Vehicle Anomaly Log Filtering for Faster Fault Monitoring
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Solution Overview
Problem
Existing vehicle monitoring systems are slow to identify anomalies and often require manual log extraction, leading to delayed issue recognition and potential loss of important data.
Innovation Solution
A vehicle monitoring apparatus and server system that detects anomalies based on user input and preset conditions, collects event status data with error levels, and transmits logs only when necessary, preventing unnecessary data collection and communication overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If continuous log monitoring and transmission is implemented, then anomaly detection speed is improved, but communication overload and database capacity issues occur
Solution Approach 1:
The system performs preliminary classification of log events into normal and abnormal categories before transmission. The log manager analyzes log content and only transmits abnormal logs to the server, while normal logs are processed locally or discarded. This preliminary action filters out unnecessary data transmission, achieving fast anomaly detection without communication overload.
Solution Approach 2:
Different processing strategies are applied to different types of logs based on their characteristics. Normal logs undergo local processing with minimal communication, while abnormal logs trigger full transmission and analysis. This localized quality approach optimizes resource allocation based on the specific needs of each log type.
2Reliability
If all logs are collected and transmitted, then complete analysis capability is improved, but database capacity requirements increase
Solution Approach 1:
The system extracts only the essential abnormal log data that requires server-side analysis, leaving normal operational logs to be processed locally or stored in lightweight formats. This extraction principle reduces the volume of data that needs to be stored in the central database while maintaining complete analysis capability for abnormal events.
Solution Approach 2:
Logs are pre-processed and filtered before being stored in the database. The log manager performs preliminary analysis to identify abnormal logs, and only these filtered logs are transmitted and stored in the central database. This preliminary action ensures database completeness for analysis while minimizing storage requirements.
3Device complexity
If manual log extraction methods are used, then system complexity is reduced, but log extraction time increases
Solution Approach 1:
The log management system performs automatic log collection, classification, and transmission without requiring manual intervention. The log manager continuously monitors log files, automatically identifies abnormal events, and initiates transmission to the server. This self-service approach maintains simple system architecture while dramatically reducing log extraction time compared to manual methods.
Data Source
AI summary
Disclosed are an apparatus and a server for monitoring a vehicle, and a method for monitoring a vehicle using the same. According to an aspect of the present disclosure, the vehicle monitoring apparatus comprises a terminal task that is configured to detect an event caused by an anomaly sign in the vehicle, and a terminal log manager configured to collect event status data comprising an error level in response to detecting the event, and configured to transmit a log matched with the event in response to a log transmission request.