Monitoring method for identifying belt coal piling through artificial intelligent video

Abstract

The invention discloses a monitoring method for identifying belt coal piling through an artificial intelligent video. The monitoring method comprises the following steps: arranging a warning line among a belt coal drop and a transferring point, and a belt; then acquiring a warning line video in real time through a mineral high-definition and explosion-proof camera; transmitting the warning line video to a server in real time by the mineral high-definition and explosion-proof camera through a network; analyzing the warning line video by the server through an artificial intelligent video identification model; when the warning line is shielded, detecting whether a target is shielded by coal piling or not; determining the belt coal piling when a detection result is that the shielding is causedby the coal piling; and judging that the belt coal piling is not caused when the detection result is that the shielding is not caused by the coal piling. The monitoring method disclosed by the invention aims at solving the problem of belt operation detection by utilizing an artificial intelligent technology; a belt coal piling phenomenon in a belt operation process is found and alarmed in time; the monitoring method is applicable to various belt conveying scenes and fixed-time correction operation of manpower on an existing detection sensor is reduced; and meanwhile, wastes of manpower and time are reduced.

Description

technical field [0001] The invention belongs to the field of belt transportation monitoring, in particular to an artificial intelligence video recognition monitoring method for belt piled coal. Background technique [0002] Coal conveying belt conveyor is the main device for transporting materials in coal mines. During the long-term operation of belt conveying, belt coal piles are frequent failures. Belt piles of coal cause system failures to stop and affect production efficiency; Belt fire and other potential safety hazards; when the belt is heavily piled with coal, the material will be jammed and blocked, which will easily cause the belt to tear. Between the reloading point and the belt at the front of the coal chute, the belt is torn when it is squeezed; as well as the reprinting point, the coal scatter outside the coal hole, etc. are not conducive to safe production, which brings great hidden dangers to safe production. . [0003] At present, coal pile detection mostly...

AI Technical Summary

Problems solved by technology

[0013] In order to overcome the defects of time-consuming, labor-intensive and weak safety and stability of the prior art, the present invention provides a monitoring method for coal-belt piles by artificial intelligence video recognition which saves time and labor and can monitor the coal piles in real time.

Images