Research on deep reinforcement learning-based power equipment event extraction technique

Power equipment public opinion events can affect all aspects of the power industry, including equipment failures, power supply interruptions, environmental impacts, and policy changes. Through public opinion event extraction, electric power companies and related government agencies can monitor and analyze public concerns and feedbacks in real time, quickly respond to potential problems, improve equipment management and maintenance, increase power supply reliability, and reduce operational risks. In this paper, we propose a deep reinforcement learning-based public opinion event extraction framework for electric power equipment, and design a reward function based on the recognition of trigger words and the detection results of related event elements, and finally, through comparative experiments, we can see that the extraction results of the proposed framework are better, and it can meet the requirements of public opinion event extraction for electric power equipment.