CN111754737A - On-line identification and evaluation device and evaluation method for installation and acceptance of metering device - Google Patents

Abstract

The invention relates to an online identification and evaluation device and an evaluation method for the installation and acceptance of a metering device, and aims to solve the problem that the evaluation of the installation and acceptance of the existing metering device is greatly affected by the external environment and subjective factors, the input of manpower and material resources is large, and the operation process cannot be monitored in real time and cannot be realized. The technical problem of automatic evaluation and alarm function, the technical solution of the present invention is: an online identification and evaluation device for installation and acceptance of a metering device, including an image acquisition preprocessing module, an image recognition analysis module, an evaluation scoring module, a processor, a storage module, a positioning module module, human-computer interaction module and alarm module. The evaluation method uses front-end acquisition, intelligent recognition and deep learning framework technology to process images, which can realize image recognition, real-time evaluation and scoring, intelligent management of metering devices, and reduce labor and material costs.

Description

On-line identification and evaluation device and evaluation method for installation and acceptance of metering device

technical field

The invention belongs to the field of electric power metering, and in particular relates to an online identification and evaluation device and an evaluation method for the installation and acceptance of a metering device.

Background technique

With the increasing complexity of power grid technology, there are more and more factors affecting power grid security, and the safety production situation is becoming increasingly severe. Although power grids and power companies have formulated complete power safety production regulations, there are still many problems of frequent violations caused by lax power safety management and weak safety awareness of operators. Modern electric power safety production puts forward the requirements of safety monitoring and evaluation system that the operation process can be monitored, the results can be traced and intelligent alarming.

At present, the evaluation of the installation and acceptance of the metering device in the electric power industry is based on the inspection of the aircraft, and then the evaluation of the pros and cons through manual visual inspection, which requires a lot of manpower and material resources. Subjective factors have a great influence, and it is impossible to accurately grasp the installation status of the equipment in a timely, objective and comprehensive manner, and cannot realize the automatic evaluation function.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the technical problems that the evaluation of the installation and acceptance of the existing metering device is greatly affected by the external environment and subjective factors, the input 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 a metering device is provided. Installation and acceptance online identification and evaluation device and evaluation method.

For achieving the above object, the technical scheme adopted in the present invention is:

An online identification and evaluation device for installation and acceptance of a metering device, comprising an image acquisition preprocessing module, an image identification 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 is composed of an image sensor, an acquisition module and an image noise reduction processing module. The image sensor captures images and/or videos of the on-site electric energy metering device to be evaluated, and converts the captured video files frame by frame. It is an RGB image; the acquisition module collects images and/or videos captured by the image sensor in real time without loss; the image noise reduction processing module removes noise in the pictures and/or videos 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 the image information sent by the image acquisition preprocessing module, marks the image to be recognized, and then uses the deep learning Gaussian_yolo framework for training to obtain model file and save it; the image prediction module loads the trained model file, sequentially performs the steps of the training process on the collected target image, multi-level sampling, convolution, pooling and full connection layer, and finally outputs the image in the Category, location and confidence information for each target;

The evaluation scoring module is composed of a scoring rule base and a quantitative scoring module. The scoring rule base specifies product scoring rules according to the state characteristics of the product and the degree of influence of the product state characteristics on the product; the quantitative scoring module is based on image recognition and analysis. The module outputs the category of each target in the image, and evaluates and scores according to the scoring rules;

The processor processes and stores the data sent by the image acquisition preprocessing module, the image recognition and analysis module, the evaluation scoring module and the positioning module, and issues an alarm instruction to the electric energy metering device to be evaluated whose comprehensive score is lower than the score threshold, The interaction between the operator and the identification evaluation device is completed through the human-computer interaction module;

The storage module stores historical comprehensive scores and scores of various categories and image recognition analysis data of the target image of the electric energy metering device to be tested;

The positioning module locates the real-time position where the electric energy metering device to be measured is located;

The human-computer interaction module: completes the interactive dialogue between the operator and the identification and evaluation device through the interface;

The alarm module receives the alarm signal of the processor and completes the alarm.

