Dynamic risk assessment method and system for hazardous chemical substance leakage and diffusion based on PINN and DBN

By combining PINN and DBN methods, the problems of low latency and high accuracy in toxic gas diffusion simulation and risk assessment under complex environments were solved, realizing real-time dynamic risk assessment and source parameter inversion, thus improving computational efficiency and the accuracy of risk assessment.

CN122887158APending Publication Date: 2026-10-09THE NAVAL MEDICAL UNIV OF PLA
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Patent Information

Application Number
CN202610956381.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-06-30
Publication Date
2026-10-09

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve high-precision, low-latency simulation of toxic gas diffusion and source term parameter inversion in complex dynamic environments. Furthermore, existing risk assessment methods lack real-time data-driven and deep-coupling capabilities, failing to accurately reflect the dynamic changes of risk factors during accident evolution.

Method used

A method based on PINN and DBN is adopted. By constructing a physical information neural network (PINN) with the advection-diffusion equation as a constraint, and combining it with a dynamic Bayesian network (DBN) for risk assessment, a deep coupling between the diffusion model and risk assessment is achieved. Real-time data is used for rolling closed-loop coupling and Bayesian inference to perform leakage source parameter inversion and risk prediction.

Benefits of technology

It achieves high-precision, low-latency diffusion simulation and source parameter inversion in complex environments, and has real-time dynamic risk assessment capabilities, improving computational efficiency and the objectivity and accuracy of risk assessment.

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Abstract

The application discloses a dynamic risk assessment method and system for dangerous chemical product leakage and diffusion based on PINN and DBN, and comprises the following steps: step S1, real-time data acquisition and preprocessing; step S2, PINN model construction, wherein the PINN model takes an advection-diffusion equation as a physical constraint, and simultaneously outputs a global concentration field and a leakage source parameter; step S3, based on the concentration field and a dangerous chemical product toxicity threshold, a multi-level influence area is demarcated and mapped to a geographic coordinate system; step S4, a DBN risk assessment topological structure is constructed, and a comprehensive risk value is calculated through Bayesian inference; and step S5, a risk grade to which the comprehensive risk value belongs is output, a main driving factor is calculated, a multi-period risk prediction is calculated, and an emergency disposal suggestion is given. The application overcomes the technical defects in the prior art that the diffusion model precision and real-time performance are difficult to be considered simultaneously, and the risk assessment cannot be dynamically updated, and provides a scientific basis for emergency response and risk management of dangerous chemical product leakage accidents.
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