CN115272572A - Power transmission line reconstruction method and device, electronic equipment and storage medium - Google Patents
Power transmission line reconstruction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for reconstructing a power transmission line, electronic equipment and a storage medium, wherein the method comprises the following steps: identifying the target image based on a pre-trained deep learning model to obtain pixel position information of a power transmission line pixel point in the target image; determining actual coordinate information corresponding to the pixel points of the power transmission line according to the pixel position information and the pose information of the pixel points of at least two power transmission lines; and obtaining parameters of the power transmission line according to the actual coordinate information, and performing line reconstruction based on the parameters of the power transmission line. Based on the technical scheme, the line point positions are accurately matched in the process of reconstructing the power transmission line, and the technical effect of improving the reconstruction accuracy of the power transmission line is achieved.
Description
Technical Field
The present invention relates to the field of power technologies, and in particular, to a method and an apparatus for reconstructing a power transmission line, an electronic device, and a storage medium.
Background
With the development and progress of the power technology, in order to facilitate the maintenance of the power transmission line, a corresponding digital model is constructed, so that the convenience of maintenance is improved.
However, in the existing model construction method, the reconstruction of the power transmission line is directly completed based on the acquired image information of the power transmission line, or the reconstruction can be completed only for a scene with single image information, so that the accuracy of the reconstructed power transmission line model is low, and the power transmission line reconstruction method cannot meet the requirements of users.
Disclosure of Invention
The invention provides a method and a device for reconstructing a power transmission line, electronic equipment and a storage medium, which are used for realizing the accurate matching of line point positions in the process of reconstructing the power transmission line and further achieving the technical effect of improving the accuracy of reconstructing the power transmission line.
In a first aspect, the present invention provides a method for reconstructing a power transmission line, including:
identifying a target image based on a pre-trained deep learning model to obtain pixel position information of a power transmission line pixel point in the target image;
determining actual coordinate information corresponding to the pixel points of the power transmission line according to pixel position information and pose information of the pixel points of at least two power transmission lines, wherein the pose information is angle information when the unmanned aerial vehicle shoots an image;
and obtaining parameters of the power transmission line according to the actual coordinate information, and performing line reconstruction based on the parameters of the power transmission line.
In a second aspect, an embodiment of the present invention further provides a device for reconstructing a power transmission line, where the device includes:
the position information acquisition module is used for identifying a target image based on a pre-trained deep learning model so as to obtain pixel position information of a power transmission line pixel point in the target image;
the coordinate information acquisition module is used for determining actual coordinate information corresponding to the pixel points of the power transmission line according to pixel position information and pose information of the pixel points of at least two power transmission lines, wherein the pose information is angle information when the unmanned aerial vehicle shoots an image;
and the line reconstruction module is used for obtaining the relevant parameters of the power transmission line according to the actual coordinate information and reconstructing the line based on the relevant parameters of the power transmission line.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for reconstructing a power transmission line according to any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the power transmission line reconstruction method according to any one of the embodiments of the present invention.
According to the technical scheme, the target image is identified based on the pre-trained deep learning model to obtain the pixel position information of the pixel points of the power transmission line in the target image, the actual coordinate information corresponding to the pixel points of the power transmission line is further determined according to the pixel position information and the pose information of the pixel points of at least two power transmission lines, the relevant parameters of the power transmission line are finally obtained according to the actual coordinate information, and the line is reconstructed based on the relevant parameters of the power transmission line.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for reconstructing a power transmission line according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for reconstructing a power transmission line according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for reconstructing a power transmission line according to an embodiment of the present invention;
fig. 4 is a block diagram of a power transmission line reconstruction apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a schematic flowchart of a method for reconstructing a power transmission line according to an embodiment of the present invention, where the embodiment is applicable to a situation where collected image information of the power transmission line is analyzed and a position of the power transmission line is reconstructed according to an analysis result, and the method may be integrated in an electronic device, where the electronic device may be a PC terminal or a server terminal.
As shown in fig. 1, the method includes:
s110, identifying a target image based on a pre-trained deep learning model to obtain pixel position information of a power transmission line pixel point in the target image.
The deep learning model can be a model constructed based on an artificial neural network, and it can be understood that the deep learning model can be an image recognition model, a semantic segmentation model and the like because the different models have different functions. The target image may be understood as an image containing the transmission line as well as the tower. The pixel position information may be position information of the transmission line pixel.
