CN111754737A - Online identification and evaluation device and method for installation acceptance of metering device - Google Patents
Online identification and evaluation device and method for installation acceptance of metering device Download PDFInfo
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Abstract
The invention relates to an online identification evaluation device and an online identification evaluation method for installation and acceptance of a metering device, aiming at solving the technical problems that the evaluation of the installation and acceptance of the existing metering device is greatly influenced by external environment and subjective factors, the input of manpower and material resources is large, the operation process can not be monitored in real time and the automatic evaluation and alarm functions can not be realized, and the technical scheme of the invention is as follows: the utility model provides an online identification evaluation device of metering device installation acceptance, includes image acquisition preprocessing module, image recognition analysis module, evaluation module of grading, treater, storage module, orientation module, man-machine interaction module and alarm module. The evaluation method adopts the front-end acquisition, intelligent identification and deep learning framework technology to process the images, can realize image identification, evaluate and score in real time, intelligently manage the metering device and reduce the cost of manpower and material resources.
Description
Technical Field
The invention belongs to the field of electric power measurement, and particularly relates to an online identification evaluation device and an online identification evaluation method for installation and acceptance of a metering device.
Background
With the continuous increase of the technical complexity of the power grid, more and more factors influencing the safety of the power grid exist, and the situation of safe production is increasingly severe. Although complete electric power safety production regulations are established by power grids and electric power enterprises, at the present stage, the problems of frequent violation behaviors caused by loose electric power safety management, weak safety consciousness of operating personnel and the like still exist. The modern power safety production puts forward the requirements of monitoring the operation process, tracing the result and intelligently alarming for the safety supervision evaluation system.
At present, the electric power industry inspects and accepts the installation of the metering device through an aircraft, and then evaluates the quality through manual visual inspection, so that a large amount of manpower and material resources are required to be input, the flight inspection is easily influenced by various uncertain factors, the manual visual inspection is greatly influenced by the subjective factors, the installation state of the equipment cannot be accurately mastered timely, objectively and comprehensively, and the automatic evaluation function cannot be realized.
Disclosure of Invention
The invention aims to solve the technical problems that the evaluation of the installation acceptance of the existing metering device is greatly influenced by external environment and subjective factors, the investment of manpower and material resources is large, the operation process cannot be monitored in real time and the automatic evaluation and alarm functions cannot be realized, and provides an online identification evaluation device and an online identification evaluation method for the installation acceptance of the metering device.
In order to achieve the purpose, the invention adopts the technical scheme that:
an online identification and evaluation device for installation and acceptance of a metering device comprises an image acquisition preprocessing module, an image identification and analysis module, an evaluation and scoring module, a processor, a storage module, a positioning module, a human-computer interaction module and an alarm module;
the image acquisition preprocessing module consists of an image sensor, an acquisition module and an image noise reduction processing module, wherein the image sensor shoots images and/or videos of the electric energy metering device to be evaluated on site and converts the shot video files into RGB images frame by frame; the acquisition module acquires images and/or videos shot by the image sensor in a real-time and lossless manner; the image noise reduction processing module is used for eliminating noise in the picture and/or the video sent by the acquisition module;
the image recognition analysis module is composed of an image training module and an image prediction module, the image training module receives image information sent by the image acquisition preprocessing module, labels images to be recognized, trains by using a deep learning Gaussian-yolo framework, obtains a model file and stores the model file; the image prediction module loads a trained model file, sequentially performs a training process on the collected target image, samples, convolutions, pooling and full-connection layers at multiple levels, and finally outputs the category, position and confidence information of each target in the image;
the evaluation scoring module consists of a scoring rule base and a quantification scoring module, wherein the scoring rule base specifies a product scoring rule according to the state characteristics of the product and the influence degree of the state characteristics of the product on the product; the quantitative scoring module is used for evaluating and scoring according to a scoring rule according to the category of each target in the image output by the image recognition and analysis module;
the processor processes and stores data sent by the image acquisition preprocessing module, the image recognition analysis module, the evaluation scoring module and the positioning module, sends an alarm instruction to the electric energy metering device to be evaluated, the comprehensive score of which is lower than a score threshold value, and completes the interaction between an operator and the recognition and evaluation device through the man-machine interaction module;
the storage module stores historical comprehensive scores and scores of various categories of the electric energy metering device to be tested and image recognition analysis data of the target image;
the positioning module is used for positioning the real-time position of the electric energy metering device to be measured;
the human-computer interaction module: finishing interactive dialogue between an operator and the recognition and evaluation device through an interface;
and the alarm module receives the alarm signal of the processor and completes the alarm.
