Early warning method and device based on non-contact perception, equipment and medium

By collecting non-contact motion sensing and thermal distribution imaging data, a real-time digital human body model is constructed to identify abnormal behavior and determine the risk level, solving the privacy and monitoring continuity issues in home health monitoring and achieving accurate identification of abnormal behavior and timely response.

CN122117401APending Publication Date: 2026-05-29PING AN TECH (SHENZHEN) CO LTD
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Patent Information

Application Number
CN202610259728.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-04
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing home health monitoring technologies cannot accurately identify abnormal behavior and determine risk levels without relying on video surveillance and wearable devices, and there are issues with privacy violations and monitoring continuity.

Method used

By collecting non-contact motion sensing data and thermal distribution imaging data, a real-time human digital model is constructed. Combined with a behavioral context analysis model, abnormal behavioral events are identified, vital sign data are generated, the risk and emergency response level is determined, and a graded emergency dispatch strategy is implemented.

Benefits of technology

It enables accurate identification of abnormal behavior and health risks without infringing on privacy, thereby improving the accuracy and timeliness of home monitoring.

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Abstract

The invention relates to the technical field of intelligent decision making, and discloses an early warning method, device and equipment based on non-contact perception and a medium, and the method comprises the steps: collecting non-contact body movement perception data and heat distribution imaging data, and carrying out the fusion construction of a real-time human body digital model; based on a real-time human body digital model, action track features and posture features are extracted, behavior situation reasoning is carried out, and abnormal behavior events are recognized; generating vital sign data when the abnormal behavior event is identified; and determining a risk emergency level according to the type of the abnormal behavior event and the vital sign data, and executing a corresponding graded emergency scheduling strategy. The method can be applied to business scenes such as home health monitoring, accurate recognition and hierarchical response to abnormal behaviors and health risks are achieved by fusing body movement and thermal imaging information and combining situation reasoning, and monitoring accuracy and timeliness are improved while privacy is guaranteed.
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