CN112623919B - Escalator intelligent monitoring management system based on computer vision - Google Patents
Escalator intelligent monitoring management system based on computer vision Download PDFInfo
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- CN112623919B CN112623919B CN202011499776.4A CN202011499776A CN112623919B CN 112623919 B CN112623919 B CN 112623919B CN 202011499776 A CN202011499776 A CN 202011499776A CN 112623919 B CN112623919 B CN 112623919B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B27/00—Indicating operating conditions of escalators or moving walkways
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B25/00—Control of escalators or moving walkways
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B29/00—Safety devices of escalators or moving walkways
Abstract
The invention discloses an escalator intelligent monitoring management system based on computer vision, which comprises: the system comprises a plurality of fixed vision sensing devices, a plurality of infrared vision sensors, a tracking vision sensing device, a photographic device, a 5G communication device and a terminal processor. The invention relates to an escalator intelligent monitoring system through a 5G communication device, and environmental parameters and unsafe behaviors of people of the escalator are monitored in real time through a fixed visual sensing device, a tracking visual sensing device and a photographic device which are arranged near the escalator, so that the linkage management of the monitoring system is realized, and the intelligent level of the system is improved; the early warning level of the escalator is determined by collecting environmental information data in the escalator area in real time and identifying unsafe behaviors of people in real time, and when the early warning level of the escalator is an abnormal level, the alarm module, the early warning prompt module and the safety early warning module can send out alarm signals, so that accurate early warning of abnormal operation of the escalator is realized, and safety accidents of the escalator can be effectively prevented.
Description
Technical Field
The invention belongs to the technical field of escalator safety monitoring, and particularly relates to an escalator intelligent monitoring management system based on computer vision.
Background
In recent years, the number of safety accidents of escalators is increasing, and the safe operation of escalators is closely related to the life and property safety of people and is highly valued by safety supervision departments in all circles of society all the time. Many escalators are operated all-weather for 24 hours, and carried passenger and article weight are difficult to the restriction, and escalator's component trouble can lead to the operation unusual, and because personnel fall down etc. artificial factors lead to the incident also can take place at any time, consequently, need establish one set of intelligent monitoring system, the all kinds of environmental information data of real time monitoring escalator to guarantee escalator's normal operating.
In order to reduce the safety accidents of the escalator, a plurality of monitoring systems such as video monitoring, load monitoring and the like are configured in a plurality of units. However, the existing monitoring systems cannot be linked and are independent of each other, the informatization and intellectualization levels of the systems are low, the identification and early warning of personnel behaviors are not accurate enough, and the safety accidents of the escalator cannot be prevented well.
In conclusion, the escalator safety monitoring system in the prior art has the problems of low overall intelligent level and inaccurate identification and early warning of unsafe behaviors of people.
Disclosure of Invention
The embodiment of the invention provides an escalator intelligent monitoring and management system based on computer vision, which is used for solving the problems of low overall intelligentization level and inaccurate identification and early warning of unsafe behaviors of people in the prior art.
