Operator Intoxication Screening With ML-Based Asset Authorization
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Solution Overview
Problem
Existing systems for intoxication examination lack effective solutions for creating and maintaining a drug-free workplace, particularly in ensuring operators are fit to operate assets safely.
Innovation Solution
A method and system utilizing a Machine Learning model to determine an operator's intoxication score and permissibility based on input data and predefined rules, allowing asset operation only when the score meets a threshold, and including self-examination, video-call, and in-person examination options with biometric verification and authorization processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional intoxication examination systems are used, then basic safety monitoring is provided, but comprehensive drug-free workplace solutions are not achieved
Solution Approach 1:
The system dynamically switches between three examination modes (self-examination, video-call examination, and in-person examination) based on operational requirements and operator availability. This dynamic adaptability allows the system to maintain reliable safety monitoring while accommodating various workplace scenarios and operational constraints.
Solution Approach 2:
The system integrates multiple examination methodologies (self-assessment, remote video verification, and physical presence verification) into a single unified platform. This multi-functional approach enables the system to provide comprehensive drug-free workplace solutions that can adapt to different operational contexts while maintaining consistent safety standards.
2Reliability
If multiple examination methods are implemented, then comprehensive screening is achieved, but system complexity increases
Solution Approach 1:
The examination system is segmented into three distinct but integrated modules: self-examination module, video-call examination module, and in-person examination module. Each module operates independently with its own verification protocol, allowing comprehensive screening while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary screening through self-examination questions before requiring more complex verification methods. This staged approach filters out obviously qualified operators early, reducing the need for complex examination procedures while maintaining detection accuracy for borderline cases.
3Productivity
If ML model-based intoxication scoring is used, then automated permissibility determination is achieved, but threshold calibration complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms where examination results and operator outcomes are continuously monitored to refine the ML model's scoring algorithm. This feedback loop enables automated high-speed authorization while progressively improving threshold calibration accuracy based on real-world performance data and incident analysis.
Solution Approach 2:
The system allows dynamic adjustment of intoxication score thresholds based on operational context, asset type, and environmental factors. This parameter flexibility enables the ML model to maintain high authorization speed while adapting precision requirements to specific operational scenarios, balancing productivity and accuracy needs.
Data Source
AI summary
The invention relates to method and system for intoxication examination of an operator for operating an asset. The method includes receiving input data corresponding to the operator prior to operating the asset from one of an asset or a client device; determining an intoxication score of the operator based on the input data using a Machine Learning (ML) model; determining permissibility of operating an asset for the operator through a plurality of predefined rules using the ML model; assigning the asset to the operator when the asset operation is determined to be permissible for the operator; transmitting authorization information to the assigned asset; authorizing, by the asset, the operator to operate the asset based on the authorization information; upon authorization, monitoring in real-time, the operator during the asset operation from real-time video data of the operator to check for compliance of the asset operation with the conditions of operation.