Image recognition and geographic information integration of power equipment sample delivery monitoring technology
To improve the transparency and safety of the sample sending process,prevent illegal interference and damage to power equipment,and optimize the accuracy and quality of the sample sending process,this paper proposes a power equipment sampling monitoring technology based on image recognition and geographic information.This technology uses image feature extraction technology based on deep learning algorithms for target recognition in video surveillance,and quickly obtains target features and their relative positions through SAR,achieving recognition of sampled objects and full process monitoring.At the same time,the use of elliptic curve technology to construct a video encryption strategy based on geographic location information has achieved security protection for surveillance videos.The validation analysis results using power equipment sampling videos as data samples show that the relevant indicators of the proposed detection method are improved by about 10% compared to similar methods,both of which remain above 85% .It can accurately monitor the entire sampling process and analyze and process position change information during the sampling process,with higher security.
power equipment sample deliverydeep learningimage recognitiongeographic informationdata encryptionelliptic curve
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