The embodiment of the invention provides an escalator intelligent monitoring and management system based on computer vision, which comprises: the system comprises a plurality of fixed visual sensing devices (1), a plurality of infrared visual sensors (2), a tracking type visual sensing device (3), a photographic device (4), a 5G communication device (5) and a terminal processor (6), wherein each fixed visual sensing device (1) is electrically connected with the infrared visual sensors (2) in the corresponding area, the fixed visual sensing devices (1) are electrically connected with the tracking type visual sensing device (3), and the fixed visual sensing devices (1), the tracking type visual sensing device (3) and the photographic device (4) are electrically connected with the terminal processor (6) through the 5G communication device (5);
a plurality of the fixed visual sense sensing devices (1) are respectively arranged on the ceiling near the escalator, and the fixed visual sense sensing devices (1) comprise: the system comprises a monitoring sensing module (11), a moving target detection module (12), a moving target tracking module (13), a tumbling detection module (14) and an alarm module (15);
the monitoring sensing module (11) is used for monitoring various types of environmental information data around the escalator, comparing the acquired various types of environmental information data with the safety set values of the corresponding environmental information data respectively, and determining whether the corresponding types of environmental information data are in abnormal states; the moving target detection module (12) is used for processing the problems of slow moving of the moving target on the escalator and reduction of time delay of background sudden change, and detecting the moving target on the escalator; the fall detection module (14) is used for solving the problems of separation, adhesion and missed detection of moving targets on the escalator, accurately and stably tracking the targets on the escalator, and improving the reliability of extracting target characteristics; the alarm module (15) is used for sending out an early warning signal to personnel in a corresponding area when the environmental information safety data of the area where the monitoring sensing module (11) is located is abnormal;
the infrared vision sensors (2) are respectively arranged on the walls around the escalator, and the infrared vision sensors (2) are used for collecting position signals of people and articles;
track formula vision sensing device (3) are laid respectively on the ceiling directly over the automatic escalator, just track formula vision sensing device (3) include: the system comprises a foreground image extraction module (31), a pedestrian abnormal behavior detection module (32), an article abnormal behavior detection module (33) and an early warning prompt module (34); the foreground image extraction module (31) is used for removing the shadow of the foreground area generated along with the movement of the target; the pedestrian abnormal behavior detection module (32) is used for extracting the abnormal behavior characteristics of the personnel, so that the recognition rate of the real-time online detection of the abnormal behavior of the personnel is improved; the article abnormal behavior detection module (33) is used for extracting the article abnormal behavior characteristics, so that the identification rate of real-time online detection of the article abnormal behavior is improved; the early warning prompt module (34) is used for judging whether the escalator is in an abnormal state according to the environmental information safety data in the escalator area and sending an early warning prompt signal to personnel;
the photographic device (4) is arranged on the side wall of the inner side of the escalator and used for acquiring images of the environment where the personnel in the escalator area are located;
and the 5G communication device (5) is used for communication of the fixed visual sensing device (1), the tracking visual sensing device (3), the photographing device (4) and the terminal processor (6).
The terminal processor (6) comprises: the system comprises a falling behavior feature library construction module (61), a monitoring video offline analysis module (62), a behavior analysis matching module (63), a crowd crowding degree analysis module (64), an escalator control module (65) and a safety early warning module (66);
the fall behavior feature library construction module (61) comprises: an action behavior analysis unit (611), a characteristic value extraction unit (612) and a building and perfecting behavior model unit (613); the action behavior analysis unit (611) is used for processing a plurality of images which are continuous in time and realizing rapid and efficient behavior analysis by mining the incidence relation among the images; the characteristic value extraction unit (612) is used for extracting key frames in the video in real time, and can effectively extract image characteristics and finish classification identification; the building and perfecting behavior model unit (613) is used for building a standard library for normal and unsafe behaviors of people commonly seen in the escalator environment and comparing the standard library with the behaviors of people appearing in a real-time video;
the surveillance video offline analysis module (62) comprises: a monitoring video offline analysis unit (621), a picture and video analysis unit (622) and a characteristic value extraction unit (623); the monitoring video off-line analysis unit (621) is used for processing a plurality of images simultaneously through a convolutional neural network with a memory unit to complete the rapid and efficient analysis of mass behavior data; the picture and video analysis unit (622) is used for realizing background modeling through a statistical graph model, fusing the model with a convolutional neural network, realizing event detection under the background cutting-out condition and effectively improving the event detection rate and the event recognition rate; the characteristic value extraction unit (623) is used for extracting key frames in an offline video, and can effectively extract image characteristics and finish classification identification;
the behavior analysis matching module (63) is used for judging whether the escalator normally operates or not by comparing the behavior characteristics extracted from the real-time video or the historical video, and sending an electric signal to the safety early warning module (66) when unsafe behaviors occur; the crowd crowding degree analysis model (64) is used for calculating the personnel density of the escalator region so as to judge whether the escalator normally runs or not, and when the personnel density exceeds a normal range, an electric signal is sent to the safety early warning module (66);
the escalator control module (65) comprises a PLC control unit (651) and an escalator brake unit (652); the PLC control unit (651) is used for controlling the operation of the escalator; the escalator brake unit (652) is used for braking in an emergency state and preventing personnel from being injured when the escalator is in abnormal operation;
the safety early warning module (66) comprises a voice early warning unit (661), a display early warning unit (662) and a control room terminal early warning unit (663); the voice early warning unit (661) is used for sending out a voice early warning signal; the display early warning unit (662) is used for displaying the environmental information data of the personnel in the escalator area in real time and determining the early warning level of the corresponding environmental data type; and the control room terminal early warning unit (663) is used for judging the early warning level of the corresponding environment data type according to the environment data information of the personnel in the escalator region and sending out corresponding early warning information according to the corresponding early warning level.
