As a product of the deep integration between next-generation information technology and industrial systems, digital twin technology has demonstrated significant advantages in real-time monitoring, predictive maintenance, and optimization decision-making for thermal power plants. To address challenges such as low equipment efficiency, high maintenance costs, and difficulties in safety risk management in traditional thermal power plants, this study developed a digital twin simulation system that covers the entire lifecycle of power generation units. The system achieves real-time collection and processing of critical parameters such as temperature, pressure, and flow rate through a collaborative architecture integrating multi-source heterogeneous sensor networks with Programmable Logic Controllers (PLCs). A three-tier processing framework handles data preprocessing, feature extraction, and intelligent analysis, while establishing a hybrid storage system combining time-series databases and relational databases to enable millisecond-level queries and data traceability. The simulation model development module employs modular design methodology, integrating multi-physics coupling algorithms including computational fluid dynamics (CFD) and thermal circulation equations. Automated parameter calibration is achieved through intelligent optimization algorithms, with model accuracy validated via unit-level verification, system-level cascaded debugging tests, and virtual test platform simulations. Based on the modular layout strategy, the user interface and interaction module integrates 3D plant panoramic view, dynamic equipment model and multi-mode interaction channel, supports cross-terminal adaptation of PC, mobile terminal and control screen, and improves fault handling efficiency through AR assisted diagnosis function.