Random number generation plays a vital role in cryptographic systems and computational applications, where uniformity, unpredictability, and robustness are essential. This paper presents the Entropy Mixing Network (EMN), a novel hybrid random number generator designed to enhance randomness quality by combining deterministic pseudo-random generation with periodic entropy injection. To evaluate its effectiveness, we propose a comprehensive assessment framework that integrates statistical tests, advanced metrics, and visual analyses, providing a holistic view of randomness quality, predictability, and computational efficiency. The results demonstrate that EMN outperforms Python's SystemRandom and MersenneTwister in critical metrics, achieving the highest Chi-squared p-value (0.9430), entropy (7.9840), and lowest predictability (-0.0286). These improvements come with a trade-off in computational performance, as EMN incurs a higher generation time (0.2602 seconds). Despite this, its superior randomness quality makes it particularly suitable for cryptographic applications where security is prioritized over speed.

翻译：随机数生成在密码系统和计算应用中起着至关重要的作用，其均匀性、不可预测性和鲁棒性至关重要。本文提出了熵混合网络（EMN），这是一种新颖的混合随机数生成器，旨在通过将确定性伪随机生成与周期性熵注入相结合来提升随机性质量。为评估其有效性，我们提出了一个综合评估框架，该框架整合了统计检验、高级度量和可视化分析，从而对随机性质量、可预测性和计算效率提供整体性评估。结果表明，EMN在关键指标上优于Python的SystemRandom和MersenneTwister，实现了最高的卡方p值（0.9430）、熵值（7.9840）和最低的可预测性（-0.0286）。这些改进以计算性能为代价，因为EMN产生了更高的生成时间（0.2602秒）。尽管如此，其卓越的随机性质量使其特别适用于安全性优先于速度的密码应用。