Computing Power Network Path Selection Using Time-Mass Entropy
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Solution Overview
Problem
The challenge of determining an optimal computing and storing path in a computing power network, particularly in a distributed topological structure, to enhance computing and storing capacities and address the exponential surge in computing power requirements, is unresolved in existing systems.
Innovation Solution
A method for determining an optimal computing and storing path in a computing power network involves calculating maximum time efficacy and mass entropies, minimal entropies, and joint optimization degrees based on a distributed topological structure, using modules for data management, path pre-judging, parsing, computing, and displaying results to optimize information transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a typical computer is used to meet computing power requirements, then the system structure is simple, but it cannot meet the exponential surge of computing power requirements
Solution Approach 1:
The patent combines typical computers and quantum computers into an organic hybrid architecture. The quantum computing power network integrates quantum computing nodes with classical computing infrastructure, merging two different computing paradigms to achieve exponential surge in computing power capacity while maintaining manageable system complexity through modular integration.
Solution Approach 2:
The computing power network is designed with multi-functionality to handle various computing power requirements. It can dynamically allocate resources between quantum and classical computing modes, serving different application scenarios (cryptanalysis, optimization problems, simulations) with a single unified system that adapts to diverse computational demands.
2Productivity
If a quantum computing power network with hybrid architecture is established to meet exponential computing power requirements, then computing power capacity increases, but the optimal path determination becomes more complex
Solution Approach 1:
The patent segments the complex path optimization problem into hierarchical layers: service ability open layer, arranging and scheduling ability layer, and resource scheduling management layer. Each layer handles specific aspects of path determination, breaking down the overwhelming complexity into manageable sub-problems that can be solved independently and combined.
Solution Approach 2:
The patent introduces intermediate optimization layers that act as mediators between the quantum computing resources and the application requirements. These intermediate layers (arranging and scheduling ability layer) translate complex quantum resource capabilities into optimized transmission paths, simplifying the overall optimization process while maintaining computing power capacity.
3Adaptability or versatility
If the computing power network uses a distributed topological structure with multiple complex networks, then resource distribution flexibility increases, but the optimal path determination problem becomes more difficult
Solution Approach 1:
The patent implements dynamic path determination mechanisms that adapt to the distributed topological structure. The optimization algorithms continuously adjust transmission paths based on real-time resource availability, network conditions, and quantum state coherence requirements, making the system capable of handling the complexity of distributed networks while maintaining resource distribution flexibility.
4Productivity
If the computing and storing path is optimized to enhance computing and storing capacities, then network efficiency improves, but the complexity of managing multiple layers and networks increases
Solution Approach 1:
The patent performs preliminary optimization by pre-calculating and caching optimal transmission paths based on historical data and predicted workloads. The resource scheduling management layer prepares optimization strategies in advance, reducing real-time management complexity while maintaining high network efficiency when actual computing and storing operations occur.
Data Source
AI summary
A method for determining an optimal computing and storing path of a computing power network and a monitoring apparatus, includes: determining a maximum time efficacy entropy of the computing and storing path of the computing power network, determining a minimal time efficacy entropy, and determining a maximum time efficacy of an information stream according to the minimal time efficacy entropy; determining a minimal mass entropy according to a total mass entropy and a maximum mass entropy of the computing and storing path of the computing power network, and determining an optimal mass of the information stream according to the minimal mass entropy; and determining a joint optimization degree of the information stream according to the maximum time efficacy, the optimal mass, and a set weight coefficient, and determining the optimal computing and storing path of the computing power network according to the joint optimization degree.