A solar silicon wafer bearing basket real-time monitoring system based on an internet of things
The real-time monitoring system for flower baskets supported by solar silicon wafers, which utilizes IoT technology and multi-source data fusion analysis, solves the problem of insufficient monitoring of single parameters in existing technologies. It enables refined monitoring and dynamic risk assessment of the entire life cycle of the flower baskets, improving the accuracy of monitoring results and early warning capabilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU LINYUAN PRECISION MACHINERY CO LTD
- Filing Date
- 2026-04-20
- Publication Date
- 2026-07-21
AI Technical Summary
Existing monitoring systems for solar silicon wafers supporting flower baskets mostly focus on monitoring single parameters and lack the ability to integrate and analyze multi-source data. They cannot combine structural response characteristics with historical process data for comprehensive evaluation, resulting in insufficient accuracy of monitoring results and early warning capabilities, and thus failing to meet usage requirements.
Design an IoT-based real-time monitoring system for solar silicon wafer-supported flower baskets. Through identification and documentation modules, process acquisition modules, excitation application modules, response acquisition modules, parameter inversion modules, damage calculation modules, threshold generation modules, and early warning output modules, the system realizes full life cycle monitoring and dynamic risk assessment of the flower baskets. By combining parameters such as local stiffness, off-center loading, and instability trend for coupled analysis, the system generates dynamic risk assessment thresholds adapted to the current service status and outputs the abnormal location and risk level of the basket.
It enables refined monitoring of the load-bearing flower baskets, allowing for early detection of potential local stiffness attenuation, eccentric load deformation, and local instability. This improves the accuracy and consistency of early warning results, avoids the judgment bias of fixed thresholds, and enhances the continuity and adaptability of the monitoring system.
Smart Images

Figure CN122431275A_ABST