CN113625770A - Autonomous navigation planning method and device for routing inspection of photovoltaic power station based on flying unmanned aerial vehicle - Google Patents

Autonomous navigation planning method and device for routing inspection of photovoltaic power station based on flying unmanned aerial vehicle Download PDF

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CN113625770A
CN113625770A CN202111045977.1A CN202111045977A CN113625770A CN 113625770 A CN113625770 A CN 113625770A CN 202111045977 A CN202111045977 A CN 202111045977A CN 113625770 A CN113625770 A CN 113625770A
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unmanned aerial
aerial vehicle
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autonomous
inspection
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CN113625770B (en
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周勇
李涛
何晓伟
韩虎虎
王鹤飞
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Guodian Siziwangqi Photovoltaic Power Generation Co ltd
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Guodian Siziwangqi Photovoltaic Power Generation Co ltd
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    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
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Abstract

The embodiment of the application discloses an autonomous navigation planning method, an autonomous navigation planning device, equipment and a storage medium for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle navigation planning.

Description

Autonomous navigation planning method and device for routing inspection of photovoltaic power station based on flying unmanned aerial vehicle
Technical Field
The application relates to the technical field of unmanned aerial vehicle navigation planning, in particular to an autonomous navigation planning method, device, equipment and storage medium for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle.
Background
In the traditional path planning of the flying unmanned aerial vehicle, the flying unmanned aerial vehicle is regarded as a particle to be analyzed; the path planning of the flying unmanned aerial vehicle usually only selects an air route with few obstacles as a final air route, but the limitation of electric quantity and flight time is ignored, so that the problem that the autonomous air route planning method is not scientific due to lack of condition factors when the autonomous air route planning is carried out on the flying unmanned aerial vehicle in the prior art is solved.
Disclosure of Invention
An object of the embodiment of the application is to provide an autonomous navigation planning method, an autonomous navigation planning device and a storage medium for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle, so as to solve the technical problems.
In order to solve the technical problem, an embodiment of the present application provides an autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle, which adopts the following technical scheme:
the invention provides an autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle, which is characterized by comprising the following steps of:
constructing a three-dimensional map library based on a multi-target photovoltaic power station to be inspected, and acquiring a three-dimensional environment global map from the three-dimensional map library to serve as a first three-dimensional map;
sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, performing three-dimensional grid planning on the first three-dimensional map based on a grid method, and dividing the first three-dimensional map into a three-dimensional grid map consisting of a plurality of unit bodies by taking a regular hexagonal grid with a preset size as a unit body;
determining the difference numbers of the unit bodies respectively corresponding to the multi-target photovoltaic power stations to be inspected, and using the difference number information to represent the photovoltaic power stations to be inspected corresponding to the multi-target photovoltaic power stations one by one;
determining the flight initial position information of the inspection flight unmanned aerial vehicle, and using A0,0,0And expressing, namely taking the multi-target photovoltaic power stations to be inspected as flight end points respectivelyEstablishing a manual potential field closing algorithm, and carrying out autonomous route planning on the inspection flying unmanned aerial vehicle based on the manual potential field closing algorithm, wherein the gravitational potential from the inspection flying unmanned aerial vehicle to the flying terminal is as follows:
Figure BDA0003251193090000021
patrol and examine flight unmanned aerial vehicle extremely the repulsion force when flying terminal point in-process through each barrier does:
Figure BDA0003251193090000022
then the manual resultant force of patrolling and examining the flying unmanned aerial vehicle to the flying terminal point is:
Figure BDA0003251193090000023
wherein L is the number of the obstacles, L is the number information of each obstacle, L is a positive integer, and the value range of L is [1, L]L is right it reaches to patrol and examine flight unmanned aerial vehicle each barrier that the flight terminal point in-process passed through plays difference identification effect, it is p to patrol and examine the distance of flight unmanned aerial vehicle relative each barrier when flightrlAnd the distance between the patrol flying unmanned aerial vehicle and the flying terminal point is prwThe European distance of the inspection flying unmanned aerial vehicle relative to each obstacle is | | prlThe European distance of the patrol flying unmanned aerial vehicle relative to the terminal point is prw||，k3Providing a minimum distance between the inspection flying unmanned aerial vehicle and each obstacle; k is a radical of1A scale factor that is an attraction potential; k is a radical of2Scale factor, k, for repulsive potential bits1And k2Are all larger than 0;
and constructing an electric quantity screening mathematical model, after the routing inspection flying unmanned aerial vehicle carries out autonomous route planning, carrying out electric quantity suitable screening on the planned autonomous route, and screening out the planned autonomous route as the final routing inspection autonomous route of the routing inspection flying unmanned aerial vehicle under the condition of preset electric quantity suitable.