Preferably, the tracking type vision sensing device (3) is arranged in a shock absorption protection bracket, and a flash lamp, a motor and an infrared receiver are arranged on the shock absorption protection bracket.
Preferably, the monitoring sensing module (11) comprises: the device comprises a sound sensor (111), a speed sensor (112), a power consumption monitor (113), a displacement sensor (114) and a vibration sensor (115).
Preferably, the early warning level includes: the escalator brake unit (652) comprises: a motor and a transmission system.
Preferably, the early warning level includes: a normal level and an exception level.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to an escalator intelligent monitoring system through a 5G communication device, and environmental parameters and unsafe behaviors of people of the escalator are monitored in real time through a fixed visual sensing device, a tracking visual sensing device and a photographic device which are arranged near the escalator, so that the linkage management of the monitoring system is realized, and the intelligent level of the system is improved; the early warning level of the escalator is determined by collecting environmental information data in the escalator area in real time and identifying unsafe behaviors of people in real time, and when the early warning level of the escalator is an abnormal level, the alarm module, the early warning prompt module and the safety early warning module can send out alarm signals, so that accurate early warning of abnormal operation of the escalator is realized, and safety accidents of the escalator can be effectively prevented.
Drawings
Fig. 1 is an overall schematic block diagram of an escalator intelligent monitoring management system based on computer vision according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a stationary visual sensing apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic block diagram of a tracking vision sensing apparatus according to an embodiment of the present invention;
FIG. 4 is a block diagram of a terminal processor according to an embodiment of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an escalator intelligent monitoring and management system based on computer vision provided by an embodiment of the present invention includes: the system comprises a plurality of fixed visual sensing devices (1), a plurality of infrared visual sensors (2), a tracking type visual sensing device (3), a photographic device (4), a 5G communication device (5) and a terminal processor (6), wherein each fixed visual sensing device (1) is electrically connected with the infrared visual sensors (2) in the corresponding area, the fixed visual sensing devices (1) are electrically connected with the tracking type visual sensing device (3), and the fixed visual sensing devices (1), the tracking type visual sensing device (3) and the photographic device (4) are electrically connected with the terminal processor (6) through the 5G communication device (5);
the invention relates to an escalator intelligent monitoring system through a 5G communication device, and environmental parameters and unsafe behaviors of people of the escalator are monitored in real time through a fixed visual sensing device 1, a tracking visual sensing device 3 and a photographic device 4 which are arranged near the escalator, so that the linkage management of the monitoring system is realized, and the intelligent level of the system is improved.
Referring to fig. 2, a plurality of the fixed visual sensing devices (1) in the embodiment of the present invention are respectively disposed on the ceiling near the escalator, and the fixed visual sensing devices (1) include: the system comprises a monitoring sensing module (11), a moving target detection module (12), a moving target tracking module (13), a tumbling detection module (14) and an alarm module (15).
It should be noted that the monitoring and sensing module (11) includes: the device comprises a sound sensor (111), a speed sensor (112), a power consumption monitor (113), a displacement sensor (114) and a vibration sensor (115). The tracking type vision sensing device (3) is arranged in a shock absorption protection support, and a flash lamp, a motor and an infrared receiver are arranged on the shock absorption protection support.
