An elevator operation scheduling method and system, an electronic device, and a storage medium
By fusing image and sensor data through a digital twin model, the risk areas of the elevator guide rail are predicted, solving the problem that existing technologies cannot identify structural hazards, enabling proactive prevention and safe avoidance, and improving the safety of elevator operation.
CN122444037APending Publication Date: 2026-07-24HUBEI UNIV OF ARTS & SCI
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUBEI UNIV OF ARTS & SCI
- Filing Date
- 2026-06-17
- Publication Date
- 2026-07-24
AI Technical Summary
Technical Problem
Existing technologies cannot effectively identify and prevent structural hazards in elevator guide rails, posing safety risks.
Method used
By employing a digital twin model that integrates real-time images and multi-source sensor data, the future stress field of the guide rails along the cage's running path is predicted, the risk index is quantified, and an intervention running path that avoids high-risk areas is planned through a path scheduling model.
Benefits of technology
This represents a leap from passive response to proactive prediction, enabling the early prediction and avoidance of high-risk areas and improving the safety and reliability of elevator operation.
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Figure CN122444037A_ABST
Abstract
The application discloses an elevator operation scheduling method and system, electronic equipment and a storage medium, and the elevator operation scheduling method is used for cage operation scheduling and comprises the following steps: acquiring an initial scheduling strategy of a cage, real-time multi-path image data and real-time multi-path sensor data, wherein the initial scheduling strategy at least comprises a cage operation path; processing the initial scheduling strategy, the real-time multi-path image data and the real-time multi-path sensor data through a preset digital twin model, predicting a future stress field of a guide rail of the cage operation path, and obtaining a risk index and a risk area. The application provides that the initial scheduling strategy, real-time image data and multi-source sensor data are fused and processed through the digital twin model, the future stress field of the guide rail on the cage operation path is predicted in advance, and the risk index and the risk area are quantitatively generated; when the risk index exceeds a safety threshold, an intervention operation path for avoiding a high-risk area is actively triggered by a path scheduling model.
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