一种电力采集终端的多任务调度采集方法及采集系统
By dynamically selecting the optimal analysis window length and constructing a prediction model specific to each task type, the problem of rigid window length in multi-task scheduling of power acquisition terminals is solved, achieving efficient task execution and resource utilization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANGZHOU HUALONG ELECTRONIC TECH CO LTD
- Filing Date
- 2026-06-22
- Publication Date
- 2026-07-17
AI Technical Summary
In existing multi-task scheduling methods for power acquisition terminals, the window length relies on manual experience to set, which leads to rigidity, inability to adapt to dynamic operating conditions, and results in task execution failure and low resource utilization efficiency.
By constructing a dynamic analysis window mechanism based on working condition characteristics and task queue characteristics, and utilizing K-Means clustering and long short-term memory network models, the optimal analysis window length is dynamically selected, and a prediction model specific to task type is constructed to achieve high-precision timeout probability prediction and scheduling optimization.
It improves the success rate of task execution and the efficiency of resource utilization, ensuring high real-time performance and stability in multi-tasking and high-concurrency scenarios.
Smart Images
Figure CN122414765A_ABST