Because the more and more complex external environment affects the safe and stable operation of power metering equipment, this paper studies the state evaluation system of power metering equipment based on BP neural network. The hardware of the system includes multi-environment parameter acquisition module, signal processing module, data transmission module and error evaluation module. The software system includes systematically collecting detection data of power metering equipment, selecting ambient temperature, humidity, magnetic field strength and atmospheric pressure as state quantities of power metering equipment, and introducing BP neural network to perform iterative calculation of state quantities to realize automatic state evaluation of power metering equipment. The experimental results show that the shortest detection time of the system is 2s, and the detection results are consistent with the actual results, which verifies that the system has a high efficiency and accuracy of the equipment status detection.
KEYWORDS: Internet of things, Sampling rates, Matrices, Telecommunications, Sustainability, Power grids, Algorithms, Software development, Data transmission, Data storage
Intelligent applications with Internet of Things terminal as the core have penetrated into all aspects of social life. With the iterative development of Internet of Things technology, the needs of users and manufacturers may constantly change, and various software anomalies may be exposed after the deployment of Internet of Things devices in the field. How to effectively ensure the sustainable renewal of IoT devices after deployment is an urgent problem to be solved. In this paper, a lightweight firmware update method for distribution IoT terminals is proposed, which adopts differential upgrade method, only the difference between the new version and the old version needs to be upgraded, and the same upgrade effect can be achieved by transferring less data. The simulation results show that the proposed firmware update method has higher upgrade success rate and upgrade efficiency, and lower memory consumption rate.
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