Worksite Machine Risk Mapping for Real-Time Alert Classification
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Solution Overview
Problem
Managing risks associated with work machines at a worksite is complex due to variables such as location, ground materials, and pedestrian traffic, necessitating a dynamic approach to optimize productivity and efficiency while minimizing hazards.
Innovation Solution
A system employing machine learning algorithms and predefined rules to classify alert events, generate reports with unique indicators, and modify work machine parameters based on risk levels, using a thematic map to visualize and broadcast alerts across machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple monitoring systems and alert events are implemented to improve safety, then risk reduction is improved, but device complexity increases
Solution Approach 1:
The patent segments alert events into distinct categories (deceleration rate, harsh brake engagement, auto brake engagement, person detection, object detection, inclination) and assigns unique indicators to each category. This segmentation allows the system to manage complex safety monitoring through organized, categorized components rather than a monolithic complex system.
Solution Approach 2:
The patent introduces a thematic map as an intermediary visual interface that aggregates and displays alert events from multiple monitoring systems. The thematic map serves as a mediator between the complex underlying monitoring systems and the user, simplifying the presentation of safety data through standardized visual representations with unique indicators.
2Reliability
If real-time alert classification and reporting is implemented to improve safety response, then productivity is improved, but loss of time increases
Solution Approach 1:
The patent implements preliminary classification of alert events into predetermined categories before they need to be responded to. By pre-defining the classification structure and using machine learning algorithms to automatically categorize alerts in advance, the system reduces the time required for real-time decision-making and response.
Solution Approach 2:
The system generates reports based on counted alert events and provides feedback through the thematic map interface. This feedback mechanism allows operators to quickly assess the status of safety monitoring and respond appropriately, reducing the effective time loss by providing immediate, actionable information about alert conditions.
3Ease of operation
If machine learning algorithms are used to classify alert events automatically, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent employs machine learning algorithms to enable the system to automatically classify alert events without requiring manual intervention or complex user input. The system serves itself by using historical data and patterns to automatically determine alert categories, reducing the operational burden on users while managing the complexity through automated learning processes.
Solution Approach 2:
The system changes the approach to alert classification by transitioning from manual categorization to automated machine learning-based classification. This parameter change in the classification method improves ease of operation by eliminating manual sorting while managing complexity through the use of learned patterns and predefined categories rather than complex real-time analysis.
Data Source
AI summary
A system and method for risk reduction during operation of a work machine at a worksite comprises a processor that performs the following operations. The operation includes receiving a location information indicating a geographic position of the work machine on a worksite, receiving a series of alert event information generated by monitoring systems, employing machine learning algorithms or predefined rules to classify each alert event into one or more predetermined categories of information, assigning a unique indicator to each classified alert event based on the classification of the alert event information in the series. The processor then receives the unique indicators and generates a report based on a count basis for display on a user interface.