Skill Evaluation Device with Flexible Time Range Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing skill evaluation methods are inadequate for assessing the long-term skills of operators in manufacturing settings, as they are designed for short-time evaluations and fail to accurately capture the operator's proficiency over extended periods.
Innovation Solution
A skill evaluation device and method that allows for flexible time range selection, enabling the extraction and analysis of specific time ranges from operation status data to calculate an index value of operator skill, which can include data associated with or adjacent to the specified time range, improving evaluation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If skill evaluation is performed over a relatively long operating time (e.g., one day), then the evaluation covers the entire work period, but the skill of the operator cannot be appropriately evaluated because the time range is too extended
Solution Approach 1:
The patent divides the long evaluation time range into multiple shorter sub-time ranges (e.g., morning shift, afternoon shift, or specific task periods). By segmenting the overall evaluation period, the system can analyze operator skill at different time intervals, capturing variations in performance that would be masked in a single aggregated long-term evaluation. This segmentation allows for more precise skill assessment by examining specific time windows where the operator's skill level can be meaningfully measured.
2Measurement precision
If skill evaluation is performed over a relatively short time range, then the skill can be evaluated in detail, but it does not reflect the operator's skill over extended periods such as one day
Solution Approach 1:
The patent combines multiple short-time range evaluation results to form a comprehensive long-term skill assessment. By merging the index values obtained from several shorter evaluation periods (e.g., combining morning and afternoon shift evaluations), the system achieves both detailed short-term analysis and comprehensive long-term assessment. This merging approach allows the evaluation to reflect both immediate skill performance and sustained skill levels over extended periods.
Solution Approach 2:
The patent introduces a hierarchical dimension to the evaluation by performing assessments at multiple time scale levels. Instead of a single linear time evaluation, the system creates a multi-dimensional evaluation structure where short-term evaluations (fine-grained time dimension) are aggregated into medium-term and long-term evaluations (coarse-grained time dimension). This dimensional transformation allows the system to maintain measurement precision at each level while covering the full extent of the operating period.
3Adaptability or versatility
If the evaluation covers the entire long operating time, then all work periods are included, but the evaluation cannot appropriately assess operator skill due to the extended duration masking detailed performance variations
Solution Approach 1:
The patent implements a dynamic evaluation approach where the time range for skill assessment can be flexibly adjusted based on evaluation needs. The system allows users to specify different time ranges (short-term, medium-term, or long-term) and dynamically switches between evaluation granularities. This dynamic adaptability enables the evaluation to maintain precision by selecting appropriate time window sizes for different assessment purposes, whether examining brief task performance or overall daily skill levels.
Data Source
AI summary
The disclosure provides a skill evaluation device, a skill evaluation method, and a storage medium capable of appropriately evaluating the skill of an operator by changing a time range for evaluating the skill of the operator. The skill evaluation device includes an acquisition part, an extraction part, a calculation part, and an output part. The acquisition part acquires time range information indicating an arbitrary time range. The extraction part extracts data of a specific time range determined based on the time range information from operation status data indicating an operation status of the operator in a time series. The calculation part calculates an index value of the skill of the operator for one or more evaluation items based on the data of the specific time range. The output part outputs index information indicating the index value.