CN115797877A - Intelligent monitoring method, system and medium for power transmission equipment - Google Patents
Intelligent monitoring method, system and medium for power transmission equipment Download PDFInfo
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Abstract
The application discloses an intelligent monitoring method, an intelligent monitoring system and an intelligent monitoring medium for power transmission equipment, relates to the technical field of data processing methods based on management purposes, and aims to solve the problems that the existing monitoring mode for the power transmission equipment is high in cost and difficult to make timely early warning according to changes of the environment. The method comprises the following steps: determining a first local defect region of the power transfer device; determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; determining a first operation risk degree of a monitoring node corresponding to a first local defect area; acquiring environment information of a monitoring node corresponding to the first local defect area, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree; according to the second operation risk degree, operation early warning is carried out on the power transmission equipment, the monitoring cost is effectively reduced, and more accurate and timely early warning can be carried out on the power transmission equipment.
Description
Technical Field
The present application relates to the field of data processing methods based on management purposes, and in particular, to an intelligent monitoring method, system, and medium for power transmission equipment.
Background
With the continuous acceleration of urbanization, the usage rate and coverage rate of power supply facilities such as power transmission equipment are also increasing year by year. Power transmission equipment adopts overhead line's mode to carry out power transmission usually, along with power transmission equipment service life's increase, can inevitably appear surface defects such as aperture, mar, breakage, these defects can influence power transmission equipment's performance, can lead to more serious incident even, consequently, in power transmission equipment's use, need carry out real-time supervision to it to improve operation security.
At present, the mode of patrolling and examining or artificially examining through unmanned aerial vehicle is monitored power transmission equipment mostly, but, above-mentioned monitoring mode consumes manpower and materials cost great, and to this kind of equipment that easily receives the environmental factor influence of power transmission equipment, also be difficult to make timely early warning to power transmission equipment's performance according to the environmental factor, both increaseed power transmission equipment's the management degree of difficulty, also probably can bring certain security problem.
Disclosure of Invention
In order to solve the above problem, the present application provides an intelligent monitoring method for power transmission equipment, including:
the method comprises the steps of obtaining operation images of a plurality of monitoring nodes corresponding to the power transmission equipment, and identifying the operation images to determine a first local defect area of the power transmission equipment;
determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect area and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment;
determining a defect influence degree corresponding to the first local defect region according to the defect type of the first local defect region, and obtaining a first operation risk degree of a monitoring node corresponding to the first local defect region according to the defect influence degree and the defect generation length;
collecting environment information of a monitoring node corresponding to the first local defect area, analyzing the environment information, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and performing operation early warning on the power transmission equipment according to the second operation risk degree.
In an implementation manner of the present application, it is right to analyze the environment information, so as to compensate the first operation risk degree according to the environment information, and obtain a compensated second operation risk degree, which specifically includes:
acquiring a running image sequence of the power transmission equipment, and screening out a historical running image which is closest to the time of the running image from the running image sequence;
identifying the historical operating image to determine a second local defect area corresponding to the power transmission equipment;
comparing the first local defect region and the second local defect region to determine a rate of change of defect between the first local defect region and the second local defect region;
determining an environmental influence coefficient corresponding to the environmental information, and determining a compensation coefficient corresponding to the environmental information according to a product between the environmental influence coefficient and the defect change rate;
compensating the first operation risk degree according to the compensation coefficient to obtain a compensated second operation risk degree; wherein the degree of compensation is positively correlated with the compensation coefficient and the environmental influence coefficient.
In an implementation manner of the present application, the environmental information at least includes an environmental temperature, an environmental humidity and an environmental landform, and the environmental influence coefficient corresponding to the environmental information is determined, which specifically includes:
determining a plurality of environment temperature intervals and a plurality of environment humidity intervals corresponding to the defect type of the first local defect area; each environment temperature interval corresponds to a first environment influence coefficient, and each environment humidity interval corresponds to a second environment influence coefficient;
respectively determining a first environmental influence coefficient and a second environmental influence coefficient corresponding to the first local defect area according to the environmental temperature and the environmental humidity;
acquiring a three-dimensional image of the environmental landform of a monitoring node where the first local defect area is located, and determining a plurality of spatial markers in the monitoring node according to the three-dimensional image;
determining a third environmental influence coefficient corresponding to the first local defect area according to the plurality of spatial markers;
and determining influence weights corresponding to the first environment influence coefficient, the second environment influence coefficient and the third environment influence coefficient respectively, and performing weighted summation on the first environment influence coefficient, the second environment influence coefficient and the third environment influence coefficient according to the influence weights to obtain the environment influence coefficients corresponding to the environment information.
