A task evolution-oriented distributed resource dynamic configuration method

By constructing a task requirement evolution model and a segmented symbolic encoding multi-strategy genetic algorithm, the problem that resource allocation schemes in existing technologies are difficult to adapt to the phased changes in task requirements is solved, and the coordinated balance of multi-stage task requirements and efficient resource allocation are achieved.

CN122593928APending Publication Date: 2026-08-18DALIAN UNIV OF TECH
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Patent Information

Application Number
CN202610650133.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-12
Publication Date
2026-08-18

AI Technical Summary

Technical Problem

Existing technologies struggle to adapt to multi-stage changes when dealing with dynamic resource allocation problems that involve the phased evolution of task requirements, leading to excessive or insufficient resource consumption. Furthermore, traditional genetic algorithms are inefficient and lack quality in solving problems involving multiple types of resources and multiple sites.

Method used

A task requirement evolution model is constructed, and a segmented symbolic coding multi-strategy genetic algorithm is adopted. The model is solved by a multi-objective nonlinear programming model and a segmented symbolic coding strategy genetic algorithm to achieve dynamic configuration of task requirements. By combining conditional probability recursive calculation and resource type boundary segmentation, the coding complexity and search space are reduced.

Benefits of technology

It achieves a quantitative description of the phased evolution of task requirements, avoids resource misallocation, improves the collaborative balance and solution efficiency of resource allocation, ensures that the resource allocation scheme meets the quantitative constraints, and avoids premature convergence.

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Abstract

This invention provides a distributed resource dynamic allocation method oriented towards task evolution, comprising the following steps: S1, constructing a task demand evolution model and recursively calculating the probability distribution of task demand events at each stage based on conditional probability; S2, receiving the probability distribution of task demand events at each stage, constructing a distributed resource allocation model oriented towards task evolution, the distributed resource allocation model taking maximizing the resource system's processing capacity in response to task demands as its primary objective and balancing the task demands at each stage as its secondary objective, establishing a multi-objective nonlinear programming model; S3, inputting the multi-objective nonlinear programming model into a heuristic solution algorithm, using a segmented symbolic encoding multi-strategy genetic algorithm for solution, and outputting the optimal resource allocation scheme. This invention, by combining with multi-stage conditional probability recursive calculation, achieves a quantitative description of the stage-wise evolution law of task demands, providing accurate probabilistic input for dynamic resource allocation.
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