Operation Cycle Segmentation Using Partial-Motion Similarity
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Solution Overview
Problem
Existing operations management systems require manual specification of a cycle interval for analyzing operations, limiting their ability to manage operations performed repeatedly without analyzing multiple tasks within a cycle.
Innovation Solution
An operations management system that includes a storage device, a first divider, a degree of similarity calculator, and an estimator to analyze operations without user-specified cycle intervals by dividing operation data sets into partial data and calculating similarity between them to optimize division points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual specification of cycle interval is required, then the system can analyze operations with defined cycles, but the system cannot automatically analyze multiple tasks within a cycle without user input
Solution Approach 1:
The system performs self-service by automatically detecting cycle intervals and dividing operation data into tasks without requiring manual user specification. The cycle interval detection unit autonomously analyzes operation data to identify repeat units, and the task division unit automatically segments these cycles into multiple tasks based on detected similarity intervals, enabling the system to serve itself rather than relying on user input.
Solution Approach 2:
The system performs preliminary action by pre-processing operation data to detect cycle intervals and identify similarity intervals before actual task analysis. The cycle interval detection unit pre-identifies repeat units in the operation data, and the task division unit pre-divides these into tasks with defined start and end points, preparing the data structure in advance for subsequent detailed analysis.
2Adaptability or versatility
If the system divides operation data into multiple tasks automatically, then it can analyze multiple tasks within a cycle, but it increases system complexity
Solution Approach 1:
The system applies segmentation by dividing the operation data analysis into distinct functional modules: a cycle interval detection unit that identifies repeat units, a task division unit that segments cycles into multiple tasks, and an analysis unit that evaluates each task. This modular segmentation allows the complex function of automatic multi-task analysis to be achieved through coordinated simple components, managing system complexity through functional decomposition.
3Measurement precision
If the system detects similarity intervals to divide tasks, then it can accurately identify task boundaries, but it requires complex calculation processes
Solution Approach 1:
The system uses feedback by calculating similarity between operation data segments and using this similarity information to refine task boundary detection. The task division unit compares operation data across detected cycle intervals, calculates similarity metrics, and uses this feedback to identify accurate task boundaries where similarity patterns change, enabling precise task segmentation through iterative similarity assessment.
Data Source
AI summary
A storage device stores operation data sets as information representing, on a repeat unit basis, a time series variation of the location of at least one point on a body of a person. A first divider divides each of the operation data sets into a predetermined number of partial data. First and second operation data sets are divided into a plurality of first partial data and a plurality of second partial data, respectively. A degree of similarity calculator calculates a degree of similarity between each of the first partial data and a corresponding one of the second partial data, and also calculates a degree of first operation data set similarity by adding together the respective degrees of similarity of all of the first partial data. The estimator estimates a division point where the first operation data set is divided to maximize the degree of first operation data set similarity.