Further, the sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, specifically, the preset analysis model at least includes:
analyzing mountainous regions, trees, buildings and multi-target photovoltaic power stations to be inspected in the first three-dimensional map, determining spatial position information of the mountainous regions, the trees, the buildings and the multi-target photovoltaic power stations to be inspected in the first three-dimensional map respectively, and marking fixed points by using four different color point values respectively, wherein the four different color point values are used for representing the mountainous regions, the trees, the buildings and the multi-target photovoltaic power stations to be inspected respectively.
Further, the grid-based method is used for performing three-dimensional grid planning on the first three-dimensional map, and the first three-dimensional map is divided into three-dimensional grid maps composed of a plurality of unit bodies by using a hexagonal grid with a preset size as a unit body, and the method further includes:
after the three-dimensional grid map is divided, three-dimensional modeling and distinguishing numbering are carried out on a plurality of divided unit bodies;
the three-dimensional modeling of the divided unit bodies specifically comprises the following operations:
establishing a three-dimensional coordinate system by taking the length, width and height of the regular hexagonal grid as coordinate units of the three-dimensional coordinate system respectively, and determining three-dimensional coordinate (x, y, z) information of each unit body in the three-dimensional coordinate system;
the distinguishing and numbering of the plurality of divided unit bodies specifically comprises the following operations:
the unit bodies are numbered along the positive direction of the x axis of the three-dimensional coordinate system, I is used for representing, I is a positive integer, the value range of I is [1, I ], and I represents the number of the unit bodies on the x axis under the condition of the same y value and the same z value;
secondly numbering the plurality of unit bodies along the positive direction of the y axis of the three-dimensional coordinate system, and expressing by using J, wherein J is a positive integer, the value range of J is [1, J ], and J expresses the number of the unit bodies on the y axis under the condition of the same x value and the same z value;
thirdly numbering the plurality of unit bodies along the positive direction of the z axis of the three-dimensional coordinate system, and expressing by using K, wherein K is a positive integer, the value range of K is [1, K ], and K expresses the number of the unit bodies on the z axis under the condition of the same x value and the same y value;
after the first number, the second number and the third number are finished, using Ai,j,kAnd as the distinguishing numbers among the unit bodies, distinguishing and identifying the unit bodies.
Further, based on the artificial potential field algorithm, the autonomous route planning is carried out on the inspection flying unmanned aerial vehicle, and the method specifically comprises the following operations:
determining the number L of passing obstacles from a flight starting position to a flight terminal of the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, marking the unit bodies where the passing obstacles are located respectively, and acquiring different number information of the unit bodies corresponding to the passing obstacles respectively;
based on a preset screening model, when the number L of the obstacles is selected as the minimum value, determining a planned path from a flight starting position to a flight terminal point of the inspection flying unmanned aerial vehicle, and taking the planned path as a first alternative autonomous air route of the flying unmanned aerial vehicle;
if the number L of the obstacles passing through in the multiple first alternative autonomous routes is the same, acquiring the number of the obstacle-free unit bodies passing through based on the difference number information of the unit bodies respectively corresponding to the passed obstacles, and selecting a path when the number of the obstacle-free unit bodies passing through is the minimum value as the secondary alternative autonomous route of the flying unmanned aerial vehicle;
and if a plurality of secondary alternative autonomous routes exist, acquiring a corresponding path when the manual potential force from the inspection flying unmanned aerial vehicle to the flying terminal point is the minimum value, and using the path as a tertiary alternative autonomous route.
Further, the obstacle includes:
patrol and examine flight unmanned aerial vehicle and pass through at the flight in-process mountain region, trees, building, the multi-target photovoltaic power plant that waits to patrol and examine.
