通过城市空间感知来诊断工业园区的创新氛围:一种多式联络型的大型语言模型方法
Xinyuan Chen1, Zhongying Song2, Li Xu2
1School of Urban Design, Wuhan University, Wuhan, 430072, China.
Scientific reports
|December 25, 2025
概括
本研究介绍了一种数据驱动的框架,使用多式大型语言模型 (MLLMs) 来评估工业园区的创新氛围. 该MLLM方法准确地诊断了空间活力和利益相关者的感知,为城市规划提供了一个新的工具.
科学领域:
- 城市规划和空间分析
- 人工智能和数据科学 人工智能和数据科学
- 人与环境的互动 互动
背景情况:
- 评估工业园区创新氛围的传统方法是主观的,是经验驱动的.
- 需要客观,数据驱动的方法来理解城市空间活力和经济活力.
- 多模式大语言模型 (MLLMs) 提供了解释复杂城市数据的潜力.
研究的目的:
- 开发和验证一个新的,数据驱动的框架,用MLLM来诊断工业园区的创新氛围.
- 量化工业园区内不同利益相关者群体的主观空间感知.
- 为城市规划和创新空间管理提供一个客观和自动化的工具.
主要方法:
- 利用多式大型语言模型 (MLLMs) 来解释来自中国武汉工业园区的多源城市数据.
- 经过验证的MLLM诊断准确性与专家评估相对应.
- 模拟各种利益相关者的观点,以量化感知异质性和分析空间活力.
主要成果:
- 双子座-2.5-pro模型显示了与专家判断的显著相关性 (r=0.890,p<0.001),证实了框架的可行性.
- MLLM框架成功量化了模拟利益相关者之间的感知差异,揭示了对城市形象和感知质量的洞察力.
- 空间分析表明,对创新氛围的总体评估是一致的,东南和西北的公园显示出更高的活力.
结论:
- 拟议的MLLM框架为诊断工业园区创新氛围提供了一个客观和自动化的方法.
- 这种方法为大数据驱动的城市规划策略提供了强大的实证支持.
- 该框架允许精细管理创新空间,以提高生产力,协作和可持续性.
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