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

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.

Improving Business Innovation Management Through Artificial Intelligence

“…Thus, the hypothesis (H) is proven. In support of Joshi and Negi (2024), Kulkarni et al (2024), and Xiao and Su (2024), it is justified that the application of AI makes it possible to improve parameters (minimizing costs and maximizing results), thereby enhancing the efficiency of innovation management in business and is preferable for innovation management compared to alternative digital technologies.…”

“…The consequences of the considered technologies in innovation management in business remain unknown. Drawing on a series of existing evidence of the benefits derived from the application of AI in innovation management (Joshi & Negi, 2024;Kulkarni et al, 2024;Xiao & Su, 2024), this research proposes the following hypothesis (H): the application of AI makes it possible to improve parameters (results and costs) of innovation management in business and is preferable for innovation management compared to alternative digital technologies. The author verifies the proposed hypothesis (H) through economic and mathematical modeling of the causal relationships between the use of AI in innovation management and the outcomes of this managerial practice in business, drawing on international experience.…”