Specifically, after the target image is obtained, the target image is identified based on a preset deep learning model, and then pixel position information of all power transmission line pixel points in the target image is obtained. For example, the acquired image may be input into an image recognition model, an image including the power transmission line is recognized, and then the image including the power transmission line is input into the model, and the pixel position information of the pixel point of the power transmission line is recognized.
On the basis of the technical scheme, before the target image is identified based on the pre-trained deep learning model, the method further comprises the following steps: and determining a target area according to the tower span and the coordinate information of the target tower, obtaining all images in the target area, and taking the images as the target images.
The target area is a circular area formed by taking a target tower as a circle center and taking the tower span as a radius. The tower span can be the horizontal distance between two suspension points of the overhead line in a plane parallel to the specific load borne by the lead between two adjacent towers. The target tower can be understood as a tower that needs to be treated. It should be noted that a tower is a support for supporting a transmission line in an overhead transmission line. The tower is mostly made of steel or reinforced concrete and is a main supporting structure of the overhead transmission line. The coordinate information of the target tower may be the coordinate information of the target tower in WGS84 coordinates, which WGS84 coordinates are a coordinate system established for use by a GPS global positioning system.
Specifically, the target tower is used as a circle center, the tower span is used as a radius, all images in the target area are determined by the position information of the unmanned aerial vehicle when the images are shot, and the images are used as the target images.
On the basis of the above technical solution, after obtaining the target image, the method further includes: and dividing the target image into at least one sub-image based on the current direction in the power transmission line, a preset identification object and the coordinate information of the target tower.
And at least one subimage corresponds to a tower with a preset model. Wherein the current direction may be a direction of current flow in the transmission line. The preset recognition object may be an object disposed on the power transmission line, such as an insulator string on the power transmission line.
Specifically, the types of the target images can be divided according to the current flow direction in the power transmission line and the preset coordinate information for identifying the object and the tower, for example, the power transmission line can be divided into three sub-images, a large tower end image, a middle target image and a small tower end image based on the coordinate information. It should be noted that the preset tower model may be a large tower and a small tower, for example, the tower into which current flows may be used as the large tower and the tower out of which current flows may be used as the small tower according to the current flow direction. Furthermore, the target image can be divided into at least one sub-image based on the type of the tower and the preset coordinate information of the recognition object and the target tower, and the sub-image can comprise a large-size tower end image, a middle target image and a small-size tower end image.
In the practical application process, a corresponding image recognition model can be set, and after a preset recognition object is recognized through the image recognition model, the target image is classified to obtain at least one sub-image.
On the basis of the technical scheme, the identifying the target image based on the deep learning model trained in advance to obtain the pixel position information of the pixel points of the power transmission line in the target image comprises the following steps: and respectively carrying out semantic segmentation on the pixels in the at least one subimage based on a pre-trained semantic segmentation model, and obtaining pixel position information of the power transmission line pixel points corresponding to each subimage.
The semantic segmentation model may be a model obtained by pre-training and used for performing semantic segmentation on the image, and it should be noted that the semantic segmentation is a process of identifying and marking an interested target in the image, that is, after at least one sub-image is subjected to speech segmentation, pixels of the power transmission line may be marked in the image. The pixel position information may be position information of a pixel corresponding to the transmission line in the sub-image.
Specifically, after semantic segmentation processing is performed on each sub-image based on a semantic segmentation model obtained through pre-training, pixel position information of a pixel point corresponding to the power transmission line in each sub-image is obtained. For example, the sub-image may be input into the semantic segmentation model, and then the corresponding image with the completed semantic mark is output, and the corresponding pixel position information is obtained.
It should be noted that, the deep learning models mentioned in the embodiments of the present invention, such as the image recognition model and the semantic segmentation model, are not limited in the present invention, and those skilled in the art may train the required models in advance according to requirements.
And S120, determining actual coordinate information corresponding to the pixel points of the power transmission line according to the pixel position information and the pose information of the at least two pixel points of the power transmission line.
And the pose information is angle information when the unmanned aerial vehicle shoots the image. The actual coordinate information can be understood as coordinate information of the pixel point in the WGS84 coordinate. It should be noted that the pose information of the drone may include yaw angle information, pitch angle information, and roll angle information of the drone when shooting.
