A dynamic priority multi-parameter cyclic sampling scheduling method and system

By adopting a dynamic priority multi-parameter cyclic sampling scheduling method, the problems of high equipment cost and low detection efficiency in water quality testing systems are solved, load balancing between modules and emergency task response are achieved, and the overall detection efficiency of the system is improved.

CN122240296APending Publication Date: 2026-06-19SICHUAN EVERGREEN PINE TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SICHUAN EVERGREEN PINE TECH CO LTD
Filing Date
2026-02-25
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing water quality testing systems suffer from high equipment procurement costs, high operating costs, and low testing efficiency when dealing with multiple testing items. Traditional scheduling methods cannot effectively coordinate differences between modules, resulting in serious idle or bottleneck phenomena between modules and overall low efficiency.

⚗Method used

A multi-parameter cyclic sampling scheduling method with dynamic priority is adopted. By generating and updating the dynamic priority score of the detection task and combining it with the time slice execution strategy, the task allocation is dynamically adjusted to achieve load balancing between modules and timely response to urgent tasks.

🎯Benefits of technology

It improved the overall system detection efficiency, ensured timely response to emergency tasks, achieved load balancing between modules, shortened the overall completion time, and improved detection efficiency and resource utilization.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a dynamic priority multi-parameter cyclic sampling scheduling method and system. The method includes: generating multiple detection tasks, assigning initial parameters to each detection task, the initial parameters including preset priority, waiting time, and real-time load rate of the processing module; calculating the dynamic priority score of each detection task based on the initial parameters, sorting the detection tasks, and executing the higher-priority detection tasks in time slices. The scheduling method effectively eliminates the idle waiting problem between modules caused by differences in the number of module channels and different detection times for different items through a dynamic load-balanced parallel sampling approach, thereby improving the overall efficiency of the system. This scheduling method ensures the priority of priority samples, guaranteeing timely response to urgent tasks, while also ensuring the timeliness of ordinary sample detection through dynamic load-balanced parallel sampling, achieving a balance between intelligent response and fairness.
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