基于正态云模型和模糊层次分析法的列车通信网络性能评估方法

贺德强; 柳国强; 陈彦君; 苗剑; 姚晓阳

doi:10.19818/j.cnki.1671-1637.2022.02.025

基于正态云模型和模糊层次分析法的列车通信网络性能评估方法

doi: 10.19818/j.cnki.1671-1637.2022.02.025

1.
广西大学机械工程学院，广西南宁 530004
2.
中车株洲电力机车研究所有限公司，湖南株洲 412001

基金项目:

国家自然科学基金项目 52072081

广西科技计划项目 AA20302010

广西自然科学基金项目 2017GXNSFDA198012

广西制造系统与先进制造技术重点实验室课题 19-050-44-S015

详细信息

作者简介:
贺德强(1973-)，男，湖南桃江人，广西大学教授，工学博士，从事列车控制和网络、信息化研究

中图分类号: U285.5
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-19
- 刊出日期: 2022-04-25

Evaluation method of train communication network performance based on normal cloud model and fuzzy analytic hierarchy process

1.
School of Mechanical Engineering, Guangxi University, Nanning 530004, Guangxi, China
2.
CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, Hunan, China

Funds:

National Natural Science Foundation of China 52072081

Science and Technology Project of Guangxi AA20302010

Natural Science Foundation of Guangxi 2017GXNSFDA198012

Key Laboratory Project of Manufacturing System and Advanced Manufacturing Technology of Guangxi 19-050-44-S015

More Information

Author Bio:
HE De-qiang(1973-), male, professor, PhD, hdqianglqy@126.com

摘要

摘要: 为保证高速列车安全、可靠运行，研究了列车通信网络性能评估方法；综合考虑列车通信网络的实时性、可靠性和服务质量，建立了合理的列车通信网络性能评价指标体系，采用模糊层次分析法确定列车通信网络性能评估指标的权重；考虑列车通信网络评估过程中具有不确定性，构建了基于正态云模型和模糊熵的二维评估模型；建立了基于交换式以太网的大容量和高可靠性列车通信网络仿真平台，获取各指标样本数据，运用二维评估模型计算各指标的隶属度，依据模糊理论最大隶属度法则确定列车通信网络性能等级。研究结果表明：在列车通信网络状态良好时，60%评估样本的网络性能等级为Ⅰ、Ⅱ级，在网络丢包率和误码率较大时，40%评估样本的评估等级为Ⅲ、Ⅳ级，表明二维评估模型能够有效地反映列车通信网络状态；与仅运用模糊综合评价法相比较，两者的评估结果基本一致，反映了二维评估模型的准确性；模糊综合评价法不能消除评估过程中不确定性因素的影响，从而导致评估结果缺乏精确度，因此，提出的方法更适合于列车通信网络性能评估。
- 高速列车 /
- 通信网络 /
- 正态云模型 /
- 网络性能评估 /
- 模糊层次分析法
Abstract: To ensure the safety and reliability of high-speed trains, a method for evaluating the performance of train communication networks (TCNs) was studied. A suitable system of performance evaluation indexes was proposed by considering the stringent requirements for TCNs in terms of real-time responsiveness, reliability, and service quality. Fuzzy analytic hierarchy process (FAHP) was used to determine the weights of performance evaluation indexes of TCN. To address the uncertainty of TCN evaluation process, a two-dimensional (2D) evaluation model based on the normal cloud model and fuzzy entropy was constructed. A TCN simulation platform was constructed by using switched Ethernet with large capacity and high reliability, and then used to obtain sample data for each index. The membership degrees of each index were computed by using the 2D evaluation model, and the performance grade of the TCN was determined by the maximum membership degree (from fuzzy theory) principle. Research results show that 60% of the evaluated samples have network performance grades of Ⅰ and Ⅱ when the TCN is in a good state. When the network has high packet loss rate and bit error rate, 40% of the evaluated samples have performance grades of Ⅲ and Ⅳ. Therefore, the result of the 2D evaluation model accurately reflects the state of the TCN. The result is largely consistent with the result from the fuzzy comprehensive evaluation (FCE), indicating that the 2D evaluation model is accurate. However, as it is not possible for the FCE method to exclude the influence of uncertainty in the evaluation process, its result lacks precision. Hence, the proposed method is more suitable for the evaluation of TCN performance. 6 tabs, 15 figs, 32 refs.
- high-speed train /
- communication network /
- normal cloud model /
- network performance evaluation /
- fuzzy analytic hierarchy process

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图 1 云图特征

Figure 1. Cloud feature

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图 2 云发生器

Figure 2. Cloud generators

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图 3 列车通信网络评价体系

Figure 3. Evaluation system of train communication network

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图 4 吞吐量评价云图

Figure 4. Evaluation cloud map of throughput

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图 5 端到端时延评价云图

Figure 5. Evaluation cloud map of end-to end delay

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图 6 丢包率评价云图

Figure 6. Evaluation cloud map of packet loss rate

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图 7 误码率评价云图

Figure 7. Evaluation cloud map of bit error rate

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图 8 链路利用率评价云图

Figure 8. Evaluation cloud map of link utilization

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图 9 基于交换式以太网的列车通信网络拓扑结构

Figure 9. Topological structure of train communication network based on switched ethernet

