Smart Gas IoT Scheduling for Maintenance Efficiency
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Solution Overview
Problem
The challenge lies in efficiently scheduling gas maintenance personnel across different areas, particularly in urban regions with complex gas pipeline networks, where insufficient manpower often leads to delayed maintenance and reduced efficiency due to the need for sequential allocation based on gas consumption features.
Innovation Solution
A smart gas Internet of Things system is implemented, utilizing a platform that includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, and a smart gas sensor network platform, which uses feature extraction methods like multilayer perceptrons, convolutional neural networks, and residual networks to determine alert vectors and on-call maintenance personnel, and performs iterative updates on scheduling capability values based on an area map to optimize real-time scheduling policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If maintenance personnel are allocated sequentially based on gas consumption features, then the allocation follows a simple rule, but the maintenance efficiency is greatly reduced and areas with insufficient staff experience delays
Solution Approach 1:
The system performs preliminary actions by predicting future maintenance demands using gas consumption features and alert vectors before actual maintenance needs arise. The iterative update mechanism pre-calculates optimal personnel allocation across multiple scenarios, enabling proactive rather than reactive scheduling and eliminating waiting delays.
Solution Approach 2:
The maintenance scheduling system transitions from static sequential allocation to dynamic iterative optimization. The system continuously updates scheduling capability values based on changing gas consumption patterns and alert levels, allowing real-time adjustment of personnel distribution to match actual demand fluctuations across different areas.
2Reliability
If more maintenance personnel are deployed to areas with high gas consumption, then the maintenance coverage is improved, but the overall resource utilization becomes unbalanced and other areas suffer from insufficient staff
Solution Approach 1:
The system applies local quality by determining area-specific scheduling capability values based on individual area characteristics including gas consumption features and alert vectors. Each area receives customized maintenance resource allocation proportional to its specific needs rather than uniform distribution, ensuring high-risk areas get adequate coverage while preventing over-allocation to low-priority areas.
Solution Approach 2:
The system dynamically changes the scheduling capability parameter for each area through iterative updates based on gas consumption patterns and alert levels. This parameter adjustment mechanism allows flexible reallocation of maintenance personnel, transforming the scheduling approach from fixed ratios to adaptive parameter-driven distribution that optimizes both coverage and resource utilization.
Data Source
AI summary
A method and a smart gas Internet of Things system for management based on gas safety are provided. The method may comprise obtaining gas-related features of at least one area; determining alert vectors of the at least one area based on the gas-related features by manners for feature extraction; determining a count of on-call maintenance personnel in the at least one area based on the alert vectors; determining whether the count of on-call maintenance personnel meets a preset threshold; and in response to a determination that there is at least one area where the count of on-call maintenance personnel meets the preset threshold, performing, based on an area map, multiple rounds of iterative update on a scheduling capability value of the at least one area that meets the preset threshold; and determining a real-time scheduling policy based on the updated scheduling capability value.