A machine learning-based system for detecting pattern deviations and autonomously correcting data integrity errors in digital networks.

Abstract

A machine learning-driven system for pattern deviation detection and autonomous correction of data integrity errors in digital networks, comprising: (a) a data acquisition interface configured to receive digital data elements from a variety of network-connected sources and to associate the digital data elements with source metadata; (b) a preprocessing and feature extraction engine configured to transform the digital data elements into feature representations that include at least structural features, temporal features, protocol compliance features, and origin features; (c) a pattern modeling engine configured to maintain a reference behavior representation derived from baseline-consistent feature representations;(d) an anomaly scoring engine configured to calculate a composite anomaly score for an incoming feature representation based on at least one reconstruction deviation and one consistency deviation relative to the reference behavior representation; (e) a fault classification engine configured to assign an integrity fault category and an affected area, which may include a field, record, packet, block, or stream segment; (f) a corrective orchestration engine configured to generate and evaluate a variety of corrective candidates and select a corrective action according to a confidence score derived from the agreement of the fault category, the origin state, the source trustworthiness, and the expected validation success;(g) a validation engine configured to check a corrected data element against integrity constraints; (h) a rollback control configured to restore a saved previous state if the check fails; and (i) an audit log generator configured to record artifacts relating to anomalies, corrections, validations, and rollbacks.