Further, the electric quantity screening mathematical model comprises:
calculating a first reference time T according to a preset time-of-flight algorithm1(ii) a Presetting a flight time algorithm formula:
Figure BDA0003251193090000051
wherein S represents the actual flying distance from the flying starting position to the flying end point of the inspection flying unmanned aerial vehicle,
Figure BDA0003251193090000052
representing the average speed, T, of the flight of the patrol flying unmanned aerial vehicle from the flight starting position to the flight end point0Indicating the elapsed time, T, of the patrol flying unmanned aerial vehicle during operation0≥0；
Calculating second reference time T according to power supply discharge time algorithm of preset flying unmanned aerial vehicle2(ii) a Presetting a power supply discharge time algorithm formula of the flying unmanned aerial vehicle:
Figure BDA0003251193090000053
wherein q is1Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the flying process1Representing the discharge coefficient of the power supply of the inspection flying unmanned aerial vehicle in unit time in the flying process, q2Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the operation process2The discharge coefficient of the power supply in unit time in the operation process of the inspection flying unmanned aerial vehicle is shown, and Q is Q1+q2Q represents the total electric energy storage quantity of the inspection flying unmanned aerial vehicle;
comparing the calculation results of the preset flight time algorithm and the power supply discharge time algorithm of the preset flight unmanned aerial vehicle, and if T is reached2≥T1And taking the screened autonomous route as a final inspection autonomous route of the inspection aircraft.
Further, if T2≥T1Taking the screened autonomous route as a final inspection autonomous route of the inspection aircraft, and the method comprises the following steps:
screening the tertiary alternative autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening condition exist in the tertiary alternative autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft;
if the autonomous routes meeting the screening conditions do not exist in the third standby autonomous routes, screening the secondary standby autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the secondary standby autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft;
and if the autonomous routes meeting the screening conditions do not exist in the secondary alternative autonomous routes, screening the primary alternative autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the primary alternative autonomous routes, taking the screened autonomous routes as the final inspection autonomous routes of the inspection aircraft.
Furthermore, the invention also provides an autonomous navigation planning device for routing inspection of the photovoltaic power station based on the flying unmanned aerial vehicle, which comprises:
the three-dimensional map building module is used for building a three-dimensional map library based on the multi-target photovoltaic power station to be inspected, and acquiring a three-dimensional environment global map from the three-dimensional map library to serve as a first three-dimensional map; sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, performing three-dimensional grid planning on the first three-dimensional map based on a grid method, and dividing the first three-dimensional map into a three-dimensional grid map consisting of a plurality of unit bodies by taking a regular hexagonal grid with a preset size as a unit body;
the photovoltaic power station position determining module is used for determining the distinguishing numbers of the unit bodies respectively corresponding to the multi-target photovoltaic power stations to be inspected, and using the distinguishing number information to represent the photovoltaic power stations to be inspected corresponding to the distinguishing numbers one by one;
autonomous routeThe simulation planning module is used for determining the flight initial position information of the inspection flying unmanned aerial vehicle and uses A0,0,0Expressing, respectively taking the multi-target photovoltaic power stations to be inspected as flight end points, constructing an artificial potential field algorithm, and carrying out autonomous air route planning on the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, wherein the gravitational potential force from the inspection flying unmanned aerial vehicle to the flight end points is as follows:
Figure BDA0003251193090000061
patrol and examine flight unmanned aerial vehicle extremely the repulsion force when flying terminal point in-process through each barrier does:
Figure BDA0003251193090000062
then the manual resultant force of patrolling and examining the flying unmanned aerial vehicle to the flying terminal point is:
Figure BDA0003251193090000063
wherein L is the number of the obstacles, L is the number information of each obstacle, L is a positive integer, and the value range of L is [1, L]L is right it reaches to patrol and examine flight unmanned aerial vehicle each barrier that the flight terminal point in-process passed through plays difference identification effect, it is p to patrol and examine the distance of flight unmanned aerial vehicle relative each barrier when flightrlAnd the distance between the patrol flying unmanned aerial vehicle and the flying terminal point is prwThe European distance of the inspection flying unmanned aerial vehicle relative to each obstacle is | | prlThe European distance of the patrol flying unmanned aerial vehicle relative to the terminal point is prw||，k3The minimum distance between the inspection flying unmanned aerial vehicle and each obstacle can be approached, and the size of the minimum distance is determined by the shape and size of the mountainous region, trees, buildings and the multi-target photovoltaic power station to be inspected, which are analyzed by the environment; k is a radical of1A scale factor that is an attraction potential; k is a radical of2Scale factor, k, for repulsive potential bits1And k2Are all larger than 0;
and the constraint model building module is used for building an electric quantity screening mathematical model, after the routing inspection flying unmanned aerial vehicle carries out autonomous route planning, the planned autonomous route is subjected to electric quantity suitable screening, and the planned autonomous route is screened out under the condition of proper preset electric quantity to serve as the final routing inspection autonomous route of the routing inspection flying machine.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer device comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to realize the steps of the autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle.
