Abstract: To improve the temperature measurement accuracy of online infrared thermal imagers in foggy weather, the effects of distance, relative humidity, and fog on temperature measurement accuracy of infrared thermal imagers were studied. A secondary thermal infrared fault data acquisition system was used to build an experimental platform for temperature measurement experiments under single-and multi-factor interference, thereby obtaining a piecewise polynomial fitting relationship between distance and error temperature. Based on the prior theory of dark channel, the quantitative description of fog was realized, and the exponential function fitting relationship between transmittance and error temperature was obtained. By way of algebraic sum, an error compensation model was proposed to compensate the measurement error caused by the interaction of distance and fog. Experimental results show that this model can significantly improve the temperature measurement accuracy of thermal imagers. For an online infrared thermal imager, collecting and storing temperature data for a long time in foggy environments are of great significance in building an equipment fault data feature database.