Smart Gas Dispatching Balancing Supply and Demand

The patent applies preliminary action by predicting future gas demand using historical data and machine learning models before emergencies occur. The system proactively adjusts dispatching plans based on predicted demand patterns, seasonal variations, and user behavior, preventing supply-demand imbalances before they lead to emergencies.

The patent implements feedback mechanisms by continuously monitoring actual gas consumption, comparing it with predicted demand, and adjusting dispatching plans accordingly. User feedback from call centers and real-time metering data are integrated into the prediction models to improve accuracy over time, creating a closed-loop system that adapts to changing conditions.

The patent applies self-service by enabling the gas distribution system to automatically adjust its own dispatching plans based on predicted demand and real-time conditions. The machine learning models autonomously analyze historical data, identify patterns, and generate optimized dispatching strategies without requiring manual intervention, allowing the system to self-optimize its performance.

The patent replaces manual mechanical dispatching processes with automated machine learning algorithms and computational models. The system uses data-driven prediction models to substitute human judgment with algorithmic optimization, processing large volumes of historical and real-time data to generate more efficient and adaptive dispatching plans.

The patent applies segmentation by dividing the complex data processing task into separate functional modules: data collection from meters and call centers, data cleaning and preprocessing, feature extraction, model training, and dispatching plan generation. This modular approach manages complexity by processing data in staged transformations rather than attempting to handle all data simultaneously.

The patent uses an intermediary data processing layer that acts as a mediator between raw data sources and the machine learning models. This intermediate layer includes data cleaning, feature engineering, and dimensionality reduction processes that simplify complex raw data into meaningful features suitable for prediction models, reducing the computational burden while maintaining accuracy.