Agrivoltaic systems demonstrate tremendous potential in synergistic land resource utilization, but conflicts exist between power generation efficiency and crop lighting requirements. This thesis proposes a dynamic coupling agrivoltaic synergistic optimization method that comprises three integrated components. The approach develops a shadow analysis algorithm based on solar trajectory tracking technology to precisely quantify ground light resource distribution patterns under various environmental conditions. Additionally, it incorporates a minute-level precision dynamic photovoltaic power generation model that integrates real-time meteorological data to achieve continuous output power evaluation and system performance monitoring. Furthermore, the methodology establishes a comprehensive multi-objective optimization model that employs module tilt angle and array spacing as primary decision variables. This optimization framework systematically solves synergistic optimal solutions that simultaneously pursue power generation maximization and shadow depth minimization objectives through advanced Pareto front identification techniques. The integrated approach provides a robust foundation for designing efficient agrivoltaic systems that balance energy production and agricultural requirements. Validation demonstrates the method's reliable prediction capability, providing quantitative decision support for agrivoltaic system design and effectively addressing the balance between energy output and crop lighting requirements.