Constraint-Based Resource Allocation with Parameter Curve Prediction
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Solution Overview
Problem
Existing resource allocation methods lack flexibility, objectivity, and adaptability, as they rely on human experience, require professional knowledge, or fail to consider multiple parameters and modeling conditions, making it difficult to optimize resource allocation effectively.
Innovation Solution
A method and device that determine a constraint condition for each item's first parameter, utilize a trained first parameter prediction model to forecast a parameter curve under different allocation conditions, and allocate resources based on these conditions to ensure compliance with parameter constraints, facilitating automatic and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If resource allocation is based on human experience and summary of strategies, then allocation decisions can be made using existing knowledge, but the method lacks flexibility and objectivity
Solution Approach 1:
The system uses automated prediction models and constraint condition determination algorithms to perform resource allocation independently, eliminating reliance on human experience while maintaining objectivity through mathematical optimization
Solution Approach 2:
The patent replaces human expert judgment (mechanical decision-making process) with automated prediction models and constraint-based optimization algorithms, achieving both flexibility and objectivity through computational methods
2Productivity
If resource allocation is adjusted based on PID control method, then dynamic adjustment can be achieved, but professional knowledge and experience are required for parameter setting
Solution Approach 1:
The prediction model automatically determines optimal resource allocation based on learned patterns from historical data, eliminating the need for manual PID parameter tuning while maintaining dynamic adjustment capabilities
Solution Approach 2:
The system transforms the complex PID parameter setting problem into automatic constraint condition determination and prediction-based optimization, changing the control approach from manual parameter tuning to automated constraint satisfaction
3Measurement precision
If direct prediction of parameter results is used under given resource allocation conditions, then allocation adjustment can be made based on predictions, but the method has poor adaptability when multiple target parameters exist
Solution Approach 1:
The patent segments the resource allocation problem into constraint condition determination for each parameter and prediction-based optimization, allowing independent handling of multiple parameters while maintaining overall system adaptability
Solution Approach 2:
The system adds the dimension of constraint conditions and prediction models to handle multiple parameters simultaneously, transforming the problem from direct prediction to constraint-based optimization that naturally accommodates multiple target parameters
4Productivity
If detailed parameter curves are predicted for all items under different allocation conditions, then comprehensive allocation optimization can be achieved, but operational resource consumption increases
Solution Approach 1:
The patent extracts only the necessary constraint conditions and key prediction results needed for optimization, avoiding computation of all possible parameter curves while maintaining allocation optimization capability
Solution Approach 2:
The system performs partial prediction focused on constraint conditions and critical parameters rather than complete parameter curves for all items, reducing computational overhead while achieving sufficient optimization
Data Source
AI summary
Provided is a method for resource allocation, including: determining a constraint condition for a first parameter of each of items; giving, in a trained first parameter prediction model, different resource allocation conditions based on current performance data of each of the items, to predict a first parameter curve of each of the items under different resource allocation conditions; and allocating resources based on a constraint condition for the first parameter of the item and the first parameter curve. Based on the above method for resource allocation, the present disclosure further provides an apparatus, an electronic device, a storage medium, and a program product for resource allocation.