CN115311587B - Intelligent generation method for photovoltaic power station inspection waypoints - Google Patents

Intelligent generation method for photovoltaic power station inspection waypoints Download PDF

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CN115311587B
CN115311587B CN202211087071.0A CN202211087071A CN115311587B CN 115311587 B CN115311587 B CN 115311587B CN 202211087071 A CN202211087071 A CN 202211087071A CN 115311587 B CN115311587 B CN 115311587B
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photovoltaic power
string
strings
power station
group
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CN115311587A (en
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李志轩
刘博�
康博雅
王峰
严超
唐东明
高海涛
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Wuxi Haina Intelligent Technology Co ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/10Terrestrial scenes
    • G06V20/17Terrestrial scenes taken from planes or by drones
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/20Image preprocessing
    • G06V10/26Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion

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Abstract

The invention provides an intelligent generation method of a photovoltaic power station inspection waypoint, which comprises the following steps: obtaining a plurality of standard group strings from the picture identification of the photovoltaic power station; acquiring the serial number of the set group required to be covered by shooting a single picture; dividing the standard group strings into a plurality of areas according to the serial number of the groups, wherein the number of the standard group strings in each area meets the line number; determining a shooting center point of each region according to the coordinates of each standard group string of each region; and generating all inspection waypoints of the photovoltaic power station based on the shooting center points of each area. The method solves the technical problems that the existing route waypoint generation method is low in efficiency and redundant data exists.

Description

Intelligent generation method for photovoltaic power station inspection waypoints
Technical Field
The invention relates to the field of photovoltaic inspection, in particular to an intelligent generation method of an inspection waypoint of a photovoltaic power station.
Background
Under the trend that the photovoltaic power generation capacity is continuously increased, full-automatic inspection is performed on the photovoltaic power station by using the unmanned aerial vehicle, so that the method gradually becomes the main flow method of operation and maintenance of the photovoltaic power station, and the method is mainly characterized by flexible, reliable, safe and stable inspection of the unmanned aerial vehicle. Generally, for the operation and maintenance of an established power station, the route planning of the field station is required, after the route is planned, the unmanned aerial vehicle is controlled to carry out inspection according to the planned route, a plurality of waypoints exist in the planned route, an unmanned opportunity takes a picture of each waypoint of the planned route, and then defects of the pictures are identified to generate an inspection report of the photovoltaic power station.
It should be noted that, the current route generation method mainly depends on manual drawing or relies on the existing tool to automatically fill a plurality of waypoints, and the plurality of waypoints form a route for inspection.
In summary, the existing route waypoint generation method is low in efficiency and redundant data exists.
In view of this, the present invention has been proposed.
Disclosure of Invention
The invention provides an intelligent generation method of a photovoltaic power station routing inspection waypoint, which aims to solve the technical problems that the existing generation method of the route waypoint is low in efficiency and redundant data exists.
The intelligent generation method of the photovoltaic power station inspection waypoints provided by the invention comprises the following steps: obtaining a plurality of standard group strings from the picture identification of the photovoltaic power station; acquiring the serial number of the set group required to be covered by shooting a single picture; dividing the standard group strings into a plurality of areas according to the serial number of the groups, wherein the number of the standard group strings in each area meets the line number; determining a shooting center point of each region according to the coordinates of each standard group string of each region; and generating all inspection waypoints of the photovoltaic power station based on the shooting center points of each area.
Further, a plurality of standard group strings are obtained from the picture identification of the photovoltaic power station, and the method comprises the following steps: performing string segmentation on the pictures of the photovoltaic power station to obtain a plurality of initial strings; calculating to obtain average size parameters of the plurality of initial strings; and cutting one or more of the plurality of initial group strings according to the size parameter of each initial group string and the average size parameter to obtain the plurality of standard group strings.
Further, performing a cutting process on one or more of the plurality of initial strings according to the size parameter of each initial string and the average size parameter, including: calculating a multiple critical value of each initial group string relative to the average size parameter, wherein the multiple critical value comprises an upper limit critical value and a lower limit critical value, and the difference value between the upper limit critical value and the lower limit critical value is smaller than a preset threshold value; dividing each initial group string into a number of segments of a lower threshold number, with the ratio of the size parameter of each initial group string to the average size parameter being between the upper and lower thresholds; and under the condition that the ratio of the size parameter of each initial group string to the average size parameter is smaller than or equal to a preset threshold value, not processing each initial group string.
