Indoor air quality prediction method and device based on neural network and electronic equipment

An indoor air quality and neural network technology, which is applied in the field of indoor air quality prediction, can solve the long-term situation that the indoor air quality prediction method is not mentioned, cannot characterize the indoor ambient air quality, and the on-site timing monitoring method cannot perform long-term data monitoring and comparison. analysis, etc.

Inactive Publication Date: 2019-07-12
YANGJIANG POLYTECHNIC
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  • Claims
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AI Technical Summary

Problems solved by technology

[0003] In actual civil life, the above two monitoring methods have defects in use. For example, the on-site regular monitoring method cannot carry out long-term data monitoring and analysis, the data lacks effective representativeness, and cannot represent the long-term status of indoor ambient air quality. High environmental quality standards and social needs
However, the online real-time sensing monitoring method has a complex system, including monitoring modules, transmission modules, storage and display modules, etc. Currently, the coverage in civilian use is small, and there are many indoor air quality parameters, and the required sensor types and quantities are also many, which is difficult Get comprehensive air quality assessment results
[0004] To sum up, in the prior art, there are only methods for monitoring indoor air quality using air quality monitoring equipment or sensor monitoring systems; and methods for predicting indoor air quality have not yet been mentioned

Method used

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  • Indoor air quality prediction method and device based on neural network and electronic equipment
  • Indoor air quality prediction method and device based on neural network and electronic equipment
  • Indoor air quality prediction method and device based on neural network and electronic equipment

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Embodiment 1

[0068] Such as image 3 As shown, the embodiment of the present invention provides a neural network-based indoor air quality prediction method, comprising the following steps:

[0069] Step S102, obtaining the indoor air quality index to be predicted in the target indoor space;

[0070] The above-mentioned target indoor space refers to the indoor space to be tested. It should be noted that the indoor space here refers to a closed or semi-closed space, such as a room without windows or with windows (for example, a residential room, a hotel room, hotel room, office room), or the interior space of a vehicle (such as a car, train, etc.).

[0071] The above indoor air quality indicators are used to characterize indoor air quality, that is, indicators that can be used to evaluate indoor air quality;

[0072] Specifically, the indoor air quality index in this embodiment includes, but is not limited to, the concentration of formaldehyde, the concentration of benzene series such as t...

Embodiment 2

[0133] Such as Figure 5 As shown, the embodiment of the present invention also provides a neural network-based indoor air quality prediction device, including:

[0134] The first index acquisition module 10 is used to acquire the indoor air quality index to be predicted in the target indoor space;

[0135] The second index acquisition module 20 is configured to acquire a predetermined indoor environment index of the target indoor space and a parameter value of the predetermined indoor environment index;

[0136] The prediction model selection module 30 is used to select a target neural network indoor air quality prediction model from a plurality of pre-trained neural network indoor air quality prediction models based on the indoor air quality index to be predicted and the predetermined indoor environment index ; Wherein each neural network indoor air quality prediction model corresponds to one or more indoor air quality indicators; each neural network indoor air quality pred...

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Abstract

The invention provides an indoor air quality prediction method and device based on a neural network and electronic equipment, and relates to the field of indoor air quality prediction, and the methodcomprises the steps: obtaining a to-be-predicted indoor air quality index of a target indoor space, a predetermined indoor environment index and a parameter value of the predetermined indoor environment index; selecting a target neural network indoor air quality prediction model from a plurality of pre-trained neural network indoor air quality prediction models based on the indoor air quality index to be predicted and the predetermined indoor environment index; and taking the parameter value of the predetermined indoor environment index of the target indoor space as the input of the target neural network indoor air quality prediction model, and outputting a to-be-predicted indoor air quality index value. The method is more flexible, more convenient and faster, the prediction precision is higher and the reliability is high while a comprehensive air quality evaluation result is obtained.

Description

technical field [0001] The invention relates to the technical field of indoor air quality prediction, in particular to a neural network-based indoor air quality prediction method, device and electronic equipment. Background technique [0002] At present, the most commonly used methods for obtaining the data of various indicators of indoor air quality are on-site timing monitoring method and online real-time sensor monitoring method. The on-site timing monitoring method is to use some complex air quality monitoring equipment to monitor on-site at specific time points to obtain relevant air quality index data; the online real-time sensor monitoring method is generally based on various monitoring sensors, using wired or wireless transmission systems, Obtain real-time and historical monitoring data of indoor air quality. [0003] In actual civil life, the above two monitoring methods have defects in use. For example, the on-site regular monitoring method cannot carry out long-t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/04
CPCG06Q10/06393G06N3/045
Inventor 关成立杨岳陈珊媛曾欣慧
Owner YANGJIANG POLYTECHNIC
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