Smart Gas Filling Station IoT Maintenance for Predictive Safety Supervision
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Solution Overview
Problem
Traditional maintenance methods for gas filling stations lack intelligent prediction of equipment status and usage, leading to inefficient and potentially risky maintenance practices that disrupt daily operations.
Innovation Solution
An IoT system is implemented to provide a method for maintaining smart gas filling stations based on safety supervision. This system includes various platforms for data collection, analysis, and decision-making, enabling dynamic adjustment of maintenance strategies based on real-time data and historical trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual maintenance methods are used based on fixed schedules, then maintenance can be performed regularly, but the maintenance efficiency is low and may disrupt daily operations
Solution Approach 1:
The system performs preliminary actions by predicting future equipment status and usage trends before actual maintenance is needed. The prediction module analyzes historical operation data to forecast when maintenance will be required, allowing the system to schedule maintenance proactively rather than reactively, thereby improving both reliability and efficiency
Solution Approach 2:
The system implements continuous feedback loops where operation data is constantly collected, analyzed, and used to adjust maintenance strategies. The feedback mechanism compares predicted vs. actual equipment status to refine prediction accuracy and dynamically optimize maintenance scheduling, resolving the contradiction between regular maintenance and operational disruption
2Adaptability or versatility
If traditional fixed maintenance schedules are implemented, then maintenance timing is predictable, but the maintenance strategy cannot adapt to real-time equipment status changes
Solution Approach 1:
The maintenance strategy transitions from static fixed schedules to dynamic adaptive scheduling. The system continuously adjusts maintenance parameters based on real-time equipment status, usage intensity, and predicted degradation trends. This dynamic approach allows the maintenance strategy to adapt to changing conditions while the information management system preserves and analyzes historical data for continuous improvement
Solution Approach 2:
The system introduces an information management platform as an intermediary between equipment operation and maintenance decision-making. This intermediary collects, stores, and analyzes operation data, transforming raw information into actionable insights that enable adaptive maintenance strategies without losing critical real-time status information
3Reliability
If routine maintenance is performed frequently to reduce equipment failure risk, then equipment reliability improves, but the impact on daily operations increases
Solution Approach 1:
The system optimizes maintenance parameters such as frequency, timing, and duration based on actual equipment conditions and usage patterns. By changing these parameters dynamically rather than using fixed schedules, the system achieves necessary reliability while minimizing operational disruption. The prediction module identifies optimal maintenance windows that balance reliability requirements with operational continuity
Data Source
AI summary
Disclosed is a method and an IoT system for maintaining a smart gas filling station based on safety supervision. The method comprises: obtaining historical gas filling data; determining, based on the historical gas filling data, a predicted usage feature; obtaining historical operation data; determining a historical operation feature based on the historical operation data; determining an operation and maintenance parameter based on the predicted usage feature and the historical operation feature, and generating a maintenance instruction; obtaining a count of reference vehicles; and generating a regulation instruction in response to determining that the count of the reference vehicles is greater than a reference threshold. The IoT system comprises a government safety supervision service platform, a government safety supervision management platform, a government safety supervision sensor network platform, a government safety supervision object platform, a gas company sensor network platform, and a gas equipment object platform.