CN117114402A - Power equipment risk assessment method based on big data - Google Patents
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Abstract
The invention relates to power equipment risk assessment, in particular to a power equipment risk assessment method based on big data, which comprises the steps of obtaining equipment data of equipment to be assessed, and determining a corresponding risk scoring table based on the equipment type of the equipment to be assessed; determining a first risk assessment result of the device to be assessed based on the risk score in the risk score table; collecting equipment images of equipment to be evaluated, and carrying out registration alignment treatment on the equipment images and the reference images; performing difference solving on the registered and aligned equipment image and the reference image to obtain a difference image; determining and identifying a difference region in the device image based on the difference image, and determining a second risk assessment result of the device to be assessed based on the difference region in the device image; determining a final risk assessment result of the equipment to be assessed by combining the first risk assessment result and the second risk assessment result of the equipment to be assessed; the technical scheme provided by the invention can overcome the defects of higher dependence on the model and lower accuracy of the risk assessment result.
Description
Technical Field
The invention relates to power equipment risk assessment, in particular to a power equipment risk assessment method based on big data.
Background
In an electric power system, operation and maintenance work of electric power equipment is an important measure for ensuring normal operation of the electric power equipment. In order to improve the efficiency of operation and maintenance work and reduce resource waste, risk assessment is generally required to be performed on the power equipment.
In the prior art, risk assessment is performed on power equipment mainly according to a device fault aging model or a reliability index issued by an electric monitoring party. In addition, according to the historical data, a state variable affecting the power equipment can be selected, and a risk assessment model between the state variable and the equipment state is established to carry out risk assessment. The method has a certain limitation on the degree of dependence on the model, and the risk assessment result for the power equipment is not accurate enough.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects existing in the prior art, the invention provides a power equipment risk assessment method based on big data, which can effectively overcome the defects of higher dependence on a model and lower accuracy of a risk assessment result existing in the prior art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the power equipment risk assessment method based on big data comprises the following steps:
s1, acquiring equipment data of equipment to be evaluated, and determining a corresponding risk scoring table based on the equipment type of the equipment to be evaluated;
s2, determining a first risk assessment result of the equipment to be assessed based on the risk score in the risk scoring table;
s3, acquiring an equipment image of equipment to be evaluated, and carrying out registration and alignment treatment on the equipment image and a reference image;
s4, carrying out difference between the registered and aligned equipment image and the reference image to obtain a difference image;
s5, determining and identifying a difference region in the equipment image based on the difference image, and determining a second risk assessment result of the equipment to be assessed based on the difference region in the equipment image;
s6, determining a final risk assessment result of the equipment to be assessed by combining the first risk assessment result and the second risk assessment result of the equipment to be assessed.
Preferably, determining a corresponding risk score table in S1 based on the device type of the device to be evaluated includes:
determining various risk indexes based on equipment data of the power equipment, and constructing a risk scoring table associated with equipment types of the power equipment based on the various risk indexes;
and constructing a risk score table library according to the equipment types of all the electric equipment and the associated risk score table, and determining a risk score table matched with the equipment types of the equipment to be evaluated from the risk score table library.
Preferably, determining a first risk assessment result of the device to be assessed based on the risk score in the risk score table in S2 includes:
determining various risk indexes in a risk scoring table, and determining risk scores corresponding to the various risk indexes based on equipment data of equipment to be evaluated;
and determining a first risk assessment result of the equipment to be assessed according to the risk scores corresponding to the risk indexes.
Preferably, the determining the first risk assessment result of the device to be assessed according to the risk scores corresponding to the risk indexes includes:
determining weight values corresponding to all risk indexes in a risk scoring table, and carrying out weighted summation calculation based on the weight values corresponding to all risk indexes and the risk scores to obtain a total risk score;
and determining the risk assessment grade of the equipment to be assessed according to the scoring range corresponding to the total risk score, and obtaining a first risk assessment result.
Preferably, the registration alignment processing for the device image and the reference image in S3 includes:
obtaining characteristic points of the equipment image and the reference image based on the SIFT algorithm, and matching the characteristic points of the equipment image and the reference image by adopting a proximity algorithm;
and performing conversion alignment on the device image and the reference image which are subjected to feature point matching.
