Task dynamic scheduling method and device based on time-sensitive rule engine, storage medium and computer device

By adopting a task dynamic scheduling method based on a time-sensitive rule engine, the problem of insufficient time-sensitivity in task scheduling in the securities industry is solved, and the dynamic optimization and reasonable allocation of tasks are realized, thereby improving the timeliness and accuracy of task processing.

CN122114447APending Publication Date: 2026-05-29CSC FINANCIAL CO LTD
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CSC FINANCIAL CO LTD
Filing Date
2026-01-16
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Traditional static scheduling algorithms are ill-suited to the uneven spatial and temporal distribution of tasks in the securities industry, the timeliness requirements of regulatory oversight, the evolution of task priorities, and the need for real-time adjustments to in-transit tasks based on risk changes. They lack time-series awareness, leading to unreasonable task scheduling.

Method used

A time-sensitive rule engine is used to determine task time priority data. By constructing cost and priority matrices, task allocation costs are adjusted and augmented paths are found to achieve dynamic optimization of task scheduling.

Benefits of technology

It improves the timeliness and accuracy of task processing, ensures that important and urgent tasks are given priority, and enhances the efficiency of task processing and the stability of the system.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122114447A_ABST
    Figure CN122114447A_ABST
Patent Text Reader

Abstract

The application discloses a task dynamic scheduling method and device based on a time-sensitive rule engine, a storage medium and computer equipment, and the method comprises the following steps: determining time priority data of a plurality of to-be-processed tasks through a time-sensitive rule engine; determining a plurality of candidate processors, constructing a cost matrix containing task allocation costs of allocating each to-be-processed task to each candidate processor, and constructing a priority matrix corresponding to the cost matrix according to each task allocation cost in the cost matrix; reducing the cost matrix to update the cost matrix, performing initial task matching on the to-be-processed tasks based on the cost matrix; finding an augmented path for an unmatched task according to the priority matrix, calculating a task allocation cost adjustment amount based on the augmented path, adjusting each task allocation cost in the cost matrix according to the allocation cost adjustment amount, continuing to find an augmented path in the next round, and obtaining a feasible path; and determining the processor of the unmatched task according to the feasible path.
Need to check novelty before this filing date? Find Prior Art