A laboratory room ventilation pressure difference adaptive regulation method and system

By using an adaptive control method for differential pressure in laboratory room ventilation, and employing a two-stage adjustment mechanism combining a self-learning model and a PID algorithm, rapid identification and precise adjustment of laboratory differential pressure are achieved. This solves the problems of poor anti-interference capability and slow response speed in existing technologies, and improves the stability and reliability of the system.

CN122408201APending Publication Date: 2026-07-17HUNAN ZHENGHAI MODERN LAB EQUIP CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUNAN ZHENGHAI MODERN LAB EQUIP CO LTD
Filing Date
2026-04-27
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing laboratory differential pressure control systems have poor anti-interference capabilities and slow response speed when facing dynamic changes, and also pose safety risks and energy waste.

Method used

An adaptive control method for ventilation pressure difference in laboratory rooms is adopted. A basic parameter library of the system is established through initialization processing, real-time multi-source data is collected, and a two-stage adjustment is performed using a self-learning model and PID algorithm. Combined with iterative control, the system can quickly identify and accurately adjust wind pressure difference interference.

Benefits of technology

It significantly shortens the differential pressure fluctuation range and recovery time, improves the system's anti-interference capability, ensures that the differential pressure remains stable within the target range under any operating conditions, and improves the system's reliability and stability.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122408201A_ABST
    Figure CN122408201A_ABST
Patent Text Reader

Abstract

本发明提供一种实验室房间通风压差自适应调控方法及系统,如此能基于数据集对主要干扰进行“预见性”补偿,极大缩短了压差波动幅度和恢复时间,提升了系统抗干扰能力;并采用“自学习模型预设调节量+实时数据修正”的双级调节机制,既利用自学习模型的历史经验保证了调节的快速性,又通过实时多源数据与目标阈值的对比修正保证了调节的精准性,显著减少了超调量和系统震荡,使房间压差能够稳定维持在目标范围内;再通过循环迭代调控机制,持续评估调节效果并动态调整调节策略,确保在任何工况下都能最终达到目标压差要求;同时,多源数据融合的方式避免了单一传感器故障导致的控制失效,提高了系统的整体可靠性。
Need to check novelty before this filing date? Find Prior Art