Intelligent scheduling method, system and device based on event resources

By constructing a dynamic resource profile library and demand sequence, and using machine learning and bipartite graph models to optimize resource allocation, the problem of low scheduling efficiency in large-scale, high-concurrency scheduling is solved, and accurate perception and automated scheduling are achieved.

CN122390310APending Publication Date: 2026-07-14JINAN YINGHONG SPORTS CULTURE DEVELOPMENT CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JINAN YINGHONG SPORTS CULTURE DEVELOPMENT CO LTD
Filing Date
2026-04-15
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing technologies often rely on human experience or simple polling rules, making it difficult to comprehensively consider complex factors such as task priority and resource matching in a short period of time, resulting in low scheduling efficiency when dealing with large-scale, high-concurrency scheduling needs.

Method used

By acquiring heterogeneous data from multiple sources of events, processing and cleaning the data, constructing a dynamic resource profile library and demand sequence, using machine learning models to automatically identify and classify event events, and optimizing resource allocation based on a weighted bipartite graph optimal matching model, we can achieve accurate quantitative perception of resource status and automated modeling of task-derived demands.

Benefits of technology

In large-scale, high-concurrency scenarios, it outputs near-optimal resource allocation schemes in real time, significantly improving scheduling efficiency and resource utilization, and realizing the transformation from manual experience-based judgment to multi-objective global automatic optimization.

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Abstract

The application discloses a smart scheduling method, system and device based on event resources, relates to the technical field of resource scheduling, and solves the technical problem that the prior art relies on manual experience or simple polling rules, is difficult to comprehensively consider complex factors such as task priority and resource matching degree in a short time, and leads to low scheduling efficiency when processing large-scale and high-concurrency scheduling requirements. Through data processing operation on event multi-source heterogeneous data, fusion data flow is obtained. Based on the fusion data flow, resource portrait updating and demand prediction are performed to obtain a dynamic resource portrait library and a dynamic resource demand sequence. An optimized allocation scheme is generated based on the dynamic resource portrait library and the dynamic resource demand sequence, the scheduling problem is formalized as an optimal matching problem of a weighted bipartite graph with the maximum global utility as the target, an intelligent optimization algorithm is used for solving, and a global optimization allocation scheme is automatically output, thereby improving the scheduling efficiency and resource utilization.
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