Using Big Data and Financial Expense Method to Construct a Dynamic Investment Planning Calculation Model

This study aims to enhance the accuracy and flexibility of investment planning for power grid infrastructure projects. By leveraging big data analytics and financial expenditure methodologies, we've developed a dynamic investment planning model tailored to the complexities and ever-changing nature of managing such projects in today's era. To accomplish this, we've adopted an intelligent compilation and dynamic adjustment approach, rigorously validating our model's reliability through simulation experiments. Methodologically, we harness big data techniques to delve into project archives, pinpointing crucial factors influencing investment strategies. Integrating the financial expenditure method, we've forged a model capable of flexible investment plan adjustments. This model adaptively formulates and revises investment plans based on varying scenarios. To substantiate our model's practicality, we've conducted a series of meticulously designed simulation experiments. The outcomes reveal that, when compared to alternative methods, our dynamic model offers superior accuracy in projecting investment needs under standard circumstances. Moreover, the investment plans it generates align seamlessly with actual execution. Additionally, the model exhibits swift responsiveness in emergency situations, enabling timely investment plan adjustments and minimizing potential losses.