CN116188838A - Artificial intelligence-based external damage hidden danger point interference judging method - Google Patents
Artificial intelligence-based external damage hidden danger point interference judging method Download PDFInfo
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Abstract
In order to solve the problems of inaccurate identification of external broken hidden danger points and easy generation of false leakage detection in the prior art, the invention provides an artificial intelligence-based external broken hidden danger point interference judging method, and the technical scheme for solving the technical problems comprises the following steps of; 1. inputting an image, namely acquiring an unmanned aerial vehicle and a monitoring photo; 2. feature extraction, namely calculating the acquired picture by using a pre-trained feature matrix to obtain a feature result; 3. feature coding, namely coding a feature result in the second step; 4. after the result classification is finished, comparing the result classification with samples in a knowledge base to finish classification; 5. and outputting a result, and decoding and outputting corresponding early warning information according to the classification type. According to the invention, the images or the monitoring images shot in the unmanned aerial vehicle inspection process are analyzed and processed by an artificial intelligence method, so that the accurate judgment of hidden danger points and the grading judgment are realized, and a more reliable basis is provided for the subsequent hidden danger point inspection and repair.
Description
Technical Field
The invention belongs to the technical field of identification of hidden danger points of external damage of a power transmission line, and particularly relates to an artificial intelligence-based method for judging interference of hidden danger points of external damage.
Background
Under the current background of the era, the safety of the power transmission line is a necessary condition for ensuring industrial and domestic electricity consumption, and accidents are easily caused by external damage in the actual operation process of the power transmission line, wherein the hidden danger points of external damage are mainly represented in the following aspects: 1. theft behavior, 2, prevention problem, 3, violation building cause.
With the development of society, unmanned aerial vehicle automatic cruising is often adopted in the inspection of transmission line now, and such operation mode has greatly improved inspection speed and has improved inspection efficiency, but unmanned aerial vehicle inspection has to external broken hidden danger point discernment inaccurately, produces the phenomenon of leaking to examine easily, therefore how to improve the accuracy of inspection in-process to external broken hidden danger point discernment always is the problem that needs to attach importance.
Disclosure of Invention
In order to solve the problems that the identification of the hidden danger points is inaccurate and the false leakage detection is easy to generate in the prior art, the invention provides the hidden danger point interference judging method based on artificial intelligence, and the image or the monitoring image shot in the unmanned aerial vehicle inspection process is analyzed and processed by the artificial intelligence method, so that the accurate judgment and the grade judgment of the hidden danger points are realized, and a more reliable basis is provided for the subsequent hidden danger point inspection and repair.
The invention provides an artificial intelligence-based external broken hidden danger point interference judging method, which solves the technical problems and comprises the following steps of;
step one, inputting an image, and collecting an unmanned aerial vehicle and a monitoring photo;
step two, extracting features, namely calculating the acquired pictures by using a feature matrix trained in advance to obtain feature results;
step three, feature coding, namely coding the feature result in the step two;
step four, classifying results, namely comparing the results with samples in a knowledge base after encoding is completed, and completing classification;
and fifthly, outputting a result, and decoding and outputting corresponding early warning information according to the classification type.
Preferably, the features in the second step include a linear feature and a nonlinear feature.
Preferably, the linear feature is the result of a linear transformation of the image bits and the two-dimensional Euclidean space neighborhood in which they are located.
Preferably, the linear filter is used for extracting the characteristic of the linear characteristic, and the specific algorithm is as follows:
wherein dst (x, y) is the target image, i.e. the linear transformation result;
kernel is a filter kernel that participates in the computation,
cols represents the line length (column number) of the filter kernel;
rows represents the column length (number of rows) of the filter kernel;
src is the original image;
x, y are bits;
x ', y' are currently calculated bits;
anchor is the anchor point of participating in calculation, and anchor has decided the step distance of kernel.
Preferably, the linear feature can be extracted into edge feature and shape feature according to the difference of the filter kernels.
Preferably, the nonlinear features are extracted by GLCM, and the nonlinear features include contrast, correlation, entropy, and homogeneity.
