Eye Movement Sensor for Real-Time Drilling Operator Safety
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Solution Overview
Problem
Current methods for identifying unsafe behaviors of drilling operators in the petroleum and natural gas industry are unable to achieve real-time monitoring and identification, which is crucial for preventing accidents.
Innovation Solution
A method and device that utilize an eye movement data sensor to collect and analyze eye movement data in real-time, processing this data to obtain characteristic parameters that are then input into an identification model to determine if the operator's behavior is safe or not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection or post-event investigation methods are used to identify unsafe behaviors, then the complexity of the monitoring system is low, but real-time monitoring capability is lost
Solution Approach 1:
The patent replaces manual inspection and post-event investigation methods with an automated eye movement tracking system. The system uses eye movement sensors to collect data, processes it through algorithms to generate characteristic parameters, and applies identification models to detect unsafe behaviors in real-time, eliminating the need for manual monitoring while achieving continuous real-time surveillance.
Solution Approach 2:
The patent introduces an intermediary processing system that bridges raw eye movement data and unsafe behavior identification. The system includes data processing modules that generate characteristic parameters from raw eye movement data, and identification models that analyze these parameters to detect unsafe behaviors, serving as intermediaries between the sensor and the final safety assessment.
2Reliability
If video monitoring with artificial intelligence algorithms is used to identify unsafe behaviors, then automation level increases, but real-time whole-course monitoring capability is still not achieved
Solution Approach 1:
The patent extracts specific critical features from comprehensive video monitoring by focusing solely on eye movement data. Instead of processing entire video streams, the system isolates and analyzes eye movement characteristics such as gaze points, gaze duration, and saccade patterns, achieving real-time monitoring with reduced computational burden while maintaining high automation.
Solution Approach 2:
The patent transforms raw eye movement data into characteristic parameters through processing modules. The system calculates parameters such as gaze point coordinates, gaze duration, saccade amplitude, and pupil diameter changes, converting complex visual data into quantifiable metrics that can be rapidly analyzed by identification models for real-time unsafe behavior detection.
3Measurement precision
If comprehensive eye movement data collection is performed to improve identification accuracy, then measurement precision increases, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant eye movement characteristics from comprehensive eye movement data. The system focuses on specific parameters such as gaze point coordinates, gaze duration, saccade amplitude, and pupil diameter changes, filtering out redundant information while maintaining high measurement precision for the critical features needed to identify unsafe behaviors.
Solution Approach 2:
The patent segments the eye movement data processing into distinct modules: data collection, characteristic parameter generation, and identification model analysis. Each module handles specific aspects of the data processing pipeline, dividing the complex task into manageable segments that can be processed efficiently while maintaining overall system precision.
Data Source
AI summary
The embodiments of the present application provide a method and a device for identifying an unsafe behavior of a drilling operator, which relate to the field of data analysis, and the method includes obtaining eye movement data information of a drilling operator by an eye movement data sensor, processing the eye movement data information to obtain an eye movement characteristic parameter corresponding to the eye movement data information, inputting the eye movement characteristic parameter to an identification model to obtain an output result from the identification model, and determining an operation behavior of the operator according to the output result and sending a warning to the operator when the operation behavior is a preset type. Through analyzing the eye movement data information in real time, whether an operation behavior meets regulations can be determined, thus realizing real-time monitoring and identification of unsafe operation behaviors of the drilling operator.