In order to solve the problems of missing alarms, false alarms, and complex data processing due to inaccurate original data in the existing big data calculation model of abnormal electricity consumption, an online monitoring sensor for distributed installation of line nodes on the primary side was developed. The reliability of data acquisition is ensured through the design of the hardware; the mathematical model of the sensor sampling error is established, the mechanism of the error formation after the sensor core is introduced into the air gap is analyzed, and the simulation optimization is carried out. The fluctuating index and coefficient of variation of the collected current signal are introduced, the centroid of the data sample is selected, and the outliers are screened according to the centroid and outlier algorithm, which greatly reduces the complexity of data calculation and improves the reliability of electricity abnormality discrimination. Functional tests show that the current data collected by the sensor is highly accurate, the relative error is less than 0. 2%, and the data synchronization error is less than 5 μs, providing reliable collection data for the calculation model.