Article Details

Abstract

To address the challenges of traditional coal mine safety inspection systems, such as long development cycles, high costs, and poor adaptability, this paper proposes an innovative solution integrating zero-code orchestration with customizable AI configuration. The approach features a visual component-based intelligent orchestration platform tailored for coal mine scenarios, enabling drag-and-drop configuration of inspection workflows, multi-source data fusion, and flexible AI model composition. The system includes preconfigured AI templates and parameters for typical mining scenarios, such as longwall faces, heading faces, and haulage roadways, allowing non-technical personnel to rapidly deploy customized intelligent inspection systems. Field implementation demonstrates that development time reduces from 7 days to 1 hour, costs decrease by 80%, inspection efficiency improves by 75%, and risk response time shortens by 83.3%. This solution offers a low-cost, high-efficiency pathway for intelligent inspection of coal mine safety inspections, demonstrating significant practical value and broad applicability.

Keywords

intelligent orchestration; coal mine safety; intelligent inspection; system design; risk early warning

References

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About the authors

Li Xuan (1999-), male, Han ethnicity, from Wuhai, Inner Mongolia, assistant engineer, bachelor's degree, research direction: coal mine intelligence; Rao Junhong (1989-), male, Han ethnicity, from Chongqing, assistant researcher, undergraduate degree, research direction: image processing, coal mine automation; Zhu Xinglin (1978-), male, Han nationality, from Yibin, Sichuan, assistant researcher, master's degree, research direction: coal mine automation control. Luo Chuan (1997-), male, Tujia ethnic group, Chongqing native, assistant researcher, master's degree, research direction: image processing.