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To solve the problem of nondestructive diagnosis of coating aging of steel pipe piles and jackets in offshore wind power booster stations，a set of coating aging monitoring and defect damage automatic identification system was designed.By arranging electrochemical impedance monitoring sensors and cameras on the surface of the tower coating in the exposed area，splash area，tidal range area，and fully immersed area of the offshore booster station，monitoring the in-situ aging status of the coating and automatic identification of coating defects and damages was achieved.With the aid of data wireless transmission and cloud service network，the changes in aging status of coatings at different zones can be online monitored.Through the image recognition system and optimized algorithm，the damage defects on coatings in different areas were automatically inspected，and the evaluation indicators of coating defects degree were quantified.Moreover，machine learning was applied to improve the accuracy of damage identification.For unmanned offshore wind power piles，the combination of electrochemical non-destructive monitoring and visual damage identification was used to establish an coating aging online monitoring network，which could achieve graded management of coating aging status in key areas，provide early warning for areas with severe coating failure，remind personnel to repair in a timely manner，and assist in the efficient and safe operation and maintenance of offshore wind power.