CN113705372B - AI identification system for join in marriage net job site violating regulations - Google Patents
AI identification system for join in marriage net job site violating regulations Download PDFInfo
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Abstract
The invention discloses an AI (artificial intelligence) identification system applied to distribution network operation site violation, which comprises the following steps: the video image acquisition module is used for acquiring regional images of different regions of the distribution network operation site; the first transmission module is respectively connected with the video image acquisition module and the edge calculation device; the edge computing device is used for receiving the area image transmitted by the first transmission module, and carrying out intelligent identification analysis on the area image to obtain an identification result; the second transmission module is respectively connected with the edge computing device and the background server; and the background server is used for receiving the identification result transmitted by the second transmission module, generating warning information according to the identification result and transmitting the warning information to the field personnel terminal. The real-time management and control of the field operation state, the personnel operation behavior and the safe and civilized construction can be realized, and the safe production management and control efficiency and the lean management level of the construction operation can be improved in all directions.
Description
Technical Field
The invention relates to the technical field of distribution network operation site detection, in particular to an AI (artificial intelligence) identification system applied to distribution network operation site violation.
Background
The Suzhou power supply company implements gridding planning as early as 2013, divides a city distribution network into 1412 grids, takes the grids as basic units, implements the planning of reserve projects, and promotes the construction target of an 'first-class' power grid. In the process of promoting the construction of the target grid frame and the implementation of the project, the implementation environment of the project construction is generally complex, the scope is wide, the number of points is large, the construction period is short, the participation units are large, the qualification of the construction units is different from the qualification of the construction team, and higher requirements are put forward for standardization, specialization and comprehension of the project construction and safety management.
From the aspect of project construction safety management, the most critical and active factor of the safety production is the most important factor influencing the safety production. Constructors have high mobility, lack safety responsibility consciousness, have no proper understanding and execution on safety work specifications and safety measures, have no proper supervision on violation behaviors and become a working key point for improving field safety management.
From the company safety operation supervision and management system, the manual on-site safety operation inspection is still the most traditional, most direct and most effective safety supervision and management means. Although the video monitoring system of the operation site is brought into the security production management and control system of the provincial company, the mobile video monitoring device is gradually applied to the outdoor operation site, all levels of supervision personnel need to check against the regulations by watching the returned video in real time or calling the stored historical video in the later period, and although some regulations can be found, a large amount of manpower, energy and material resources are consumed, and possible careless situations can be artificially judged. In addition, with the increase of the number of the monitoring cameras in the operation field, the requirement of transmitting real-time video streams on network transmission bandwidth is higher and higher, the network bandwidth requirement is greatly increased, and meanwhile, the pressure of a rear-end server is increased.
The intelligent management and control of the operation site safety is realized, the intelligent management and control of the operation site safety are flattened, the urgent problem of improving the safety management quality and effect of the current operation site is solved, and therefore an AI identification system applied to the violation of the distribution network operation site is urgently needed.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above. Therefore, the invention aims to provide an AI identification system applied to distribution network operation site violation, which integrates the existing PMS production business system, business outsourcing safety management system and safety production operation risk management and control system according to the requirements of 'construction standardization, management standardization, team specialization and cooperative team', and realizes real-time management and control of site operation state, personnel operation behavior and safety civilization construction by applying the latest artificial intelligent image identification technology, visual perception edge calculation technology and monocular visual ranging technology on the basis of the existing operation site video monitoring system, thereby comprehensively improving the safety production management and control efficiency and the lean management level of construction operation.
In order to achieve the above object, an embodiment of the present invention provides an AI identification system applied to distribution network operation site violation, including:
the video image acquisition module is used for acquiring regional images of different regions of the distribution network operation site;
the first transmission module is respectively connected with the video image acquisition module and the edge calculation device;
the edge computing device is used for receiving the area image transmitted by the first transmission module, and performing intelligent identification analysis on the area image to obtain an identification result;
the second transmission module is respectively connected with the edge computing device and the background server;
and the background server is used for receiving the identification result transmitted by the second transmission module, generating warning information according to the identification result and transmitting the warning information to the field personnel terminal.
According to some embodiments of the invention, the edge computing device comprises:
the first acquisition module is used for receiving the area image transmitted by the first transmission module and acquiring the attribute information of the area image;
the first determining module is used for determining the priority order of image analysis on the area images according to the attribute information;
the second acquisition module is used for acquiring authority level information and calculation information of the edge calculation nodes;
the resource allocation management module is used for determining the control relation among a plurality of edge computing nodes according to the authority level information, and the edge computing nodes with high authority levels carry out resource allocation management on the edge computing nodes with low authority levels according to the priority order and the computing information;
the edge computing node performs image analysis on the distributed area images to obtain image analysis results; and judging whether the target area corresponding to the area image has area abnormality according to the image analysis result, and marking the target area with the area abnormality to obtain an identification result.
According to some embodiments of the invention, the video image acquisition module comprises at least one of an intelligent safety helmet, an intelligent deployment and control ball, and an intelligent unmanned aerial vehicle.
According to some embodiments of the invention, the edge computing device comprises:
the extraction module is used for extracting the characteristics of the region image to obtain a human body image;
the first positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain head key points and arm key points; respectively positioning a head area and an arm area according to the head key points and the arm key points;
the first recognition module is used for inputting the head area into a pre-trained head recognition model, recognizing the wearing condition of the safety helmet and obtaining a first recognition result;
and the second recognition module is used for inputting the arm area into a pre-trained skin color model, recognizing the short sleeve wearing condition and obtaining a second recognition result.
