Autonomous Resource Configuration for Dynamic Task-Driven Process Control
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Solution Overview
Problem
Existing software defined manufacturing systems struggle to adapt to real-time changes in processing resource availability and failure constellations, limiting flexibility and efficiency in handling dynamic production conditions.
Innovation Solution
A resource object and processing resource pool system that utilizes task specifications and resource signatures to autonomously configure and manage task execution, allowing for dynamic allocation of resources based on capability and status matching, with an observation-based feedback loop for parallel operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software defined manufacturing systems use traditional processing resource oriented approaches with early decisions on resource usage, then system stability and control are maintained, but adaptability to real-time changes in processing resource availability and failure constellations deteriorates
Solution Approach 1:
The patent implements dynamic task execution release by continuously monitoring processing resource availability and failure constellations in real-time. The system transitions from static, pre-determined resource allocation to dynamic adaptation where task execution is released or suspended based on current system state, enabling the system to respond flexibly to changing conditions without requiring complete reconfiguration
Solution Approach 2:
The patent segments the manufacturing system into independent processing resources and task execution units. Each processing resource can be monitored and controlled independently, allowing the system to isolate failures and adapt to partial resource availability. This modular segmentation enables selective task release based on specific resource states without affecting the entire system
2Productivity
If software defined manufacturing systems implement continuous monitoring and autonomous resource configuration, then flexibility and efficiency in handling dynamic conditions improve, but device complexity and control infrastructure requirements worsen
Solution Approach 1:
The patent implements self-service mechanisms where processing resources automatically report their availability status and failure conditions to the control system. The system autonomously determines task execution release decisions based on monitored conditions without requiring manual intervention or complex centralized control logic, reducing the burden on control infrastructure while maintaining high productivity
Solution Approach 2:
The patent establishes continuous feedback loops where the state of processing resources is constantly monitored and fed back to the task execution control mechanism. This real-time feedback enables automatic adjustment of task execution based on current system conditions, improving productivity through responsive resource allocation while keeping control infrastructure relatively simple through rule-based decision making
Data Source
Figure 1(a)~1(d)
Figure 2
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AI summary
The present invention relates to a resource object (26) operated in a modular processing system (10) for execution of at least one task (28) in support of modular processing system operation. Every task (28) is defined by a requested capability specification representing an expected functionality for task execution and/or by a requested resource specification representing at least one processing resource expected for task execution. As tasks describe capabilities and/or resources to be provided for related task execution the resource object (26) can execute autonomous process resource configuration. The present invention also relates to a processing resource pool providing processing resources for use through the resource object (26) during autonomous process resource configuration.