使用计算机视觉和LLM-RAG的LEED认证自动化集成平台
An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
AI总结：
本文提出一个LEED认证自动化集成平台，结合计算机视觉、EnergyPlus与Gemma3-RAG框架，实现文档分析、能耗建模与报告生成，自动化覆盖率达82%，文档时间减少70%。
摘要：
能源与环境设计先锋（LEED）认证过程的特点是数据处理、模拟和文档编制的劳动密集型要求。本文提出了一个旨在简化LEED认证关键环节的自动化平台。该平台集成了基于PySide6的用户界面、用于流程编排的Review Manager，以及用于积分合规性、通过EnergyPlus进行能耗建模和基于位置评估的多个分析引擎。关键组件包括用于文档分析的基于OpenCV的预处理流水线和由Gemma3大语言模型结合检索增强生成（RAG）框架驱动的报告生成模块。本文详细介绍了实现技术——包括用于文档分析的计算机视觉、结构化LLM提示设计以及基于RAG的报告生成。试点项目部署的初步结果显示，与传统手动工作流相比，效率和准确性均有所提升，实现了82%的自动化覆盖率，文档编制时间最多减少70%。该平台展示了绿色建筑认证自动化的实用可扩展性。
英文摘要：
The Leadership in Energy and Environmental Design (LEED) certification process is characterized by labor-intensive requirements for data handling, simulation, and documentation. This paper presents an automated platform designed to streamline key aspects of LEED certification. The platform integrates a PySide6-based user interface, a review Manager for process orchestration, and multiple analysis engines for credit compliance, energy modeling via EnergyPlus, and location-based evaluation. Key components include an OpenCV-based preprocessing pipeline for document analysis and a report generation module powered by the Gemma3 large language model with a retrieval-augmented generation framework. Implementation techniques - including computer vision for document analysis, structured LLM prompt design, and RAG-based report generation - are detailed. Initial results from pilot project deployment show improvements in efficiency and accuracy compared to traditional manual workflows, achieving 82% automation coverage and up to 70% reduction in documentation time. The platform demonstrates practical scalability for green building certification automation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
