【摘要】 为提高全自动生化分析仪的测试精度与检测效率，提出一种基于格拉姆角场（GAF）与并行KConvNeXt网络的检测模型，以准确判定采样系统在采样过程中由样本针堵塞所产生的异常情况。首先运用GAF方法，将一维采样压力信号的时间序列转化为二维图像。随后，采用改进的注意力机制结合并行双通道KConvNeXt网络对压力信号进行分类，最终实现94.58%的分类准确率。实验结果表明，提出的方法能有效捕捉采样压力信号的关键特征，为生化分析仪采样系统异常压力检测提供一种高效的解决方案，具有重要的实际应用价值。更多还原

【Abstract】 A detection model based on Gramian angular field(GAF) and parallel KConvNeXt network is proposed for accurately detecting the abnormal conditions caused by sample needle blockage in the sampling system during the sampling,thus improving the testing accuracy and detection efficiency of automated biochemical analyzers. GAF-based method is employed to transform the time series of one-dimensional pressure signals into two-dimensional image representations.Subsequently, an improved attention mechanism integrated with a parallel dual-channel KConvNeXt network is used to classify the pressure signals, and achieves a final classification accuracy of 94.58%. The experimental results show that the proposed method can effectively capture the key characteristics of the pressure signals, offering an efficient solution for the anomalous pressure detection in biochemical analyzer sampling system and exhibiting important practical significance.更多还原