According to some embodiments of the invention, the edge computing device further comprises:
the second positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain trunk key points, and positioning a trunk area according to the trunk key points;
and the third recognition module is used for inputting the trunk area into a pre-trained trunk recognition model, recognizing the safety belt condition and obtaining a third recognition result.
According to some embodiments of the present invention, the system further includes an encryption module, connected to the edge computing device, for encrypting the identification result obtained by the edge computing device to obtain encrypted data, and transmitting the encrypted data to the background server based on the second transmission module.
According to some embodiments of the invention, the edge computing device further comprises:
the second determining module is used for determining the abnormal object in the marking area, and identifying the action posture of the abnormal object to obtain the abnormal behavior characteristic of the abnormal object;
the third determining module is used for inquiring an abnormal behavior feature-abnormal score table according to the abnormal behavior feature and determining an abnormal score corresponding to the abnormal behavior feature;
a fourth determining module, configured to determine a total abnormal score according to the abnormal score corresponding to the abnormal behavior feature and the number of the abnormal behavior features;
and the first alarm module is used for determining the abnormal grade of the abnormal object according to the abnormal total score and sending out first alarm prompts of different grades according to different abnormal grades.
According to some embodiments of the invention, the second determining module comprises:
the determining submodule is used for determining an action posture contour map of the abnormal object;
the marking submodule is used for marking the positions of all limbs of the abnormal object on the action posture contour map according to the human body proportion model;
and the obtaining submodule is used for respectively identifying whether each limb of the abnormal object wears a protective tool or not based on a preset standard, and obtaining the abnormal behavior characteristics when the protective tool is not worn on the limb.
According to some embodiments of the invention, the edge computing device further comprises:
a fifth determining module, configured to determine a target image in the mark region;
the characteristic acquisition module is used for extracting the characteristics of the target image to acquire hue characteristics, saturation characteristics and brightness characteristics;
the comparison module is used for comparing the hue characteristics with preset hues respectively, comparing the saturation characteristics with preset saturations respectively and comparing the brightness characteristics with preset brightness respectively, and when the hue characteristics, the saturations and the brightness are determined to be consistent with each other, indicating that a fire disaster occurs in a marked area;
and the second alarm module is used for determining the range and the severity of the fire when the fire in the marked area is determined, and further sending out second alarm prompts in different grades.
In one embodiment, the edge computing device includes:
the synthesis module is used for carrying out image synthesis on the plurality of area images to obtain a high dynamic range image;
the query module is used for acquiring the environment brightness corresponding to the plurality of area images, calculating to obtain the average environment brightness, and querying a preset data table according to the average environment brightness to obtain a noise reduction model;
the first noise reduction processing module is used for carrying out first noise reduction processing on the high dynamic range image according to the noise reduction model to obtain a first noise reduction image;
the conversion module is used for converting the first noise reduction image into a Bayer image based on a Bayer color filter and determining a noise reduction point and texture intensity corresponding to the noise reduction point on the Bayer image according to a preset rule;
the second noise reduction processing module is used for inquiring a preset texture intensity-noise reduction coefficient table according to the texture intensity, determining a corresponding noise reduction coefficient, and performing second noise reduction processing on the Bayer image according to the noise reduction coefficient to obtain a second noise reduction image;
and the output module is used for inputting the second noise reduction image into a pre-trained intelligent recognition model for recognition and outputting a recognition result.
Has the advantages that:
1. the edge computing device realizes violation identification, and an artificial intelligent edge computing module is added in the traditional fixed camera, the mobile distribution control ball and the intelligent safety helmet equipment to form a fixed visual analysis device and a mobile visual analysis device, so that the identification and judgment of the on-site violation behaviors can be directly carried out, and the on-site violation behaviors are actively reported to a background server. The edge calculation has the characteristics of low time delay, safety, strong flexibility and independence on cloud and network. The artificial intelligence AI recognition edge calculation is completed at the video monitoring terminal, and the problems of untimely cloud calculation response and high power consumption are solved. The most important change of edge calculation and video monitoring is to change passive monitoring into active analysis and early warning, so that the problem that massive monitoring data needs to be manually processed is solved. The edge calculation removes redundant information by preprocessing a video image, so that part or all of video analysis is migrated to the edge, thereby reducing the demands on cloud center calculation, storage and network bandwidth and improving the video analysis speed. Meanwhile, the invalid video storage is reduced, the storage space is reduced, the occupation of a large amount of high-definition video network transmission bandwidth is greatly reduced, the 'in-the-fact' evidence video data is stored to the maximum extent, the reliability of evidence information is enhanced, and the utilization rate of the video data storage space is improved.
2. The convolutional network model based on the attention mechanism realizes standard dressing identification, and a convolutional neural model based on the attention mechanism is constructed, so that local significant characteristics of a human body can be captured quickly, and the learning performance of the arm skin color characteristics is excellent; therefore, the skin color is used as foreground information, the clothing is used as background information, an area candidate Network (RPN) is introduced into an attention mechanism, and the skin color of the arm of the person is subjected to example segmentation, so that high-precision identification of standard clothing is realized.
3. After violation is identified, fast pushing rectification and real-time intervention of the background server are combined, the result of identification of the violation behaviors on the site is calculated by the edge, rectification information is pushed to the site personnel terminal in the background server, real-time intervention is achieved in operation safety construction of the power grid industry by the Internet of things technology, and early discovery and early processing are achieved within 1 minute of corresponding time.
