Workflow Decision Management for Automated Device Attribute Adjustment
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Solution Overview
Problem
Conventional networked devices require user intervention to change attribute values, which is inefficient as they often follow consistent usage patterns and scenarios, necessitating a method for workflow decision management that automatically adjusts device attributes based on identified usage patterns and scenarios without user input.
Innovation Solution
The system maintains a device state history to identify usage patterns and scenarios, determines workflow administration capacity, and executes workflows to adjust device attributes automatically, incorporating conflict resolution and tolerance management to prevent device damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated workflow decision management is implemented to adjust device attributes without user intervention, then productivity and ease of operation are improved, but device complexity and risk of harmful factors increase
Solution Approach 1:
The system segments workflow management into distinct components: workflow scenarios define conditional logic, workflows contain specific attribute adjustment actions, and workflow administration capacities manage execution limits. This segmentation allows complex automation to be broken into manageable, independently configurable parts, reducing overall system complexity while maintaining high productivity.
Solution Approach 2:
The system performs preliminary actions by pre-defining workflow scenarios and workflows before actual device operation. Usage patterns are identified from historical data, and corresponding workflows are prepared in advance with specified attribute adjustments. When conditions match, pre-configured workflows are executed automatically, enabling efficient automation without real-time decision complexity.
2Adaptability or versatility
If multiple workflows are executed simultaneously to handle various usage patterns, then adaptability is improved, but harmful factors and reliability risks increase due to potential conflicts
Solution Approach 1:
The system applies preliminary anti-action by determining workflow administration capacities that explicitly prevent harmful effects. Before workflows are executed, the system identifies potential conflicts between multiple workflows and establishes capacity limits to prevent simultaneous execution of conflicting workflows. This preemptive conflict resolution eliminates device damage risks while maintaining the ability to execute multiple adaptive workflows.
Solution Approach 2:
The system uses feedback mechanisms to monitor device state changes resulting from workflow executions. When a workflow modifies device attributes, the system tracks these changes and uses them to determine whether to execute subsequent workflows. This feedback loop ensures that multiple workflows adapt to actual device states while preventing harmful interactions through capacity-based execution control.
3Reliability
If workflow administration capacity is determined through conflict analysis and tolerance management, then reliability is improved, but device complexity increases
Solution Approach 1:
The system applies partial action by determining workflow administration capacities that allow selective execution of workflows based on conflict analysis. Rather than preventing all workflow executions or allowing unlimited parallel execution, the system determines optimal capacities that enable safe workflow operation. This partial control mechanism ensures reliability while avoiding the complexity of comprehensive workflow management.
4Adaptability or versatility
If device state history is maintained and analyzed to identify usage patterns, then adaptability is improved, but loss of time and processing overhead increase
Solution Approach 1:
The system performs preliminary action by maintaining device state history and identifying usage patterns in advance of actual workflow execution needs. Historical device states are analyzed to pre-identify usage patterns, which are then stored and matched against current conditions. This preliminary pattern recognition reduces real-time processing time while maintaining high adaptability to device usage variations.
Data Source
AI summary
Methods, systems, and products are provided for workflow decision management. Embodiments include maintaining a device state history; identifying a plurality of device usage patterns in dependence upon the device state history; identifying a plurality of workflow scenarios in dependence upon the device usage patterns; determining a workflow administration capacity in dependence upon the plurality of workflow scenarios; identifying a plurality of workflows in dependence upon the workflow scenario; executing the plurality of workflows in dependence upon the workflow administration capacity.