In an implementation manner of the present application, determining, according to the plurality of spatial markers, a third environmental impact coefficient corresponding to the first local defect area specifically includes:
carrying out image segmentation on the three-dimensional image, and screening out a plurality of target sub three-dimensional images to which the space markers belong from the sub three-dimensional images obtained after segmentation aiming at each space marker;
determining mark points corresponding to the space marker in the plurality of target sub-three-dimensional images respectively, and sequencing the mark points according to the sequence of the vertical coordinates of the mark points from high to low so as to determine the space marker points of the space marker; the space marker point is used for representing the highest position of the space marker;
projecting the three-dimensional image onto a two-dimensional plane, and determining the distance between the space mark point and the power transmission equipment based on the two-dimensional plane;
and determining a third environmental influence coefficient corresponding to the first local defect area according to the distance interval to which the distance belongs.
In an implementation manner of the present application, identifying the operation image to determine a first local defect area of the power transmission device specifically includes:
carrying out gray level processing on the running image, determining a gray level average value of each row of pixel points of the running image after the gray level processing, and determining a background area and a target area to be identified in the running image according to the gray level average value; wherein the average value of the gray scale of the background area is smaller than the average value of the gray scale of the target area;
respectively determining neighborhood pixel points corresponding to each pixel point neighborhood in the target region, and gray standard difference and gray average value between gray values corresponding to the neighborhood pixel points;
determining dispersion coefficients corresponding to the pixel points according to the ratio of the standard deviation of the gray scale to the average value of the gray scale;
for each pixel point, comparing the dispersion coefficient corresponding to the pixel point with a preset value, and clustering the pixel points in the target area according to a comparison result to determine a first local defect area of the power transmission equipment; and the dispersion coefficient corresponding to the first local defect area is larger than the preset value.
In one implementation of the present application, after determining the first local defect region of the power transfer device, the method further comprises:
carrying out image binarization on the first local defect area to obtain a binarized first local defect area image; wherein the first local defect region image is composed of a plurality of defect pixels;
for each defective pixel point, sequentially traversing the first local defective region image by taking the defective pixel point as a starting point so as to determine an adjacent defective pixel point which has an adjacent relation with the defective pixel point;
connecting the adjacent defect pixel points to obtain a plurality of sub-defect regions corresponding to the first local defect region;
determining the number of pixel points in the plurality of sub-defect regions and the number of standard pixel points corresponding to the plurality of sub-defect regions respectively, and comparing the number of the pixel points corresponding to each sub-defect region with the number of the standard pixel points so as to determine whether the number of the standard pixel points is greater than the number of the pixel points or not;
and if so, deleting the sub-defect area from the first local defect area.
In an implementation manner of the present application, before determining the influence weights corresponding to the first environmental influence coefficient, the second environmental influence coefficient, and the third environmental influence coefficient, respectively, the method further includes:
determining a monitoring time period in which the power transmission equipment is currently located and time period correction coefficients corresponding to the environmental information in the monitoring time period respectively;
and respectively correcting the influence weights corresponding to the first environment influence coefficient, the second environment influence coefficient and the third environment influence coefficient according to the time interval correction coefficient.
In an implementation manner of the present application, determining a defect generation length of the first local defect region according to the defect feature parameter specifically includes:
constructing a multiple linear regression power transmission equipment defect prediction model by taking the defect characteristic parameters as independent variables and the defect growth as dependent variables;
and substituting the defect characteristic parameters into the multiple linear regression power transmission equipment defect prediction model to determine the defect generation length of the first local defect area.
The embodiment of the application provides an intelligent monitoring system of power transmission equipment, the system includes:
the system comprises an identification module, a first local defect area determination module and a second local defect area determination module, wherein the identification module is used for acquiring running images of a plurality of monitoring nodes corresponding to the power transmission equipment and identifying the running images so as to determine the first local defect area of the power transmission equipment;
the defect evaluation module is used for determining a defect characteristic parameter corresponding to the first local defect area and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect areas and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment;
the operation risk evaluation module is used for determining the defect influence degree corresponding to the first local defect area according to the defect type of the first local defect area, and obtaining a first operation risk degree of the monitoring node corresponding to the first local defect area according to the defect influence degree and the defect generation length;
the compensation module is used for acquiring environment information of a monitoring node corresponding to the first local defect area, analyzing the environment information, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and the early warning module is used for carrying out operation early warning on the power transmission equipment according to the second operation risk degree.
