Smart Gas IoT Platform Predicting Pipeline Operation Progress
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing the operation progress of a smart gas pipeline network is challenging due to frequent dynamic changes, requiring efficient prediction and management methods to improve construction and operation efficiency and user experience.
Innovation Solution
A method and system using a smart gas IoT platform that predicts future operation progress through a machine learning model, generates prompt messages for operators and associated objects, and adjusts parallel project sets based on construction risk, incorporating convolutional and fully connected layers for feature mapping and risk prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual progress management methods are used in gas pipeline network operations, then flexibility in handling dynamic changes is maintained, but management efficiency and accuracy deteriorate due to frequent manual interventions and updates
Solution Approach 1:
The patent replaces manual mechanical progress tracking with an automated machine learning-based prediction system. The progress prediction model automatically forecasts operation progress based on historical data and current status, eliminating the need for manual progress updates and calculations, thereby improving management efficiency while reducing operational complexity
Solution Approach 2:
The system enables self-service progress management through automated prompt message generation and distribution. The model automatically generates progress predictions, creates prompt messages for different stakeholders, and distributes them through the messaging module, allowing the system to manage its own progress tracking without external intervention
2Measurement precision
If real-time progress tracking and prediction systems are implemented, then operation progress accuracy is improved, but information processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training the progress prediction model on historical operation data before actual use. This pre-training phase allows the model to learn patterns and relationships in advance, so that during actual operation, predictions can be generated quickly with high accuracy without requiring extensive real-time computational resources
Solution Approach 2:
The patent applies partial action by focusing the prediction model on key progress indicators and critical path activities rather than tracking every single operation detail. This selective approach maintains high prediction accuracy for the most important metrics while reducing overall processing time and computational burden
3Productivity
If automated prompt message generation is implemented for operators and associated objects, then communication efficiency is improved, but system complexity and development costs increase
Solution Approach 1:
The prompt message generation module implements universality by using a single machine learning model to generate predictions that serve multiple stakeholders (operators, associated objects, management personnel) with different information needs. The same core prediction engine adapts its output format and content based on the recipient type, eliminating the need for separate prediction systems for each user group and reducing overall system complexity
Data Source
AI summary
The embodiments of the present disclosure provide methods and Internet of Things (IoT) system for managing an operation progress of a smart gas pipeline network. The method includes: obtaining a construction type and a progress sequence of a gas operation based on a smart terminal and a sensing unit; predicting a future operation progress at a future moment through a progress prediction model based on the construction type and the progress sequence, the progress prediction model being a machine learning model; generating a first prompt message and a second prompt message based on the future operation progress, the first prompt message including the progress reminder data of the gas operation, and the second prompt message including an approach plan of a gas associated object; and sending the first prompt message to a gas operator and sending the second prompt message to the gas associated object.