4. The large-area automatic operation and violation identification are realized by utilizing the AI edge calculation of the unmanned aerial vehicle, so that the application of the intelligent unmanned aerial vehicle technology in the field of violation identification of operation safety construction in the power grid industry is further expanded, and the detection efficiency is improved for multi-angle remote interference-free violation detection on a large-area construction operation site.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a block diagram of an AI identification system applied to distribution network job site violation according to one embodiment of the present invention;
FIG. 2 is a block diagram of an edge computing device according to one embodiment of the invention;
FIG. 3 is a block diagram of an edge computing device according to yet another embodiment of the invention;
FIG. 4 is a schematic diagram of an AI identification system applied to network distribution job site violations in accordance with an embodiment of the present invention;
FIG. 5 is a flow diagram of performing violation identification according to one embodiment of the present invention;
fig. 6 is a flow chart for performing violation identification according to yet another embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, an embodiment of the present invention provides an AI identification system applied to distribution network job site violation, including:
the video image acquisition module is used for acquiring regional images of different regions of the distribution network operation site;
the first transmission module is respectively connected with the video image acquisition module and the edge calculation device;
the edge computing device is used for receiving the area image transmitted by the first transmission module, and carrying out intelligent identification analysis on the area image to obtain an identification result;
the second transmission module is respectively connected with the edge computing device and the background server;
and the background server is used for receiving the identification result transmitted by the second transmission module, generating warning information according to the identification result and transmitting the warning information to the field personnel terminal.
The working principle of the technical scheme is as follows: the video image acquisition module is used for acquiring area images of different areas of the distribution network operation site; the first transmission module is respectively connected with the video image acquisition module and the edge calculation device; the first transmission module is a wireless transmission module; the edge computing device is used for receiving the area image transmitted by the first transmission module, and carrying out intelligent identification analysis on the area image to obtain an identification result; the second transmission module is respectively connected with the edge computing device and the background server; the second transmission module may be a 4Gapn module. And the background server is used for receiving the identification result transmitted by the second transmission module, generating warning information according to the identification result and transmitting the warning information to the field personnel terminal. The video image acquisition module comprises at least one of an intelligent safety helmet, an intelligent distribution control ball and an intelligent unmanned aerial vehicle.
The beneficial effects of the above technical scheme are that: the edge computing system for identifying the violation behaviors in the operation field deploys the deep learning intelligent identification algorithm to the operation field end by marginalizing the computing platform. When the front-end visual analysis device identifies the violation behaviors, the identification result is transmitted to the background server, the conversion from passive monitoring to active early warning is realized, and the network bandwidth and the pressure of the background server are greatly reduced. Meanwhile, aiming at the problems of high power consumption, insufficient stock and the like of a video monitoring camera, the edge computing module is deployed in a conventional distribution control ball, an intelligent safety helmet and the like, so that violation identification is realized on the basis of recycling stock monitoring equipment, and the waste of equipment resources is avoided. The result of the identification of the site violation behaviors is calculated by the edge, and the rectification information is pushed to the site personnel terminal in the background server, so that the technology of the Internet of things is interfered in real time in the operation safety construction of the power grid industry, and the corresponding time in the period only needs 1 minute, thereby realizing early discovery and early processing. And large-area automatic operation and violation identification are realized by using the AI edge calculation of the unmanned aerial vehicle. The unmanned aerial vehicle automatic cruise monitoring technology is used for developing and testing a flight mode of an unmanned aerial vehicle execution program design, so that the unmanned aerial vehicle generates a flight path through existing data and automatically executes a patrol monitoring plan; and transmitting the power grid operation site video acquired by the unmanned aerial vehicle to the edge computing device in real time by utilizing an automatic unmanned aerial vehicle and a real-time streaming media transmission protocol.
As shown in fig. 2, the edge calculation apparatus includes:
the first acquisition module is used for receiving the area image transmitted by the first transmission module and acquiring the attribute information of the area image;
the first determining module is used for determining the priority order of image analysis on the area images according to the attribute information;
the second acquisition module is used for acquiring authority level information and calculation information of the edge calculation nodes;
the resource allocation management module is used for determining the control relation among a plurality of edge computing nodes according to the authority level information, and the edge computing nodes with high authority levels perform resource allocation management on the edge computing nodes with low authority levels according to the priority sequence and the computing information;
the edge computing node performs image analysis on the distributed area images to obtain image analysis results; and judging whether the target area corresponding to the area image has area abnormality according to the image analysis result, and marking the target area with the area abnormality to obtain an identification result.
The working principle of the technical scheme is as follows: the edge computing means comprises a number of computing edge nodes. The first acquisition module is used for receiving the area image transmitted by the first transmission module and acquiring the attribute information of the area image; the attribute information comprises the resolution ratio of the regional image, the size of occupied memory and the like; the first determining module is used for determining the priority order of image analysis on the area images according to the attribute information; in an example, the larger the memory occupied by the area image is, the more priority is given to image analysis. The resource allocation management module is used for determining the control relation among a plurality of edge computing nodes according to the authority level information, and the edge computing nodes with high authority levels carry out resource allocation management on the edge computing nodes with low authority levels according to the priority order and the computing information; the calculation information includes calculation load capacity, calculation cost and the like of the edge calculation node. The edge computing node performs image analysis on the distributed area images to obtain image analysis results; and judging whether the target area corresponding to the area image has area abnormality according to the image analysis result, and marking the target area with the area abnormality to obtain an identification result.