An embodiment of the present application provides a non-volatile computer storage medium storing computer-executable instructions configured to:
the method comprises the steps of obtaining operation images of a plurality of monitoring nodes corresponding to the power transmission equipment, and identifying the operation images to determine a first local defect area of the power transmission equipment;
determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect areas and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment;
determining the defect influence degree corresponding to the first local defect region according to the defect type of the first local defect region, and obtaining a first operation risk degree of a monitoring node corresponding to the first local defect region according to the defect influence degree and the defect generation length;
collecting environmental information of a monitoring node corresponding to the first local defect area, and analyzing the environmental information to compensate the first operation risk degree according to the environmental information to obtain a compensated second operation risk degree;
and performing operation early warning on the power transmission equipment according to the second operation risk degree.
The intelligent monitoring method for the power transmission equipment can bring the following beneficial effects:
the defects are positioned according to the running image of the power transmission equipment, and the monitoring through manpower or inspection equipment is not needed, so that the monitoring cost is effectively reduced. The first local defect area with defects is analyzed, after the first operation risk degree of the power transmission equipment is obtained, the first operation risk degree is compensated through the environmental information, the influence of the environmental information on the performance of the power transmission equipment can be comprehensively considered on the basis of determining the operation state of the power transmission equipment, and therefore more accurate and timely early warning is made for the power transmission equipment, safety accidents are avoided, and management efficiency is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart of an intelligent monitoring method for power transmission equipment according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an intelligent monitoring system of a power transmission device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, an intelligent monitoring method for a power transmission device according to an embodiment of the present application includes:
101: the method comprises the steps of obtaining operation images of a plurality of monitoring nodes corresponding to the power transmission equipment, and identifying the operation images to determine a first local defect area of the power transmission equipment.
For overhead power transmission equipment, when monitoring the overhead power transmission equipment, multi-region and all-around monitoring can be carried out in a mode of arranging a plurality of monitoring nodes, so that the monitoring precision is improved. Each monitoring node is provided with a corresponding monitoring device, such as a camera, a laser radar and the like, and is used for acquiring the running image corresponding to the power transmission equipment under the current monitoring node. After the operation images of the plurality of monitoring nodes corresponding to the power transmission equipment are obtained, the operation images need to be identified, so that whether the current power transmission equipment has defects or not and a first local defect area where the defects are located are determined.
In one embodiment, the operation image of the power transmission device is divided into a background area and a target area where the power transmission device is located, and if a defect of the power transmission device is to be identified, the power transmission device in the operation image needs to be located first, and then a specific defect area needs to be located. The brightness values of the background area and the target area are greatly different, and the corresponding gray values are also greatly different, so that the target area can be screened out from the running image through the gray values of different pixel points.
Firstly, performing gray processing on an operation image, determining the gray average value of pixel points of each row of the operation image after the gray processing because the power transmission equipment is regularly distributed in the operation image, and further determining a background area and a target area to be identified in the operation image according to the gray average value. Wherein, the gray level average value of the background area is smaller than that of the target area.
Secondly, after the target area where the power transmission equipment is located is identified, the defect area needs to be more accurately positioned. The gray value of the edge of the defective area of the power transmission equipment can generate certain fluctuation, so that for each pixel point in the target area, the pixel point can be used as a central position, and the areas of the pixel points at eight positions, namely, upper, lower, left, right, upper left, upper right, lower left and lower right, of the pixel point are neighborhoods of the pixel point, wherein the pixel points in the neighborhoods are neighborhood pixel points. After the neighborhood pixel points corresponding to the neighborhood of each pixel point in the target region are respectively determined, the gray value corresponding to each neighborhood pixel point is also determined, the gray average value between the gray values is calculated through a formula (1), and the gray standard deviation between the gray values is calculated through a formula (2). Wherein, the formula is as follows:
wherein the content of the first and second substances,
representing the gray value corresponding to the neighborhood pixel point of a certain pixel point. After obtaining the gray standard deviation and the gray average value, the ratio of the gray standard deviation to the gray average value is used
Determining a dispersion coefficient C corresponding to the pixel point, wherein the dispersion coefficient is used for expressing the fluctuation degree of the gray value between the neighborhood pixel points in the neighborhood of the current pixel point, and the larger the dispersion coefficient is, the gray value of the domain pixel point is shownThe greater the degree of value fluctuation. At this time, for each pixel point, the corresponding dispersion coefficient is compared with a preset value, so as to obtain a corresponding comparison result. The preset value represents the maximum value of the gray value discrete degree, and if the dispersion coefficient is larger than the preset value, the gray value in the neighborhood of the current pixel point is changed violently, and the power transmission equipment has certain defects in the neighborhood. Therefore, according to the comparison result, the pixel points in the target area where the power transmission equipment is located can be clustered, and therefore the first local defect area is obtained.