Preferably, the performing conversion alignment on the device image and the reference image which complete feature point matching includes:
and aligning the device image and the reference image which are subjected to feature point matching by adopting a homography matrix through a rotation transformation mode.
Preferably, in S4, the difference between the registered and aligned device image and the reference image is obtained, which includes:
and performing matrix difference on the registered and aligned equipment image and the reference image, and taking the absolute value of the obtained matrix difference as a difference image.
Preferably, after taking the absolute value of the obtained matrix difference as the difference image, the method includes:
performing edge detection processing on the reference image to obtain an edge detection image;
the edge detection image is multiplied with the difference image to eliminate edge noise data of the difference image.
Preferably, determining and identifying the difference region in the device image based on the difference image in S5 includes:
extracting the maximum pixel value of the difference image, and performing binarization processing on the difference image when the maximum pixel value is larger than a preset pixel threshold value to obtain a binarized image;
and marking the region where the pixel points with the pixel values being the preset pixel values are located in the binarized image to obtain a difference region.
Preferably, the identifying the region where the pixel point with the pixel value being the preset pixel value in the binarized image is located, before obtaining the difference region, includes:
traversing all pixel points with the pixel value of 255 in the binarized image, judging the pixel point as a noise point if the total number of the pixel points with the pixel value of 255 around the pixel point is smaller than a preset number threshold value, and setting the pixel value of the pixel point as 0.
(III) beneficial effects
Compared with the prior art, the power equipment risk assessment method based on big data has the following beneficial effects:
1) Acquiring equipment data of equipment to be evaluated, determining a corresponding risk scoring table based on the equipment type of the equipment to be evaluated, and determining a first risk evaluation result of the equipment to be evaluated based on the risk score in the risk scoring table, so that a proper risk scoring table and a corresponding risk index can be determined according to the equipment type of the equipment to be evaluated, and the first risk evaluation result can be ensured to reflect the risk state of the equipment to be evaluated more accurately;
2) Acquiring an equipment image of equipment to be evaluated, carrying out registration alignment processing on the equipment image and a reference image, carrying out difference solving on the registered and aligned equipment image and the reference image to obtain a difference image, determining and identifying a difference region in the equipment image based on the difference image, and determining a second risk assessment result of the equipment to be evaluated based on the difference region in the equipment image, so that the difference region relative to the reference image in the equipment image can be determined and identified, and the second risk assessment result can accurately reflect the risk state of the equipment to be evaluated from the appearance angle of the equipment;
3) The final risk assessment result of the equipment to be assessed is determined by combining the first risk assessment result and the second risk assessment result of the equipment to be assessed, so that the final risk assessment result can reflect the risk state of the equipment to be assessed more accurately and comprehensively, the dependence on the model is effectively reduced, and the more accurate risk assessment result can be obtained.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a flowchart illustrating a first risk assessment method for determining a device under evaluation according to the present invention;
fig. 3 is a schematic flow chart of determining a second risk assessment result of a device under assessment in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. 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 utility model provides a power equipment risk assessment method based on big data, as shown in fig. 1 and 2, (1) obtain the equipment data of the equipment to be assessed, and confirm corresponding risk score table based on the equipment type of the equipment to be assessed.
Specifically, determining a corresponding risk score table based on the device type of the device to be evaluated includes:
determining various risk indexes based on equipment data of the power equipment, and constructing a risk scoring table associated with equipment types of the power equipment based on the various risk indexes;
and constructing a risk score table library according to the equipment types of all the electric equipment and the associated risk score table, and determining a risk score table matched with the equipment types of the equipment to be evaluated from the risk score table library.
(2) Determining a first risk assessment result of the device to be assessed based on the risk score in the risk scoring table, wherein the first risk assessment result specifically comprises:
determining various risk indexes in a risk scoring table, and determining risk scores corresponding to the various risk indexes based on equipment data of equipment to be evaluated;
and determining a first risk assessment result of the equipment to be assessed according to the risk scores corresponding to the risk indexes.
Specifically, determining a first risk assessment result of the device to be assessed according to risk scores corresponding to the risk indexes includes:
determining weight values corresponding to all risk indexes in a risk scoring table, and carrying out weighted summation calculation based on the weight values corresponding to all risk indexes and the risk scores to obtain a total risk score;
and determining the risk assessment grade of the equipment to be assessed according to the scoring range corresponding to the total risk score, and obtaining a first risk assessment result.
