IoT Pipeline Repair Scheduling for Urgency-Based Equipment Dispatch
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Solution Overview
Problem
Gas pipelines are prone to failures due to external factors, and existing manual scheduling of repair equipment is often inefficient and unreasonable, leading to potential gas supply disruptions and management challenges.
Innovation Solution
An IoT system for pipeline repair that includes a citizen user platform, government supervision platforms, a gas company management platform, and equipment platforms, utilizing machine learning models to determine a rational repair sequence and prioritize equipment based on performance and urgency, ensuring timely repairs and maintaining gas supply.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing is used to schedule repair equipment, then the system complexity is reduced, but the repair scheduling efficiency and reasonability deteriorate
Solution Approach 1:
The system enables automatic self-scheduling of repair equipment through the IoT platform. The platform automatically collects pipeline data, evaluates repair urgency using machine learning models, and assigns appropriate repair equipment without requiring manual intervention, thereby improving scheduling efficiency while managing system complexity through automation
Solution Approach 2:
The patent replaces manual mechanical scheduling processes with an automated digital system. The IoT platform uses sensors, communication networks, and machine learning algorithms to automatically evaluate and schedule repairs, substituting human decision-making with an automated intelligent system that processes information and makes scheduling decisions efficiently
2Reliability
If more professional repair equipment is deployed, then the repair capability is improved, but the equipment scheduling difficulty increases
Solution Approach 1:
The system continuously collects feedback from sensors monitoring pipeline conditions and repair equipment status. This real-time feedback is processed by the IoT platform to dynamically adjust scheduling decisions, ensuring that the right equipment is assigned to the right pipelines at the right time, thereby improving reliability while simplifying scheduling through continuous optimization
Solution Approach 2:
The patent uses machine learning models to dynamically change scheduling parameters based on real-time data. The system adjusts repair urgency assessments, equipment assignment priorities, and scheduling timelines based on varying pipeline conditions, gas supply demands, and equipment availability, making the scheduling process adaptive and easier to manage despite having multiple pieces of equipment
3Manufacturing precision
If repair time is extended to ensure thorough repairs, then the repair quality is improved, but the gas supply disruption increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring pipeline conditions through sensors and pre-evaluating repair needs using machine learning models. This allows the system to identify and schedule repairs in advance, planning the optimal repair sequence and equipment allocation before actual failures occur, thereby ensuring high repair quality while minimizing gas supply disruption through proactive scheduling
Solution Approach 2:
The patent implements dynamic scheduling that adapts repair timelines based on real-time conditions. The system can adjust repair priorities, extend or shorten repair windows, and reassign equipment dynamically based on gas supply demands, pipeline criticality, and repair progress, allowing the system to optimize between repair quality and supply disruption for each specific situation
Data Source
AI summary
Provided is a method and an Internet of Things (IoT) system for pipeline repair. The method includes: obtaining pipeline repair equipment information and pipeline data to be repaired; determining a performance level of a pipeline repair equipment based on the pipeline repair equipment information; determining a target pipeline repair equipment based on the performance level; constructing a pipeline map to be repaired; determining a repair urgency for each pipeline to be repaired; determining at least one target repair pipeline; determining a pipeline repair sequence; determining repair commands based on the pipeline repair sequence; and controlling, based on the repair commands, the target pipeline repair equipment that is in an idle state and has the performance level not less than a performance threshold to prioritize processing a target repair pipeline with the repair urgency not less than an urgency threshold.