CN103955907A - Method for telemetering pointer type SF6 gas density meter - Google Patents
Method for telemetering pointer type SF6 gas density meter Download PDFInfo
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- CN103955907A CN103955907A CN201410154212.5A CN201410154212A CN103955907A CN 103955907 A CN103955907 A CN 103955907A CN 201410154212 A CN201410154212 A CN 201410154212A CN 103955907 A CN103955907 A CN 103955907A
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Abstract
The invention discloses a method for telemetering a pointer type SF6 gas density meter. The method comprises the steps that images of the pointer type SF6 gas density meter are collected, and are transmitted to a reading end; the collected images are converted into black-and-white images; image regulation processing is carried out on the black-and-white images, so that violent changes caused by violent changes of illumination to the gray levels of the images are removed; a pointer recognition area of the pointer type SF6 gas density meter is set, and image binaryzation is carried out on the processed black-and-white images in the pointer recognition area; plaque noise in the pointer recognition area is removed; a linear equation corresponding to a pointer in the pointer recognition area is searched for, and the slope of the linear equation is determined; a number corresponding to the pointing direction of the pointer is determined. According to the method, the pointer type SF6 gas density meter is telemetered instead of on-site reading through personnel, the human cost is saved, existing devices are made full use of, and the method accords with the development trend of unmanned transformer substations in a smart power grid.
Description
Technical field
The present invention relates to a kind of read method of meter reading, relate in particular to a kind of SF
6the read method of air density meter.
Background technology
SF
6gas has the features such as dielectric strength is high, arc extinction performance is good, is the first-selected insulating medium of current high-tension apparatus, has been widely used in SF
6primary cut-out, GIS, SF
6mutual inductor, SF
6on transformer.SF
6whether gas density is to determine SF within rated range
6effectively whether the insulation of gas and arc extinction performance one of key parameter, and therefore power industry operating standard regulation must be regularly to SF
6the density of gas detects.Current, to SF
6the detection of gas density mainly relies on staff to check and read mechanical pointer SF to scene
6the reading of air density meter, operating experience shows that this kind of detection mode not only waste a large amount of manpower and materials, and work efficiency is not high, do not meet the development trend of intelligent grid unattended operation transformer station, be therefore necessary current detection equipment or detection scheme to carry out technology upgrading or transformation.Mechanical pointer SF by automation equipment to existing checkout equipment
6the air density meter on-the-spot reading of replacement personnel that takes remote measurement is a kind of cost-effective technical solution, and this scheme does not need to change and comprises mechanical pointer SF
6air density meter is at interior existing checkout equipment.
Summary of the invention
The object of the present invention is to provide a kind of pointer SF
6the method of telemetering of air density meter, this method of telemetering can be to the mechanical pointer SF of existing checkout equipment
6air density meter takes remote measurement, thereby the on-the-spot reading of replacement personnel does not need replacing to comprise mechanical pointer SF
6air density meter is at interior existing checkout equipment, when having saved human cost, make full use of existing equipment, minimizing improvement cost, be a kind of cost-effective technical solution, and the method can be carried out by automation equipment, the solution of robotization can be got rid of artificial uncertain factor, raise the efficiency, real-time interconnection detects data, meets the development trend of intelligent grid unattended operation transformer station.
To achieve these goals, the present invention proposes a kind of pointer SF
6the method of telemetering of air density meter, it comprises step:
Gather pointer SF
6the image of air density meter, and by image transmitting to reading end;
Change the image collecting into black white image;
Black white image is carried out to image specification processing, to remove the acute variation of the gradation of image causing because of illumination acute variation in black white image;
Set pointer SF
6the pointer identified region of air density meter, carries out image binaryzation to the treated black white image in pointer identified region;
Remove the patch noise in pointer identified region;
Find the corresponding straight-line equation of pointer in pointer identified region, and determine the slope of this straight-line equation;
Determine the reading that pointer direction is corresponding.
