A risk identification method for abnormal key data in the whole process of production project Online publication date: Mon, 17-Apr-2023
by Ya Zhou; Yaopeng Zhao; Wei Li; Jianhui Liang; Qiwu Zou
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 27, No. 1/2/3, 2022
Abstract: In order to improve the identification accuracy of risk categories in each stage of production projects, a method for identifying abnormal risks of key data in the whole process of production projects is proposed. Extract the risk factors of the whole process data of the production project, and use the improved fuzzy comprehensive evaluation method to judge whether the risk factor risk item is empty; screen the data with high correlation with the risk factor as abnormal key data; identify the risk according to the corresponding risk item of the abnormal data category. Taking the artificial intelligence production project as an example, the screening risk data is used as the test data to set up a comparative experiment. The results show that the proposed method has a higher recognition accuracy rate of risk categories, and the recognition results are more accurate and reliable.
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