A GenAI-powered multi-modal root cause analysis system with reinforcement learning from human feedback, comprising:
- a structural housing configured to support interconnected computational and electronic components;
- a multi-modal acquisition unit operatively coupled with a plurality of communication interfaces and configured to receive heterogeneous operational information from a plurality of data sources including image streams, machine telemetry signals, sensor measurements, textual event records, maintenance documents, audio signals, user interactions, environmental observations, and event notifications;
- a preprocessing processor operatively coupled with the multi-modal acquisition unit and configured to perform signal normalization, temporal synchronization, noise reduction, semantic extraction, image transformation, feature extraction, sequence segmentation, and data standardization operations upon the heterogeneous operational information;
- a data harmonization processor configured to generate synchronized contextual datasets by aligning heterogeneous operational information according to temporal relationships, spatial associations, semantic dependencies, and operational event identifiers;
- a contextual embedding processor configured to transform the synchronized contextual datasets into unified high-dimensional representational structures corresponding to multimodal operational conditions;
- a cross-modal association processor configured to identify relationships among image information, textual information, acoustic information, telemetry information, and operational event information through contextual attention processing and semantic dependency extraction;
- a causal graph generation processor configured to generate dynamic graph structures comprising interconnected nodes representing operational entities and graph edges representing causal relationships, temporal dependencies, probabilistic associations, and contextual interactions among the operational entities;
- a generative reasoning processor configured to receive the unified representational structures and the dynamic graph structures and generate one or more root cause hypotheses corresponding to detected anomalous conditions;
- a human feedback acquisition unit configured to receive evaluative inputs including correctness indicators, explanatory annotations, ranking information, and corrective observations from human operators;
- a reinforcement optimization processor configured to convert the evaluative inputs into adaptive reward information and modify inferential parameters associated with the generative reasoning processor according to the adaptive reward information;
- and
- a memory unit configured to store historical event information, contextual representations, graph relationships, feedback histories, and inferential adaptation parameters, wherein the reinforcement optimization processor continuously updates analytical behavior according to received human feedback such that root cause identification accuracy and interpretability progressively improve during system operation.