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Optimization of STP Innovation Management Mechanisms Driven by Advanced Evolutionary IoT Arithmetic
Tianxiang Wang1, Qingqing Ma2, Jinxi Li3
1Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan 650500, China.
This study optimizes high technology policy management using advanced evolutionary Internet of Things (IoT) arithmetic. It enhances national innovation capacity by analyzing factors and improving power prediction algorithms for manufacturing competitiveness.
Area of Science:
- Manufacturing Innovation
- High Technology Policy
- Internet of Things (IoT) Arithmetic
Background:
- Manufacturing faces global competition and declining advantage, necessitating enhanced national high technology innovation.
- Optimizing high technology policy innovation management is crucial for economic development.
- Popular information technologies like Big Data, AI, and IoT are increasingly integrated into industrial applications, including microgrids.
Purpose of the Study:
- To introduce an effective method for optimizing high technology policy innovation management driven by advanced evolutionary Internet of Things (IoT) arithmetic.
- To analyze and rank factors influencing high technology policy innovation using the STP (Stimulation, Transformation, Production) innovation management mechanism.
- To develop improved algorithms for wind and photovoltaic (PV) power prediction to support high-tech policy management.
Main Methods:
- Conceptual analysis of Big Data, Artificial Intelligence, and Internet of Things (IoT) technologies and their microgrid applications.
- Application of the STP innovation management mechanism to classify and rank influencing factors into cause and effect categories.
- Development of a data mining-based wind power prediction algorithm and a deep neural network-based PV power prediction algorithm.
Main Results:
- Factors influencing high technology policy innovation management were classified and ranked by importance.
- An improved wind power prediction algorithm and a PV power prediction algorithm were established.
- The study proposes a majorization of the high technology policy innovation management mechanism driven by advanced evolutionary IoT arithmetic.
Conclusions:
- Advanced evolutionary IoT arithmetic provides an effective framework for optimizing high technology policy innovation management.
- The integration of Big Data, AI, and IoT technologies, coupled with improved prediction algorithms, enhances manufacturing competitiveness.
- The proposed approach supports the enhancement of national high technology innovation capacity and policy management.
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