Research on Real-Time Optimal Strategies Based on Multi-Method Integration

In a data-driven research environment, precise analysis of real-time optimal strategies (RTOs) is crucial for strategic formulation, and integrating multiple methods helps to better understand dynamic changes and promote corresponding trends. This paper proposed a real-time optimization strategy based on momentum function as the evaluation model to address this issue. The model adopted a weight method based on information entropy of discrete quantitative variables and constructed a momentum model based on a sigmoid form logistic function, with an accuracy rate of up to 80.00%. According to Spearman rank correlation analysis, it has been validated the strong correlation between the momentum model and the current state. Afterward, the support vector machine (SVM) regression model was used to optimize the model, and the average error was reduced by 4.25%. In the end, this paper selected the changing trends of certain significant indicators under different states and verified through the Analytic Hierarchy Process (AHP) and Grey Relational Analysis that the correlation degree can reach 92.90%, proving that the momentum model can effectively adjust the project state and achieve real-time optimization.