基于智能体的水利科技报告形式审查系统构建研究OA

Report review serves as a core link in project quality control.Traditional manual review methods face chal-lenges such as inefficiency and inconsistent standard implementation,while existing general-purpose review systems struggle to adapt to the multiple challenges posed by the complex structural characteristics,multidimensional review tasks,and domain knowledge dependencies inherent in water resources science and technology reports.To address this,this study proposed an agent-based formal review system architecture for such reports.Through agent-driven dynamic task planning and collaborative review mechanisms,adaptive parsing of complex report structures and multi-task orchestration were realized.A water resources domain knowledge enhancement mechanism was established based on LoRA fine-tuning and Retrieval-Augmented Generation(RAG)technology.A terminology knowledge base con-taining 27,005 specialized water resources terms and a computational relationship database were constructed,form-ing a command fine-tuning dataset with 10,358 samples.The system's review capabilities in tasks involving special-ized terminology,computational logic,and contractual consistency was enhanced.Benchmarked against human review results,the agent system achieved an average F1 score above 80%across eight types of review tasks.Experi-mental results demonstrate that this system enables fully intelligent processing for the formal review of water resources science and technology reports,significantly improving efficiency while ensuring review accuracy.The intelligent review system developed in this study provides standardized tool support for quality management of water resources project documentation and offers a reference technical pathway for the intelligent review of texts in specialized domains.