Abstract: In order to address the problem that the number of connected devices in the OGIoT(Oil and Gas IoT) has increased dramatically, resulting in insufficient computing power of the edge nodes in the EC ( Edge Computing) system, and it is difficult to effectively identify the service collapse caused by malicious attacks from other edge nodes, an EMLDI(Efficient Machine Learning method for Improved Data Contamination Detection of Oil and Gas IoT algorithm) is proposed, which solves the problem of fluctuating and inaccurate results of edge nodes due to their poor robustness, data distortion or mild qualitative changes. The problem of large and inaccurate edge node results due to robustness of edge nodes and data distortion or mild qualitative changes is solved. The network is trained by adding GN(Gaussian Noise) to the expanded data set through randomly selected batch samples, which enables the network to have broader data fitting and prediction capabilities, and solves the problem of systemic collapse due to the difficulty of implementing correct operations at the edge nodes when the data is severely corrupted. The algorithm is able to identify noise contaminated and random label contaminated samples more effectively and the algorithm achieves the best results within the specified training batches.

Key words: oil and gas iot, gaussian noise, data pollution, machine learning