CN116485122A - Power transmission line personnel presence supervision method and system for preventing external damage - Google Patents
Power transmission line personnel presence supervision method and system for preventing external damage Download PDFInfo
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Abstract
The invention provides a method and a system for supervising the presence of an external broken prevention person of a power transmission line, which are used for constructing a figure database sample and a face database sample; training the extracted human image sample and the human face picture sample respectively to obtain a human image recognition model and a human face detection model; the background periodically captures and grabs the camera pictures once through the interface service and transmits the camera pictures to the server; the background system compares the uploaded portrait information and face information by using a recognition algorithm, and combines the compared and uploaded GPS position information to confirm the presence evidence of the squatting person, so that the defect that the squatting task cannot be truly realized due to serious in-place rate of the person in the traditional external damage prevention work is overcome, and meanwhile, the actual execution efficiency of the external damage prevention work of the power transmission line can be greatly improved.
Description
Technical Field
The invention belongs to the technical field of external damage prevention of a power transmission line of a power grid, and particularly relates to a method and a system for monitoring the presence of external damage prevention personnel of the power transmission line.
Background
The transmission line is an aorta transmitted by a power grid, is an ultra-large scale system with wide area distribution on the ground, is inevitably influenced by natural environment and human factors, and has an influence on safe operation of the power grid due to faults. Because the transmission line is positioned in the field, the point is multiple, the coverage area is large, the operation and maintenance radius is long, the transmission line is influenced by climate, environment and human factors, the occurrence of external damage has strong randomness, the operation and maintenance management of the transmission line is difficult, the on-site real-time management and control capability is weak, and the safe operation of a power grid is seriously influenced. The external breaking tripping is always a permanent fault, the problem can not be solved through automatic reclosing after an accident occurs, the work such as fault inspection, equipment rush repair and the like is required to be carried out, power transmission can not be recovered in a short time, equipment is damaged, an electric power department can suffer huge economic loss, and more importantly, the stability of a power grid is damaged, and the development of industrial and agricultural production and the normal life of people are influenced. Therefore, we need to have conscious knowledge of the damage caused by external force and keep high importance.
At present, part of power transmission line operation and maintenance has established 'line length system', and aiming at the traffic difficult areas, the lines far away from villages and towns and the special line sections with long patrol period, the public line protection points are established, and the public line protection staff is engaged in carrying out patrol squatting on a daily basis. Although the problem of insufficient strength of professional patrols is solved, the trip frequency of external broken lines is greatly reduced, the patrolling supervision of the masses ' line guards has no effective measure, and only sends daily field patrolling photos and videos to supervise by means of WeChat, so that the external broken line control points of ' in-and-out ' and ' on-site squatting ' are required to have no effective supervision measure. With the continuous increase of the power grid scale, the operation and maintenance workload of the line is rapidly increased, the safe operation of the line is increasingly threatened by external force damage such as a crane, foreign matters and the like, and inspection blind spots in time and space are easy to generate.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides the method and the system for monitoring the presence of the external damage prevention personnel of the power transmission line, which solve the defects that the in-place rate of the personnel is seriously insufficient and the squatting task cannot be really realized in the traditional external damage prevention work, and can greatly improve the actual execution efficiency of the external damage prevention work of the power transmission line.
The invention adopts the following technical scheme.
A power transmission line personnel presence supervision method for preventing external damage comprises the following steps:
step 1, collecting external broken line inspection squat figure information and face image information, and constructing a figure database sample and a face database sample as deep learning samples;
step 2, training the extracted human image sample and the human face picture sample respectively to obtain a human image recognition model and a human face detection model;
step 3, when the external broken line inspection gatekeeper arrives at and leaves the target site, the app on the mobile terminal equipment is utilized to punch a card on the face, and the app end program uploads the face picture and GPS position information and time information to the server end;
step 4, capturing and capturing a camera picture through the interface service periodically by the background, and transmitting the captured camera picture to a server;
and 5, comparing the uploaded portrait information and face information by using an identification algorithm by the background system, and combining with the compared and uploaded GPS position information to confirm the presence evidence of the squatting person.
Preferably, in step 1, frame extraction is performed on the video of the external broken line inspection personnel by a video image 'sheet extraction' technology, and a figure database sample and a face database sample of the external broken line inspection personnel are respectively constructed by an image enhancement and element recognition technology and an image face extraction technology.
Preferably, in step 2, the system adopts a target detection algorithm, combines a DNN neural network and a pre-training model, performs frame analysis on video, extracts facial features, and forms a current patrol operator feature database; the automatic training engine processes the feature database, trains the feature database according to two conditions of a near view picture and a far view picture to obtain a near view feature set 1 and a far view feature set 2, and constructs a near view human image recognition model, a far view human image recognition model, a near view human face detection model and a far view human face detection model according to the near view feature set 1 and the far view feature set 2.
The system comprises a patrol task creation and execution module, a feature extraction module and a patrol behavior analysis module.
