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<p>S&amp;T management data has a wide range of sources and types, and the innovation of S&amp;T management data service model is an important way for the efficient utilization of S&amp;T resources in the new era, so this paper creates an innovative model of S&amp;T management data service based on Data-Information-Knowledge-Wisdom model and artificial intelligence technology. Heterogeneous data mining technology based on association rules is used to obtain the connection between S&amp;T management data, Lagrange interpolation is used for heterogeneous data cleaning to predict the missing values of S&amp;T management data, and data lineage resolution technology is used to solve the challenges brought by the complex and diverse S&amp;T management data components. Experimental analyses are conducted from both S&amp;T management data processing and data service cases to verify the effectiveness and scientificity of the S&amp;T management data service innovation model proposed in this paper. The results show that in S&amp;T management data processing, this paper’s method consumes less than 2.45s for associated data rule mining, which has high mining efficiency, and the duplication rate and missing rate of data are below 0.0260 and 0.0222. Through the analysis of the service quality data, it can be seen that the degree of explanation of the service process quality problems of the model proposed in this paper tends to be close to 1, which can reflect the differences in the service process quality problems, and provide accurate, intelligent and personalized services for the main body of science and technology innovation.</p>
</abstract>

S&T management data has a wide range of sources and types, and the innovation of S&T management data service model is an important way for the efficient utilization of S&T resources in the new era, so this paper creates an innovative model of S&T management data service based on Data-Information-Knowledge-Wisdom model and artificial intelligence technology. Heterogeneous data mining technology based on association rules is used to obtain the connection between S&T management data, Lagrange interpolation is used for heterogeneous data cleaning to predict the missing values of S&T management data, and data lineage resolution technology is used to solve the challenges brought by the complex and diverse S&T management data components. Experimental analyses are conducted from both S&T management data processing and data service cases to verify the effectiveness and scientificity of the S&T management data service innovation model proposed in this paper. The results show that in S&T management data processing, this paper’s method consumes less than 2.45s for associated data rule mining, which has high mining efficiency, and the duplication rate and missing rate of data are below 0.0260 and 0.0222. Through the analysis of the service quality data, it can be seen that the degree of explanation of the service process quality problems of the model proposed in this paper tends to be close to 1, which can reflect the differences in the service process quality problems, and provide accurate, intelligent and personalized services for the main body of science and technology innovation.

Research on the Innovation of Science and Technology Management Data Service Mode under Artificial Intelligence Technology

Department of Science and Technology Development

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

S&T management data has a wide range of sources and types, and the innovation of S&T management data service model is an important way for the efficient...

Research on the Innovation of Science and Technology Management Data Service Mode under Artificial Intelligence Technology

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 23. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0118

Schlüsselwörter
Artificial intelligence, Association rules, Data cleaning, Bloodline analysis, Data service model

© 2024 Xiaohua Xiao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Innovation of Science and Technology Management Data Service Mode under Artificial Intelligence Technology

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 23. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0118

SchlüsselwörterArtificial intelligence, Association rules, Data cleaning, Bloodline analysis, Data service model

© 2024 Xiaohua Xiao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Artificial intelligence, Association rules, Data cleaning, Bloodline analysis, Data service model