A combinatorial empowerment-cloud model-based prediction method for artificial intelligence manufacturing evaluation

Jiawen Zhang; Huameng Gao; Rong He

doi:10.1117/12.2680834

23 May 2023 A combinatorial empowerment-cloud model-based prediction method for artificial intelligence manufacturing evaluation

Jiawen Zhang, Huameng Gao, Rong He

Author Affiliations +

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264529 (2023) https://doi.org/10.1117/12.2680834
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

The level of development of artificial intelligence in manufacturing industry directly affects the development of the country, is the centralized embodiment of the comprehensive strength of the country, directly provides technical, material and resource support for the development of the national economy and is the most basic industry that drives economic development. In this context, this paper builds an evaluation index system based on hierarchical analysis method and cloud model theory, uses fuzzy hierarchical analysis method to determine the weight values of indicators at all levels, then outputs the standard cloud diagram and evaluation cloud diagram by building a cloud model to visually reflect the fit, and finally introduces examples to verify the scientific and rationality of the model method, and through data Finally, we introduce examples to verify the scientific and reasonableness of the model method, and reflect the superiority of the model in prediction and evaluation through the comparison of data.

Citation Download Citation

Jiawen Zhang, Huameng Gao, and Rong He "A combinatorial empowerment-cloud model-based prediction method for artificial intelligence manufacturing evaluation", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264529 (23 May 2023); https://doi.org/10.1117/12.2680834

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