Geographical Network Node Prediction for Resource Conservation
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Solution Overview
Problem
It is challenging to predict the effects of performing actions, such as deactivating or activating nodes, on the performance of a set of geographically distributed nodes due to the complexity of data and numerous confounding variables, which hinders efficient management and resource allocation in retail or service-based networks.
Innovation Solution
The implementation groups nodes into geographical networks based on shared entities and geographical proximity, using transaction and node information to generate predictive models that simulate the effects of actions, thereby reducing the number of nodes to process and conserving processor resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive models are generated for all nodes using complete transaction data, then prediction accuracy improves, but computational burden and processing time increase significantly
Solution Approach 1:
The patent segments the set of nodes into multiple geographical networks based on shared entities and spatial proximity. Instead of processing all nodes globally, the system divides them into smaller regional clusters (e.g., first geographical network, second geographical network), allowing parallel or sequential processing of subsets. This segmentation maintains prediction accuracy within each regional context while significantly reducing the computational burden and processing time compared to a monolithic global model.
2Reliability
If complete transaction data from all entities is processed for each node, then prediction reliability improves, but device complexity and computational resources required increase
Solution Approach 1:
The patent applies local quality by tailoring the predictive modeling approach to each geographical network's specific characteristics. Each geographical network is processed with data and parameters relevant to its local context (shared entities, regional transaction patterns), rather than applying a uniform complex model to all nodes. This localized approach maintains prediction reliability for each region while reducing overall device complexity and computational resource requirements.
3Productivity
If geographical networks are created based on shared entities and proximity, then resource allocation efficiency improves, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-grouping nodes into geographical networks based on shared entities and spatial proximity before executing the predictive modeling and resource allocation processes. This pre-processing step creates organized clusters that simplify subsequent data processing and resource allocation decisions. The initial grouping structure enables more efficient processing during the actual prediction and allocation phases, offsetting the initial complexity of creating the geographical network divisions.
Data Source
AI summary
A device may include one or more processors. The device may receive first information identifying a plurality of nodes and transactions associated with the plurality of nodes. The transactions may be between nodes, of the plurality of nodes, and entities of a plurality of entities. The device may determine geographical locations corresponding to the plurality of nodes. The device may determine second information, based on the first information, that may identify nodes, of the plurality of nodes, that are associated with shared entities. The device may generate, based on the geographical locations and the second information, a geographical network. The device may select a selected node, of the geographical network, on which to perform an action. The device may determine third information based on predicting future performance of the geographical network assuming that the action is performed. The device may store or provide the third information.