Neural Network Data Routing Optimization
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Solution Overview
Problem
Current computer networks lack optimized data routing methods, leading to inefficient utilization of computational resources and resource consumption during data transfers, such as video files or currency transactions, as they are typically handled in a single format without consideration for minimal resource usage.
Innovation Solution
A system and method that utilize a clustering model and neural network to select an optimal routing path for data transfers based on feature vectors, group characteristics, and cost metrics, allowing for real-time selection of source nodes and routing paths that minimize resource consumption and maximize efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data transfer is carried out in a single format without optimized routing, then the system is simple to operate, but computational resources are not efficiently utilized
Solution Approach 1:
The patent pre-calculates and stores optimal routing paths and format conversion requirements before actual data transfer occurs. The system maintains a database of pre-analyzed routing options considering various data formats, network conditions, and resource availability, enabling rapid decision-making during execution without real-time computation overhead
Solution Approach 2:
The patent introduces an intermediary routing system that acts as a mediator between data sources and destinations. This intermediary component analyzes data format requirements, selects appropriate conversion formats, and determines optimal routing paths, thereby decoupling the complexity of format handling and routing optimization from the end systems while improving overall resource utilization
2Adaptability or versatility
If multiple computer systems with different data formats are used to transfer different types of data, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal routing system that can handle multiple data formats through a single standardized interface. The system maintains a format conversion capability that can transform various data types (video files, currency transactions, etc.) into appropriate formats for transmission, allowing one system architecture to serve multiple data format requirements without requiring separate specialized systems for each format
Solution Approach 2:
The patent dynamically adjusts data format parameters based on the specific transfer requirements and network conditions. The system analyzes the source and destination system capabilities, then automatically selects and applies appropriate format conversion parameters, enabling flexible adaptation to different data types and system configurations without requiring permanent multi-format support in each system
3Loss of energy
If data routing is performed without optimization algorithms, then the routing process is simple and fast, but resource consumption is high
Solution Approach 1:
The patent pre-calculates optimal routing paths by analyzing network topology, resource availability, and data format requirements before actual data transfer. This preliminary analysis stores routing decisions and resource allocation strategies in advance, allowing the system to execute pre-determined optimal paths during data transfer without performing complex real-time calculations, thereby reducing computational resource consumption during operation
Solution Approach 2:
The patent implements a feedback mechanism that monitors actual resource consumption and routing performance during data transfer operations. The system uses this feedback to refine and update routing algorithms and format conversion strategies, enabling continuous improvement of resource efficiency while managing algorithm complexity through iterative optimization based on real-world performance data
Data Source
AI summary
A system and a method of optimizing a plurality of data elements including one or more nodes within a first network of nodes may include: receiving a value of one or more data transfer parameters pertaining to one or more data transfers conducted over one or more nodes of a first computer network; perturbating a value of one or more elements; creating a simulated computer network based on the one or more perturbated values; for each network of the first computer network and the simulated computer network, calculating a value of at least one performance parameter; and generating, based on the calculation, a suggestion for optimizing the data elements, wherein the suggestion may include at least one perturbated data element value.