An evaluation method of an online identification and evaluation device for installation and acceptance of a metering device comprises the following steps:
1) establishing a scoring rule base: according to the state characteristics of the product and the influence degree of the state characteristics of the product on the product, making a product scoring rule and warehousing the product scoring rule;
2) image acquisition and preprocessing: shooting images and/or videos of the electric energy metering device to be evaluated on site, converting the shot video files into RGB images frame by frame, collecting the shot images and/or videos in a real-time and lossless manner, and then carrying out noise reduction processing on the collected images and/or videos;
3) identification and analysis of images: marking the preprocessed target image by using a marking tool, then training by using a deep learning Gaussian _ yolo frame to obtain and store a model file, then loading the trained model file, sequentially carrying out the steps of a training process on the acquired target image, and obtaining and outputting the category, position and confidence information of each target in the image through sampling, convolution, pooling and full connection layers at multiple levels;
4) evaluation of the images was scored: scoring according to the category, position and confidence information of each target in the image and the established scoring rule;
5) and (4) alarming: judging whether to alarm or not according to the target comprehensive score given by the scoring, and sending an alarm signal and alarming when the score is lower than a preset score threshold value;
6) data storage: and storing the image identification analysis data and the historical comprehensive score data of the target image.
Further, the product scoring rule in the step 1) is the provision of meter box process detection, meter box installation correction, meter box surface detection, meter box surface two-dimensional code extraction, meter detection, meter installation correction, meter lead sealing, meter incoming line, safety detection, line leakage detection and wiring process scoring.
Further, the labeling in the step 3) is to perform characteristic labeling on the ammeter, wiring, damaged and exposed areas in the image.
Further, the step of training in step 3) includes:
(1) configuring a training file and batching image files;
(2) cutting the size of each image of each batch according to the configured training file, and zooming to the same size;
(3) sampling, convolving, pooling and carrying out feature extraction on each image in a multi-level manner according to a training file;
(4) carrying out regression analysis on the extracted features and the marked feature regions;
(5) and updating the value of each layer of neuron, and obtaining and storing a model file after training for N times.
Further, the evaluation rule in the step 4) is performed according to a percentage system, and the evaluation rule specifically includes: category score (total number of detected targets-number of abnormal targets)/total number of detected targets × score assignment of the category × 1/2
And when the number of the abnormal targets is zero, the category is full, and the sum of the category scores of all the categories is the comprehensive score.
Further, the training file in the training step 1) includes the size of the processed image, the sampling, convolution, pooling and full connection at multiple levels, and parameters of each processing.
The invention has the beneficial effects that:
1. the online identification and evaluation device and the evaluation method for the installation and acceptance of the metering device provided by the invention utilize image identification and AI technology to carry out front-end acquisition and intelligent identification on the meter box process, the meter component, the line safety and the like in all directions, the intelligent image identification algorithm can distinguish the appearances, internal structures and installation processes of different types of meter boxes, and carry out comprehensive evaluation and scoring on identification results, so that the influence of artificial subjective factors can be eliminated to the maximum extent, the identification efficiency is improved, and the device and the method are convenient and rapid.
2. The online identification and evaluation device for installation and acceptance of the metering device has the characteristics of convenience in carrying, real-time monitoring and identification, objective evaluation and the like, and workers can carry the equipment to collect the safety of the installed meter box process, the meter components and the line in real time on site by an image identification method, evaluate and score in real time, intelligently manage the metering device and reduce the cost of manpower and material resources.
Drawings
FIG. 1 is a schematic block diagram of an installation acceptance online identification and evaluation device of a metering device according to the present invention;
FIG. 2 is a flow chart of the evaluation method of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
As shown in fig. 1, the online identification and evaluation device for installation and acceptance of a metering device in the present embodiment includes an image acquisition and preprocessing module, an image identification and analysis module, an evaluation and scoring module, a processor, a storage module, a positioning module, a human-computer interaction module, and an alarm module;
the image acquisition preprocessing module consists of an image sensor, an acquisition module and an image noise reduction processing module, wherein the image sensor shoots images and/or videos of the electric energy metering device to be evaluated on site and converts the shot video files into RGB images frame by frame; the acquisition module acquires images and/or videos shot by the image sensor in a real-time and lossless manner; the image noise reduction processing module is used for eliminating noise in the picture and/or the video sent by the acquisition module;
the image recognition analysis module is composed of an image training module and an image prediction module, the image training module receives image information sent by the image acquisition preprocessing module, labels images to be recognized, trains by using a deep learning Gaussian-yolo framework, obtains a model file and stores the model file; the image prediction module loads a trained model file, sequentially performs a training process on the collected target image, samples, convolutions, pooling and full-connection layers at multiple levels, and finally outputs the category, position and confidence information of each target in the image;
the evaluation scoring module consists of a scoring rule base and a quantification scoring module, wherein the scoring rule base specifies a product scoring rule according to the state characteristics of the product and the influence degree of the state characteristics of the product on the product; the quantitative scoring module is used for evaluating and scoring according to a scoring rule according to the category of each target in the image output by the image recognition and analysis module;
the processor processes and stores data sent by the image acquisition preprocessing module, the image recognition analysis module, the evaluation scoring module and the positioning module, sends an alarm instruction to the electric energy metering device to be evaluated, the comprehensive score of which is lower than a score threshold value, and completes the interaction between an operator and the recognition and evaluation device through the man-machine interaction module;
the storage module stores historical comprehensive scores and scores of various categories of the electric energy metering device to be tested and image recognition analysis data of the target image;
the positioning module is used for positioning the real-time position of the electric energy metering device to be measured;
the human-computer interaction module finishes interaction dialogue between an operator and the recognition and evaluation device through an interface;
and the alarm module receives the alarm signal of the processor and completes the alarm.
