CN113296537A - Electric power unmanned aerial vehicle inspection method and system based on electric power tower model matching - Google Patents
Electric power unmanned aerial vehicle inspection method and system based on electric power tower model matching Download PDFInfo
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/10—Simultaneous control of position or course in three dimensions
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Abstract
The invention provides an electric power unmanned aerial vehicle inspection method and system based on electric power pole tower model matching, belonging to the technical field of electric power facility inspection, and the electric power unmanned aerial vehicle inspection method based on electric power pole tower model matching comprises S1, obtaining three-dimensional model data of an electric power pole tower and three-dimensional model data of an unmanned aerial vehicle; s2, drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information; s3, simulating flight along the target flight line by using the three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data; s4, sending a control instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection; the method has the advantages that the actual tower body characteristics and the geographical position information of the electric power tower are collected, and the data obtained in the simulation process are corrected, so that the working efficiency is greatly improved, the system channel is prevented from being too large, and a favorable basis is provided for deep learning of a neural network.
Description
Technical Field
The invention belongs to the technical field of electric power facility inspection, and particularly relates to an electric power unmanned aerial vehicle inspection method and system based on electric power tower model matching.
Background
In recent years, the electric power system in China is developed rapidly, and according to the forecast of a GlobalData consulting organization, the total mileage of the electric transmission line in China is increased to more than 159 thousands of meters by 2020. China is vast, the terrain is changeable, and especially, ultrahigh voltage transmission lines are mostly distributed in severe environments such as mountainous areas, hills and the like, which causes great difficulty in the maintenance and operation of power grids.
The manual inspection work of the areas has large workload, and the life safety of inspection personnel cannot be well guaranteed. In recent years, the rise of the unmanned aerial vehicle industry brings changes to various industries, the unmanned aerial vehicle is utilized to carry out the routing inspection work of the power transmission line, the work efficiency can be effectively improved, the line inspection cost is reduced, the work safety of routing inspection personnel is guaranteed, and the potential safety hazard on the high-voltage line can be timely noticed through the image data recorded by the airborne cloud deck.
The unmanned aerial vehicle path planning is the fundamental guarantee of the flight safety and the task completion condition of the unmanned aerial vehicle, and is required to have high reliability and practicability. At present, before the unmanned aerial vehicle patrols and examines, all control unmanned aerial vehicle through manual mode and fly once along every electric power tower, obtain tower body characteristic and position parameter of every electric power tower through camera and the positioning device on the unmanned aerial vehicle, finally plan the flight line with data integration through the computer. Thus, factors including flight environment, flight altitude and flight control present significant challenges to the operator if the operator is neglected slightly to cause the drone to collide or fall.
Disclosure of Invention
The embodiment of the invention provides a method and a system for inspecting an electric power unmanned aerial vehicle based on electric power tower model matching, and aims to solve the problem that the existing unmanned aerial vehicle needs to be manually controlled to acquire data of each electric power tower before inspection.
In view of the above problems, the technical solution proposed by the present invention is:
the invention provides an electric power unmanned aerial vehicle inspection method based on electric power tower model matching, which comprises the following steps:
s1, obtaining three-dimensional model data of the electric power tower and three-dimensional model data of the unmanned aerial vehicle;
s2, drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information;
s3, simulating flight along the target flight line by using the three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data;
s4, sending a control instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection;
s5, receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information collected by the positioning equipment;
s6, revising the three-dimensional model data of the power tower according to the video image set, revising the target route according to the real-time geographic position information to obtain an actual route, and simulating flight according to the actual route to obtain real motion attitude data;
and S7, establishing a training sample set by using the three-dimensional model data, the actual air route and the real motion attitude data of the electric power tower, and training the training sample set by using a neural network to obtain an identification model to realize automatic inspection.
As a preferred technical solution of the present invention, the drawing of the target route by combining the three-dimensional model data of the power tower and the defined geographic position information specifically comprises: the method comprises the steps of obtaining a topographic layer of an electric power tower area, setting corresponding defined geographical position information in the topographic layer, importing three-dimensional model data of the electric power tower, corresponding the three-dimensional model data of each electric power tower to the defined geographical position information, establishing a serial number, and drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographical position information.
As a preferred technical scheme of the invention, the target route and the actual route respectively comprise a route distance, a starting point coordinate, a routing inspection starting point coordinate, a path point coordinate, an inspection ending point coordinate and a landing point coordinate.
As a preferred technical solution of the present invention, the virtual motion attitude data and the real motion attitude data include a flight trajectory, a flight speed, a flight time, and a flight stability parameter.
