Smart Gas Supply Supervision With Batch-Based Resource Allocation
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Solution Overview
Problem
Current gas full-cycle supervision systems face challenges in efficiently managing different batches of gas due to varying stages and durations, leading to unreasonable allocation of supervision resources and potential safety issues.
Innovation Solution
A smart gas full-cycle supervision system and method that utilizes a government safety supervision management platform to assess resource consumption and availability, adjust gas supply based on importance degrees calculated from hazard and user influence levels, and optimize data collection frequencies to ensure effective supervision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the same supervision scheme is applied to different batches of gas, then the supervision process is simplified and standardized, but unreasonable allocation of supervision resources occurs and safety problems cannot be detected timely
Solution Approach 1:
The patent segments the gas supervision process by dividing gas batches into different supervision groups based on their full-cycle stages. Each group receives tailored supervision schemes according to their specific characteristics (production, storage, transportation, distribution, sales, or use stages), rather than applying a uniform scheme to all batches. This segmentation enables reasonable resource allocation while maintaining supervision standardization within each group.
Solution Approach 2:
The supervision scheme is made dynamic by automatically adjusting the grouping and supervision parameters based on real-time gas batch characteristics and full-cycle stage information. The system dynamically reconfigures supervision resources according to the current state of different gas batches, allowing the supervision approach to adapt to changing conditions while maintaining operational simplicity.
2Ease of operation
If supervision resources are allocated uniformly to all gas batches, then resource allocation is simple and manageable, but supervision efficiency decreases and critical safety issues are missed
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor gas batch characteristics, full-cycle stages, and supervision effectiveness. Based on this feedback, the system automatically adjusts the grouping and resource allocation for different gas batches, optimizing supervision efficiency while maintaining manageable operational complexity through automated decision-making.
Solution Approach 2:
The supervision parameters (such as data collection frequency, monitoring intensity, and resource allocation) are changed based on the grouped characteristics of different gas batches. High-priority batches receive enhanced supervision parameters while lower-priority batches receive standard supervision, optimizing overall supervision efficiency without requiring complex manual allocation decisions.
3Measurement precision
If data collection frequency is increased for all gas batches, then supervision accuracy is improved, but resource consumption increases and optimization opportunities are lost
Solution Approach 1:
The system applies local quality by setting different data collection frequencies for different gas batch groups based on their specific needs and risk levels. Critical gas batches undergo frequent monitoring with high data collection frequency, while less critical batches are monitored at standard frequencies. This localized approach improves supervision accuracy where needed while conserving computing resources overall.
Data Source
AI summary
The present disclosure provides a system and method for gas supply management of smart gas full-cycle, the method includes: obtaining gas production data; determining base supervision data based on the gas production data; obtaining residual computing resources at a preset frequency; in response to determining that a sum of the reference resource consumption and the residual computing resources satisfying a preset requirement, performing operations including: generating and sending a control instruction to a gas company management platform; evaluating an importance degree of each of the all batches of gas in the target gas based on the target data and the gas production data of the target gas; and adjusting a gas supply volume of the at least one of all batches of gas in the target gas based on the importance degree of the each of the all batches of gas in the target gas and the target data.