The specific functions of the modules in the fixed visual sensor device 1 are described as follows:
the monitoring sensing module (11) is used for monitoring various types of environmental information data around the escalator, comparing the acquired various types of environmental information data with the safety set values of the corresponding environmental information data respectively, and determining whether the corresponding types of environmental information data are in abnormal states; the moving target detection module (12) is used for processing the problems of slow moving of the moving target on the escalator and reduction of time delay of background sudden change, and detecting the moving target on the escalator; the fall detection module (14) is used for solving the problems of separation, adhesion and missed detection of moving targets on the escalator, accurately and stably tracking the targets on the escalator, and improving the reliability of extracting target characteristics; the alarm module (15) is used for sending out an early warning signal to personnel in a corresponding area when the environmental information safety data of the area where the monitoring sensing module (11) is located is abnormal;
it should be noted that the early warning levels include: a normal level and an exception level.
According to the invention, the early warning level of the escalator is determined by monitoring the environmental parameters and unsafe behaviors of people in the escalator area in real time, and when the early warning level of the escalator is an abnormal level, the alarm module, the early warning prompt module and the safety early warning module can send out alarm signals, so that the accurate early warning of abnormal operation of the elevator is realized, and the safety accident of the escalator can be effectively prevented.
The infrared vision sensors (2) are respectively arranged on the walls around the escalator, and the infrared vision sensors (2) are used for collecting position signals of people and articles;
referring to fig. 3, the tracking type visual sensing devices (3) in the embodiment of the present invention are respectively disposed on the ceiling right above the escalator, and the tracking type visual sensing devices (3) include: the system comprises a foreground image extraction module (31), a pedestrian abnormal behavior detection module (32), an article abnormal behavior detection module (33) and an early warning prompt module (34); the foreground image extraction module (31) is used for removing the shadow of the foreground area generated along with the movement of the target; the pedestrian abnormal behavior detection module (32) is used for extracting the abnormal behavior characteristics of the personnel, so that the recognition rate of the real-time online detection of the abnormal behavior of the personnel is improved; the article abnormal behavior detection module (33) is used for extracting the article abnormal behavior characteristics, so that the identification rate of real-time online detection of the article abnormal behavior is improved; and the early warning prompt module (34) is used for sending an early warning prompt signal to personnel when judging that the escalator is in an abnormal state according to the environmental information safety data in the escalator area.
It should be noted that the tracking type visual sensing device (3) is arranged in a shock absorption protection bracket, and a flash lamp, a motor and an infrared receiver are arranged on the shock absorption protection bracket.
According to the invention, the tracking type visual sensing device (3) arranged on the ceiling right above the escalator can effectively remove shadows generated by the movement of a foreground region along with a target, can extract abnormal behavior characteristics of personnel, classifies the extracted falling characteristics by using a support vector machine, trains a classifier, and displays the recognition rate of online monitoring of the personnel and articles in real time, so that the effective recognition and accurate early warning of unsafe behaviors of the personnel are realized, and the device is convenient and practical.
The photographic device (4) is arranged on the side wall of the inner side of the escalator and used for acquiring images of the environment where the personnel in the escalator area are located;
and the 5G communication device (5) is used for communication of the fixed visual sensing device (1), the tracking visual sensing device (3), the photographing device (4) and the terminal processor (6).
