A model construction method and system based on digital twinning

By dynamically correcting the credibility and assigning weights to multi-source heterogeneous data, and by adopting a lightweight iteration strategy, the problems of data distortion and insufficient computing power in digital twin models are solved, and the accurate construction and efficient operation of the model are achieved.

CN122263370APending Publication Date: 2026-06-23GUANGDONG KELI INTELLIGENT TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGDONG KELI INTELLIGENT TECH CO LTD
Filing Date
2026-02-08
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

In existing technologies, during the construction of digital twin models, multi-source data is easily affected by equipment accuracy, transmission links and environmental interference, resulting in data distortion. Fixed weight allocation in the data fusion stage cannot adapt to differences in data credibility. During model iteration, computing power is limited and iteration accuracy is insufficient, which cannot meet the rapid update requirements in dynamic scenarios.

Method used

By collecting multi-source heterogeneous data of physical entities, dynamic credibility correction is performed. Dynamic weight allocation is carried out by combining sensitivity parameters and real-time requirement parameters. A lightweight iterative strategy is used to update the core parameters of the digital twin model to construct the digital twin model.

Benefits of technology

It achieves accurate mapping between digital twin models and physical entities, enhances the dynamic adaptability of the models, ensures stable application of the models in complex scenarios, and reduces computing power consumption.

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Abstract

The application discloses a model construction method and system based on digital twinning, and the method comprises the following steps: collecting multi-source heterogeneous data of a physical entity, wherein the multi-source heterogeneous data comprises running data, environment data and state data; based on data collection errors, transmission delays and environmental interference factors, the multi-source heterogeneous data is dynamically corrected in credibility; based on the corrected credibility result, the sensitivity parameters output by the digital twinning model in combination with the multi-source heterogeneous data and the real-time requirement parameters of the model application scene corresponding to the multi-source heterogeneous data are used for dynamic weight distribution; based on the allocated dynamic weight, the core parameters of the digital twinning model are updated by using a lightweight iteration strategy to construct the digital twinning model. The system corresponds to realize the above method. The application solves the problems of multi-source data distortion, fixed weight and iteration calculation power consumption, realizes accurate model construction, and adapts to dynamic scene requirements.
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