CN111931565A - Photovoltaic power station UAV-based autonomous inspection and hot spot identification method and system - Google Patents
Photovoltaic power station UAV-based autonomous inspection and hot spot identification method and system Download PDFInfo
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Abstract
The invention discloses an autonomous inspection and hot spot identification method and system based on a photovoltaic power station UAV (unmanned aerial vehicle), which comprises the steps of collecting original infrared data of a photovoltaic module and transmitting the data to a ground base station in real time for preprocessing; extracting image edge characteristics of the preprocessed infrared data by using the pixel matrix; identifying hot spots of a photovoltaic assembly in an image by combining a temperature matrix, and preliminarily positioning to the position of a hot spot area heating point by utilizing a real-time dynamic carrier phase dynamic differential strategy; constructing a three-dimensional map of the photovoltaic power station based on the actual spatial position of the on-site photovoltaic module; and performing fusion processing by using the primary positioning result and the three-dimensional map to obtain the hot spot position after secondary positioning, generating a patrol report and displaying the patrol report in the terminal. According to the invention, through autonomous detection, positioning and automatic generation of a field report of the photovoltaic module hot spot unmanned aerial vehicle, the problems of long operation and maintenance time consumption and low working efficiency of the photovoltaic module corresponding to the existing photovoltaic power station are solved.
Description
Technical Field
The invention relates to the technical field of photovoltaic power stations, unmanned aerial vehicles and pattern recognition, in particular to an autonomous inspection and hot spot recognition method and system based on a photovoltaic power station UAV.
Background
Along with the completion of a large amount of large-scale photovoltaic power stations, the quantity of the routing inspection work of the photovoltaic modules is also larger and larger, the attention of a plurality of students and entrepreneurs is attracted, according to statistics, in a photovoltaic power station system, the problems of stains, sheltering, hot spots and the like of the photovoltaic modules account for more than 50% of the fault rate of power station equipment, and if the problems can be found in time and processed in time, the overall efficiency of the power station is greatly improved. At present, the fault monitoring of a photovoltaic module is generally carried out by two methods, namely, the fault monitoring is carried out by monitoring the output voltage and the output power of the photovoltaic module during power generation, and the method has the defects that only rough fault detection can be carried out, the specific photovoltaic module is difficult to accurately position, and the internal fault of a cell is difficult to find; and secondly, with the help of the characteristic that all faults are abnormal in temperature, the heating components are measured and recorded one by manually carrying a temperature measuring instrument.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides an autonomous inspection and hot spot identification method and system based on a photovoltaic power station UAV, which can realize autonomous detection, positioning and field report generation of a photovoltaic module hot spot unmanned aerial vehicle.
In order to solve the technical problems, the invention provides the following technical scheme: collecting original infrared data of a photovoltaic module and transmitting the data to a ground base station in real time for preprocessing; extracting image edge characteristics of the preprocessed infrared data by using a pixel matrix; identifying hot spots of the photovoltaic module in the image by combining the temperature matrix and preliminarily positioning to the position of a hot spot area heating point by utilizing a real-time dynamic carrier phase dynamic differential strategy; constructing a three-dimensional map of the photovoltaic power station based on the actual spatial position of the on-site photovoltaic module; and performing fusion processing by using the primary positioning result and the three-dimensional map to obtain the hot spot position after secondary positioning, generating a patrol report and displaying the patrol report in the terminal.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: before the original infrared data are collected, planning an autonomous flight route based on a track of a three-dimensional point cloud map; leading the planned autonomous flight route into the ground base station, and monitoring the autonomous inspection state and data of the unmanned aerial vehicle in real time; and acquiring the original infrared data of the photovoltaic module by using an infrared thermal phase instrument of the unmanned aerial vehicle and transmitting the original infrared data to the ground base station in real time.