Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913584640950272 |
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| author | Wang, Siqi Liang, Chao Gao, Yunfan Liu, Yang Li, Jing Wang, Haofen |
| author_facet | Wang, Siqi Liang, Chao Gao, Yunfan Liu, Yang Li, Jing Wang, Haofen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT Wang, Siqi Liang, Chao Gao, Yunfan Liu, Yang Li, Jing Wang, Haofen Artificial Intelligence Computers and Society Social and Information Networks I.2.0; I.2.7; H.3.3; H.4.0 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. |
| title | Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT |
| topic | Artificial Intelligence Computers and Society Social and Information Networks I.2.0; I.2.7; H.3.3; H.4.0 |
| url | https://arxiv.org/abs/2411.15758 |