US 12,455,377 B2
System and method for capturing movement trajectories of particulate matter
Shu Tao, Beijing (CN); Xin He, Beijing (CN); Shuxiu Zheng, Beijing (CN); Heng Zhang, Beijing (CN); Xiaoqiao Jiao, Beijing (CN); and Guofeng Shen, Beijing (CN)
Assigned to PEKING UNIVERSITY, Beijing (CN)
Filed by Peking University, Beijing (CN)
Filed on Apr. 26, 2022, as Appl. No. 17/660,825.
Claims priority of application No. 202210131678.8 (CN), filed on Feb. 14, 2022.
Prior Publication US 2023/0258803 A1, Aug. 17, 2023
Int. Cl. G01S 17/58 (2006.01); G01S 7/481 (2006.01); G01S 17/86 (2020.01); G06T 7/246 (2017.01)
CPC G01S 17/58 (2013.01) [G01S 7/4815 (2013.01); G01S 17/86 (2020.01); G06T 7/246 (2017.01); G06T 2207/30241 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A method for capturing movement trajectories of particulate matter, applied to a system for capturing movement trajectories of particulate matter, said system comprising:
micro lidar measurement equipment and a camera, wherein
the micro lidar measurement equipment comprises: a laser device, a pitching platform, and a protective casing for the laser device, and the laser device is located on the pitching platform;
the laser device is configured to emit a laser; and
the camera is configured to project the laser, and determine particulate matter concentrations by referencing a grayscale of pixels and a height of the pixels in one or more images;
said method comprising:
obtaining scattered light images of the particulate matter by the camera;
performing a distance calibration on the scattered light images of the particulate matter;
extracting grayscale pixels at different heights in a laser light path from the calibrated scattered light images of the particulate matter;
building a particulate matter concentration prediction model; wherein an expression of the particulate matter concentration prediction model is as follows:
C=p0+p1×H+p2×G+p3×(H)2+p4×H×G, and
C represents the particulate matter concentration, H represents an attenuation distance, G represents the grayscale, and p0, p1, p2, p3 and p4 are all constants;
predicting particulate matter concentrations based on the particulate matter concentration prediction model; and
determining the movement trajectories of the particulate matter based on differences in spatial distribution of particulate matter concentrations at different times.