面向室内火灾应急疏散的智能体寻径方法研究

doi:10.16182/j.issn1004731x.joss.25-0881

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (2): 532-543.doi: 10.16182/j.issn1004731x.joss.25-0881

• 应急管理应用 • 上一篇

面向室内火灾应急疏散的智能体寻径方法研究

田傲¹, 张健钦¹, 文政¹, 胡超男¹, 赵红², 沈博³

^1.北京建筑大学测绘与城市空间信息学院，北京 102616
^2.华中农业大学文法学院，湖北武汉 430070
^3.苍穹数码技术股份有限公司，北京 100023

收稿日期:2025-09-12 修回日期:2025-10-23 出版日期:2026-02-18 发布日期:2026-02-11
通讯作者: 张健钦
第一作者简介:田傲(2001-)，男，硕士生，研究方向应急模拟仿真等。
基金资助:
国家自然科学基金(42371416);安全生产智能精准执法关键技术及装备研发(2024EMST121204);北京建筑大学2025年度博士研究生科研能力提升项目(DG2025036)

Agent-based Pathfinding Method for Indoor Fire Emergency Evacuation

Tian Ao¹, Zhang Jianqin¹, Wen Zheng¹, Hu Chaonan¹, Zhao Hong², Shen bo³

^1.School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
^2.College of Humanities & Social Sciences, Huazhong Agricultural University, Wuhan 430070, China
^3.KQ GEO Technologies Co. , Ltd. , Beijing 100023, China

Received:2025-09-12 Revised:2025-10-23 Online:2026-02-18 Published:2026-02-11
Contact: Zhang Jianqin

摘要/Abstract

摘要：

为提高动态火灾场景中应急疏散效率，减少人员伤亡，提出一种基于智能体和动态A*算法框架的实时路径重规划方法。对智能体行为与动作进行建模，设计奖励函数构建智能体疏散框架；依托火灾模拟数据，构建包含热辐射、烟雾能见度和CO浓度等参数的动态代价网络，实现时空连续的火灾环境建模；通过动态权重分配优化复合代价函数，结合改进的启发函数和动态搜索机制，实现疏散路径局部重规划与智能体疏散寻路。仿真结果表明：该方法具备更低的路径重规划响应延迟与更优的路径安全性，在动态危险环境中具有良好的适应性与安全性。

关键词: 动态A*算法, 局部重规划, 应急疏散, 建筑火灾

Abstract:

To improve emergency evacuation efficiency and reduce casualties in dynamic fire scenarios, a real-time path re-planning method based on agents and dynamic A* algorithm framework is proposed. The behavior and actions of the agent is modeled. A reward function is designed, and an agent-based evacuation framework is constructed. Based on fire simulation data, a dynamic cost network involving parameters such as thermal radiation, smoke visibility, and CO concentration is constructed to achieve spatiotemporally continuous modeling of fire environments. By optimizing the composite cost function through dynamic weight allocation, combined with an improved heuristic function and dynamic search mechanism, local re-planning of evacuation paths and agent-based evacuation pathfinding are achieved. The simulation results show that this method has lower path re-planning response delay and better path safety, demonstrating good adaptability and safety in dynamic hazardous environments.

Key words: dynamic A* algorithm, local re-planning, emergency evacuation, building fire

中图分类号:

TP391.9

田傲,张健钦,文政等 . 面向室内火灾应急疏散的智能体寻径方法研究[J]. 系统仿真学报, 2026, 38(2): 532-543.

Tian Ao,Zhang Jianqin,Wen Zheng,et al . Agent-based Pathfinding Method for Indoor Fire Emergency Evacuation[J]. Journal of System Simulation, 2026, 38(2): 532-543.

图/表 18

图1

表1

火灾不同阶段的风险因素的权重值

权重 $ω z$	温度 $ω t$	能见度 $ω v$	CO浓度 $ω C O$
初始期 $ω 1$	0.25	0.55	0.20
蔓延期 $ω 2$	0.50	0.30	0.20
全面期 $ω 3$	0.30	0.20	0.50

表1

表2

图2

图 3

图4

表3

图5

图6

图7

图8

图9

图10

表4

图 11

表5

表6

图12

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温度范围/℃	能见度范围/m	CO体积分数×10^-6	综合风险场	动态路径代价值	状态
20~30	24~30	0~100	0~0.162	1~2.62	通畅
30~40	18~24	100~250	0.162~0.331	2.26~4.31	正常
40~50	12~18	250~400	0.331~0.509	4.31~6.09	高风险
50~65	8~12	400~550	0.509~0.684	6.09~7.84	极高风险
＞65	—	—	∞	∞	禁行
—	＜8	—	∞	∞	禁行
—	—	＞550	∞	∞	禁行

输入参数	属性
环境温度/℃	20
能见度/m	30
CO浓度	0
风速	0
材料类型	聚氨酯
燃烧表面积/m²	4
模拟时间/s	500

方法	路径效率	距离/m	时间/s	最大通行代价	平均通行代价	平均响应时间/ms	拐点
A*	1.02	60.64	57.66	4.52	2.72	29.60	10
JPS	1.04	61.48	59.49	17.85	3.34	62.39	8
双向A*	1.33	78.70	74.84	37.75	3.14	100.62	11
改进A*	1.02	60.35	56.47	1.36	1.18	8.27	7

方法	路径效率	距离/m	时间/s	最大通行代价	平均通行代价	平均响应时间/ms	拐点
A*	1.11	163.06	154.40	29.58	4.65	102.97	34
JPS	1.04	151.69	181.33	17.62	4.39	276.97	12
双向A*	1.15	168.64	181.36	62.14	21.94	181.36	10
改进A*	1.02	149.38	155.52	11.17	1.39	95.06	8

评价指标	改进A*算法	Pathfinder
ASET/s	137.1	137.1
RSET/s	128.6	130.5
平均疏散距离/m	47.47	55.80
重规划响应时间/ms	70.831	—
出口利用率/%	5~28	1~37

面向室内火灾应急疏散的智能体寻径方法研究

Agent-based Pathfinding Method for Indoor Fire Emergency Evacuation

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