CN111126193A - Artificial intelligence recognition system based on deep learning coal mine underground unsafe behavior - Google Patents
Artificial intelligence recognition system based on deep learning coal mine underground unsafe behavior Download PDFInfo
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- CN111126193A CN111126193A CN201911259677.6A CN201911259677A CN111126193A CN 111126193 A CN111126193 A CN 111126193A CN 201911259677 A CN201911259677 A CN 201911259677A CN 111126193 A CN111126193 A CN 111126193A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
Abstract
The invention discloses an artificial intelligence recognition system for underground unsafe behaviors of a coal mine based on deep learning, which comprises a mine field example material collection, a training data set, an AI training platform, an algorithm model, a control platform and the like, wherein the training data set generated by the mine field example material collection is loaded through the AI training platform based on the deep learning technology, the underground unsafe behaviors of the coal mine are trained, after an algorithm model is obtained, the algorithm model is directly led into an AI camera or NVR through the control platform, the AI camera or NVR identifies the trained unsafe behaviors through the artificial intelligence analysis of real-time video flow, the inference results are pushed to the control platform, the control platform pushes the unsafe information to an administrator, the unsafe information is audited and communicated with the field to confirm the unsafe behaviors, the unsafe behaviors are stopped and the control platform pushes the unsafe information to an unsafe place at the same time of the administrator, the scene is warned by sound and light; the accident is reduced, and the application range is wide.
Description
Technical Field
The invention relates to an artificial intelligence recognition system for underground unsafe behaviors of a coal mine based on deep learning, and belongs to the field of artificial intelligence.
Background
Most coal mines in China have the characteristics of complex geological conditions, high mining difficulty, multiple disaster types, wide distribution range and the like, and the hidden dangers of unsafe behaviors of people, unsafe states of objects and the like are numerous, so that coal mine safety production accidents happen occasionally, and human casualties are caused. As the coal mine has multiple points and wide links under the coal mine, a field security supervisor cannot meet the requirements and cannot stare at a certain place within 24 hours, the safety supervision blind points and risk points are multiple, and the coal mine safety management cost is increased.
Disclosure of Invention
The invention aims to provide an artificial intelligence recognition system for underground unsafe behaviors of a coal mine based on deep learning, and aims to solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the following technical scheme: an artificial intelligence identification system based on deep learning coal mine underground unsafe behaviors comprises: mine site instance material collection (S1), training data sets (S2), AI training platform (S3), algorithm model (S4), control platform (S5), site acousto-optic warning (S6), AI camera/NVR (S7), unsafe behavior information auditing (S8) and communication (S9);
the method comprises the steps that an AI training platform (S3) is an artificial intelligence training platform based on deep learning, unsafe behaviors in a coal mine are collected through mine site example material collection (S1), a training data set (S2) is generated, the training data set is uploaded to the AI training platform (S3), the AI training platform (S3) carries out classification training on the unsafe behavior materials in the coal mine through loading the training data set (S2), and a coal mine unsafe algorithm model (S4) can be obtained through training and checking the unsafe materials in the coal mine;
the AI camera/NVR (S7), the scene acousto-optic warning (S6) and the unsafe behavior information auditing (S8) can be controlled through the control platform (S5), real-time video streams and videos of the AI camera/NVR (S7) can be read, and iterative training of the algorithm model can be realized through the control platform (S5).
As a further scheme of the invention, an underground coal mine unsafe algorithm model (S4) is obtained through an AI training platform (S3), and is led into an AI camera/NVR (S7) through a control platform (S5) or is directly led into the AI camera/NVR (S7), so that the AI camera/NVR (S7) has the capability of intelligently identifying the underground unsafe behavior of the trained coal mine.
As a further scheme of the invention, when the AI camera/NVR (S7) intelligently identifies the unsafe behavior of the underground coal mine to be trained, the control platform (S5) receives the inference result by pushing the inference result to the control platform (S5), the control platform (S5) pushes the unsafe information to the administrator for auditing the unsafe behavior information (S8), the administrator communicates with the site where the unsafe behavior occurs (S9) to ensure that the unsafe behavior is effectively prevented, the control platform pushes the unsafe behavior information to the administrator and simultaneously pushes the site acousto-optic warning (S6) to the site where the unsafe behavior occurs, and the site where the unsafe behavior occurs is warned by sound and light to achieve the purpose of preventing the unsafe behavior.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, based on the intelligent analysis requirement of unsafe behaviors of coal mine enterprises, an artificial intelligent identification system of unsafe behaviors of coal mine underground is constructed by utilizing an AI open platform based on deep learning, the unsafe behaviors of coal mine underground can be detected through video stream, and warning information is sent out, so that the dilemma encountered in the safety production management process of coal mine enterprises is solved, the safety management investment is reduced, the intelligent management level of safety production is improved, and finally the occurrence of accidents is reduced. Meanwhile, the invention can intelligently identify unsafe behaviors through video streaming, record the unsafe behaviors and warn the unsafe behaviors on site, and has wide application range.