It should be noted that there may be a certain error in the first local defect region determined through the above steps, and a small range region around the first local defect region is likely to be identified as a defect region, so after the first local defect region is obtained, it needs to be further filtered to obtain a more accurate defect location result.
Specifically, image binarization is performed on the first local defect area, so that a binarized first local defect area image is obtained. The first local defect area image after binarization consists of a plurality of defect pixel points. And for each defective pixel point, sequentially traversing the first local defect area image by taking the defective pixel point as a starting point, so as to determine an adjacent defective pixel point which has an adjacent relation with the defective pixel point. And if the adjacent defective pixel points exist, sequentially connecting the adjacent defective pixel points until all defective pixel points after the first local defective region image is traversed, and obtaining a plurality of sub-defective regions corresponding to the first local defective region. It should be noted that there may be one or more sub-defect regions.
After the binarization is carried out on the part where the local defect area is located, the background and the foreground of the image are substantially divided, and then whether the local defect area really has defects or not can be judged according to the number of pixel points in the sub-defect area. The number of the pixels in the unit area in the operation image is fixed, the number of the pixels in the unit area can be used as the number of the standard pixels, and then the number of the standard pixels is compared with the number of the pixels in each sub-defect area, so that whether the number of the standard pixels is larger than the number of the pixels or not is determined. If the current sub-defect area is larger than the first partial defect area, the current sub-defect area is possibly mistakenly identified due to the fact that part of pixel points are missing, at this time, the sub-defect area needs to be deleted from the first partial defect area, and therefore it is guaranteed that the finally identified first partial defect areas all have actual defects.
102: determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect areas and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment.
When the power transmission apparatus is defective, the more significant the defect characteristic parameter of the first local defective region is, the more the operation performance of the power transmission apparatus is affected. Therefore, after the first local defect area is determined, corresponding defect feature parameters at least including a defect area and a defect depth value need to be determined, and then, according to the defect feature parameters, the defect generation length of the first local defect area is determined. The defect growth degree is used for representing the operation state of the power transmission equipment, and the larger the defect growth degree is, the larger the defect degree corresponding to the power transmission equipment is, that is, the larger the defect area and the defect depth value of the first local defect area are, the wider the radiation range representing the surface defect of the power transmission equipment is, and the performance of the power transmission equipment is more affected.
The defect growth length and the defect characteristic parameter value are in a linear relation, the defect characteristic parameter can be used as an independent variable, the defect growth length is used as a dependent variable, and a multivariate linear regression power transmission equipment defect prediction model is constructed, wherein the model can be expressed as:
wherein the content of the first and second substances,
、
、
the regression coefficient is represented, s represents the defect area, d represents the defect depth value, and F represents the defect growth length.
After a multiple linear regression power transmission equipment defect prediction model is constructed, defect characteristic parameters of the first local defect area are substituted into the model, and therefore the defect generation length of the first local defect area can be determined.
103: and determining the defect influence degree corresponding to the first local defect area according to the defect type of the first local defect area, and obtaining a first operation risk degree of the monitoring node corresponding to the first local defect area according to the defect influence degree and the defect generation length.
Power transmission equipment may have a variety of surface defect types such as dirt on the insulation surface, insulation scratches, delamination of the outer semiconducting layer, pinholes, etc. For example, if the surface of the insulating layer of the power transmission device is stained, the transmission performance of the power transmission device is not adversely affected, the defect of insulation scratch does not greatly affect the performance of the power transmission device in a short time, and if the surface of the power transmission device has small holes, the power transmission speed may be reduced or the power transmission may be interrupted if the surface is not processed in time.
Therefore, after the first local defect area is identified, the defect influence degree corresponding to the current first local defect area needs to be determined from the preset mapping table according to the defect type of the first local defect area. And further, carrying out weighted summation on the defect influence degree and the defect growth degree corresponding to the first local defect area, so as to obtain a first operation risk degree of the monitoring node corresponding to the first local defect area. The first operational risk level represents a degree of performance impact of the first local defect area on the power transfer device.
