With the development of society and the continuous improvement of residents' living standards, residents have higher and higher requirements for the technical level of power system. This paper mainly studies how to apply intelligent technology to the research of instrument electrical control. Nowadays, the traditional electrical instruments cannot meet the needs of the public, so the research on intelligent instruments is the most important problem at present. This article uses the RSA public key encryption algorithm based on the public blockchain. In the experimental results of this article, big data shows that from 2016 to mid-2017, there were 50 merchants using non-smart technologies in electrified dashboards in 2016. There are 40 merchants using smart technology in electrified dashboards, which is lower than the number of non-smart control technologies. However, in 2017, the number of merchants using non-intelligent control was 35, and by November 2017, it was 120, a decrease of 5% from 2016. The number of merchants using smart technology in electrified dashboards is increasing every month, from 45 in January 2016 to 285 in November 2017, an increase of 13% in the same year. This shows that intelligent control is more and more widely used in electrical automation instrument panels. Therefore, the research of a more intelligent control system of electrical automation instruments is a problem that has attracted much attention today.
The aging of the population gradually intensifies. The health status and medical problems of the elderly have aroused widespread concern in society. Wearable health monitoring system is a typical application of wearable computing in the medical field, which can achieve continuous and dynamic acquisition of human status under low physiological and psychological loads. Fall detection monitoring plays an important role in eldercare. This paper establishes a wearable health monitoring system for fall detection based on a three‐axis accelerometer. First, the acceleration signals are collected through a three‐axis accelerometer which is installed into a wearable device. Second, the collected acceleration signals are represented as 20 features, including mean of acceleration signal, SD of acceleration signal, coefficient Kurtosis, coefficient of skewness etc. Third, the acceleration signal features are used to learn a covariance‐guided one‐class support vector machine due to the difficulty to obtain fall acceleration signals. The experiments and simulations show the effectiveness of the proposed system for fall detection.
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