Electrical impedance tomography (EIT) is an imaging technique that realizes the image reconstruction of conductivity distribution in the field. The existing EIT algorithms ignore the hidden fuzzy features during imaging, making the EIT technique exhibit a high degree of uncertainty, imprecision, incompleteness, and inconsistency in the actual use process, resulting in a low spatial resolution of the reconstructed images. In order to solve this problem, we introduce fuzzy linear programming into EIT imaging. On the basis of analyzing the fuzzy features of EIT in detail, a new model of fuzzy optimization is built up, whose optimal solution is obtained by using constrained coefficient fuzzy linear programming. To this end, we devise two types of simulation experiments to verify the performance of the optimization algorithm. Experimental results prove that compared with the traditional Tikhonov regularization algorithm, the correlation coefficient of the reconstructed image of the proposed algorithm is higher, the relative error value is smaller.

电阻抗层析成像技术（Electrical impedance tomography，EIT）是一种实现现场电导率分布图像重建的成像技术。现有的EIT算法忽略了成像过程中隐藏的模糊特征，使得EIT技术在实际使用过程中表现出高度的不确定性、不精确性、不完整性和不一致性，导致重建图像的空间分辨率较低。为了解决这个问题，我们将模糊线性规划引入到EIT成像中。在详细分析EIT模糊特性的基础上，建立了一种新的模糊优化模型，利用约束系数模糊线性规划求得其最优解。为此，我们设计了两种类型的仿真实验来验证优化算法的性能。