Further, acquiring a set serial number of groups required to be covered by taking a single picture, including: the serial columns of the group are all odd.
Further, generating all patrol waypoints of the photovoltaic power station based on the shooting center points of each area includes: acquiring an inclination angle of a photovoltaic string of the photovoltaic power station and a digital surface model of the photovoltaic power station; and generating the heights and horizontal plane coordinates of all the inspection waypoints of the photovoltaic power station based on the inclination angle of the photovoltaic string, the digital surface model and the shooting center point of each area.
Further, generating the altitude and horizontal plane coordinates of all inspection waypoints of the photovoltaic power station based on the inclination angle of the photovoltaic string, the digital surface model and the shooting center point of each region, including: indexing from the digital surface model through coordinates of shooting center points of each region to obtain actual elevation of the shooting center points; acquiring a first distance from a preset inspection navigation point to the shooting center; and calculating to obtain the heights and horizontal plane coordinates of all the inspection waypoints of the photovoltaic power station according to the actual elevation of the shooting center point, the first distance and the inclination angle of the photovoltaic group string.
Further, the group string segmentation is carried out on the pictures of the photovoltaic power station through the trained segmentation model to obtain a plurality of initial group strings, wherein the training of the segmentation model comprises the following steps: acquiring an orthographic image and a digital surface model of a photovoltaic power station; determining a minimum altitude of the photovoltaic power plant from the digital surface model; obtaining a surface model of the relative elevation of the photovoltaic power plant from all elevations on the digital surface model and the lowest elevation; extracting a first RGB three-channel image of the surface model of the relative elevation and a second RGB three-channel image of the orthographic image; and processing the first RGB three-channel image and the second RGB three-channel image into four-channel images, and inputting the four-channel images into a depth network for training to obtain the segmentation model.
Further, dividing the plurality of standard group strings into a plurality of areas according to the number of the group serial columns includes: acquiring coordinates of a plurality of standard group strings and sequencing the coordinates to obtain the sequence of each standard group string; respectively carrying out differential processing on coordinates of every two standard group strings adjacent in sequence in the plurality of standard group strings based on the sequence of each standard group string to obtain a plurality of differential results; dividing the standard group strings into rows according to the differential results to obtain a plurality of rows of standard group strings; dividing the multi-row standard group string into a plurality of areas according to the group serial number.
The invention also provides an electronic device comprising a memory and a processor, the memory having stored thereon computer instructions, characterized in that the computer instructions, when executed by the processor, are performed by any of the methods described above.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, causes any of the above methods to be performed.
The invention provides an intelligent generation method of a photovoltaic power station inspection waypoint, which comprises the following steps: obtaining a plurality of standard group strings from the picture identification of the photovoltaic power station; acquiring the serial number of the set group required to be covered by shooting a single picture; dividing the standard group strings into a plurality of areas according to the serial number of the groups, wherein the number of the standard group strings in each area meets the line number; determining a shooting center point of each region according to the coordinates of each standard group string of each region; and generating all inspection waypoints of the photovoltaic power station based on the shooting center points of each area. The method solves the technical problems that the existing route waypoint generation method is low in efficiency and redundant data exists.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an intelligent generation method of a photovoltaic power station inspection waypoint;
FIG. 2 is a schematic diagram of a shooting center calculated by a plurality of initial strings;
fig. 3 is a schematic diagram of a cutting string provided by the present invention.
Detailed Description
To further clarify the above and other features and advantages of the present invention, a further description of the invention will be rendered by reference to the appended drawings. It should be understood that the specific embodiments presented herein are for purposes of explanation to those skilled in the art and are intended to be illustrative only and not limiting.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the specific details need not be employed to practice the present invention. In other instances, well-known steps or operations have not been described in detail in order to avoid obscuring the invention.
Example 1
The invention provides an intelligent generation method of a photovoltaic power station inspection waypoint, which comprises the following steps of:
And S11, identifying a plurality of standard group strings from the pictures of the photovoltaic power station.