An evaluation method for an online identification and evaluation device for installation and acceptance of a metering device, comprising the following steps:

1) Establishment of scoring rule base: formulate product scoring rules and incorporate them into the library according to the status characteristics of the product and the degree of influence of the product status characteristics on the product;

2) Image collection and preprocessing: image and/or video are captured on the electric energy metering device to be evaluated on site, and the captured video files are converted into RGB images frame by frame, and the captured images and/or Capture video, and then perform noise reduction processing on the captured pictures and/or videos;

3) Image recognition and analysis: Label the preprocessed target image with the labeling tool, and then use the deep learning Gaussian_yolo framework for training, obtain the model file and save it, then load the trained model file, and sequence the collected target images. Steps in the training process, multi-level sampling, convolution, pooling and fully connected layers, to obtain and output the category, location and confidence information of each target in the image;

4) Image evaluation and scoring: score according to the category, location and confidence information of each target in the image and the established scoring rules;

5) Alarm: according to the comprehensive score of the target given by the score to determine whether to alarm, when the score is lower than the preset score threshold, an alarm signal is issued and an alarm is performed;

6) Data storage: the image recognition analysis data of the target image and the historical comprehensive score data are stored.

Further, the product scoring rules described in the step 1) are the process detection of the meter box, the installation of the meter box, the surface detection of the meter box, the extraction of the two-dimensional code on the surface of the detection meter box, the detection of the electricity meter, the installation of the electricity meter Provisions for lead sealing, meter entry, safety testing, leak detection and wiring workmanship scoring.

Further, the labeling in the step 3) is to perform feature labeling on the electric meter, wiring, damage and exposed line areas in the image.

Further, the step of training in the step 3) includes:

(1) Configure training files and batch image files;

(2) Cut the size of each image in each batch according to the configured training file and scale it to the same size;

(3) Perform feature extraction on each image through multi-level sampling, convolution, pooling and fully connected layers according to the training file;

(4) Perform regression analysis on the extracted features and marked feature regions;

(5) Update the value of each level of neurons, after training N times, get the model file and save it.

Further, the evaluation rules in the step 4) are carried out according to the percentage system, and the evaluation rules are specifically: category score = (total number of detection targets - number of abnormal targets)/total number of detection targets × score allocation of this category × 1/2

When the number of abnormal targets is zero, the category is full score, and the sum of the category scores of each category is the comprehensive score.

Further, the training file in the training step 1) includes the size of the processed image, multi-level sampling, convolution, pooling and full connection, as well as the parameters of each processing.

The beneficial effects of the present invention are:

1. The metering device installation acceptance online identification evaluation device and evaluation method provided by the present invention use image recognition and AI technology to carry out front-end collection and intelligent identification of meter box technology, meter components and line safety in an all-round way. The intelligent image recognition algorithm can Distinguish the appearance, internal structure and installation process of different types of meter boxes, and comprehensively evaluate and score the identification results, which can eliminate the influence of artificial subjective factors to the greatest extent, and improve the identification efficiency, which is convenient and fast.

2. The online identification and evaluation device for installation and acceptance of the metering device provided by the present invention has the characteristics of being easy to carry, real-time monitoring and identification, objective evaluation, etc. The staff can carry this device with them on the spot through the method of image recognition to check the meter box process and electricity meter after installation. Component and line safety are collected in real time, evaluated and scored in real time, and the metering device is intelligently managed to reduce labor and material costs.

Description of drawings

1 is a schematic diagram of the module structure of the online identification and evaluation device for installation and acceptance of a metering device according to the present invention;

Fig. 2 is a flow chart of the evaluation method of the present invention.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

As shown in FIG. 1 , an online identification and evaluation device for installation and acceptance of a metering device in this embodiment includes an image acquisition preprocessing module, an image recognition analysis module, an evaluation scoring module, a processor, a storage module, a positioning module, and a human-computer interaction module. modules and alarm modules;