For the mechanical pointer SF to existing checkout equipment
6air density meter takes remote measurement, and the present invention proposes above-mentioned pointer SF
6the method of telemetering of air density meter.In the method, pointer SF
6the collection of the image of air density meter can be completed by camera, and the identification extraction that the image based on this collection is realized reading is automatically the key point of the method.In order to realize gathering pointer SF corresponding to image
6the automatic identification of the reading of air density meter is extracted, the method has been carried out a series of processing to gathering image, comprise by the image collecting change into black white image, to black white image carry out image specification processing, set pointer identified region and carry out image binaryzation, remove patch noise, find the corresponding straight-line equation of pointer and determine this straight-line equation slope, determine the reading of the corresponding pointer direction of this straight-line equation, thereby complete the remote measurement to pointer SF6 air density meter.
In above-mentioned treatment step, changing by the image collecting the described black white image coming is gray level image, and tonal range is 0～255, totally 256 grades, each gray level of the new images that described image specification processing obtains will have the probability density of prior regulation, therefore can remove the acute variation of the gradation of image causing because of illumination acute variation in described black white image, due to the image in the total indicator reading circle that only association comprises pointer, this circle can be made as to pointer identified region, follow-up algorithm process is only processed for this region, the whole white pixel that are set as beyond this region, the binaryzation of image is exactly that the gray level image of 256 brightness degrees is chosen and obtained the binary image that still can reflect integral image and local feature by suitable threshold value, the binaryzation of image is conducive to the further processing of image, make image become simple, and data volume reduces, can highlight the profile of interested target, the pixel that all gray scales are more than or equal to threshold value is judged as and belongs to certain objects, its gray-scale value is 255, otherwise these pixel gray-scale values are 0, represent the object area of background or exception, thereby make whole image present obvious black and white effect, carrying out after binary conversion treatment, prospect frame becomes the image sequence being made up of black and white, but due to factors such as effect of light, noise, moving object disturbances, cause prospect pointer intra-zone and profile incomplete, in order to make us can correctly detect panel board pointer, we need to remove the patch noise in pointer identified region to the foreground image after binaryzation, through the image that step process is crossed before, can start to find the corresponding straight-line equation of pointer in pointer identified region, and determine the slope of this straight-line equation, due to this straight slope and pointer SF
6the pointer actual slope of air density meter is consistent, once slope is decided, can calculate the actual corresponding reading of pointer by mathematical conversion easily, thereby complete the remote measurement to pointer SF6 air density meter.
Further, in the method for telemetering of pointer SF6 air density meter of the present invention, adopt histogram specification method to carry out image specification processing to black white image.
Histogrammic regulation is to make histogram according to the processing procedure of specifying rule to distribute.Statistical relationship between the pixel count (number of the pixel in this gray level) to each gray level and its appearance in gray level image is drawn, represent gray level with horizontal ordinate, ordinate represents pixel count, obtains the grey level histogram (characterizing intensity profile probability density) of this gray level image.Image after the specification processing of image histogram has with certain standard picture and has similar grey level histogram shape, can overcome like this acute variation of illumination.
Further, in the method for telemetering of pointer SF6 air density meter of the present invention, described histogram specification method comprises step:
Adopt histogram equalization to carry out equalization processing to black white image, obtain gray level s;
The gray level probability density function of the black white image obtaining according to hope, obtains transforming function transformation function G(z);
Adopt transforming function transformation function G(z) gray level s is carried out to inverse transformation z=G
-1(s).
The concrete derivation of above-mentioned steps is:
Suppose pr (r) and pz (z) represent normalization respectively original image intensity profile probability density function and wish the intensity profile probability density function of the image that obtains.First original image is carried out to histogram equalization processing, the transforming function transformation function of the gray level r of the even gray level s that asks original image to original image:
Suppose that the gray level of wishing the image that obtains also can utilize following transforming function transformation function equalization
Its inverse process is z=G
-1(v), by wishing that the even gray level v of image obtains wishing the gray level z of image.Because to original image with wish that image all made equalization processing, so ps (s) and pz (z) have same uniform density.Therefore,, if the even gray level s that can obtain from original image replaces the even gray level v of the hope image in inverse process, its result wishes that the gray level z=G-1 (s) of image will have desired probability density of wishing image.
According to above-mentioned principle, obtain the process of histogram specification processing:
(1) utilize histogram equalization to carry out equalization processing to original image;
(2) the gray level probability density function pz (z) of the image obtaining according to hope, obtains transforming function transformation function G (z);
(3) the gray level s that utilizes step (1) to obtain makes inverse transformation z=G-1 (s).