Specifically, in this solution, a device with a data processing function, such as a server, may be used as an execution body of the method steps in this solution, where the photovoltaic power station may be a centralized photovoltaic power station or a distributed photovoltaic power station, and a plurality of photovoltaic strings (herein, the photovoltaic strings may be simply referred to as strings) may be included in the photovoltaic power station.
Alternatively, the picture of the photovoltaic power station may be an orthophoto DOM of the photovoltaic power station.
Step S13, the serial number of the set groups required to be covered by the shot single picture is obtained.
Specifically, the user may set the number of strings to be covered (or included) in order to take a single picture, i.e., the number of horizontal and vertical strings to be covered in a single picture of the unmanned aerial vehicle camera when the unmanned aerial vehicle is taking a picture if at a waypoint.
And S15, dividing the plurality of standard group strings into a plurality of areas according to the group serial number, wherein the number of standard group strings in each area meets the rank number.
Specifically, the standard group string can be divided into a plurality of areas, and the number of rows and columns of the group string in each area is the number of group serial columns covered by a single picture set by a user. For example, the number of group strings in each region is "3 rows and 2 columns" after dividing the plurality of standard group strings by the group serial number "3 rows and 2 columns" when the number of group serial columns required to be covered by a single picture set by a user is 3 rows and 2 columns.
And S17, determining a shooting center point of each area according to the coordinates of each standard group string of each area.
Specifically, in the scheme, after the plurality of areas are divided, the scheme determines the shooting center point of each area according to the coordinates of all the strings of each area, and here, the shooting center point refers to the shooting viewpoint of the unmanned aerial vehicle camera after the unmanned aerial vehicle is located at the waypoint in the future, each area corresponds to one shooting viewpoint, and the unmanned aerial vehicle camera can take the pictures of the strings of the area according to the shooting viewpoints. The scheme can obtain the shooting center points of all areas in the photovoltaic power station through the method of the step S17.
Specifically, step S17, determining a shooting center point of each area according to coordinates of each standard group string of each area, includes:
step S171, connecting the coordinates of each standard group string of the current area, thereby determining a polygon;
step S172, obtaining a plurality of standard group strings within a preset distance from the vertexes of the polygon;
Step S172, obtaining a shooting center point of the current area according to the coordinates of the plurality of standard group strings within the preset distance.
Specifically, referring to fig. 2, if there are 3 rows and 2 columns of strings in the current area, the 3 rows and 2 columns of strings include string 1, string 2, string 3, string 4, string 5, and string 6, and coordinates of the six strings (in this embodiment, the coordinates of the string are coordinates of the string center) are connected to determine a polygon.
As will be exemplified below with reference to fig. 2, the center coordinates of group string 1 are (a 1, a 2), the center coordinates of group string 2 are (b 1, b 2), the center coordinates of group string 5 are (c 1, c 2), and the center coordinates of group string 6 are (d 1, d 2), which for the current area, the coordinates (x, y) of the shooting center point can be calculated by the following formula:
by the method, the shooting center point of the current area can be rapidly calculated, all group strings of the current area can be shot through the shooting center point, and omission of the group strings can not occur.
And S19, generating all the inspection waypoints of the photovoltaic power station based on the shooting center points of each area.
And S21, generating all inspection waypoints of the photovoltaic power station based on the shooting center points of each area.
Specifically, in the scheme, the inspection waypoints of the current area can be generated based on the shooting center of each area, so that all the inspection waypoints of all the areas of the photovoltaic power station are obtained, all the inspection waypoints form an inspection route of the unmanned aerial vehicle, and the unmanned aerial vehicle can fly according to the inspection route when performing the inspection task, so that the acquisition of the photovoltaic group string picture is realized.