As a preferred technical solution of the present invention, the receiving of the video image set shot by the camera of the unmanned aerial vehicle and the real-time geographic location information collected by the positioning device specifically includes: after receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information of each electric power tower collected by positioning equipment, splitting the video image set into a plurality of frame-by-frame image data, and splitting the plurality of frame-by-frame image data into a plurality of frame-by-frame image sets according to the sequence of each electric power tower.
As a preferred technical solution of the present invention, the revising of the three-dimensional model data of the power tower according to the video image set, the determining of the actual flight path according to the real-time geographic location information, and the obtaining of the actual motion attitude data according to the simulated flight of the actual flight path specifically include: scanning a plurality of frame-by-frame image sets to obtain tower body characteristics of each electric power tower, revising three-dimensional model data of each electric power tower according to the tower body characteristics, determining an actual flight path according to real-time geographic position information, and simulating flight according to the actual flight path to obtain real motion attitude data.
As a preferred technical scheme of the invention, the method for establishing the training sample set by using the three-dimensional model data, the actual air route and the real motion attitude data of the electric power tower and training the training sample set by using the neural network to obtain the recognition model to realize automatic inspection specifically comprises the following steps: marking three-dimensional model data, an actual air route and a real motion attitude data interest point area of the power tower to establish a training sample set, wherein the training sample set is divided into a training set and a testing set.
As a preferred technical scheme of the invention, after the three-dimensional model data, the actual route and the real motion attitude data interest point area of the power tower are labeled to establish the training sample set, the following steps are carried out: and building a neural network, initializing the network, dividing sample data in the training set into a plurality of batches, training the neural network by adopting the sample data of one batch, and updating the weight parameters of the neural network.
As a preferred technical solution of the present invention, the building of the neural network, the network initialization, the dividing of the sample data in the training set into a plurality of batches, the training of the neural network with the sample data of one batch, and the updating of the weight parameters of the neural network are as follows: and verifying the trained neural network by adopting the test set, calculating the loss value of the neural network, judging whether the loss value is smaller than a set threshold value, if so, outputting the trained neural network to obtain an identification model, and otherwise, turning to the previous step to train the sample data of the next batch.
On the other hand, the invention also provides an electric power unmanned aerial vehicle inspection system based on electric power pole tower model matching, which comprises:
the acquisition module is used for acquiring three-dimensional model data of the electric power tower and three-dimensional model data of the unmanned aerial vehicle;
the drawing module is used for drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information;
the simulation module is used for simulating flight along a target flight line by using three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data;
the control module is used for sending a control instruction to the unmanned aerial vehicle so as to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection;
the system comprises an acquisition module, a positioning device and a control module, wherein the acquisition module is used for receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information acquired by the positioning device;
the revision module is used for revising the three-dimensional model data of the power tower according to the video image set, determining an actual flight line according to the real-time geographic position information, and simulating flight according to the actual flight line to obtain real motion attitude data;
the learning module is used for establishing a training sample set from three-dimensional model data, an actual air route and real motion attitude data of the electric power tower, and training the training sample set by adopting a neural network to obtain a recognition model for realizing automatic inspection.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the invention, the target air line is drawn through the three-dimensional model data of the electric power tower and the defined geographic position information, the virtual motion attitude data is obtained by using the three-dimensional model data of the unmanned aerial vehicle to perform a simulation flight experiment along the target air line, so that the acquisition of flight parameters of the unmanned aerial vehicle is further completed, the data acquired by manually controlling the unmanned aerial vehicle can be reduced, and the purpose of avoiding the damage of the unmanned aerial vehicle is achieved.
(2) According to the invention, the unmanned aerial vehicle flies truly along the target route according to the virtual motion attitude data, the actual tower body characteristics and the geographic position information of the electric power tower are collected, and the data obtained in the simulation process are corrected, so that the working efficiency is greatly improved, the system channel is prevented from being too thick, and a favorable basis is provided for deep learning of a neural network.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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FIG. 1 is a schematic flow chart of a method for routing inspection of an electric unmanned aerial vehicle based on electric tower model matching, disclosed by the invention;
fig. 2 is a schematic structural diagram of an intelligent subscription system based on a federation blockchain disclosed in the present invention.
Description of reference numerals: 110. an acquisition module; 120. a drawing module; 130. a simulation module; 140. a control module; 150. an acquisition module; 160. a revision module; 170. and a learning module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the equipment or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Example one
Referring to the attached figure 1, the invention provides a technical scheme: the electric power unmanned aerial vehicle inspection method based on electric power tower model matching comprises the following steps:
and S1, obtaining three-dimensional model data of the electric power tower and three-dimensional model data of the unmanned aerial vehicle.
