This application discloses a method, apparatus, device, and storage medium for identifying transaction anomaly risks, relating to the field of
anomaly detection in microservice systems. The method includes: real-time collection of transaction
business data, microservice operation data, and link call data, followed by
feature extraction to obtain standardized real-time multi-dimensional risk correlation features; inputting these real-time multi-dimensional risk correlation features into an
adaptive optimization model to obtain preliminary
risk identification results;
synchronizing these features to a digital twin of the microservice transaction link; using the digital twin to simulate the current transaction flow state and generate real-time
simulation risk assessment results; and combining the preliminary
risk identification results with the real-time
simulation risk assessment results to query a dynamic causal graph, identify risk transmission paths and root causes, and determine the final
risk level. Through the above methods, this application improves the accuracy, real-time performance, and adaptability of
risk identification in complex microservice environments.