AI CBRN Threat Prediction via Sensor Data Correction

The patent creates a virtual copy of the physical sensor network through AI-based simulation. The system uses pollution diffusion prediction modeling to generate virtual sensor data that replicates what physical sensors would measure, eliminating the need for actual sensor installation while maintaining measurement capability. This is achieved by inputting weather data, geography information, and building data into the prediction model to simulate pollution concentration at various locations.

The patent replaces the mechanical sensor-based measurement system with an AI-based prediction system. Instead of using physical sensors to directly measure pollution concentrations, the system uses machine learning models that process weather, geography, and emission data to predict pollution levels. This substitution eliminates the need for physical sensor installation and maintenance while providing comparable or superior measurement precision.

The patent divides the prediction task into multiple specialized components: a weather model for atmospheric conditions, a pollution diffusion prediction model for concentration calculations, and an AI-based correction model for refinement. Each component handles a specific aspect of the prediction, allowing the system to maintain simplicity while improving accuracy through modular specialization. The segmentation enables parallel processing of different data types and independent optimization of each model component.

The patent implements a feedback mechanism where the AI model corrects the output of the pollution diffusion prediction model based on patterns learned from historical data. The correction model receives initial predictions, compares them with actual sensor measurements when available, and adjusts the predictions accordingly. This feedback loop continuously improves prediction accuracy in complex urban environments without requiring complete redesign of the prediction system.