Data-driven multi-task collaborative partitioning management method and device
Through the data-driven multi-task collaborative division management method, the problems of data conflicts and inefficient task scheduling in the existing technology are solved, and efficient tasks are implemented and the resource utilization is achieved.
CN118796460BActive Publication Date: 2025-03-25SUZHOU XIAOMAN FINANCIAL DATA TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202410786943.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-18
- Publication Date
- 2025-03-25
- Estimated Expiration
- 2044-06-18
AI Technical Summary
Technical Problem
Data from different sources in the prior art conflicts or inconsistent, making it difficult to coordinate and unify, resulting in unreasonable task division, scheduling and inefficient.
Method used
The data-driven multi-task collaborative division management method is adopted, and the optimal task division management solution is achieved by collecting task feature data and processing unit feature data, combining historical resource data, dimensionality reduction analysis, configuring resource weights, optimizing task division, and realizing the optimal task division management solution.
Benefits of technology
The rational division and scheduling of tasks are realized, the execution efficiency of multi-tasks is improved, and the rational utilization of resources is ensured.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention discloses a data-driven multi-task collaborative partitioning management method and device, which relates to the field of task partitioning technology. The method includes: collecting multiple task feature data of multiple tasks to be partitioned and managed, obtaining multiple historical resource data sets, performing dimensionality reduction analysis on the multiple historical resource data sets, obtaining multiple task resource complexities and multiple dimensionality reduction task resources, collecting multiple unit feature data of multiple processing units currently processing tasks, setting task partitioning constraints, obtaining an optimal task partitioning management plan, allocating multiple tasks to multiple processing units, and completing task partitioning management. The present invention solves the technical problems in the prior art that data from different sources conflict or are difficult to coordinate and unify, task partitioning scheduling management is unreasonable, and efficiency is low, and achieves reasonable partitioning and scheduling, so that multiple tasks can be executed more efficiently.
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