用大模型+知识图谱提升工业园区规划决策能力
Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT
- 构建多模态工业区知识图谱，融合街景、企业、地理等数据
- 通过蒙特卡洛树搜索增强大模型推理，提升选址与功能规划精度
- 适合城市规划、智能决策研究者使用，推动园区智能化管理
工业园区是城市经济增长的关键。然而，其发展常因产业需求与城市服务不匹配而受阻，亟需科学规划与运营。本文提出IndustryScopeKG——首个大规模多模态、多层次的工业园区知识图谱，整合街景、企业、社会经济及地理信息，刻画园区内复杂关系与语义。同时，我们构建IndustryScopeGPT框架，利用大语言模型（LLMs）结合蒙特卡洛树搜索，强化工具增强型推理与决策能力，显著提升园区选址推荐与功能规划效果。该方法展示了将大模型与结构化数据结合在工业园区规划与运营（IPPO）中的潜力，为智能IPPO研究树立新基准，并为城市工业发展提供坚实基础。数据集与代码已开源：https://github.com/Tongji-KGLLM/IndustryScope。
原文摘要 · Abstract (English)
Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://github.com/Tongji-KGLLM/IndustryScope.
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