IoT Gas Inspection Routing for Mixed Drone, Robot, and Manual Patrols
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Solution Overview
Problem
Unmanned inspection equipment for gas pipelines faces limitations in uneven terrain and high costs, leading to ineffective inspections and increased labor and time expenditure due to repeated manual interventions.
Innovation Solution
An IoT system for smart gas inspection that integrates a public user platform, citizen cloud service platform, government gas supervision management platform, sensor networks, and inspection equipment (drone, robot) to determine optimal inspection types and routes based on historical data and machine learning models, ensuring accurate and efficient inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If unmanned inspection equipment (drone, robot) is used to replace manual inspection, then inspection intelligence and efficiency are improved, but application scope is limited and inspection cost increases
Solution Approach 1:
The patent introduces a multi-level platform system (government gas supervision management platform, gas company sensor network platform, public user platform) as an intermediary that coordinates between manual and unmanned inspection resources. This mediator optimizes task allocation based on terrain conditions, pipeline characteristics, and resource availability, allowing the system to select the most appropriate inspection method for each specific scenario rather than forcing unmanned equipment in all situations.
2Productivity
If unmanned inspection equipment is used, then inspection intelligence is improved, but inspection cost increases and resource waste occurs due to ineffective or repeated inspection
Solution Approach 1:
The system performs preliminary actions by conducting terrain analysis, pipeline condition assessment, and resource availability evaluation before deploying inspection tasks. The government gas supervision management platform pre-processes inspection route information and matches it with appropriate inspection resources based on historical data and real-time conditions, ensuring that unmanned equipment is only deployed when conditions are favorable, thereby avoiding wasted inspection costs.
Solution Approach 2:
The patent implements feedback mechanisms where inspection results, terrain data, and resource performance information are continuously collected and fed back to the management platform. This feedback loop allows the system to learn from past inspection outcomes and optimize future task allocation, preventing repeated ineffective inspections and reducing overall inspection costs through data-driven decision making.
3Reliability
If manual inspection is performed repeatedly due to unavailable data from unmanned equipment, then inspection coverage is maintained, but labor and time cost increases
Solution Approach 1:
The system dynamically adjusts inspection strategies based on real-time conditions and equipment performance. When unmanned equipment encounters terrain limitations or technical issues that prevent data collection, the platform automatically reassigns those specific segments to manual inspection while maintaining unmanned inspection for suitable segments. This dynamic resource allocation ensures complete inspection coverage without the need for entirely manual re-inspection, minimizing time and labor costs.
Data Source
AI summary
Disclosed is an IoT system for smart gas inspection supervision and distribution, comprising: a public user platform, a citizen cloud service platform, a government gas supervision management platform, a government gas supervision sensor network platform, a government gas supervision object platform, a gas company sensor network platform, and a gas equipment object platform. The government gas supervision management platform is configured to obtain an inspection route; determine a plurality of inspection sub-routes; generate a calling instruction; generate an acquisition instruction and send the acquisition instruction to the gas equipment object platform; determine an inspection type parameter of the inspection sub-routes; divide the inspection sub-routes into three categories of objects to be inspected, and determine candidate assignment parameters of the three categories of objects to be inspected; determine predicted inspection time; determine an inspection assignment parameter; generate an inspection instruction and send the inspection instruction to the gas equipment object platform.