An elevator intelligent scheduling system and method based on deep learning

A technology of deep learning and intelligent scheduling, which is applied in the direction of instruments, biological neural network models, character and pattern recognition, etc., can solve the problem of inability to achieve real-time intelligent scheduling of elevators, limited information of passengers, and inability to analyze the occupations of passengers, etc. body shape etc.

Inactive Publication Date: 2019-05-31
ZHEJIANG NEW ZAILING TECH CO LTD
View PDF6 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in this solution, the AI ​​processing module that mainly analyzes image data mainly analyzes face data. The AI ​​processing module cannot analyze inf

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
  • An elevator intelligent scheduling system and method based on deep learning
  • An elevator intelligent scheduling system and method based on deep learning
  • An elevator intelligent scheduling system and method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0072] refer to figure 1 , is shown as a flow chart of the steps of a deep learning-based elevator intelligent scheduling method according to an embodiment of the present invention, which includes the following steps:

[0073] S1, collect image information, and obtain all information of people waiting for each elevator;

[0074] The above steps include but are not limited to monitoring cameras placed in the designated elevator monitoring area, i...

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 an elevator intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting image information, and obtaining all information ofpeople waiting for elevator taking in each elevator; storing and preprocessing video images, collected in real time, of people waiting for elevator taking in real time, which comprise key frame selection of monitoring continuous videos, and the standard of the key frame selection is one frame, most capable of reflecting information of all elevator taking people, in each section of elevator takingpeople waiting video; Finally, obtaining attribute information of the number of elevator passengers, the gender, the shape and the weight estimation of the elevator passengers in a key frame in the monitoring video, and sending the information obtained through algorithm analysis to an elevator intelligent scheduling module; Obtaining the stopping position of the current elevator in real time, andfeeding back an elevator dispatching control center distribution result to an elevator operation monitoring module in real time according to the obtained number of people taking the elevator and attribute information; The elevator operation monitoring module is responsible for elevator safety, elevator passenger safety and convenient maintenance.

Description

technical field [0001] The invention belongs to the field of elevator applications, and in particular relates to an elevator intelligent scheduling system and method based on deep learning. Background technique [0002] In modern life, whether in residential buildings, office buildings or shopping malls, the importance of elevators as an important means of commuting has become increasingly prominent. However, due to factors such as construction costs and building areas, the number of elevators in buildings is limited. For example, during the morning and evening peaks of residential buildings on weekdays and weekends in shopping malls, people taking elevators can only wait on the corresponding floors, which greatly wastes time. [0003] The publication number is CN 108217352A, and the application name is a Chinese patent application for an intelligent elevator dispatching system, which belongs to the field of intelligent elevator control, including a sensor system module, a...

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
IPC IPC(8): G06K9/00G06N3/04
Inventor 王伟王超陈国特
Owner ZHEJIANG NEW ZAILING TECH CO LTD
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