A dynamic risk prediction method and terminal

By constructing a trajectory database and utilizing graph neural networks and environmental data evaluation models, dynamic risk prediction of monitored targets is achieved, solving the problem of lagging risk assessment in existing systems, improving the accuracy and timeliness of risk identification, and enhancing security capabilities.

CN122198641APending Publication Date: 2026-06-12FUJIAN XINGHAI COMM TECH
0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
FUJIAN XINGHAI COMM TECH
Filing Date
2026-03-16
Publication Date
2026-06-12

AI Technical Summary

⚠Technical Problem

Existing monitoring systems lack the ability to analyze the future movement trends of people, making it difficult to identify and quantify potential risks in advance in complex scenarios. This results in delayed early warnings and fails to meet the proactive protection needs of personnel safety in high-risk scenarios.

⚗Method used

By acquiring real-time location data of monitored targets to build a trajectory database, using graph neural networks to analyze expected locations, and combining environmental data and disaster information to build a risk assessment model, dynamic and forward-looking assessment of potential risks can be achieved.

🎯Benefits of technology

It improves the timeliness and accuracy of risk identification, enhances security capabilities, enables early quantitative assessment of potential risks, reduces misjudgments, and improves the system's adaptability and overall security.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The application discloses a kind of dynamic risk prediction method and terminal, and the positioning data of monitoring target is acquired in real time, and real-time trajectory database is constructed according to positioning data;Real-time trajectory database is analyzed using preset graph neural network, and the expected position of monitoring target is generated;The environmental data and disaster information of the region where monitoring target is located are acquired, and risk assessment model is constructed based on environmental data and disaster information;Based on the expected position of monitoring target, the risk score of monitoring target is obtained using risk assessment model analysis, and the risk prediction result of monitoring target is determined according to risk score.The application carries out risk score to expected position, is no longer limited to the identification of danger that has occurred, but quantitatively evaluates potential risk in advance, to realize dynamic, forward-looking risk prediction, improve the timeliness and accuracy of risk identification, enhance overall security capability.
Need to check novelty before this filing date? Find Prior Art