In order to solve the above technical problem, an embodiment of the present application further provides a nonvolatile computer-readable storage medium, which adopts the following technical solutions:
a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of a method for autonomous navigation planning for routing inspection of a photovoltaic power plant based on a flying drone, as set forth in an embodiment of the present application.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the embodiment of the application discloses autonomous navigation planning method, device, equipment and storage medium for patrolling and examining photovoltaic power station based on flying unmanned aerial vehicle, through constructing three-dimensional map library and constraint model, obtain the position of multi-target photovoltaic power station, carry out autonomous route planning to cruising flying unmanned aerial vehicle, through flying unmanned aerial vehicle through the quantity of barrier, the quantity of barrier-free unit body, artifical resultant field force minimum, flight time constraint and power supply constraint, carry out autonomous planning route for flying unmanned aerial vehicle based on cyclic mode, the scientificity and the suitability of autonomous planning have been guaranteed.
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In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is a diagram of an exemplary system architecture to which embodiments of the present application may be applied;
fig. 2 is a flowchart of an embodiment of the autonomous navigation planning method for routing inspection of the photovoltaic power station based on the flying drone in the embodiment of the present application;
fig. 3 is an execution logic flow diagram of the autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying drone in the embodiment of the present application;
fig. 4 is a schematic structural diagram of an embodiment of the autonomous navigation planning apparatus for routing inspection of a photovoltaic power station based on a flying drone in the embodiment of the present application;
FIG. 5 is a schematic structural diagram of a constraint model building module in the embodiment of the present application;
fig. 6 is a schematic structural diagram of an embodiment of a computer device in an embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the autonomous navigation planning method for polling the photovoltaic power station based on the flying unmanned aerial vehicle provided by the embodiment of the present application is generally executed by the server/terminal device, and accordingly, the autonomous navigation planning device for polling the photovoltaic power station based on the flying unmanned aerial vehicle is generally arranged in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Continuing to refer to fig. 2, a flowchart of an embodiment of the autonomous navigation planning method for polling the photovoltaic power station based on the flying drone of the present application is shown, and the autonomous navigation planning method for polling the photovoltaic power station based on the flying drone includes the following steps:
step 201, a three-dimensional map library is constructed based on a multi-target photovoltaic power station to be inspected, and a three-dimensional environment global map is obtained from the three-dimensional map library and is used as a first three-dimensional map.
Step 202, sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, performing three-dimensional grid planning on the first three-dimensional map based on a grid method, and dividing the first three-dimensional map into a three-dimensional grid map consisting of a plurality of unit bodies by taking a regular hexagonal grid with a preset size as a unit body.
In this embodiment of the present application, the sending the first three-dimensional map to a preset analysis model for performing a three-dimensional environment analysis, specifically, the preset analysis model at least includes:
analyzing mountainous regions, trees, buildings and multi-target photovoltaic power stations to be inspected in the first three-dimensional map, determining spatial position information of the mountainous regions, the trees, the buildings and the multi-target photovoltaic power stations to be inspected in the first three-dimensional map respectively, and marking fixed points by using four different color point values respectively, wherein the four different color point values are used for representing the mountainous regions, the trees, the buildings and the multi-target photovoltaic power stations to be inspected respectively.
