Vehicle Driving Data Mining for Analysis Error Reduction
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Solution Overview
Problem
The rapid increase in vehicle driving data requires an efficient method for processing and analyzing large amounts of time-series data to extract useful information for managers and users, such as transportation organizations, to improve safety, efficiency, and prevent accidents.
Innovation Solution
A method and system for processing and analyzing vehicle driving big data through data refinement, statistical data generation, and mining analysis, which includes outlier detection, statistical calculation, and pattern analysis to provide actionable insights on driver behavior, accident risk, and maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vehicle driving data are collected and accumulated over time, then the amount of data available for analysis increases, but the difficulty of processing and analyzing the data increases exponentially
Solution Approach 1:
The patent segments the data processing workflow into distinct modules: data collection from multiple sources (sensors, logs, databases), data storage in structured formats, data processing through filtering and aggregation, and data analysis using mining techniques. This modular segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while managing large volumes of vehicle driving data
Solution Approach 2:
The patent introduces intermediate data structures and processing layers that mediate between raw data collection and final analysis. These intermediaries include standardized data formats, aggregation buffers, and preprocessing pipelines that transform heterogeneous vehicle driving data into uniform, analysis-ready formats, thereby simplifying the processing burden on downstream systems
2Measurement precision
If systematic data refining and mining procedures are implemented, then the quality and usefulness of extracted information improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary data refining and aggregation procedures during the data collection and storage phases. By pre-processing data to remove obvious outliers, standardize formats, and aggregate redundant information before analysis, the system reduces the computational burden during actual mining operations, thereby maintaining high information quality while reducing processing time
Solution Approach 2:
The patent implements selective data refining that focuses computational resources on the most critical data quality issues and the most relevant data subsets for specific analysis objectives. Rather than applying exhaustive refinement to all data uniformly, the system applies targeted refinement procedures only where necessary to achieve the required analysis quality, thus optimizing the balance between information quality and processing efficiency
Data Source
AI summary
Provided is a method of processing and analyzing vehicle driving big data, the method including: refining vehicle driving data of raw data; acquiring statistical data based on the refined vehicle driving data; and performing mining analysis based on at least one of the refined vehicle driving data and the acquired statistical data.