It should be noted that the three-dimensional model data for establishing the electric power tower and the unmanned aerial vehicle is three-dimensional software which can be obtained by current market application, and the three-dimensional software comprises any one of Pro/Engineer, Solidworks, CATIA, UG-NX and Cimatron.
Specifically, the proportion of the three-dimensional model data of the electric tower and the three-dimensional model data of the unmanned aerial vehicle in establishing the model data should be the same as that of a real object.
And S2, drawing the target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information.
Further, the drawing of the target route by combining the three-dimensional model data of the electric power tower and the definition of the geographic position information is specifically as follows: the method comprises the steps of obtaining a topographic layer of an electric power tower area, setting corresponding defined geographical position information in the topographic layer, importing three-dimensional model data of the electric power tower, corresponding the three-dimensional model data of each electric power tower to the defined geographical position information, establishing a serial number, and drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographical position information.
Specifically, the content of the imported terrain map layer should include features of terrain and landforms, and after the importing is completed, corresponding defined geographical position information is set in the terrain map layer according to actual position parameters of the power tower, meanwhile, the three-dimensional model data of the corresponding power tower is imported according to the defined geographical position information, and serial number sequencing is established, so that a target air route is conveniently drawn, the actual air route is conveniently determined, and the unmanned aerial vehicle can fly along the target air route or the actual air route according to the sequence of the power tower. For example, the geographical location information (X, Y, Z) of the actual power tower, where X represents longitude, Y represents latitude, and Z represents altitude, and if there are power towers numbered 1, 2, and N, the corresponding geographical location information is (X)1、Y1、Z1)、(X2、Y2、Z2) And (X)N、YN、ZN)。
And S3, simulating flight along the target flight path by using the three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data.
Specifically, three-dimensional model data of the unmanned aerial vehicle are imported, the unmanned aerial vehicle is enabled to carry out simulated flight along a target route, and virtual motion attitude data can be measured and calculated in the flight process.
And S4, sending a control instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection.
And S5, receiving the video image set shot by the camera of the unmanned aerial vehicle and the real-time geographic position information collected by the positioning equipment.
Further, receiving the video image set shot by the camera of the unmanned aerial vehicle and the real-time geographical location information collected by the positioning device is specifically as follows: after receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information of each electric power tower collected by positioning equipment, splitting the video image set into a plurality of frame-by-frame image data, and splitting the plurality of frame-by-frame image data into a plurality of frame-by-frame image sets according to the sequence of each electric power tower.
Specifically, the process is that a real unmanned aerial vehicle flies along a target air route through virtual motion attitude data, the unmanned aerial vehicle shoots videos through a camera to obtain a video image set, and meanwhile, real-time geographic position information collected by a positioning device is obtained; certainly, in the actual flight process, the target route has errors with the actual situation, so that when the unmanned aerial vehicle deviates, a control instruction can be sent to the unmanned aerial vehicle, the target route at the stage is stopped to be used, and when the real-time geographic position information acquired through the positioning equipment is revised on the target route, the stage stopped to be used is readjusted according to the real-time geographic position information; it can be understood that if there is no error in the target route in actual flight, no revision is needed, which can greatly reduce the work rate and improve the work efficiency.
And S6, revising the three-dimensional model data of the power tower according to the video image set, revising the target route according to the real-time geographic position information to obtain an actual route, and simulating flight according to the actual route to obtain real motion attitude data.
Furthermore, the three-dimensional model data of the power tower is revised according to the video image set, the actual route is determined according to the real-time geographic position information, and the actual motion attitude data obtained by simulating flight according to the actual route is specifically as follows: scanning a plurality of frame-by-frame image sets to obtain tower body characteristics of each electric power tower, revising three-dimensional model data of each electric power tower according to the tower body characteristics, determining an actual flight path according to real-time geographic position information, and simulating flight according to the actual flight path to obtain real motion attitude data.
Specifically, after a plurality of frame-by-frame image sets are scanned by shooting the real tower body characteristics of the power tower, the defects and the omissions are found for the three-dimensional model data of the power tower, and the reference basis is provided for the unmanned aerial vehicle in the flight process after the three-dimensional model data of the power tower is deeply learned.
And S7, establishing a training sample set by using the three-dimensional model data, the actual air route and the real motion attitude data of the electric power tower, and training the training sample set by using a neural network to obtain an identification model to realize automatic inspection.
