Neural Network Emission Analysis with Blockchain Ledger

The patent creates a virtual copy of the emission measurement process through neural network simulation. Instead of using physical sensors to directly measure emitted substances, the system generates signals from a signal source and processes them through a trained neural network model that replicates the measurement function, thereby avoiding the need for expensive physical sensor hardware while maintaining measurement capability

The patent replaces the mechanical/physical sensor-based measurement system with an information-processing-based system. The neural network processes signals computationally to determine emission amounts, substituting the physical sensing mechanism with an algorithmic approach that uses signal processing and machine learning to achieve the same measurement objective

The patent implements a feedback mechanism where the neural network is trained using reference measurements from reliable sources, and the system continuously processes signals and compares results against predefined reference values. This feedback loop ensures the virtual measurement system maintains high reliability by constantly validating its outputs against known standards and adjusting its predictions accordingly

The patent performs preliminary training of the neural network model using extensive datasets before deployment. The signal source and neural network are pre-configured with learned patterns and relationships, allowing the system to reliably determine emission amounts from the start of operation without requiring complex real-time calibration procedures during actual measurement