It should be noted that in the prior art, the inspection waypoints are easy to be missed and have low efficiency by manually drawing, and the waypoints are automatically filled according to the specific area by the existing software, and the existing software is only required to cover the specific area, so that a large number of redundant unnecessary waypoints are generated, and the amount of inspection acquired data is increased. On the one hand, the scheme is different from the prior art that the waypoints are manually drawn by manpower, the inspection waypoints are automatically generated by identifying the coordinates of each photovoltaic group string, the efficiency of the waypoint generation is improved, and on the other hand, as the waypoints of the scheme are generated according to the coordinates of each standard group string of each area, the redundancy of the quantity of the waypoints inspected by the scheme can not occur, each group string can be just covered, the scheme can acquire pictures of all the group strings of the power station by the minimum waypoints, and the redundancy of acquired data is reduced.
Optionally, step S11 identifies a plurality of standard strings from the picture of the photovoltaic power station, including:
step S111, performing string segmentation from the pictures of the photovoltaic power station to obtain a plurality of initial strings.
Specifically, after the orthographic image of the photovoltaic power station is obtained, the group string segmentation model can be adopted to perform group string segmentation or identification on the picture of the photovoltaic power station, so that a plurality of initial group strings are obtained.
Step S113, calculating to obtain the average size parameters of the plurality of initial group strings.
Specifically, the method can respectively count the size parameters of each initial group string, and calculate to obtain the average size parameter of the initial group string, where the size parameter can be the width or height of the group string, and the average size parameter is the average width of all initial group stringsOr average height/>
And step S115, cutting one or more of the plurality of initial group strings according to the size parameter and the average size parameter of each initial group string to obtain the plurality of standard group strings.
Specifically, regarding an initial group string that is significantly higher than the average size parameter in width or height, it is indicated that an error occurs during image recognition, and that two group strings may be mistakenly recognized as one group string, at this time, the size of the initial group string after being mistakenly recognized is necessarily higher than the average size, so for an initial group string with a size parameter higher than the average size parameter, the present solution performs cutting processing and obtains a plurality of standard group strings, the present solution determines an initial group string with a size parameter higher than the average size parameter from the initial group strings, then performs cutting, and the cut plurality of group strings and the group string without cutting are the plurality of standard group strings, where the sizes of the plurality of standard group strings do not exceed the average size parameter.
Optionally, the size parameter of each initial group string is a width and a height of each initial group string, and step S115 includes performing a cutting process on one or more of the plurality of initial group strings according to the size parameter of each initial group string and the average size parameter to obtain the plurality of standard group strings, where the step S115 includes:
Step S1151, calculating a multiple critical value of each initial group string with respect to the average size parameter, where the multiple critical value includes an upper boundary critical value and a lower boundary critical value, and a difference between the upper boundary critical value and the lower boundary critical value is smaller than a preset threshold value;
Step S1152, where the ratio of the size parameter of each initial group string to the average size parameter is between the upper limit critical value and the lower limit critical value, dividing each initial group string into a number of segments of the lower limit critical value number;
And under the condition that the ratio of the size parameter of each initial group string to the average size parameter is smaller than or equal to a preset threshold value, not processing each initial group string.
The following exemplifies the above-described step S1151 to step S1152:
Each initial group string is any one of a plurality of initial group strings, the size parameter of the initial group string can be width W and height H, and the average size parameter is the median of the width W High H median/>First calculate W, H forAssuming that the upper bound corresponds to W max、Hmax and the lower bound corresponds to W min、Hmin, wherein W max、Hmax、Wmin、Hmin is a positive integer greater than 1, and:
Wmax-Wmin=1
Hmax-Hmin=1
The method meets the following conditions:
at this time, W should be divided into W min segments and H into H min segments, when Or/>And if so, the current group string is not processed.
Next, step S1151 to step S1152 are further illustrated, for example, the parameter of the initial group string (for example, the width) is 11, and the average size parameter of the initial group string (the average height of all the initial group strings) is 5, then, by calculation, 11++5=2.2, where the quotient 2 of the parameter of the initial group string and the average size parameter is the lower threshold, and where the quotient of the parameter of the initial group string and the average size parameter is 1 if there is a remainder, i.e., 2+1=3, and 3 is the upper threshold. Then 2.2 is between the lower bound 2 and the upper bound 3, then the initial group string is divided into 2 segments.
