Research on Application of AI Enabled Industrial Safety Service Support System Architecture
Abstract
continue to be complicated, and the traditional experience led safety control mode has obvious shortcomings, which can not meet the dynamic
safety control needs of modern industry. With the technical advantages of data mining, intelligent identification and trend prediction, artificial
intelligence has become the core support for the intelligent upgrading of industrial safety service system. Combined with the actual working
conditions of industrial safety production, this paper systematically summarizes various defects of the traditional industrial safety service support system, constructs a hierarchical industrial safety service support system architecture enabled by artificial intelligence, expounds the core
technology principles and application advantages of the system, combs the practical bottlenecks of the current technology and puts forward
targeted optimization paths. The research results can realize the transformation of industrial safety management and control from passive disposal to active pre judgment, and provide theoretical reference and technical support for the construction of industrial safety intelligent governance and standardized service system.
Keywords
Full Text:
PDFReferences
[1] Wang Lei, Li Gang. Research on the application of artificial intelligence technology in industrial safety risk early warning [J]. Journal of
Mine Automation, 2022, 48 (07): 102-107.
[2] Chen Xi, zhang Wenjie. Design of industrial safety intelligent monitoring system based on multi-source data fusion [J]. Computer Engineering and Applications, 2023, 59 (12): 231-238.
[3] Liu Jianing, Wang Hao. Construction of intelligent security service support system in the industrial Internet scenario [J]. Manufacturing
Automation, 2023, 45 (09): 156-160.
[4] Zhao Qiming, Sun Yang. Research on Upgrading Path of industrial safety production management and control enabled by artificial intelligence [J]. Journal of Safety and Environment, 2022, 22 (08): 3452-3458.
DOI: http://dx.doi.org/10.70711/aitr.v4i2.9833
Refbacks
- There are currently no refbacks.