基于电力大数据的电力骨干通信网络毁伤韧性评估方法

doi:10.11959/j.issn.1000-0801.2023105

摘要/Abstract

摘要：

研究了基于电力大数据的电力骨干通信网络毁伤韧性评估方法，利用毁伤韧性评估结果制定保护策略，提升网络的生存能力。将电力骨干通信网络与攻击方设为博弈双方，利用极大极小法确定双方的纳什均衡策略，利用纳什均衡策略获取节点的毁伤概率，将节点毁伤概率转化为网络破坏形态。针对网络破坏形态，利用云计算技术调取电力大数据中包含的修复条件，依据各节点重要度为节点分配修复资源以及修复预算，构建电力骨干通信网络的性能时程响应曲线，输出网络毁伤韧性的动态评估结果。实验结果表明，利用云计算技术处理海量电力大数据的所有操作时间不超过 330 ms，当修复预算与修复资源均为最高时，电力骨干通信网络的毁伤韧性最高可达0.925，说明该方法可以有效评估电力骨干通信网络的毁伤韧性。

关键词: 电力大数据, 骨干通信网络, 毁伤韧性, 评估方法, 博弈双方, 云计算

Abstract:

The damage toughness assessment method of power backbone communication network based on power big data was studied, and the damage toughness assessment results were used to formulate protection strategies and improve the network survivability.The power backbone communication network and the attacker were set as the two sides of the game, and the Nash equilibrium strategy of both sides was determined by the minimax method.The damage probability of nodes was obtained by the Nash equilibrium strategy, and the node damage probability was transformed into the network damage form.According to the network damage pattern, the repair conditions contained in the power big data were obtained by cloud computing technology.According to the importance of each node, the repair resources and repair budget were allocated to the nodes in order.The performance time-history response curve of the power backbone communication network was constructed, and the dynamic evaluation results of the network damage toughness were output.The experimental results show that all operations using cloud computing technology to process massive power big data can not exceed 330 ms.When the repair budget and repair resources are the highest, the damage toughness of the power backbone communication network can reach 0.925 at most, indicating that this method can effectively evaluate the damage toughness of the power backbone communication network.

Key words: power big data, backbone communication network, damage toughness, assessment method, the two sides of the game, cloud computing

中图分类号:

TM76

韩雪. 基于电力大数据的电力骨干通信网络毁伤韧性评估方法[J]. 电信科学, 2023, 39(5): 136-143.

Xue HAN. Damage toughness assessment method of power backbone communication network based on power big data[J]. Telecommunications Science, 2023, 39(5): 136-143.

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链路序号	节点对	业务流量/(Gbit·s^-1)	链路序号	节点对	业务流量/(Gbit·s^-1)
1	1,2	3.854	15	14,15	3.854
2	2,3	2.851	16	15,16	1.687
3	3,4	1.687	17	16,17	2.915
4	5,6	2.695	18	17,18	1.815
5	3,6	3.648	19	17,23	3.645
6	1,8	4.051	20	18,19	2.781
7	7,8	3.945	21	18,20	1.854
8	8,9	2.874	22	20,21	2.495
9	9,10	3.156	23	21,25	3.851
10	6,11	2.945	24	24,25	4.854
11	11,12	2.945	25	23,24	5.618
12	10,12	2.495	26	22,23	3.648
13	10,13	3.185	27	22,25	2.648
14	13,14	4.185	28	14,24	1.854

节点序号	节点重要度	节点序号	节点重要度	节点序号	节点重要度
1	0.352	10	0.284	18	0.785
2	0.294	11	0.394	19	0.564
3	0.384	12	0.615	20	0.648
4	0.465	13	0.581	21	0.584
5	0.584	14	0.495	22	0.165
6	0.684	15	0.671	23	0.485
7	0.481	16	0.584	24	0.384
8	0.594	17	0.465	25	0.451
9	0.854	—	—	—	—