The patrol task creation and execution module is divided into two parts, namely an administrator creates the patrol task and an patrol personnel executes the task; an administrator selects a place needing to be patrolled through a WEB management page, then allocates patrolling personnel, creates a corresponding patrolling plan, and a background program pushes the task to the patrolling personnel needing to be patrolled through an app; the patrol personnel receives the task at the app end and then catches up with the task approach, and the information such as the GPS position, the face picture, the time, the field picture and the like of the patrol personnel are uploaded by adopting the functions of face card punching and hidden danger reporting;
the feature extraction module adopts a target detection algorithm, combines a DNN neural network and a pre-training model, performs frame analysis on the video, extracts facial features and forms a current inspector feature database; the automatic training engine processes the feature database, trains the feature database according to two conditions of a near view picture and a far view picture to obtain a near view feature set and a far view feature set, and constructs a near view human image recognition model, a far view human image recognition model, a near view human face detection model and a far view human face detection model according to the near view feature set and the far view feature set;
the inspection behavior analysis module adopts a background to judge whether a squatting person arrives at the scene in time by comparing face pictures uploaded by the app; the background service periodically grabs the camera pictures on site and transmits the camera pictures to the server, the program firstly carries out target detection on the pictures to identify the human images in the pictures, then the human images are matched with the patrol personnel by combining a face recognition algorithm, the site monitoring pictures of the patrol personnel are sorted, the occurrence rate of the patrol personnel is counted, and after the minimum threshold is met, the presence of the patrol personnel is judged, otherwise, the absence of the patrol personnel is judged.
The invention has the advantages that compared with the prior art,
1. the method and the system for monitoring the presence of the power transmission line anti-external-damage personnel realize non-repudiation of external-damage inspection personnel, ensure inspection in-place rate and improve execution efficiency of a squatting task;
2. the intelligent control level of the external damage prevention of the power grid is improved, and the direct damage of the external damage to the power grid equipment is reduced;
3. the invention avoids dependence on background supervisory personnel, realizes dynamic inspection analysis and early warning reminding based on artificial intelligence, and greatly releases labor cost.
Drawings
Fig. 1 is a flow chart of a method for monitoring the presence of an anti-external-broken person on a power transmission line;
fig. 2 is a schematic diagram of a power transmission line personnel presence supervision system for preventing external damage.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without inventive faculty, are within the scope of the invention, based on the spirit of the invention.
The method for supervising the presence of the outer broken prevention personnel of the power transmission line specifically comprises the following steps:
step 1, collecting external broken line inspection squat figure information and face image information, and constructing a figure database sample and a face database sample as deep learning samples;
in the step 1, frame extraction is carried out on the video of the external broken line inspection personnel by a video image 'sheet extraction' technology, and a figure database sample and a face database sample of the external broken line inspection personnel are respectively constructed by technologies such as an image enhancement and element recognition technology, an image face extraction technology and the like.
Step 2, training the extracted human image sample and the human face picture sample respectively to obtain a human image recognition model and a human face detection model;
in the step 2, the system adopts a target detection algorithm, combines a DNN neural network and a pre-training model, performs frame analysis on video, extracts facial features and forms a current patrol operator feature database; the automatic training engine processes the feature database, trains the feature database according to two conditions of a near view picture and a far view picture to obtain a near view feature set 1 and a far view feature set 2, and constructs a near view human image recognition model, a far view human image recognition model, a near view human face detection model and a far view human face detection model according to the near view feature set 1 and the far view feature set 2.
Step 3, when the external broken line inspection gatekeeper arrives at and leaves the target site, the app on the mobile terminal equipment is utilized to punch a card on the face, and the app end program uploads the face picture and GPS position information and time information to the server end;
step 4, capturing and capturing the camera pictures at intervals of 10 minutes periodically by the background through the interface service, and transmitting the captured camera pictures to the server, or actively capturing a picture by the background and storing the picture on the server;
and 5, comparing the uploaded portrait information and face information by using an identification algorithm by the background system, and combining with the compared and uploaded GPS position information to confirm the presence evidence of the squatting person.
In step 5, the system firstly uses a target detection algorithm to obtain all persons on site, then uses the target recognition algorithm in combination with the feature set 2 to detect whether the patrolling agent appears in the site monitoring photo, and after the site monitoring photo at non-patrol time is excluded, the site monitoring photo of the patrolling agent is sorted, the occurrence rate of the patrolling agent is counted, and after the minimum threshold is met, the system judges that the patrolling agent is on site. The threshold is an adjustable parameter and can be configured according to the actual running condition of the application system.
An external broken-preventing personnel on-site supervision system for a power transmission line is shown in figure 2,
the system comprises a patrol task creation and execution module, a feature extraction module and a patrol behavior analysis module;
the patrol task creation and execution module is divided into two parts, namely an administrator creates the patrol task and an patrol personnel executes the task. An administrator selects a place needing to be patrolled through a WEB management page, then allocates patrolling personnel, creates a corresponding patrolling plan, and a background program pushes the task to the patrolling personnel needing to be patrolled through an app; the patrol personnel receives the task at the app end and then catches up with the task approach, and the information such as the GPS position, the face picture, the time, the field picture and the like of the patrol personnel are uploaded by adopting functions such as face punching, hidden danger reporting and the like.
