A mobile crane operation state real-time monitoring method based on digital twinning
By deploying multi-source sensors on mobile cranes for clock synchronization and resampling, and inputting aligned multimodal feature data into a physical information neural network for calculation, combined with the fusion analysis of twin data layers and closed-loop intervention of virtual and real mutual control, the problem of insufficient monitoring and control lag in existing monitoring systems under complex working conditions is solved, and dynamic monitoring and risk warning of crane operation status are realized.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- EUROCRANE (CHINA) CO LTD
- Filing Date
- 2026-06-11
- Publication Date
- 2026-07-17
AI Technical Summary
Existing mobile crane operation monitoring systems struggle to achieve clock synchronization and spatiotemporal alignment of global multimodal characteristics under complex working conditions. They fail to deeply couple data-driven processes with physical mechanisms, resulting in an inability to predict the dynamic swaying and overturning risks of heavy objects. Furthermore, the incomplete reverse control link makes it difficult to output flexible feedforward constraint commands during potential risk periods, leading to problems such as insufficient monitoring capabilities, poor interactivity, and low digitalization.
By deploying multi-source heterogeneous sensors to collect data in real time and performing clock synchronization and resampling spatiotemporal alignment, using physical information neural networks to perform multimodal feature calculations, and combining twin data layers for fusion analysis, real-time swing posture data is generated and a dynamic safety margin index is output. Virtual and real mutual control closed-loop intervention is performed to achieve visualized anomaly tracing.
It enables dynamic monitoring of the operating status of mobile cranes, visual tracing of abnormal processes, and early warning of potential risks, thereby improving the safety and intelligent management of the operation process and overcoming the problems of insufficient monitoring and control lag in existing systems under complex working conditions.
Smart Images
Figure CN122403281A_ABST