Workflow Node Traversal for Network Control Efficiency
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Solution Overview
Problem
Existing methods for performing control tasks in communication networks and devices lack efficiency and effectiveness, necessitating improved approaches for maintenance and repair.
Innovation Solution
A computer-implemented method that loads a data structure with nodes representing steps of a workflow, allowing for the execution of control tasks by traversing a subset of nodes, determining node types, rendering graphical representations, receiving user inputs, and advancing to next nodes based on response values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used for performing control tasks in communication networks, then the processes can be completed, but the efficiency and effectiveness are insufficient
Solution Approach 1:
The control task is segmented into multiple discrete nodes representing individual steps or decisions. Each node can be independently executed and evaluated, allowing the workflow to be broken down into manageable units that can be traversed systematically through the data structure.
Solution Approach 2:
The workflow execution is made dynamic by adapting the traversal path based on user inputs and node responses. The system dynamically determines the next node to visit based on conditional logic embedded in the data structure, allowing the control task to respond to real-time conditions and optimize execution efficiency.
2Reliability
If a structured workflow with multiple nodes is implemented, then the effectiveness of control tasks improves, but the system complexity increases
Solution Approach 1:
The data structure serves multiple functions: it stores workflow definition, contains decision logic, tracks execution state, and guides navigation. This multi-functional design consolidates what could be separate complex components into a unified structure, managing complexity while maintaining effectiveness.
Solution Approach 2:
The computational device acts as an intermediary that loads and executes the workflow data structure. This mediator layer separates the complex workflow logic from the user interface and network operations, managing complexity by providing a structured intermediate representation that can be systematically processed.
3Reliability
If adaptive workflow execution based on user inputs is implemented, then the effectiveness of control tasks improves, but the time required for task completion may increase
Solution Approach 1:
The system incorporates feedback loops where user inputs and node responses are evaluated to determine the next execution path. This feedback mechanism ensures that the workflow adapts to actual conditions, improving effectiveness by making informed decisions while systematically progressing through the data structure to minimize unnecessary delays.
Data Source
AI summary
Embodiments perform a control task for a real-world communications network. First, a data structure is loaded in memory. The data structure includes node(s) corresponding to step(s) of a workflow. Next, the workflow is executed, to perform the task, by traversing a subset of the node(s). Traversing includes, at each node of the subset, determining a node type of the node. Based on the node type, a graphical representation of the node is rendered on a display. Responsive to the rendering, a data input is received. Further, responsive to determining the node type is not a final node type: (i) a next node of the subset is determined based on the input and response value(s) (associated with the node type) of the node and (ii) a move to the next node occurs. Responsive to determining the node type is the final node type, performance of the task is completed.