This paper discusses the application of multi-parameter optimization techniques based on the Monte Carlo algorithm for IoT device access, specifically focusing on power grids, and effectively ensures the accuracy of device access debugging based on physical models. Considering the complexity of the IoT environment and the diversity of devices within power grids, traditional parameter tuning methods often face issues of inefficiency and insufficient flexibility, particularly in the context of power grids. By dynamically analyzing the operational status and business requirements of terminal devices, this study proposes a Monte Carlo-based adaptive access strategy tailored to optimize the interaction efficiency and access reliability between devices and business platforms within power grids. The effectiveness of this method in improving device access performance and stability is verified through simulation experiments that mimic grid conditions, providing a new solution for optimizing IoT device access in critical infrastructure settings. This approach is expected to ensure the accuracy and reliability of terminal device access debugging and optimization based on physical models, which is crucial for maintaining operational integrity and achieving reliable diagnostics in real-time applications, demonstrating significant implications for the reliability and accuracy of terminal device access within power grids.