基于混合Copula函数的电子商务系统可靠性建模

Reliability Modeling of E-Commerce Systems Based on Mixed Copula Functions

摘要: 随着电子商务系统业务规模的迅猛扩张，系统在高并发访问、复杂交易流程及突发异常流量冲击下的稳健性面临严峻挑战。电子商务系统可靠性不仅依赖于单一组件的性能，更受到模块间复杂非线性依赖与联合失效机制的深刻影响。传统可靠性建模方法在刻画尾部相关性、集群失效等极端风险时存在明显局限。针对该问题，本文提出了一种基于混合Copula函数的电子商务系统可靠性模型，利用期望最大化算法进行参数估计与优化。该模型中Copula函数用于从依赖结构中分离边缘分布，实现对模块间关联系统的精准建模。利用蒙特卡洛方法随机生成符合实际业务场景的故障数据，实验结果表明，本文提出的模型能够精确地描述模块间的异质依赖结构，在拟合优度与风险识别能力方面均显著优于单一Copula模型。本研究从方法论层面拓展了系统可靠性建模的理论工具，为电商平台在高并发促销、异常流量管控等现实场景中的运维策略制定提供了科学依据。

Abstract: With the rapid expansion of e-commerce system operations, the robustness of these systems under high-concurrency access, complex transaction processes, and sudden abnormal traffic surges faces severe challenges. The reliability of e-commerce systems depends not only on the performance of individual components but is also profoundly influenced by complex nonlinear dependencies and joint failure mechanisms among modules. Traditional reliability modeling methods exhibit significant limitations in capturing extreme risks such as tail dependence and cluster failures. To address this issue, this paper proposes a reliability model for e-commerce systems based on mixed Copula functions, utilizing the Expectation-Maximization algorithm for parameter estimation and optimization. In this model, Copula functions are employed to separate marginal distributions from the dependency structure, enabling precise modeling of the interdependencies among modules. Using the Monte Carlo method to randomly generate failure data that aligns with real-world business scenarios, experimental results demonstrate that the proposed model accurately captures the heterogeneous dependency structures among modules and significantly outperforms single Copula models in terms of goodness-of-fit and risk identification capabilities. This study expands the theoretical toolkit for system reliability modeling from a methodological perspective and provides a scientific basis for formulating operational strategies in real-world scenarios such as high-concurrency promotions and abnormal traffic management in e-commerce platforms.