Preferably, in the third step, feature encoding is performed based on GBDT classification algorithm: for the input feature matrix and the prior result, a classifier is set to satisfy the following relation:
wherein: n is the number of nodes of the classifier, i is the state of the classifier;
let the total iteration number be M, the current iteration period be M, then there are:
finally f i (x) The output of the feature encoder is a feature value.
Preferably, in the fourth step, the characteristic value and the target value are compared, wherein in the comparison process, different information is respectively compared by using different vector values, and a value with the smallest variance is selected.
In summary, the technical scheme of the invention has at least the following beneficial effects:
1. the characteristic matrix is adopted to extract the characteristics of the pictures shot in the unmanned aerial vehicle inspection process, the shot pictures can be converted into characteristic information, and subsequent analysis and processing are facilitated;
2. the linear characteristic and the nonlinear characteristic of the image are respectively extracted, so that the subsequent comparison is more accurate;
3. different levels of setting can be realized according to different characteristic values by giving different alarm information to the target value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides an artificial intelligence-based external damage hidden danger point interference judging method, which is shown in a figure 1 and comprises the following steps of;
step one, inputting an image, and collecting an unmanned aerial vehicle and a monitoring photo; in the process of inspection, the unmanned aerial vehicle is controlled or automatically cruises to shoot, or monitoring photos are adopted in certain areas, and shot pictures/images are acquired to input;
step two, extracting features, namely calculating the acquired pictures by using a feature matrix trained in advance to obtain feature results;
step three, feature coding, namely coding the feature result in the step two;
step four, classifying results, namely comparing the results with samples in a knowledge base after encoding is completed, and completing classification;
and fifthly, outputting a result, and decoding and outputting corresponding early warning information according to the classification type.
It should be noted that the features in the second step in the present invention include linear features and nonlinear features, and the linear features and the nonlinear features are extracted respectively by a corresponding algorithm. Wherein the linear characteristic is the result of a linear transformation of the image bits and the two-dimensional Euclidean space neighborhood in which they are located. In the specific calculation, the linear filter is used for extracting the characteristic of the linear characteristic, and the specific algorithm is as follows:
wherein dst (x, y) is the target image, i.e. the linear transformation result;
kernel is a filtering kernel involved in computation, i.e. a two-dimensional vector, different filtering kernels extracting different features
Information;
cols represents the line length (column number) of the filter kernel;
rows represents the column length (number of rows) of the filter kernel;
src is the original image;
x, y is a bit;
x ', y' are currently calculated bits;
anchor is the anchor point of participating in calculation, and anchor has decided the step distance of kernel, decides the density of feature extraction.
It should be noted that in the process of extracting the linear features, the invention can extract the edge features and the shape features according to the different filter kernels.
The nonlinear characteristics in the invention are extracted through GLCM, wherein the nonlinear characteristics mainly comprise contrast, correlation, entropy and homogeneity. In the specific calculation and extraction, the method is as follows:
for a Gray level picture of a color picture, the Gray level co-occurrence Matrix (GLCM) is expressed as follows:
wherein (Δx, Δy) is a set of offsets, the offsets being a vector having a length and a direction;
c is gray level co-occurrence matrix, I is input gray level picture, x, y is bit;
after extracting GLCM, the GLCM is calculated to obtain the following common characteristic values (p (i, j) as
Bits in GLCM):
contrast ratio:
correlation (μ is a correlation coefficient):
entropy:
homogeneity:
the above four feature values are the nonlinear features to be extracted in the invention and the calculation precautions thereof.
It should be noted that, the feature encoding in the third step of the present invention is performed based on GBDT classification algorithm, specifically as follows: for the input feature matrix and the prior result, a classifier is set to satisfy the following relation:
wherein: n is the number of nodes of the classifier, i is the state of the classifier;
let the total iteration number be M, the current iteration period be M, then there are:
Obtaining a result and updating:
finally f i (x) The output of the feature encoder is a feature value.
In the fourth step of the invention, the obtained characteristic value is compared with the target value, because different early warning information is represented by different characteristic values, the output result is compared with the prior characteristic value, the value with the smallest variance is selected, and different information is compared by using different vector values, so that the identification of a specific region (prior interest region) in the infrared picture can be realized, and the category of the specific region (prior interest region) can be identified.