The gray level z of the new images obtaining in this way will have the probability density pz (z) of prior regulation.Two transforming function transformation function T (r) and G-1 (s) can be combined into a funtcional relationship,
z＝G
-1[T(r)]
It can produce the grey level distribution of wishing from original image to utilize this formula.
In addition, when
G
-1[T(r)]＝T(r)
Time, histogram specification strengthens processing and is just reduced to histogram equalization processing.
Further, pointer SF of the present invention
6in the method for telemetering of air density meter, the patch noise at least one of them removal region of employing medium filtering and morphologic filtering.
Owing to carrying out after binary conversion treatment, prospect frame becomes the image sequence being made up of black and white, but due to factors such as effect of light, noise, moving object disturbances, cause prospect pointer intra-zone and profile incomplete, in order correctly to detect the image of panel board pointer, need to carry out to the foreground image after binaryzation the further operation of medium filtering and/or morphologic filtering.
Median filtering method is a kind of ill-defined nonlinear smoothing method that reduces, and its thought is to replace the current gray-scale value of image with the Mesophyticum of gray scale in neighborhood, is mainly used in removing image random noise.Its principle can be described as: to each pixel in image, centered by it, appoint and get a symmetrical region, institute's pixel value a little in region is sorted, get a pixel value that value is handled point of centre.Because sudden change pixel is after sequence, the possibility of centre that is positioned at sequencing queue is very little, so these values of suddenling change pixels can be replaced by the intermediate pixel value in region, thereby have effectively removed the high-frequency random noises in image.The process of image median filter is first the pixel in window to be sorted by gray-scale value, then the value of getting sequence intermediate point is as intermediate value, and is worth the output valve as wave filter using this.
Morphologic filtering is exactly a mobile structural element in image, then structural element and bianry image is below carried out to the set operations such as intersecting and merging.First corrode the process expanding afterwards and be called opening operation, it has elimination small objects, in the effect on very thin place separating objects and level and smooth larger object border; The process of post-etching of first expanding is called closed operation, and it has tiny cavity in the object of filling, connects the effect of adjacent object and smooth boundary.Expanding (dilation) and corroding (erosion) is computing the most basic in mathematical morphology, and extraction and identification to image are important.Expansion is that the destination object in image is increased to pixel, and corrosion is to the destination object place to go pixel in image, and they are dual operations.Increase or remove pixel number depend on image process in the size and shape of structural element.The operation rule expanding is: the pixel value of output image is the maximal value in input picture field, in a bianry image, is 1 as long as there is a pixel value, and corresponding output pixel value is 1; The operation rule of corrosion is: the pixel value of output image is the minimum value in input picture field, in a bianry image, is 0 as long as there is a pixel value, and corresponding output pixel value is 0.In above-mentioned morphology operations, the central element of structural element is in the area-of-interest of input picture, but edge pixel to image, the certain fields that defined by structural element just likely exceed the border of image.For definition boundary pixel, morphology function is that a pixel value is specified on these undefined borders, is equivalent to for whole image has increased other row and column, and this process is called Boundary filling.The Boundary filling rule expanding is: the part being beyond the boundary is appointed as the minimum value of the data type of image, and for bianry image, border pixel values is 0, and to gray level image, if data type is uint8, border pixel values is 0; The Boundary filling rule of corrosion is: the part being beyond the boundary is appointed as the maximal value of the data type of image, and for bianry image, border pixel values is 1, and to gray level image, if data type is uint8, border pixel values is 255.
Further, pointer SF of the present invention
6in the method for telemetering of air density meter, the black picture element in pointer identified region is carried out to Hough conversion, to find the corresponding straight-line equation of pointer.
Consider that the panel board pointer image finally obtaining is straight line, the present invention searches out straight line by the method for Hough conversion.In computing machine identification, usually need to find from image the figure of given shape, be obviously difficult to realize if directly utilize image lattice to search for judgement, at this moment just image pixel need to be mapped to parameter space by certain algorithm.Hough conversion provides a kind of method that image pixel information is mapped to parameter space by coordinate, and the parameter space building by it can easily judge given shape.Hough conversion is that a kind of global characteristics that utilizes image couples together the edge pixel of given shape, form a kind of method at continuously smooth edge, it realizes the identification to known analytic expression curve by the point on source images is mapped to for cumulative parameter space.Hough conversion is usually used in the straight line in image and circle to identify.
