Intelligent inspection robot state anomaly diagnosis system based on large model

Abstract

The application discloses a large model-based intelligent inspection robot state anomaly diagnosis system, which comprises a state coding module, an anomaly decoupling module, an enhanced diagnosis module and a diagnosis analysis module.The state coding module receives multi-modal time series data from the intelligent inspection robot, and generates a state semantic vector sequence representing the comprehensive running state of the robot.The anomaly decoupling module uses a time series neural network to model the context of the state semantic vector sequence, and separates enhanced time series state semantic features through a decoupling representation learning network to output an abnormal mode vector with a category label.The enhanced diagnosis module retrieves relevant historical fault case texts and maintenance knowledge from a preset fault knowledge graph to form global diagnosis data, inputs the global diagnosis data and key feature segments of original multi-modal data corresponding to the abnormal mode vector into a preset improved large language model to output a structured diagnosis report.The diagnosis analysis module performs anomaly analysis according to the structured diagnosis report to realize intelligent inspection robot state anomaly diagnosis.

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