(untitled) (Informatica, 2025)
With the development of the power industry, ensuring the safety of live work is increasingly vital. However, traditional monitoring methods often struggle to identify and predict this abnormal behavior accurately. Therefore, this paper proposes a new technology to detect abnormal behavior in field work using the hidden Markov model. Introducing the hidden Markov model establishes a dynamic work behavior model to detect abnormal behavior. In the research, the behavior pattern of the field work process is analyzed first, and the factors affecting the change of behavior state are determined. Then, the hidden Markov model is used to build a state transition model of real-time work behavior, and the model is trained and optimized by historical data. The experiment found that the model can capture the dynamic behavior of workers in the field work and predict their next behavioral state. Many experiments and simulation results show that the abnormal behavior detection technology based on Hidden Markov Model can accurately identify abnormal behavior in the work process, provide timely warning, and ensure the safety and efficiency of fieldwork. In addition, the model is robust and scalable and can adapt to different working environments and operator changes. The abnormal behavior detection technology based on the hidden Markov model proposed in this paper not only improves the safety and efficiency of live work but also provides vital support for the sustainable development of the electric power industry. Through experimental verification, the detection accuracy of this method reaches more than 90%, the false alarm rate is controlled within 5%, and the average processing speed is 20 frames per second, which can meet the requirements of real-time detection. These indicators show that the technology has high reliability and effectiveness in the detection of abnormal behavior, and provides strong support for the safe and stable operation of the power system.