为解决高海拔隧道施工过程中存在的通风效率低、能耗高等问题，开发一种基于生成对抗网络（GANs）的高海拔隧道施工通风数据模拟与优化系统。首先，采用GANs技术生成高精度的施工环境数据，替代传统的CFD仿真方法，通过学习施工环境中多维数据的分布特征，实时生成模拟数据。随后，结合遗传算法（GA）和粒子群优化（PSO）算法，优化通风系统的参数配置，提高系统的响应速度和稳定性。试验结果表明： 1）基于GANs的通风优化策略在提高通风效率和降低能耗方面具有显著优势，通风效率提升了12%，能耗降低了8%。2）GANs生成的数据与实际测量数据的误差小于15%，具有较高的准确性和适应性。3）通过在多种不同的施工环境下进行试验，验证了该优化策略在复杂条件下的有效性和鲁棒性，尤其在极端条件下表现尤为突出。4）通过引入生成对抗网络技术，显著提高了隧道施工通风系统的优化效率，降低了施工过程中存在的安全隐患，为高海拔隧道施工的智能化管理提供了新的技术支持。

The ventilation efficiency is low and energy consumption is high in high-altitude tunnel construction. To address these challenges, a ventilation data simulation and optimization system based on generative adversarial networks (GANs) is developed. This system uses GANs technology to generate high-precision construction environment data, replacing traditional computational fluid dynamics simulation methods. By learning the distribution characteristics of multidimensional data in the construction environment, it generates simulated data in real-time. Subsequently, genetic algorithms and particle swarm optimization are combined to optimize the parameter configuration of the ventilation system, enhancing the system′s response speed and stability. Experimental results show that: (1) The GANs-based ventilation optimization strategy significantly improves ventilation efficiency and reduces energy consumption, with ventilation efficiency increasing by 12% and energy consumption decreasing by 8%. (2) The error between GANs-generated data and actual measurement data is less than 15%, demonstrating high accuracy and adaptability. (3) Tests conducted in various construction environments confirm the effectiveness and robustness of the optimization strategy under complex conditions, especially in extreme conditions. (4) By integrating GANs technology, the optimization efficiency of the tunnel construction ventilation system is significantly improved, reducing safety risks during construction and providing new technological support for intelligent management of high-altitude tunnel construction.