Enterprise Process Monitoring Intelligence Based on Multi-Source Heterogeneous Data Fusion: A Coupling Coordination Degree Model Perspective

DOI

10.2991/978-94-6239-721-7_26How to use a DOI?

Keywords

multi-source heterogeneous data; process monitoring; coupling coordination; industrial IoT; intelligent systems

Abstract

The convergence of next-generation information technology and manufacturing has created new pathways for enterprises to achieve intelligent transformation in process monitoring. However, the integration of massive heterogeneous data and the improvement of cross-system coordination efficiency remain key technical challenges. This study introduces a system coupling coordination degree model to construct a hierarchical evaluation framework for enterprise process monitoring. The framework encompasses four dimensions: infrastructure, data resources, protocol standards, and intelligent algorithms, with 12 key indicators. The entropy weight method is employed for objective weight quantification. Experimental validation based on real industrial data from 12-month studies demonstrates that the proposed model achieves 98.7% temperature detection accuracy and 81.4% balanced classification accuracy. The coupling coordination degree can be improved from 0.38 to 0.89, representing a 134.2% enhancement. This research provides theoretical support and empirical reference for the digital and intelligent collaborative upgrading of enterprise process monitoring.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yi Hou
AU  - Haisheng Hong
AU  - Jiajun Tong
AU  - Yongshu Chen
AU  - Ronglin Li
AU  - Huanhuan Li
AU  - Xiaoyun Wang
AU  - Jianyu Zhao
PY  - 2026
DA  - 2026/07/06
TI  - Enterprise Process Monitoring Intelligence Based on Multi-Source Heterogeneous Data Fusion: A Coupling Coordination Degree Model Perspective
BT  - Proceedings of the 2026 6th International Conference on Public Management and Intelligent Society (PMIS 2026)
PB  - Atlantis Press
SP  - 278
EP  - 295
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-721-7_26
DO  - 10.2991/978-94-6239-721-7_26
ID  - Hou2026
ER  -