Optimization of Statistical and Monitoring Methods for High-Tech Industries in Shandong Province

Qing Zhu; Xiaoxia Liu; Mengying Guo; Chen Zhang; Xuancheng Du

doi:10.2991/978-94-6239-721-7_30

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Optimization of Statistical and Monitoring Methods for High-Tech Industries in Shandong Province

Authors

Qing Zhu², Xiaoxia Liu³, Mengying Guo², Chen Zhang¹, Xuancheng Du³^{, *}

¹Shandong Shanke Industrial Development Research Institute Co., Ltd., Jinan, Shandong, China

²Shandong Institute of Innovation and Development, Jinan, Shandong, China

³Shandong Scitech Innovation Group Co., Ltd., Jinan, Shandong, China

^*Corresponding author. Email: duxc@sdscicom.com

Corresponding Author

Xuancheng Du

Available Online 6 July 2026.

DOI: 10.2991/978-94-6239-721-7_30 How to use a DOI?
Keywords: High-tech industry; statistical scope; monitoring and early warning; dynamic adjustment; Shandong Province
Abstract: The high-tech industry is a core driver of high-quality regional economic development, and the scientific rigor of its statistical and monitoring work directly impacts the precision of policy formulation. This paper systematically reviews domestic and international practices in the statistics and monitoring of high-tech industries, and thoroughly analyzes the current issues in Shandong Province’s statistical scope, including outdated definitions, poor alignment with national standards, and the lack of a dynamic adjustment mechanism. To address these challenges, this paper proposes an optimization path based on a three-dimensional reconstruction of the statistical scope (“industry + enterprise + product”), a dynamic revision scheme for the statistical catalog, and an integrated monitoring operation mechanism. The aim is to provide theoretical support and practical reference for establishing a scientific, precise, and dynamic statistical and monitoring system for high-tech industries in Shandong Province.
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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Volume Title: Proceedings of the 2026 6th International Conference on Public Management and Intelligent Society (PMIS 2026)
Series: Advances in Engineering Research
Publication Date: 6 July 2026
ISBN: 978-94-6239-721-7
ISSN: 2352-5401
DOI: 10.2991/978-94-6239-721-7_30 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Qing Zhu
AU  - Xiaoxia Liu
AU  - Mengying Guo
AU  - Chen Zhang
AU  - Xuancheng Du
PY  - 2026
DA  - 2026/07/06
TI  - Optimization of Statistical and Monitoring Methods for High-Tech Industries in Shandong Province
BT  - Proceedings of the 2026 6th International Conference on Public Management and Intelligent Society (PMIS 2026)
PB  - Atlantis Press
SP  - 331
EP  - 338
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-721-7_30
DO  - 10.2991/978-94-6239-721-7_30
ID  - Zhu2026
ER  -

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