CN112270274A - Intelligent identification method for electric power safety tool - Google Patents
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Abstract
The invention discloses an intelligent identification method for an electric power safety tool, and belongs to the technical field of tool management. The intelligent identification method for the electric power safety tools and appliances can realize the one-to-one correspondence among the safety tools and appliances, RFID tags and operating personnel through the steps of binding the tools and appliances, taking the tools and appliances, positioning the tools and appliances, identifying the tools and appliances, making a combined decision and the like, and can be used for independently managing the life cycles of the tools and appliances of different types, thereby effectively improving the standardized management level of the life cycles of the tools.
Description
Technical Field
The invention relates to the technical field of tool management, in particular to an intelligent identification method for an electric power safety tool.
Background
The electric power is life pulse of people, the safety is the life line of the electric power, and the guideline of 'safety is first, prevention is first, and comprehensive treatment' is a permanent theme of the electric power industry. With the continuous development of economy in China, the electric power industry realizes the leap-type progress, and the electric power safety production management is taken as the key point of national supervision and is more and more concerned by various social circles. At present, electric power safety accidents still occur occasionally, and irreparable great loss is caused to countries, enterprises and related families.
The electric power enterprise highly attaches importance to safe production and management work, especially can discover in time and correct the violation of regulations operation, is dedicated to strengthening the safe production of electric wire netting operation and monitoring management, emphasizes the safety control of the whole life cycle of the electric power safety tool. The safety tool is a special tool and an apparatus for electric power enterprises to ensure personal safety of operators and prevent accidents such as electric shock, burn, fall and the like, and is necessary equipment for electric power operators to carry out daily operation and maintenance.
The existing intelligent tool cabinet or intelligent storehouse for storing electric power safety tools has a plurality of problems in tool intelligent identification, and firstly, an infrared correlation type detection method is used for detecting whether a tool exists at the position of a certain fixed device or not, but the device cannot be identified; secondly, through installing the hyperfrequency card reader at the storehouse gate, paste hyperfrequency electronic tags on the instrument, only need manage through the infrared ray that detects receiving electronic tags when passing in and out the door, nevertheless appear judging chaotic problem when a plurality of equipment pass in and out simultaneously easily.
Disclosure of Invention
1. Technical problem to be solved
Aiming at the problems in the prior art, the invention aims to provide an intelligent identification method for an electric power safety tool, which can realize the one-to-one correspondence among the safety tool, an RFID (radio frequency identification) tag and an operator, can be used for independently managing the life cycles of different types of tools and effectively improving the standardized management level of the tool life cycle.
2. Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
An intelligent identification method for an electric power safety tool comprises the following steps:
step one, binding tools and instruments, storing effective identity information of an operator in an RFID label in advance, sticking the label on a safety tool and instrument matched with the operator, and placing the safety tool and instrument in a warehouse;
step two, taking out the tools, enabling an operator to enter a warehouse according to the effective identity information of the operator, scanning the RFID label through the communication mobile equipment, bouncing out of an identity verification page after scanning, taking out the tools after the operator passes identity verification, and then taking out the tools from the warehouse through scanning the RFID label;
step three, positioning the tool, wherein after an operator carries the tool to enter an operation site, the RFID tag carried on the tool enters a magnetic field of movable equipment, whether the tool is complete or not is judged through the movable equipment, and an identified result is displayed;
identifying the tools, capturing pictures of various tools in the operation site by using a camera, detecting and classifying targets by using a deep learning technology under the assistance of a GPU, and displaying results on the movable equipment;
and step five, joint decision, namely judging whether the tools carried by the operating personnel are complete and qualified through the joint decision.
Further, the valid identity information of the operator in the first step includes but is not limited to face information, name, age, gender, job number, department and mobile phone number.
Further, the effective identity information of the operating personnel entering the warehouse in the second step is face information or work number information, and the operating personnel enter the warehouse by scanning faces or inputting work numbers.
