On-Duty Status Detection Model for Personnel Safety
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Solution Overview
Problem
High-risk personnel in industries such as numerical control and construction face cognitive resource depletion due to transient states, leading to misoperations and safety risks, necessitating effective monitoring and early-warning systems to prevent such incidents.
Innovation Solution
A personnel on-duty status early-warning method and apparatus that continuously collects human body data, determines on-duty status using a pre-trained detection model, and outputs early-warning prompts when preset conditions are met, incorporating simulated working environments and subjective perception data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used for personnel status, then the system complexity is low, but the detection accuracy of on-duty status is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training a detection model offline using historical human body data and on-duty status annotation information. This pre-trained model is then deployed for real-time status detection, allowing the system to achieve high detection accuracy without complex real-time processing infrastructure. The preliminary preparation of training data and model optimization resolves the contradiction between accuracy and system complexity.
Solution Approach 2:
The patent introduces an intermediate detection model that bridges raw human body data and on-duty status determination. This model acts as a mediator, processing physiological signals, cognitive load indicators, and emotional state data to infer personnel status. The intermediary model simplifies the overall system architecture while maintaining high detection accuracy through specialized algorithmic processing.
2Measurement precision
If comprehensive human body data collection is implemented, then the detection accuracy improves, but the loss of time for data processing increases
Solution Approach 1:
The patent performs data preprocessing, feature extraction, and model training in advance before real-time deployment. Historical data is pre-processed and stored in optimized formats, and the detection model is pre-trained to enable rapid inference. This preliminary action eliminates time-consuming processing steps during actual monitoring, resolving the contradiction between comprehensive data analysis and processing speed.
Solution Approach 2:
The patent extracts only the most relevant features from comprehensive human body data for real-time processing. Instead of analyzing all raw data continuously, the system identifies and processes key indicators such as heart rate variability, cognitive task performance metrics, and emotional response patterns. This selective extraction maintains detection accuracy while significantly reducing processing time.
3Reliability
If real-time monitoring and early-warning system are deployed, then personnel safety is improved, but the device complexity increases
Solution Approach 1:
The patent implements a self-service monitoring system where personnel wearable devices automatically collect human body data, process it through embedded algorithms, and generate early warnings without requiring complex external monitoring infrastructure. The system uses onboard processing and pre-trained models to autonomously detect status changes and trigger alerts, reducing overall system complexity while maintaining high reliability for personnel safety.
Data Source
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AI summary
Provided is a personnel on-duty status early-warning method, apparatus, device, system and medium. The method includes: continuously collecting human body data of personnel in an actual working environment; determining on-duty status of the personnel corresponding to the collected human body data based on the obtained human body data by using a state detection model, the state detection model is obtained by pre-training a preset model based on a training dataset, and the training dataset includes human body data and corresponding on-duty status annotation information; performing on-duty status monitoring on the personnel based on the on-duty status determined for the personnel; and outputting early-warning prompt information in response to the on-duty status of the personnel during the on-duty status monitoring meeting a preset early-warning condition. The method can improve the detection accuracy of the personnel on-duty status.