Specifically, the actual coordinate information corresponding to the pixel points of the power transmission line is determined jointly based on the pixel position information of the pixel points of at least two power transmission lines and the yaw angle information, the pitch angle information and the roll angle information of the unmanned aerial vehicle when the unmanned aerial vehicle shoots the corresponding image.
On the basis of the technical scheme, the determining of the actual coordinate information corresponding to the pixel point of the power transmission line according to the pixel position information of at least two pixel points in the point set and the space attitude information of the target image comprises the following steps: processing the pixel position information and the pose information of the at least two pixel points based on a space forward intersection method to obtain actual coordinate information corresponding to the pixel points of the power transmission line
The spatial forward intersection can be a method for determining the spatial position of a model point by utilizing intersection of rays with the same name after light beams during stereopair photography are recovered and a geometric model is built.
Specifically, the actual coordinate information corresponding to the pixel point can be obtained by processing the pixel position information and the pose information of at least two pixel points based on a spatial forward intersection method.
And S130, obtaining parameters of the power transmission line according to the actual coordinate information, and reconstructing the line based on the parameters of the power transmission line.
The parameter may be parameter information for reconstructing the power transmission line.
Specifically, after the actual coordinate information is obtained, parameters of the power transmission line can be obtained based on the actual coordinate information, and then reconstruction of the power transmission line on the coordinate system is completed based on the parameter information, so as to construct a corresponding three-dimensional model, for example, the actual coordinates of all pixels can be directly displayed on the coordinate system, and then reconstruction of the power line is completed based on points in the coordinate system.
On the basis of the above technical solution, before obtaining the relevant parameters of the power transmission line according to the actual coordinate information and performing line reconstruction based on the relevant parameters of the power transmission line, the method further includes: and classifying the actual coordinate information corresponding to the pixel points of the power transmission line based on the coordinate information of the target tower, and sequencing according to height information in the actual coordinate information corresponding to the pixel points of the power transmission line.
Here, the height information may be understood as z-axis coordinate information in the actual coordinate information.
Specifically, the actual coordinate information corresponding to the pixel points of the power transmission line is divided into large-size tower coordinate information and small-size tower coordinate information according to the large-size tower coordinates and the small-size tower coordinates between the single tower spans, for example, distance equipartition points between two towers can be found according to the tower span information, then the position information of the pixel points of the power transmission line is classified based on the positions of the equipartition points, and the pixel points are sequenced according to the height information of the pixel points of the power transmission line. It should be noted that, because a plurality of lines may be mounted on one tower, the lines at different heights need to be ordered according to the height information, and thus, repeated selection of point locations can be avoided.
On the basis of the technical scheme, the obtaining of the parameters of the power transmission line according to the actual coordinate information and the line reconstruction based on the parameters of the power transmission line comprise: and bringing the actual coordinate information of at least three points in the actual coordinate information into a catenary equation to obtain related parameters of the catenary equation, and finishing the reconstruction of the power transmission line based on the related parameters.
The catenary equation may be a curve shape of a chain (with uniform thickness and mass distribution) and soft (incapable of extending) with two fixed ends under the action of gravity. Specifically, the actual coordinate information of three point locations can be selected from the actual coordinate information and brought into the catenary equation, so that the parameter information of the catenary equation can be solved, and the reconstruction of the power transmission line can be completed based on the parameter information. It should be noted that, after the coordinate system is properly selected, the equation of the catenary is a hyperbolic cosine function, and the standard equation is as follows: y = a cosh (x/a), where a is the distance of the curve vertex from the abscissa axis.
On the basis of the technical scheme, the method further comprises the following steps: projecting all the reconstructed power transmission lines onto a horizontal plane, calculating the slopes of the corresponding horizontal plane projections, and if the slopes are not equal, recalculating the parameters of the power transmission lines; if the slopes are equal, continuing to equally divide all the power transmission lines which are completely reconstructed, projecting the power transmission lines onto a vertical plane, calculating a vertical coordinate difference value between every two equal divisions, and if the vertical coordinate difference value meets a preset condition, keeping a reconstruction result; and if the vertical coordinate difference value does not meet the preset condition, recalculating the parameters of the power transmission line.
