Audit Risk Warning Model for Power Grid Enterprises Based on Data Mining
In order to improve the effectiveness of audit risk management in power grid enterprises,a risk indicator system for power grid enterprise audits is constructed through big data technology mining.Firstly,principal component analysis and expert method are used to screen out the main risk factors of power grid enterprises.Secondly,the random forest algorithm is introduced to construct an audit risk warning model,and the lion swarm algorithm is introduced to optimize the parameter problem and construct an improved random forest warning model.In accuracy and recall tests,the three models studied performed the best,with values of 0.968 and 0.986,respectively,outperforming support vector machine models and traditional random forest models.At the same time,warning tests were conducted on 13 risk indicators,and the research model's warning accuracy was 96.5%,which is better than other models.It can be seen that the overall application effect of the proposed early warning model is better,and the research content provides important technical support for audit risk management and intelligent development of power grid enterprises.
Random forestMaster data miningComponent analysis methodAudit risksEarly warning model
国家科技期刊平台
NSTL
万方数据