The beneficial effects of the above technical scheme are that: the edge computing nodes are communicated with one another, resources are reasonably distributed according to the priority sequence, the computing capacity and the computing cost of the edge computing nodes, the load balance of the edge computing nodes is guaranteed, the total computing cost is reduced, the computing cost is reduced, accurate analysis of area images is achieved, area abnormity is monitored more accurately, the distribution of the edge computing nodes is more reasonable, effective scheduling of computing resources of the edge computing nodes is achieved to achieve maximization of computing benefits, computing speed is improved, quick response is achieved on the edge side based on the edge computing nodes according to the area images acquired by the video image acquisition module, the requirement of intelligent monitoring is met, marking is conducted when area abnormity occurs, accurate identification results are convenient to obtain, timely and accurate alarming is convenient for a background server, and personnel in the corresponding area are reminded to take corresponding measures in time.
According to some embodiments of the invention, the video image acquisition module comprises at least one of an intelligent safety helmet, an intelligent deployment and control ball, and an intelligent unmanned aerial vehicle.
The working principle and the beneficial effects of the technical scheme are as follows: the intelligent safety helmet is provided with an integrated 4G individual safety helmet device with an artificial intelligent edge computing module, on-site video image acquisition can be realized, and through analysis and processing technologies such as spatial positioning sensing and artificial intelligence, on-site unsafe factors and violation behaviors are intelligently identified and fed back to the background server in real time, so that risks are prompted in time. The intelligent ball distributing and controlling device is provided with an integrated ball distributing and controlling device with an artificial intelligent edge computing module, can realize field video image acquisition, intelligently identifies field unsafe factors and violation behaviors through analysis and processing technologies such as spatial positioning sensing and artificial intelligence, and feeds back the unsafe factors and the violation behaviors to a background server in real time so as to prompt risks in time. Intelligent unmanned aerial vehicle possesses the unmanned aerial vehicle of artificial intelligence edge calculation (ground satellite station) module, can realize remote video safety supervision, the discernment violating the regulations in job site to real-time feedback uploads backstage server, in time points out the risk.
As shown in fig. 4, in an embodiment, the edge computing device is used as a core processor for connecting an engineering construction site and a violation management and control platform, and the obtained site monitoring picture/video data of stock video monitoring equipment (stock guns, stock ball machines, stock distribution control balls), video safety helmets, unmanned aerial vehicles and the like are transmitted to the edge computing device for processing, so that a set of front-end video analysis system is established; and transmitting the front-end analysis result to a background server through the APN/wired public network/wireless public network, realizing refined test and autonomous early warning, and finally uploading the important early warning data to the background server or a violation management and control platform. And the management mode is optimized, and the management and control efficiency is improved. The AI identification system for distribution network operation site violation integrates the management functions of the existing PMS production service system, the service outsourcing safety management and control system, the safety production operation risk management and control system and the operation site video monitoring system, and changes the previous chain type management state of the company to the project management department and the project management department for supervising the operation points layer by layer and guiding the operation points by level into the linear management of the company and the project management department on the same operation site at the same time and different places, thereby greatly improving the management efficiency.
The intelligent identification personnel, the safety of entering the field is highlighted. The AI identification system combines intelligent image analysis and face recognition technology, collects field information of operators through a field video device, realizes face recognition identity authentication of the operators entering and leaving a field, increases the identification of safety helmets and dressing standards of the personnel before entering the field, and automatically sends out an alarm in real time for the personnel not in a list library, the safety helmets and the dressings which do not meet the requirements of the specifications. In addition, based on the video image intelligent analysis technology, a virtual electronic fence is established in a video picture, so that the working personnel are prevented from mistakenly entering a surrounding electrified dangerous area and mistakenly touching electrified equipment, and external personnel are prevented from approaching a construction operation area.
The front end identifies violation and increases the management and control site. The AI identification system adopts the fusion application of the video distribution control ball and the video safety helmet, so that the monitoring range of an operation site is greatly enlarged, and the newly-added artificial intelligent AI edge calculation module is used for carrying out front-end real-time identification judgment and real-time alarm on typical violation behaviors such as safety helmets, personnel dressing, safety belts and safety measures, so that the violation identification timeliness is improved. The front-end identification can effectively reduce redundant video transmission and storage space, greatly reduce the requirements on cloud center calculation, storage and network bandwidth, and improve the video analysis speed.
And (4) intelligently and comprehensively evaluating and standardizing operation behaviors. The intelligent evaluation system carried by the project records the discovered violation behaviors in real time, and identifies and hooks the violation behaviors, constructors and construction units by comparing the violation behaviors with data of a business management system such as production management, operation planning, risk management and control, outsourcing personnel management and the like, so that the intelligent evaluation system provides a powerful basis for companies and construction management departments to master and know the safety condition of field operation in time and comprehensively evaluate and assess the construction units.
Finally, the result of the identification of the site violation behaviors is calculated by the edge, and the rectification information is pushed to the site personnel terminal in the background server, so that the technology of the Internet of things is interfered in real time in the operation safety construction of the power grid industry.
As shown in fig. 3 and 5, the edge calculating apparatus includes:
the extraction module is used for extracting the characteristics of the region image to obtain a human body image;
the first positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain head key points and arm key points; respectively positioning a head area and an arm area according to the head key points and the arm key points;
the first recognition module is used for inputting the head area into a pre-trained head recognition model, recognizing the wearing condition of the safety helmet and obtaining a first recognition result;
and the second recognition module is used for inputting the arm area into a pre-trained skin color model, recognizing the wearing condition of the short sleeves and obtaining a second recognition result.
The working principle of the technical scheme is as follows: the edge calculation device comprises an extraction module, a detection module and a processing module, wherein the extraction module is used for extracting the characteristics of the region image to obtain a human body image; the first positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain head key points and arm key points; respectively positioning a head area and an arm area according to the head key points and the arm key points; the first recognition module is used for inputting the head area into a pre-trained head recognition model, recognizing the wearing condition of the safety helmet and obtaining a first recognition result; and the second recognition module is used for inputting the arm area into a pre-trained skin color model, recognizing the wearing condition of the short sleeves and obtaining a second recognition result. The head recognition model is obtained by obtaining a head sample picture and performing ResNet training.