The image acquisition preprocessing module is composed of an image sensor, an acquisition module and an image noise reduction processing module. The image sensor captures images and/or videos of the on-site electric energy metering device to be evaluated, and converts the captured video files frame by frame. It is an RGB image; the acquisition module collects images and/or videos captured by the image sensor in real time without loss; the image noise reduction processing module removes noise in the pictures and/or videos 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 the image information sent by the image acquisition preprocessing module, marks the image to be recognized, and then uses the deep learning Gaussian_yolo framework for training to obtain model file and save it; the image prediction module loads the trained model file, sequentially performs the steps of the training process on the collected target image, multi-level sampling, convolution, pooling and full connection layer, and finally outputs the image in the Category, location and confidence information for each target;

The evaluation scoring module is composed of a scoring rule base and a quantitative scoring module. The scoring rule base specifies product scoring rules according to the state characteristics of the product and the degree of influence of the product state characteristics on the product; the quantitative scoring module is based on image recognition and analysis. The module outputs the category of each target in the image, and evaluates and scores according to the scoring rules;

The processor processes and stores the data sent by the image acquisition preprocessing module, the image recognition and analysis module, the evaluation scoring module and the positioning module, and issues an alarm instruction to the electric energy metering device to be evaluated whose comprehensive score is lower than the score threshold, The interaction between the operator and the identification evaluation device is completed through the human-computer interaction module;

The storage module stores historical comprehensive scores and scores of various categories and image recognition analysis data of the target image of the electric energy metering device to be tested;

The positioning module locates the real-time position where the electric energy metering device to be measured is located;

The human-computer interaction module completes the interactive dialogue between the operator and the identification and evaluation device through the interface;

The alarm module receives the alarm signal of the processor and completes the alarm.

The above-mentioned evaluation method for an online identification and evaluation device for installation and acceptance of a metering device includes the following steps:

1) Establishment of the scoring rule base: According to the product status characteristics and the degree of influence of the product status characteristics on the product, formulate product scoring rules and incorporate them into the warehouse; Regulations on meter box surface inspection, detection of QR code extraction on meter box surface, meter inspection, meter installation bias, meter lead sealing, meter entry, safety inspection, leak detection and wiring process scoring, specifically: meter box process inspection 30 points, 10 points for meter box installation, 10 points for meter box surface inspection, 10 points for detection of QR code extraction on the surface of meter box; 30 points for meter inspection, 10 points for meter installation, 10 points for meter lead sealing, meter entry 10 points; 40 points for safety inspection, 20 points for leak detection, and 20 points for wiring process;

2) Image collection and preprocessing: image and/or video are captured on the electric energy metering device to be evaluated on site, and the captured video files are converted into RGB images frame by frame, and the captured images and/or Capture video, and then perform noise reduction processing on the captured pictures and/or videos;

3) Image recognition and analysis: Use annotation tools to label the electricity meter, wiring, damage and exposed areas in the preprocessed target image, and then use the deep learning Gaussian_yolo framework for training, obtain the model file and save it, and then load it After the trained model file, perform the steps of the training process on the collected target images in turn, including multi-level sampling, convolution, pooling and full connection layers, to obtain and output the category, location and confidence information of each target in the image. ; Wherein, the step of described training comprises:

(1) configure the training file and batch the image files; the training file includes the size of the processed image, multi-level sampling, convolution, pooling and full connection, and the parameters of each processing;

(2) Cut the size of each image in each batch according to the configured training file and scale it to the same size;

(3) Perform feature extraction on each image through multi-level sampling, convolution, pooling and fully connected layers according to the training file;

(4) Perform regression analysis on the extracted features and marked feature regions;

(5) Update the value of each level of neurons, after training N times, get the model file and save it;

4) Image evaluation and scoring: according to the category, location and confidence information of each target in the image, and the established scoring rules, scoring; the evaluation rules are carried out according to the percentage system, with a full score of 100 points, and the evaluation rules are specifically:

Category score = (total number of detected targets - number of abnormal targets)/total number of detected targets × score distribution of this category × 1/2

When the number of abnormal targets is zero, the category is full score, and the sum of the category scores of each category is the comprehensive score;

5) Alarm: according to the comprehensive score of the target given by the score to determine whether to alarm, when the score is lower than the preset score threshold, an alarm signal is issued and an alarm is performed;

6) Data storage: the image recognition analysis data of the target image and the historical comprehensive score data are stored.

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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