Further, the identity verification mode in the second step is face information verification or short message verification code verification.
Furthermore, the movable equipment in the third step is equipment with a GPS receiver, an RFID reader, a singlechip and a touch display screen; the special radio frequency signal is sent by the RFID reader, the RFID tag can send out tool information stored in a chip by virtue of energy obtained by induced current after receiving the special radio frequency signal sent by the RFID reader, whether tools are complete or not is judged according to all returned tool information, and the special radio frequency signal can be sent out and used for identifying whether tools carried by operating personnel are complete or not; the GPS receiver is used for positioning the position of the operation site.
Further, the result of whether the tools and the instruments identified in the third step are complete and the result of target detection and classification in the fourth step are displayed on the touch display screen.
Further, the camera is mounted on the mobile device.
Further, the target detection mode in the fourth step is the Faster R-CNN algorithm.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
(1) the scheme can realize the one-to-one correspondence among the safety tools, the RFID tags and the operating personnel, the life cycles of the tools of different types are managed independently, and the standardized management level of the tool life cycle is effectively improved.
(2) The movement track of the tools after being taken out of the warehouse is positioned and the using process is monitored, so that the corresponding tools can be identified on the fixing equipment.
(3) The one-to-one correspondence relationship among the safety tools, the RFID tags and the operators is established, so that the problem of judgment confusion is not easy to occur when a plurality of tools enter and exit simultaneously.
(4) The performance of the Faster R-CNN network model is enhanced by combining an online difficult sample mining mechanism, the problem of unbalance of positive and negative samples in the network training process is solved, the detection result is more robust, and the accuracy of wearing safety tools by detection operators is further improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of the identification, location and joint decision portion of the tool of the present invention;
FIG. 3 is a schematic diagram showing the overall structure of the Faster R-CNN of the present invention.
Detailed Description
The drawings in the embodiments of the invention will be combined; the technical scheme in the embodiment of the invention is clearly and completely described; obviously; the described embodiments are only some of the embodiments of the invention; but not all embodiments, are based on the embodiments of the invention; all other embodiments obtained by a person skilled in the art without making any inventive step; all fall within the scope of protection of the present invention.
Referring to fig. 1-3, an intelligent identification method for an electric power safety tool includes the following steps:
step one, binding tools and instruments, storing effective identity information of an operator in an RFID label in advance, sticking the label on a safety tool and instrument matched with the operator, and placing the safety tool and instrument in a warehouse;
step two, taking out the tools, enabling an operator to enter a warehouse according to the effective identity information of the operator, scanning the RFID label through the communication mobile equipment, bouncing out of an identity verification page after scanning, taking out the tools after the operator passes identity verification, and then taking out the tools from the warehouse through scanning the RFID label;
step three, positioning the tool, wherein after an operator carries the tool to enter an operation site, the RFID tag carried on the tool enters a magnetic field of movable equipment, whether the tool is complete or not is judged through the movable equipment, and an identified result is displayed;
identifying the tools, capturing pictures of various tools in the operation site by using a camera, detecting and classifying targets by using a deep learning technology (visual intelligent algorithm) under the assistance of a GPU (and a CPU), and displaying results on the movable equipment;
and step five, joint decision, namely judging whether the tools carried by the operating personnel are complete and qualified through the joint decision.
The effective identity information of the operator in the first step includes but is not limited to face information, name, age, sex, job number, department and mobile phone number.
And in the second step, the effective identity information of the operator entering the library is face information or work number information, and the operator enters the library by scanning the face or inputting the work number.
And in the second step, the identity verification mode is face information verification or short message verification code verification.
The movable equipment in the third step is equipment with a GPS receiver, an RFID reader, a singlechip and a touch display screen; the special radio frequency signal is sent by the RFID reader, the RFID tag can send out tool information stored in a chip by virtue of energy obtained by induced current after receiving the special radio frequency signal sent by the RFID reader, whether tools are complete or not is judged according to all returned tool information, and the special radio frequency signal can be sent out and used for identifying whether tools carried by operating personnel are complete or not; the GPS receiver is used for positioning the position of the operation site.
