In order to achieve high-precision detection and transmission of remote fiber optic sensing networks under noise interference conditions, an optimized differential (OD) denoising method is proposed. Construct a new differential algorithm based on the moving average method, and combine low-pass algorithm and median algorithm to predict edge information and signal values; Using double discrimination to compensate for the shortcomings of edge information misjudgment and optimize the ability to accurately locate edge information; Combining weighted average method and Kalman filter (KF), the accuracy of signal prediction is optimized. The experimental verification results show that using this method for data preprocessing will enable the system to detect data normally, with a measurement accuracy of 0.76%, and improve the anti noise performance of the detection system. The comparative experimental results with an advanced wavelet threshold denoising method show that the slope optimization method can improve the measurement accuracy by more than 0.62 times under ≥10 dB noise interference.