The beneficial effects of the above technical scheme are that: the key identification of the illegal behaviors of the distribution network operation site, including safety helmet and dressing conditions, is realized by adopting a convolution neural model based on an attention mechanism aiming at constructors in a monitoring video, so that local remarkable characteristics of a human body can be rapidly captured, and particularly, the learning performance of the arm skin color characteristics is excellent; therefore, the skin color is used as foreground information, the clothing is used as background information, an area candidate Network (RPN) is introduced into an attention mechanism, and the skin color of the arm of the person is subjected to example segmentation, so that high-precision identification of standard clothing is realized. Firstly, a sample image of the head of a constructor is acquired, and the image comprises three types: (1) no safety helmet; (2) wearing safety helmets not standardized; and (3) correctly dressing. The project constructs a depth residual error network to identify and classify the three images, and trains on the collected samples to obtain a classification model. For the non-standard dressing (short sleeves), the arm area of the constructor is obtained through an attitude estimation algorithm, and then a skin color detection model is adopted to judge whether the arm is exposed. The skin color is one of the obvious characteristics of the human body surface, although the skin color of the human body presents different colors due to different races, the skin color tone is basically consistent after the influence of brightness, visual environment and the like on the skin color is eliminated, and theoretical basis is provided for the skin color segmentation by utilizing color information. The safety helmet and the wearing condition can be conveniently and accurately identified, and whether the violation behaviors exist or not can be accurately judged. The convolutional network model based on the attention mechanism realizes standard dressing identification, and the workload of project implementation is mainly focused on applying a deep learning convolutional neural network self-learning technology and implementing a violation behavior detection module. The action recognition technology based on deep learning overcomes the visual angle of operators and the dynamic change of clothes, and accurately positions the human body behavior of the power grid operation video key frame.
As shown in fig. 6, according to some embodiments of the invention, the edge computing device further comprises:
the second positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain trunk key points, and positioning a trunk area according to the trunk key points;
and the third recognition module is used for inputting the trunk area into a pre-trained trunk recognition model, recognizing the safety belt condition and obtaining a third recognition result.
The working principle of the technical scheme is as follows: the edge calculation means further includes: the second positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain trunk key points, and positioning a trunk area according to the trunk key points; and the third recognition module is used for inputting the trunk area into a pre-trained trunk recognition model, recognizing the safety belt condition and obtaining a third recognition result. Utilize monocular vision principle to judge the operation personnel height during the operation of ascending a height, the analysis personnel of ascending a height safety belt wearing condition whether have during the use safety belt hang high with the phenomenon, whether someone's support when using the ladder, whether use the metal ladder that does not conform to safety regulation, whether the operation personnel stand more than the safety line of ladder highest point, whether have high altitude parabolic phenomenon etc..
The beneficial effects of the above technical scheme are that: to joining in marriage key discernment of net job site violation of regulations, including the construction safety belt condition of wearing of ascending a height, at first, gather constructor truck sample image, contain two kinds of types: (1) no safety belt is used; and (2) normally using the safety belt. The method is characterized in that a depth residual error network is adopted to identify and classify two images as a safety helmet identification network, and a classification model is obtained by training an acquired sample. The wearing condition of the safety belt for the climbing construction is conveniently and accurately identified, and then whether the violation behaviors exist or not is accurately judged. And the first table is a statistical table of identification results of the violation behaviors.
Watch 1
In one embodiment, intelligent image analysis and face recognition technology are utilized, face recognition identity authentication of personnel entering and leaving a field of production operation personnel and illegal helmet and wearing recognition are achieved, face recognition is added during entering the field, people unmatched to face recognition are pushed and alarmed at the backstage, in addition, field face pictures are collected, post-affair evidence checking is facilitated, common cheating behaviors such as card replacement, imposition and the like are stopped, and quality safety management of construction projects is further enhanced. The voice alarm is given to the irregular dressing personnel who do not correctly wear the safety helmet and the like.
According to some embodiments of the present invention, the system further includes an encryption module, connected to the edge computing device, configured to encrypt the identification result obtained by the edge computing device to obtain encrypted data, and transmit the encrypted data to the background server based on the second transmission module.
The beneficial effects of the above technical scheme are that: the safety and the reliability of data transmission are ensured.
In one embodiment, for the stock ball distribution and control equipment, the ball distribution and control equipment deployed in the construction and overhaul field is connected to the edge computing device through wifi, the edge computing device performs intelligent identification analysis on the acquired image, and the image with the violation alarm in the field is sent to the background server through a Nanrui or Pu Hua Jiami chip and 4G. For the guns or the dome cameras deployed in the stored transformer substation, the intelligent edge computing device is accessed through the Ethernet through a transformer substation local area network cabinet, the acquired images are intelligently identified and analyzed, and the pictures with the on-site warning of violating badges are sent to the background server through the intranet.
According to some embodiments of the invention, the edge computing device further comprises:
the second determination module is used for determining the abnormal object in the marking area, and identifying the action posture of the abnormal object to acquire the abnormal behavior characteristic of the abnormal object;
the third determining module is used for inquiring an abnormal behavior feature-abnormal score table according to the abnormal behavior feature and determining an abnormal score corresponding to the abnormal behavior feature;
the fourth determining module is used for determining the total abnormal score according to the abnormal score corresponding to the abnormal behavior characteristics and the number of the abnormal behavior characteristics;
and the first alarm module is used for determining the abnormal grade of the abnormal object according to the abnormal total score and sending out first alarm prompts of different grades according to different abnormal grades.
