The industrial Internet of Things (IIoT) has attracted enormous attention from academics and industries, which is a significant component of the future transformation of industrial systems. The analysis, understanding, and application of all kinds of fragmented data in IIoT is one of the critical technologies that need to be studied in the future. The identity resolution technology is the most promising technology to achieve this goal at present. Although many identity resolution systems based on domain name system (DNS) have been proposed, there are technical problems with a single root node causing system performance bottlenecks and slow resolution rates. In this paper, we focus on a widely used distributed structured peer-to-peer (P2P) object name system (ONS) and redesign a novel identity resolution system based on a Dual-Chord algorithm for IIoT. The support vector machine (SVM) algorithm is first introduced in the system designed in this paper to implement the clustering of the P2P network. And then, the Dual-Chord algorithm is proposed, which can get better performance than the classical Chord algorithm. This system resolves the mismatch between logical and physical addresses and improves the resolution rate of the system. Finally, through PeerSim network simulation software, this paper evaluates the simulation performance of the improved identity resolution system based on the Dual-Chord algorithm. Simulation results prove that the system proposed in this paper is a more efficient identity resolution system.

工业物联网（IIoT）引起了学术界和工业界的极大关注，是未来工业系统转型的重要组成部分。对工业物联网中各种碎片化数据的分析、理解和应用是未来需要研究的关键技术之一。身份解析技术是目前实现这一目标最有前途的技术。虽然已经提出了很多基于域名系统（DNS）的身份解析系统，但是存在单一根节点的技术问题，导致系统性能瓶颈和解析速度慢。在本文中，我们专注于广泛使用的分布式结构化对等 (P2P) 对象名称系统 (ONS)，并基于 IIoT 的双弦算法重新设计了一种新颖的身份解析系统。本文设计的系统中首次引入支持向量机(SVM)算法来实现P2P网络的聚类。然后提出了Dual-Chord算法，该算法比经典的Chord算法具有更好的性能。该系统解决了逻辑地址和物理地址不匹配的问题，提高了系统的解析率。最后，通过PeerSim网络仿真软件，对改进后的基于Dual-Chord算法的身份解析系统的仿真性能进行了评估。