Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis.Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction modelbased on long short-term memory (LSTM) is used to predict PM2.5 concentration, achieving ideal results.
With the economic and social development of China, the scale of the power grid continues to expand. Rapid location and diagnosis of power failures have become significant for China to maintain its stable development of power system. In recent years, the Internet of Things (IoT) based on 5G technology has been applied to power grid more widely. Meanwhile, given the fact that the blockchain is traceable and tamper-resistant, the combination of the blockchain and IoT is considered to locate power failures quickly and assist professional maintenance personnel to deduce the cause of failures, minimizing economic loss. With the foundation of IoT sensor node data, this paper designs a decentralized electronic certificate scheme based on blockchain and Interplanetary File System (IPFS) to collect data of each node of the power system and store it in the blockchain. The model of data sharding, storage and certificate optimizes the utilization of storage space of the blockchain, reducing the time required for system access to nodes. Traceability of data stored on blockchain data is employed to quickly and accurately trace faults of the power system, providing strong technical support for the safe and stable operation of China’s power system.
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