Industrial Machine Software Update Scheduling
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Solution Overview
Problem
Industrial machines require software updates that can cause downtime, disrupting critical workflows and incurring significant costs, as existing methods lack a systematic approach to schedule updates without impacting workflow execution.
Innovation Solution
A method and system that analyze historical data and IoT feeds to identify optimal timing and sequence for software updates on industrial machines, using machine learning and digital twin simulations to minimize downtime impacts, and proactively reconfigure resource allocation to maintain workflow continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software updates are performed on industrial machines, then software functionality and security are improved, but machine downtime increases and workflow disruption occurs
Solution Approach 1:
The system performs preliminary analysis of workflow patterns, machine roles, and activity sequences before scheduling updates. Historical data is analyzed to predict optimal update timing windows that minimize disruption to critical workflows, allowing updates to be scheduled proactively rather than reactively
Solution Approach 2:
The update scheduling system dynamically adjusts timing and sequencing based on real-time workflow monitoring and machine status. The system can adapt update schedules based on actual workflow execution patterns, machine availability, and predicted impact, transforming a static update process into a dynamic optimization
2Adaptability or versatility
If software updates are scheduled during workflow execution, then update timing flexibility is improved, but workflow disruption and negative impact increase
Solution Approach 1:
The system continuously monitors workflow execution, machine status, and update progress, using this feedback to optimize scheduling decisions. Real-time data about workflow patterns and machine availability is fed back into the scheduling algorithm to dynamically adjust update timing and minimize disruption
Solution Approach 2:
The system creates virtual copies or simulations of workflow execution patterns to predict update impact before actually performing updates. By modeling potential disruption scenarios, the system can identify safe update windows that avoid critical workflow periods
3Productivity
If multiple industrial machines are updated simultaneously, then update management efficiency is improved, but overall system downtime increases
Solution Approach 1:
The system divides the update process into segmented, machine-specific time windows based on individual machine roles and workflow dependencies. Rather than updating all machines simultaneously, the system schedules updates in coordinated batches that respect workflow relationships, allowing some machines to be updated while others remain operational
Solution Approach 2:
The system performs preliminary sequencing and grouping of machines based on workflow dependencies and criticality. By pre-determining update sequences and identifying which machines can be updated simultaneously without impacting critical workflows, the system optimizes the balance between update efficiency and system availability
Data Source
AI summary
Embodiments of the present invention provide an approach for scheduling a software update on an industrial machine based on a specification, predicted usage and role of the machine. Specially, based on a historical learning, the system and method provide for analyzing an activity workflow sequence of a workflow execution in an industrial floor. The analysis includes examining how the industrial machines are collaborating with each other, whether the activities are performed in parallel or in sequence, a time duration involvement of the machines while performing the activities, a time required an installation of the software update, and any scheduled industrial machine maintenance. Based on the analysis, an appropriate time and sequence when for software update installation can be performed is identified so that there is little or no negative impact during workflow execution.