Cerebral stroke risk early warning method and system based on digital twinning

By analyzing the acquisition control data to evaluate the delay and completeness of acquisition and updates, optimizing the data update process, the data accuracy problem caused by frequent updates of digital twin models is solved, and the accuracy of stroke risk warning is improved.

CN120221067AActive Publication Date: 2025-06-27THE FIRST MEDICAL CENT CHINESE PLA GENERAL HOSPITAL
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Patent Information

Application Number
CN202510226977.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-27
Publication Date
2025-06-27
Estimated Expiration
2045-02-27

AI Technical Summary

Technical Problem

In the prior art, due to frequent changes in physiological status of cerebrovascular patients, the digital twin model needs to be updated frequently, resulting in inaccurate charging and discharging control of blood pressure monitors, and delayed data update and synchronization, affecting the accuracy of stroke risk warning data.

Method used

By analyzing the acquisition control data, obtaining the acquisition evaluation value, and determining whether to perform acquisition and update delay evaluation and acquisition and update integrity evaluation. If necessary, perform data update delay analysis and completeness analysis to determine whether to perform optimization to improve the accuracy of the data.

Benefits of technology

This has achieved the reliability of the stroke risk prediction level output by the digital twin model, and thus improved the accuracy of stroke risk warning data.

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Abstract

The invention provides a cerebral apoplexy risk early warning method and system based on digital twinning, and relates to the technical field of cerebral apoplexy risk early warning. The method comprises the following steps: acquiring and evaluating; acquiring update delay evaluation; acquiring and updating integrity evaluation; and cerebral apoplexy risk prediction. The method comprises the following steps: analyzing acquisition control data, judging whether to carry out acquisition update delay evaluation and acquisition update integrity evaluation, if the acquisition update delay evaluation is carried out, judging whether to carry out acquisition update delay optimization, and if the acquisition update integrity evaluation is carried out, judging whether to carry out acquisition update integrity optimization. And finally, obtaining the cerebral apoplexy risk prediction level, thereby achieving the effect of improving the accuracy of the cerebral apoplexy risk early warning data, and solving the problem that the accuracy of the cerebral apoplexy risk early warning data is not high in the prior art.
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