Distributed Processing System Accuracy Parameter Standardization
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Solution Overview
Problem
Distributed processing systems face limitations in maximizing processing capacity and efficiency due to unique evaluations by independent processing units, which can lead to inconsistent and time-consuming processing tasks.
Innovation Solution
The system employs multiple independent processing units to provide evaluations for the same data, combining them using an accuracy parameter to standardize results and direct data to the best-suited processor, and ranks data groups to simplify processing tasks, thereby increasing efficiency and reducing the number of evaluations needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple independent processing units evaluate the same data, then processing capacity is maximized, but evaluation consistency deteriorates
Solution Approach 1:
The patent introduces accuracy parameters that quantify the reliability of each processing unit's evaluations. By changing the parameter set to include these accuracy metrics, the system can weight and combine evaluations from multiple units while accounting for their varying reliability, thus maintaining consistency despite using multiple independent evaluators
Solution Approach 2:
The server acts as an intermediary that receives evaluations from multiple independent processing units, standardizes them using accuracy parameters, and combines them into a final result. This intermediary role reconciles the diverse evaluations into a consistent outcome while preserving the benefits of parallel processing
2Productivity
If multiple independent processing units evaluate the same data, then processing capacity is maximized, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing accuracy parameters for each processing unit before actual data evaluation begins. This allows the server to quickly weight and combine evaluations without time-consuming calculations during the processing phase, reducing overall processing time while maintaining capacity
Solution Approach 2:
The patent segments the processing task into independent evaluation phases performed by multiple units simultaneously, followed by a consolidation phase. This segmentation allows parallel processing of different data portions while the standardized combination method ensures efficient merging of results without sequential bottlenecks
3Productivity
If data is sent to multiple processing units, then processing capacity is maximized, but the number of evaluations increases
Solution Approach 1:
The system applies partial action by sending data to only the necessary number of processing units based on the accuracy parameters, rather than distributing to all available units. This selective approach achieves sufficient processing capacity while minimizing the total number of evaluations required to reach a reliable conclusion
Data Source
AI summary
A distributed processing system is disclosed herein. The distributed processing system includes a server, a database server, and an application server that are interconnected via a network, and connected via the network to a plurality of independent processing units. The independent processing units can include an analysis engine that is machine learning capable, and thus uniquely completes its processing tasks. The server can provide one or several pieces of data to one or several of the independent processing units, can receive an analysis results from the one or several independent processing units, and can update the result based on a value characterizing the machine learning of the independent processing unit.