Referring to fig. 3, fig. 3 is an example of a plurality of initial strings after image recognition, and in fig. 2, there are 5 strings, a string a, a string B, a string C, a string D, and a string E, where the size of the string E is significantly different from that of the strings a, B, C, and D, and the method may determine to cut the string D after the calculation in steps S1151 to S1152, so as to cut the string D into two segments.
Optionally, the serial columns of the groups required to be covered by the single picture are all odd. The technical effects of this arrangement are described below:
when the number of serial groups required to be covered by a single picture is odd, the shooting center can be positioned at the centers of all the serial groups in the single picture, so that when the inspection navigation point acquires an inspection image, the number of serial groups is as much as possible, and the influence of distortion in the processing of the inspection image in the later stage is reduced as much as possible.
In the prior art, distortion is necessarily generated at the boundary of an image in actual inspection shooting, and the distortion affects subsequent image processing.
The method and the device have the advantages that the number of lines and the number of columns required to be covered by shooting a single picture are determined, then the shooting center and the waypoints are calculated based on the number of lines and the number of columns required to be covered, and the image is acquired through the shooting center and the waypoints, so that the image influence caused by distortion can be effectively relieved.
Optionally, step S19 generates all the inspection waypoints of the photovoltaic power station based on the shooting center point of each area, including:
Step S191, obtaining the inclination angle of the photovoltaic power station photovoltaic group string and the Digital Surface Model (DSM) of the photovoltaic power station.
Step S192, generating all inspection waypoints of the photovoltaic power station based on the inclination angle θ of the photovoltaic string, a Digital Surface Model (DSM), and a photographing center point of each region.
Optionally, step S192 generates all inspection waypoints of the photovoltaic power station based on the inclination angle of the photovoltaic string, the digital surface model, and the shooting center point of each region, including:
in step S1921, the coordinates of the shooting center point of each area are indexed from the digital surface model, so as to obtain the actual elevation of the shooting center point.
Step S1922, a first distance from the preset patrol waypoint to the shooting center is obtained.
Step S1923, calculating to obtain the heights and horizontal plane coordinates of all the inspection waypoints of the photovoltaic power station according to the actual elevation of the shooting center point, the first distance and the inclination angle of the photovoltaic group string.
Specifically, the present solution may index the actual altitude h d of the shooting center on the digital surface model DSM according to the coordinate (x 1,y1) of the shooting center, and assuming that the inclination angle of the cluster is θ, the distance between the waypoint and the shooting center is a fixed value m (i.e., the first distance), then the actual flight altitude (i.e., the flight altitude of the waypoint) of the unmanned aerial vehicle is h d +msin θ, and correspondingly, the coordinates of the projection of the horizontal plane of the coordinates of the routing inspection waypoint are (x 2,y2):
x2=x1
y2=y1-m cosθ
optionally, the group string segmentation is performed on the picture of the photovoltaic power station through the trained segmentation model to obtain a plurality of initial group strings, wherein the training of the segmentation model comprises:
step S1101, obtaining an orthographic image and a digital surface model of the photovoltaic power station.
Step S1102, determining a lowest altitude of the photovoltaic power plant by the digital surface model.
Step S1103 obtains a surface model of the relative elevation of the photovoltaic power plant from all elevations on the digital surface model and the lowest elevation.
Step S1104 extracts a first RGB three-channel image of the surface model of the relative elevation and a second RGB three-channel image of the orthographic image.
Step S1105, processing the first RGB three-channel image and the second RGB three-channel image into four-channel images, and inputting the four-channel images into a depth network for training to obtain the segmentation model.
The following describes the steps S1101 to S1105 further:
The orthophoto (DOM) contains the appearance information and the geographical coordinates of the power station, and the Digital Surface Model (DSM) contains the elevation information of the power station. For the processing of the surface model, firstly, interval images for training are extracted according to the same geographical interval and range, then the lowest elevation of the whole DSM is calculated, then the lowest elevation of the DSM is subtracted from the elevation of all geographical coordinates on the whole DSM to obtain the surface model representing the relative elevation, and finally, the surface model containing the relative elevation is normalized to obtain the final processing result of the DSM. Extracting R, G, B channels of images of the DOM, carrying out normalization processing on each channel to obtain a DOM processing result, then carrying out CAT processing on the 3 channel images processed by the DOM and the 3 channel images processed by the DSM, and sending the images as a normal 4 channel image to a deep learning model for training.
