Evaluation of the quality of grassroots statistics based on a combined empowerment cloud model

Kexin Wang; Xing Huang; Qinghua Lu; Jiayuan Mao

doi:10.1117/12.3036834

21 July 2024 Evaluation of the quality of grassroots statistics based on a combined empowerment cloud model

Kexin Wang, Xing Huang, Qinghua Lu, Jiayuan Mao

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 1321931 (2024) https://doi.org/10.1117/12.3036834
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

High-quality grass-roots statistics are an important guarantee to objectively reflect the development of grass-roots social and economic operations and provide strong data support for government decision-making. A combined empowerment cloud model is constructed to evaluate the quality of grass-roots statistics. Firstly, the index system for evaluating the quality of grassroots statistics is proposed based on GB/T 36344-2018 national standard framework; secondly, to calculate the weights of subjective and objective indicators, AHP-CRITIC is used in combination with game theory to estimate combination weights; then, the cloud model is constructed to evaluate the quality of grassroots statistics; finally, the proposed method is proved to be reasonable and effective through example analysis, and the cloud The model takes into account the randomness and fuzziness of some indicators and can provide a scientific and reliable decision basis for the evaluation of the quality of grass-roots statistics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kexin Wang, Xing Huang, Qinghua Lu, and Jiayuan Mao "Evaluation of the quality of grassroots statistics based on a combined empowerment cloud model", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 1321931 (21 July 2024); https://doi.org/10.1117/12.3036834

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