Digital employee semantic interaction enhancement method based on large language model and medium

Abstract

The present application relates to the technical field of semantic processing, and in particular to a digital employee semantic interaction enhancement method and medium based on a large language model. According to valve opening degree data at different times within a neighborhood range at each moment and flow measurement data distribution, flow measurement compensation data after delay compensation at each moment is obtained, and whether an actuator-sensor is connected is judged. If connected, operator adjustment feature labels at each moment are obtained according to valve opening degree data at different times within a local range at each moment and flow measurement compensation data after delay compensation. If not connected, the operator adjustment feature labels at each moment are set as a fault mode. Constraint prompt words are constructed based on the operator adjustment feature labels and the actuator-sensor connection state, the constraint prompt words are input into a large language model, and interaction feedback information is generated. The present application accurately converts physical facts into semantic constraints, and improves the physical consistency of semantic interaction.

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