In this embodiment of the present application, the grid-based method is used to perform a three-dimensional grid planning on the first three-dimensional map, and the first three-dimensional map is divided into three-dimensional grid maps composed of a plurality of unit bodies by using a hexagonal grid with a preset size as a unit body, and the method further includes: after the division of the three-dimensional grid map is completed, three-dimensional modeling and distinguishing numbering are carried out on the divided unit bodies, and the three-dimensional modeling is carried out on the divided unit bodies, which specifically comprises the following operations: establishing a three-dimensional coordinate system by taking the length, width and height of the regular hexagonal grid as coordinate units of the three-dimensional coordinate system respectively, and determining three-dimensional coordinate (x, y, z) information of each unit body in the three-dimensional coordinate system;
the distinguishing and numbering of the plurality of divided unit bodies specifically comprises the following operations: the unit bodies are numbered along the positive direction of the x axis of the three-dimensional coordinate system, I is used for representing, I is a positive integer, and the value range of I is [1, I ]]I represents the number of the unit bodies on the x axis under the condition of the same y value and the same z value; and secondly numbering the plurality of unit bodies along the positive direction of the y axis of the three-dimensional coordinate system, and using J to represent, wherein J is a positive integer, and the value range of J is [1, J]J represents the number of the unit bodies on the y axis under the condition of the same x value and the same z value; thirdly numbering the plurality of unit bodies along the positive direction of the z axis of the three-dimensional coordinate system, and expressing by using K, wherein K is a positive integer, and the value range of K is [1, K ]]K represents the number of the unit bodies on the z axis under the condition of the same x value and the same y value; after the first number, the second number and the third number are finished, using Ai,j,kAnd as the distinguishing numbers among the unit bodies, distinguishing and identifying the unit bodies.
And 203, determining the distinguishing numbers of the unit bodies respectively corresponding to the multi-target photovoltaic power stations to be inspected, and representing the photovoltaic power stations to be inspected corresponding to the distinguishing numbers one by using the distinguishing number information.
Step 204, determining the flight initial position information of the inspection flying unmanned aerial vehicle, and using A to obtain0,0,0Representing, respectively serving the multi-target photovoltaic power stations to be inspected as flight end points, constructing an artificial potential field algorithm, and carrying out autonomous air route planning on the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, wherein the inspection flying unmanned aerial vehicle is up to the flying unmanned aerial vehicleThe attractive force at the end point is:
Figure BDA0003251193090000111
patrol and examine flight unmanned aerial vehicle extremely the repulsion force when flying terminal point in-process through each barrier does:
Figure BDA0003251193090000112
then the manual resultant force of patrolling and examining the flying unmanned aerial vehicle to the flying terminal point is:
Figure BDA0003251193090000113
wherein L is the number of the obstacles, L is the number information of each obstacle, L is a positive integer, and the value range of L is [1, L]L is right it reaches to patrol and examine flight unmanned aerial vehicle each barrier that the flight terminal point in-process passed through plays difference identification effect, it is p to patrol and examine the distance of flight unmanned aerial vehicle relative each barrier when flightrlAnd the distance between the patrol flying unmanned aerial vehicle and the flying terminal point is prwThe European distance of the inspection flying unmanned aerial vehicle relative to each obstacle is | | prlThe European distance of the patrol flying unmanned aerial vehicle relative to the terminal point is prw||，k3The minimum distance between the inspection flying unmanned aerial vehicle and each obstacle can be approached, and the size of the minimum distance is determined by the shape and size of the mountainous region, trees, buildings and the multi-target photovoltaic power station to be inspected, which are analyzed by the environment; k is a radical of1A scale factor that is an attraction potential; k is a radical of2Scale factor, k, for repulsive potential bits1And k2Are all greater than 0.
In this application embodiment, based on the artificial potential field algorithm, it is right to patrol and examine flight unmanned aerial vehicle and carry out autonomic airline planning, specifically include the following operation:
determining the number L of passing obstacles from a flight starting position to a flight terminal of the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, marking the unit bodies where the passing obstacles are located respectively, and acquiring different number information of the unit bodies corresponding to the passing obstacles respectively;
selecting a planned path from a flight starting position to a flight terminal point of the inspection flying unmanned aerial vehicle as a first alternative autonomous air route of the flying unmanned aerial vehicle when the number L of the obstacles is the minimum value based on a preset screening model; if the number L of the obstacles passing through in the multiple first alternative autonomous routes is the same, acquiring the number of the obstacle-free unit bodies passing through based on the difference number information of the unit bodies respectively corresponding to the passed obstacles, and selecting a path when the number of the obstacle-free unit bodies passing through is the minimum value as the secondary alternative autonomous route of the flying unmanned aerial vehicle; and if a plurality of secondary alternative autonomous routes exist, acquiring a corresponding path when the manual potential force from the inspection flying unmanned aerial vehicle to the flying terminal point is the minimum value, and using the path as a tertiary alternative autonomous route.
