CN203325007U - Mode identification apparatus and system of fully mechanized coal mining face abnormal statuses - Google Patents
Mode identification apparatus and system of fully mechanized coal mining face abnormal statuses Download PDFInfo
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- CN203325007U CN203325007U CN2013202867233U CN201320286723U CN203325007U CN 203325007 U CN203325007 U CN 203325007U CN 2013202867233 U CN2013202867233 U CN 2013202867233U CN 201320286723 U CN201320286723 U CN 201320286723U CN 203325007 U CN203325007 U CN 203325007U
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Abstract
The utility model discloses a mode identification apparatus and system of fully mechanized coal mining face abnormal statuses. The mode identification apparatus of coal mining face abnormal statuses extracts images from collected video information in fully mechanized coal mining faces, analyzes the extracted images and obtains real-time safe case information of the fully mechanized coal mining faces. If the collected information shows security abnormity of the fully mechanized coal mining faces, the abnormal information is sent to other monitoring units and alarm information is emitted and / or an automatic emergency system is started.
Description
Technical field
The utility model relates to mine safety, coal mining and mechanical automation technical field, particularly a kind of pattern recognition device of fully-mechanized mining working unusual condition and system.
Background technology
Under mine fully-mechanized mining working because the changeable complexity of geologic condition, environment badly exist many such as roof fall, wall caving, catch fire, the dangerous matter sources of ponding, brought very large hidden danger to mine operation and downhole production personnel's personal safety.And intelligentized fully-mechanized mining working supervisory system will make borehole operation safe and the few people of security monitoring change into as possibility.Wherein video monitoring is a kind of mode that realizes the intellectual comprehensive mechanized mining face monitoring.
In at present common prior art, having a kind of is to utilize the coalcutter positioning system to follow the tracks of coalcutter, show the real-time working scene that coalcutter is mined by the video camera in the Switch Video acquisition system in ground display system, thereby reach the purpose with the machine video monitoring.Another kind of technical scheme is that video camera is arranged on rocker arm of coal mining machine, working condition when Real-Time Monitoring is mined, and send when abnormal conditions and remind and report to the police.Although technique scheme has solved the real-time video monitoring problem for the operating mode at coal winning machine position place, it remains in fact by manually watching the guarded region image.If monitor a plurality of zones in long-armed fully-mechanized mining working simultaneously, still need the within a short period of time of the picture of switching monitoring video camera seat in the plane to show that different seat in the plane is monitored rapidly, whether there is security exception by artificial judgment.This has just caused prior art higher for artificial requirement, and easily occurs that in the Picture switch process monitoring is omitted or the discovery dangerous situation waits situation not in time.
Therefore, developing a kind of technology of can automatic real time monitoring and identifying the unusual condition of optional position in fully-mechanized mining working is very important.
The utility model content
In order to address the above problem, the utility model proposes a kind of brand-new fully-mechanized mining working unusual condition pattern recognition device and system, by the image to the video monitoring system collection, analyzed, realized the automatic real time monitoring to full fully-mechanized mining working.
The utility model device particular content is as follows:
A kind of pattern recognition device of fully-mechanized mining working unusual condition, comprise video decode module, image processing module and information transfer module;
Described video decode module contacts by described information transfer module and the external world, receives the video information of workplace video monitoring unit transmission and is decoded, and extracts the image in video information;
The image that described image processing module receiver, video decoder module extracts extracts characteristic parameter information and carries out the pattern-recognition computing with prestored information, thereby judging whether to exist security exception from image information;
Described image processing module sends to other monitoring units by judged result by information transfer module after to the image information analyzing and processing.
The utility model system particular content is as follows:
A kind of pattern recognition system of fully-mechanized mining working unusual condition, comprise workplace video monitoring unit, workplace unusual condition pattern recognition device, other monitoring units;
The video information of the full fully-mechanized mining working of described workplace video monitoring unit collection also is sent to described workplace unusual condition pattern recognition device, described workplace unusual condition pattern recognition device receives described video information, therefrom extract image, extract characteristic parameter information and carry out the pattern-recognition computing with prestored information from image information, thereby judge whether to exist security exception, if exist, extremely abnormal information is sent to described other monitoring units;
Described other monitoring units receive described abnormal information and send warning message.