The feature extraction module adopts a target detection algorithm, combines a DNN neural network and a pre-training model, performs frame analysis on the video, extracts facial features and forms a current inspector feature database; the automatic training engine processes the feature database, trains the feature database according to two conditions of a near view picture and a far view picture to obtain a near view feature set 1 and a far view feature set 2, and constructs a near view human image recognition model, a far view human image recognition model, a near view human face detection model and a far view human face detection model according to the near view feature set 1 and the far view feature set 2.
The inspection behavior analysis module adopts a background to judge whether a squatting person arrives at the scene in time by comparing face pictures uploaded by the app; the background service periodically grabs the camera pictures on site and transmits the camera pictures to the server, the program firstly carries out target detection on the pictures to identify the human images in the pictures, then the human images are matched with the patrol personnel by combining a face recognition algorithm, the site monitoring pictures of the patrol personnel are sorted, the occurrence rate of the patrol personnel is counted, and after the minimum threshold is met, the presence of the patrol personnel is judged, otherwise, the absence of the patrol personnel is judged.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (6)
1. The method for supervising the presence of the outer broken prevention personnel of the power transmission line is characterized by comprising the following steps:
step 1, collecting external broken line inspection squat figure information and face image information, and constructing a figure database sample and a face database sample as deep learning samples;
step 2, training the extracted human image sample and the human face picture sample respectively to obtain a human image recognition model and a human face detection model;
step 3, when the external broken line inspection gatekeeper arrives at and leaves the target site, the app on the mobile terminal equipment is utilized to punch a card on the face, and the app end program uploads the face picture and GPS position information and time information to the server end;
step 4, capturing and capturing a camera picture through the interface service periodically by the background, and transmitting the captured camera picture to a server;
and 5, comparing the uploaded portrait information and face information by using an identification algorithm by the background system, and combining with the compared and uploaded GPS position information to confirm the presence evidence of the squatting person.
2. The method for supervising the presence of power transmission line anti-external-damage personnel according to claim 1, wherein the method comprises the following steps:
in the step 1, frame extraction is carried out on the video of the external broken line inspection personnel by a video image sheet extraction technology, and a figure database sample and a face database sample of the external broken line inspection personnel are respectively constructed by an image enhancement and element recognition technology and an image face extraction technology.
3. The method for supervising the presence of power transmission line anti-external-damage personnel according to claim 1, wherein the method comprises the following steps:
in the step 2, the system adopts a target detection algorithm, combines a DNN neural network and a pre-training model, performs frame analysis on video, extracts facial features and forms a current patrol operator feature database; the automatic training engine processes the feature database, trains the feature database according to two conditions of a near view picture and a far view picture to obtain a near view feature set 1 and a far view feature set 2, and constructs a near view human image recognition model, a far view human image recognition model, a near view human face detection model and a far view human face detection model according to the near view feature set 1 and the far view feature set 2.
4. The utility model provides a transmission line prevents broken personnel present supervisory systems for realize the transmission line prevents broken personnel present supervisory methods of any one of claims 1-3, including patrol task creation and execution module, feature extraction module, patrol action analysis module, its characterized in that:
the patrol task creation and execution module is divided into two parts, namely an administrator creates the patrol task and an patrol personnel executes the task; an administrator selects a place needing to be patrolled through a WEB management page, then allocates patrolling personnel, creates a corresponding patrolling plan, and a background program pushes the task to the patrolling personnel needing to be patrolled through an app; the patrol personnel receives the task at the app end and then catches up with the task approach, and the information such as the GPS position, the face picture, the time, the field picture and the like of the patrol personnel are uploaded by adopting the functions of face card punching and hidden danger reporting;
the feature extraction module adopts a target detection algorithm, combines a DNN neural network and a pre-training model, performs frame analysis on the video, extracts facial features and forms a current inspector feature database; the automatic training engine processes the feature database, trains the feature database according to two conditions of a near view picture and a far view picture to obtain a near view feature set and a far view feature set, and constructs a near view human image recognition model, a far view human image recognition model, a near view human face detection model and a far view human face detection model according to the near view feature set and the far view feature set;
the inspection behavior analysis module adopts a background to judge whether a squatting person arrives at the scene in time by comparing face pictures uploaded by the app; the background service periodically grabs the camera pictures on site and transmits the camera pictures to the server, the program firstly carries out target detection on the pictures to identify the human images in the pictures, then the human images are matched with the patrol personnel by combining a face recognition algorithm, the site monitoring pictures of the patrol personnel are sorted, the occurrence rate of the patrol personnel is counted, and after the minimum threshold is met, the presence of the patrol personnel is judged, otherwise, the absence of the patrol personnel is judged.
5. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-3.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-3.
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| CN (1) | CN116485122A (en) |
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2023
- 2023-04-11 CN CN202310380855.0A patent/CN116485122A/en active Pending
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