Multi-mode driven cross-industry digital twin universal platform architecture and implementation method

By adopting a multimodal-driven cross-industry digital twin platform architecture, the problem of insufficient cross-modal fusion accuracy and adaptability in cross-industry scenarios is solved, achieving efficient cross-industry adaptation and real-time decision-making, supporting lightweight deployment and edge computing, and improving decision-making accuracy and response speed in complex scenarios.

CN120951233APending Publication Date: 2025-11-14INSPUR SOFTWARE CO LTD
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Patent Information

Application Number
CN202511007524.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-22
Publication Date
2025-11-14

AI Technical Summary

Technical Problem

Existing technologies suffer from insufficient cross-modal fusion accuracy, weak cross-industry adaptability, and low collaborative efficiency of heterogeneous systems in cross-industry scenarios. In particular, they are difficult to achieve efficient collaboration when combining large models with digital twins, and their lightweight deployment capabilities are insufficient, failing to meet the needs of low-latency scenarios.

Method used

Adopting a multimodal-driven, cross-industry digital twin general platform architecture, it achieves access to multi-source heterogeneous data and unified feature representation through layered management including a data access layer, a multimodal fusion layer, a large model scheduling layer, a twin engine layer, and an interactive application layer. It combines models such as Transformer-GNN, BERT, PointNet++, and LSTM for cross-modal coding, supports lightweight deployment and edge node computing, and introduces knowledge distillation and meta-learning algorithms for model optimization, forming a two-way feedback mechanism of large model strategy generation, digital twin simulation verification, and physical device execution.

Benefits of technology

It significantly improves the accuracy of multimodal fusion, enhances cross-industry adaptation efficiency and real-time decision-making capabilities, reduces deployment costs and latency, supports independent operation of edge nodes, meets the low-latency requirements of complex scenarios, and provides full-dimensional decision support capabilities.

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Abstract

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.
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