CN111401289A - Intelligent identification method and device for transformer component - Google Patents
Intelligent identification method and device for transformer component Download PDFInfo
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- CN111401289A CN111401289A CN202010214107.1A CN202010214107A CN111401289A CN 111401289 A CN111401289 A CN 111401289A CN 202010214107 A CN202010214107 A CN 202010214107A CN 111401289 A CN111401289 A CN 111401289A
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- 238000000034 method Methods 0.000 title claims abstract description 39
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- 238000012549 training Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 4
- 239000003086 colorant Substances 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention relates to an intelligent identification method and device of a transformer component, wherein the transformer component comprises a sleeve, and the method comprises the following steps: s1: acquiring a transformer image, loading the transformer image into a pre-trained first model, identifying each transformer component in the transformer image, and acquiring the area of each transformer component in the transformer image; s2: cutting out the region of the bushing in the transformer component based on the region of each transformer component acquired in step S1; s3: and loading the regional image of the sleeve into the pre-trained second model, and identifying the color and character information of the sleeve so as to determine the phase of the sleeve. Compared with the prior art, the method can further identify the phase of the sleeve, and has the advantages of high identification precision, stability and reliability and the like.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an intelligent identification method and device for transformer components.
Background
Along with the development of artificial intelligence technology, more and more advanced intelligent perception technology is applied to the operation and maintenance of the transformer substation to assist operation and maintenance personnel to more conveniently and rapidly process data image data, form correlation analysis and further improve the equipment management level.
Existing techniques for identifying transformer components focus on identification of transformer components and are not further expanded.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent identification method and device for transformer components.
The purpose of the invention can be realized by the following technical scheme:
a method of intelligent identification of a transformer component, the transformer component comprising a bushing, the method comprising the steps of:
s1: acquiring a transformer image, loading the transformer image into a pre-trained first model, identifying each transformer component in the transformer image, and acquiring the area of each transformer component in the transformer image;
s2: cutting out the region of the bushing in the transformer component based on the region of each transformer component acquired in step S1;
s3: and loading the regional image of the sleeve into the pre-trained second model, and identifying the color and character information of the sleeve so as to determine the phase of the sleeve.
Further, the transformer part still includes conservator, fan and transformer body.
Further, the training process of the first model comprises: obtaining a transformer sample image, performing deconstruction classification labeling on the transformer according to the conservator, the fan, the sleeve and the transformer body, and generating a first training set and a first testing set so as to train the first model.
Further, the training process of the second model comprises: and acquiring a sleeve sample image, performing color and character information labeling on the sleeve sample image, and generating a second training set and a second testing set so as to train the second model.
Further, the first model is built based on a convolutional neural network.
Further, the identification process of the transformer component by the first model comprises the following steps:
s1: extracting a two-dimensional feature vector from the transformer image;
s2: generating different anchor points in each grid of the two-dimensional feature vector;
s3: labeling the identified area by using a labeling frame;
s4: and performing regression processing on the labeling frames and the generated anchor points, and performing cutting classification on different labeling frames.
Further, the colors of the sleeve include red, green and yellow.
Further, the character information includes "a" characters, "B" characters, and "C characters.
The invention also provides an intelligent identification device of the transformer component, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method.
Compared with the prior art, the invention has the following advantages:
(1) according to the method, after each part of the transformer is identified through the first model, the phase of the sleeve in the transformer is further identified through the second model, so that the identification of the phase of the sleeve is further realized, and the equipment management level is improved; and the identification precision of the casing phase is improved, and the method is simple, stable and reliable.
(2) The method and the device separately train the first model and the second model, and can improve the recognition accuracy of the first model and the second model.
(3) According to the invention, the first model generates different anchor points by extracting the two-dimensional characteristic vector from the transformer image, and performs regression processing with the marking frame of the identification component, so that the marking frame is closer to the actual position of the component, and the identification precision of the second model is improved.
Drawings
Fig. 1 is a schematic flow chart of an intelligent identification method of a transformer component according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
As shown in fig. 1, the present embodiment provides an intelligent identification method for transformer components, which is used for identifying a conservator, a fan, a bushing and a transformer body, and the method includes the following steps:
s1: acquiring a transformer image, loading the transformer image into a pre-trained first model, identifying each transformer component in the transformer image, and acquiring the area of each transformer component in the transformer image;
the identification process of the transformer component by the first model specifically comprises the following steps:
s101: extracting a two-dimensional feature vector from the transformer image;
s102: generating different anchor points in each grid of the two-dimensional feature vector;
s103: labeling the identified area by using a labeling frame;
s104: and performing regression processing on the marking frame and the generated anchor point to enable the marking frame to be closer to the actual position of the part, and performing cutting classification on different marking frames.
S2: cutting out the region of the bushing in the transformer component based on the region of each transformer component acquired in step S1;
s3: and loading the regional image of the sleeve into the pre-trained second model, and identifying the color and character information of the sleeve so as to determine the phase of the sleeve.
The specific implementation is that a neural network is adopted to identify red, green and yellow colors, characters A, characters B and characters C of three phases of the bushing ABC, so as to confirm the phase difference of the bushing.
The training process of the first model comprises: obtaining a transformer sample image, performing deconstruction classification labeling on the transformer according to the conservator, the fan, the sleeve and the transformer body, and generating a first training set and a first testing set so as to train the first model. The first model is built based on a convolutional neural network.
The training process of the second model comprises the following steps: and acquiring a sleeve sample image, performing color and character information labeling on the sleeve sample image, and acquiring a second training set and a second testing set so as to train the second model.
The embodiment also provides an intelligent identification device of the transformer component, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the intelligent identification method of the transformer component.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (9)
1. A method for intelligent identification of a transformer component, the transformer component comprising a bushing, the method comprising the steps of:
s1: acquiring a transformer image, loading the transformer image into a pre-trained first model, identifying each transformer component in the transformer image, and acquiring the area of each transformer component in the transformer image;
s2: cutting out the region of the bushing in the transformer component based on the region of each transformer component acquired in step S1;
s3: and loading the regional image of the sleeve into the pre-trained second model, and identifying the color and character information of the sleeve so as to determine the phase of the sleeve.
2. The intelligent identification method for the transformer component according to claim 1, wherein the transformer component further comprises a conservator, a fan and a transformer body.
3. The intelligent identification method for the transformer component according to claim 1, wherein the training process of the first model comprises: obtaining a transformer sample image, classifying and labeling the transformer sample image according to the conservator, the fan, the sleeve and the transformer body, generating a first training set and a first testing set, and training the first model.
4. The intelligent identification method for transformer components according to claim 1, wherein the training process for the second model comprises: and acquiring a sleeve sample image, performing color and character information labeling on the sleeve sample image, and generating a second training set and a second testing set so as to train the second model.
5. The intelligent identification method for transformer components according to claim 1, characterized in that the first model is established based on a convolutional neural network.
6. The intelligent identification method for the transformer component according to claim 1, wherein the identification process of the transformer component by the first model comprises the following steps:
s101: extracting a two-dimensional feature vector from the transformer image;
s102: generating different anchor points in each grid of the two-dimensional feature vector;
s103: labeling the identified area by using a labeling frame;
s104: and performing regression processing on the labeling frames and the generated anchor points, and performing cutting classification on different labeling frames.
7. The intelligent identification method for transformer components according to claim 1, wherein the colors of the sleeves comprise red, green and yellow.
8. The intelligent identification method for the transformer component according to claim 1, wherein the character information comprises "a" characters, "B" characters and "C" characters.
9. An intelligent identification device for transformer components, characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method according to any one of claims 1 to 8.
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