When the string segmentation is performed, the appearance information of the orthophoto DOM and the elevation information of the digital surface model DSM can be fused for combined training so as to achieve the purpose of improving the string extraction precision.
It should be further noted that in this embodiment, the DOM and DSM fusion training mode is adopted, so that the accuracy of group string segmentation is improved, meanwhile, multiple post-processing methods are adopted for the segmentation result, the robustness of the route generating tool is ensured, meanwhile, the inspection center is set according to the arrangement of the group string, the image of the whole power station can be acquired with the least shooting points, and the redundancy of acquired data is reduced.
Optionally, step S15 divides the plurality of standard group strings into a plurality of areas according to the number of the group serial columns, including:
Step S151, coordinates of a plurality of standard group strings are obtained and sequenced, and the sequence of each standard group string is obtained.
Specifically, the coordinates of the plurality of standard strings may be ordinate, and the scheme may sort the plurality of standard strings according to the size of the ordinate or the order of the plurality of standard strings, where it should be noted that, in this document, the coordinates of the strings refer to the coordinates of the center of the extracted string. Assume that the ordinate arrangement order (order) of the extraction group string center is y 1、y2、y3…yn, where y 1≤y2≤y3…yn is satisfied.
And step S152, respectively performing differential processing on coordinates of every two adjacent standard group strings in sequence in the plurality of standard group strings based on the sequence of each standard group string to obtain a plurality of differential results.
Specifically, the above ordinate may be differentiated, and the differential result is :d1=y2-y1,d2=y3-y2,…dn-1=yn-yn-1;, and the d 1 to d n-1 are multiple differential results, which should be noted that the differential result may be a row spacing of the group string.
And step 153, dividing the standard group strings into rows according to the differential results to obtain a plurality of rows of standard group strings.
Specifically, the differential results are counted by the scheme, more specifically, the differential results can be clustered by DBSCAN based on distance of 1.5For the cluster radius, 2/>And clustering the effective intervals, wherein a single class in the obtained clustering result is a row class result divided according to row spacing, namely the scheme classifies the difference values as a group string of the same row in the same range.
The above steps S151 to S153 may be a process of grouping strings according to the line pitch.
And step S154, dividing the multi-row standard group string into a plurality of areas according to the group serial number.
Specifically, after knowing a plurality of rows of group strings (i.e. which row that group string belongs to), the scheme can quickly and accurately divide the group strings into a plurality of areas from a plurality of rows of standard group strings according to the set number of group strings to be covered by a single photo.
It is to be understood that the specific features, operations and details described herein before with respect to the method of the invention may be similarly applied to the apparatus and system of the invention, or vice versa. In addition, each step of the method of the present invention described above may be performed by a corresponding component or unit of the apparatus or system of the present invention.
It is to be understood that the various modules/units of the apparatus of the invention may be implemented in whole or in part by software, hardware, firmware, or a combination thereof. The modules/units may each be embedded in a processor of the computer device in hardware or firmware or separate from the processor, or may be stored in a memory of the computer device in software for invocation by the processor to perform the operations of the modules/units. Each of the modules/units may be implemented as a separate component or module, or two or more modules/units may be implemented as a single component or module.
In one embodiment, a computer device is provided that includes a memory and a processor having stored thereon computer instructions executable by the processor, which when executed by the processor, instruct the processor to perform the steps of the method of embodiments of the present invention. The computer device may be broadly a server, a terminal, or any other electronic device having the necessary computing and/or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc. connected by a system bus. The processor of the computer device may be used to provide the necessary computing, processing and/or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may have an operating system, computer programs, etc. stored therein or thereon. The internal memory may provide an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface and communication interface of the computer device may be used to connect and communicate with external devices via a network. Which when executed by a processor performs the steps of the method of the invention.
The present invention may be implemented as a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes steps of a method of an embodiment of the present invention to be performed. In one embodiment, the computer program is distributed over a plurality of computer devices or processors coupled by a network such that the computer program is stored, accessed, and executed by one or more computer devices or processors in a distributed fashion. A single method step/operation, or two or more method steps/operations, may be performed by a single computer device or processor, or by two or more computer devices or processors. One or more method steps/operations may be performed by one or more computer devices or processors, and one or more other method steps/operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step/operation or two or more method steps/operations.