Further, a training sample set is established by the three-dimensional model data, the actual air route and the real motion attitude data of the electric power tower, and the neural network is adopted to train the training sample set to obtain an identification model, so that the automatic inspection is specifically: marking three-dimensional model data, an actual air route and a real motion attitude data interest point area of the power tower to establish a training sample set, wherein the training sample set is divided into a training set and a testing set.
After the three-dimensional model data, the actual route and the real motion attitude data interest point area of the power tower are labeled and a training sample set is established, the method comprises the following steps: and building a neural network, initializing the network, dividing sample data in the training set into a plurality of batches, training the neural network by adopting the sample data of one batch, and updating the weight parameters of the neural network.
Building a neural network, initializing the network, dividing sample data in a training set into a plurality of batches, training the neural network by adopting the sample data of one batch, and updating weight parameters of the neural network: and verifying the trained neural network by adopting the test set, calculating the loss value of the neural network, judging whether the loss value is smaller than a set threshold value, if so, outputting the trained neural network to obtain an identification model, and otherwise, turning to the previous step to train the sample data of the next batch.
In the embodiment of the invention, the target route and the actual route respectively comprise a route distance, a flying point coordinate, a patrol starting point coordinate, a path point coordinate, a patrol finishing point coordinate and a landing point coordinate. However, the coordinates of the departure point, the coordinates of the inspection start point, the coordinates of the path point, the coordinates of the inspection end point, and the coordinates of the landing point should be identified with reference to the geographical location information.
In an embodiment of the invention, the virtual motion attitude data and the real motion attitude data comprise flight trajectory, flight speed, flight time and flight stability parameters.
Example two
Referring to the attached figure 2, the electric power unmanned aerial vehicle inspection system based on electric power tower model matching comprises:
the acquisition module is used for acquiring three-dimensional model data of the electric power tower and three-dimensional model data of the unmanned aerial vehicle;
the drawing module 120 is used for setting and defining geographic position information to draw a target route;
the simulation module 130 is used for simulating flight along a target flight line by using three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data;
the control module 140 is used for sending a control instruction to the unmanned aerial vehicle so as to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection;
the acquisition module 150 is used for receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information acquired by the positioning equipment;
the revising module 160 is used for revising the three-dimensional model data of the power tower according to the video image set, determining an actual flight line according to the real-time geographic position information, and simulating flight according to the actual flight line to obtain real motion attitude data;
and the learning module 170 is used for establishing a training sample set from the three-dimensional model data, the actual air route and the real motion attitude data of the electric power tower, and training the training sample set by adopting a neural network to obtain an identification model for realizing automatic inspection.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
(1) according to the invention, the target air line is drawn through the three-dimensional model data of the electric power tower and the defined geographic position information, the virtual motion attitude data is obtained by using the three-dimensional model data of the unmanned aerial vehicle to perform a simulation flight experiment along the target air line, so that the acquisition of flight parameters of the unmanned aerial vehicle is further completed, the data acquired by manually controlling the unmanned aerial vehicle can be reduced, and the purpose of avoiding the damage of the unmanned aerial vehicle is achieved.
(2) According to the invention, the unmanned aerial vehicle flies truly along the target route according to the virtual motion attitude data, the actual tower body characteristics and the geographic position information of the electric power tower are collected, and the data obtained in the simulation process are corrected, so that the working efficiency is greatly improved, the system channel is prevented from being too thick, and a favorable basis is provided for deep learning of a neural network.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (10)
1. An electric power unmanned aerial vehicle inspection method based on electric power tower model matching is characterized by comprising the following steps:
s1, obtaining three-dimensional model data of the electric power tower and three-dimensional model data of the unmanned aerial vehicle;
s2, drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information;
s3, simulating flight along the target flight line by using the three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data;
s4, sending a control instruction to the unmanned aerial vehicle to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection;
s5, receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information collected by the positioning equipment;
s6, revising the three-dimensional model data of the power tower according to the video image set, revising the target route according to the real-time geographic position information to obtain an actual route, and simulating flight according to the actual route to obtain real motion attitude data;
and S7, establishing a training sample set by using the three-dimensional model data, the actual air route and the real motion attitude data of the electric power tower, and training the training sample set by using a neural network to obtain an identification model to realize automatic inspection.
