针对海上导管架平台健康监测系统误报警率过高问题，提出了一种基于早期预警信号（EWS）的联合累计概率分布预警方法。首先，通过分析平台的环境监测与结构响应数据，提取一阶自相关系数与Lyapunov指数，利用自回归积分滑动平均模型（ARIMA）预测残差序列。进一步，为准确刻画不同预警指标间的非线性依赖关系，采用核密度估计构建边缘分布函数，并基于优选后的Gumbel Copula函数构建EWS指标残差的联合分布。结果表明，联合残差在非尾部区域相关性较弱，而在尾部，尤其是上尾，相关性显著增强。最后，依据3σ准则绘制联合累计概率分布等高线图，分析不同区域的落点，实现多指标联合预警。以南海某导管架平台为例，基于单一指标的模型虚假报警率为11.49%~12.08%，而基于Copula的联合累计概率分布预警模型将虚假报警率降至4.36%，显著提升预警准确性，为海上导管架结构的安全管理提供参考。

The traditional early warning methods for offshore jacket structures heavily rely on single models, resulting in a high frequency of false alarms, which fail to meet the practical demands of engineering applications. To improve the accuracy and reliability of early warnings, this paper proposes a joint cumulative probability distribution-based early warning method utilizing early warning signals (EWS). This method first extracts the first-order autocorrelation coefficient and Lyapunov exponent from the platform's environmental monitoring and structural response data, and then predicts their residual sequence using the ARIMA model for subsequent analysis. Subsequently, to precisely characterize the nonlinear dependencies between different warning indicators, kernel density estimation is used to construct the marginal distribution functions. Based on the optimized Gumbel Copula function, a joint distribution of the residuals of the EWS indicators is established. The results show that the joint residuals exhibit weak correlation in the non-tail regions, but significantly stronger correlation in the tail regions, particularly in the upper tail. Finally, contour plots of the joint cumulative probability distribution are generated based on the 3σ criterion of probability statistics, and the points of intersection in different regions are analyzed to achieve multi-indicator joint early warnings. The case study shows that, due to the limitations of model structure and sequence characteristics, false alarm rates in single-indicator models can range from 11.49% to 12.08%. In contrast, the early warning model based on the joint cumulative probability distribution using Copula effectively integrates the probability distribution characteristics of EWS indicators, reducing the false alarm rate to 4.36%, thus improving the accuracy of early warnings. This approach provides valuable reference for the operational safety management of offshore jacket structures.