Wherein the horizontal plane can be understood as the xoy plane. The slope may be the degree of tilt of the transmission line in the horizontal plane projection. The vertical plane can be the xoz plane, and the corresponding vertical coordinate difference can be understood as the height value of the projection of each vertical plane. The preset condition may be that the height values of the vertical plane projections of each segment are equal, or that the error is within a preset range, for example, the error is within 0.1 meter.
Specifically, in order to ensure the reconstruction accuracy of the power transmission line, multiple sets of coordinates can be selected and brought into a catenary equation to obtain corresponding parameters and complete reconstruction, the reconstructed power transmission line is projected onto an xoy plane, slopes on the xoy plane are respectively calculated and compared, when the slopes are the same, the reconstructed line is projected onto an xoz plane and is divided into 4 equal parts on an X axis, the height values between the equal parts are compared, if the height values meet preset conditions, the current reconstruction result is retained, and further the accuracy of the reconstruction result is ensured. For example, after taking a Z value on each equal part and sorting each Z value according to size, the Z values between adjacent parts are differentiated. If the differences on each equal part are basically equal and each difference has no negative number, the catenary equation of each group is reserved, if the differences on each equal part are not basically equal and each difference has no negative number, the catenary equation of each group is reserved, and the conditions are returned to the calculation of the catenary equation.
According to the technical scheme, the target image is identified based on the deep learning model trained in advance, so that the pixel position information of the pixel points of the power transmission line in the target image is obtained, the actual coordinate information corresponding to the pixel points of the power transmission line is further determined according to the pixel position information and the pose information of the pixel points of at least two power transmission lines, the relevant parameters of the power transmission line are finally obtained according to the actual coordinate information, and the line is reconstructed based on the relevant parameters of the power transmission line.
Example two
Fig. 2 is a flowchart of a power transmission line reconstruction method according to an embodiment of the present invention, and this embodiment further details an implementation flow of the power transmission line reconstruction method based on the above example, and a specific implementation manner of the method may refer to the technical solution of this embodiment. The technical terms that are the same as or corresponding to the above embodiments are not repeated herein.
It should be noted that after receiving image data collected by the unmanned aerial vehicle, relevant images in the span of the tower are screened out according to the coordinates of the tower, and the images are subjected to the space-three triangulation technology to calculate photo postures and elements in the camera and outside. And performing semantic segmentation on the power line of the image by utilizing deep learning, and finally, bringing the segmented pixel points into a power line catenary equation to fit the power line after matching.
As shown in fig. 2, taking the calculation of the line between the single gear pitches as an example, the specific implementation process of the method is as follows:
firstly, image data I0 acquired by a single unmanned aerial vehicle needs to be acquired, and then the image data acquired by the single unmanned aerial vehicle is analyzed by an aerial triangulation technical means to obtain spatial attitude information (yaw angle yaw, pitch angle pitch, roll angle) of a photo. The aerial triangulation is a measuring method for encrypting control points indoors according to a small number of field control points in stereo photogrammetry to obtain the elevation and the plane position of the encrypted points.
Further, image data In a single span is screened out for I0 by utilizing WGS84 coordinate information of the tower, and meanwhile, related image data Imid of a middle position of the single span is screened out for I0 by utilizing the WGS84 coordinate information of the tower. It should be noted that the related image data Imid of the middle position of the single span may refer to image information of the middle position of the power transmission line.
And identifying the position Ix (x =1, 2, 3 \8230n; n) of the insulator string by using deep learning, specifically, pre-training a corresponding image identification model, and marking the position of the insulator string after the image is identified by the image identification model. And dividing the positions Ix of the insulator strings into a small pole tower end Iminx and a large pole tower end Imaxx by utilizing WGS84 coordinate information of the pole towers. It should be noted that, the towers may be divided into a large tower and a small tower based on the current flowing direction, and the current flowing direction is from the small tower to the large tower.
Pixel positions IPminxn (n =1, 2, 3 \8230; n) and ipmaxn (n =1, 2, 3 \8230; n) in the power line are divided pixel by deep learning at the corresponding Iminx and Imaxx positions in the image data containing Iminx and Imaxx. The pixel position IPmidn (n =1, 2, 3 \8230; n) of the power line in the image data Imid is segmented pixel by deep learning. Specifically, the semantic segmentation model may be used to process the image, so as to achieve the effect of segmenting the pixel position pixel by pixel.
