A Decision Support Agent and Allied Applications with Artificial Intelligent Generative Content
At this stage,artificial intelligence generative content(AIGC)based large models that have undergone professional data fine-tuning still have difficulty in addressing users'needs for immediate reasoning and decision-making in industrial manufacturing,energy production,asset management,and other scenarios due to the private attributes of data asset privacy,variation of quality of data administration and processing,expertise entrance regarding to industrial mechanism as well as complicate continuous operation after AIGC model been deployed.We propose a multi-role,self-closed-loop intelligent agent collaborative framework that can compensate for the shortcomings of large models in professional semantic understanding,multi-round self-interaction,and decision-making based on feedback and self-supervision between multiple different role agents in question-answering decision-making scenarios.At the same time,it explores the feasibility of applying medium-sized large-scale AI models to vertical scenarios to achieve performance similar to that of very large-scale base models.Through allied application with AIGC large models in three high-frequency scenarios:drilling well control,device asset operation and maintenance management,and refining device operation guidance,it is proven that the proposed method is effective in reducing the engineering application threshold of large models,explaining and reducing application costs.
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