Simulation Mapping System for Interactive Training Data Synchronization
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Solution Overview
Problem
Existing interactive computer simulation systems face challenges in accurately collecting and synchronizing data from multiple dynamic subsystems during training activities, leading to inconsistencies in performance evaluation and inadequate assessment of user performance due to missing data and differing time steps across subsystems.
Innovation Solution
A simulation mapping system that utilizes a processor module to construct datasets with target time steps by synchronizing data from multiple dynamic subsystems, inferring missing data from co-related subsystems, and applying linear quadratic estimation or probabilistic directed acyclic graphical models to provide standardized performance metric values for grading systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected from multiple dynamic subsystems during training activities, then comprehensive performance evaluation is enabled, but data synchronization and consistency become problematic due to different time steps and missing data
Solution Approach 1:
The patent introduces an intermediary processing layer that receives data from multiple dynamic subsystems with different time steps and reconstructs a unified, synchronized dataset. This intermediary component performs temporal alignment and data interpolation to ensure consistent timing across all subsystems, resolving the contradiction between comprehensive data collection and data consistency.
Solution Approach 2:
The system performs preliminary data synchronization and reconstruction before performance evaluation. By pre-aligning data from multiple subsystems to a common time step and inferring missing values in advance, the system ensures that subsequent performance metrics are calculated from consistent, complete data without encountering synchronization issues during evaluation.
2Measurement precision
If data from multiple dynamic subsystems is synchronized to a target time step, then performance metric accuracy is improved, but system complexity increases due to data reconstruction requirements
Solution Approach 1:
The system employs self-service mechanisms where the data processing automatically performs synchronization, temporal alignment, and missing data inference without requiring manual intervention. The processor autonomously reconstructs the unified dataset by identifying relationships between subsystems and inferring missing values, reducing operational complexity while maintaining high measurement precision.
3Reliability
If missing data is inferred from co-related subsystems, then complete performance assessment is achieved, but data processing time increases due to reconstruction operations
Solution Approach 1:
The system changes the temporal parameter of the data by resampling and aligning all subsystems to a common target time step. This parameter transformation enables consistent performance metric calculation across subsystems with originally different sampling rates, achieving complete performance assessment while managing processing time through efficient resampling algorithms.
Data Source
AI summary
A simulation mapping system and method for determining a plurality of performance metric values in relation to a training activity performed by a user in an interactive computer simulation, the interactive computer simulation simulating a virtual element comprising a plurality of dynamic subsystems. A processor module obtains dynamic data related to the virtual element being simulated in an interactive computer simulation station comprising a tangible instrument module. The dynamic data captures actions performed by the user on tangible instruments. The processor module constructs a dataset corresponding to the plurality of performance metric values from the dynamic data having a target time step by synchronizing dynamic data and by inferring, for at least one missing dynamic subsystems of the plurality of dynamic subsystems missing from the dynamic data, a new set of data into the dataset from dynamic data associated to one or more co-related dynamic subsystems.