A digital air duct control system and method for air conditioning system

Data collected through IoT devices, a temperature and humidity prediction model was established and combined with PID control algorithms were solved, and the problems of slow PID control and temperature overshoot in air conditioning systems were solved, achieving more efficient air conditioning control.

CN119245169BActive Publication Date: 2025-03-25JINGJIANG YIKAI VENTILATION EQUIP CO LTD
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Patent Information

Application Number
CN202411765275.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-04
Publication Date
2025-03-25
Estimated Expiration
2044-12-04

AI Technical Summary

Technical Problem

In existing air-conditioning systems, PID control is prone to slow regulation speed and temperature overshoot, which cannot meet actual needs.

Method used

The environment parameters and air conditioner air duct status parameters are collected through IoT devices, the sample set is constructed and pre-processed, a temperature and humidity prediction model is established, and the model is trained through a genetic algorithm, and the air conditioner air duct control volume is calculated by combining the PID control algorithm.

Benefits of technology

It improves the real-time and accuracy of digital air duct control, avoiding the problems of slow adjustment speed and temperature overshoot.

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Abstract

The present invention relates to the technical field of air conditioning, and discloses a digital air duct control system and method for an air conditioning system. The method collects environmental parameters and air conditioning duct state parameters through an Internet of Things device, and constructs a sample set based on the collected air conditioning duct state parameter data, and pre-processes the air conditioning duct state parameter data in the constructed sample set, and establishes a temperature and humidity prediction model based on the obtained pre-processed air conditioning duct state parameter data and the collected environmental parameters; at the same time, the established temperature and humidity prediction model is trained through a genetic algorithm, and the trained temperature and humidity prediction model is output, and based on the obtained trained temperature and humidity prediction model, the air conditioning duct control amount is calculated through a PID control algorithm, and the air conditioning duct is controlled based on the calculated air conditioning duct control amount, thereby improving the real-time performance and accuracy of digital air duct control.
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