An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, trad...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/488 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587516596715520 |
|---|---|
| author | Yi Sun Faxiu Ji |
| author_facet | Yi Sun Faxiu Ji |
| author_sort | Yi Sun |
| collection | DOAJ |
| description | Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety. |
| format | Article |
| id | doaj-art-a3d35d8fda764efdad1d8f9b8b011e77 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a3d35d8fda764efdad1d8f9b8b011e772025-01-24T13:49:07ZengMDPI AGSensors1424-82202025-01-0125248810.3390/s25020488An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language ModelsYi Sun0Faxiu Ji1School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaArtificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.https://www.mdpi.com/1424-8220/25/2/488coal mine safety assessmentembodied intelligencemulti-source sensor datamulti-level architecturelogical reasoning |
| spellingShingle | Yi Sun Faxiu Ji An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models Sensors coal mine safety assessment embodied intelligence multi-source sensor data multi-level architecture logical reasoning |
| title | An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models |
| title_full | An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models |
| title_fullStr | An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models |
| title_full_unstemmed | An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models |
| title_short | An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models |
| title_sort | embodied intelligence system for coal mine safety assessment based on multi level large language models |
| topic | coal mine safety assessment embodied intelligence multi-source sensor data multi-level architecture logical reasoning |
| url | https://www.mdpi.com/1424-8220/25/2/488 |
| work_keys_str_mv | AT yisun anembodiedintelligencesystemforcoalminesafetyassessmentbasedonmultilevellargelanguagemodels AT faxiuji anembodiedintelligencesystemforcoalminesafetyassessmentbasedonmultilevellargelanguagemodels AT yisun embodiedintelligencesystemforcoalminesafetyassessmentbasedonmultilevellargelanguagemodels AT faxiuji embodiedintelligencesystemforcoalminesafetyassessmentbasedonmultilevellargelanguagemodels |