Escalator monitoring and early warning method and system combining event driving and visual large model

By combining event-driven and visual big data model-based escalator monitoring and early warning methods, dynamically adjusting the image acquisition frequency and constructing an edge inference cluster, the problems of resource consumption and recognition robustness are solved, and efficient risk identification for high-altitude operations is achieved.

CN122157139APending Publication Date: 2026-06-05CHINA DATANG GRP DIGITAL TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA DATANG GRP DIGITAL TECH CO LTD
Filing Date
2026-01-12
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies suffer from huge resource consumption and rigid edge computing scheduling mechanisms due to the use of full-time high-frequency continuous analysis of video streams. This results in insufficient understanding of key actions such as climbing and escalator movements, and the multimodal fusion method is fragile, making it impossible to achieve accurate risk identification of escalator behavior during high-altitude operations.

Method used

An escalator monitoring and early warning method combining event-driven and visual big data models is proposed. By using low-frequency frame acquisition, edge inference clusters, and visual multimodal big data models, the image acquisition frequency is dynamically adjusted. A lightweight visual model instance resource pool and multi-core load balancing scheduling are constructed to perform spatial topology and behavior analysis, and generate escalator compliance judgments and risk warnings.

Benefits of technology

Achieve low resource consumption and high-precision visual semantic understanding in large-scale, multi-channel video surveillance scenarios, improve the accuracy of action recognition in complex scenes, and enhance the robustness of risk monitoring for high-altitude operations in industrial environments.

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Abstract

The application provides an escalator monitoring and early warning method and system combining event driving and a visual large model, relates to the technical field of monitoring and early warning, and comprises the following steps: accessing a video monitoring network to obtain real-time video streams of a work site; normalizing frames of the real-time video streams with a low acquisition frequency as an initial state; performing image recognition on the initial acquisition image frames, configuring an image acquisition frequency according to the obtained event state, and performing layered frame acquisition on the real-time video streams; constructing an edge inference cluster composed of a single-core multi-model instance resource pool and a multi-core load balancing scheduling group; inputting the screened acquisition image frames into a visual multi-modal large model, performing semantic analysis based on spatial topological relations, escalator boarding behaviors, escalator behaviors and work contexts, and generating escalator compliance judgment and risk early warning results. The application can solve the technical problem of poor escalator monitoring and early warning accuracy in the prior art and achieve the technical effect of improving escalator monitoring and early warning accuracy.
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