The working principle of the technical scheme is as follows: the second determination module is used for determining the abnormal object in the marking area, and identifying the action posture of the abnormal object to acquire the abnormal behavior characteristic of the abnormal object; a third determining module, configured to query an abnormal behavior feature-abnormal score table according to the abnormal behavior feature, and determine an abnormal score corresponding to the abnormal behavior feature; the fourth determining module is used for determining the total abnormal score according to the abnormal score corresponding to the abnormal behavior characteristics and the number of the abnormal behavior characteristics; and the first alarm module is used for determining the abnormal grade of the abnormal object according to the abnormal total score and sending out first alarm prompts of different grades according to different abnormal grades.
The beneficial effects of the above technical scheme are that: the user can accurately acquire the abnormal grade of the marked area, and then corresponding countermeasures are taken.
According to some embodiments of the invention, the second determining module comprises:
the determining submodule is used for determining an action posture contour map of the abnormal object;
the marking submodule is used for marking the positions of all limbs of the abnormal object on the action posture contour map according to the human body proportion model;
and the obtaining submodule is used for respectively identifying whether each limb of the abnormal object wears a protective tool or not based on a preset standard, and obtaining the abnormal behavior characteristics when the protective tool is not worn on the limb.
The working principle of the technical scheme is as follows: the determining submodule is used for determining an action posture contour map of the abnormal object; the marking submodule is used for marking the positions of all limbs of the abnormal object on the action posture contour map according to the human body proportion model; and the obtaining submodule is used for respectively identifying whether each limb of the abnormal object wears a protective tool or not based on a preset standard, and obtaining the abnormal behavior characteristics when the protective tool is not worn on the limb. In an example, when non-local staff is detected in an area, a safety helmet is preset to be worn when the non-local staff enters the area, whether the head of the non-local staff wears the safety helmet or not is judged, and abnormal behavior characteristics are obtained.
The beneficial effects of the above technical scheme are that: the method and the device realize effective monitoring of the abnormal objects in the marked area and accurately acquire the abnormal behavior characteristics.
According to some embodiments of the invention, the edge computing device further comprises:
a fifth determining module, configured to determine a target image in the mark region;
the characteristic acquisition module is used for extracting the characteristics of the target image to acquire hue characteristics, saturation characteristics and brightness characteristics;
the comparison module is used for comparing the hue characteristics with preset hues respectively, comparing the saturation characteristics with preset saturations respectively and comparing the brightness characteristics with preset brightness respectively, and when the hue characteristics, the saturations and the brightness are determined to be consistent with each other, indicating that a fire disaster occurs in a marked area;
and the second alarm module is used for determining the range and the severity of the fire when the fire in the marked area is determined, and further sending out second alarm prompts in different grades.
The beneficial effects of the above technical scheme are that: whether the fire disaster is found in the area is intelligently achieved, second alarm prompts of different levels are achieved, a user is reminded to timely process the fire disaster, loss is reduced, and meanwhile accuracy of area abnormity monitoring is guaranteed.
Aiming at the violation identification behaviors of the power grid operation field, edge calculation and artificial intelligence are used as core devices. The traditional fixed or mobile camera can be accessed, artificial intelligent edge calculation is realized at the front end of the site, unsafe factors and violation behaviors in the site are intelligently identified, and real-time feedback and uploading to a back-end server and automatic early warning are carried out; the device such as edge computing device, video safety helmet can realize scene video image acquisition, through analysis and processing techniques such as spatial localization perception, artificial intelligence, intelligent recognition scene insecurity factor and the act of breaking rules and regulations and real-time feedback upload back-end server, in time indicate the risk. Based on the image deep learning algorithm, the functions of automatic snapshot, comparison, alarm and pushing of the illegal behaviors such as no person in the picture, no safety helmet wearing correctly, no safety belt wearing correctly (short sleeve wearing), no safety belt fastening, low hanging height of the safety belt and the like are realized through edge calculation. Intelligent unmanned aerial vehicle. The unmanned aerial vehicle with the artificial intelligent edge computing (ground station) module can realize remote video safety supervision and violation identification on an operation site, feed back and upload the information to a rear-end server in real time, and prompt risks in time.
In one embodiment, an edge computing device, comprising:
the synthesis module is used for carrying out image synthesis on the plurality of area images to obtain a high dynamic range image;
the query module is used for acquiring the environment brightness corresponding to the plurality of area images, calculating to obtain average environment brightness, and querying a preset data table according to the average environment brightness to obtain a noise reduction model;
the first noise reduction processing module is used for carrying out first noise reduction processing on the high dynamic range image according to the noise reduction model to obtain a first noise reduction image;
the conversion module is used for converting the first noise reduction image into a Bayer image based on a Bayer color filter and determining a noise reduction point and texture intensity corresponding to the noise reduction point on the Bayer image according to a preset rule;
the second noise reduction processing module is used for inquiring a preset texture intensity-noise reduction coefficient table according to the texture intensity, determining a corresponding noise reduction coefficient, and performing second noise reduction processing on the Bayer image according to the noise reduction coefficient to obtain a second noise reduction image;
and the output module is used for inputting the second noise reduction image into a pre-trained intelligent recognition model for recognition and outputting a recognition result.