Those of ordinary skill in the art will appreciate that the method steps of the present invention may be implemented by a computer program, which may be stored on a non-transitory computer readable storage medium, to instruct related hardware such as a computer device or a processor, which when executed causes the steps of the present invention to be performed. Any reference herein to memory, storage, database, or other medium may include non-volatile and/or volatile memory, as the case may be. Examples of nonvolatile memory include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage, hard disk, solid state disk, and the like. Examples of volatile memory include Random Access Memory (RAM), external cache memory, and the like.
The technical features described above may be arbitrarily combined. Although not all possible combinations of features are described, any combination of features should be considered to be covered by the description provided that such combinations are not inconsistent.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (7)

1. An intelligent generation method of a photovoltaic power station inspection waypoint is characterized by comprising the following steps:
A plurality of standard group strings are obtained from the picture identification of the photovoltaic power station, wherein the standard group strings are in a certain range in terms of size, and the size of each group string is within a certain range;
acquiring a set serial number of groups required to be covered by shooting a single picture, wherein the serial number of groups is an odd number;
Dividing the standard group strings into a plurality of areas according to the serial number of the groups, wherein the number of the standard group strings in each area meets the line number;
determining a shooting center point of each region according to the coordinates of each standard group string of each region;
Generating all inspection waypoints of the photovoltaic power station based on the shooting center points of each area; the method for identifying and obtaining the plurality of standard group strings from the picture of the photovoltaic power station comprises the following steps: performing string segmentation on the pictures of the photovoltaic power station by adopting a string segmentation model to obtain a plurality of initial strings; calculating to obtain average size parameters of the plurality of initial strings; cutting one or more of the plurality of initial group strings according to the size parameter of each initial group string and the average size parameter to obtain a plurality of standard group strings;
Wherein cutting one or more of the plurality of initial strings according to the size parameter of each initial string and the average size parameter comprises: calculating a multiple critical value of each initial group string relative to the average size parameter, wherein the multiple critical value comprises an upper limit critical value and a lower limit critical value, and the difference value between the upper limit critical value and the lower limit critical value is smaller than a preset threshold value; dividing each initial group string into a number of segments of a lower threshold number, with the ratio of the size parameter of each initial group string to the average size parameter being between the upper and lower thresholds; and under the condition that the ratio of the size parameter of each initial group string to the average size parameter is smaller than or equal to a preset threshold value, not processing each initial group string.
2. The method of claim 1, wherein generating all patrol waypoints of the photovoltaic power plant based on the capture center points of each region comprises:
acquiring an inclination angle of a photovoltaic string of the photovoltaic power station and a digital surface model of the photovoltaic power station;
And generating the heights and horizontal plane coordinates of all the inspection waypoints of the photovoltaic power station based on the inclination angle of the photovoltaic string, the digital surface model and the shooting center point of each area.
3. The method of claim 2, wherein generating the altitude and horizontal coordinates of all inspection waypoints of the photovoltaic power plant based on the inclination of the string of photovoltaic groups, the digital surface model, and the shooting center point of each region comprises:
indexing from the digital surface model through coordinates of shooting center points of each region to obtain actual elevation of the shooting center points;
Acquiring a first distance from a preset inspection navigation point to the shooting center;
and calculating to obtain the heights and horizontal plane coordinates of all the inspection waypoints of the photovoltaic power station according to the actual elevation of the shooting center point, the first distance and the inclination angle of the photovoltaic group string.
4. The method of claim 1, wherein the group string segmentation is performed on the pictures of the photovoltaic power plant by a trained segmentation model to obtain a plurality of initial group strings, wherein training the segmentation model comprises:
Acquiring an orthographic image and a digital surface model of a photovoltaic power station;
determining a minimum altitude of the photovoltaic power plant from the digital surface model;
Obtaining a surface model of the relative elevation of the photovoltaic power plant from all elevations on the digital surface model and the lowest elevation;
Extracting a first RGB three-channel image of the surface model of the relative elevation and a second RGB three-channel image of the orthographic image;
And processing the first RGB three-channel image and the second RGB three-channel image into four-channel images, and inputting the four-channel images into a depth network for training to obtain the segmentation model.