Drawings
FIG. 1 is a schematic block diagram of the system of the present invention.
In the figure: s1, mine site example material collection, S2, a training data set, S3, an AI training platform, S4, an algorithm model, S5, a control platform, S6, site acousto-optic warning, S7, an AI camera/NVR, S8, unsafe behavior information auditing, S9 and communication.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely explained below with reference to the drawings in the embodiments of the present invention.
An artificial intelligence identification system based on deep learning coal mine underground unsafe behaviors comprises: mine site instance material collection (S1), training data sets (S2), AI training platform (S3), algorithm model (S4), control platform (S5), site acousto-optic warning (S6), AI camera/NVR (S7), unsafe behavior information auditing (S8) and communication (S9);
the method comprises the steps that an AI training platform (S3) is an artificial intelligence training platform based on deep learning, unsafe behaviors in a coal mine are collected through mine site example material collection (S1), a training data set (S2) is generated, the training data set is uploaded to the AI training platform (S3), the AI training platform (S3) carries out classification training on the unsafe behavior materials in the coal mine through loading the training data set (S2), and a coal mine unsafe algorithm model (S4) can be obtained through training and checking the unsafe materials in the coal mine;
the AI camera/NVR (S7), the scene acousto-optic warning (S6) and the unsafe behavior information auditing (S8) can be controlled through the control platform (S5), real-time video streams and videos of the AI camera/NVR (S7) can be read, and iterative training of the algorithm model can be realized through the control platform (S5).
And obtaining an underground coal mine unsafe algorithm model (S4) through an AI training platform (S3), and leading the model into an AI camera/NVR (S7) through a control platform (S5) or directly leading the model into the AI camera/NVR (S7), so that the AI camera/NVR (S7) has the capability of intelligently identifying the underground coal mine unsafe behavior trained.
When the AI camera/NVR (S7) intelligently identifies the unsafe behavior of the underground coal mine to be trained, the control platform (S5) pushes the reasoning result, the control platform (S5) receives the reasoning result, the unsafe information is pushed to the administrator to verify the unsafe behavior information (S8), the administrator communicates with the site where the unsafe behavior occurs (S9) to ensure that the unsafe behavior is effectively prevented, the control platform pushes the unsafe behavior information to the administrator and simultaneously pushes the site acousto-optic alarm (S6) to the site where the unsafe behavior occurs, and the site where the unsafe behavior occurs is warned by sound and light, so that the purpose of preventing the unsafe behavior is achieved.
The identification method of the invention comprises the following steps:
the method comprises the steps of material collection, unsafe behavior model training, unsafe behavior algorithm model loading and unsafe behavior identification.
1. Collecting materials;
the method comprises the steps of firstly determining underground unsafe behaviors of the coal mine, such as the underground coal mine without wearing safety helmets, mining lamps, dust masks, coats, rubber boots, safety belts, pedestrians during driving, off-duty monitoring of equipment operators and the like, and then collecting mine field example materials according to different underground unsafe behaviors of the coal mine to generate a training data set.
2. Training an unsafe behavior algorithm model;
firstly, loading a training data set on an AI training platform based on a deep learning technology, classifying different unsafe behaviors, and training an algorithm only aiming at a region by setting the region of interest. And through training and checking of the underground unsafe behavior materials of the coal mine, an underground unsafe behavior algorithm model of the coal mine can be generated.
3. Loading an unsafe behavior model;
and after the training of the unsafe behavior model is finished, generating an algorithm model through an AI training platform. The deployment and the task issuing of the algorithm model can be loaded to a front-end AI camera or NVR through a control platform, or can be directly loaded to the front-end AI camera or NVR, and the control platform carries out iterative training on the algorithm model so as to achieve the best effect.
4. Identification of unsafe behaviors;
the method comprises the steps that whether unsafe behaviors exist in a shot video is analyzed through a front-end AI camera or NVR loaded with an underground coal mine unsafe behavior algorithm model in an artificial intelligence mode, if the unsafe behaviors exist, an inference result is pushed to a control platform, the control platform pushes unsafe behavior information to an administrator, the administrator is in communication with the site where the unsafe behaviors occur, the unsafe behaviors are effectively prevented, the control platform pushes the unsafe behavior information to the administrator, meanwhile, according to equipment correlation information, warning information is pushed to an audible and visual alarm of the site where the unsafe behaviors occur, and audible and visual warning of the unsafe behaviors is conducted on site.