Firstly, determining a forward shot image as a unique data source for identifying an external damage, acquiring altitude information of a picture and tower account data on the basis of intelligently identifying an external damage construction hidden trouble point, dividing an image by combining a focal length of a cradle head of an unmanned aerial vehicle, and distributing pixel values for the target position according to the difference between a similarity matrix of the target position and a similarity matrix of a neighborhood position to obtain a gray level image; and (3) carrying out region segmentation on the gray level image to obtain a region segmented image, wherein the region segmented image can be divided into a defect hidden danger area within a certain range of a line channel, a safety precaution area at a far point and a concerned area at a far point, and further carrying out grading research and judgment on the risk degree of the risk point.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (8)
1. The method for judging the interference of the hidden danger points based on the artificial intelligence is characterized by comprising the following steps of;
step one, inputting an image, and collecting an unmanned aerial vehicle and a monitoring photo;
step two, extracting features, namely calculating the acquired pictures by using a feature matrix trained in advance to obtain feature results;
step three, feature coding, namely coding the feature result in the step two;
step four, classifying results, namely comparing the results with samples in a knowledge base after encoding is completed, and completing classification;
and fifthly, outputting a result, and decoding and outputting corresponding early warning information according to the classification type.
2. The method for determining the disturbance of the hidden danger point based on artificial intelligence according to claim 1, wherein the features in the second step include linear features and nonlinear features.
3. The artificial intelligence based method of claim 2, wherein the linear features are the result of a linear transformation of the image bits and the two-dimensional euclidean space neighborhood in which they reside.
4. The method for judging the external damage hidden trouble point interference based on artificial intelligence according to claim 3, wherein the linear characteristics are extracted by using a linear filter, and the specific algorithm is as follows:
wherein dst (x, y) is the target image, i.e. the linear transformation result;
kernel is a filter kernel that participates in the computation,
cols represents the line length (column number) of the filter kernel;
rows represents the column length (number of rows) of the filter kernel;
src is the original image;
x, y are bits;
x ', y' are currently calculated bits;
anchor is the anchor point of participating in calculation, and anchor has decided the step distance of kernel.
5. The method for judging the disturbance of the hidden danger point of external damage based on artificial intelligence according to claim 3, wherein the linear features can be extracted into edge features and shape features according to different filter kernels.
6. The method for judging the external damage hidden trouble point interference based on artificial intelligence according to claim 1, wherein the nonlinear features are extracted by GLCM, and the nonlinear features comprise contrast, correlation, entropy and homogeneity.
7. The method for determining the disturbance of the hidden danger point of external damage based on artificial intelligence according to claim 1, wherein in the third step, feature coding is performed based on a GBDT classification algorithm: for the input feature matrix and the prior result, a classifier is set to satisfy the following relation:
wherein: n is the number of nodes of the classifier, i is the state of the classifier;
let the total iteration number be M, the current iteration period be M, then there are:
Obtaining a result and updating:
finally f i (x) The output of the feature encoder is a feature value.
8. The method for determining the interference of the hidden danger point based on artificial intelligence according to claim 1, wherein the comparison in the fourth step is to compare the characteristic value with the target value, wherein in the comparison process, different information is respectively compared by using different vector values, and the value with the smallest variance is selected.
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| CN113781450A (en) * | 2021-09-14 | 2021-12-10 | 中科百惟(云南)科技有限公司 | Automatic intelligent defect analysis system based on unmanned aerial vehicle image acquisition of power transmission and distribution line |
| CN115294094A (en) * | 2022-08-26 | 2022-11-04 | 国网安徽省电力有限公司超高压分公司 | Extra-high voltage line insulator fault diagnosis method and device based on heterogeneous data |
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- 2022-12-16 CN CN202211623942.6A patent/CN116188838A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103413150A (en) * | 2013-06-28 | 2013-11-27 | 广东电网公司电力科学研究院 | Power line defect diagnosis method based on visible light image |
| CN105825207A (en) * | 2016-04-20 | 2016-08-03 | 北京航空航天大学 | Fragmented high-voltage line detection method and device |
| CN108257114A (en) * | 2017-12-29 | 2018-07-06 | 天津市万贸科技有限公司 | A kind of transmission facility defect inspection method based on deep learning |
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