Image feature point matching is carried out on an image data set with IPminxn, IPmaxxn and IPImidn pixel points, and a point group IPZminxn (n =1, 2, 3 \8230; n), IPZmaxxn (n =1, 2, 3 \8230; n) and IPZmidn (n =1, 2, 3 \8230; n) corresponding to each point set of IPminxnn, IPmaxxn and IPmidn are sequentially found. And performing space forward intersection on two points in each of the point groups IPZminxn, IPZmaxxn and IPZmidn by using the space posture information of the photos to obtain coordinate values of corresponding WGS84 coordinates IPZminxn, IPZWmaxxn and IPZmidn.
And dividing the coordinates of IPZWminxn, IPZWmaxxn and IPZWmidn into left and right coordinates of IPZWLminxn, IPZWLmaxxn, IPZWLmidn, IPZWRminxn, IPZWRmaxxn and IPZWRmidn by using the coordinates of a single-gear-pitch large-small-size tower. And arranging the obtained point location information IPZWLminxn, IPZWLmaxxn, IPZWLmidn, IPZWRminxn, IPZWRmaxxn and IPZWRmidn from small to large according to the altitude height.
Firstly, respectively randomly taking a first point, a second point, a third point and the like in IPZWLminxn, IPZWLmaxxn and IPZWLmidn to form a group, and bringing the first group, the second group, the third group and the like into a catenary equation in sequence to solve the parameters related to the catenary equation.
In the practical application process, after the calculation of the parameters is completed and the electric transmission line is reconstructed, the reconstructed electric transmission line also needs to be verified, so that the reconstruction accuracy is ensured, the verification method is as shown in fig. 3, firstly, catenary equations obtained by the first group, the second group, the third group and the like are projected on an XOY plane, whether the slopes of the catenary equations projected on the XOY plane of each group are equal or not is calculated, if the slopes are equal, the point sets of each group are reserved, and if the slopes are not equal, the point set calculation is returned to the previous step again to continue. And further, on the basis that corresponding clicks are reserved on equal slopes, dividing the X axis of each group of catenary equations into 4 equal parts, taking a Z value on each equal part, and after each Z value is sorted according to the size, obtaining the difference value of the Z values between adjacent Z values. If the differences on each equal part are basically equal and each difference has no negative number, the catenary equation of each group is reserved, if the differences on each equal part are not basically equal and each difference has no negative number, the catenary equation of each group is reserved, and the conditions are returned to the calculation of the catenary equation.
According to the technical scheme, the target image is identified based on the pre-trained deep learning model to obtain the pixel position information of the pixel points of the power transmission line in the target image, the actual coordinate information corresponding to the pixel points of the power transmission line is further determined according to the pixel position information and the pose information of the pixel points of at least two power transmission lines, the relevant parameters of the power transmission line are finally obtained according to the actual coordinate information, and the line is reconstructed based on the relevant parameters of the power transmission line.
EXAMPLE III
Fig. 4 is a device for reconstructing a power transmission line according to an embodiment of the present invention. The device includes: a position information acquisition module 410, a coordinate information acquisition module 420, and a route reconstruction module 430.
The position information acquiring module 410 is configured to identify a target image based on a pre-trained deep learning model to obtain pixel position information of a power transmission line pixel point in the target image;
the coordinate information acquiring module 420 is configured to determine actual coordinate information corresponding to the pixel points of the power transmission line according to pixel position information and pose information of the pixel points of the at least two power transmission lines, where the pose information is angle information when the unmanned aerial vehicle shoots an image;
and a line reconstruction module 430, configured to obtain parameters of the power transmission line according to the actual coordinate information, and perform line reconstruction based on the parameters of the power transmission line.
On the basis of the above technical solution, the apparatus further comprises:
and the target image acquisition module is used for determining a target area according to the tower span and the coordinate information of the target tower, obtaining all images in the target area, and taking the images as target images, wherein the target area is a circular area formed by taking the target tower as the center of a circle and taking the tower span as the radius.
On the basis of the above technical solution, the target image acquisition module includes:
the image dividing unit is used for dividing the target image into at least one sub-image based on the current direction in the power transmission line, a preset identification object and the coordinate information of the target tower; and the at least one subimage corresponds to a tower of a preset model.
