Abstract: To address the shortcomings of traditional model robustness evaluation methods, such as low description consistency and difficulty in obtaining accurate scene matching data, a new power artificial intelligence index algorithm model of multi scenario robustness evaluation method is proposed. The multi scene data is extracted, the disturbance range interval of multi scene data in local space is set, the interval movement distance of spatial range is controlled, and the data acquisition results of sample points within the interval range are predicted. The basic feature parameters of the algorithm model are input, the multiple scene data is selected to obtain distance range values while increasing the input parameter dimension, and the initial data evaluation operations are performed based on the selected values. Based on the characteristics of uncertain control objectives, conduct data foundation analysis to ensure that the system is in a stable state and maintains its dynamic characteristics. Effectively analyze the differences between different system parameters, construct a range of deviation values, judge the multi scenario characteristics of the algorithm model, and achieve data evaluation. The experimental results show that the multi scenario robustness evaluation method of the electric power artificial intelligence index algorithm model can effectively transform the coordinates of sampling points, ensure the invariance of multi scenario sampling point data images, overcome the problem of scene data rotation sensitivity, and improve response speed. Compared with traditional evaluation methods, the proposed evaluation method has strong advantages in interference robustness and affine deformation robustness. 

Key words:  , electric power labor, artificial intelligence, index algorithm, model multi-scenario, robustness evaluation, evaluation method