And displaying the result of whether the tools and the instruments identified in the third step are complete and the result of target detection and classification in the fourth step on the touch display screen.
The camera is installed on the mobile device.
The target detection mode in the fourth step is a fast R-CNN algorithm, and the fast R-CNN is suitable for small target detection and occasions with higher detection precision.
The Fast R-CNN algorithm, which consists of generating a Region-proposed Network RPN (Region probable Network, RPN) of candidate regions for extracting candidate boxes and detecting Fast R-CNN based on the prosol extracted by the RPN and recognizing a target in the prosol.
The Faster R-CNN algorithm consists of four parts: (1) a feature extraction section: extracting feature maps from an original image by conv, relu and poolling, wherein the feature maps are used for extracting the features of the image, the feature maps are input into the whole image, and the extracted features are output and called as the feature maps; (2) the RPN network is used for recommending candidate regions, wherein the RPN network and the FastR-CNN share the same CNN, the input is feature maps, and the output is a plurality of candidate regions; (3) the input and the output are the same as the input and the output of the RoI pooling in the Faster R-CNN; (4) classification and regression, the output of this layer is the final objective, the class to which the candidate region belongs, and the precise location of the candidate region in the image are output, and its overall structure is shown in fig. 3.
The RPN training principle and the loss function RPN use two classifications, only distinguishing background from object, but do not predict the class of object, i.e. class-aggregation. Because the coordinate values are predicted at the same time, when training the RPN, the prior frame is matched with the ground-route box, and the principle is as follows: first, the IoU highest prior box with a certain group-route box; secondly, a priori box with IoU value greater than 0.7 with a certain group-channel box can match a group-channel as long as one is satisfied, so that the priori box is a positive sample (object) and the group-channel is taken as a regression target.
For those prior boxes with IoU values below 0.3 with any one of the group-channel box, which are considered negative samples, then network training is performed and parameters are fine-tuned, the loss function of the image is defined as:
wherein: i represents the ith candidate frame index in the small batch processing; p is a radical ofiIs the probability that the ith candidate frame is the target, if i is the candidate target, then pi *Is 1, otherwise is 0; t is ti＝{tx，ty，tw，thIs a vector representing the predicted parameterized candidate box coordinates; t is ti *Corresponding to the coordinate vector of the real target box. t is tiAnd ti *Is defined as:
wherein: (x, y) is the coordinates of the center point of the bounding box; (x)a，ya) Coordinates of the candidate frame; (x)*，y*) Bounding box coordinates for the real region; w and h are the width of the bounding boxAnd high; the objective of the algorithm is to find a relationship that maps the original box P to a regression box closer to the real box G, the loss function L of the classificationclsIs defined as:
the regression loss function Lreg is defined as:
wherein R is smoothL1Function, smoothL1The function is as follows:
the Faster R-CNN model adopts a 4-step iterative training strategy: firstly, pre-training RPN on an image library ImageNet, and fine-tuning on a PASCAL VOC data set; secondly, a Fast R-CNN model is trained independently by using region primers generated by the trained PRN, and the model is also pre-trained on ImageNet; thirdly, initializing RPN by using a CNN model part of Fast R-CNN, and then performing fine-tuning on the residual layer in the RPN, wherein feature extractors of the Fast R-CNN and the RPN are shared; fourthly, fixing a feature extractor, and performing fine-tuning on the rest layer of Fast R-CNN; thus, through multiple iterations, Fast R-CNN can be organically fused with RPN to form a unified network.
Two inputs of the joint decision are respectively from the tool product information of the operation field tool positioning identification module based on the RFID technology and the global positioning technology and the tool type information obtained by the operation field tool identification module based on the visual perception algorithm, and a conclusion whether the tools are complete and qualified is finally given through the joint decision.