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: preprocessing the original infrared data comprises the steps of obtaining the highest temperature of a hot spot area in a hot spot picture and the highest temperature of a non-hot spot picture according to a large number of hot spot and non-hot spot sample infrared pictures to form suspected hot spot temperature value data; analyzing the suspected hot spot temperature value data to obtain a temperature value which is higher than the non-hot spot picture temperature value and lower than the highest temperature value of the hot spot picture area and serve as a suspected temperature value; and screening out the pictures with the temperature values larger than the suspected hot spots in the original infrared data, and carrying out distortion correction and haze removal on the pictures to obtain the data pictures of the suspected hot spots.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: extracting the image edge features comprises marking the position of a light spot in the preprocessed infrared data; inputting the infrared data picture before marking into a deep learning network for detection and classification to obtain a pre-judgment result; comparing the marked light spot position with the pre-judging result by utilizing an NMS strategy, and carrying out iterative optimization to remove weak features until a perfect stored picture feature model is output; and inputting the infrared picture into the storage picture characteristic model for hot spot identification, wherein the storage picture characteristic model utilizes the pixel matrix to extract the characteristics similar to the color and texture in the storage picture characteristic model.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: identifying the hot spots comprises the steps of utilizing the stored picture characteristic model to carry out characteristic coding, and identifying the edge characteristics of the photovoltaic module by combining a clustering strategy to generate a hot spot prediction frame; determining the category and confidence value of the hot spot prediction frame according to the category confidence; filtering the prediction boxes belonging to the background and the prediction boxes with lower confidence thresholds respectively; decoding the reserved hot spot prediction frame and obtaining a real position parameter according to the prior frame; and filtering the prediction boxes with larger overlapping degree by the NMS, and identifying the hot spots by detecting the finally remaining prediction boxes.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: the preliminary positioning comprises positioning the position of the hot spot pixel point of the infrared photo according to the finally remained prediction frame; calculating the hot spot pixel point position of the infrared photo by using an aerial imaging strategy and a similar triangle strategy to obtain the actual longitude and latitude position of the hot spot; and judging the photovoltaic module to which the hot spot belongs by utilizing the actual longitude and latitude position of the hot spot.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: the building of the photovoltaic power station three-dimensional map comprises the steps of building a photovoltaic power station three-dimensional map database and storing the three-dimensional information of each photovoltaic assembly.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: the fusion process comprises gridding the infrared data; and automatically corresponding the primary positioning result with the three-dimensional information in the constructed three-dimensional map database of the photovoltaic power station to obtain the hot spot position of secondary positioning.
As a preferred scheme of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV, the method comprises the following steps: the inspection report specifically comprises the name of the inspected equipment, the inspection place, the inspection result, the inspection personnel, the inspection purpose, the inspection basis and the mark of the infrared photo and the hot spot photovoltaic panel corresponding to the navigation point photovoltaic module.
As a preferred scheme of the photovoltaic power station UAV-based autonomous inspection and hotspot identification system of the present invention, wherein: the device comprises an acquisition module, a control module and a display module, wherein the acquisition module is used for acquiring original infrared data, a holder angle, flight attitude information and distance information of the photovoltaic module; the data processing center module is used for receiving, calculating, storing and outputting data information to be processed and comprises an operation unit, a database and an input and output management unit, wherein the operation unit is connected with the acquisition module and used for receiving the data information acquired by the acquisition module to calculate the hot spot pixel point position and the actual longitude and latitude position of the hot spot of the infrared photo, the database is connected with each module and used for storing all the received data information and providing allocation supply service for the data processing center module, and the input and output management unit is used for receiving the information of each module and outputting the operation result of the operation unit; the identification module is connected with the data processing center module and used for receiving the operation result of the operation unit and judging whether the coordinate position obtained by identification and calculation is the hot spot area; the positioning module is connected with the identification module and used for reading the identification result of the identification module and carrying out centimeter-level positioning by combining the real-time dynamic carrier phase dynamic difference with the coordinate position.
