Widespread video sensors record rainfall information continuously. Video-based rainfall data estimation， with high spatio-temporal resolution， has become one of the most promising methods of rainfall data collection to date. However， due to the complexity and variability of sensor devices， video scenarios， etc.， the quality of rainfall data estimated can often contrast between individual visual sensors. Further processing is required to ensure the quality of rainfall inversion results. Inspired by Tobler's First Law of Geography， this study presents a precision control model （PCM） for video-based-rainfall inversion results correction. The model uses the spatio-temporal information between camera nodes， within the Visual Sensor Network， as the constraint. Rainfall events were analyzed from the dimensions of spatio-temporal consistency， situational consistency， and correlation， to achieve a high-precision representation of rainfall data. A multi-granularity filtering method was adopted for rainfall inversion using mutual verification of rainfall information among video nodes. The experimental results show that the PCM model can effectively improve rainfall inversion accuracy and stability in various rainfall scenarios. The mean value of the relative error of rainfall intensity （RI） is reduced by approximately 14.85% in light or medium rainfall scenarios， and approximately 19.90% in heavy or violent rainfall scenarios； For the standard deviation of the related error of RI， approximately 40.87% reduction for medium and light rain scenarios， and approximately 40.96% reduction for heavy rain scenarios. The results of this study confirm that the proposed PCM can provide support to produce high-quality rainfall data.