Research On The Application Of Machine Learning-Based Fault Early Warning Methods In Risk Management Of Internet Of Vehicles Projects

The promotion and development of intelligent transportation,as well as continuous innovation and application of computer and communication technologies,has enabled the processing,storage,transmission,and exchange of vehicle information,thereby driving the development of the Internet of Vehicles(Io V)projects.In China,the development space of the Io V projects is broad and the investment scale is relatively large.Therefore,it is necessary to carry out risk management for the Io V projects.The Io V projects is a complex system engineering that involves multiple areas,and there are many problems in the implementation process.Vehicle failures are one potential risk factor.Effective warning and management of vehicle faults is a pressing issue that needs to be addressed in the Io V projects.By reducing the probability of vehicle failures through fault early warning,the investment risk and losses of the project can be reduced,and the smooth implementation and success of the project can be ensured.This article aims to explore the application of machine learning-based fault early warning methods in risk management of Io V projects,and to achieve the goal of risk management in Io V projects by analyzing and mining useful data,extracting the features of faults,selecting appropriate machine learning algorithms,and establishing corresponding prediction models.The main contents of this study are as follows:(1)Starting from the project risk management theory,current research on fault early warning,research on risk management of Io V projects,and the application of machine learning models in risk management,this study selects more than 70 features to construct a vehicle failure prediction model and uses the chi-square test method to verify the significant correlation between these features and prediction model construction.The experimental results further indicate that the selection of these features has certain reference significance for the research and practice of vehicle failure warning.(2)Using various machine learning algorithms to construct vehicle failure prediction models,including stacked ensemble learning,decision trees,support vector machines,logistic regression,extremely randomized trees,random forests,adaptive boosting,and gradient boosting decision trees.Meanwhile,the prediction time is advanced from 1 minute to 10 minutes,and multiple evaluation indicators are used to evaluate the performance of these models.The experimental results show that the integrated model has better prediction performance than other single models,especially the stacking model shows the best performance.In addition,the results show that the closer the predicted time is to the occurrence of the failure,the higher the accuracy of the model prediction.(3)Further verifying the cost-benefit of the fault prediction model constructed in this study in practice,the latest production data of a well-known Chinese auto enterprise’s Io V projects is used for empirical analysis.The empirical results show that the fault prediction model constructed in this study has good prediction performance and accuracy,and can effectively predict and warn vehicle failures.In addition,through cost-benefit analysis of the applied prediction model,the advantages and feasibility of the machine learning-based fault early warning methods in fault risk control of Io V projects are verified,which provides decision-makers with strong technical support and information reference and helps improve the efficiency and accuracy of fault risk control in auto enterprises.Based on the above research content,this article mainly contributes in three aspects:(1)This article conducts in-depth research on risk management in the Io V projects from the perspective of fault risk management.Despite some research results in project risk management during the rapid development of the Io V projects,research on fault risk management is still relatively scarce.This study implements a vehicle engine water temperature fault early warning to help car owners detect engine water temperature anomalies in a timely manner and take corresponding measures to reduce the fault risk of the Io V projects effectively.(2)This article adopts machine learning methods for risk management in the Io V projects.Although machine learning methods have been widely applied in various fields of risk management,their application in the risk management of the Io V projects is still relatively limited.This study uses machine learning methods to establish a vehicle fault prediction model applicable to commercial diesel engines,providing new ideas and methods for risk management in the Io V projects.(3)This article provides modeling references for subsequent research related to the Io V projects.The vehicle fault early warning methods proposed in this article uses machine learning algorithms to construct a vehicle fault prediction model by analyzing and mining the characteristics of vehicle operation data,effectively predicting the occurrence of vehicle failures.This innovative idea and method not only demonstrate the applicability of machine learning but also make new valuable contributions to the selection of machine learning models and the selection of features.