Smart Gas Management Platform Work Order Fulfillment
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Solution Overview
Problem
Current gas work order fulfillment systems lack the ability to accurately determine fulfillment personnel and time limits, leading to inefficient processing and potential safety issues due to the lack of specific allocation rules for personnel and time arrangements.
Innovation Solution
The proposed IoT system for smart gas platforms analyzes demand information to determine a fulfillment mode, which includes self-service or manual fulfillment, and allocates fulfillment personnel and sets time limits based on this information, optimizing the fulfillment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a gas work order is not fulfilled properly and timely, then people's normal life order is affected and personal and property safety is compromised, but implementing a comprehensive fulfillment system increases system complexity
Solution Approach 1:
The fulfillment system is segmented into multiple components: demand information acquisition module, fulfillment mode determination module, and fulfillment plan generation module. Each module handles specific tasks independently, reducing overall system complexity while maintaining comprehensive functionality for reliable work order fulfillment.
Solution Approach 2:
The system performs preliminary actions by pre-determining fulfillment modes and generating fulfillment plans before actual work order execution. This includes pre-acquiring demand information, pre-assessing fulfillment requirements, and pre-arranging fulfillment resources, which ensures reliable fulfillment without requiring complex real-time decision-making systems.
2Productivity
If detailed fulfillment plans with specific personnel and time arrangements are implemented, then work order processing efficiency is improved, but the complexity of determining fulfillment plans increases
Solution Approach 1:
The fulfillment plan determination is made dynamic and adaptive. The system adjusts fulfillment plans based on real-time demand information, availability of personnel, and changing work conditions. This dynamic approach improves processing efficiency by allocating resources optimally without requiring overly complex predetermined schedules.
Solution Approach 2:
The system incorporates feedback mechanisms where fulfillment progress and outcomes are continuously monitored and fed back into the plan determination process. This allows the system to learn from past performance and improve future fulfillment plans, enhancing productivity while managing determination complexity through iterative optimization rather than complex upfront planning.
3Measurement precision
If comprehensive demand information is collected and analyzed, then accurate fulfillment plans can be determined, but information processing time and system complexity increase
Solution Approach 1:
The system extracts and focuses on key demand information elements that are most critical for accurate fulfillment planning. Rather than processing all possible information equally, it identifies and prioritizes essential data points such as work order type, urgency level, required skills, and time constraints, achieving high accuracy while reducing processing time.
Solution Approach 2:
Different levels of information analysis are applied to different aspects of fulfillment planning. Critical time-sensitive parameters receive intensive real-time analysis, while less time-critical parameters use standardized assessment protocols. This localized quality approach ensures high accuracy where needed while minimizing unnecessary processing time elsewhere.
Data Source
AI summary
The embodiments of the present disclosure provide a method and Internet of things (IoT) system for smart gas platform work order fulfillment. The method is executed through the IoT system, and the IoT system for smart gas platform work order fulfillment includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform. The method is executed by a smart gas management platform, including: obtaining demand information of at least one gas work order of a gas platform, determining, based on the demand information, a fulfillment mode of the at least one gas work order, and in response to that the fulfillment mode is the manual fulfillment, determining a work order fulfillment plan of the at least one gas work order based on the demand information and personnel information of the gas platform.