Objective With the rapid advancement of refined oil logistics networks toward multimodal collaboration, multi-level integration, and multi-agent cooperation, traditional technical frameworks have encountered performance bottlenecks such as insufficient reliability, limited adaptability to diverse scenarios, and low computational efficiency when optimizing ultra-large-scale complex networks. To overcome these challenges, developing an autonomous and controllable refined oil logistics optimization platform aligned with the national information technology application innovation strategy is of great strategic importance for enhancing China’s innovation capabilities in key core technologies, ensuring the lifecycle security of energy data, and establishing a robust defense line for national energy security.

Methods This paper provides an in-depth analysis of the challenges faced by existing logistics optimization platforms in refined oil storage and transportation management across four dimensions: design concept, software functionality, intelligence level, and information security. It reviews the current development status of refined oil logistics networks in China and traces the evolution of bottlenecks in logistics optimization, emphasizing the urgent need to establish a domestic refined oil logistics optimization platform. Furthermore, the paper proposes a system architecture for such a platform, oriented toward engineering requirements and supported by intelligent technologies, with the aim of advancing innovation-driven logistics management.

Results The architecture design for the domestic refined oil logistics optimization platform consists of the infrastructure layer, core computing layer, business application layer, and security assurance layer. Additionally, the paper provides a detailed discussion of three key technologies within the core computing layer: global coordinated optimization of multi-level logistics, collaborative optimization of multimodal transport via pipeline, railway, waterway, and road, and data-driven closed-loop intelligent decision-making.

Conclusion The research results provide theoretical guidance and technical support for developing an autonomous and controllable refined oil logistics optimization platform against the backdrop of the ongoing information technology application innovation initiative. The development of future refined oil logistics networks will focus on the following key features: pursuing innovation as the core driver, ensuring autonomy and controllability as foundational guarantees, enhancing multidisciplinary integration, and improving full-process data accumulation. These efforts aim to build efficient, safe, and intelligent modern refined oil logistics systems.