Abstract: Meteorological equipment operates for long periods of time under extreme climatic conditions,and obtaining timely and accurate health status is essential to ensure its continued,reliable operation.However,the health assessment of meteorological equipment faces the problems of complex evaluation indexes,low degree of correlation between devices,long data collection pe-riod,many parameters of qualitative indexes and lack of quantitative indexes.Current health evaluation methods are difficult to be directly applied to the health assessment of meteorological equipment.To address these issues,this study aims to systematically propose a set of evaluation index system with value for engineering practice,and to obtain the data set through multiple data preprocessing methods.On this basis,this paper firstly uses Lasso regression for feature extraction to get the priority queue of each indicator in the assessment result.Then,combining AHP and expert scoring,an importance matrix is constructed and the weights of the indicators are calculated.Finally,the fuzzy comprehensive evaluation method is used to construct comment sets and their corresponding affiliation functions for different indicators,and the health status of meteorological equipment is analysed by combining the weights and affiliation matrix calculation.The experimental test results show that the proposed method exhibits good performance in the recognition of health status,with the accuracy of 0.949,the recall rate of 0.966,and the F1 Score of 0.957.This study provides a new method for assessing the healthiness of meteorological equipment,which can help to better maintain and manage meteorological equipment and ensure its stable operation under extreme weather conditions.

Key words: Health evaluation, Analytic hierarchy process, Fuzzy comprehensive evaluation method, Lasso feature extraction, Meteorological equipment