The evaluation method of the online identification and evaluation device for installation and acceptance of the metering device comprises the following steps:
1) establishing a scoring rule base: according to the state characteristics of the product and the influence degree of the state characteristics of the product on the product, making a product scoring rule and warehousing the product scoring rule; the rule of grading of product for detecting case technology, table case installation just, table case surface detection, detect table case surface two-dimensional code and draw, the ammeter detects, the ammeter installation is just, the ammeter lead sealing, the ammeter is gone into line, safety inspection, leak the rule of detecting and the rule of technology of working a telephone switchboard grade, specifically do: the meter box process detection is 30 minutes, the meter box is installed for 10 minutes, the meter box surface detection is 10 minutes, and the two-dimensional code on the meter box surface detection is 10 minutes; the method comprises the following steps of (1) detecting an electric meter for 30 minutes, rightly installing the electric meter for 10 minutes, sealing the lead of the electric meter for 10 minutes, and enabling the electric meter to enter a line for 10 minutes; the safety detection is carried out for 40 minutes, the missing line detection is carried out for 20 minutes, and the wiring process is carried out for 20 minutes;
2) image acquisition and preprocessing: shooting images and/or videos of the electric energy metering device to be evaluated on site, converting the shot video files into RGB images frame by frame, collecting the shot images and/or videos in a real-time and lossless manner, and then carrying out noise reduction processing on the collected images and/or videos;
3) identification and analysis of images: carrying out characteristic labeling on ammeter, wiring, damaged and exposed areas in the preprocessed target image by using a labeling tool, then training by using a deep learning Gaussian-yolo frame to obtain and store a model file, then loading the trained model file, sequentially carrying out the steps of a training process on the acquired target image, and obtaining and outputting the category, position and confidence information of each target in the image through sampling, convolution, pooling and full connection layers at multiple levels; wherein the step of training comprises:
(1) configuring a training file and batching image files; the training file comprises the size of the processed image, multi-level sampling, convolution, pooling and full connection, and parameters of each processing;
(2) cutting the size of each image of each batch according to the configured training file, and zooming to the same size;
(3) sampling, convolving, pooling and carrying out feature extraction on each image in a multi-level manner according to a training file;
(4) carrying out regression analysis on the extracted features and the marked feature regions;
(5) updating the value of each layer of neuron, and obtaining and storing a model file after training for N times;
4) evaluation of the images was scored: scoring according to the category, position and confidence information of each target in the image and the established scoring rule; the evaluation rule is carried out according to a percentage system, the full score is 100, and the evaluation rule specifically comprises the following steps:
category score (total number of detected targets-number of abnormal targets)/total number of detected targets × score assignment of the category × 1/2
When the number of the abnormal targets is zero, the category is full, and the sum of the category scores of all the categories is a comprehensive score;
5) and (4) alarming: judging whether to alarm or not according to the target comprehensive score given by the scoring, and sending an alarm signal and alarming when the score is lower than a preset score threshold value;
6) data storage: and storing the image identification analysis data and the historical comprehensive score data of the target image.