The invention has the beneficial effects that: according to the method, the automatic inspection application of the unmanned aerial vehicle and the automatic infrared image identification are carried out aiming at the local abnormal heating of the photovoltaic module of the photovoltaic power station, the fault of the module is detected, the automatic detection, the positioning and the automatic generation of the field report of the hot spot unmanned aerial vehicle of the photovoltaic module are realized, and the problems of long operation and maintenance time consumption and low working efficiency of the photovoltaic module corresponding to the existing photovoltaic power station are solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be 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 to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of an autonomous inspection and hot spot identification method based on a photovoltaic power station UAV according to a first embodiment of the present invention;
fig. 2 is a schematic view of a photovoltaic module hot spot positioning process of an autonomous inspection and hot spot identification method based on a photovoltaic power station UAV according to a first embodiment of the present invention;
fig. 3 is a schematic flow chart of a suspected hot spot temperature value obtaining method based on autonomous inspection and hot spot identification of a photovoltaic power station UAV according to a first embodiment of the present invention;
fig. 4 is a schematic diagram of a stored picture feature model training process of an autonomous inspection and hotspot identification method based on a photovoltaic power station UAV according to a first embodiment of the present invention;
fig. 5 is a schematic diagram of a preliminary positioning process of an autonomous inspection and hot spot identification method based on a photovoltaic power station UAV according to a first embodiment of the present invention;
fig. 6 is a schematic positioning principle diagram of an autonomous inspection and hot spot identification method based on a photovoltaic power station UAV according to a first embodiment of the present invention;
fig. 7 is a schematic diagram illustrating comparison test output curves of two methods of the autonomous inspection and hot spot identification method based on the photovoltaic power station UAV according to the first embodiment of the present invention;
fig. 8 is a schematic diagram of a distribution of a module structure of an autonomous inspection and hot spot identification system based on a photovoltaic power station UAV according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
The hot spot problem not only affects the power generation benefit of the photovoltaic module, but even causes fire in serious cases, so that the realization of real-time positioning detection of the hot spot has important application value on maintenance work of a photovoltaic power station, but some existing operation and maintenance detection methods mostly aim at defects generated in the manufacturing process of the photovoltaic module, the defect detection research on actual operation and maintenance is less, and the problems of poor generalization capability and insufficient accuracy exist.
Referring to fig. 1 to 7, a first embodiment of the present invention provides an autonomous inspection and hot spot identification method based on a photovoltaic power station UAV, including:
s1: and collecting original infrared data of the photovoltaic module and transmitting the data to a ground base station in real time for preprocessing. It should be noted that, referring to fig. 3, before acquiring the raw infrared data, the method further includes:
planning an autonomous flight route based on the route of the three-dimensional point cloud map;
leading the planned autonomous flight route into a ground base station, and monitoring the autonomous inspection state and data of the unmanned aerial vehicle in real time;
the method comprises the steps of collecting original infrared data of a photovoltaic module by using an infrared thermal phase instrument of an unmanned aerial vehicle and transmitting the original infrared data to a ground base station in real time.
Specifically, preprocessing the raw infrared data includes:
acquiring the highest temperature of a hot spot area and the highest temperature of a non-hot spot picture in a hot spot picture according to a large number of hot spot and non-hot spot sample infrared pictures to form suspected hot spot temperature value data;
analyzing the suspected hot spot temperature value data to obtain a temperature value which is higher than the non-hot spot picture temperature value and lower than the highest temperature value of the hot spot picture area and taking the temperature value as a suspected temperature value;
and screening out pictures with the temperature values larger than the suspected hot spots in the original infrared data, and carrying out distortion correction and haze removal on the pictures to obtain data pictures of the suspected hot spots.
S2: and extracting the image edge characteristics of the preprocessed infrared data by using the pixel matrix. In this step, referring to fig. 4, the extracting the edge feature of the image includes:
marking the position of a light spot in the preprocessed infrared data;
inputting the infrared data picture before marking into a deep learning network for detection and classification to obtain a pre-judgment result;
comparing the marked light spot position and the pre-judging result by utilizing an NMS strategy, and carrying out iterative optimization to remove weak features until a perfect stored picture feature model is output;
and inputting the infrared picture into a storage picture characteristic model for hot spot identification, and extracting the characteristics similar to the color and the texture in the storage picture characteristic model by using the pixel matrix in the storage picture characteristic model.
