Network type fresh air system based on big data analysis and regulation and control method

Abstract

The invention discloses a network type fresh air system based on big data analysis and a regulation and control method. A big data analysis network platform and dispersed user fresh air systems jointly form an outdoor air quality monitoring network; the user fresh air system at least comprises an air quality monitoring module which is arranged in an air supply channel and used for monitoring the quality of outdoor air entering the air supply channel, and the air quality monitoring module serves as a data acquisition terminal of a monitoring network. The monitoring network is formed by all widely dispersed user fresh air systems, change data of outdoor air quality of all positions are collected, the data are summarized and then subjected to big data analysis, accurate prediction of air quality change in the network is achieved, and operation of all the user fresh air systems is optimized and adjusted on the basis of the prediction data.

Description

technical field [0001] The invention relates to fresh air system technology, in particular to a networked fresh air system and a control method based on big data analysis. Background technique [0002] The fresh air system is an air treatment system composed of an air supply system and an exhaust system. It continuously introduces outdoor fresh air into the room and replaces the indoor air. The indoor air is discharged outside through the exhaust channel; at the same time, the outdoor air introduced into the room is purified. , so that the indoors can continue to obtain high-quality clean air. The principle of the existing fresh air system is mainly to adopt the method of multi-stage filtration, and several purification modules with different functions can also be added to work together to gradually filter or remove harmful components and inhalable particles in the air. The normal operation mode of the fresh air system is 24-hour automatic operation, but it is not ruled out...

AI Technical Summary

Problems solved by technology

[0004] 1. High energy consumption and low air exchange rate: Compared with natural ventilation with windows open, the air exchange rate of the fresh air system is only a fraction or even a tenth of that of natural ventilation with windows open, and the air supply requires power consumption ;For example, in the time period when the weather conditions are suitable and the outdoor air quality is excellent, the fresh air system has little advantage compared with the natural ventilation with windows open;

[0005] 2. Low degree of intelligence: the filter device in the fresh air system is designed according to the worst outdoor air quality. Large fluctuations, different seasons, different dates, and different time periods of the day, the data of outdoor air quality are different, sometimes fluctuating violently, if the fresh air system can avoid the time period of poor air quality, and at the same time Using the time period with better air quality can not only reduce the consumption of filter consumables, but also provide more and better quality fresh air for the room; however, the influence of this factor is not considered in the design of the existing fresh air system

Existing artificial intelligence algorithms rely on monitoring data from public observation points, and due to the small number of public observation points, they cannot provide accurate local outdoor air quality changes related to a certain building (latitude and longitude) and a certain floor (height) data, and cannot monitor local sudden changes in outdoor air quality in a small area of ​​the relevant area; therefore, the existing air quality prediction system cannot play an accurate guiding role in the operation strategy of each user's fresh air system

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