基于小时间尺度网络中断故障识别的数学建模仿真

Mathematical Modeling and Simulation of Network Interruption Fault Identification Based on Small Time Scale

摘要: 随着信息技术的迅猛发展和普及，网络通信已成为现代社会不可或缺的一部分。网络为人们提供了高效、便捷的信息传输和交流渠道，广泛应用于各个领域。然而，网络中断故障频发，给人们的生产生活带来了极大的不便和损失。特别是在小时间尺度下，网络中断故障的发生更为频繁和难以预测，对网络的稳定性和可靠性提出了更高的要求。本文深入研究了小时间尺度网络中断故障识别问题，通过数学建模仿真方法，实现了信号校正、故障定位以及容错识别。研究过程中，首先对信号进行了校正处理，以消除噪声和干扰，提高故障定位的精度；其次，构建了容错数学模型，包括可重构服务承载网络模型、资源紧迫度评估以及容错模型构建，以应对网络中断故障；最后，通过实验与分析验证了模型的置信度和识别效率。实验结果表明，本文提出的建模方法在小时间尺度网络中断故障识别中具有较高的准确性和效率，为实际网络故障的快速处理提供了有效的理论支持。

Abstract: With the rapid development and popularization of information technology, network communication has become an indispensable part of modern society. Network provides people with efficient and convenient information transmission and communication channels, and is widely used in various fields. However, frequent network interruption faults bring great inconvenience and loss to people’s production and life. Especially in the small time scale, the occurrence of network interruption fault is more frequent and difficult to predict, which puts forward higher requirements for the stability and reliability of the network. In this paper, we deeply study the problem of network interruption fault identification in small time scale, and realize signal correction, fault location and fault tolerance identification through mathematical modeling and simulation. In the research process, the signal is first corrected to eliminate noise and interference and improve the accuracy of fault location. Secondly, a fault-tolerant mathematical model is constructed, which includes reconfigurable service bearing network model, resource urgency evaluation and fault-tolerant model construction to deal with network interruption faults. Finally, the reliability and recognition efficiency of the model are verified by experiments and analysis. The experimental results show that the modeling method proposed in this paper has high accuracy and efficiency in the identification of network interruption faults in small time scales, and provides effective theoretical support for the fast processing of practical network faults.