High static pressure difference prediction method for piston pressure gauge
By using displacement sensors and high-precision pressure sensors in the piston pressure gauge to collect data, perform error detection and model correction, the accuracy and accuracy problems in high-static pressure differential measurement are solved, and efficient prediction results are achieved.
CN120274936AInactive Publication Date: 2025-07-08SOUTHWEST UNIV
View PDF 0 Cites 0 Cited by
Patent Information
- Application Number
- CN202510351407.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-07-08
- Estimated Expiration
- Not applicable · inactive patent
Smart Images

Figure CN120274936A_ABST
Abstract
The invention relates to the field of piston pressure prediction, and discloses a high static pressure difference prediction method for a piston pressure gauge, and the method comprises the steps: collecting and recording the displacement data and pressure data during the movement of a piston, recording the environment data during the movement of the piston, and carrying out the preprocessing and error analysis of the collected data, thereby obtaining a high static pressure difference prediction result. The data acquisition module is used for judging whether the acquired data is accurate or not, obtaining a current differential pressure value through differential pressure analysis, calculating an actual differential pressure evaluation coefficient by combining the current differential pressure value with a differential pressure value within a period of historical time, and predicting the high static pressure difference of the piston pressure gauge by constructing a mathematical model of the piston pressure gauge. And performing error analysis on the differential pressure prediction result predicted by the model and the actual differential pressure evaluation coefficient to obtain an error factor, correcting the error generated by the model through the error factor, and outputting the prediction result after error correction, thereby being beneficial to ensuring the comprehensiveness and accuracy of data acquisition, and improving the accuracy of data acquisition. And the accuracy and reliability of model prediction are further improved.
Need to check novelty before this filing date? Find Prior Art