Cyber-Physic System is a new type of complex system which combines sensor network, communication system, computer network etc. The complicated structure of CPS will result in producing large amounts of data, so how to reduce the network load and to improve the energy utilization is a challenge. Data aggregation is a promising technique which can reduce the amount of data transmitted in network by converting the raw data to summary data, leading to the purpose of saving energy. Meanwhile, since the aggregated data contains more comprehensive information, it can greatly improve the analysis and decision-making ability in the network. In this paper, we ifrst applied data aggregation technology to the context of CPS, and proposed a new algorithm called STAC (Spatial-Temporal-Attribute Correlation) algorithm, combining spatial aggregation, temporal aggregation, and the most important attribute aggregation. STAC turn the large amounts of raw data into GI (general information) based on the spatial-temporal correlation at ifrst. Then according to the weighted Bayesian theory, the algorithm generated second aggregated data CI (comprehensive information) based attribute correlation of different types of data. The simulation results on NS2 show that the algorithm can effectively reduce packet transmission capacity, predict network event type accurately, and achieve intelligent closed-loop control in CPS.%信息物理系统CPS（Cyber-Physic System）是融合了传感器网络、通信系统、计算机网络等多种网络和技术的新型复杂系统。其结构复杂性决定了网络中会产生大量数据，如何减小网络负载，提高能量利用率是CPS面临的一项重要挑战。数据融合通过将信息综合化，减少在网络内传输的数据量，能够有效地降低网络负载，从而达到节省能量开支的效果。同时通过将各类原始数据融合成具有意义的概括数据，大大提高了网络的数据分析和决策能力。本文将数据融合技术应用于信息物理系统，基于数据的时空和属性特点，提出了同时包含时间融合、空间融合、属性融合的STAC(Spatial-Temporal-Attribute Correlation)算法。该算法基于时空相关性对原始数据进行首次融合，进而在加权贝叶斯理论的基础上，根据不同类型数据的属性相关性对数据进行二次融合，形成有综合意义的概括数据。基于NS2平台的仿真结果证明，该算法能够有效减少数据包传输量，并且能够准确预测CPS网络事件类型，实现CPS的智能闭环控制。
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