Aiming at solving network delay caused by large chunks of data in industrial Internet of Things, a data compression algorithm based on edge computing is creatively put forward in this paper. The data collected by sensors need to be handled in advance and are then processed by different single packet quantity K and error threshold e for multiple groups of comparative experiments, which greatly reduces the amount of data transmission under the premise of ensuring the instantaneity and effectiveness of data. On the basis of compression processing, an outlier detection algorithm based on isolated forest is proposed, which can accurately identify the anomaly caused by gradual change and sudden change and control and adjust the action of equipment, in order to meet the control requirement. As is shown by experimental simulation, the isolated forest algorithm based on partition outperforms box graph and K-means clustering algorithm based on distance in anomaly detection, which verifies the feasibility and advantages of the former in data compression and detection accuracy.

中文翻译：

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基于孤立森林算法和数据压缩的工业物联网传感器异常检测

针对工业物联网中大数据量引起的网络时延问题，提出了一种基于边缘计算的数据压缩算法。传感器采集到的数据需要事先处理，然后通过不同的单个数据包数量K和错误阈值e进行多组比较实验处理，在确保实时性和有效性的前提下，大大减少了数据传输量。数据。在压缩处理的基础上，提出了一种基于孤立森林的离群值检测算法，该算法可以准确识别出由渐进变化和突然变化引起的异常，并对设备的动作进行控制和调整，从而满足控制要求。如实验模拟所示，