Distributed Node Reconciliation for Machine Control
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Solution Overview
Problem
Current systems for controlling machine operations through data reconciliation in distributed clusters face inefficiencies and delays due to the centralized processing of large datasets, which can lead to slow response times and inaccuracies in real-time data analysis.
Innovation Solution
A distributed computing environment where multiple nodes process data in parallel, each handling a portion of the dataset using independent computer threads to perform reconciliation tasks, allowing for rapid analysis and control of machine operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized processing is used to reconcile data in distributed clusters, then data consistency can be maintained, but processing time increases and real-time responsiveness decreases
Solution Approach 1:
The patent segments the centralized reconciliation process into distributed parallel processing across multiple nodes. Each node independently processes portions of the dataset using separate computer threads, dividing the monolithic processing task into concurrent sub-tasks that can execute simultaneously, thereby reducing overall processing time while maintaining data consistency through coordinated reconciliation.
Solution Approach 2:
The patent transitions from a single-dimensional centralized processing model to a multi-dimensional distributed architecture. By adding the dimension of parallelism across multiple nodes and threads, the system processes data along multiple computational paths simultaneously, exponentially increasing processing capacity without sacrificing the reconciliation accuracy that ensures data consistency.
2Loss of information
If centralized processing handles large datasets, then comprehensive data analysis is achieved, but response time for real-time control deteriorates
Solution Approach 1:
The patent segments large datasets into manageable portions distributed across multiple nodes, with each node processing assigned data segments in parallel. This segmentation enables comprehensive analysis of the entire dataset while maintaining fast response times, as the aggregate processing power of multiple concurrent threads compensates for the divided data scope at each individual node.
Solution Approach 2:
The patent merges the processing results from multiple parallel threads and nodes to achieve comprehensive data analysis. By combining the outputs of distributed processing while maintaining coordination through reconciliation mechanisms, the system recovers the full analytical capability needed for real-time control decisions without being bottlenecked by centralized processing limitations.
3Productivity
If distributed parallel processing is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent implements a universal node architecture where each distributed node can perform multiple functions: data processing, thread management, and reconciliation participation. This multi-functionality reduces overall system complexity by eliminating the need for specialized components for each function, as any node can assume any role based on workload requirements, simplifying the control structure despite the distributed nature.
Solution Approach 2:
The patent enables nodes to autonomously manage their own processing threads and local data reconciliation without requiring constant central coordination. Each node independently handles its processing tasks and contributes to the overall reconciliation process, reducing the complexity of centralized control while maintaining processing speed through self-organizing parallel execution.
Data Source
AI summary
The operation of a machine can be controlled by performing reconciliation using a cluster of nodes. In one example, a node can receive parent timestamped data from a parent dataset and child timestamped data from child datasets that are children of the parent dataset in a hierarchical relationship. The parent timestamped data and the child timestamped data can relate to an operational characteristic of the machine. The node can generate computer processing-threads. Each computer processing-thread can solve one or more respective reconciliation problems between a parent data point that has a particular timestamp in the parent timestamped data and child data points that also have the particular timestamp in the child timestamp data to generate a reconciled dataset. An operational setting of the machine can then be adjusted based on the reconciled dataset.