The working principle and the beneficial effects of the technical scheme are as follows: the synthesis module is used for carrying out image synthesis on the plurality of area images to obtain a high dynamic range image; the query module is used for acquiring the environment brightness corresponding to the plurality of area images, calculating to obtain average environment brightness, and querying a preset data table according to the average environment brightness to obtain a noise reduction model; the preset data table is an average ambient brightness-noise reduction model corresponding table. The method comprises a plurality of noise reduction models which are obtained by training sample images under different environment brightness. The first noise reduction processing module is used for carrying out first noise reduction processing on the high dynamic range image according to the noise reduction model to obtain a first noise reduction image; the method can effectively avoid overlapping shadows caused by alignment errors between images when the images are synthesized, simultaneously eliminates noise caused by different brightness, improves the image purity to a certain extent and keeps more detailed characteristics of the images. The conversion module is used for converting the first noise reduction image into a Bayer image based on a Bayer color filter and determining a noise reduction point and texture intensity corresponding to the noise reduction point on the Bayer image according to a preset rule; the Bayer image is subjected to image segmentation and is divided into a plurality of data blocks, and each data block comprises two green pixel units, one blue pixel unit and one red pixel unit. The preset rule is that the fixed position based on the data block is set as a noise reduction point row by row and column by column. The second denoising processing module is used for inquiring a preset texture strength-denoising coefficient table according to the texture strength, determining a corresponding denoising coefficient, and performing second denoising processing on the Bayer image according to the denoising coefficient to obtain a second denoising image; and performing noise reduction based on the similarity of the noise reduction point and the surrounding pixel units of the same color channel. The noise caused by different colors can be effectively eliminated. According to the method and the device, the luminance noise and the chrominance noise in the image are denoised respectively, the denoising effect is improved, and the recognition accuracy of inputting the denoised second denoising image into a pre-trained intelligent recognition model for recognition is further improved. The intelligent recognition model is obtained based on sample image training.
In an embodiment, the apparatus further includes a channel transmission rate detection module, configured to detect a channel transmission rate of the second transmission module, determine whether the channel transmission rate is less than a preset channel transmission rate, and send an alarm prompt when it is determined that the channel transmission rate is less than the preset channel transmission rate;
detecting a channel transmission rate of a second transmission module, comprising:
determining a data packet queue sent by the edge computing device to the second transmission module, and computing the waiting time delay M of the data packet of each threshold value i based on the length of the data packet queue i ：
Wherein m is i The number of effective data packets which are successfully sent at a sampling time interval t corresponding to a threshold value i for a transmission node; x is the number of ij J, sent in a sampling time interval t for the transmission node corresponding to a threshold iA data packet; t (x) ij ) For data packet x ij Dequeue time in a data packet queue; p (x) ij ) For data packet x ij An enqueue time in a data packet queue; [ T (x) ij )-P(x ij )]As a data packet x ij Latency in the data packet queue; l is the number of the corresponding threshold values of the transmission nodes;
calculating the average value of the waiting time delay of the data packet of each threshold value i
Calculating the channel transmission rate V of the second transmission module:
wherein H is the channel bandwidth; epsilon is the channel gain; w is transmission power; n is noise power spectral density; e is a natural constant; m 0 Is a preset time delay.
The working principle and the beneficial effects of the technical scheme are as follows: the channel transmission rate detection module is used for detecting the channel transmission rate of the second transmission module, judging whether the channel transmission rate is smaller than a preset channel transmission rate or not, and sending an alarm prompt when the channel transmission rate is smaller than the preset channel transmission rate; the channel transmission rate of the second transmission module is convenient to guarantee, and further the data transmission rate between the edge computing device and the background server is guaranteed, the timeliness of system response is convenient to guarantee, and user experience is improved. In the data packet queue, the end-to-end time delays corresponding to different thresholds are different, and the selection of the thresholds greatly influences the network time delay performance, and further influences the channel transmission rate. The pre-set delay is a delay determined based on multiple experiments that is satisfactory to most customers. Based on the formula, the channel transmission rate of the second transmission module is accurately calculated, and the accuracy of judging the channel transmission rate and the preset channel transmission rate is improved.
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 (9)
1. The utility model provides an AI identification system for join in marriage net job site violating regulations which characterized in that includes:
the video image acquisition module is used for acquiring regional images of different regions of the distribution network operation site;
the first transmission module is respectively connected with the video image acquisition module and the edge calculation device;
the edge computing device is used for receiving the area image transmitted by the first transmission module, and carrying out intelligent identification analysis on the area image to obtain an identification result;
the second transmission module is respectively connected with the edge computing device and the background server;
the background server is used for receiving the identification result transmitted by the second transmission module, generating warning information according to the identification result and transmitting the warning information to the field personnel terminal;
the edge computing device comprises:
the synthesis module is used for carrying out image synthesis on the plurality of area images to obtain a high dynamic range image;
the query module is used for acquiring the environment brightness corresponding to the plurality of area images, calculating to obtain the average environment brightness, and querying a preset data table according to the average environment brightness to obtain a noise reduction model;
the first denoising module is used for performing first denoising processing on the high dynamic range image according to the denoising model to obtain a first denoising image;
the conversion module is used for converting the first noise reduction image into a Bayer image based on a Bayer color filter and determining a noise reduction point and texture intensity corresponding to the noise reduction point on the Bayer image according to a preset rule;
the second denoising processing module is used for inquiring a preset texture strength-denoising coefficient table according to the texture strength, determining a corresponding denoising coefficient, and performing second denoising processing on the Bayer image according to the denoising coefficient to obtain a second denoising image;
the output module is used for inputting the second noise reduction image into a pre-trained intelligent recognition model for recognition and outputting a recognition result;
the device also comprises a channel transmission rate detection module, a first transmission module and a second transmission module, wherein the channel transmission rate detection module is used for detecting the channel transmission rate of the second transmission module, judging whether the channel transmission rate is smaller than a preset channel transmission rate or not, and sending an alarm prompt when the channel transmission rate is smaller than the preset channel transmission rate;
detecting a channel transmission rate of a second transmission module, comprising:
determining a data packet queue sent by the edge computing device to the second transmission module, and computing the waiting time delay M of the data packet of each threshold value i based on the length of the data packet queue i ：
Wherein m is i The number of effective data packets which are successfully sent at a sampling time interval t corresponding to a threshold value i for a transmission node; x is a radical of a fluorine atom ij Transmitting a jth data packet corresponding to a threshold i in a sampling time interval t for the transmission node; t (x) ij ) As a data packet x ij Dequeue time in a data packet queue; p (x) ij ) For data packet x ij An enqueue time in a data packet queue; [ T (x) ij )-P(x ij )]For data packet x ij Latency in the data packet queue; l is the number of the corresponding threshold values of the transmission nodes;
calculating the average value of the waiting time delay of the data packet of each threshold value i
Calculating the channel transmission rate V of the second transmission module:
wherein H is the channel bandwidth; epsilon is the channel gain; w is transmission power; n is noise power spectral density; e is a natural constant; m is a group of 0 Is a preset time delay.
