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Research on an Adaptive Weighted Fusion Algorithm Under the Edge Computing Framework of Power Internet of Things

机译:电网边缘计算框架下自适应加权融合算法研究

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In the power system, each province has nearly millions of sensors, and a large amount of data is generated. In the process of multi-sensor data collection, there are many problems such as inaccurate monitoring accuracy, waste of collection nodes, and zero drift of the sensor itself, which affect the inaccurate data reporting of the central station and the inaccurate execution of feedback information. At the same time, the sensor cannot do it once Acquisition, multiple applications, especially in power distribution rooms, station areas and other areas with high environmental requirements. There are distribution automation, electricity information collection, and dozens of sensor terminals such as temperature and humidity, smoke, video, robots, etc. Highly accurate information collection and environment configuration. To this end, an improved adaptive weighted fusion algorithm is proposed to achieve accurate terminal collection and data fusion upload based on the edge side of the power IoT edge computing framework. The design scheme can improve the accuracy of terminal data collected in power distribution rooms, station areas and other regions. Simulation and experiment proves the effectiveness of the algorithm in terms of acquisition accuracy and data fusion.
机译:在电力系统中,每个省份具有近百万个传感器,并产生大量数据。在多传感器数据收集的过程中,存在许多问题,例如不准确的监视精度,收集节点浪费和传感器本身的零漂移,这影响了中央站的不准确的数据报告和反馈信息的不准确执行。与此同时,传感器一旦采集,多种应用,特别是在配电室,站点区域和具有高环境要求的其他区域。有配电自动化,电力信息收集和数十个传感器端子,如温度和湿度,烟雾,视频,机器人等高度准确的信息收集和环境配置。为此,提出了一种改进的自适应加权融合算法,以实现基于Power IoT边缘计算框架的边缘侧的准确终端收集和数据融合上传。设计方案可以提高电力分配室,站区域和其他地区收集的终端数据的准确性。模拟和实验证明了在采集精度和数据融合方面的算法的有效性。

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