喷油特性智能感知与控制是发动机实现可控高效燃烧的关键前沿技术。为准确获取实时喷油特性的可信信息，提出了融合变分贝叶斯与双向长短期记忆网络（VB-BiLSTM）的喷油特性感知方法，通过变分推断与自适应矩估计算法协同优化，确定了超参数概率密度分布的最优变分近似，实现了喷油速率可信区间确定与精确感知。以不同典型机器学习模型为对照，在不同强度噪声干扰下评估了感知性能，并在试验台上进行了喷油特性在线感知验证。研究结果表明：VBBiLSTM模型在不同信噪比条件下抗噪性能均显著优于其他模型，喷油速率试验数据处于感知喷油速率的95%可信区间内，感知喷油量与实际喷油量的最大误差小于6%，端到端的感知时间仅为毫秒级。

Intelligent perception and control of fuel injection characteristics is a key frontier technology for achieving engine controllable and efficient combustion. To accurately obtain reliable real-time information on fuel injection characteristics, a fuel injection characteristic perception method that integrated variational Bayesian and bidirectional long short-term memory networks (VB-BiLSTM) was proposed. By the collaborative optimization of variational inference and adaptive moment estimation algorithm, the optimal variational approximation of hyperparameter probability density distribution was determined, and the determination of credible interval for fuel injection rate and the precise perception was achieved. The perception performance was evaluated under different levels of noise interference using different typical deep learning models as controls, and online perception verification of fuel injection characteristics was conducted on a test bench. The research results show that the VB-BiLSTM model has significantly better noise resistance performance compared to other models. The experimental data of fuel injection rate are within the 95% credible interval of perceived injection rate, and the maximum error between the perceived and actual value is less than 6%. The end-to-end perception time is on the order of milliseconds.