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An Intelligent Monitoring System for the Safety of Building Structure under the W2T Framework

机译:W2T框架下的建筑结构安全智能监控系统

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Monitoring systems for the safety of building structure (SBS) can provide peoplewith important data related to main supporting points in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring a challenge for data management and data mining. In order to meet this challenge, under the framework of Wisdom Web of Things (W2T), we design a monitoring system for the SBS by using the semantic andthe multisource data fusion technologies. This system establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world (computers) and provides various services in the monitoring process to alleviate engineers’ workload. Furthermore, all data in the cyber world are organized as the raw data, the semantic information, and themultisource knowledge. Based on this organization, we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered by the semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e., inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoning is conducted to provide personalized service.
机译:建筑结构安全监控系统(SBS)可以为人们提供与建筑物主要支撑点有关的重要数据,然后帮助人们制定合理的维护计划。但是,越来越多的数据给数据管理和数据挖掘带来了挑战。为了应对这一挑战,我们在智慧物联网(W2T)的框架下,利用语义和多源数据融合技术设计了SBS监视系统。该系统在物理世界(建筑物),社交世界(人类)和网络世界(计算机)之间建立动态数据循环,并在监视过程中提供各种服务以减轻工程师的工作量。此外,网络世界中的所有数据都组织为原始数据,语义信息和多源知识。基于此组织,我们可以从时间,空间和多传感器的角度集中精力进行数据融合。最后,从样本性能和时间消耗方面对基于语义平台LarKC的原型系统进行了测试。特别地,通过多源知识的融合来检测噪声数据(即,不一致,异常或错误数据),并且进行一些基于规则的推理以提供个性化服务。

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