Further, pointer SF of the present invention
6in the method for telemetering of air density meter, adopt adaptive threshold split plot design to carry out described image binaryzation.
At present image cut apart in the most conventional dividing method be the threshold method of the maximum equation difference.Gray scale difference between target part and background in image is less, when the double-hump characteristics of grey level histogram is not obvious, directly just not too easily determines a suitable threshold value with histogram.And the maximum variance between clusters being proposed by Ostu is the threshold method of selecting to cut apart two class regions, when the shape of image grey level histogram has bimodal but without obvious low ebb or bimodal and low ebb is all not obvious, the result that adopts maximum variance adaptive threshold method often can be comparatively satisfied with, the method is comparatively simple simultaneously, is a kind of threshold value system of selection of widespread use.In the present invention, based on Ostu algorithm, consider the adaptive response that threshold value is selected simultaneously, cut apart utilizing auto-thresholding algorithm to carry out image, specific as follows:
If the grey level histogram of certain image comprises two regions, T is the threshold value that separates two regions.
Region 1 accounts for the Area Ratio of whole image:
Region 2 accounts for the Area Ratio of whole image:
The average gray of entire image:
The average gray in region 1:
The average gray in region 2:
The pass of the average gray of entire image between the average gray value in region 1, region 2 is:
μ＝μ
1θ
1+μ
2θ
2
If the same area has gray scale similar characteristic, and shows as obvious gray difference between zones of different, in the time that two interregional gray scale differences that separated by threshold value T are larger, the average gray μ in two regions
1, μ
2also larger with the difference of the average gray μ of entire image, interregional variance is exactly to describe the actual parameter of this species diversity, and its expression formula is:
In formula:
represent that image cut apart the variance between latter two region by threshold value T.Obviously, there is different T values, will obtain different interregional variances.When divided two interregional variances reach maximum, be considered to the optimal separation state in two regions, definite threshold T thus:
Not needing artificially to set other parameters with maximum variance decision threshold, is a kind of method of automatic selection threshold value.
The prospect of the above-mentioned threshold value energy differentiation place image based on iteration and the main region place of background, the threshold value of interative computation gained is good to the segmentation effect of image, can meet the requirement of panel board binaryzation.
Pointer SF of the present invention
6the method of telemetering of air density meter, by being used the telemetry equipment of this method of telemetering to the mechanical pointer SF of existing checkout equipment
6air density meter takes remote measurement, and has replaced the on-the-spot reading of personnel, does not need to change to comprise mechanical pointer SF
6air density meter is at interior existing checkout equipment, when having saved human cost, take full advantage of existing equipment, minimize improvement cost, be a kind of cost-effective technical solution, and the solution of robotization gets rid of artificial uncertain factor, improved efficiency, real-time interconnection detects data, meets the development trend of intelligent grid unattended operation transformer station.
Brief description of the drawings
Fig. 1 is pointer SF of the present invention
6the schematic flow sheet of the method for telemetering of air density meter under a kind of embodiment.
Fig. 2 is pointer SF of the present invention
6the pointer SF through adaptive iteration method thresholding method binaryzation after of the method for telemetering of air density meter under a kind of embodiment
6the remote measurement image of air density meter.
Fig. 3 is pointer SF of the present invention
6the pointer SF through medium filtering and morphologic filtering after of the method for telemetering of air density meter under a kind of embodiment
6the remote measurement image of air density meter.
Fig. 4 is pointer SF of the present invention
6the method of telemetering of air density meter under a kind of embodiment to pointer SF
6the remote measurement image of air density meter carries out Hough and converts rectilinear picture corresponding to straight-line equation obtaining.
Embodiment
Below in conjunction with Figure of description and specific embodiment to pointer SF of the present invention
6the method of telemetering of air density meter is made further explanation and explanation.
Fig. 1 has illustrated pointer SF of the present invention
6the flow process of the method for telemetering of air density meter under a kind of embodiment.
