Objective The intelligence level of refined oil batch planning directly impacts the economic efficiency and safety of pipeline network operations. Traditional methods rely heavily on manual data processing, model invocation, and human–machine interaction, resulting in low efficiency, poor adaptability, and a high risk of errors. These limitations hinder real-time, accurate, and closed-loop decision-making required by smart pipeline networks.

Methods A comprehensive technology system for intelligent professional models in refined oil batch planning was proposed, structured around a three-tier architecture: data layer, model layer, and application layer. The data layer emphasized intelligent management of multi-source heterogeneous data, ensuring high-quality model inputs through semantic parsing, data cleaning, and standardized mapping. The model layer enabled intelligent model invocation and solution, including input data validation, automatic model matching, efficient computation, and visualization of batch migration and dispatching instructions. The application layer enhanced intelligent human–machine collaboration through automated dispatching instruction generation, natural language Q&A, and dynamic fine-tuning of the plan driven by semantic instructions, achieving a closed-loop from result generation to instruction execution.

Results This technology system systematically restructured the traditional fragmented, experience-driven planning process, achieving standardization, automation, and intelligence throughout. Data preparation and model solution efficiency increased by an order of magnitude, with significant reductions in manual intervention. The planning cycle was shortened from hours to minutes, and dispatching instructions could be generated and issued within seconds. The integrated constraint conflict detection mechanism effectively mitigated operational risks from manual adjustments, significantly enhancing the executability and safety of plans.

Conclusion A preliminary application of intelligent fine-tuning for batch planning was conducted on a refined oil pipeline in western China, validating the system’s core value and establishing a technical foundation for transitioning from “automatic” to “intelligent” batch planning. The data layer overcomes barriers of multi-source heterogeneous data, ensuring high-quality, consistent model inputs. The model layer provides autonomous decision-making and error correction, ensuring robust and optimal solutions under complex conditions. The application layer seamlessly integrates AI computing efficiency with dispatcher expertise through natural language interaction and dynamic closed-loop processes, forming a complete intelligent cycle of “cognition, decision, execution, and feedback”.