With respect to prior art, the beneficial effects of the utility model are: the utility model makes supervisory system can automatically identify monitoring area whether to have potential safety hazard by install workplace unusual condition pattern recognition device additional in supervisory system, greatly reduce underground safety monitoring to artificial degree of dependence in, realized the automatic real time monitoring to arbitrary region in whole long-armed fully-mechanized mining working, and solved the discovery dangerous situation easily occurred in the manual monitoring and wait not in time problem.
The accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, below will the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described, apparently, the accompanying drawing the following describes is only some embodiment that put down in writing in the application, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the utility model method the first embodiment process flow diagram;
Fig. 2 is the utility model method the second embodiment process flow diagram;
The embodiment schematic diagram that Fig. 3 is the utility model device;
The embodiment schematic diagram that Fig. 4 is the utility model system.
Embodiment
In order to make those skilled in the art person understand better the utility model scheme, below in conjunction with the accompanying drawing in the utility model embodiment, technical scheme in the utility model embodiment is clearly and completely described, obviously, described embodiment is only the utility model part embodiment, rather than whole embodiment.Embodiment based in the utility model, those of ordinary skills are not making under the creative work prerequisite the every other embodiment obtained, and all belong to the scope of the utility model protection.
Fig. 1 is the utility model method the first embodiment process flow diagram, and as shown in Figure 1, the utility model method comprises the steps:
Step 101: workplace video monitoring unit obtains video information; Described workplace video monitoring system can be one or more explosion-proof web cameras, and video camera is taken the image information in its zone of monitoring.
Step 102: workplace video monitoring unit is sent to workplace unusual condition pattern recognition device by described video information; Explosion-proof web camera is sent to workplace unusual condition pattern recognition device by the image information of shooting by Ethernet.
Step 103: described pattern recognition device extracts image from video information; The described image that extracts from video information can be that extraction one frame can be also to extract multiple image, and the image of extraction need to be enough to identify its viewing area and whether have security exception.
Step 104: described pattern recognition device extracts characteristic parameter information from image information, and carries out the pattern-recognition computing with the mathematical model information prestored and judge whether to exist security exception;
Can use one or more algorithms in neural network algorithm, hidden Markov algorithm, genetic algorithm to carry out characteristic parameter extraction and Model Matching computing, mathematical model when described and the mathematical model prestored are mated mathematical model while training various unusual condition of the image information that refers in advance the various different fully-mechanized mining working unusual conditions that will obtain and image information under normal circumstances and normal condition, and pre-stored in the middle of described pattern recognition device, when the Model Matching computing, the characteristic parameter information that to extract from image information is carried out matching operation with each mathematical model prestored with corresponding algorithm, the model classification that matching degree is the highest is exactly the workplace situation classification of this image information, comprise normal, wall caving, roof fall, catch fire, ponding etc.
Under actual production work condition, the image that the workplace video monitoring system is obtained may not directly be analyzed, and sometimes before analysis, also needs extracted image is done to some works for the treatment of in advance; After pattern recognition device judges the security situation of guarded region, according to the difference of judged result, also should make different reactions; In addition, also have some preferred versions at some details places.Therefore, the second embodiment of the utility model method is proposed at this.
The another kind of embodiment process flow diagram that Fig. 2 is the utility model method, as shown in Figure 2:
Step 201: workplace video monitoring unit obtains image information; Workplace video monitoring unit can be mounted in the explosion-proof web camera on hydraulic support, and the explosion-proof web camera of installation can be one also can be several, also can on a plurality of supports, install; The web camera of installing is taken the image information in its guarded region.