Claims (7)
1. The utility model provides a metering device installation is checked and is accepted on-line identification evaluation device which characterized in that: the system comprises an image acquisition preprocessing module, an image recognition analysis module, an evaluation scoring module, a processor, a storage module, a positioning module, a human-computer interaction module and an alarm module;
the image acquisition preprocessing module consists of an image sensor, an acquisition module and an image noise reduction processing module, wherein the image sensor shoots images and/or videos of the electric energy metering device to be evaluated on site and converts the shot video files into RGB images frame by frame; the acquisition module acquires images and/or videos shot by the image sensor in a real-time and lossless manner; the image noise reduction processing module is used for eliminating noise in the picture and/or the video sent by the acquisition module;
the image recognition analysis module consists of an image training module and an image prediction module, wherein the image training module receives image information sent by the image acquisition preprocessing module, labels an image to be recognized, and then trains by using a deep learning Gaussian-yolo framework to obtain and store a model file; the image prediction module loads a trained model file, sequentially performs a training process on the collected target image, samples, convolutions, pooling and full-connection layers at multiple levels, and finally outputs the category, position and confidence information of each target in the image;
the evaluation scoring module consists of a scoring rule base and a quantification scoring module, wherein the scoring rule base specifies a product scoring rule according to the state characteristics of the product and the influence degree of the state characteristics of the product on the product; the quantitative scoring module is used for evaluating and scoring according to a scoring rule according to the category of each target in the image output by the image recognition and analysis module;
the processor processes and stores data sent by the image acquisition preprocessing module, the image recognition analysis module, the evaluation scoring module and the positioning module, sends an alarm instruction to the electric energy metering device to be evaluated, the comprehensive score of which is lower than a score threshold value, and completes the interaction between an operator and the recognition and evaluation device through the man-machine interaction module;
the storage module stores historical comprehensive scores and scores of various categories of the electric energy metering device to be tested and image recognition analysis data of the target image;
the positioning module is used for positioning the real-time position of the electric energy metering device to be measured;
the human-computer interaction module finishes interaction dialogue between an operator and the recognition and evaluation device through an interface;
and the alarm module receives the alarm signal of the processor and completes the alarm.
2. The evaluation method of the online identification and evaluation device for the installation acceptance of the metering device according to claim 1, characterized in that: the method comprises the following steps:
1) establishing a scoring rule base: according to the state characteristics of the product and the influence degree of the state characteristics of the product on the product, making a product scoring rule and warehousing the product scoring rule;
2) image acquisition and preprocessing: shooting images and/or videos of the electric energy metering device to be evaluated on site, converting the shot video files into RGB images frame by frame, collecting the shot images and/or videos in a real-time and lossless manner, and then carrying out noise reduction processing on the collected images and/or videos;
3) identification and analysis of images: marking the preprocessed target image by using a marking tool, then training by using a deep learning Gaussian _ yolo frame to obtain and store a model file, then loading the trained model file, sequentially carrying out the steps of a training process on the acquired target image, and obtaining and outputting the category, position and confidence information of each target in the image through sampling, convolution, pooling and full connection layers at multiple levels;
4) evaluation of the images was scored: scoring according to the category, position and confidence information of each target in the image and the established scoring rule;
5) and (4) alarming: judging whether to alarm or not according to the target comprehensive score given by the scoring, and sending an alarm signal and alarming when the score is lower than a preset score threshold value;
6) data storage: and storing the image identification analysis data and the historical comprehensive score data of the target image.
3. The evaluation method according to claim 2, characterized in that: the product scoring rule in the step 1) is a rule for detecting meter box processes, installing a meter box rightly, detecting the surface of the meter box, extracting two-dimensional codes on the surface of the detected meter box, detecting an electric meter, installing the electric meter rightly, sealing the lead of the electric meter, entering the electric meter, detecting safety, detecting missing lines and scoring a wiring process.
4. The evaluation method according to claim 2, characterized in that: and the marking in the step 3) is to mark the characteristics of the ammeter, the wiring, the damaged area and the exposed area in the image.
5. The evaluation method according to claim 2, wherein the step of training in step 3) comprises:
(1) configuring a training file and batching image files;
(2) cutting the size of each image of each batch according to the configured training file, and zooming to the same size;
(3) sampling, convolving, pooling and carrying out feature extraction on each image in a multi-level manner according to a training file;
(4) carrying out regression analysis on the extracted features and the marked feature regions;
(5) and updating the value of each layer of neuron, and obtaining and storing a model file after training for N times.
6. The evaluation method according to claim 2, characterized in that: the evaluation rule in the step 4) is carried out according to a percentage system, and the evaluation rule specifically comprises the following steps:
category score (total number of detected targets-number of abnormal targets)/total number of detected targets × score assignment of the category × 1/2
And when the number of the abnormal targets is zero, the category is full, and the sum of the category scores of all the categories is the comprehensive score.
7. The evaluation method according to claim 5, characterized in that: the training file in the training step 1) comprises the size of the processed image, the sampling, convolution, pooling and full connection under multiple levels and parameters of each processing.
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| CN116228166A (en) * | 2023-04-21 | 2023-06-06 | 广州智算信息技术有限公司 | Water heater installation acceptance system based on artificial intelligence |
| CN116228166B (en) * | 2023-04-21 | 2023-08-04 | 广州智算信息技术有限公司 | Water heater installation acceptance system based on artificial intelligence |
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