The working principle of the invention is as follows: the method is suitable for artificial intelligence recognition of unsafe behaviors of coal mines, and comprises the steps of training the unsafe behaviors of the coal mines through an AI training platform based on a deep learning technology, obtaining an algorithm model, leading the algorithm model into an AI camera or NVR, and recognizing the trained unsafe behaviors through artificial intelligence analysis of real-time video streams by the AI camera or NVR, pushing reasoning results to a control platform, pushing unsafe behavior information to an administrator by the control platform, carrying out information verification and communication confirmation on the unsafe behavior information, and pushing information to the unsafe behavior occurrence places to prompt scene acousto-optic warning.
The foregoing is a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that variations, modifications, substitutions and alterations can be made in the embodiment without departing from the principles and spirit of the invention.
Claims (3)
1. The utility model provides a colliery is unsafe behavior artificial intelligence identification system in pit based on deep learning which characterized in that includes: mine site instance material collection (S1), training data sets (S2), AI training platform (S3), algorithm model (S4), control platform (S5), site acousto-optic warning (S6), AI camera/NVR (S7), unsafe behavior information auditing (S8) and communication (S9);
the method comprises the steps that an AI training platform (S3) is an artificial intelligence training platform based on deep learning, unsafe behaviors in a coal mine are collected through mine site example material collection (S1), a training data set (S2) is generated, the training data set is uploaded to the AI training platform (S3), the AI training platform (S3) carries out classification training on the unsafe behavior materials in the coal mine through loading the training data set (S2), and a coal mine unsafe algorithm model (S4) can be obtained through training and checking the unsafe materials in the coal mine;
the AI camera/NVR (S7), the scene acousto-optic warning (S6) and the unsafe behavior information auditing (S8) can be controlled through the control platform (S5), real-time video streams and videos of the AI camera/NVR (S7) can be read, and iterative training of the algorithm model can be realized through the control platform (S5).
2. The artificial intelligence recognition system based on deep learning coal mine underground unsafe behavior as claimed in claim 1, wherein an underground coal mine unsafe algorithm model (S4) is obtained through an AI training platform (S3), and is led into an AI camera/NVR (S7) through a control platform (S5) or is directly led into the AI camera/NVR (S7), so that the AI camera/NVR (S7) has the ability of intelligently recognizing the underground unsafe behavior of the trained coal mine.
3. The artificial intelligence recognition system for the underground unsafe behaviors of the coal mine based on deep learning of the claim 1 is characterized in that when an AI camera/NVR (S7) intelligently recognizes the underground unsafe behaviors of the trained coal mine, the control platform (S5) pushes reasoning results to a control platform (S5) and receives the reasoning results, unsafe information is pushed to an administrator for auditing the unsafe behavior information (S8), the administrator communicates with the site where the unsafe behaviors occur (S9) to ensure that the unsafe behaviors are effectively stopped, the control platform pushes a site acousto-optic alarm (S6) to the site where the unsafe behaviors occur while pushing the unsafe behavior information to the administrator, and the site where the unsafe behaviors occur is warned through sound and light to stop the unsafe behaviors.
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| CN111783575A (en) * | 2020-06-18 | 2020-10-16 | 合肥恒翔电子科技有限公司 | Underground artificial intelligence video drilling system |
| CN112836648A (en) * | 2021-02-05 | 2021-05-25 | 湖南嘿哈猫网络科技有限公司 | User behavior analysis model construction and system application based on deep learning |
| CN113179386A (en) * | 2021-03-19 | 2021-07-27 | 江西铜业股份有限公司 | Mining area safety intelligent monitoring broadcasting system and intelligent broadcasting method thereof |
| CN113763659A (en) * | 2021-09-08 | 2021-12-07 | 山西华拓电气有限公司 | Mine area personnel invasion monitoring system based on visual identification |
| CN114666553A (en) * | 2022-05-18 | 2022-06-24 | 深圳酷源数联科技有限公司 | Coal mine underground large-visual-angle security monitoring system |
| CN115146903A (en) * | 2022-05-09 | 2022-10-04 | 中国中煤能源集团有限公司 | Coal mine unsafe behavior recognition early warning system based on key skeleton points |
| CN116030391A (en) * | 2023-01-06 | 2023-04-28 | 滨州邦维信息科技有限公司 | Intelligent monitoring method for personnel risk of coal discharge port |
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