The invention discloses a
parallel optimization method for a hydroelectric power generation equipment knowledge multi-
modal large model, and the method comprises the following steps: S1, collecting multi-
modal data, and taking the multi-
modal data as the input of an LORA fine-tuning
large model; s2, adjusting parameters in the LORA
fine tuning large model through an optimization
algorithm; s3, feeding multi-
modal data to an LORA
fine tuning model improved based on an LLAMA3 structure, training, and performing modal
switching algorithm parameter adjustment through an LORA technology in combination with an optimization
algorithm to form an optimal model; and S4, using a reasoning interface to enable the user to perform question and answer type use, namely inputting picture or text data by the user and proposing a corresponding question, reasoning by a
static model to obtain an output result, and displaying the output result in a window for output. The problem of difficulty in manual query is solved by combining a large-model hydroelectric power generation knowledge recommendation technology,
knowledge question and answer recommendation is performed by combining various equipment, maintenance reports, specifications, various standards and the like of the
hydropower station, production employment of the
hydropower station is well assisted, and power and a foundation are provided for intelligent construction of the hydroelectric
power station.