Early warning system and method for health risk of pollutant exposure based on dynamic monitoring

By using the triplet identifier of 'individual ID + timestamp + spatial coordinates' and an AI-driven multimodal deep learning model, the shortcomings of existing technologies in individualized pollutant exposure assessment are addressed, achieving dynamic health risk early warning with high spatiotemporal resolution, and significantly improving the accuracy and timeliness of the early warning.

CN122245760APending Publication Date: 2026-06-19HUBEI PROVINCIAL ACADEMY OF ECO-ENVIRONMENTAL SCIENCES(PROVINCIAL ECOLOGICAL ENVIRONMENT ENGINEERING ASSESSMENT CENTER)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUBEI PROVINCIAL ACADEMY OF ECO-ENVIRONMENTAL SCIENCES(PROVINCIAL ECOLOGICAL ENVIRONMENT ENGINEERING ASSESSMENT CENTER)
Filing Date
2026-03-17
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing environmental health monitoring systems cannot achieve individualized and dynamic pollutant exposure assessments and lack high spatiotemporal resolution data correlation mechanisms, resulting in delayed early warnings, high false alarm and false alarm rates, and difficulty in achieving early warning of health risks.

Method used

Using a triplet identification technology of 'individual ID + timestamp + spatial coordinates', combined with a bidirectional long short-term memory network and a self-attention mechanism, an individualized health baseline is established. Through high-precision positioning and multi-source data fusion, the cumulative dose of individual pollutants is calculated, and an AI-driven multimodal deep learning model is used for risk assessment and early warning.

Benefits of technology

It enables individualized and dynamic pollutant exposure assessment with minute-level temporal resolution and meter-level spatial accuracy, significantly improving the accuracy and timeliness of early warning, reducing false alarm and missed alarm rates, supporting personalized intervention measures, and enhancing the precision and scientific nature of environmental health risk management.

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Abstract

This invention discloses an early warning system and method for health risks from pollutant exposure based on dynamic monitoring. The system constructs a "personal ID-timestamp-spatial coordinates" triple using a data association engine to achieve precise matching of biomarker data and environmental exposure information. The system integrates a data association engine, a personalized health baseline modeling module, a spatiotemporal exposure profile construction module, an omics detection extension module, an AI-driven risk assessment module, an early warning decision generator, and a feedback optimization module. It fuses multi-source biomarker detection, high-precision individual activity trajectories, and environmental monitoring data. A dynamic health baseline is established using bidirectional LSTM and a self-attention mechanism, and multi-level anomaly detection and risk prediction are performed through a multi-modal deep learning model with cross-modal attention fusion. The system supports multi-scenario adaptation and concept drift detection, achieving closed-loop management from exposure assessment to health early warning. This invention significantly improves the early identification capability of health anomalies, providing precise and intelligent technical support for environmental health risk management.
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