Geological safety monitoring and resource scheduling method and system based on large language model

Abstract

The application provides a geological safety monitoring and resource scheduling method and system based on a large language model. The method comprises: constructing an end-cloud collaborative system model comprising an end-side layer, an edge layer and a cloud layer; converting multi-source perception data into input prompt word sequences of an autoregressive time series reasoning task, constructing a computing power demand model to evaluate the floating point calculation amount of the pre-filling stage and the autoregressive decoding stage, and constructing a bottom communication model to evaluate the dynamic data transmission rate; calculating the expected total delay and the expected total energy consumption, and constructing a system utility function; abstracting the joint decision of the task offloading location, the communication bandwidth allocation and the cloud computing power segmentation into a Markov decision process; using a TD3 algorithm with an experience buffer division mechanism to solve the optimal resource allocation and offloading strategy; in response to the strategy, performing collaborative reasoning at the end side or the cloud, and outputting geological safety time series prediction and response instructions. The application effectively reduces the system delay and energy consumption, and improves the task completion rate and service quality.

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