低温推进剂加注液位信号智能处理技术研究

低温加注系统是运载火箭发射场地面支持设备的重要组成部分，包括低温介质的储存、运输、供给、控制以及安全等内容。由于低温推进剂本身存在低温沸腾、易挥发的特性，其加注过程十分复杂，为满足新一代运载火箭推进剂精准的加注要求，需要实时准确监测加注过程中贮箱内的液位高度。本文针对火箭地面加注过程的液位信号数据，对其三角波电压和线性波电压的特征进行分析、提取，基于BP（Back Propagation，反向传播）神经网络算法完成对不同加注状态的识别检测，并应用于传感器节数判别，优化了液位计算算法，降低了节数人为干预需求，提高了液位测量准确性。经实验测试验证，该方法可有效识别低温加注状态，识别准确率达到90%以上，用于液位信号处理中可显著提升液位高度计算的准确性。

Research on intelligent processing technology of cryogenic propellant filling level signal

The cryogenic refueling system is an important part of the launch site ground support equipment, includs the storage, transportation, supply, control and safety of cryogenic medium. Due to the low temperature boiling and volatile characteristics of cryogenic propellant, its refueling process is very complicatal. In order to meet the precise refueling requirements of the new generation of launch vehicle propellant, the liquid level in the tank needs to be monitored accurately during the refueling process in real time. This paper analyzes and extracts the characteristics of the triangular wave voltage and linear wave voltage for the liquid level signal data of the rocket ground refueling process, completes the identification and detection of different refueling states based on the BP neural network algorithm, and applies it to the sensor node discrimination, which can optimize the level calculation algorithm, reduce the demand for human intervention and improve the accuracy of liquid level measurement. The experimental results show that the method can identify the low temperature refueling state effectively, and the accuracy rate is over 90%, and can be used in liquid level signal processing to significantly improve the accuracy of level height calculation.