According to the technical scheme, the equipment data of the equipment to be evaluated are obtained, the corresponding risk scoring table is determined based on the equipment type of the equipment to be evaluated, and the first risk evaluation result of the equipment to be evaluated is determined based on the risk score in the risk scoring table, so that the appropriate risk scoring table and the corresponding risk index can be determined according to the equipment type of the equipment to be evaluated, and the first risk evaluation result can be ensured to reflect the risk state of the equipment to be evaluated more accurately.
As shown in fig. 1 and 3, (3) acquiring an apparatus image of an apparatus to be evaluated, and performing registration alignment processing on the apparatus image and a reference image.
Specifically, performing registration alignment processing on the device image and the reference image includes:
obtaining characteristic points of the equipment image and the reference image based on the SIFT algorithm, and matching the characteristic points of the equipment image and the reference image by adopting a proximity algorithm;
and performing conversion alignment on the device image and the reference image which are subjected to feature point matching.
Specifically, performing conversion alignment on the device image and the reference image that complete feature point matching includes:
and aligning the device image and the reference image which are subjected to feature point matching by adopting a homography matrix through a rotation transformation mode.
(4) Performing difference solving on the registered and aligned equipment image and the reference image to obtain a difference image, wherein the method specifically comprises the following steps of:
and performing matrix difference on the registered and aligned equipment image and the reference image, and taking the absolute value of the obtained matrix difference as a difference image.
Specifically, after taking the absolute value of the obtained matrix difference as the difference image, it includes:
performing edge detection processing on the reference image to obtain an edge detection image;
the edge detection image is multiplied with the difference image to eliminate edge noise data of the difference image.
(5) A region of difference in the device image is determined and identified based on the difference image, and a second risk assessment result for the device under assessment is determined based on the region of difference in the device image.
Specifically, determining and identifying a region of disparity in an image of a device based on a disparity image includes:
extracting the maximum pixel value of the difference image, and performing binarization processing on the difference image when the maximum pixel value is larger than a preset pixel threshold value to obtain a binarized image;
and marking the region where the pixel points with the pixel values being the preset pixel values are located in the binarized image to obtain a difference region.
Specifically, identifying a region where a pixel point with a pixel value being a preset pixel value in a binarized image is located, and before obtaining a difference region, the method includes:
traversing all pixel points with the pixel value of 255 in the binarized image, judging the pixel point as a noise point if the total number of the pixel points with the pixel value of 255 around the pixel point is smaller than a preset number threshold value, and setting the pixel value of the pixel point as 0.
According to the technical scheme, the equipment image of the equipment to be evaluated is acquired, registration alignment processing is carried out on the equipment image and the reference image, difference is carried out on the registered and aligned equipment image and the reference image, a difference image is obtained, the difference region in the equipment image is determined and identified based on the difference image, and the second risk assessment result of the equipment to be evaluated is determined based on the difference region in the equipment image, so that the difference region in the equipment image relative to the reference image can be determined and identified, and the risk state of the equipment to be evaluated can be accurately reflected by the second risk assessment result from the equipment appearance angle.
As shown in fig. 1, (6) determining a final risk assessment result of the device to be assessed by combining the first risk assessment result and the second risk assessment result of the device to be assessed.
According to the technical scheme, the final risk assessment result of the equipment to be assessed is determined by combining the first risk assessment result and the second risk assessment result of the equipment to be assessed, so that the final risk assessment result can reflect the risk state of the equipment to be assessed more accurately and comprehensively, the dependence on the model is effectively reduced, and the more accurate risk assessment result can be obtained.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The power equipment risk assessment method based on big data is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring equipment data of equipment to be evaluated, and determining a corresponding risk scoring table based on the equipment type of the equipment to be evaluated;
s2, determining a first risk assessment result of the equipment to be assessed based on the risk score in the risk scoring table;
s3, acquiring an equipment image of equipment to be evaluated, and carrying out registration and alignment treatment on the equipment image and a reference image;
s4, carrying out difference between the registered and aligned equipment image and the reference image to obtain a difference image;
s5, determining and identifying a difference region in the equipment image based on the difference image, and determining a second risk assessment result of the equipment to be assessed based on the difference region in the equipment image;
s6, determining a final risk assessment result of the equipment to be assessed by combining the first risk assessment result and the second risk assessment result of the equipment to be assessed.
