Era of big data, digitalization and intelligentization: New paradigm of the whole process and total factor quality control

ZUO Qunchao; ZHANG Zhihui; SONG Yue

doi:10.12029/gc20240919002

2025 Vol. 52, No. 3

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ZUO Qunchao, ZHANG Zhihui, SONG Yue. 2025. Era of big data, digitalization and intelligentization: New paradigm of the whole process and total factor quality control[J]. Geology in China, 52(3): 890-914. doi: 10.12029/gc20240919002

Citation:

ZUO Qunchao, ZHANG Zhihui, SONG Yue. 2025. Era of big data, digitalization and intelligentization: New paradigm of the whole process and total factor quality control[J]. Geology in China, 52(3): 890-914. doi: 10.12029/gc20240919002

Era of big data, digitalization and intelligentization: New paradigm of the whole process and total factor quality control

1.
Development and Research Center of China Geological Survey, Beijing 100037, China
2.
Command Center of Natural Resource Comprehensive Survey of China Geological Survey, Beijing 100055, China

Fund Project: Supported by the projects of China Geological Survey (No.DD20190570, No.DD20160052), National Mineral Resources Potential Evaluation Plan project (2006−2013), Deep and Peripheral Prospecting Plan for Old Mines (2012−2014), National Special Project for Prospecting Replacement Resources in Crisis Mines (2004−2010).

More Information

Author Bio: Zuo Qunchao, male, born in 1964, professor−level senior engineer, engaged in mineral geological survey and deep prospecting prediction, construction and application of geological big data, and computer geological intelligent methods and technologies; E-mail: zuoqc163@163.com

Received Date: 19 September 2024

Revised Date: 21 October 2024

Publish Date: 25 May 2025

Abstract

Abstract

This paper is the result of mineral exploration engineering.
Objective
The current hotspots on big data are almost focused on how to discover or reveal the hidden value or secrets of big data, and there is an extreme lack of systematic, in−depth, and practical research or solutions on how to improve the quality of big data construction. However, the quality of big data construction is crucial for successfully discovering or revealing the hidden value or secrets of big data, and for making or implementing scientific and accurate decisions.
Methods
Firstly, by analyzing and summarizing the existing relevant research results, including the quality control experiences of the big data construction of China's National Mineral Resource Potential Evaluation (2006−2013), the entity data models for ore−searching prognosis in mineralization concentrating areas, and the basic framework of quality control models in the field of earth science. Then, with the help of big data thinking and digital and intelligent technologies, a digital and intelligent system of the whole process and total factor quality control for ore−searching prognosis and big data entity construction in mineralization concentrating areas is established. Finally, a new paradigm of the whole process and total factor quality control is proposed, which can be extended and supported to the quality control of big data asset construction in the field of earth science and beyond.
Results
This study has proposed the quality control theory and method on data division and graininessl, established a digital and intelligent system of the whole process and total factor quality control for ore−searching prognosis and big data entity construction in mineralization concentrating areas, developed quality control software, formulated standards and specifications for data quality check and evaluation, and efficiently supported and fulfilled the quality control works such as self check, mutual check, special check, supervision and check, field acceptance, initial review, final review, re−examination, and confirmation of acceptance for the construction of big data entities for ore−searching prognosis in mineralization concentrating areas.
Conclusions
The digital and intelligent system of the whole process and total factor quality control for ore−searching prognosis and big data entity construction in mineralization concentrating areas has originality, practicality, efficiency and universality, which can directly be expand to and support the quality control of big data asset construction in the field of earth science and beyond. The proposed new paradigm of the whole process and total factor quality control also is scientific, effective, and universally applicable. The proposed concept of ontology constraint for data model has universality and practical significance, that is, the definition of data model should meet the requirements of conceptualization, sharing, explicitness, and formalization, applicable to two−dimensional or high−dimensional map−type data entities, as well as simple(e.g. data sheet) or complex(e.g. relation database)（relation database） table−type data entities.
- data quality control /
- big data thinking /
- digital transformation /
- intelligent transformation /
- digital and intelligent technologies /
- geological survey engineering /
- mineral exploration engineering

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