104: and collecting the environment information of the monitoring node corresponding to the first local defect area, and analyzing the environment information to compensate the first operation risk degree according to the environment information to obtain a compensated second operation risk degree.
First operation risk degree is through carrying out defect location and defect influence degree analysis to power transmission equipment and obtaining, however, overhead power transmission equipment not only can receive the potential safety hazard factor that its self exists when the operation, still can receive the influence of environmental factor, for example, weather factor, environmental landform etc.. When environmental factor changes, the personnel of patrolling and examining or unmanned aerial vehicle probably receive certain restriction when the operation, still can have the hysteretic possibility of monitoring to lead to the emergence of incident. Therefore, the environmental information is analyzed by collecting the environmental information of the monitoring node corresponding to the first local defect area, and the first operation risk degree is compensated according to the environmental information to obtain the compensated second operation risk degree.
Specifically, an operation image sequence of the power transmission equipment is obtained, and a historical operation image which is closest to the time of the operation image is screened from the operation image sequence. And identifying the historical operation image, and determining a second local defect area corresponding to the power transmission equipment. The first local defect region may be a defect region obtained by continuing the development of the first local defect region, or may be a newly-appeared defect region. And after the second local defect area is obtained, comparing the first local defect area with the second local defect area, and determining the defect change rate between the first local defect area and the second local defect area. The defect change rate is used for representing the development degree of the defects of the power transmission equipment and can be obtained by calculating the difference value of the characteristic parameters of the defects, and for a certain power transmission equipment, the larger the defect change rate is, the larger the defect development degree of the power transmission equipment is.
Further, after the defect change rate of the first local defect area is obtained, an environmental influence coefficient corresponding to the environmental information needs to be determined, and a compensation coefficient corresponding to the environmental information is determined according to a product of the environmental influence coefficient and the defect change rate.
It should be noted that the environmental information at least includes an ambient temperature, an ambient humidity, and an environmental landscape, where the environmental landscape refers to an external landscape in a monitoring range corresponding to each monitoring node, such as a tree, a space building, and the like. The space building is usually fixed, and the influence generated by the space building in the operation process of the power transmission equipment does not change in real time, so that the space building with the activity capability such as trees can be referred to when determining the environmental influence coefficient of the first local defect area through the environmental landform.
The environmental influence coefficient is adapted to the environmental information, a plurality of environmental temperature intervals and a plurality of environmental humidity intervals corresponding to the defect types of the first local defect area need to be determined, each environmental temperature interval corresponds to one first environmental influence coefficient, each environmental humidity interval corresponds to one second environmental influence coefficient, and therefore the first environmental influence coefficient and the second environmental influence coefficient corresponding to the current first local defect area can be determined according to the collected environmental temperature and the collected environmental humidity. For the environmental factor of the environmental landscape, it needs to be considered whether the highest point of the spatial marker such as a tree will contact with the power transmission device in the current monitoring period, and if the highest point of the spatial marker contacts with the power transmission device, the spatial marker may damage the surface of the power transmission device, thereby affecting the performance of the power transmission device to a certain extent. According to the method and the device, the three-dimensional image of the environmental landform of the monitoring node where the first local defect area is located can be generated through the environmental information collected by the laser radar, and therefore the plurality of spatial markers in the monitoring node can be determined in the three-dimensional image.
After the plurality of spatial markers are determined, a third environmental impact coefficient corresponding to the first local defect region may be determined according to the plurality of spatial markers. The third environmental influence coefficient is determined by a spatial height difference between the spatial marker and the power transmission device, and if the spatial height difference is to be determined, the three-dimensional image needs to be segmented, and for each spatial marker, a plurality of target sub three-dimensional images to which the spatial marker belongs are screened out from the segmented sub three-dimensional images. The whole image formed by splicing the multiple target sub three-dimensional images contains a space marker. After the target sub three-dimensional images are screened out, the mark points corresponding to the space marker in the target sub three-dimensional images can be determined, and the mark points are sequenced according to the sequence from high to low of the vertical coordinates of the mark points, so that the space marker points of the space marker can be determined. The mark point refers to the highest position of a part of the space marker in a certain target sub three-dimensional image, and the space marker point is used for representing the highest position of the space marker. After the space mark point is determined, the three-dimensional image is integrally projected onto a two-dimensional plane, and the distance between the space mark point and the power transmission equipment can be determined based on the two-dimensional plane. In this case, the third environmental impact coefficient corresponding to the first local defect region may be determined based on the distance section to which the distance belongs.
