面向多模态认知与具身决策的“文录”大脑系统：通用大模型与行业知识深度融合的安全化新架构

s artificial intelligence rapidly penetrates diverse industries and application scenarios, a critical challenge in constructing next-generation intelligent systems lies in effectively integrating the linguistic comprehension capabilities of general-purpose large models with domain-specific knowledge bases for complex real-world applications. This paper proposes "Wenlu," an embodied brain system based on multimodal cognitive decision-making, designed to achieve secure integration of private knowledge with public models, unified processing of multimodal data (e.g., images and speech), and closed-loop decision-making from cognition to automated hardware code generation. By employing a brain-inspired memory tagging and replay mechanism, the system organically combines user-private data, domain-specific knowledge, and general language models to deliver precise and efficient multimodal services for enterprise decision support, medical analysis, autonomous driving, and robotic control. Compared to existing solutions, "Wenlu" demonstrates superior capabilities in multimodal processing, privacy preservation, end-to-end hardware control code generation, as well as self-learning and sustainable updating, thereby establishing a foundation for next-generation intelligent cognitive systems.