S3: and identifying hot spots of the photovoltaic assembly in the image by combining the temperature matrix, and preliminarily positioning to the position of a hot spot area by utilizing a real-time dynamic carrier phase dynamic differential strategy. It should be further noted that, with reference to fig. 5 and 6, the identifying the hot spot includes:
performing feature coding by using a stored picture feature model, and identifying the edge features of the photovoltaic module by combining a clustering strategy to generate a hot spot prediction frame;
determining the category and the confidence coefficient value of the hot spot prediction frame according to the category confidence coefficient;
respectively filtering a prediction frame belonging to the background and a prediction frame with a lower confidence threshold;
decoding the reserved hot spot prediction frame and obtaining a real position parameter according to the prior frame;
and (4) filtering the prediction boxes with larger overlapping degree by using the NMS, and identifying the hot spots by detecting the final residual prediction boxes.
Further, the preliminary positioning includes:
positioning the hot spot pixel point position of the infrared photo according to the final residual prediction frame;
calculating the hot spot pixel point position of the infrared photo by using an aerial imaging strategy and a similar triangle strategy to obtain the actual longitude and latitude position of the hot spot;
and judging the photovoltaic module to which the hot spot belongs by utilizing the actual longitude and latitude position of the hot spot.
S4: and constructing a three-dimensional map of the photovoltaic power station based on the actual spatial position of the on-site photovoltaic module. What should be further illustrated in this step is that constructing the three-dimensional map of the photovoltaic power station includes:
and establishing a three-dimensional map database of the photovoltaic power station, and storing the three-dimensional information of each photovoltaic assembly.
S5: and performing fusion processing by using the primary positioning result and the three-dimensional map to obtain the hot spot position after secondary positioning, generating a patrol report and displaying the patrol report in the terminal. It should be noted that the fusion process includes:
gridding the infrared data;
and automatically corresponding the primary positioning result with the three-dimensional information in the constructed three-dimensional map database of the photovoltaic power station to obtain the hot spot position of the secondary positioning.
Specifically, the patrol report includes:
the method comprises the steps of detecting the name of a detected device, a routing inspection place, a routing inspection result, routing inspection personnel, a routing inspection purpose, a routing inspection basis, a marked infrared photo and the position of a hot spot photovoltaic panel corresponding to a waypoint photovoltaic module.
In a popular way, referring to fig. 2, a ground base station or a network RTK acquires the antenna position, the ground height, the posture and the photographing angle of an unmanned aerial vehicle which is automatically patrolled by the unmanned aerial vehicle, an infrared camera carried by the unmanned aerial vehicle calibrates the camera and the absolute position of the antenna of the unmanned aerial vehicle, the central position of the infrared camera is obtained through calculation, the ground base station constructs a three-dimensional map of a photovoltaic power station based on an early-stage field photovoltaic assembly, the found camera central position automatically corresponds to the space in the three-dimensional map, and therefore an accurate hot spot position is obtained.
Preferably, this embodiment is further to be explained in the prior art that the existing method for detecting infrared hot spots of a photovoltaic module performs video framing processing on an infrared video corresponding to the photovoltaic module, the frame image is used as an infrared image to be detected, the frame image is segmented to obtain an infrared image of a photovoltaic area and converted into a gray image, the gray image is binarized for each gray threshold to obtain a first number of binarized images to determine an abnormal area of the photovoltaic module, an extracted feature profile is obtained by using a profile extraction function and calculated, a circular large hot spot and a rectangular large hot spot are determined according to a calculation result and a preset value range, the hot spots caused by a junction box in the circular large hot spot and the hot spots caused by illumination in the rectangular large hot spot are removed, and the method mainly solves the technical problem of how to improve the accuracy and the analysis efficiency of hot spot analysis, the device can not be automatically positioned to the hot spot position of the photovoltaic module in real time, and still needs manual operation and maintenance; the method comprises the steps of extracting image features by using a pixel matrix, identifying hot spots of a photovoltaic assembly in an image by combining a temperature matrix, preliminarily positioning to the position of a hot spot region heating point by using a real-time dynamic carrier phase dynamic difference strategy, automatically fusing a preliminary positioning result and an actually constructed three-dimensional graph, determining the position of a hot spot pixel point by using longitude and latitude, finally identifying and positioning the hot spot position of the photovoltaic assembly, and autonomously producing a routing inspection report without manual operation and maintenance to achieve real-time performance, autonomy performance and high-accuracy positioning and identification performance.
