Pressure mapping method and device for high-precision pressure sensor array
By introducing technical means such as double-cubic spline interpolation with tension coefficient compensation and depth separation convolutional networks in the pressure sensor array, the accuracy and reliability problems of traditional pressure sensor arrays reflecting continuous pressure distribution are solved, and high-precision and real-time pressure mapping is achieved.
Patent Information
- Application Number
- CN202411841486.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-04-01
- Estimated Expiration
- 2044-12-13
AI Technical Summary
Traditional pressure sensor arrays have nonlinear response characteristics and discreteness of spatial distribution when reflecting continuous pressure distribution, resulting in insufficient measurement accuracy and reliability. The existing pressure mapping methods are difficult to effectively deal with noise interference and zero-point drift, with high computational complexity and poor real-time performance.
The continuous pressure distribution between the sensing units is reconstructed using a double-cubic spline interpolation algorithm with tension coefficient compensation, and spatial feature extraction and channel feature fusion through a deep separable convolutional network, combining the pressure state probability model and confidence propagation mechanism, a data reliability evaluation system is established, and the fusion weight is adaptively calculated through the deep Q learning network.
The pressure mapping accuracy is improved, high real-timeness is ensured, and the problems of data distortion, low computing efficiency and insufficient reliability are solved, and the intelligent fusion of pressure data and multi-scale feature extraction are realized.