Research on Building the Science and Technology Evaluation Scientific Data Center of Gansu Province Utilizing Hadoop and ThinkPHP Technologies
Through analyzing the construction of scientific data both domestically and internationally, the resource construction system for establishing a scientific data center for scientific and technological evaluation is clarified, and the Gansu science and technology data center for scientific and technological evaluation is constructed. Based on key technologies such as QR algorithm, Hadoop technology, and ThinkPHP framework, the functional requirements of the system are analyzed. The construction of the resource system is scientifically planned, the system architecture is designed, data import and export are optimized, and the Gansu science and technology evaluation scientific data center system is constructed to ensure the comprehensive aggregation and efficient integration of data and to realize the expansion of applications. Through the construction of the Gansu science and technology evaluation scientific data center, centralized storage and efficient display of scientific and technological data are achieved. The collection, collation, storage, and management of scientific data are comprehensively planned, and the utilization efficiency of scientific data is enhanced. It provides comprehensive, accurate, and timely data support for scientific and technological evaluation and also offers a powerful basis for scientific and technological decision-making, promoting scientific and technological innovation and development, and contributing to the scientific and technological progress and economic development of the Gansu province.
Authors
- Fei Liu (ORCID: https://orcid.org/0000-0001-7141-5105)
- Xinhong Yin
- Xianbo Li (ORCID: https://orcid.org/0000-0002-2949-2753)
Institutions
- University of Electronic Science and Technology of China (CN)
- Institute of Scientific and Technical Information (CN)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/aeis65978.2024.00019
- Primary Topic
- Advanced Decision-Making Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00