KG storage and CoT reasoning： Construct the intelligent agent for vehicle risk analysis

YI Zhijun; WEI Bangyan; XU Yonggao; WU Xiaoyun; ZHU Yiting; HE Zhaocheng

doi:10.11714/acta.snus.ZR20250213

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KG storage and CoT reasoning： Construct the intelligent agent for vehicle risk analysis

Research articles | 更新时间：2026-01-19

- KG storage and CoT reasoning： Construct the intelligent agent for vehicle risk analysis
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 65, Issue 1, Pages: 85-92(2026)
- 作者机构：
  
  1.广东省道路运输事务中心，广东广州 510199
  2.广东省智能交通系统重点实验室 / 中山大学智能工程学院，广东深圳 518107
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20250213
  CLC： U491
- Received：30 September 2025，
  
  Revised：2025-10-31，
  
  Accepted：31 October 2025，
  
  Online First：02 January 2026，
  
  Published：25 January 2026
- 稿件说明：
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易智君,韦邦彦,徐永高等.KG存储协同CoT推理：构建车辆风险分析智能体[J].中山大学学报(自然科学版)(中英文),2026,65(01):85-92.

YI Zhijun,WEI Bangyan,XU Yonggao,et al.KG storage and CoT reasoning： Construct the intelligent agent for vehicle risk analysis[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(01):85-92.
易智君,韦邦彦,徐永高等.KG存储协同CoT推理：构建车辆风险分析智能体[J].中山大学学报(自然科学版)(中英文),2026,65(01):85-92. DOI： 10.11714/acta.snus.ZR20250213.

YI Zhijun,WEI Bangyan,XU Yonggao,et al.KG storage and CoT reasoning： Construct the intelligent agent for vehicle risk analysis[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(01):85-92. DOI： 10.11714/acta.snus.ZR20250213.

摘要

文中提出了一种基于知识图谱与思维链（CoT）引导的大语言模型的车辆风险分析智能体框架。首先，面向重点车辆的安全监管，将分散的风险驾驶行为、交通事故与路网数据等信息建模为统一的风险知识图谱语义网络。然后，设计了基于提示词工程的分级推理CoT架构，引导大语言模型实现理解问题、查询数据、确认结果和修正查询的可溯源推理过程，提升问答的准确性与鲁棒性。实验证明，车辆风险分析智能体能够有效支持自然语言交互下的风险分析与关联挖掘，在多种复杂查询任务中表现良好，并能结合子图分析为交通管理提供高效、智能和可解释的决策分析工具。

Abstract

This paper proposes a vehicle risk analysis intelligent agent framework integrating knowledge graph technology and large language model（LLM） guided by chain-of-thought（CoT）. we construct a risk knowledge graph tailored for critical vehicle safety supervision to unify fragmented data including risky driving behaviors， traffic accidents， and road network information into a cohesive semantic network. And a hierarchical CoT reasoning architecture grounded in prompt engineering is designed to steer the LLM through a traceable inference pipeline comprising query comprehension，data retrieval，result validation and query refinement，thereby enhancing the accuracy and robustness of question-answering systems. Experimental validation confirms that the proposed intelligent agent of vehicle risk analysis effectively facilitates natural language-driven risk assessment and association mining across diverse complex query scenarios，delivering an efficient，intelligent，and interpretable decision-support tool for traffic management with demonstrable superiority in operational efficacy and analytical transparency.

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references

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