In an embodiment of the present application, the obstacle includes: patrol and examine flight unmanned aerial vehicle and pass through at the flight in-process mountain region, trees, building, the multi-target photovoltaic power plant that waits to patrol and examine.
Step 205, constructing an electric quantity screening mathematical model, after the routing inspection flying unmanned aerial vehicle finishes planning the autonomous route, carrying out electric quantity suitable screening on the planned autonomous route, and using the planned autonomous route screened under the condition of suitable preset electric quantity as the final routing inspection autonomous route of the routing inspection flying machine.
In the embodiment of the application, the electric quantity screening mathematical model comprises a step of calculating a first reference time T according to a preset flight time algorithm1(ii) a Presetting a flight time algorithm formula:
Figure BDA0003251193090000131
wherein S represents the actual flying distance from the flying starting position to the flying end point of the inspection flying unmanned aerial vehicle,
Figure BDA0003251193090000132
representing the average speed, T, of the flight of the patrol flying unmanned aerial vehicle from the flight starting position to the flight end point0Indicating said inspectionTime consuming, T, of flying unmanned aerial vehicle when performing operations0≥0；
Calculating second reference time T according to power supply discharge time algorithm of preset flying unmanned aerial vehicle2(ii) a Presetting a power supply discharge time algorithm formula of the flying unmanned aerial vehicle:
Figure BDA0003251193090000133
wherein q is1Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the flying process1Representing the discharge coefficient of the power supply of the inspection flying unmanned aerial vehicle in unit time in the flying process, q2Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the operation process2The discharge coefficient of the power supply in unit time in the operation process of the inspection flying unmanned aerial vehicle is shown, and Q is Q1+q2Q represents the total electric energy storage quantity of the inspection flying unmanned aerial vehicle; comparing the calculation results of the preset flight time algorithm and the power supply discharge time algorithm of the preset flight unmanned aerial vehicle, and if T is reached2≥T1And if not, re-executing the steps 201 to 205 to plan the autonomous route of the inspection flying unmanned aerial vehicle.
In the embodiment of the application, if T2≥T1Taking the screened autonomous route as a final inspection autonomous route of the inspection aircraft, and the method comprises the following steps: screening the tertiary alternative autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening condition exist in the tertiary alternative autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft; if the autonomous routes meeting the screening conditions do not exist in the third standby autonomous routes, screening the secondary standby autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the secondary standby autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft; if the second preparation is selected fromAnd if the autonomous routes meeting the screening conditions do not exist in the main routes, screening the first alternative autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the first alternative autonomous routes, taking the screened autonomous routes as the final inspection autonomous routes of the inspection aircraft.
According to the autonomous navigation planning method for patrolling and examining the photovoltaic power station based on the flying unmanned aerial vehicle, the position of the multi-target photovoltaic power station can be obtained by constructing the three-dimensional map library and the constraint model, autonomous route planning is carried out on the cruising flying unmanned aerial vehicle, the number of barrier-free unit bodies is increased by the flying unmanned aerial vehicle through barriers, the minimum value of the manual closed field force is obtained, the flying time is constrained, the power supply is constrained, autonomous route planning is carried out on the flying unmanned aerial vehicle based on a circulating mode, and the scientificity and the applicability of the autonomous planning are guaranteed.
