基于 DC-SAM 液位识别模型的非满管流量计

在工业生产过程中超声流量计凭借非接触测量、适用多种流体介质等优点发挥着重要作用。针对目前超声流量检测抗干扰能力差，检测精度较低等问题，提出了一种结合模式识别的四声道非满管超声流量计；该系统通过带浮点运算的高性能ARM处理器进行FFT运算，求出四声道的综合流速；配合边缘计算芯片来识别出管道的液位，并基于液位修正模型提升识别稳定性。在液位识别模型中利用特征提取模块和空间注意力机制模块来提取有效特征，使用随机森林进行液位分类。实验结果表明，DC-SAM算法相对于其他模型更快收敛，准确率达到96.6%;该系统在流量实验中，对比标定流量计能达到96.5%的准确度，且具有较好的线性度；可以准确识别液位和流量，在保持较高测量精度的同时满足检测的稳定性要求。

Non-full tube flowmeter based on DC-SAM liquid level identification model

In the industrial production process, ultrasonic flowmeter plays an important role with the advantages of non-contact measurement and suitable for various fluid media. Aiming at the problems of poor anti-interference ability and low detection accuracy of ultrasonic flow detection, a four-channel non-full tube ultrasonic flowmeter combined with pattern recognition is proposed. The system uses a high-performance chip with floating point operation for FFT calculation and calculates the integrated velocity of four channels, and identifies the liquid level of the non-full pipe with the edge calculation chip, and then improves the identification stability through the liquid level correction model. In the liquid level recognition model, the feature extraction module and the spatial attention mechanism module are used to extract the effective features, and the random forest classification module is used to classify the liquid level. The experimental results show that the DC-SAM algorithm can converge faster than other models, and the accuracy can reach 96. 6%. In the flow experiment, the system can achieve 96. 5% accuracy and good linearity compared with the calibrated flowmeter. The system can accurately identify liquid level and non-full pipe flow, and meet the stability requirements of detection while maintaining high measurement accuracy, which proves the feasibility of operation and deployment of edge calculation in ultrasonic flow detection.