Referring to fig. 4, the terminal processor (6) includes: the system comprises a falling behavior feature library construction module (61), a monitoring video offline analysis module (62), a behavior analysis matching module (63), a crowd crowding degree analysis module (64), an escalator control module (65) and a safety early warning module (66);
the specific functions of the modules in the terminal processor (6) are described as follows:
the fall behavior feature library construction module (61) comprises: an action behavior analysis unit (611), a characteristic value extraction unit (612) and a building and perfecting behavior model unit (613); the action behavior analysis unit (611) is used for processing a plurality of images which are continuous in time and realizing rapid and efficient behavior analysis by mining the incidence relation among the images; the characteristic value extraction unit (612) is used for extracting key frames in the video in real time, and can effectively extract image characteristics and finish classification identification; the building and perfecting behavior model unit (613) is used for building a standard library for normal and unsafe behaviors of people commonly seen in the escalator environment and comparing the standard library with the behaviors of people appearing in a real-time video;
the surveillance video offline analysis module (62) comprises: a monitoring video offline analysis unit (621), a picture and video analysis unit (622) and a characteristic value extraction unit (623); the monitoring video off-line analysis unit (621) is used for processing a plurality of images simultaneously through a convolutional neural network with a memory unit to complete the rapid and efficient analysis of mass behavior data; the picture and video analysis unit (622) is used for realizing background modeling through a statistical graph model, fusing the model with a convolutional neural network, realizing event detection under the background cutting-out condition and effectively improving the event detection rate and the event recognition rate; the characteristic value extraction unit (623) is used for extracting key frames in an offline video, and can effectively extract image characteristics and finish classification identification;
the behavior analysis matching module (63) is used for judging whether the escalator normally operates or not by comparing the behavior characteristics extracted from the real-time video or the historical video, and sending an electric signal to the safety early warning module (66) when unsafe behaviors occur; the crowd crowding degree analysis model (64) is used for calculating the personnel density of the escalator region so as to judge whether the escalator normally runs or not, and when the personnel density exceeds a normal range, an electric signal is sent to the safety early warning module (66);
the escalator control module (65) comprises a PLC control unit (651) and an escalator brake unit (652); the PLC control unit (651) is used for controlling the operation of the escalator; the escalator brake unit (652) is used for braking in an emergency state and preventing personnel from being injured when the escalator is in abnormal operation;
the safety early warning module (66) comprises a voice early warning unit (661), a display early warning unit (662) and a control room terminal early warning unit (663); the voice early warning unit (661) is used for sending out a voice early warning signal; the display early warning unit (662) is used for displaying the environmental information data of the personnel in the escalator area in real time and determining the early warning level of the corresponding environmental data type; and the control room terminal early warning unit (663) is used for judging the early warning level of the corresponding environment data type according to the environment data information of the personnel in the escalator region and sending out corresponding early warning information according to the corresponding early warning level.
The invention collects the environmental information data of the personnel and the articles entering the escalator area through the computer vision technology and the radio frequency technology, can monitor various environmental information data of the escalator, can display the identification rate of the online monitoring of the personnel in real time, improves the accuracy rate of the identification of unsafe behaviors of the personnel, and carries out real-time monitoring and early warning on various types of environmental information data related to the escalator, the corresponding early warning level, the unsafe behaviors of the personnel, the abnormal falling of the articles and the abnormal operation of the escalator through visibility, thereby improving the monitoring accuracy rate and realizing the rapid, intelligent and linkage management of an escalator monitoring system.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (5)
1. The utility model provides an automatic staircase intelligent monitoring management system based on computer vision which characterized in that: the method comprises the following steps: the system comprises a plurality of fixed visual sensing devices (1), a plurality of infrared visual sensors (2), a tracking type visual sensing device (3), a photographic device (4), a 5G communication device (5) and a terminal processor (6), wherein each fixed visual sensing device (1) is electrically connected with the infrared visual sensors (2) in the corresponding area, the fixed visual sensing devices (1) are electrically connected with the tracking type visual sensing device (3), and the fixed visual sensing devices (1), the tracking type visual sensing device (3) and the photographic device (4) are electrically connected with the terminal processor (6) through the 5G communication device (5);
a plurality of the fixed visual sense sensing devices (1) are respectively arranged on the ceiling near the escalator, and the fixed visual sense sensing devices (1) comprise: the system comprises a monitoring sensing module (11), a moving target detection module (12), a moving target tracking module (13), a tumbling detection module (14) and an alarm module (15);
