Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT

TL;DR

This paper presents IndustryScopeKG, a large-scale industrial park knowledge graph, and IndustryScopeGPT, a framework using LLMs and Monte Carlo Tree Search to improve urban industrial park planning and management.

Contribution

Introduction of IndustryScopeKG and IndustryScopeGPT, integrating multi-modal urban data with LLMs for enhanced industrial park decision-making.

Findings

Improved site recommendation accuracy

Enhanced functional planning capabilities

Set a new benchmark for IPPO research

Abstract

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.