2. The electric power unmanned aerial vehicle inspection method based on electric power tower model matching according to claim 1, wherein the drawing of the target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information is specifically as follows: the method comprises the steps of obtaining a topographic layer of an electric power tower area, setting corresponding defined geographical position information in the topographic layer, importing three-dimensional model data of the electric power tower, corresponding the three-dimensional model data of each electric power tower to the defined geographical position information, establishing a serial number, and drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographical position information.
3. The electric power unmanned aerial vehicle inspection method based on electric power tower model matching of claim 1, wherein the target route and the actual route each include a route distance, a departure point coordinate, an inspection start point coordinate, a path point coordinate, an inspection end point coordinate and a landing point coordinate.
4. The electric power unmanned aerial vehicle inspection method based on electric power tower model matching of claim 1, wherein the virtual motion attitude data and the real motion attitude data comprise flight trajectory, flight speed, flight time and flight stability parameters.
5. The electric power tower model matching-based unmanned aerial vehicle inspection method according to claim 1, wherein the receiving of the video image set shot by the camera of the unmanned aerial vehicle and the real-time geographical location information collected by the positioning device is specifically: after receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information of each electric power tower collected by positioning equipment, splitting the video image set into a plurality of frame-by-frame image data, and splitting the plurality of frame-by-frame image data into a plurality of frame-by-frame image sets according to the sequence of each electric power tower.
6. The electric power tower model matching-based unmanned aerial vehicle inspection method according to claim 1, wherein the three-dimensional model data of the electric power tower is revised according to the video image set, the actual flight line is determined according to the real-time geographic position information, and the actual motion attitude data obtained by simulating flight according to the actual flight line is specifically: scanning a plurality of frame-by-frame image sets to obtain tower body characteristics of each electric power tower, revising three-dimensional model data of each electric power tower according to the tower body characteristics, determining an actual flight path according to real-time geographic position information, and simulating flight according to the actual flight path to obtain real motion attitude data.
7. The electric power unmanned aerial vehicle inspection method based on electric power tower model matching of claim 1, wherein the method comprises the steps of establishing a training sample set from three-dimensional model data, actual flight paths and real motion attitude data of an electric power tower, training the training sample set by adopting a neural network to obtain a recognition model, and realizing automatic inspection specifically: marking three-dimensional model data, an actual air route and a real motion attitude data interest point area of the power tower to establish a training sample set, wherein the training sample set is divided into a training set and a testing set.
8. The electric power unmanned aerial vehicle inspection method based on electric power tower model matching of claim 7, wherein the following steps are performed after the three-dimensional model data, the actual route and the real motion attitude data interest point region of the electric power tower are labeled to establish the training sample set: and building a neural network, initializing the network, dividing sample data in the training set into a plurality of batches, training the neural network by adopting the sample data of one batch, and updating the weight parameters of the neural network.
9. The electric power unmanned aerial vehicle inspection method based on electric power tower model matching of claim 8, wherein the neural network is built, network initialization is performed, sample data in a training set is divided into a plurality of batches, the neural network is trained by adopting the sample data of one batch, and after the weight parameters of the neural network are updated, the method comprises the following steps: and verifying the trained neural network by adopting the test set, calculating the loss value of the neural network, judging whether the loss value is smaller than a set threshold value, if so, outputting the trained neural network to obtain an identification model, and otherwise, turning to the previous step to train the sample data of the next batch.
10. The electric power unmanned aerial vehicle inspection system based on electric power tower model matching is applied to the electric power unmanned aerial vehicle inspection method based on electric power tower model matching, which comprises the following steps of:
the acquisition module is used for acquiring three-dimensional model data of the electric power tower and three-dimensional model data of the unmanned aerial vehicle;
the drawing module is used for drawing a target route by combining the three-dimensional model data of the electric power tower and the defined geographic position information;
the simulation module is used for simulating flight along a target flight line by using three-dimensional model data of the unmanned aerial vehicle to obtain virtual motion attitude data;
the control module is used for sending a control instruction to the unmanned aerial vehicle so as to control the unmanned aerial vehicle to fly according to the virtual motion attitude data and the target route for inspection;
the system comprises an acquisition module, a positioning device and a control module, wherein the acquisition module is used for receiving a video image set shot by a camera of the unmanned aerial vehicle and real-time geographic position information acquired by the positioning device;
the revision module is used for revising the three-dimensional model data of the power tower according to the video image set, determining an actual flight line according to the real-time geographic position information, and simulating flight according to the actual flight line to obtain real motion attitude data;
the learning module is used for establishing a training sample set from three-dimensional model data, an actual air route and real motion attitude data of the electric power tower, and training the training sample set by adopting a neural network to obtain a recognition model for realizing automatic inspection.
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