Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Yongtao Wang (), Yinhui Feng, Chengfeng Xi, Bochao Wang, Bo Tang and Yanzhao Geng
Additional contact information
Yongtao Wang: Innovation Research Institute, Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China
Yinhui Feng: Innovation Research Institute, Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China
Chengfeng Xi: Innovation Research Institute, Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China
Bochao Wang: Innovation Research Institute, Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China
Bo Tang: Innovation Research Institute, Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China
Yanzhao Geng: Innovation Research Institute, Beijing Tianma Intelligent Control Technology Co., Ltd., Beijing 101399, China

Energies, 2024, vol. 17, issue 20, 1-16

Abstract: Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

Keywords: cloud–edge collaboration; real-time data warehouse; stream processing; massively parallel processing (MPP) database; knowledge base; large language model (LLM); retrieval-augmented generation (RAG) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/20/5205/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/20/5205/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:20:p:5205-:d:1502221

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().