Aiming at the problem that traditional methods tend to have serious risks of underestimation or overestimation in the construction period prediction of hydropower projects during the rainy season

this paper intends to realize the scientific prediction of the construction period through the Monte Carlo model. Traditional methods rely on single-point estimation

making it difficult to handle random factors such as rainfall intensity and equipment failure

as well as the impact of risk superposition

which leads to deviations in construction period judgment. The research first sorts out key risk factors such as natural environment and construction resources

and assigns probability distributions to them based on historical data and expert judgment; then constructs a construction period calculation model that includes process dependencies and connection logic; conducts tens of thousands of random simulations using Python to generate a probability histogram and cumulative distribution curve of the construction period

which intuitively present the occurrence frequency and compliance probability of different construction periods. The results show that this method can effectively quantify uncertainties

accurately capture the impact of risk superposition

and provide construction period references for projects under multiple probability scenarios. Its application promotes the transformation of construction period management for hydropower projects during the rainy season from experience-based judgment to data-driven decision-making

improving the project's risk resistance and management scientificity.