Pipeline impact force observation-based intelligent measurement method for liquid flow

This paper proposes an innovative measurement method that uses the impact force generated when the liquid flows through the pipe as an observation indicator, and successfully establishes a non-linear mapping relationship between the impact force sequence and the weight of the flowing liquid by training and learning the collected impact force sequence through the CLCD (CNN-LSTM-CNN-Double) network architecture. In response to the challenges such as the prevalent interference factors and inconsistent flow time lengths in the collected data, this paper introduces a new weight ratio algorithm, WRP (Weight-Ratio-Process), which effectively improves the robustness and accuracy of data processing. The experimental results show that the effective detection rate of the method reaches 90 % when the weighing error is set to ±5g on the constructed fluid impact force test platform. When the error range is relaxed to ±15g, the effective detection rate is increased to 98 %. This achievement demonstrates the broad application potential and practical value of the method in the field of fluid transport measurement.