IoT Gas Batch Supervision With Dynamic Resource Allocation
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Solution Overview
Problem
Current gas full-cycle supervision systems face challenges in efficiently allocating supervision resources due to varying stages and durations across different gas batches, leading to potential security and quality issues.
Innovation Solution
A smart gas full-cycle supervision system based on Internet of Things (IoT) technology, which includes a government safety supervision management platform that obtains gas production data, determines base supervision data, and adjusts gas supply volumes based on importance degrees calculated from target data and gas production data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the same supervision scheme is applied to different batches of gas, then the supervision process is simplified, but unreasonable allocation of supervision resources occurs and security problems cannot be found timely
Solution Approach 1:
The patent segments the gas supervision process by dividing gas batches into different categories based on their full-cycle stages (production, storage, transportation, distribution, sales, use). Each segment receives customized supervision schemes tailored to its specific characteristics, rather than applying a uniform supervision approach to all gas batches. This segmentation enables reasonable allocation of supervision resources and timely detection of security problems in each stage.
Solution Approach 2:
The patent implements dynamic supervision schemes that adapt to the changing stages of gas batches. As gas moves through different stages (production → storage → transportation → distribution → sales → use), the supervision parameters, frequency, and intensity are dynamically adjusted according to the current stage characteristics and risk levels, ensuring optimal resource allocation throughout the gas lifecycle.
2Reliability
If supervision frequency is increased for all gas batches, then security problems can be detected timely, but computing resources are wasted on low-risk batches
Solution Approach 1:
The patent applies local quality by differentiating supervision intensity based on the specific characteristics of each gas batch and its current stage. High-risk batches or those in critical stages (such as transportation or storage) receive increased supervision frequency and more rigorous monitoring, while low-risk batches in stable stages receive reduced supervision. This localized approach ensures timely detection of security problems where needed while conserving computing resources in low-risk areas.
Solution Approach 2:
The patent dynamically changes supervision parameters (frequency, intensity, monitoring depth) based on risk assessment results and gas batch characteristics. The system adjusts these parameters in real-time according to factors such as gas type, storage conditions, transportation routes, and historical safety data, optimizing the balance between detection timeliness and resource consumption.
3Productivity
If customized supervision schemes are applied to different gas batches, then resource allocation is optimized, but the supervision system complexity increases
Solution Approach 1:
The patent manages system complexity through structured segmentation of gas batches into standardized categories based on their full-cycle stages. Each segment has predefined supervision protocols and parameters, which simplifies the overall system architecture despite the customization. The segmentation framework provides a clear organizational structure that makes the complex customized supervision manageable and scalable.
Solution Approach 2:
The patent employs a universal platform that can handle multiple gas batches with different supervision requirements through a common architecture. The system uses standardized data interfaces, unified risk assessment models, and modular supervision modules that can be configured for different gas types and stages, reducing overall system complexity while enabling customized supervision schemes.
Data Source
AI summary
The present disclosure provides a system and method for smart gas full-cycle supervision based on IOT, 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.