On the basis of the above technical solution, the coordinate information acquisition module is configured to: and respectively carrying out semantic segmentation on the pixels in the at least one sub-image based on a preset semantic segmentation model, and obtaining pixel position information of the power transmission line pixel points corresponding to each image.
On the basis of the above technical solution, the coordinate information obtaining module is further configured to: and processing the pixel position information and the pose information of the at least two pixel points based on a space forward intersection method to obtain actual coordinate information corresponding to the pixel points of the power transmission line.
On the basis of the above technical solution, the coordinate information obtaining module further includes:
and the classification unit is used for classifying the actual coordinate information corresponding to the pixel point of the power transmission line based on the coordinate information of the target tower and sequencing according to the height information in the actual coordinate information corresponding to the pixel point of the power transmission line.
On the basis of the above technical solution, the line reconstruction module is specifically configured to: and bringing the actual coordinate information of at least three points in the actual coordinate information into a catenary equation to obtain related parameters of the catenary equation, and finishing the reconstruction of the power transmission line based on the related parameters.
On the basis of the above technical solution, the line reconstruction module further includes:
the verification unit is used for projecting all the power transmission lines which are completely reconstructed onto a horizontal plane, calculating the corresponding slopes of the horizontal plane projections, and if the slopes are not equal, recalculating the relevant parameters of the power transmission lines; if the slopes are equal, continuing to equally divide all the power transmission lines which are completely reconstructed, projecting the power transmission lines onto a vertical plane, calculating a vertical coordinate difference value between every two equal divisions, and if the vertical coordinate difference value meets a preset condition, keeping a reconstruction result; and if the vertical coordinate difference value does not meet the preset condition, recalculating the related parameters of the power transmission line.
According to the technical scheme, the target image is identified based on the pre-trained deep learning model to obtain the pixel position information of the pixel points of the power transmission line in the target image, the actual coordinate information corresponding to the pixel points of the power transmission line is further determined according to the pixel position information and the pose information of the pixel points of at least two power transmission lines, the relevant parameters of the power transmission line are finally obtained according to the actual coordinate information, and the line is reconstructed based on the relevant parameters of the power transmission line.
The power transmission line reconstruction device provided by the embodiment of the invention can execute the power transmission line reconstruction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
Example four
FIG. 5 illustrates a block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
In some embodiments, the power transmission line reconstruction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power transmission line reconstruction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the power line reconstruction method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for reconstructing a power transmission line, comprising:
identifying a target image based on a pre-trained deep learning model to obtain pixel position information of a power transmission line pixel point in the target image;
determining actual coordinate information corresponding to the pixel points of the power transmission line according to the pixel position information and the pose information of the pixel points of the at least two power transmission lines, wherein the pose information is angle information when the unmanned aerial vehicle shoots an image;
and obtaining parameters of the power transmission line according to the actual coordinate information, and performing line reconstruction based on the parameters of the power transmission line.
2. The method of claim 1, further comprising, prior to identifying the target image based on the pre-trained deep learning model:
determining a target area according to the tower span and the coordinate information of the target tower, obtaining all images in the target area, and taking the images as target images, wherein the target area is a circular area formed by taking the target tower as the center of a circle and taking the tower span as the radius.
3. The method of claim 2, after obtaining the target image, further comprising:
dividing the target image into at least one sub-image based on the current direction in the power transmission line, a preset identification object and the coordinate information of the target tower;
and the at least one subimage corresponds to a tower of a preset model.
4. The method of claim 1, wherein the identifying the target image based on the pre-trained deep learning model to obtain pixel position information of a pixel point of the power transmission line in the target image comprises:
and respectively carrying out semantic segmentation on the pixels in the at least one sub-image based on a preset semantic segmentation model, and obtaining pixel position information of the power transmission line pixel points corresponding to the sub-images.
5. The method according to claim 1, wherein the determining of the actual coordinate information corresponding to the pixel point of the power transmission line according to the pixel position information of at least two pixel points in the point set and the spatial attitude information of the target image comprises:
and processing the pixel position information and the pose information of the at least two pixel points based on a space forward intersection method to obtain actual coordinate information corresponding to the pixel points of the power transmission line.
