Urban air quality grade predicting method based on multi-field characteristics

A technology for air quality classification and prediction method, applied in the field of air quality prediction, can solve the problems of moderate pollution, heavy pollution, unsuitability, etc., to ensure global convergence, ensure super-linear convergence speed, overcome marker bias and conditional independence hypothetical effect

Inactive Publication Date: 2014-12-10
ZHEJIANG HONGCHENG COMP SYST
View PDF5 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the other hand, the traditional air quality forecast usually predicts the air quality of the next whole day. There is a drawback in this kind of coarse-grained air quality forecast. People who are more sensitive to substances can carry out outdoor activities, but in fact, the air quality in certain time periods (such as 8:00-9:00 am, 5:00-6:00 pm) may be moderately polluted or heavily polluted. People who are more sensitive to air pollution are not suitable for outdoor activities during these time periods

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Urban air quality grade predicting method based on multi-field characteristics
  • Urban air quality grade predicting method based on multi-field characteristics
  • Urban air quality grade predicting method based on multi-field characteristics

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0033] Embodiment: the present invention proposes the AQI grade prediction method based on multi-field feature, and flow process is as figure 1 shown. The method is divided into three stages: data preprocessing, training and prediction. Among them, the data flow in the preprocessing stage is represented by a dotted line, the data flow in the training stage is represented by a dotted line, and the data flow in the prediction stage is represented by a solid line.

[0034] The process of data preprocessing stage is as follows: figure 2 As shown, its main steps include:

[0035] 1) For a certain city, collect historical and real-time data and meteorological forecast data for a certain period of time in the future in multiple fields that affect air quality, such as meteorology, traffic, air pollutants, etc.;

[0036] 2) Divide city a into disjoint grids, each grid g=g.wxg.h has the same length g.w and width g.h, use g. c Indicates the center point of the grid g. use g a (w, ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to an urban air quality grade predicting method based on multi-field characteristics. The method comprises a data preprocessing stage, a training stage and a predicting stage. In the data preprocessing stage, mesh generation is carried out to obtain a training sample, and a training data set is obtained. In the training stage, the optimal parameter of a conditional random field model is estimated, an optimal model shown in the specification is obtained, and the conditional random field model is output. In the predicting stage, the maximum output sequence, shown in the specification, of the conditional probability P(Y/X<f>) is obtained by combining the viterbi algorithm and the conditional probability model shown in the specification and obtained in learning of the training stage, and a prediction result is output. The AQI grade can be predicted more accurately, the AQI grade prediction in the following time quantum of all stations is carried out, and the requirement for selecting the time quantum with the high air quality to go out and take activities of people is met.

Description

technical field [0001] The invention relates to a method for predicting city AQI grades, in particular to a method for predicting AQI grades of air quality monitoring stations based on multi-field features. Background technique [0002] Air is a substance that the living things on the earth depend on for survival, and it is an essential substance. Ambient air quality is closely related to people's daily life, and it also plays an important role in the comprehensive evaluation of urban environment. However, with the development of human civilization and economy, air pollution is becoming more and more serious. How to improve air quality and reasonably predict and warn air environment quality is becoming more and more important. According to air quality prediction, people can take corresponding measures such as wearing masks, Try to avoid going out, etc., and protect yourself from air pollutants. [0003] Traditional air quality prediction methods generally only consider the...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 王敬昌陈岭赵江奇袁翠丽鲁东丽李纺
Owner ZHEJIANG HONGCHENG COMP SYST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap