Heavy Machine Workflow Adjustment for Accident Response
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Solution Overview
Problem
Current mechanisms are inadequate for effectively addressing a wide range of accidental scenarios in heavy machinery operations, requiring immediate detection and dynamic allocation of resources to mitigate potential accidents while maintaining operational efficiency.
Innovation Solution
A computer-implemented method using trained artificial intelligence models to detect or predict accidental scenarios and dynamically adjust workflows by identifying and deploying alternative heavy machines to assist in mitigating these scenarios, ensuring operational efficiency is maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current mechanisms are used to address accidental scenarios, then response time is reduced, but operational efficiency deteriorates due to inadequate resource allocation
Solution Approach 1:
The system dynamically adjusts workflows and reallocates heavy machines based on real-time accident detection and severity assessment. The workflow adjuster continuously monitors operational data and modifies task assignments dynamically, allowing the system to adapt to changing conditions while maintaining overall productivity through automated resource optimization.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where operational data from heavy machines is continuously collected, analyzed by the accident detector, and used to generate real-time recommendations. The workflow adjuster receives feedback on accident mitigation effectiveness and continuously optimizes resource allocation to balance safety responses with operational efficiency.
2Reliability
If heavy machines are reallocated to mitigate accidental scenarios, then safety is improved, but operational efficiency deteriorates due to workflow disruptions
Solution Approach 1:
The system applies partial reallocation of heavy machines based on accident severity. Instead of fully stopping all operations, the workflow adjuster selectively reassigns only the necessary machines to mitigation tasks while maintaining other workflows. This partial action approach ensures safety responses are adequate without causing complete operational shutdowns.
Solution Approach 2:
The system changes operational parameters dynamically by adjusting workflow priorities and machine allocation based on accident conditions. The workflow adjuster modifies task parameters, timing, and resource distribution to balance safety requirements with continued operational productivity, allowing non-critical operations to continue while safety responses are executed.
3Measurement precision
If AI models are deployed to detect and respond to accidental scenarios, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The AI-based accident detector and workflow adjuster serve multiple functions: detecting accidents, assessing severity, predicting outcomes, generating recommendations, and dynamically adjusting workflows. This multi-functionality consolidates what would otherwise require separate systems into a unified platform, managing complexity while enhancing detection and response capabilities.
Solution Approach 2:
The system implements self-service through automated AI-driven detection and response mechanisms. The accident detector automatically identifies and assesses accidents without human intervention, and the workflow adjuster autonomously reallocates resources based on AI-generated recommendations. This automation reduces the need for complex manual monitoring and decision-making processes.
Data Source
AI summary
Described are techniques for dynamic workflow adjustments to assist heavy machines involved in accidental scenarios. Real-time data associated with an activity area where a heavy machine is performing an activity is monitored. The monitored data may then be analyzed by a first trained artificial intelligence (AI) model to determine if an accidental scenario is detected or predicted. Upon detecting or predicting an accidental scenario, a knowledge repository including information, such as the capabilities of heavy machines, is analyzed. Based on the analysis of the knowledge repository, a second AI model identifies a heavy machine to mitigate the accidental scenario. Furthermore, the second AI model adjusts the workflow for the heavy machine providing the assistance and/or for the heavy machine engaged in the activity involving the detected or predicted accidental scenario. The identified heavy machine may then be deployed to perform the adjusted workflow to mitigate the accidental scenario.