2. The AI identification system for distribution network job site violations of claim 1 wherein said edge computing device includes:
the first acquisition module is used for receiving the area image transmitted by the first transmission module and acquiring the attribute information of the area image;
the first determining module is used for determining the priority order of image analysis on the area images according to the attribute information;
the second acquisition module is used for acquiring authority level information and calculation information of the edge calculation nodes;
the resource allocation management module is used for determining the control relation among a plurality of edge computing nodes according to the authority level information, and the edge computing nodes with high authority levels carry out resource allocation management on the edge computing nodes with low authority levels according to the priority order and the computing information;
the edge computing node carries out image analysis on the distributed area image to obtain an image analysis result; and judging whether the target area corresponding to the area image has area abnormality according to the image analysis result, and marking the target area with the area abnormality to obtain an identification result.
3. The AI identification system as recited in claim 1, applied to a network distribution job site violation, wherein said video image capture module comprises at least one of an intelligent safety helmet, an intelligent distribution and control ball, and an intelligent drone.
4. The AI identification system for distribution network job site violation according to claim 1, wherein said edge computing device comprises:
the extraction module is used for extracting the characteristics of the region image to obtain a human body image;
the first positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain head key points and arm key points; respectively positioning a head area and an arm area according to the head key points and the arm key points;
the first recognition module is used for inputting the head area into a pre-trained head recognition model, recognizing the wearing condition of the safety helmet and obtaining a first recognition result;
and the second recognition module is used for inputting the arm area into a pre-trained skin color model, recognizing the short sleeve wearing condition and obtaining a second recognition result.
5. The AI identification system for distribution network job site violations of claim 4 wherein the edge computing device further comprises:
the second positioning module is used for detecting key points of the human body image through a posture estimation algorithm to obtain trunk key points, and positioning a trunk area according to the trunk key points;
and the third recognition module is used for inputting the trunk area into a pre-trained trunk recognition model, recognizing the safety belt condition and obtaining a third recognition result.
6. The AI identification system for distribution network job site violation according to claim 1 further comprising an encryption module coupled to said edge computing device for encrypting said identification result obtained by said edge computing device to obtain encrypted data and transmitting said encrypted data to a background server based on a second transmission module.
7. The AI identification system for distribution network job site violations of claim 2 wherein the edge computing device further comprises:
the second determination module is used for determining the abnormal object in the marking area, and identifying the action posture of the abnormal object to acquire the abnormal behavior characteristic of the abnormal object;
the third determining module is used for inquiring an abnormal behavior feature-abnormal score table according to the abnormal behavior feature and determining an abnormal score corresponding to the abnormal behavior feature;
the fourth determining module is used for determining the total abnormal score according to the abnormal score corresponding to the abnormal behavior characteristics and the number of the abnormal behavior characteristics;
and the first alarm module is used for determining the abnormal grade of the abnormal object according to the abnormal total score and sending out first alarm prompts of different grades according to different abnormal grades.
8. The AI identification system for distribution network job site violations of claim 7 wherein said second determination module includes:
the determining submodule is used for determining an action posture contour map of the abnormal object;
the marking submodule is used for marking the positions of all limbs of the abnormal object on the action posture contour map according to the human body proportion model;
and the obtaining submodule is used for respectively identifying whether each limb of the abnormal object wears a protective tool or not based on a preset standard, and obtaining the abnormal behavior characteristics when the protective tool is not worn on the limb.
9. The AI identification system for distribution network job site violations of claim 2 wherein the edge computing device further comprises:
a fifth determining module, configured to determine a target image in the mark region;
the characteristic acquisition module is used for extracting the characteristics of the target image to acquire hue characteristics, saturation characteristics and brightness characteristics;
the comparison module is used for comparing the hue characteristics with preset hues respectively, comparing the saturation characteristics with preset saturations respectively and comparing the brightness characteristics with preset brightness respectively, and when the hue characteristics, the saturations and the brightness are determined to be consistent with each other, indicating that a fire disaster occurs in a marked area;
and the second alarm module is used for determining the range and the severity of the fire when the fire in the marked area is determined, and further sending out second alarm prompts in different grades.
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| CN115797850B (en) * | 2023-02-06 | 2023-04-18 | 中国石油大学(华东) | Oil field production safety early warning analysis system based on video stream |
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