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Given the limitations of traditional monitoring methods in assessing the temperature of overhead line connectors in complex environments, a new approach is proposed: the CNN-DQN algorithm, which combines Convolutional Neural Networks (CNN) and Deep Q-Networks (DQN). The method extracts spatial features of temperature data through CNN and inputs these features into a Markov decision process (MDP) model using DQN to learn and optimize connector temperature monitoring strategies. Experimental results show that the CNN-DQN algorithm has a success rate of 93% in temperature detection and early warning. Compared with traditional models, it has significantly improved accuracy, stability and robustness. 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