Preferably, in order to verify and explain the technical effects adopted in the method of the present invention, the present embodiment selects a traditional photovoltaic module infrared hot spot detection method to perform a comparison test with the method of the present invention, and compares the test results by means of scientific demonstration to verify the real effect of the method of the present invention; the traditional infrared hot spot detection method for the photovoltaic module has the problems of poor real-time performance and autonomy, can only detect hot spots but cannot perform positioning, needs manual operation and maintenance, is high in cost and low in operation and maintenance efficiency, compared with the traditional method, the method provided by the invention has good real-time performance, autonomy, more accurate positioning performance and identification performance, and the traditional method and the method provided by the invention are adopted in the embodiment to perform real-time measurement and comparison on the photovoltaic module in a certain solar photovoltaic engineering respectively.
And (3) testing environment: (1) the method comprises the following steps of collecting image data by a Ti25 thermal infrared imager, building a model framework by Tensorflow, taking Python as a programming language, and performing acceleration by using a GPU, SPYDER operation and MATLB simulation operation;
(2) randomly shooting 1000 pictures by using an unmanned aerial vehicle as a data set, and respectively carrying out feature extraction and identification positioning on the pictures by using two methods;
(3) when the method is adopted, automatic test equipment (an unmanned aerial vehicle receives ground base station identification information to carry out autonomous positioning) is started.
Referring to fig. 7, the solid line is the curve output by the method of the present invention, the dotted line is the curve output by the traditional method, according to the schematic diagram of fig. 7, it can be seen intuitively that the trend of the solid line is more gradual than that of the dotted line, and the identification accuracy of both methods is reduced with the increase of the number of infrared data sets, but the trend of the dotted line is steeper and the reduction is more obvious.
Example 2
Referring to fig. 8, a second embodiment of the present invention, which is different from the first embodiment, provides an autonomous inspection and hot spot identification system based on a photovoltaic power station UAV, including:
and the acquisition module 100 is used for acquiring original infrared data, holder angles, flight attitude information and distance information of the photovoltaic module.
The data processing center module 200 is used for receiving, calculating, storing and outputting data information to be processed, and comprises an operation unit 201, a database 202 and an input/output management unit 203, wherein the operation unit 201 is connected with the acquisition module 100 and used for receiving the data information acquired by the acquisition module 100 to calculate the hot spot pixel point position and the hot spot actual longitude and latitude position of the infrared photo, the database 202 is connected with each module and used for storing all the received data information and providing allocation supply service for the data processing center module 200, and the input/output management unit 203 is used for receiving the information of each module and outputting the operation result of the operation unit 201.
The identification module 300 is connected to the data processing center module 200, and is configured to receive the operation result of the operation unit 201 and determine whether the coordinate position obtained by the identification calculation is a hot spot region.
The positioning module 400 is connected to the identification module 300, and is configured to read the identification result of the identification module 300, and perform centimeter-level positioning by combining the coordinate position through real-time dynamic carrier phase dynamic difference.
