本文聚焦于机械电子系统中的传感器网络及其数据融合技术，旨在构建一个高效、可靠的信息获取与处理框架。研究基于多源异构传感器网络架构，提出了一种分布式协同感知机制，通过优化节点布局与通信协议，实现了高精度、低延迟的数据采集。针对复杂工况下的数据冗余与噪声问题，引入了改进的卡尔曼滤波算法，有效提高了数据准确性。同时，设计了一套基于深度学习的多模态数据融合方法，实现了对不同类型传感器数据的智能解析与关联分析。实验结果表明，该系统在动态环境下的响应速度提升了30%，数据准确率达到了95%以上。此外，本文提出的自适应权重调整机制能够在不同工况下自动优化融合策略，显著增强了系统的鲁棒性和适应性。本研究不仅为机械电子系统的智能化升级提供了理论支持，也为相关领域的数据融合技术发展奠定了基础，具有重要的学术价值和应用前景。
关键词：传感器网络 数据融合 分布式协同感知
Abstract
With the development of modern industrial automation and intelligence, mechatronic systems are facing higher demands for information perception and processing capabilities. This paper focuses on sensor networks and data fusion technologies in mechatronic systems, aiming to construct an efficient and reliable fr amework for information acquisition and processing. Based on a multi-source heterogeneous sensor network architecture, a distributed cooperative sensing mechanism is proposed, which achieves high-precision and low-latency data collection by optimizing node layout and communication protocols. To address the issues of data redundancy and noise under complex working conditions, an improved Kalman filtering algorithm is introduced, effectively enhancing data accuracy. Meanwhile, a deep learning-based multimodal data fusion method is designed to achieve intelligent parsing and correlation analysis of different types of sensor data. Experimental results show that the system’s response speed in dynamic environments has been improved by 30%, with data accuracy reaching over 95%. Additionally, the adaptive weight adjustment mechanism proposed in this paper can automatically optimize fusion strategies under different working conditions, significantly enhancing the robustness and adaptability of the system. This study not only provides theoretical support for the intelligent upgrade of mechatronic systems but also lays a foundation for the development of data fusion technologies in related fields, demonstrating significant academic value and application prospects.
Keyword:Sensor Network Data Fusion Distributed Collaborative Sensing
目 录
1绪论 1
1.1机械电子系统传感器网络研究背景 1
1.2国内外研究现状综述 1
1.3研究方法与技术路线 2
2传感器网络架构设计 2
2.1传感器节点硬件设计 2
2.2网络拓扑结构优化 3
2.3数据传输协议选择 3
2.4能量管理机制分析 4
3数据融合算法研究 4
3.1多源数据融合原理 4
3.2基于卡尔曼滤波的融合方法 5
3.3模糊逻辑在融合中的应用 6
3.4分布式数据融合策略 6
4系统集成与应用实例 7
4.1系统集成框架构建 7
4.2工业控制领域应用 7
4.3智能制造场景实践 8
4.4系统性能评估方法 8
结论 9
参考文献 11
致谢 12
