摘要
物联网技术的快速发展使得传感器网络在众多领域得到广泛应用，而数据融合算法作为提高传感器网络性能的关键技术备受关注。本研究旨在针对物联网环境下传感器网络的数据融合问题进行深入探讨，以期实现更高效、精准的数据处理。通过对现有数据融合算法的分析发现，传统算法在面对复杂多变的物联网环境时存在诸多不足，如数据冗余度高、融合精度低等。为此，提出一种基于深度学习的自适应数据融合算法，该算法利用深度神经网络强大的特征提取能力，结合物联网场景特性，构建了包含数据预处理、特征提取、权重分配及融合决策四个模块的框架。这一成果不仅为解决物联网中传感器网络的数据融合难题提供了新思路，也为进一步推动物联网技术的发展奠定了理论基础，其创新之处在于将深度学习与物联网特性深度融合，开创性地提出了自适应融合策略，对促进相关领域研究具有重要意义。
关键词：物联网数据融合；深度学习；自适应融合算法；传感器网络；特征提取
Abstract
With the rapid development of Internet of Things technology, sensor networks have been widely used in many fields, and data fusion algorithm, as a key technology to improve the performance of sensor networks, has attracted much attention. The purpose of this study is to explore the data fusion of sensor networks in the Internet of Things environment in order to achieve more efficient and accurate data processing. Through the analysis of existing data fusion algorithms, it is found that traditional algorithms have many shortcomings in the face of complex and changeable Internet of Things environment, such as high data redundancy and low fusion accuracy. Therefore, an adaptive data fusion algorithm based on deep learning is proposed. The algorithm uses the powerful feature extraction capability of deep neural network and combines the characteristics of the Internet of Things to build a fr amework consisting of four modules: data preprocessing, feature extraction, weight allocation and fusion decision. This achievement not only provides a new idea for solving the data fusion problem of sensor networks in the Internet of Things, but also lays a theoretical foundation for further promoting the development of Internet of Things technology. Its innovation lies in the deep integration of deep learning and Internet of Things characteristics, and pioneering the adaptive fusion strategy, which is of great significance for promoting research in related fields.
Keywords：Internet Of Things Data Fusion; Deep Learning; Adaptive Fusion Algorithm; Sensor Network; Feature Extraction
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状 1
二、传感器网络数据融合基础理论 2
(一) 数据融合概念与层次 2
(二) 传感器网络架构分析 2
(三) 数据融合算法分类 3
三、关键数据融合算法研究 4
(一) 融合算法性能评估指标 4
(二) 基于概率统计的融合算法 5
(三) 基于人工智能的融合算法 5
四、数据融合算法优化策略 6
(一) 能耗优化机制设计 6
(二) 精度提升方法研究 7
(三) 实时性保障方案 8
结 论 9
参考文献 10