It should be further noted that, the data processing center module 200 is mainly divided into three layers, including a control layer, an operation layer and a storage layer, where the control layer is a command control center of the data processing center module 200 and is composed of an instruction register IR, an instruction decoder ID and an operation controller OC, and the control layer can sequentially fetch each instruction from a memory according to a program pre-programmed by a user, place the instruction in the instruction register IR, analyze and determine the instruction in the instruction decoder, notify the operation controller OC to operate, and send a micro-operation control signal to a corresponding component according to a determined time sequence; the operation layer is the core of the data processing center module 200, can execute arithmetic operation (such as addition, subtraction, multiplication, division and addition operation thereof) and logical operation (such as shift, logical test or two-value comparison), is connected to the control layer, and performs operation by receiving a control signal of the control layer; the storage layer is a database of the data processing center module 200, and can store data (data to be processed and data already processed).
Preferably, the positioning module 400 adopts a network RTK positioning strategy, which can provide a three-dimensional positioning result of the station in a specified coordinate system in real time, and achieve centimeter-level accuracy, the positioning accuracy of a common GPS is greater than 1 meter, the probability of 50% signal error can reach more than 2 meters, and meanwhile, the GPS cannot support accurate height setting, and the error can reach more than ten meters; in the RTK (carrier phase differential technology), the error idea of the GPS is separated, a mobile base station is arranged on a reference point with a known position to obtain the deviation of a positioning signal, the deviation is sent to a mobile station needing to be positioned to obtain more accurate position information, and in the RTK operation mode, the base station collects satellite data and carries out real-time carrier phase differential processing on an observed value and a received data chain through a data chain to obtain a centimeter-level positioning result.
Still further, it should be noted that, in this embodiment, the system further includes an unmanned aerial vehicle inspection platform and a field data processing station, where the unmanned aerial vehicle inspection platform carries an infrared thermal phase instrument to collect original infrared data of the photovoltaic module, and returns the infrared data to the ground station in real time to detect and identify a hot spot of the photovoltaic module, and automatically generates an inspection report; specifically, an infrared thermal imaging camera carried by an unmanned aerial vehicle platform integrates flight attitude, RTK positioning data and other auxiliary sensor information, so that the unmanned aerial vehicle and collected influence data reach centimeter-level positioning accuracy and an infrared camera picture can be remotely checked in real time; the field data processing station can realize autonomous flight path planning flight and save multi-task flight paths so as to achieve unmanned aerial vehicle remote state monitoring and operation remote control, during operation, the unmanned aerial vehicle flies fully autonomously in a photovoltaic power station according to a set route and a set height, and the flight and power supply states of the unmanned aerial vehicle are monitored in real time through communication with the unmanned aerial vehicle.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. An autonomous inspection and hot spot identification method based on a photovoltaic power station UAV is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting original infrared data of the photovoltaic module and transmitting the data to a ground base station in real time for preprocessing;
extracting image edge characteristics of the preprocessed infrared data by using a pixel matrix;
identifying hot spots of the photovoltaic module in the image by combining the temperature matrix and preliminarily positioning to the position of a hot spot area heating point by utilizing a real-time dynamic carrier phase dynamic differential strategy;
constructing a three-dimensional map of the photovoltaic power station based on the actual spatial position of the on-site photovoltaic module;
and performing fusion processing by using the primary positioning result and the three-dimensional map to obtain the hot spot position after secondary positioning, generating a patrol report and displaying the patrol report in the terminal.
2. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 1, wherein: prior to the collecting of the raw infrared data also includes,
planning an autonomous flight route based on the route of the three-dimensional point cloud map;
leading the planned autonomous flight route into the ground base station, and monitoring the autonomous inspection state and data of the unmanned aerial vehicle in real time;
and acquiring the original infrared data of the photovoltaic module by using an infrared thermal phase instrument of the unmanned aerial vehicle and transmitting the original infrared data to the ground base station in real time.
3. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method according to claim 1 or 2, characterized in that: pre-processing the raw infrared data includes,
acquiring the highest temperature of a hot spot area and the highest temperature of a non-hot spot picture in a hot spot picture according to a large number of hot spot and non-hot spot sample infrared pictures to form suspected hot spot temperature value data;
analyzing the suspected hot spot temperature value data to obtain a temperature value which is higher than the non-hot spot picture temperature value and lower than the highest temperature value of the hot spot picture area and serve as a suspected temperature value;
and screening out the pictures with the temperature values larger than the suspected hot spots in the original infrared data, and carrying out distortion correction and haze removal on the pictures to obtain the data pictures of the suspected hot spots.
4. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 3, wherein: the extracting of the image edge feature includes,
marking the position of a light spot in the preprocessed infrared data;
inputting the infrared data picture before marking into a deep learning network for detection and classification to obtain a pre-judgment result;
comparing the marked light spot position with the pre-judging result by utilizing an NMS strategy, and carrying out iterative optimization to remove weak features until a perfect stored picture feature model is output;
and inputting the infrared picture into the storage picture characteristic model for hot spot identification, wherein the storage picture characteristic model utilizes the pixel matrix to extract the characteristics similar to the color and texture in the storage picture characteristic model.
5. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 4, wherein: identifying the hot spot includes identifying the hot spot,
carrying out feature coding by using the stored picture feature model, and identifying the edge feature of the photovoltaic module by combining a clustering strategy to generate a hot spot prediction frame;
determining the category and confidence value of the hot spot prediction frame according to the category confidence;
filtering the prediction boxes belonging to the background and the prediction boxes with lower confidence thresholds respectively;
decoding the reserved hot spot prediction frame and obtaining a real position parameter according to the prior frame;
and filtering the prediction boxes with larger overlapping degree by the NMS, and identifying the hot spots by detecting the finally remaining prediction boxes.
6. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 5, wherein: the preliminary positioning includes the steps of,
positioning the position of the hot spot pixel point of the infrared photo according to the finally remained prediction frame;
calculating the hot spot pixel point position of the infrared photo by using an aerial imaging strategy and a similar triangle strategy to obtain the actual longitude and latitude position of the hot spot;
and judging the photovoltaic module to which the hot spot belongs by utilizing the actual longitude and latitude position of the hot spot.
7. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 6, wherein: the building of the photovoltaic power station three-dimensional map comprises the steps of building a photovoltaic power station three-dimensional map database and storing the three-dimensional information of each photovoltaic assembly.
8. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 7, wherein: the fusion process includes the steps of,
gridding the infrared data;
and automatically corresponding the primary positioning result with the three-dimensional information in the constructed three-dimensional map database of the photovoltaic power station to obtain the hot spot position of secondary positioning.
9. The photovoltaic power station UAV-based autonomous inspection and hotspot identification method of claim 8, wherein: the patrol report specifically includes a list of the patrol reports,
the device name to be inspected, the inspection place, the inspection result, the inspection personnel, the inspection purpose, the inspection basis and the mark correspond to the positions of the waypoint photovoltaic modules of the infrared photo and the hot spot photovoltaic panel.
10. The utility model provides an independently patrol and examine and hot spot identification system based on photovoltaic power plant UAV which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the acquisition module (100) is used for acquiring original infrared data, a holder angle, flight attitude information and distance information of the photovoltaic module;
the data processing center module (200) is used for receiving, calculating, storing and outputting data information to be processed, and comprises an operation unit (201), a database (202) and an input and output management unit (203), wherein the operation unit (201) is connected with the acquisition module (100) and used for receiving the data information acquired by the acquisition module (100) to calculate the hot spot pixel point position and the actual longitude and latitude position of the hot spot of the infrared photo, the database (202) is connected with each module and used for storing all the received data information and providing allocation and supply service for the data processing center module (200), and the input and output management unit (203) is used for receiving the information of each module and outputting the operation result of the operation unit (201);
the identification module (300) is connected to the data processing center module (200) and is used for receiving the operation result of the operation unit (201) and judging whether the coordinate position obtained by identification and calculation is the hot spot area;
the positioning module (400) is connected with the identification module (300) and used for reading the identification result of the identification module (300) and carrying out centimeter-level positioning by combining the coordinate position through real-time dynamic carrier phase dynamic difference.
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