User Evaluation Prediction Model for Natural Gas Networks
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Solution Overview
Problem
Current natural gas network management systems lack an effective method for obtaining user evaluations, which are crucial for improving user experience and network construction, as they rely on outdated and inefficient data collection and analysis processes.
Innovation Solution
A method and system that utilize machine learning models to predict user evaluations by integrating natural gas statistics data with user feedback data, adjusting predictions based on fault influence factors, and analyzing user opinion models to generate accurate service evaluation data, thereby improving data integration and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection and analysis processes are used, then system simplicity is maintained, but user evaluation accuracy and timeliness deteriorate
Solution Approach 1:
The system segments the user evaluation process into multiple independent modules: data collection module (collecting natural gas consumption data, billing data, user information), data processing module (cleaning, preprocessing), analysis module (predicting user evaluations using machine learning models), and feedback module (providing results to network construction). This segmentation enables accurate user evaluation while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw natural gas network data and user evaluation results. The model includes preprocessing components (data cleaning, feature extraction) and prediction components (evaluating user satisfaction based on consumption patterns, billing information, and network performance), enabling accurate evaluation without requiring direct complex analysis of all raw data.
2Adaptability or versatility
If forward-looking user evaluation methods are implemented, then user experience improvement is enabled, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing natural gas consumption data, billing data, and network performance data before user evaluations are needed. The machine learning model is trained in advance on historical data to predict user satisfaction trends, enabling forward-looking user experience optimization without requiring complex real-time processing during evaluation moments.
Solution Approach 2:
The patent implements a feedback mechanism where predicted user evaluations are fed back into the natural gas network construction and optimization process. The system continuously monitors prediction accuracy and adjusts model parameters based on actual user feedback, enabling adaptive user experience improvement while managing complexity through iterative refinement rather than complex static systems.
3Productivity
If machine learning models are used for prediction, then user evaluation timeliness is improved, but computational resource requirements increase
Solution Approach 1:
The system applies partial action by selectively processing only the most relevant data features for user evaluation prediction (e.g., consumption patterns, billing information, network performance metrics) rather than analyzing all available data. The machine learning model focuses on key predictive features, achieving timely user feedback processing while reducing unnecessary computational energy consumption on irrelevant data.
Data Source
AI summary
The present disclosure provides a method and system for obtaining a user evaluation configured to a natural gas energy measuring. The method may include obtaining natural gas statistics data and regional user data during a preset time period in a target area; obtaining a user evaluation prediction result based on the natural gas statistics data and the regional user data by a user evaluation model; determining whether the user evaluation prediction result is abnormal based on an actual user evaluation; and if the user evaluation prediction result is abnormal, adjusting the user evaluation prediction result.