6. The method according to claim 1, further comprising, before obtaining parameters of the power transmission line according to the actual coordinate information and performing line reconstruction based on the parameters of the power transmission line:
and classifying the actual coordinate information corresponding to the pixel points of the power transmission line based on the coordinate information of the target tower, and sequencing according to height information in the actual coordinate information corresponding to the pixel points of the power transmission line.
7. The method according to claim 1, wherein the obtaining of the parameters of the power transmission line according to the actual coordinate information and the line reconstruction based on the parameters of the power transmission line comprise:
and bringing the actual coordinate information of at least three points in the actual coordinate information into a catenary equation to obtain related parameters of the catenary equation, and completing the reconstruction of the power transmission line based on the related parameters.
8. The method of claim 1, further comprising:
projecting all the power transmission lines which are completely reconstructed onto a horizontal plane, calculating the slopes of the corresponding horizontal plane projections, and if the slopes are not equal, recalculating the relevant parameters of the power transmission lines;
if the slopes are equal, continuing to equally divide all the power transmission lines which are completely reconstructed, projecting the power transmission lines onto a vertical plane, calculating a vertical coordinate difference value between every two equal divisions, and if the vertical coordinate difference value meets a preset condition, keeping a reconstruction result;
and if the vertical coordinate difference does not meet the preset condition, recalculating the related parameters of the power transmission line.
9. A transmission line reconstruction apparatus, comprising:
the position information acquisition module is used for identifying a target image based on a pre-trained deep learning model so as to obtain pixel position information of a power transmission line pixel point in the target image;
the coordinate information acquisition module is used for determining actual coordinate information corresponding to the pixel points of the power transmission line according to pixel position information and pose information of the pixel points of at least two power transmission lines, wherein the pose information is angle information when the unmanned aerial vehicle shoots an image;
and the line reconstruction module is used for obtaining the relevant parameters of the power transmission line according to the actual coordinate information and reconstructing the line based on the relevant parameters of the power transmission line.
10. An electronic device, characterized in that the electronic device comprises:
one or more processors; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the power transmission line reconstruction method of any one of claims 1-8.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115760725A (en) * | 2022-11-04 | 2023-03-07 | 广东安恒电力科技有限公司 | Power transmission line external force invasion monitoring method, medium and equipment based on laser radar |
| CN115984489A (en) * | 2023-03-21 | 2023-04-18 | 广东数字生态科技有限责任公司 | Three-dimensional reconstruction method and device for power transmission line and processing equipment |
| CN116151168A (en) * | 2023-04-23 | 2023-05-23 | 广东电网有限责任公司汕尾供电局 | Method, device, equipment and storage medium for determining equivalent conductor of grounding grid |
| CN116295031A (en) * | 2023-02-24 | 2023-06-23 | 中国测绘科学研究院 | Sag measurement method, sag measurement device, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115760725A (en) * | 2022-11-04 | 2023-03-07 | 广东安恒电力科技有限公司 | Power transmission line external force invasion monitoring method, medium and equipment based on laser radar |
| CN115760725B (en) * | 2022-11-04 | 2024-02-20 | 广东安恒电力科技有限公司 | Laser radar-based transmission line external force intrusion monitoring method, medium and equipment |
| CN116295031A (en) * | 2023-02-24 | 2023-06-23 | 中国测绘科学研究院 | Sag measurement method, sag measurement device, computer equipment and storage medium |
| CN116295031B (en) * | 2023-02-24 | 2024-03-12 | 中国测绘科学研究院 | Sag measurement method, sag measurement device, computer equipment and storage medium |
| CN115984489A (en) * | 2023-03-21 | 2023-04-18 | 广东数字生态科技有限责任公司 | Three-dimensional reconstruction method and device for power transmission line and processing equipment |
| CN115984489B (en) * | 2023-03-21 | 2023-09-19 | 广东数字生态科技有限责任公司 | Three-dimensional reconstruction method, device and processing equipment of power transmission line |
| CN116151168A (en) * | 2023-04-23 | 2023-05-23 | 广东电网有限责任公司汕尾供电局 | Method, device, equipment and storage medium for determining equivalent conductor of grounding grid |
| CN116151168B (en) * | 2023-04-23 | 2023-07-07 | 广东电网有限责任公司汕尾供电局 | Method, device, equipment and storage medium for determining equivalent conductor of grounding grid |
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