the monitoring sensing module (11) is used for monitoring various types of environmental information data around the escalator, comparing the acquired various types of environmental information data with the safety set values of the corresponding environmental information data respectively, and determining whether the corresponding types of environmental information data are in abnormal states; the moving target detection module (12) is used for processing the problems of slow moving of the moving target on the escalator and reduction of time delay of background sudden change, and detecting the moving target on the escalator; the fall detection module (14) is used for solving the problems of separation, adhesion and missed detection of moving targets on the escalator, accurately and stably tracking the targets on the escalator, and improving the reliability of extracting target characteristics; the alarm module (15) is used for sending out an early warning signal to personnel in a corresponding area when the environmental information safety data of the area where the monitoring sensing module (11) is located is abnormal;
the infrared vision sensors (2) are respectively arranged on the walls around the escalator, and the infrared vision sensors (2) are used for collecting position signals of people and articles;
track formula vision sensing device (3) are laid respectively on the ceiling directly over the automatic escalator, just track formula vision sensing device (3) include: the system comprises a foreground image extraction module (31), a pedestrian abnormal behavior detection module (32), an article abnormal behavior detection module (33) and an early warning prompt module (34); the foreground image extraction module (31) is used for removing the shadow of the foreground area generated along with the movement of the target; the pedestrian abnormal behavior detection module (32) is used for extracting the abnormal behavior characteristics of the personnel, so that the recognition rate of the real-time online detection of the abnormal behavior of the personnel is improved; the article abnormal behavior detection module (33) is used for extracting the article abnormal behavior characteristics, so that the identification rate of real-time online detection of the article abnormal behavior is improved; the early warning prompt module (34) is used for judging whether the escalator is in an abnormal state according to the environmental information safety data in the escalator area and sending an early warning prompt signal to personnel;
the photographic device (4) is arranged on the side wall of the inner side of the escalator and used for acquiring images of the environment where the personnel in the escalator area are located;
the 5G communication device (5) is used for communication of the fixed visual sensing device (1), the tracking visual sensing device (3), the photographing device (4) and the terminal processor (6);
the terminal processor (6) comprises: the system comprises a falling behavior feature library construction module (61), a monitoring video offline analysis module (62), a behavior analysis matching module (63), a crowd crowding degree analysis module (64), an escalator control module (65) and a safety early warning module (66);
the fall behavior feature library construction module (61) comprises: an action behavior analysis unit (611), a characteristic value extraction unit (612) and a building and perfecting behavior model unit (613); the action behavior analysis unit (611) is used for processing a plurality of images which are continuous in time and realizing rapid and efficient behavior analysis by mining the incidence relation among the images; the characteristic value extraction unit (612) is used for extracting key frames in the video in real time, and can effectively extract image characteristics and finish classification identification; the building and perfecting behavior model unit (613) is used for building a standard library for normal and unsafe behaviors of people commonly seen in the escalator environment and comparing the standard library with the behaviors of people appearing in a real-time video;
the surveillance video offline analysis module (62) comprises: a monitoring video offline analysis unit (621), a picture and video analysis unit (622) and a characteristic value extraction unit (623); the monitoring video off-line analysis unit (621) is used for processing a plurality of images simultaneously through a convolutional neural network with a memory unit to complete the rapid and efficient analysis of mass behavior data; the picture and video analysis unit (622) is used for realizing background modeling through a statistical graph model, fusing the model with a convolutional neural network, realizing event detection under the background cutting-out condition and effectively improving the event detection rate and the event recognition rate; the characteristic value extraction unit (623) is used for extracting key frames in an offline video, and can effectively extract image characteristics and finish classification identification;
the behavior analysis matching module (63) is used for judging whether the escalator normally operates or not by comparing the behavior characteristics extracted from the real-time video or the historical video, and sending an electric signal to the safety early warning module (66) when unsafe behaviors occur; the crowd crowding degree analysis module (64) is used for calculating the personnel density of the escalator area so as to judge whether the escalator normally runs or not, and when the personnel density exceeds a normal range, an electric signal is sent to the safety early warning module (66);
the escalator control module (65) comprises a PLC control unit (651) and an escalator brake unit (652); the PLC control unit (651) is used for controlling the operation of the escalator; the escalator brake unit (652) is used for braking in an emergency state and preventing personnel from being injured when the escalator is in abnormal operation;
the safety early warning module (66) comprises a voice early warning unit (661), a display early warning unit (662) and a control room terminal early warning unit (663); the voice early warning unit (661) is used for sending out a voice early warning signal; the display early warning unit (662) is used for displaying the environmental information data of the personnel in the escalator area in real time and determining the early warning level of the corresponding environmental data type; and the control room terminal early warning unit (663) is used for judging the early warning level of the corresponding environment data type according to the environment data information of the personnel in the escalator region and sending out corresponding early warning information according to the corresponding early warning level.