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图 10 吞吐量

Figure 10. Throughput

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图 11 端到端时延

Figure 11. End-to-end delay

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图 12 丢包率

Figure 12. Packe tloss rate

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图 13 误码率

Figure 13. Bit error rate

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图 14 链路利用率

Figure 14. Link utilization

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图 15 列车通信网络性能评估流程

Figure 15. Flow of performance evaluation of train communication network

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表 1 各评价等级的含义

Table 1. Meanings of each evaluation level

列车通信网络等级	含义
Ⅰ	网络性能理想，适当增加娱乐信息业务量
Ⅱ	网络性能较好，适当增加重要数据业务量
Ⅲ	网络性能一般，能完成正常通信
Ⅳ	网络性能差，处于瘫痪临界状态
Ⅴ	网络瘫痪，可能造成事故

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表 2 列车通信网络性能评价指标赋值标准

Table 2. Assignment standards of performance evaluation indexes of train communication network

列车通信网络等级	端到端时延/ms	丢包率/%	误码率/%	链路利用率/%	吞吐量/Mbps
Ⅰ	≤1	≤2	≤2	≤20	≤70
Ⅱ	≤4	≤4	≤6	≤40	≤55
Ⅲ	≤7	≤6	≤10	≤60	≤40
Ⅳ	≤10	≤10	≤25	≤80	≤20
Ⅴ	≤20	≥10	≥25	≥80	≤10

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表 3 评价指标重要程度判断准则

Table 3. Importance judging criteria of evaluation indexes

重要性	三角模糊数
同等重要	(0.5, 0.5, 0.5)
稍微重要	(0.5, 0.6, 0.7)
明显重要	(0.6, 0.7, 0.8)
强烈重要	(0.7, 0.8, 0.9)
绝对重要	(0.8, 0.9, 1.0)
反比较	1-r_ij

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表 4 列车通信网络云数字特征

Table 4. Cloud digital features of train communication network

列车通信网络等级	云数字特征	端到端延时/ms	丢包率/%	误码率/%	链路利用率/%	吞吐量/Mbps
Ⅰ	E	0.0	0.0	0.0	0.0	70.0
	F	0.33	0.67	0.67	6.67	5.00
	H	0.01	0.01	0.02	0.05	0.05
Ⅱ	E	2.5	3.0	4.0	30.0	47.5
	F	1.00	0.67	1.33	6.67	5.00
	H	0.01	0.01	0.02	0.05	0.05
Ⅲ	E	5.5	5.0	8.0	50.0	30.0
	F	1.00	0.67	1.33	6.67	6.67
	H	0.01	0.01	0.02	0.05	0.05
Ⅳ	E	8.5	8.0	17.5	70.0	15.0
	F	1.00	1.33	5.00	6.67	3.33
	H	0.01	0.01	0.02	0.05	0.05
Ⅴ	E	20.0	20.0	40.0	90.0	5.0
	F	3.33	3.33	5.00	6.67	3.33
	H	0.01	0.01	0.02	0.05	0.05

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表 5 指标权重

Table 5. Weights of indexes

网络性能指标	权重
端到端时延	0.248 3
丢包率	0.249 9
吞吐量	0.152 2
误码率	0.202 8
链路利用率	0.110 8

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表 6 评估结果

Table 6. Evaluation results

样本编号	综合隶属度					模糊熵	本文评估结果	模糊综合评价法评估结果
样本编号	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	模糊熵	本文评估结果	模糊综合评价法评估结果
1	8.9×10^-7	8.9×10^-2	8.8×10^-4	0.0	0.0	1.2×10^-1	Ⅱ	Ⅱ
2	3.5×10^-1	7.1×10^-3	3.1×10^-8	0.0	0.0	2.8×10^-1	Ⅰ	Ⅱ
3	1.0×10^-9	1.7×10^-2	0.0	0.0	0.0	3.4×10^-2	Ⅱ	Ⅱ
4	3.6×10^-1	7.2×10^-3	9.8×10^-4	0.0	0.0	1.3×10^-1	Ⅰ	Ⅰ
5	1.6×10^-6	9.9×10^-2	8.2×10^-4	0.0	0.0	3.4×10^-6	Ⅱ	Ⅱ
6	0.0	3.6×10^-9	3.2×10^-7	0.0	0.0	2.9×10^-6	Ⅲ	Ⅲ
7	0.0	4.1×10^-6	2.7×10^-5	0.0	0.0	1.5×10^-4	Ⅲ	Ⅲ
8	0.0	0.0	0.0	6.9×10^-6	0.0	3.5×10^-5	Ⅳ	Ⅲ
9	0.0	1.4×10^-9	1.7×10^-7	1.9×10^-7	0.0	2.6×10^-6	Ⅳ	Ⅳ
10	4.0×10^-4	2.1×10^-1	1.7×10^-7	0.0	0.0	2.1×10^-1	Ⅱ	Ⅱ

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