Remote Operation Irregularity Detection Using Event Relevance
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Solution Overview
Problem
Existing remote operation systems lack accurate determination of whether a remote operation is regular or irregular, leading to unsatisfactory determination accuracy due to the absence of considering remote operation information and event information.
Innovation Solution
An irregularity determination method that utilizes remote operation information and associated event information, including relevance information, time difference thresholds, and variance of acquisition times, to accurately determine the regularity of remote operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simple threshold-based determination is used for remote operation irregularity detection, then the determination process is fast and simple, but the determination accuracy is unsatisfactory
Solution Approach 1:
The system performs preliminary learning during a training period to acquire remote operation information and event information in advance. This preliminary action enables the system to establish baseline patterns and relevance relationships before actual irregularity detection occurs, improving accuracy without adding complexity to the real-time determination process.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously comparing current remote operation information and event information against learned patterns. The relevance determination unit provides feedback on whether operations conform to expected patterns, enabling accurate irregularity detection through iterative comparison and adjustment.
2Measurement precision
If remote operation information and event information are considered together, then determination accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The system segments the determination process into distinct modules: a learning unit that processes historical data separately, a relevance determination unit that evaluates current operations against learned patterns, and an irregularity determination unit that makes final judgments. This segmentation enables efficient processing by dividing complex tasks into manageable, parallelizable components.
Solution Approach 2:
The learning unit performs preliminary processing of remote operation information and event information during training periods, pre-computing relevance relationships and patterns. This preliminary action reduces the computational burden during real-time operation, as the system only needs to compare current data against pre-processed patterns rather than analyzing everything from scratch.
3Reliability
If a high threshold for irregularity determination is used, then false alarms are reduced, but legitimate irregular operations may be missed
Solution Approach 1:
The system dynamically adjusts determination thresholds based on learned patterns and contextual information rather than using fixed static thresholds. The relevance determination unit continuously refines its assessment based on the specific patterns learned during training, enabling the system to adapt thresholds to each situation and achieve both low false alarms and high detection accuracy.
Solution Approach 2:
The system changes determination parameters based on the specific characteristics of remote operation information and event information. By analyzing the relevance between different types of operations and events, the system adjusts its determination criteria dynamically, allowing it to distinguish between legitimate variations and actual irregularities with high precision while maintaining low false alarm rates.
Data Source
AI summary
An information processing device performs: acquiring remote operation information concerning a remote operation to an appliance; acquiring event information associated with the remote operation and indicating an occurrence of a specific event related to an action of a user; determining whether the remote operation is regular or irregular on the basis of: relevance information indicating a relevance between the remote operation and the event; the acquired remote operation information; and the acquired event information; and outputting an irregularity notification when the remote operation is determined to be irregular.