Resource Processing Strategy Combination for Flexible Allocation
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Solution Overview
Problem
Existing resource allocation systems in APPs face challenges in flexibility and cost-effectiveness, as they require customized modifications and complex code maintenance to adapt to changing user interaction scenarios, leading to increased development costs and reduced maintainability.
Innovation Solution
A resource processing method and apparatus that preconfigure the relationship between resource allocation scenarios and strategy combinations, allowing for flexible adjustment of resource pools and allocation strategies without modifying codes, enabling precise resource allocation by executing predefined allocation strategies and combining resources based on predefined operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If customized modifications are made for different user interaction scenarios, then resource allocation accuracy is improved, but system complexity and code maintenance difficulty increase
Solution Approach 1:
The system segments resource allocation into independent, configurable strategy modules that can be selectively combined. Each allocation strategy is divided into discrete components (resource pools, allocation rules, combination operations) that can be independently configured and recombined based on different user interaction scenarios, reducing overall system complexity while maintaining allocation accuracy.
Solution Approach 2:
The system implements dynamic configuration capabilities where resource pools, allocation strategies, and combination operations can be flexibly adjusted without code modifications. The strategy combination configuration allows runtime adaptation to different user interaction scenarios through configurable parameters, enabling the system to dynamically respond to changing requirements while maintaining accuracy.
2Manufacturing precision
If customized modifications are made for different user interaction scenarios, then resource allocation accuracy is improved, but development cost increases
Solution Approach 1:
The system creates a universal resource allocation framework that can handle multiple user interaction scenarios through a single configurable platform. The strategy combination configuration and allocation strategies are designed to be scenario-agnostic, allowing the same core system to serve diverse purposes (video streaming, VR, gaming, etc.) by simply configuring different resource pools and allocation rules, thereby eliminating the need for separate customized developments.
Solution Approach 2:
The system performs preliminary configuration of resource pools, allocation strategies, and combination operations that can be reused across different scenarios. By pre-establishing a flexible configuration framework with predefined strategy templates and combination operations, the system eliminates the need for repeated customized development work when facing new user interaction scenarios.
3Adaptability or versatility
If code modifications are made to adapt to changing scenarios, then resource allocation flexibility is improved, but code maintainability deteriorates
Solution Approach 1:
The system replaces static code-based adaptation with dynamic configuration capabilities. Resource pools, allocation strategies, and combination operations are defined through configurable parameters rather than hard-coded logic, allowing the system to adapt to changing user interaction scenarios by modifying configuration files or database entries without touching the source code, thereby maintaining code integrity and ease of maintenance.
Solution Approach 2:
The system introduces a configuration layer as an intermediary between the user interaction scenarios and the core allocation logic. This configuration layer (strategy combination configuration) acts as a mediator that translates diverse scenario requirements into standardized allocation operations, isolating the core code from scenario-specific variations and improving maintainability while preserving flexibility.
4Manufacturing precision
If multiple allocation strategies are combined, then resource allocation precision is improved, but system complexity increases
Solution Approach 1:
The system segments the combination of multiple allocation strategies into modular, independently configurable components. Each allocation strategy is a discrete unit that can be selectively activated and combined through the strategy combination configuration, allowing precise resource allocation through multiple strategies without creating monolithic complexity in the system architecture.
Solution Approach 2:
The system provides built-in combination operations that automatically merge results from multiple allocation strategies according to predefined rules. The strategy combination configuration specifies how to combine resources from different strategies (e.g., aggregation, selection, weighting), automating the complexity management and allowing precise multi-strategy allocation without manually managing the complexity of interactions between strategies.
Data Source
AI summary
A resource processing method and apparatus are proposed in the embodiment of the present disclosure. The method includes: obtaining a resource allocation scenario; querying a corresponding strategy combination configuration according to the resource allocation scenario; executing each allocation strategy to determine a target resource pool allocated with the allocation strategy; obtaining resources corresponding to each allocation strategy from the target resource pool allocated with the allocation strategy; executing the combination operation on the resources corresponding to the allocation strategy to obtain target resources.