城市规划学刊Issue(1)：12-20,9.DOI:10.16361/j.upf.202601002
人工智能辅助国土空间诊断的理论范式建构与思行方法变革
Theoretical Paradigm and Methodology of AI-assisted Territorial Spatial Diagnosis
摘要
Abstract
Faced with the complex"urban diseases"within territorial spaces,territo-rial spatial diagnosis(TSD)often resorts to fragmented,symptomatic solutions and arbitrary decision-making amid the rapid practice of territorial spatial planning.This tendency risks rendering territorial spatial planning detached from reality,akin to castles in the air.Recently,Artificial Intelligence(AI)technologies,characterized by higher-level,holistic,and complex reasoning,as well as ecological rationality,have demonstrated significant potential in enhancing the scientific rigor of territorial spa-tial planning.However,a critical gap remains:the lack of effective theoretical para-digms and practical methodologies for AI-assisted TSD.This paper builds on the theoretical foundations and core tasks of TSD.It systematically reviews the evolution-ary trajectory of TSD technologies and identifies the key challenges confronting their intelligent advancement.Based on this analysis,the paper constructs a novel Dual-Helix paradigm and a Diamond scaffolding framework for TSD,underpinned by hy-brid human-machine intelligence.Furthermore,the paper proposes five key directions for transformative shifts in thinking approaches and six systems for enhancing practi-cal methods following the integration of AI into TSD.These advancements offer sub-stantial promise for systematically addressing the inherent limitations of traditional di-agnostic approaches.Consequently,they are anticipated to significantly elevate the ra-tionality of territorial spatial planning,ultimately fostering the sustainable develop-ment of territorial spaces.关键词
国土空间诊断/人工智能技术/理论范式/思行方法/空间规律/人机混智Key words
territorial spatial diagnosis/artificial intelligence technology/theo-retical paradigm/cognitive and action appoach/spatial law/human-machine hybrid enhanced intelligence分类
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刘晓畅,吴志强..人工智能辅助国土空间诊断的理论范式建构与思行方法变革[J].城市规划学刊,2026,(1):12-20,9.基金项目
国家社科基金青年项目"基于多模态机器学习的城市街区建成环境降碳机制与策略研究"(项目编号:25CJY130)资助 （项目编号:25CJY130）
