The satellite-based measurement-device-independent quantum key distribution can promote the realization of quantum communication networks. Under the condition of the limited data set, it is necessary to optimize all parameters. For communication networks, real-time prediction and optimization are also indispensable. With the development of machine learning, cross-combination with machine learning has also become the mainstream of parameter optimization in various disciplines. This paper discusses the asymmetric MDI-QKD based on the satellite in the case of statistical fluctuations, and uses the local search algorithm to achieve full parameter optimization under the condition of considering the probability of sending the signal. Compared with fixed related parameters, the key rate is increased by an order of magnitude. On this basis, random forest is used to predict the high-precision optimal parameters, thereby eliminating the simulation and iteration required by the search method to meet the real-time optimization of the future QKD network.

独立于卫星测量设备的量子密钥分发可以促进量子通信网络的实现。在数据集有限的情况下，需要对所有参数进行优化。对于通信网络来说，实时预测和优化也是必不可少的。随着机器学习的发展，与机器学习的交叉结合也成为各学科参数优化的主流。本文讨论了在统计波动情况下基于卫星的非对称MDI-QKD，在考虑发送信号概率的情况下，采用局部搜索算法实现全参数优化。与固定相关参数相比，密钥率提高了一个数量级。在此基础上，