Step 202: send video information to workplace situation pattern recognition unit; Described explosion-proof web camera is sent to workplace unusual condition pattern recognition device by Ethernet by the video information photographed;
Step 203: workplace situation pattern recognition unit is extracted image from video information; The image extracted can be a frame or multiframe, and the requirement that can meet the needed image of later analysis with the image extracted is as the criterion;
Step 204: extracted image is carried out to pre-service; Because may there be the problem that some can the impact analysis result precisions in the image extracted, therefore in order to make analysis result more accurately will carry out some pre-service, that pre-service work comprises is anti-shake, enhancing etc.;
Step 205: pattern recognition device extracts characteristic parameter information from image information, and carries out the pattern-recognition computing with the mathematical model information prestored and judge whether to exist security exception;
In described pattern recognition device, storing the mathematical model information that means various potential safety hazards, these mathematical model information are to adopt respective algorithms to train respectively acquisition by the various image information of potential safety hazard and the image informations of normal condition of existing that gather in advance; The picture that pattern recognition device utilizes one or more algorithms in neural network algorithm, hidden Markov algorithm or genetic algorithm to show image carries out the characteristic parameter information extraction;
The characteristic parameter information that to extract from image information is carried out matching operation with each mathematical model prestored with corresponding algorithm, the model classification that matching degree is the highest is exactly the workplace situation classification of this image information, thoroughly does away with the highest model classification of matching degree and judges whether to exist security exception;
As judge the no abnormal next monitoring circulation that enters, as noting abnormalities, judgement enters step 206;
Step 206: abnormal conditions information is sent to other monitoring units;
Pattern recognition device sends to face support controller by abnormal information by the CAN bus, and face support controller gives the alarm and abnormal information is sent to the down-hole centralized control room after receiving abnormal information; The down-hole centralized control room gives the alarm and display display frame is switched to the video camera place that abnormal information occurs after receiving abnormal information, then abnormal information is sent to integrated monitoring unit, ground; Integrated monitoring unit, ground gives the alarm and display display frame is switched to the video camera place that abnormal information occurs after receiving abnormal information.
In addition, step 206 can also be by described pattern recognition unit respectively to the one or more direct transmission abnormal information in described bracket controller, down-hole centralized control room, integrated monitoring unit, ground; Bracket controller is receiving that abnormal information gives the alarm, down-hole centralized control room and/or integrated monitoring unit, ground can only give the alarm, can only display display frame be switched to the video camera place that abnormal information occurs after receiving abnormal information, also can give the alarm and also display display frame be switched to the video camera place that abnormal information occurs.
Step 207: the unmanned intervention of alarm starts the automatic emergency device;
After described bracket controller gives the alarm; In described down-hole centralized control room, integrated monitoring unit, ground one or more give the alarm or switching display display frame to the video camera place that abnormal information occurs; Intervene as unmanned, start the automatic emergency device, described automatic emergency device includes but not limited to fire extinguishing system, unwatering system, apparatus of oxygen supply.
In order better to realize the utility model method, the utility model also provides a kind of embodiment of fully-mechanized mining working unusual condition pattern recognition device.
Fig. 3 is the utility model device embodiment schematic diagram, and as shown in the figure, the utility model device comprises:
After 302 pairs of image information analyzing and processing of described image processing module, judged result is sent to other monitoring units by described information transfer module 303.
In order better to realize the utility model intention, better apply the utility model device, the utility model also provides a kind of embodiment of fully-mechanized mining working unusual condition pattern recognition system.
Fig. 4 is the utility model system embodiment schematic diagram, and as shown in the figure, the utility model system comprises:
Workplace video monitoring unit 401, workplace unusual condition recognition device 402, other monitoring units 403;
Workplace video monitoring unit 401 gathers the video information of full fully-mechanized mining working and is sent to workplace unusual condition pattern recognition device 402;
Workplace unusual condition pattern recognition device 402 receives described video information, therefrom extract image, then extract characteristic parameter information from image information, and carry out matching judgment with the mathematical model information prestored and whether have security exception, extremely abnormal information is sent to described other monitoring units 403 if exist; Described other monitoring units 403 comprise the one or more unit in face support controller 4031, down-hole centralized control room 4032, integrated monitoring unit, ground 4033.
Described other monitoring units receive described abnormal information and send warning message;
Preferably, described other monitoring units are intervened as unmanned after giving the alarm, and start the automatic emergency device.
