AI Driven Risk Management Model for the Entire Lifecycle of Power Transmission and Transformation Projects

As the complexity of power transmission and transformation projects continues to increase, the risks they face are becoming more diverse and unpredictable. Traditional risk management methods can no longer meet the accuracy and efficiency requirements of modern power transmission and transformation projects. To address this problem, this paper introduces artificial intelligence technology and adopts a full life cycle risk management model based on AI (Artificial Intelligence), aiming to improve the accuracy and timeliness of risk prediction, assessment and management through intelligent means, thereby reducing engineering risks and improving safety. Firstly, this paper uses big data technology and LSTM (Long Short-Term Memory) algorithm to identify the risks in each life cycle stage of power transmission and transformation projects and builds a risk assessment model. Then, based on the clustering algorithm and reinforcement learning technology, an adaptive risk warning and control strategy is proposed, which can dynamically adjust the response measures according to the real-time monitoring data. Finally, combined with actual engineering cases, the effectiveness and feasibility of the model are verified. In each group of experiments, there are differences between the minimum and maximum response times, but the fluctuations are relatively stable. For example, when there are 5 samples, the response time fluctuated between 0.30 and 0.40 seconds. The AI-driven power transmission and transformation project risk management model proposed in this paper not only innovatively supplements traditional risk management methods in theory, but also verifies its advantages in practice.