Radioactive aerosol real-time monitoring method and system

Through multimodal data fusion technology and machine learning algorithms, combined with decision support systems, real-time monitoring and evaluation of radioactive aerosols are achieved, solving the problems of long monitoring cycles and poor real-time performance in traditional methods, and improving the accuracy and adaptability of monitoring.

CN119903412APending Publication Date: 2025-04-29HUANENG NUCLEAR ENERGY TECH RES INST CO LTD
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Patent Information

Application Number
CN202411644363.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-18
Publication Date
2025-04-29

AI Technical Summary

Technical Problem

The traditional radioaerosol monitoring methods have the disadvantages of long monitoring cycles, poor real-time performance, and complex operation, which are difficult to meet the real-time and accuracy requirements of modern environmental monitoring.

Method used

Using multimodal data fusion technology, advanced machine learning algorithms and decision support systems, we collect and fuse real-time multimodal data, use pre-trained recognition models to identify nuclides, and infer the semantic properties of unknown types of nuclides through the zero-sample learning model, and finally generate a suggested decision.

Benefits of technology

A comprehensive, real-time and accurate monitoring and evaluation of radioactive aerosols is achieved, and the comprehensiveness and accuracy of monitoring data is improved. It can quickly and accurately identify known and unknown types of nuclides, and provides strong technical support to reduce the risk of radioactive contamination.

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Abstract

The invention relates to the technical field, and discloses a radioactive aerosol real-time monitoring method and system, and the method comprises the steps: collecting real-time multi-modal data of aerosol monitoring in a target region, and obtaining the fusion features of the real-time multi-modal data; performing nuclide identification on the fusion features of the real-time multi-modal data by using a pre-trained identification model to obtain an identification result; on the basis of the recognition result, semantic attributes of nuclides of known types are obtained by judging the semantic attributes of the nuclides, and semantic attributes of nuclides of unknown types are speculated by using a zero sample learning model; and based on the attribute of the nuclide corresponding to the real-time multi-modal data, generating a suggested decision by using a predefined decision support model. Through real-time monitoring and rapid identification, risk early warning can be given out in time, a scientific basis is provided for emergency response, and the problem that sudden radiation events cannot be handled in time is solved.
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