针对电阻抗断层成像(EIT)技术中FCM聚类算法的灵敏度系数信息缺失以及测量电压的利用率低两方面问题,提出一种新的成像算法。在该算法中引入灵敏度系数矩阵信息修正各个剖分单元的电压。同时提出了将测量电压数据按照其权系数进行处理的方法,该方法可应用于所有EIT经典反演算法之中。理论和数值仿真结果均表明,与已有的FCM聚类算法相比,优化后算法对两相流型的定位准确度更高,得到的重建图像的空间分辨率与之前相比相对误差降低了5%~15%,相关系数提高了5%~20%。 Aiming at the problem of lacking of sensitivity coefficient information and low utilization rate of measurement voltage in FCM clustering algorithm in electrical impedance tomography (EIT), a new imaging algorithm is proposed. Sensitivity coefficient matrix information is introduced into the algorithm to correct the voltage of each cell. At the same time, a method of processing measured voltage data according to its weight coefficient is proposed, which can be applied to all EIT classical inversion algorithms. Both theoretical and numerical simulation results show that compared with the existing FCM clustering algorithm, the proposed algorithm has higher localization accuracy for the two-phase flow pattern and the relative error of the spatial resolution of the reconstructed image is lower than before 5% ~ 15%, the correlation coefficient increased by 5% ~ 20%.