LNG Station Address Matching Using Deep Learning Semantic Extraction
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Solution Overview
Problem
Traditional address matching methods for LNG station management are inefficient due to their reliance on literal address text matching, which struggles with accuracy and speed when handling massive multi-source heterogeneous address data, and lack precise positioning and supervision of LNG tanker transportation.
Innovation Solution
A deep learning algorithm is used to extract semantic features from address texts, combined with a prediction model that includes a feature extracting layer, a predicting layer, and a confidence level determination layer, to improve address matching efficiency and monitor LNG tanker transportation by predicting driving directions based on relative location and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional address matching methods are used, then the system is simple to implement, but the matching accuracy and speed deteriorate when handling massive multi-source heterogeneous address data
Solution Approach 1:
The patent replaces traditional mechanical address matching methods with deep learning algorithms. The system uses neural networks to extract semantic features from address texts and perform intelligent matching, substituting the manual rule-based approach with an automated learning-based system that handles heterogeneous data more effectively
Solution Approach 2:
The patent changes the parameters of address matching by introducing semantic feature extraction instead of literal string matching. The system transforms address data into semantic representations and uses learned parameters from training data to improve matching accuracy while maintaining reasonable system complexity
2Productivity
If deep learning algorithms are used for address matching, then the matching efficiency and accuracy improve, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models on large datasets before deployment. The semantic feature extraction capabilities are established in advance through training, allowing the system to perform rapid matching during operation without performing the heavy computational work in real-time
Solution Approach 2:
The patent extracts only the essential semantic features from address data using the trained deep learning model, rather than processing all raw data. This extraction approach reduces the computational burden by focusing on the most relevant features for matching while discarding redundant information
3Measurement precision
If traditional transportation supervision methods are used, then the system is easy to operate, but the positioning precision and safety monitoring capability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring tanker position, movement direction, and path deviations. The system compares actual tanker trajectories with planned routes and provides real-time feedback for corrective actions, improving positioning precision while maintaining operational simplicity through automated monitoring
Solution Approach 2:
The patent applies multi-functionality by integrating multiple supervision functions into a single system. The platform simultaneously performs positioning, path tracking, deviation detection, and safety monitoring, reducing the need for separate systems and maintaining ease of operation through a unified interface
Data Source
AI summary
The present disclosure discloses a method for liquefied natural gas (LNG) station management and an Internet of Things system. The method includes: acquiring historical gas filling data of a gas station based on a preset acquiring time; determining LNG demand data at at least one future moment and a predicted confidence level of the LNG demand data based on the historical gas filling data through a prediction model; determining a reliable moment based on the predicted confidence level; determining at least one set of candidate delivery plans based on the LNG demand data and LNG storage data; and determining a target delivery plan based on at least one piece of gas filling cost data of the at least one set of candidate delivery plans and the reliable moment.