Abstract: Large language models (LLM) are increasingly being applied in software engineering, but current automated code generation research primarily focuses on general-purpose functional code, lacking effective solutions for the specific requirements of embedded systems. We propose RD2ESC (requirements documents to embedded system code), a prompt-based fine-tuning method that enables LLMs to understand the complex relationships between embedded code and requirements documents, and constructs a multi-agent collaborative code generation framework capable of rapidly generating high-quality embedded code using requirements documents and reference code. Experimental results demonstrate that RD2ESC improves the Pass@1 metric from 0.15 to 0.71 compared with the GPT-4o baseline model, achieving a test pass rate of 0.75 and compilation pass rate of 0.96. Sensitivity analysis reveals that the method exhibits certain dependency on reference code quality, with Pass@1 declining from 0.68 to 0.47 under 10%~50% perturbation conditions and dropping to 0.25 without reference code, while still maintaining basic code generation capabilities. Ablation experiments confirm the synergistic effects among multi-agents, with the complete system demonstrating significant performance improvements compared with individual components. This research provides an effective technical framework for embedded code automatic generation, substantially enhancing embedded system development efficiency.