As shown in Figure 1, the method for telemetering of the present embodiment comprises the steps:
(1) gather pointer SF
6the image of air density meter, and by image transmitting to reading end;
(2) change the image collecting into black white image;
(3) adopt histogram specification method to carry out image specification processing to black white image, to remove the acute variation of the gradation of image causing because of illumination acute variation in black white image; This histogram specification method comprises step: adopt histogram equalization to carry out equalization processing to black white image, obtain the even gray level s of original image; The gray level probability density function of the black white image obtaining according to hope, obtains transforming function transformation function G(z); Adopt transforming function transformation function G(z) even original image gray level s is carried out to inverse transformation and obtains wishing the gray level z=G of the black white image obtaining
-1(s);
(4) set pointer SF
6the pointer identified region of air density meter, adopts adaptive iteration method thresholding method to carry out image binaryzation to the treated black white image in pointer identified region;
(5) adopt medium filtering and morphologic filtering to remove the patch noise in pointer identified region;
(6) black picture element in pointer identified region is carried out to Hough conversion, to find the corresponding straight-line equation of pointer in pointer identified region, and the slope of definite this straight-line equation;
(7) by the slope of above-mentioned straight-line equation and pointer SF
6the dial plate of air density meter compares conversion, determines the reading that pointer direction is corresponding, thereby completes pointer SF
6the remote measurement of air density meter.
The above-mentioned method of telemetering step of the present embodiment realizes by automation equipment, and wherein step (1) realizes by camera, and step (2)-(7) are by the computer realization being connected with this camera.Fig. 2 has illustrated the remote measurement image after adaptive threshold split plot design binaryzation of the present embodiment.Fig. 3 has illustrated the remote measurement image after medium filtering and morphologic filtering of the present embodiment.Fig. 4 has illustrated that in the present embodiment, remote measurement image being carried out to Hough converts rectilinear picture corresponding to straight-line equation obtaining.
In the above-mentioned method of telemetering step of the present embodiment, the concrete transform method of step (2) is: the RGB component of setting a pixel in image is respectively R, G, B, and the computing method of the gray-scale value of this pixel are:
G＝0.299r+0.587g+0.114b
In image, each pixel is changed in the manner described above, obtains gray level image (black white image).
In the above-mentioned method of telemetering step of the present embodiment, the image that step (4) obtains after processing is as Fig. 2.
In the above-mentioned method of telemetering step of the present embodiment, the image that step (5) obtains after processing is as Fig. 3.
In the above-mentioned method of telemetering step of the present embodiment, the specific algorithm step of step (6) is:
(61) quantization parameter space suitably;
(62) each unit of supposition parameter space is a totalizer;
(63) accumulator initialization is 0;
(64) every bit to image space adds 1 on totalizer corresponding to its satisfied parametric equation;
(65) the corresponding model parameter of the maximal value of accumulator array.
Convert rectilinear picture that the straight-line equation that obtains is corresponding as shown in Figure 4 through Hough.
In the above-mentioned method of telemetering step of the present embodiment, the concrete method of step (7) is: from pointer SF
6on the dial plate of air density meter, extract two true plots and find the corresponding pointer slope of these two readings, calculate by mathematical conversion the reading that pointer is corresponding.
Be noted that above enumerate only for specific embodiments of the invention, obviously the invention is not restricted to above embodiment, have many similar variations thereupon.If all distortion that those skilled in the art directly derives or associates from content disclosed by the invention, all should belong to protection scope of the present invention.
Claims (6)
1. a pointer SF
6the method of telemetering of air density meter, is characterized in that, comprises step:
Gather pointer SF
6the image of air density meter, and by image transmitting to reading end;
Change the image collecting into black white image;
Black white image is carried out to image specification processing, to remove the acute variation of the gradation of image causing because of illumination acute variation in black white image;
Set pointer SF
6the pointer identified region of air density meter, carries out image binaryzation to the treated black white image in pointer identified region;
Remove the patch noise in pointer identified region;
Find the corresponding straight-line equation of pointer in pointer identified region, and determine the slope of this straight-line equation;
Determine the reading that pointer direction is corresponding.
2. pointer SF as claimed in claim 1
6the method of telemetering of air density meter, is characterized in that, adopts histogram specification method to carry out image specification processing to black white image.
