Virtual-Real Collaboration State Feedback for Ergonomic Monitoring
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Solution Overview
Problem
Existing human-machine collaboration systems lack effective methods for real-time feedback and adaptation based on virtual-real integration, leading to inefficiencies and safety risks in human-machine interactions.
Innovation Solution
A method and apparatus for feedbacking a human-machine collaboration state through virtual-real integration, utilizing ergonomic data and operation scene images to recognize collaboration and personnel states, enabling real-time adjustments and decision-making for improved efficiency and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and recognition of human-machine collaboration state is implemented, then collaboration efficiency and safety are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the collaboration state recognition into multiple independent modules: ergonomic data acquisition module, operation scene image acquisition module, collaboration state recognition module, and personnel state recognition module. Each module handles specific aspects of monitoring, reducing overall system complexity while maintaining comprehensive safety monitoring.
Solution Approach 2:
The patent introduces a state recognition system as an intermediary that processes ergonomic data and operation scene images to generate collaboration state information. This intermediary layer simplifies the interaction between raw sensors and control systems, making the overall system more manageable despite the increased monitoring capabilities.
2Measurement precision
If multiple data sources (ergonomic data and operation scene images) are integrated for state recognition, then recognition accuracy is improved, but data processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing of ergonomic data and operation scene images before integration, including feature extraction and preliminary classification. This preliminary action reduces the complexity of subsequent integration processing, maintaining high recognition accuracy while reducing overall processing time.
Solution Approach 2:
The patent implements selective processing where only relevant features from ergonomic data and operation scene images are processed in detail, while less critical data receives simplified processing. This partial action approach maintains sufficient recognition accuracy while significantly reducing computational load and processing time.
3Productivity
If continuous monitoring of operation subject state is performed, then real-time feedback capability is improved, but energy consumption and system resource usage increase
Solution Approach 1:
The system implements periodic monitoring with variable intervals, where the monitoring frequency is adjusted based on the current collaboration state and task requirements. During stable states, monitoring occurs at lower frequency to reduce energy consumption, while during critical transitions or high-risk operations, monitoring frequency increases to maintain real-time feedback capability.
Solution Approach 2:
The monitoring system dynamically adjusts its operation based on detected state changes and task priorities. When the operation subject enters a new task phase or when anomalies are detected, the system intensifies monitoring temporarily, then returns to normal periodic monitoring, optimizing energy consumption while maintaining productivity.
Data Source
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AI summary
Provided are a method and an apparatus for feedbacking a human-machine collaboration state based on virtual-real integration, and an electronic device. Ergonomic data of an operation subject in a human-machine collaboration process for a current operation task and an operation scene image that is obtained by shooting the human-machine collaboration process and at least includes an operation device and a setting parameter of an operation environment are acquired. A human-machine collaboration state recognition is performed based on the operation scene image to obtain target collaboration state data corresponding to the current operation task, and a personnel state recognition is performed based on the ergonomic data to obtain personnel state data of the operation subject. Based on the target collaboration state data and the personnel state data, a state of the human-machine collaboration process is feedbacked.