It should be noted that, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby make the process, method, article or the equipment that comprise a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or also be included as the intrinsic key element of this process, method, article or equipment.In the situation that not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment that comprises described key element and also have other identical element.
For system embodiment, because it corresponds essentially to embodiment of the method, so relevant part gets final product referring to the part explanation of embodiment of the method.System embodiment described above is only schematic, the wherein said unit as the separating component explanation can or can not be also physically to separate, the parts that show as unit can be or can not be also physical locations, can be positioned at a place, or also can be distributed on a plurality of network element.Can select according to the actual needs some or all of module wherein to realize the purpose of the present embodiment scheme.Those of ordinary skills in the situation that do not pay creative work, can understand and implement.
The above is only embodiment of the present utility model; it should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the utility model principle; can also make some improvements and modifications, these improvements and modifications also should be considered as protection domain of the present utility model.
Claims (6)
1. the pattern recognition device of a fully-mechanized mining working unusual condition, is characterized in that comprising video decode module, image processing module and information transfer module;
Described video decode module contacts by described information transfer module and the external world, receives the video information of workplace video monitoring unit transmission and is decoded, and extracts the image in video information;
The image that described image processing module receiver, video decoder module extracts extracts characteristic parameter information and carries out the pattern-recognition computing with prestored information, thereby judging whether to exist security exception from image information;
Described image processing module sends to other monitoring units by judged result by information transfer module after to the image information analyzing and processing.
2. device according to claim 1, is characterized in that, described image processing module comprises dsp chip or micro-chip processor.
3. according to claim 1,2 described devices, it is characterized in that, described information transfer module comprises one or more in Ethernet interface or CAN controller.
4. the pattern recognition system of a fully-mechanized mining working unusual condition, is characterized in that, comprises workplace video monitoring unit, workplace unusual condition pattern recognition device, other monitoring units;
Described workplace video monitoring unit gathers the video information of fully-mechanized mining working and is sent to described workplace unusual condition pattern recognition device, described workplace unusual condition pattern recognition device receives described video information, therefrom extract image, extract characteristic parameter information and carry out the pattern-recognition computing with prestored information from image information, thereby judge whether to exist security exception, if exist, extremely abnormal information is sent to described other monitoring units;
Described other monitoring units receive described abnormal information and send warning message.
5. system according to claim 4, is characterized in that, described other monitoring units comprise one or more in bracket controller, down-hole centralized control room, integrated monitoring unit, ground.
6. system according to claim 5, is characterized in that, one or more in described bracket controller, down-hole centralized control room, integrated monitoring unit, ground after sending warning message as unmanned the intervention, start the automatic emergency system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2013202867233U CN203325007U (en) | 2013-05-23 | 2013-05-23 | Mode identification apparatus and system of fully mechanized coal mining face abnormal statuses |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2013202867233U CN203325007U (en) | 2013-05-23 | 2013-05-23 | Mode identification apparatus and system of fully mechanized coal mining face abnormal statuses |
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| CN203325007U true CN203325007U (en) | 2013-12-04 |
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| CN2013202867233U Expired - Lifetime CN203325007U (en) | 2013-05-23 | 2013-05-23 | Mode identification apparatus and system of fully mechanized coal mining face abnormal statuses |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103248878A (en) * | 2013-05-23 | 2013-08-14 | 南车株洲电力机车有限公司 | Pattern recognition method, device and system of abnormal situation of fully mechanized coal mining face |
-
2013
- 2013-05-23 CN CN2013202867233U patent/CN203325007U/en not_active Expired - Lifetime
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103248878A (en) * | 2013-05-23 | 2013-08-14 | 南车株洲电力机车有限公司 | Pattern recognition method, device and system of abnormal situation of fully mechanized coal mining face |
| CN103248878B (en) * | 2013-05-23 | 2016-03-09 | 南车株洲电力机车有限公司 | A kind of mode identification method, equipment and system of fully-mechanized mining working unusual condition |
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Granted publication date: 20131204 |
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