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A monitoring method for artificial intelligence video recognition of belt deviation

A video recognition and artificial intelligence technology, applied in conveyor objects, conveyor control devices, transportation and packaging, etc., can solve the problems of time-consuming, labor-intensive, safety and stability

Active Publication Date: 2021-04-13
JINGYING SHUZHI TECH HLDG CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the defects of time-consuming, labor-intensive and weak security and stability in the prior art, the present invention proposes a monitoring method for belt deviation by artificial intelligence video recognition which saves time and labor and can monitor belt deviation in real time

Method used

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  • A monitoring method for artificial intelligence video recognition of belt deviation
  • A monitoring method for artificial intelligence video recognition of belt deviation
  • A monitoring method for artificial intelligence video recognition of belt deviation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] like Figures 1 to 4 As shown, the artificial intelligence video recognition method for monitoring the belt deviation of this embodiment firstly selects no less than two idlers 3 on both sides of the belt 2 as more than two tracking targets 4, and then passes the mine high-definition explosion-proof camera. 1 Real-time acquisition of the tracking target video, mine high-definition explosion-proof camera 1 transmits the tracking target video to the server in real time through the network, and the server analyzes the tracking target video through the artificial intelligence video recognition model. When the tracking target 4 is blocked, it is detected whether it is blocked by the belt 2. In order to track target 4, the detection result is that the occlusion caused by the belt 2 determines the deviation of the belt 2, and the detection result is not the occlusion caused by the belt 2, and it is not determined that the belt 2 deviates.

[0023] Preferably, the tracking targ...

Embodiment 2

[0031] like Figures 1 to 4 As shown, the artificial intelligence video recognition monitoring method of belt deviation in this embodiment firstly selects no less than two outer ends of idlers 3 on both sides of the belt 2 as more than two tracking targets 4, and then passes the mining high-definition Explosion-proof camera 1 collects and tracks the target video in real time, mine HD explosion-proof camera 1 transmits the tracking target video to the server in real time through the network, and the server analyzes the tracking target video through the artificial intelligence video recognition model. When the tracking target 4 is blocked, it detects whether it is a belt 2. The tracking target 4 is blocked. The detection result is that the blocking caused by the belt 2 determines the deviation of the belt 2. The detection result is not the blocking caused by the belt 2, and it is not determined that the belt 2 is running.

[0032] Preferably, the tracking target 4 is no less tha...

Embodiment 3

[0040] like Figures 1 to 4 As shown, the artificial intelligence video recognition monitoring method of belt deviation in this embodiment firstly selects no less than two outer ends of idlers 3 on both sides of the belt 2 as more than two tracking targets 4, and then passes the mining high-definition Explosion-proof camera 1 collects and tracks the target video in real time, mine HD explosion-proof camera 1 transmits the tracking target video to the server in real time through the network, and the server analyzes the tracking target video through the artificial intelligence video recognition model. When the tracking target 4 is blocked, it detects whether it is a belt 2. The tracking target 4 is blocked. The detection result is that the blocking caused by the belt 2 determines the deviation of the belt 2. The detection result is not the blocking caused by the belt 2, and it is not determined that the belt 2 is running.

[0041] Preferably, the tracking target 4 is no less tha...

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Abstract

The invention discloses a method for monitoring belt deviation by artificial intelligence video identification, which comprises the following steps: firstly select no less than two rollers on both sides of the belt as more than two tracking targets, and then use a mine-used high-definition explosion-proof camera Real-time acquisition of tracking target video, mining high-definition explosion-proof camera transmits tracking target video to the server in real time through the network, the server analyzes the tracking target video through artificial intelligence video recognition model, when the tracking target is blocked, detects whether the belt is blocking the tracking target, If the detection result is the occlusion caused by the belt, the belt deviation is determined. If the detection result is not the occlusion caused by the belt, it is not judged as the belt deviation. The present invention aims to use artificial intelligence technology to solve the problem of belt running detection, detect and alarm in time when the belt deviation occurs during the belt running process, is applicable to various belt transportation scenarios, and reduces manual operations on the timing and effectiveness of existing detection sensors, thereby Ensure the safety of coal mine operations while reducing the waste of manpower and time.

Description

technical field [0001] The invention belongs to the field of belt transportation monitoring, and particularly relates to a monitoring method for identifying belt deviation through artificial intelligence video. Background technique [0002] Conveying coal belt conveyor is the main equipment for transporting materials in coal mines. During the long-term operation of the belt conveyor, the deviation of the belt is a common fault. When the deviation of the belt reaches a certain level, the belt will trigger the emergency stop device for deviation prevention, causing the operation system to stop and affecting the production process; causing abnormal damage to the main components of the equipment; it is easy to cause potential safety hazards; due to the serious deviation of the belt, It causes the belt to roll over the material, causing the unilateral force of the belt to exceed the longitudinal breaking force of the belt, thereby causing potential safety hazards such as lateral...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B65G43/02
CPCB65G43/02B65G2203/0283B65G2203/041
Inventor 吴喆峰曹凌基朱晓宁
Owner JINGYING SHUZHI TECH HLDG CO LTD
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