摘要基于传感器的监测系统使用多个传感器来识别信息，这些信息是通过处理底层传感媒体流获得的，传感媒体流通常是含有噪声和不精确的，导致很多错误的结果，比如数据不正确、数据不完整、数据不一致等。62974

因此，需要一种机制来计算基于传感器的信息质量，来帮助用户或系统做出决策和提高自动化监控效率。在本文中，以智慧城市管理为应用背景，以倾角仪数据为研究对象，研究一种高效的基于传感器观测的数据质量评估方法。本文描述了多传感器多媒体监控系统方面的信息质量，度量了信息的准确性、确定性、和及时性。本文采用线性加权融合的方法获取目标信息，计算参与观察的传感器的信息质量属性，帮助用户或系统更好的做出决策。最后，本文实现了所提出的数据质量评估算法，并通过实验验证该算法的性能。

毕业论文关键词：传感器 数据质量 质量评估

毕业设计说明书（论文）外文摘要

Title        The data quality evaluation that under the background of Wisdom city 

Abstract

Current sensor-based monitoring systems use multiple sensors in order to identify high-level information based on the events that take place in the monitored environment. This information is obtained through low-level processing of sensory media streams, which are usually noisy and imprecise, leading to many undesired consequences such as incorrect data or incomplete data, inconsistent data.

Therefore, we need a mechanism to compute the quality of sensor-driven information that would help a user or a system in making an informed decision and improve the automated monitoring process. In this article, with wisdom city management as the application background, the inclinometer data as the research object, researching a kind of efficient data quality evaluation method based on sensor observations.And we propose a model to characterize such quality of information in a multisensor multimedia monitoring system in terms of certainty, accuracy/confidence and timeliness. Our model adopts a multimodal fusion approach to obtain the target information and dynamically compute these attributes based on the observations of the participating sensors. Finally, the proposed data quality assessment algorithm has been implemented in this paper, and the performance of the algorithm is verified by experiment.

Key words: sensors  data quality  the quality evaluation

1. 绪论 1

1.1 研究背景 1

1.2 研究意义 2

1.3  相关技术的发展研究现状 4

1.3.1 基于传感器的信息质量测量 4

1.3.2非感官信息的质量测量 6

1.4 本文研究内容及篇章结构 7

1.4.1 研究内容 7

1.4.2 篇章结构 7

2. 物联网感知应用中检测事件的通用框架 9

2.1 通用事件检测框架 9

2.2 相关概念 10

3. 数据质量度量模型 10

3.1 问题形式化 11

3.2 建立模型 11

3.2.1 模拟质量属性 12

3.2.2信息质量的聚合机制 16

4.算法实现 18

4.1 确定性算法实现