Machine learning-based batched measurement method and system for street space sunshine durations

A technology of machine learning and measurement method, applied in sunshine time recorder, measuring device, meteorology and other directions, can solve the problems of huge simulation accuracy error, long time consumption, and difficulty in simulating the sunlight occlusion of tree canopy by urban geometric models. To achieve the effect of efficient operation and high accuracy

Active Publication Date: 2019-08-27
NANJING UNIV
View PDF3 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

One category uses a 3D model to simulate meteorological changes to measure the sunshine hours, but this type of simulation software has problems such as time-consuming and high-quality 3D model data acquisition difficulties
The more fatal flaw is that the simplified urban geometric model is difficult to simulate the sunlight shading of the real tree canopy, and the error of the simulation accuracy is extremely large
The other type uses remote sensing maps to simulate the sunshine hours, and the simulation accuracy is not enough to meet the quantitative requirements of the street space.

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
  • Machine learning-based batched measurement method and system for street space sunshine durations
  • Machine learning-based batched measurement method and system for street space sunshine durations
  • Machine learning-based batched measurement method and system for street space sunshine durations

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0081] figure 1 It is a flow chart of the method for batch measurement of sunshine hours in street space based on machine learning in the present invention. Such as figure 1 As shown, a batch measurement method of street space sunshine hours based on machine learning, including:

[0082] Step 101: Acquiring the panorama of the observation point, specifically including:

[0083] Obtain the latitude and longitude location information of each observation point through the network street view map.

[0084] The panorama of all observation points is determined according to the latitude and longitude position information of each observation point.

[0085] Step 102: Classify and identify the panorama using image semantic segmentation technology to obtain multiple panoramas after classification and identification, specifically including:

[0086] The convolutional neural network model in the image semantic segmentation technology is used to classify and identify the panorama, and ...

Embodiment 2

[0100] figure 2 It is a structural diagram of the batch measurement system for street space sunshine hours based on machine learning in the present invention. Such as figure 2 As shown, a machine learning-based batch measurement system of sunshine hours in street space includes:

[0101] The first acquiring module 201 is configured to acquire a panorama of an observation point.

[0102] The classification and recognition module 202 is configured to classify and recognize the panorama by using image semantic segmentation technology to obtain a plurality of classified and recognized panoramas.

[0103] The conversion processing module 203 is configured to perform conversion processing on each of the classified and recognized panoramic images to obtain multiple fisheye images.

[0104] The second acquiring module 204 is configured to acquire solar path trajectories on multiple set dates.

[0105] The sunshine hours determining module 205 is configured to superimpose each of...

Embodiment 3

[0121] In specific implementation, a machine learning-based method for batch measurement of sunshine duration in street space includes:

[0122] Step 1: Obtain a panorama of the observation point

[0123] Step 1.1: Obtain the longitude and latitude position information of a series of observation points.

[0124] Based on the selected street view map, first obtain the road network information through the API provided by the map itself, and then use the GIS software to generate observation points at regular intervals for the selected roads, and obtain the latitude and longitude position information of all observation points. The data sources of the present invention are street view maps provided by companies such as Baidu on the Internet, including Baidu Street View, Tencent Street View, Google Street View, and the like.

[0125] Step 1.2: Obtain a panoramic photo.

[0126] Based on the obtained longitude and latitude information of the observation points, the API provided by ...

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 discloses a machine learning-based batched measurement method and system for street space sunshine durations. The method comprises the steps of obtaining a panoramic image of an observation point; carrying out classification and recognition on the panoramic image by adoption of an image semantic segmentation technology so as to obtain a plurality of classified and recognized panoramic images; carrying out conversion processing on the classified and recognized panoramic images to obtain a plurality of fisheye images; obtaining sun path trajectories of a plurality of set dates; andoverlapping the fisheye images with the corresponding sun path trajectories to obtain a sunshine duration. By adoption of the method or system, the sunshine durations of a large number of observationpoints can be processes in batches, so that the operation is efficient and the correctness is high.

Description

technical field [0001] The invention relates to the field of sunshine hours measurement, in particular to a method and system for batch measurement of street space sunshine hours based on machine learning. Background technique [0002] The measurement of sunshine hours currently mainly includes the following three types of measurement methods: meteorological base station measurement method, manual measurement method and software simulation method. [0003] (1) Meteorological base station measurement method. Due to the uneven distribution of meteorological base stations, the number of samples of sunshine hours collected is insufficient, and it is difficult to meet the quantitative requirements of sunshine hours in street space. [0004] (2) Manual measurement method. Measure sunshine hours using homemade fisheye photos, heliometers, daylight sensors, and more. However, this kind of work requires a lot of manual processing, takes a long time, and is difficult to measure in ...

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): G01W1/12
CPCG01W1/12
Inventor 童滋雨宫传佳徐沙杨华武
Owner NANJING UNIV
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