基于AHP-DS异质数据融合的建筑空间占用感知算法

Building Space Sensing Algorithm Based on AHP-DS Heterogeneous Data Fusion

智慧建筑智能控制系统旨在优化建筑物的建筑空间舒适度及能源分配,近年来受到了广泛关注,而建筑空间占用信息对智慧建筑管理系统的决策具有重要作用。现有建筑空间占用感知方法尚未针对如何融合具有不确定性、冲突性的多源数据问题提出有效解决方案,基于此,本文将D-S证据理论引入建筑空间占用预测中,并构建了基于建筑空间占用检测的mass函数确保D-S证据理论在该应用中的性能。另外,本文引入层次分析法(Analytic Hierarchy Process,AHP)解决多源异构数据源间的冲突所导致的建筑空间占用误测的问题,以提升感知精准度和系统性能。

The intelligent control system of smart buildings aims to optimize the comfort and energy distribution of building space,and has received widespread attention in recent years.Building space occupancy information plays an important role in the decision-making of smart building management systems.The existing methods for perceiving building space occupancy have not yet proposed effective solutions to the problem of integrating multi-source data with uncertainty and conflict.Based on this,this paper introduces D-S evidence theory into building space occupancy prediction and constructs a mass function based on building space occupancy detection to ensure the performance of D-S evidence theory in this application.In addition,this article introduces the Analytic Hierarchy Process(AHP)to solve the problem of mismeasurement of building space occupancy caused by conflicts between multiple heterogeneous data sources,in order to improve perception accuracy and system performance.