After a first environment influence coefficient, a second environment influence coefficient and a third environment influence coefficient corresponding to the environment information are respectively determined, influence weights corresponding to the environment influence coefficients are determined through a preset weight mapping table. The weight mapping table comprises mapping relations between the environment information and the influence weights corresponding to the environment information, and the specific numerical value can be determined and obtained through overhaul data of operation and maintenance personnel. And according to the influence weight, carrying out weighted summation on the first environment influence coefficient, the second environment influence coefficient and the third environment influence coefficient to obtain the environment influence coefficient corresponding to the environment information.
The same thing applies to the environmental landscape, the environmental temperature and the environmental humidity, which are all affected by the monitoring period, for example, the summer environmental humidity and the summer environmental temperature are significantly higher than in other seasons, and for example, for spatial signs such as trees, the growth rate in the spring and summer seasons is significantly higher than in the spring and autumn seasons. Therefore, when the influence of the environmental information on the power transmission equipment can be measured, the factor of the monitoring time period is also considered, and after the monitoring time period in which the power transmission equipment is currently located and the time period correction coefficient corresponding to the environmental information in the current monitoring time period are determined, the influence weights corresponding to the first environmental influence coefficient, the second environmental influence coefficient and the third environmental change influence coefficient are respectively corrected according to the time period correction coefficient. The time interval correction coefficients corresponding to the first environmental influence coefficient and the second environmental influence coefficient are respectively in direct proportion to the environmental temperature and the environmental humidity; and the time interval correction coefficient corresponding to the third environmental influence coefficient is in direct proportion to the growth speed of the space marker.
Furthermore, after the compensation coefficient is determined, the first operation risk degree is compensated according to the compensation coefficient, the compensation coefficient and the first operation risk degree are subjected to product operation, and an obtained operation result is a compensated second operation risk degree. Wherein, the compensation coefficient is larger than 1, and the compensation degree is in positive correlation with the compensation coefficient and the environmental influence coefficient.
105: and performing operation early warning on the power transmission equipment according to the second operation risk degree.
The second operation risk degree is an evaluation result finally obtained on the basis of analyzing the defects of the power transmission equipment and considering the influence of the environmental and geomorphic conditions of the power transmission equipment on the performance of the power transmission equipment. Therefore, in the real-time operation process of the power transmission equipment, the performance change of the power transmission equipment can be monitored, so that operation early warning is carried out when environmental factors are changed, and safety accidents can be effectively avoided. The higher the second operation risk degree is, the larger the operation hidden danger of the power transmission equipment is, and operation and maintenance personnel can take corresponding management measures to overhaul the power transmission equipment according to the second operation risk degree.
The above is the method embodiment proposed by the present application. Based on the same idea, some embodiments of the present application further provide a system and a non-volatile computer storage medium corresponding to the above method.
Fig. 2 is a schematic structural diagram of an intelligent monitoring system of a power transmission device according to an embodiment of the present disclosure. As shown in fig. 2, the system includes:
the identification module 201 is configured to acquire operation images of a plurality of monitoring nodes corresponding to the power transmission equipment, and identify the operation images to determine a first local defect area of the power transmission equipment;
a defect evaluation module 202, configured to determine a defect feature parameter corresponding to the first local defect region, and determine a defect generation length of the first local defect region according to the defect feature parameter; the defect characteristic parameters at least comprise defect areas and defect depth values, and the defect growth degree is used for representing the running state of the power transmission equipment;
the operation risk evaluation module 203 is configured to determine a defect influence degree corresponding to the first local defect region according to the defect type of the first local defect region, and obtain a first operation risk degree of the monitoring node corresponding to the first local defect region according to the defect influence degree and the defect generation length;
the compensation module 204 is configured to acquire environment information of a monitoring node corresponding to the first local defect area, analyze the environment information, and compensate the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and the early warning module 205 is configured to perform operation early warning on the power transmission device according to the second operation risk.
