To address the rapid decision-making needs in the regulation of large-scale flexible load resources

a fast decision-making method for precise control is proposed

aimed at enhancing the efficiency and accuracy of power load regulation and ensuring optimal resource allocation. A fundamental framework of a decision support system is introduced

and a comprehensive mathematical model is constructed based on actual power load data. By employing genetic algorithms for multi-generational iterative optimization and integrating linear regression models

optimal strategies for power load regulation are explored. Throughout the research process

model parameters are continuously optimized to achieve precise control of load regulation. The study reveals that the load adjustment coefficient optimized by the genetic algorithm falls within the range of 0.4 to 0.8

which can effectively regulate users' power load demand. The linear regression model predicts a user load completion rate of 95% to 99%

demonstrating high goodness of fit and predictive accuracy

further validating the effectiveness of the proposed regulation method.