The invention relates to the technical field of
power system automation, and particularly discloses a power network fault early warning method based on a
large model, and the method comprises the steps: directly mapping the data of a high-frequency
power sensor into a
time sequence soft prompt vector which can be understood by the
large model through a lightweight
encoder and a linear projection technology; deep alignment of continuous signals and discrete
semantics is realized, and
information loss and efficiency
bottleneck caused by numerical textualization are avoided. And then, directionally retrieving an operation and maintenance
knowledge base by using the soft prompt vector, introducing a cross-
modal affinity matrix to construct a semantic gating mechanism, dynamically screening and enhancing retrieved key knowledge fragments according to real-time waveform characteristics, and automatically inhibiting redundant text
noise irrelevant to the current working condition. And finally, deep attention interaction and reasoning are performed on a
composite sequence formed by an instruction, enhanced knowledge and soft prompt through a
large model, a
control signal containing fault classification and disposal suggestions is output, and real-time accurate early warning considering high-frequency
signal sensitivity and expert knowledge logicality is realized.