An embodiment of the present application provides a non-volatile computer storage medium, which stores computer-executable instructions configured to:
acquiring running images of a plurality of monitoring nodes corresponding to the power transmission equipment, and identifying the running images to determine a first local defect area of the power transmission equipment;
determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise a defect area and a defect depth value, and the defect growth degree is used for representing the running state of the power transmission equipment;
determining the defect influence degree corresponding to the first local defect area according to the defect type of the first local defect area, and obtaining a first operation risk degree of the monitoring node corresponding to the first local defect area according to the defect influence degree and the defect generation length;
collecting environment information of a monitoring node corresponding to the first local defect area, analyzing the environment information, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and performing operation early warning on the power transmission equipment according to the second operation risk degree.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system and media embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, where relevant, reference may be made to some descriptions of the method embodiments.
The system and the medium provided by the embodiment of the application correspond to the method one to one, so the system and the medium also have the beneficial technical effects similar to the corresponding method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An intelligent monitoring method for a power transmission apparatus, the method comprising:
acquiring operation images of a plurality of monitoring nodes corresponding to power transmission equipment, and identifying the operation images to determine a first local defect area of the power transmission equipment;
determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect area and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment;
determining a defect influence degree corresponding to the first local defect region according to the defect type of the first local defect region, and obtaining a first operation risk degree of a monitoring node corresponding to the first local defect region according to the defect influence degree and the defect generation length;
collecting environment information of a monitoring node corresponding to the first local defect area, analyzing the environment information, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and carrying out operation early warning on the power transmission equipment according to the second operation risk degree.
2. The intelligent monitoring method of power transmission equipment according to claim 1, wherein the analyzing the environmental information is performed to compensate the first operation risk degree according to the environmental information, so as to obtain a compensated second operation risk degree, specifically comprising:
acquiring a running image sequence of the power transmission equipment, and screening out a historical running image which is closest to the time of the running image from the running image sequence;
identifying the historical operating image to determine a second local defect area corresponding to the power transmission equipment;
comparing the first local defect region and the second local defect region to determine a rate of change of defect between the first local defect region and the second local defect region;
determining an environmental influence coefficient corresponding to the environmental information, and determining a compensation coefficient corresponding to the environmental information according to a product between the environmental influence coefficient and the defect change rate;
compensating the first operation risk degree according to the compensation coefficient to obtain a compensated second operation risk degree; wherein the degree of compensation is positively correlated with the compensation coefficient and the environmental influence coefficient.
3. The intelligent monitoring method of the power transmission equipment according to claim 2, wherein the environmental information at least includes an environmental temperature, an environmental humidity and an environmental landscape, and the determining the environmental influence coefficient corresponding to the environmental information specifically includes:
determining a plurality of environment temperature intervals and a plurality of environment humidity intervals corresponding to the defect type of the first local defect area; each environment temperature interval corresponds to a first environment influence coefficient, and each environment humidity interval corresponds to a second environment influence coefficient;
respectively determining a first environmental influence coefficient and a second environmental influence coefficient corresponding to the first local defect area according to the environmental temperature and the environmental humidity;
acquiring a three-dimensional image of the environmental landform of a monitoring node where the first local defect area is located, and determining a plurality of spatial markers in the monitoring node according to the three-dimensional image;
determining a third environmental influence coefficient corresponding to the first local defect area according to the plurality of spatial markers;
and determining the first environmental influence coefficient, the second environmental influence coefficient and the influence weight corresponding to the third environmental influence coefficient respectively, and weighting and summing the first environmental influence coefficient, the second environmental influence coefficient and the third environmental influence coefficient according to the influence weights to obtain the environmental influence coefficients corresponding to the environmental information.
4. The intelligent monitoring method for power transmission equipment according to claim 3, wherein determining a third environmental impact coefficient corresponding to the first local defect area according to the plurality of spatial markers specifically comprises:
performing image segmentation on the three-dimensional image, and screening a plurality of target sub three-dimensional images to which the space markers belong from the sub three-dimensional images obtained after segmentation for each space marker;
determining mark points corresponding to the space marker in the plurality of target sub-three-dimensional images respectively, and sequencing the mark points according to the sequence of the vertical coordinates of the mark points from high to low so as to determine the space marker points of the space marker; the space marker point is used for representing the highest position of the space marker;
projecting the three-dimensional image onto a two-dimensional plane, and determining the distance between the space mark point and the power transmission equipment based on the two-dimensional plane;
and determining a third environmental influence coefficient corresponding to the first local defect area according to the distance interval to which the distance belongs.
