Build a Multimodal Interaction and Multi-Agent Collaborative Decision-Making Mechanism Enhanced by Large Models in the Intelligent Decision-Making System for Distribution Network Production

With the rapid development of the new power system, the complexity of distribution network operation has put forward higher requirements for the real-time, accuracy, and intelligence of production command. Traditional decision-making systems have bottlenecks in heterogeneous data fusion, human–computer interaction efficiency, and decision-making science. To address the above challenges, this paper proposes an intelligent decision-making system for distribution network production enhanced by large language models (LLMs). The core contributions of this system are as follows: (1) A three-layer heterogeneous intelligent architecture integrating perception, cognition, and execution is constructed to realize the complete process from multimodal data input to closed-loop control; (2) An LLM-driven multimodal fusion and interaction mechanism is designed to uniformly encode unstructured information such as SCADA time-series data, on-site images, and voice commands into high-dimensional semantic features, realizing the comprehensiveness of situation awareness (SA) and the naturalness of human–computer interaction; (3) A multi-agent collaborative decision-making framework based on the improved contract net protocol (CNP) is proposed. The command intelligent agent empowered by the LLM decomposes and schedules complex tasks, driving each professional intelligent agent to perform parallel optimization. The system is verified in a typical distribution network fault handling scenario. The results show that compared with the traditional manual method, the proposed system reduces the end-to-end decision-making time from more than 30[Formula: see text]min to within 4[Formula: see text]min, with a reduction rate of more than 85%; the comprehensive accuracy rate of its fault location and recovery strategy reaches more than 98%, and the generated recovery strategy is significantly superior to the static rule-based expert system in terms of safety and economy. This research provides an innovative paradigm and technical path for the in-depth application of large model technology in the field of power critical infrastructure.