2. The escalator intelligent monitoring and management system based on computer vision according to claim 1, characterized in that the tracking vision sensing device (3) is arranged in a shock-absorbing protection bracket, and a flash lamp, a motor and an infrared receiver are arranged on the shock-absorbing protection bracket.
3. The escalator intelligent monitoring and managing system based on computer vision according to claim 1, characterized in that the monitoring sensing module (11) comprises: the device comprises a sound sensor (111), a speed sensor (112), a power consumption monitor (113), a displacement sensor (114) and a vibration sensor (115).
4. The computer vision-based escalator intelligent monitoring and management system according to claim 1, characterized in that, the escalator brake unit (652) comprises: a motor and a transmission system.
5. The computer vision-based escalator intelligent monitoring and management system according to claim 1, characterized in that said early warning level comprises: a normal level and an exception level.
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| CN113697652A (en) * | 2021-09-23 | 2021-11-26 | 佛山市和众电梯技术有限公司 | Escalator and moving sidewalk safe operation monitoring device and monitoring method |
| CN114998828B (en) * | 2022-05-18 | 2023-04-07 | 慧之安信息技术股份有限公司 | Offshore platform personnel stair ascending and descending management method based on Internet of things platform |
| CN117557949A (en) * | 2023-04-12 | 2024-02-13 | 无锡八英里电子科技有限公司 | Escalator safety device based on image recognition edge calculation |
| CN116434145B (en) * | 2023-04-21 | 2024-04-16 | 北京日立电梯工程有限公司 | Escalator passenger dangerous behavior analysis and monitoring system based on image recognition |
| CN116206266B (en) * | 2023-05-06 | 2023-08-15 | 济宁盛世照明电器有限公司 | Vision counterpoint system based on capper |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1013599A1 (en) * | 1998-12-21 | 2000-06-28 | Inventio Ag | Safety device for an escalator or a moving walkway |
| CN207632306U (en) * | 2017-08-03 | 2018-07-20 | 深圳市天俊智能卡有限公司 | A kind of escalator Monitoring System |
| JP2019018993A (en) * | 2017-07-21 | 2019-02-07 | 三菱電機ビルテクノサービス株式会社 | Passenger conveyor alarm system |
| CN110002329A (en) * | 2018-12-29 | 2019-07-12 | 广州地铁设计研究院股份有限公司 | A kind of escalator on-line monitoring early warning system and method based on cloud platform |
| CN110203803A (en) * | 2019-06-06 | 2019-09-06 | 快意电梯股份有限公司 | Escalator safeguard method and device based on AI intelligent monitoring |
| CN111517204A (en) * | 2020-05-08 | 2020-08-11 | 广东省特种设备检测研究院佛山检测院 | Escalator safety monitoring method, device, equipment and readable storage medium |
| CN111731979A (en) * | 2020-07-03 | 2020-10-02 | 安徽理工大学 | Automatic staircase takes advantage of personnel identification system that tumbles based on computer vision |
-
2020
- 2020-12-18 CN CN202011499776.4A patent/CN112623919B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1013599A1 (en) * | 1998-12-21 | 2000-06-28 | Inventio Ag | Safety device for an escalator or a moving walkway |
| JP2019018993A (en) * | 2017-07-21 | 2019-02-07 | 三菱電機ビルテクノサービス株式会社 | Passenger conveyor alarm system |
| CN207632306U (en) * | 2017-08-03 | 2018-07-20 | 深圳市天俊智能卡有限公司 | A kind of escalator Monitoring System |
| CN110002329A (en) * | 2018-12-29 | 2019-07-12 | 广州地铁设计研究院股份有限公司 | A kind of escalator on-line monitoring early warning system and method based on cloud platform |
| CN110203803A (en) * | 2019-06-06 | 2019-09-06 | 快意电梯股份有限公司 | Escalator safeguard method and device based on AI intelligent monitoring |
| CN111517204A (en) * | 2020-05-08 | 2020-08-11 | 广东省特种设备检测研究院佛山检测院 | Escalator safety monitoring method, device, equipment and readable storage medium |
| CN111731979A (en) * | 2020-07-03 | 2020-10-02 | 安徽理工大学 | Automatic staircase takes advantage of personnel identification system that tumbles based on computer vision |
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