Defect knowledge graph construction and application in multi-cloud IoT

Abstract

As the State Grid Multi-cloud IoT platform grows and improves, an increasing number of IoT applications generate massive amounts of data every day. To meet the demands of intelligent management of State Grid equipment, we proposed a scheme for constructing the defect knowledge graph of power equipment based on multi-cloud. The scheme is based on the State Grid Multi-cloud IoT architecture and adheres to the design specifications of the State Grid SG-EA technical architecture. This scheme employs ontology design based on a fusion algorithm and proposes a knowledge graph reasoning method named GRULR based on logic rules to achieve a consistent and shareable model. The model can be deployed on multiple clouds independently, increasing the system’s flexibility, robustness, and security. The GRULR method is designed with two independent components, Reasoning Evaluator and Rule Miner, that can be deployed in different clouds to adapt to the State Grid Multi-cloud IoT architecture. By sharing high-quality rules across multiple clouds, this method can avoid vendor locking and perform iterative updates. Finally, the experiment demonstrates that the GRULR method performs well in large-scale knowledge graphs and can complete the reasoning task of the defect knowledge graph efficiently.

Recurrent Neural Network (RNN) Inference Cloud Computing

“…It aims to increase the precision and effectiveness of healthcare services, particularly in places with limited access to medical information. The process for creating a faulty knowledge graph tailored for multi-cloud IoT systems is described by the authors in a study [21] Wenqing Yang and others(2022). It explains the process of extracting defect-related data from many clouds and Internet of Things devices, organizing it into a knowledge graph, and facilitating more effective defect identification, resolution, and knowledge sharing within the ecosystem.…”

A Deep Learning-Based Method for Preventing Data Leakage in Electric Power Industrial Internet of Things Business Data Interactions

“…Determine the fixed structure of sensitive data, 𝑅 𝑈 𝑖 1 = 𝑟 𝑈 𝑖 1 , 𝑟 𝑈 𝑖 2 , … . , 𝑟 𝑈 𝑖 𝑅 , for each specific user 𝑢 𝑖 .…”

“…With the accelerated advancement of technologies like cloud computing, artificial intelligence, and blockchain, the Industrial Internet of Things has emerged as a pivotal force driving the digital, networked, and intelligent evolution of industries, serving as a catalyst for societal progress. Among its vital branches, the Electric Power Industrial Internet of Things stands out for its dedication to seamlessly integrating Internet technology into the power system, aiming to foster intelligent and efficient power production, transmission, distribution, and management [1]. The blockchain-based Industrial Internet of Things (BIIoT) ensures the security and transparency of data through its distributed ledger and non-tamperable characteristics, while improving the data management and collaboration efficiency of the power system [2,3].…”

Lightweight similarity checking for English literatures in mobile edge computing

“…Specifically, with the advent of COVID-19 pandemic, people’s activity ranges are further limited. In this situation, mobile devices have gradually become one of the major ways to aid people’s various activities including academic research [ 8 – 10 ]. Today, more and more people are apt to take mobile devices as their major academic research tools and hence generate a series of mobile devices-enabled academic research tools such as online academic meeting, multi-party academic collaborations, etc.…”