融合大语言模型与MCP协议的离心泵智能优化平台研究OA

Traditional design optimization methods for centrifugal pump often suffer from weak multi-objective coordination,low levels of intelligence,and high user barriers.To address these issues,this study proposes an intelligent optimization platform for centrifugal pumps that integrates Large Language Models(LLMs)with the Model Context Protocol(MCP).Utilizing advanced LLMs such as GPT-4 as its core,the platform enables natural language requirement parsing,automatic mapping of structural parameters,and knowledge reasoning,significantly enhancing both user interaction and platform intelligence.By in-tegrating the NSGA-II multi-objective genetic algorithm and Gaussian Process Regression(GPR)for performance prediction,the platform achieves collaborative multi-objective optimization,while the GPR confidence intervals are used to quantify uncertainty and assess risks in the optimization results.Through the MCP protocol,the system realizes standardized and secure integration with heterogeneous resources,in-cluding enterprise-level databases and CFD simulation platforms.A typical industrial pump case is used to validation,and the results demonstrates that the developed platform can automatically obtain the Pareto optimal solution sets with minimal manual intervention.The optimization efficiency is improved by over 60%,and the maximum error in performance prediction remains below 1%.These results fully demon-strate the platform's high efficiency,reliability,and broad application potential in industrial settings.