Clean room intelligent operation adaptation method and system based on environmental constraint linkage

By collecting and processing cleanroom data in real time, assessing the urgency of constraints, and adjusting the supply and exhaust air and fan frequencies, the system has resolved constraint conflicts and energy consumption fluctuations in cleanroom systems under dynamic operating conditions, thereby improving the stability and economy of cleanroom operation.

CN122284425APending Publication Date: 2026-06-26JIANGSU ZHIPU YOUNENG TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610369840.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-25
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing cleanroom systems struggle to adaptively adjust priorities under dynamic conditions with multiple constraints, leading to constraint conflicts and mismatches, energy consumption fluctuations, and operational instability.

Method used

By collecting multi-constraint operation data in real time, performing time alignment, anomaly correction and normalization preprocessing, assessing the urgency of environmental constraints, generating multi-constraint urgency comparison results, adjusting the supply and exhaust valve ratio, fan frequency and filter unit operating level, monitoring the frequency change characteristics of the supply fan in real time, and optimizing the stability of linkage control.

Benefits of technology

It achieves adaptive and dominant switching between cleanliness indicators and energy-saving targets, suppresses sudden increases in energy consumption and frequent fluctuations in air volume, improves the stability and economy of cleanroom operation, and forms a traceable closed-loop operation link.

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Abstract

The invention discloses a clean room intelligent operation adaptation method and system based on environmental constraint linkage, and relates to the technical field of clean room operation adjustment. Comprising the steps of S1, collecting multi-constraint operation data in real time, and performing data preprocessing; s2, analyzing the change characteristics of the multi-constraint operation data, evaluating the urgency degree of the environmental constraint, generating a multi-constraint urgency comparison result, judging the type of the current dominant constraint, and outputting a priority control strategy; s3, performing conflict judgment on the relationship between the energy consumption cost and the pressure difference income by utilizing the change characteristics of the multi-constraint operation data, and adjusting the ratio of an air supply valve and an air exhaust valve, the frequency amplification of a fan and the operation gear of a filter unit; and S4, the operation frequency and change characteristics of the air feeder are monitored, the stability of linkage control is evaluated, and optimization and adjustment actions are implemented. The problems that under the multi-constraint dynamic working condition of the clean room, the fixed priority is difficult to adjust in a self-adaptive mode, constraint conflict mismatch is likely to be caused, and energy consumption fluctuation and unstable operation are caused are solved.
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