Confidence-Aware Service Pattern Optimization Method
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Solution Overview
Problem
Current service pattern optimization methods fail to comprehensively analyze collaboration among participants in complex service systems, ignoring the influence of service deployment platforms and lacking a holistic approach to improving quality of service, efficiency, and rationality of data, resource, and value transfer.
Innovation Solution
A confidence-aware service pattern optimization method that initializes a candidate list, temperature, confidence, and termination threshold, and iteratively searches for an optimized pattern using pattern optimization indexes, dynamically adjusting search speed and step size based on confidence, to optimize workflow, data flow, resource flow, and value flow among participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional service pattern optimization methods are used, then optimization can be performed, but optimization time is excessive and optimization effect is insufficient
Solution Approach 1:
The patent pre-calculates and stores confidence values for different service patterns before optimization begins. This preliminary preparation allows the optimization algorithm to quickly evaluate candidate patterns without performing full QoS assessments during the optimization process, significantly reducing optimization time while maintaining optimization effectiveness.
Solution Approach 2:
The patent replaces the traditional exhaustive search mechanism with a confidence-guided search mechanism. By using pre-computed confidence values to guide the search direction, the system substitutes brute-force evaluation with a more intelligent search strategy that focuses on promising areas of the solution space, reducing optimization time while improving optimization effect.
2Reliability
If comprehensive analysis of collaboration among participants is performed, then optimization quality improves, but computational complexity increases
Solution Approach 1:
The patent pre-computes confidence values that encapsulate the results of comprehensive collaboration analysis. By performing this complex analysis beforehand and storing the results as confidence metrics, the system avoids repeating computationally intensive calculations during optimization while still benefiting from comprehensive analysis, thus improving optimization quality without proportionally increasing computational complexity.
Solution Approach 2:
The patent transforms the complex multi-dimensional collaboration analysis into a simplified confidence parameter. This parameter transformation allows the system to capture the essence of comprehensive collaboration analysis in a single metric that can be efficiently used during optimization, reducing computational complexity while maintaining optimization quality.
3Adaptability or versatility
If service deployment platform influence is considered, then optimization comprehensiveness improves, but analysis complexity increases
Solution Approach 1:
The patent merges the analysis of service deployment platform influence with the confidence calculation process. By integrating platform considerations into the confidence metric computation, the system avoids separate complex analyses while still accounting for platform effects, thus improving optimization comprehensiveness without proportionally increasing analysis complexity.
Data Source
AI summary
Disclosed in the present invention is a confidence-aware service pattern optimization method, wherein the service pattern optimization method comprises the following steps: (1) inputting an original pattern Pa to be optimized; (2) initializing a candidate list PaList of the original pattern Pa; (3) initializing a temperature T; (4) initializing confidence C; (5) initializing a maximum number of iterations IterMax; (6) initializing a termination threshold Th; (7) circularly searching a target pattern Pa* according to pattern optimization indexes, wherein the number of circulations is IterMax; (8) reducing the temperature T; (9) if the pattern Pa* obtained at the end of the cycle in step (7) remains consistent for consecutive Th times, obtaining the Pa* as an optimized target pattern; otherwise, jumping to step (7). By introducing the confidence mechanism, the search speed and search step size can be dynamically adjusted in the search space with different optimization potentials, which greatly saves the optimization time and improves the optimization effect.