基于CN-FRAM的公共交通设备设施系统运营安全韧性度量

doi:10.16265/j.cnki.issn1003-3033.2024.03.0658

摘要/Abstract

摘要：

设备设施故障是公共交通系统运营安全事故发生的主要原因,为更好地度量和增强系统的安全韧性,提出融合复杂网络(CN)与功能共振分析方法(FRAM)的CN-FRAM运营安全韧性度量模型,并将系统韧性定义为扰动下系统性能损失与性能基线之比。首先,根据设备设施系统构成和节点功能,建立CN;其次,将FRAM模型嵌入到CN中,以扩展节点和连接,构建CN-FRAM模型;然后,基于CN-FRAM韧性度量模型分析系统组件之间功能变化的聚合,并在量化系统韧性时综合考虑网络整体效益和组件之间的耦合程度;最后,以南京市地铁信号系统为例,验证方法的可行性和有效性。结果表明:该模型可以量化系统破坏-恢复全过程的韧性,计算故障对系统的影响程度,并以韧性值最大化为目标,展现不同修复策略下的韧性表现,从而为确定最佳恢复顺序提供依据。对比现有方法,该方法所确定的最优恢复策略能显著减少系统因故障造成的整体性能损失,从而提高系统的韧性。

关键词: 复杂网络(CN)与功能共振分析方法(FRAM), 公共交通, 设备设施系统, 运营安全韧性, 韧性度量

Abstract:

Equipment and facility failures were the primary cause of safety accidents in public transportation systems. In order to better quantify and enhance the safety resilience of systems, CN-FRAM operational safety resilience measurement model, integrating CN and FRAM, were proposed. System resilience was defined as the ratio of system performance loss to performance baseline under perturbations. Firstly, based on the composition and functional nodes of the equipment and facility system, a CN was established. Secondly, the FRAM model was embedded into the CN to expand nodes and connections, constructing the CN-FRAM model. Then, based on the CN-FRAM resilience measurement model, the aggregation of functional changes between system components was analyzed, and when quantifying system resilience, the overall efficiency of the network and the degree of coupling between components were considered comprehensively. Finally, using the metro signal system in Nanjing as an example, the feasibility and effectiveness of the method were validated. The results show that the model can quantify the resilience of the system throughout the disruption-recovery process, calculate the impact of failures on the system, and maximize resilience values as the objective, demonstrating resilience performance under different repair strategies, thereby providing a basis for determining the optimal recovery sequence. Compared with existing methods, the optimal recovery strategies identified by this method can significantly reduce the overall performance loss caused by failures, thus enhancing system resilience.

Key words: complex network(CN) and functional resonance analysis method (FRAM), public transportation systems, equipment and facilities system, safe and resilient operation, resilience measurement method

中图分类号:

X951交通运输安全

申玲, 唐令怡, 廖洁. 基于CN-FRAM的公共交通设备设施系统运营安全韧性度量[J]. 中国安全科学学报, 2024, 34(3): 45-54.

SHEN Ling, TANG Lingyi, LIAO Jie. Operational safety resilience measure for public transportation equipment and facility systems based on CN-FRAM[J]. China Safety Science Journal, 2024, 34(3): 45-54.

图/表 13

图1

表1

基于时间和精度的可变性与耦合效应评分值

$V i T$	评分	$V i P$	评分	$α i j T$	评分	$α i j P$	评分
准时	1	精确	1	放大	2	放大	2
太早	2	可接受	2	无影响	1	无影响	1
太晚	3	不精确	3	阻尼	0.5	阻尼	0.5
不发生	4	错误	4	—	—	—	—

表1

表2

图2

表3

图3

表4

2对耦合参数示例

耦合编号	功能名称	耦合功能名称	$α i j T$ $α i j P$
C1	车载信标天线	应答器接收器	2
C1	车载信标天线	应答器接收器	1
C8	雷达	车载记录系统	2
C8	雷达	车载记录系统	1

表4

表5

图4

图5

表6

图6

表7

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时间	准时	过早	过晚	不发生
可能概率分布1	0.65	0.09	0.24	0.02
可能概率分布2	0.75	0.13	0.08	0.04
精度	精确	可接受	不精确	错误
可能概率分布1	0.75	0.15	0.07	0.03
可能概率分布2	0.76	0.16	0.04	0.04

编号	功能名称	λ	μ	MTTR	MTBF	耦合功能	方面	耦合编号	Z_ij
1	车载信标天线	0.000 1	0.9	3	30 000	应答器接收器	前提	C1	8
2	应答器接收器	0.000 3	0.85	4	20 000	车载机柜	前提	C2	12
2	应答器接收器	0.000 3	0.85	4	20 000	车载记录系统	输入	C3	12
3	编码里程计	0.000 2	0.9	4	50 000	车载记录系统	输入	C4	8
3	编码里程计	0.000 2	0.9	4	50 000	车载机柜	输入	C5	8
4	速度传感器	0.000 1	0.9	3	40 000	车载记录系统	输入	C6	12
4	速度传感器	0.000 1	0.9	3	40 000	车载机柜	输入	C7	12
5	雷达	0.000 5	0.8	9	60 000	车载记录系统	输入	C8	18
5	雷达	0.000 5	0.8	9	60 000	车载机柜	输入	C9	18
6	车载记录系统	0.000 3	0.85	5	30 000	车载人机界面	输入	C10	8
7	车载数据库存储单元	0.000 2	0.9	6	50 000	车载机柜	输入	C11	8
8	车载机柜	0.000 3	0.9	6	40 000	车载记录系统	输入	C12	12
						车载人机界面	输入	C13	12
						无线天线	控制	C14	12
9	车载人机界面	0.000 1	0.9	3	60 000	车载记录系统	输入	C15	12
10	无线天线	0.000 2	0.95	3	50 000	车载机柜	前提	C16	8

组件序号	拓扑结构重要度	功能耦合重要度	综合重要度
1	0.226 495	12	2.717 940
2	0.193 079	24	4.633 904
3	0.069 575	16	1.113 193
4	0.069 575	24	1.669 790
5	0.069 575	36	2.504 685
6	0.007 822	8	0.062 578
7	0.066 579	8	0.532 631
8	0.161 147	36	5.801 294
9	0.007 822	12	0.093 867
10	0.128 331	8	1.026 650

修复策略排名	1	2	3	4	5
修复顺序	[2, 1, 8, 5, 6]	[2, 8, 1, 5, 6]	[1, 2, 8, 5, 6]	[8, 2, 1, 5, 6]	[1, 8, 2, 5, 6]
全过程韧性	0.703 3	0.701 8	0.700 8	0.693 7	0.692 7
修复策略排名	6	7	8	9	10
修复顺序	[8, 1, 2, 5, 6]	[2, 1, 8, 6, 5]	[2, 8, 1, 6, 5]	[1, 2, 8, 6, 5]	[8, 2, 1, 6, 5]
全过程韧性	0.691 1	0.682 6	0.681 1	0.680 1	0.673 0

模型	最佳修复策略	全过程韧性值
FRAM模型	[8,5,2,1,6]	0.609 9
文献^[17]节点度模型	[8,6,2,5,1]	0.579 6
CN-FRAM模型	[2,1,8,5,6]	0.703 3