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Multi-Source Data Fusion and Virtual Human-Driven Metaverse Digital Twins for Intelligent Power Marketing Service Halls

Fangzheng Sheng1, Bin Wu1, Ming Chen1, Cong Wang1, Jin Yu1
1State Grid Shanghai Shinan Electric Power Supply Company, Shanghai, 201103, China

Abstract

To address fragmented business data, limited interaction modalities, substantial demands on manual consultation, and inadequate presentation of low-carbon services in conventional power marketing service halls, this study proposes a human-centered metaverse digital twin framework for power marketing service halls. The framework takes the physical service hall as its operational foundation, uses digital twins to establish a continuously updated correspondence between physical facilities and their virtual representations, and positions digital humans as the primary user-interaction interface. It integrates marketing business systems, cyber-physical system (CPS) energy-management platforms, electricity consumption data collection systems, the State Grid Online platform, RPA process data, and smart home sub-metering data to develop an integrated service environment offering guided tours, business consultation, information visualization, service recommendations, and carbon-footprint displays. In addition, the proposed architecture encompasses a heterogeneous data-fusion model, a mechanism for generating digital-human service scripts, a method for constructing metaverse scenes, and a carbon-footprint assessment component. Together, these elements support a closed-loop workflow extending from the interpretation of customer requests to business responses, scene interaction, and feedback-informed service refinement. The reported evaluation indicates shorter service-completion times, improved question-answering accuracy and confidence calibration, more favorable assessments of immersion and interaction, and stronger capabilities for displaying marketing information and communicating low-carbon energy services. These results are reported outcomes of the study and require confirmation against the underlying evaluation data.

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Citation

Fangzheng Sheng, Bin Wu, Ming Chen, Cong Wang, Jin Yu. Multi-Source Data Fusion and Virtual Human-Driven Metaverse Digital Twins for Intelligent Power Marketing Service Halls[J], Archives Des Sciences, Volume 76, Issue 4, 2026. 240-253. DOI: https://doi.org/10.68304/as/76425.