Human resource data integration system based on Artificial Intelligence
In order to improve the intelligent level of the automatic test method of substation debugging and maintenance and re-duce the manual operation and maintenance debugging work,this paper proposes a method of constructing LightGBM machine learning model for intelligent analysis of the automatic test results of substation debugging and maintenance.Firstly,construct a LightGBM ma-chine learning model and tune and train its parameters;Then,the LightGBM machine learning model is tested using the data obtained from automatic testing of substation debugging and maintenance;Meanwhile,an XGBoost machine learning model was constructed as the experimental control group,and trained and tested using the same experimental method;Finally,compare the comprehensive per-formance of two machine learning models.The experimental results show that the LightGBM machine learning model has better fitting performance;The XGBoost machine learning model has the highest accuracy rate of 90.1％in analyzing the error data of automatic detection method fault category prediction;The judgment accuracy of the LightGBM machine learning model remained above 95％,reaching a maximum of 96.9％.It can be seen that the LightGBM machine learning model selected in this article is more suitable and stable for intelligent analysis of automatic testing results for substation debugging and maintenance,and can achieve the goal of impro-ving the intelligent level of automatic testing methods for substation debugging and maintenance.
万方数据
维普
