Gas Pipeline IoT Monitoring for Dynamic Maintenance Prioritization

The patent replaces manual periodic inspection with an automated sensor network platform that collects pipeline operation data (pressure, flow, temperature) and gas user consumption data. This mechanical-to-automated substitution dramatically improves monitoring efficiency while the modular platform architecture keeps system complexity manageable.

The system enables self-service through automatic data collection from sensors, automated risk assessment algorithms that evaluate pipeline safety without human intervention, and automated generation of maintenance instructions. This eliminates the need for manual inspection while maintaining operational simplicity.

The patent implements dynamic inspection scheduling where the inspection frequency and focus are automatically adjusted based on real-time pipeline operation data and gas consumption patterns. High-risk pipelines with significant usage variations receive more frequent monitoring, while stable pipelines require less attention, making the system adaptable without being operationally complex.

The system changes monitoring parameters (inspection frequency, sensor thresholds, risk weights) based on pipeline-specific operational data and gas consumption patterns. This allows each pipeline to be monitored according to its actual conditions rather than a fixed schedule, achieving high adaptability through automated parameter adjustment.

The patent creates a universal sensor network platform that can monitor multiple gas pipelines simultaneously using standardized sensor nodes. Each sensor node is multi-functional, measuring pressure, flow, and temperature while also detecting leakage. This universal approach improves reliability through comprehensive monitoring while keeping individual node complexity low.

The system implements continuous feedback loops where sensor data from multiple pipelines is automatically collected, analyzed, and used to update risk assessments. This feedback mechanism improves monitoring reliability by continuously adapting to actual pipeline conditions and failure patterns across the entire network, rather than relying on static resident/building-based classifications.

The patent applies local quality by storing comprehensive monitoring data only for high-risk pipelines and key parameters for all pipelines, while storing summarized or reduced data for low-risk pipelines. The sensor network platform automatically prioritizes data retention based on pipeline risk assessment, maintaining high assessment accuracy for critical pipelines while reducing overall storage requirements.

The system extracts and stores only the most critical monitoring parameters (pressure anomalies, flow deviations, temperature changes indicating leakage) at high resolution, while storing other parameters at lower resolution or aggregating them over time. This selective extraction maintains assessment accuracy for safety-critical parameters while significantly reducing total storage resource consumption.