Study on Early Warning System of Fixed Assets Operation Risk Based on Rule Integration Learning

In recent years, with the complexity of market environment and diversification of asset structure, the identification and early warning of operational risk has become a key part of asset management. Traditional risk assessment methods are often difficult to give consideration to accuracy and interpretability, which easily leads to information loss and risk omission. Therefore, a risk early warning model based on rule integration learning is proposed. Through the dynamic weight distribution mechanism, the steps of rule extraction, conflict resolution, and priority ranking are integrated, which can flexibly deal with different business scenarios. In this paper, a multidimensional data system is constructed, and cross-validation and hierarchical evaluation are carried out to ensure the robustness of the model's performance. The results show that the model has good generalization ability and risk identification ability. This study provides reference and support for the risk early warning and decision-making of fixed assets operation.