The information such as the monitoring picture captured by the camera, the position positioned by the GPS receiver, the tool information, the joint decision data and the like is transmitted to the remote management personnel in an information mutual transmission mode, for the remote management personnel, the information can be input and output, the input is mainly voice, the output comprises the monitoring picture, the operation site positioning information, the tool information, the decision data and the like, and for the site construction personnel, the output is only voice call data or warning.
The one-to-one correspondence among the safety tools, the RFID tags and the operators can be realized, the life cycles of the tools of different types are managed independently, and the standardized management level of the tool life cycle is effectively improved.
The above; but are merely preferred embodiments of the invention; the scope of the invention is not limited thereto; any person skilled in the art is within the technical scope of the present disclosure; the technical scheme and the improved concept of the invention are equally replaced or changed; are intended to be covered by the scope of the present invention.
Claims (8)
1. An intelligent identification method for an electric power safety tool is characterized in that: the method comprises the following steps:
step one, binding tools and instruments, storing effective identity information of an operator in an RFID label in advance, sticking the label on a safety tool and instrument matched with the operator, and placing the safety tool and instrument in a warehouse;
step two, taking out the tools, enabling an operator to enter a warehouse according to the effective identity information of the operator, scanning the RFID label through the communication mobile equipment, bouncing out of an identity verification page after scanning, taking out the tools after the operator passes identity verification, and then taking out the tools from the warehouse through scanning the RFID label;
step three, positioning the tool, wherein after an operator carries the tool to enter an operation site, the RFID tag carried on the tool enters a magnetic field of movable equipment, whether the tool is complete or not is judged through the movable equipment, and an identified result is displayed;
identifying the tools, capturing pictures of various tools in the operation site by using a camera, detecting and classifying targets by using a deep learning technology under the assistance of a GPU, and displaying results on the movable equipment;
and step five, joint decision, namely judging whether the tools carried by the operating personnel are complete and qualified through the joint decision.
2. The intelligent identification method for the electric power safety tool according to claim 1, characterized in that: the effective identity information of the operator in the first step includes but is not limited to face information, name, age, sex, job number, department and mobile phone number.
3. The intelligent identification method for the electric power safety tool according to claim 2, characterized in that: and in the second step, the effective identity information of the operator entering the library is face information or work number information, and the operator enters the library by scanning the face or inputting the work number.
4. The intelligent identification method for the electric power safety tool according to claim 1, characterized in that: and in the second step, the identity verification mode is face information verification or short message verification code verification.
5. The intelligent identification method for the electric power safety tool according to claim 1, characterized in that: the movable equipment in the third step is equipment with a GPS receiver, an RFID reader, a singlechip and a touch display screen; the special radio frequency signal is sent by the RFID reader, the RFID tag can send out tool information stored in a chip by virtue of energy obtained by induced current after receiving the special radio frequency signal sent by the RFID reader, whether tools are complete or not is judged according to all returned tool information, and the special radio frequency signal can be sent out and used for identifying whether tools carried by operating personnel are complete or not; the GPS receiver is used for positioning the position of the operation site.
6. The intelligent identification method for the electric power safety tool according to claim 5, characterized in that: and displaying the result of whether the tools and the instruments identified in the third step are complete and the result of target detection and classification in the fourth step on the touch display screen.
7. The intelligent identification method for the electric power safety tool according to claim 1, characterized in that: the camera is installed on the mobile device.
8. The intelligent identification method for the electric power safety tool according to claim 1, characterized in that: the target detection mode in the fourth step is a Faster R-CNN algorithm.
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| CN109559081A (en) * | 2019-02-01 | 2019-04-02 | 沈阳宝通门业有限公司 | A kind of storehouse tool management system and control method based on RFID label tag |
| CN110991574A (en) * | 2019-11-07 | 2020-04-10 | 北京交通大学 | RFID-based intelligent supervision system for field tools |
| CN111259893A (en) * | 2020-01-19 | 2020-06-09 | 柳潆林 | Intelligent tool management method based on deep learning |
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