Under the background of the new power system, parameter identification and correction based on measurement data are key requirements for the digitalization and intelligence of distribution networks. A parameter identification method is proposed that integrates the mathematical model of the distribution network with measurement data collected from the low-voltage side of distribution transformers, aiming to address the online identification and correction of line and transformer parameters. Firstly, the topology is obtained by parsing CIM files, and the initial parameters of components are set according to asset data. Then, a quasi-Newton-Raphson method is employed to construct the Jacobian matrix based on multiple sets of measurement data, enabling iterative parameters updated by solving underdetermined or overdetermined equations. Finally, the identified parameters are used to recalculate the low-voltage side voltages, which are then compared with the measured values for validation. Experimental results based on data from a practical distribution network in South China demonstrate that, with increasing data sets and iteration times, the average relative errors converge to a stable level. This verifies that the proposed method achieves high accuracy and practicality, and can provide effective support for power flow calculation, state estimation, and digital twin construction in distribution networks.