针对爆炸冲击波外场实测中超压波形受干扰而缺失或失真，进而导致冲击波特征值提取失效的问题，提出一种融合局部形态特征与全局时序依赖的深度学习信号修复与评估方法。该方法以残缺序列和二值缺失掩码作为输入，利用一维卷积捕捉微秒级激波阵面突变，级联双向长短期记忆网络与自注意力机制重构长程衰减趋势。为克服传统回归造成的峰值低估，文中引入分位数回归与动态峰值加权损失函数联合优化，并借助时序生成对抗网络实施样本增强以提升泛化能力。实测毁伤过程冲击波数据处理结果表明：该方法有效抑制了削峰现象，显著降低了峰值超压、正压作用时间与比冲量的绝对误差；在90%置信水平下预测区间覆盖率达0.94以上，且区间宽度随局部信息缺失程度自适应扩大。研究表明，该方法在保真重构强瞬态物理参数的同时，实现了修复质量的量化度量，为武器毁伤效能评估与工程风险判读提供了高可信度的数据支撑。

The overpressure waveforms are disturbed，missing，or distorted in the field measurements of explosive shock waves，resulting in the failure to extract the feature values of shockwave.Therefore，a deep learning signal restoration and evaluation method that integrates local morphological features with global temporal dependencies is proposed.This method takes the incomplete sequences and binary missing masks as inputs，employs one-dimensional convolution to capture the microsecond-level abrupt changes in the shock wave front，and uses a cascaded bidirectional long short-term memory network with a self-attention mechanism to reconstruct the long-range attenuation trends.To overcome the underestimation of peak caused by traditional regression，the quantile regression and a dynamic peak-weighted loss function are jointly used for optimization，and the temporal generative adversarial networks are employed for sample augmentation to improve the generalization capability.The shockwave data measured during the damage process is processed.The results indicate that the proposed method effectively suppresses peak clipping，significantly reducing the absolute errors of peak overpressure，positive phase duration and specific impulse； at a 90% confidence level，the coverage rate of the predictive interval reaches over 0.94，and the interval width adaptively increases according to the degree of local information loss.The study demonstrates that the proposed method not only faithfully reconstructs the strong transient physical parameters，but also provides the quantitative metrics for restoration quality，offering highly reliable data support for the assessment of weapon damage effectiveness and the interpretation of engineering risk.