5. The intelligent monitoring method for the power transmission equipment according to claim 1, wherein identifying the operation image to determine the first local defect area of the power transmission equipment specifically comprises:
carrying out gray level processing on the running image, determining a gray level average value of each row of pixel points of the running image after the gray level processing, and determining a background area and a target area to be identified in the running image according to the gray level average value; wherein the average value of the gray scale of the background area is smaller than the average value of the gray scale of the target area;
respectively determining neighborhood pixel points corresponding to the neighborhood of each pixel point in the target region, and gray standard difference and gray average value between corresponding gray values of the neighborhood pixel points;
determining dispersion coefficients corresponding to the pixel points according to the ratio of the gray standard deviation to the gray average value;
for each pixel point, comparing the dispersion coefficient corresponding to the pixel point with a preset value, and clustering the pixel points in the target area according to a comparison result to determine a first local defect area of the power transmission equipment; and the dispersion coefficient corresponding to the first local defect area is larger than the preset value.
6. The intelligent monitoring method of a power transmission apparatus according to claim 5, wherein after determining the first local defect region of the power transmission apparatus, the method further comprises:
carrying out image binarization on the first local defect area to obtain a binarized first local defect area image; wherein the first local defect region image is composed of a plurality of defect pixels;
for each defective pixel point, sequentially traversing the first local defect area image by taking the defective pixel point as a starting point so as to determine an adjacent defective pixel point which has an adjacent relation with the defective pixel point;
connecting the adjacent defect pixel points to obtain a plurality of sub-defect regions corresponding to the first local defect region;
determining the number of pixel points in the plurality of sub-defect regions and the number of standard pixel points corresponding to the plurality of sub-defect regions respectively, and comparing the number of the pixel points corresponding to each sub-defect region with the number of the standard pixel points so as to determine whether the number of the standard pixel points is greater than the number of the pixel points;
and if so, deleting the sub-defect area from the first local defect area.
7. The intelligent monitoring method for power transmission equipment according to claim 3, wherein before determining the influence weights corresponding to the first environmental influence coefficient, the second environmental influence coefficient and the third environmental influence coefficient, respectively, the method further comprises:
determining a monitoring time period in which the power transmission equipment is currently located and time period correction coefficients corresponding to the environmental information in the monitoring time period respectively;
and respectively correcting the influence weights corresponding to the first environment influence coefficient, the second environment influence coefficient and the third environment influence coefficient according to the time interval correction coefficient.
8. The intelligent monitoring method for power transmission equipment according to claim 1, wherein determining the defect generation length of the first local defect region according to the defect characteristic parameter specifically comprises:
constructing a multiple linear regression power transmission equipment defect prediction model by taking the defect characteristic parameters as independent variables and the defect growth as dependent variables;
and substituting the defect characteristic parameters into the multiple linear regression power transmission equipment defect prediction model to determine the defect generation length of the first local defect area.
9. An intelligent monitoring system for a power transmission device, the system comprising:
the system comprises an identification module, a first local defect area determination module and a second local defect area determination module, wherein the identification module is used for acquiring running images of a plurality of monitoring nodes corresponding to the power transmission equipment and identifying the running images so as to determine the first local defect area of the power transmission equipment;
the defect evaluation module is used for determining a defect characteristic parameter corresponding to the first local defect area and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect areas and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment;
the operation risk evaluation module is used for determining the defect influence degree corresponding to the first local defect area according to the defect type of the first local defect area, and obtaining a first operation risk degree of the monitoring node corresponding to the first local defect area according to the defect influence degree and the defect generation length;
the compensation module is used for acquiring environment information of a monitoring node corresponding to the first local defect area, analyzing the environment information, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and the early warning module is used for carrying out operation early warning on the power transmission equipment according to the second operation risk degree.
10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions configured to:
acquiring operation images of a plurality of monitoring nodes corresponding to power transmission equipment, and identifying the operation images to determine a first local defect area of the power transmission equipment;
determining a defect characteristic parameter corresponding to the first local defect area, and determining the defect generation length of the first local defect area according to the defect characteristic parameter; the defect characteristic parameters at least comprise defect areas and defect depth values, and the defect growth degree is used for representing the operation state of the power transmission equipment;
determining a defect influence degree corresponding to the first local defect region according to the defect type of the first local defect region, and obtaining a first operation risk degree of a monitoring node corresponding to the first local defect region according to the defect influence degree and the defect generation length;
collecting environment information of a monitoring node corresponding to the first local defect area, analyzing the environment information, and compensating the first operation risk degree according to the environment information to obtain a compensated second operation risk degree;
and performing operation early warning on the power transmission equipment according to the second operation risk degree.
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