Article: Construction of a natural gas pipeline intelligent early warning platform based on the integration of multi-source heterogeneous data management and GAN reinforcement learning Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.7 pp.1 - 25 Abstract: The platform proposed in this paper is a combination of two technologies (a data governance module and an intelligent early warning model). It features a metadata-driven heterogeneous data fusion model that provides a means of efficiently cleaning, aligning, and building feature sets to integrate multiple sources of data (such as SCADA and GIS) by providing a solution for the challenge of integrating disparate types of datasets. The combination of a conditional generative adversarial network (CGAN) and reinforcement learning enables the generation of high-quality synthetic anomaly sample datasets based on the 'expectation' of future performance, informed by conditional fault characteristics (CFC). The newly generated enhanced data will be utilised to train an early warning agent using a deep Q-QN model. Using this approach to maximise cumulative rewards will provide a means to train the best possible early warning strategy, enabling a proactive and accurate approach to identify and manage risk in pipelines. Inderscience Publishers - linking academia, business and industry through research

Title: Construction of a natural gas pipeline intelligent early warning platform based on the integration of multi-source heterogeneous data management and GAN reinforcement learning

Authors: Huichao Guo; Runhua Huang; Yanzhi Huang

Addresses: School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430000, Hubei, China; Zhejiang Business College, Hangzhou, 310000, Zhejiang, China ' Zhejiang Business College, Hangzhou, 310000, Zhejiang, China; The Chinese University of Hong Kong, Shenzhen, 518100, Guangdong, China ' Zhejiang Business College, Hangzhou, 310000, Zhejiang, China

Abstract: The platform proposed in this paper is a combination of two technologies (a data governance module and an intelligent early warning model). It features a metadata-driven heterogeneous data fusion model that provides a means of efficiently cleaning, aligning, and building feature sets to integrate multiple sources of data (such as SCADA and GIS) by providing a solution for the challenge of integrating disparate types of datasets. The combination of a conditional generative adversarial network (CGAN) and reinforcement learning enables the generation of high-quality synthetic anomaly sample datasets based on the 'expectation' of future performance, informed by conditional fault characteristics (CFC). The newly generated enhanced data will be utilised to train an early warning agent using a deep Q-QN model. Using this approach to maximise cumulative rewards will provide a means to train the best possible early warning strategy, enabling a proactive and accurate approach to identify and manage risk in pipelines.

Keywords: multi-source heterogeneous data governance; conditional generative adversarial network; deep Q-network; DQN; natural gas pipeline; intelligent early warning; conditional fault characteristics; CFC.

DOI: 10.1504/IJDMB.2026.154768

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.7, pp.1 - 25

Received: 06 Jan 2026
Accepted: 01 Apr 2026
Published online: 13 Jul 2026 *

Title: Construction of a natural gas pipeline intelligent early warning platform based on the integration of multi-source heterogeneous data management and GAN reinforcement learning

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