3. pointer SF as claimed in claim 2
6the method of telemetering of air density meter, is characterized in that, described histogram specification method comprises step:
Adopt histogram equalization to carry out equalization processing to black white image, obtain gray level s;
The gray level probability density function of the black white image obtaining according to hope, obtains transforming function transformation function G(z);
Adopt transforming function transformation function G(z) gray level s is carried out to inverse transformation z=G
-1(s).
4. pointer SF as claimed in claim 1
6the method of telemetering of air density meter, is characterized in that, the patch noise at least one of them removal region of employing medium filtering and morphologic filtering.
5. pointer SF as claimed in claim 1
6the method of telemetering of air density meter, is characterized in that, the black picture element in pointer identified region is carried out to Hough conversion, to find the corresponding straight-line equation of pointer.
6. pointer SF as claimed in claim 1
6the method of telemetering of air density meter, is characterized in that, adopts adaptive threshold split plot design to carry out described image binaryzation.
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| CN201410154212.5A CN103955907A (en) | 2014-04-17 | 2014-04-17 | Method for telemetering pointer type SF6 gas density meter |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104320580A (en) * | 2014-10-25 | 2015-01-28 | 国家电网公司 | Equipment and method for identifying SF6 density meter pointer based on image acquisition |
| CN107038444A (en) * | 2016-02-03 | 2017-08-11 | 上海慕荣电气有限公司 | A kind of image-recognizing method of pointer dial plate |
| CN107133623A (en) * | 2017-05-11 | 2017-09-05 | 安徽慧视金瞳科技有限公司 | A kind of pointer position accurate detecting method positioned based on background subtraction and the center of circle |
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| CN109146806A (en) * | 2018-07-29 | 2019-01-04 | 国网上海市电力公司 | Gauge pointer position detection recognition methods based on shadow removing optimization in remote monitoriong of electric power |
| CN112989963A (en) * | 2021-02-24 | 2021-06-18 | 唐山不锈钢有限责任公司 | Rockwell hardness test process supervision and judgment method based on graph recognition |
| CN113947720A (en) * | 2021-12-20 | 2022-01-18 | 广东科凯达智能机器人有限公司 | Method for judging working state of density meter |
| CN114972218A (en) * | 2022-05-12 | 2022-08-30 | 中海油信息科技有限公司 | Pointer meter reading identification method and system |
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2014
- 2014-04-17 CN CN201410154212.5A patent/CN103955907A/en active Pending
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104320580A (en) * | 2014-10-25 | 2015-01-28 | 国家电网公司 | Equipment and method for identifying SF6 density meter pointer based on image acquisition |
| CN107038444A (en) * | 2016-02-03 | 2017-08-11 | 上海慕荣电气有限公司 | A kind of image-recognizing method of pointer dial plate |
| CN107133623A (en) * | 2017-05-11 | 2017-09-05 | 安徽慧视金瞳科技有限公司 | A kind of pointer position accurate detecting method positioned based on background subtraction and the center of circle |
| CN107133623B (en) * | 2017-05-11 | 2020-03-24 | 安徽慧视金瞳科技有限公司 | Pointer position accurate detection method based on background difference and circle center positioning |
| CN107368815A (en) * | 2017-07-25 | 2017-11-21 | 上海控创信息技术股份有限公司 | Instrument detecting method and system |
| CN109146806A (en) * | 2018-07-29 | 2019-01-04 | 国网上海市电力公司 | Gauge pointer position detection recognition methods based on shadow removing optimization in remote monitoriong of electric power |
| CN112989963A (en) * | 2021-02-24 | 2021-06-18 | 唐山不锈钢有限责任公司 | Rockwell hardness test process supervision and judgment method based on graph recognition |
| CN112989963B (en) * | 2021-02-24 | 2022-10-18 | 唐山不锈钢有限责任公司 | Rockwell hardness test process supervision and judgment method based on pattern recognition |
| CN113947720A (en) * | 2021-12-20 | 2022-01-18 | 广东科凯达智能机器人有限公司 | Method for judging working state of density meter |
| CN114972218A (en) * | 2022-05-12 | 2022-08-30 | 中海油信息科技有限公司 | Pointer meter reading identification method and system |
| CN114972218B (en) * | 2022-05-12 | 2023-03-24 | 中海油信息科技有限公司 | Pointer meter reading identification method and system |
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