多层概率决策的网络大数据协作融合算法

The network big data cooperative fusion algorithm based on multi layer probabilistic joint decision

为了改善网络大数据传输效率及其精度,降低网络数据传输负荷,基于多层概率网络模型和联合决策研究了一种网络大数据协作融合算法。首先,以复杂异构多层网络的数据采集与缓存为对象,以实时感知数据及其准确处理为优化目标,设计了一种多层概率联合决策模型。接着,通过主层-分层和信号强度进行网络大数据的多维描述,结合3步分解和三性融合,以逆变换去噪为驱动,提出了网络大数据协作数据融合算法。最后,实验和仿真结果表明,与实验统计值相比,所提算法在数据融合精度和效率等方面具有明显优势。

In order to improve the efficiency and accuracy of network large data transmission and reduce the network data transmis-sion load, a network large data fusion algorithm based on multilayer probabilistic network model and joint decision making is stud-ied. Firstly, based on the data acquisition and caching of complex heterogeneous multi-layer networks, a multi-level probabilistic joint decision model is designed, which takes real-time sensing data and its accurate processing as the optimization objective.Then, through the main layer stratification and the signal strength of multidimensional network big data description, combined with the three step decomposition and three fusion, driven by transform denoising, the network data collaboration data fusion algorithm is proposed. Finally, the experimental and simulation results show that the proposed algorithm has obvious advantages in terms of data fusion accuracy and efficiency compared with experimental statistics.