5. The method of claim 1, wherein dividing the plurality of standard group strings into a plurality of regions according to the number of group strings comprises:
Acquiring coordinates of a plurality of standard group strings and sequencing the coordinates to obtain the sequence of each standard group string;
Respectively carrying out differential processing on coordinates of every two standard group strings adjacent in sequence in the plurality of standard group strings based on the sequence of each standard group string to obtain a plurality of differential results;
dividing the standard group strings into rows according to the differential results to obtain a plurality of rows of standard group strings;
dividing the multi-row standard group string into a plurality of areas according to the group serial number.
6. An electronic device comprising a memory and a processor, the memory having stored thereon computer instructions, which when executed by the processor result in the method of any of claims 1 to 5 being performed.
7. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, causes the method of any of claims 1 to 5 to be performed.
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Families Citing this family (2)

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Publication number Priority date Publication date Assignee Title
CN117132419B (en) * 2023-10-26 2024-01-23 北京图知天下科技有限责任公司 Numbering method of photovoltaic module
CN117968705A (en) * 2024-04-02 2024-05-03 广东电网有限责任公司佛山供电局 Cable line inspection method and device, readable storage medium and processor

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109636787A (en) * 2018-12-11 2019-04-16 哈尔滨工业大学 Battery point quality automatic testing method when a kind of high-precision real based on deep learning
CN110244766A (en) * 2019-07-03 2019-09-17 王峰 A kind of method and system for planning in photovoltaic plant unmanned plane inspection course line
CN112034878A (en) * 2020-11-05 2020-12-04 北京云圣智能科技有限责任公司 Unmanned aerial vehicle-based power inspection method and device and electronic equipment
CN113311861A (en) * 2021-05-14 2021-08-27 国家电投集团青海光伏产业创新中心有限公司 Automatic detection method and system for photovoltaic module subfissure characteristics
CN113591729A (en) * 2021-08-03 2021-11-02 浙江农林大学 Urban forest single-tree crown detection method combining RGB-DSM image and deep learning
CN114758002A (en) * 2022-06-15 2022-07-15 南开大学 Photovoltaic string position determining method and system based on aerial image
CN114935942A (en) * 2022-05-20 2022-08-23 无锡海纳智能科技有限公司 Method for determining inspection route of distributed photovoltaic power station and electronic equipment

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9740200B2 (en) * 2015-12-30 2017-08-22 Unmanned Innovation, Inc. Unmanned aerial vehicle inspection system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109636787A (en) * 2018-12-11 2019-04-16 哈尔滨工业大学 Battery point quality automatic testing method when a kind of high-precision real based on deep learning
CN110244766A (en) * 2019-07-03 2019-09-17 王峰 A kind of method and system for planning in photovoltaic plant unmanned plane inspection course line
CN112034878A (en) * 2020-11-05 2020-12-04 北京云圣智能科技有限责任公司 Unmanned aerial vehicle-based power inspection method and device and electronic equipment
CN113311861A (en) * 2021-05-14 2021-08-27 国家电投集团青海光伏产业创新中心有限公司 Automatic detection method and system for photovoltaic module subfissure characteristics
CN113591729A (en) * 2021-08-03 2021-11-02 浙江农林大学 Urban forest single-tree crown detection method combining RGB-DSM image and deep learning
CN114935942A (en) * 2022-05-20 2022-08-23 无锡海纳智能科技有限公司 Method for determining inspection route of distributed photovoltaic power station and electronic equipment
CN114758002A (en) * 2022-06-15 2022-07-15 南开大学 Photovoltaic string position determining method and system based on aerial image

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Inspection Path Planning for Aerial Vehicles via Sampling-based Sequential Optimization;Liping Shi et al.;2021 International Conference on Unmanned Aircraft Systems (ICUAS);第679-687页 *
基于React的输电线路智能机巡系统的设计与实现;张欢;万方数据;第1-89页 *

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