2. The big data based power equipment risk assessment method of claim 1, wherein: in S1, determining a corresponding risk scoring table based on the equipment type of the equipment to be evaluated, wherein the risk scoring table comprises the following steps:
determining various risk indexes based on equipment data of the power equipment, and constructing a risk scoring table associated with equipment types of the power equipment based on the various risk indexes;
and constructing a risk score table library according to the equipment types of all the electric equipment and the associated risk score table, and determining a risk score table matched with the equipment types of the equipment to be evaluated from the risk score table library.
3. The big data based power equipment risk assessment method of claim 2, wherein: and S2, determining a first risk assessment result of the equipment to be assessed based on the risk scores in the risk score table, wherein the first risk assessment result comprises the following steps:
determining various risk indexes in a risk scoring table, and determining risk scores corresponding to the various risk indexes based on equipment data of equipment to be evaluated;
and determining a first risk assessment result of the equipment to be assessed according to the risk scores corresponding to the risk indexes.
4. A big data based power equipment risk assessment method according to claim 3, characterized in that: the determining a first risk assessment result of the equipment to be assessed according to the risk scores corresponding to the risk indexes comprises the following steps:
determining weight values corresponding to all risk indexes in a risk scoring table, and carrying out weighted summation calculation based on the weight values corresponding to all risk indexes and the risk scores to obtain a total risk score;
and determining the risk assessment grade of the equipment to be assessed according to the scoring range corresponding to the total risk score, and obtaining a first risk assessment result.
5. The big data based power equipment risk assessment method of claim 1, wherein: in S3, performing registration alignment processing on the device image and the reference image, including:
obtaining characteristic points of the equipment image and the reference image based on the SIFT algorithm, and matching the characteristic points of the equipment image and the reference image by adopting a proximity algorithm;
and performing conversion alignment on the device image and the reference image which are subjected to feature point matching.
6. The big data based power equipment risk assessment method of claim 5, wherein: the step of performing conversion alignment on the device image and the reference image which are subjected to feature point matching comprises the following steps:
and aligning the device image and the reference image which are subjected to feature point matching by adopting a homography matrix through a rotation transformation mode.
7. The big data based power equipment risk assessment method of claim 5, wherein: and S4, carrying out difference between the registered and aligned equipment image and the reference image to obtain a difference image, wherein the step comprises the following steps of:
and performing matrix difference on the registered and aligned equipment image and the reference image, and taking the absolute value of the obtained matrix difference as a difference image.
8. The big data based power equipment risk assessment method of claim 7, wherein: after taking the absolute value of the obtained matrix difference value as a difference image, the method comprises the following steps:
performing edge detection processing on the reference image to obtain an edge detection image;
the edge detection image is multiplied with the difference image to eliminate edge noise data of the difference image.
9. The big data based power equipment risk assessment method of claim 7, wherein: determining and identifying a difference region in the device image based on the difference image in S5 includes:
extracting the maximum pixel value of the difference image, and performing binarization processing on the difference image when the maximum pixel value is larger than a preset pixel threshold value to obtain a binarized image;
and marking the region where the pixel points with the pixel values being the preset pixel values are located in the binarized image to obtain a difference region.
10. The big data based power equipment risk assessment method of claim 9, wherein: the identifying the region where the pixel point with the pixel value being the preset pixel value in the binarized image is located, before obtaining the difference region, comprises:
traversing all pixel points with the pixel value of 255 in the binarized image, judging the pixel point as a noise point if the total number of the pixel points with the pixel value of 255 around the pixel point is smaller than a preset number threshold value, and setting the pixel value of the pixel point as 0.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117455903A (en) * | 2023-12-18 | 2024-01-26 | 深圳市焕想科技有限公司 | Sports apparatus state evaluation method based on image processing technology |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117455903A (en) * | 2023-12-18 | 2024-01-26 | 深圳市焕想科技有限公司 | Sports apparatus state evaluation method based on image processing technology |
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