For convenience of understanding, the following further description and examples are provided with reference to specific application scenarios, specifically referring to fig. 3, where fig. 3 is an execution logic flow diagram of the autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying drone in the embodiment of the present application, and specifically the following steps are provided:
constructing a three-dimensional map library based on a multi-target photovoltaic power station to be inspected to obtain a first three-dimensional map; analyzing mountainous regions, trees, buildings and multi-target photovoltaic power stations to be inspected in a first three-dimensional map, respectively carrying out fixed point marking by using four different color point values, carrying out three-dimensional grid planning on the first three-dimensional map based on a grid method, carrying out three-dimensional modeling and differential numbering on a plurality of divided unit bodies, determining the differential numbering of the unit bodies respectively corresponding to the multi-target photovoltaic power stations to be inspected, and representing the photovoltaic power stations to be inspected corresponding to the multi-target photovoltaic power stations one by using differential numbering information; determining flight initial position information of the inspection flying unmanned aerial vehicle, taking multi-target photovoltaic power stations to be inspected as flight end points respectively, constructing an artificial potential field algorithm, determining the number of obstacles passing through the inspection flying unmanned aerial vehicle from the flight initial position to the flight end points, marking the unit bodies where the passing obstacles are located respectively, and acquiring different numbering information of the unit bodies corresponding to the passing obstacles respectively; based on a preset screening model, when the number L of the obstacles is selected as the minimum value, a first alternative autonomous route is obtained; if the number L of the obstacles in the plurality of first alternative autonomous routes is the same, acquiring a secondary alternative autonomous route; if there are many secondary autonomic air routes of selecting oneself, obtain the route that corresponds when patrolling and examining unmanned aerial vehicle to the artifical momentum of flight terminal point is the minimum, as cubic autonomic air route of selecting oneself, establish electric quantity screening mathematical model, after patrolling and examining unmanned aerial vehicle and carrying out autonomic air route planning and accomplishing, carry out the suitable screening of electric quantity to the autonomic air route of planning, the autonomic air route of planning of the screening under the suitable condition of predetermined electric quantity is as patrolling and examining the autonomic air route of final patrol and examining of patrolling and examining the aircraft, wherein, the screening step includes:
firstly, screening the three candidate autonomous routes, and if the autonomous routes meeting the screening condition exist in the three candidate autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft; if the autonomous routes meeting the screening conditions do not exist in the third standby autonomous routes, screening the secondary standby autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the secondary standby autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft; and if the autonomous routes meeting the screening conditions do not exist in the secondary alternative autonomous routes, screening the primary alternative autonomous routes based on the electric quantity screening mathematical model, if the autonomous routes meeting the screening conditions exist in the primary alternative autonomous routes, taking the screened autonomous routes as the final inspection autonomous routes of the inspection flying machine, otherwise, re-executing the autonomous route planning method of the inspection flying unmanned aerial vehicle.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 4, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an autonomous navigation planning apparatus for routing inspection of a photovoltaic power station based on a flying drone, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 4, the autonomous navigation planning apparatus 4 for routing inspection of a photovoltaic power station based on a flying drone in this embodiment includes: the system comprises a three-dimensional map building module 401, a photovoltaic power station position determining module 402, an autonomous route simulation planning module 403 and a constraint model building module 404. Wherein:
the three-dimensional map building module 401 is used for building a three-dimensional map library based on a multi-target photovoltaic power station to be inspected, and acquiring a three-dimensional environment global map from the three-dimensional map library as a first three-dimensional map; sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, performing three-dimensional grid planning on the first three-dimensional map based on a grid method, and dividing the first three-dimensional map into a three-dimensional grid map consisting of a plurality of unit bodies by taking a regular hexagonal grid with a preset size as a unit body;
a photovoltaic power station position determining module 402, configured to determine difference numbers of unit bodies respectively corresponding to the multiple target photovoltaic power stations to be inspected, and use the difference number information to characterize the photovoltaic power stations to be inspected, which correspond to the difference numbers one by one;
an autonomous flight path simulation planning module 403, configured to determine flight start position information of the inspection flying unmanned aerial vehicle, and set a as a0,0,0Expressing, respectively taking the multi-target photovoltaic power stations to be inspected as flight end points, constructing an artificial potential field algorithm, and carrying out autonomous air route planning on the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, wherein the gravitational potential force from the inspection flying unmanned aerial vehicle to the flight end points is as follows:
Figure BDA0003251193090000171
patrol and examine flight unmanned aerial vehicle extremely the repulsion force when flying terminal point in-process through each barrier does:
Figure BDA0003251193090000172
then the manual resultant force of patrolling and examining the flying unmanned aerial vehicle to the flying terminal point is:
Figure BDA0003251193090000173
wherein L is the number of the obstacles, L is the number information of each obstacle, L is a positive integer, and the value range of L is [1, L]L is right it reaches to patrol and examine flight unmanned aerial vehicle each barrier that the flight terminal point in-process passed through plays difference identification effect, it is p to patrol and examine the distance of flight unmanned aerial vehicle relative each barrier when flightrlAnd the distance between the patrol flying unmanned aerial vehicle and the flying terminal point is prwThe European distance of the inspection flying unmanned aerial vehicle relative to each obstacle is | | prlThe European distance of the patrol flying unmanned aerial vehicle relative to the terminal point is prw||，k3Do patrol and examine flight unmanned aerial vehicle and instituteEach barrier can approach to a minimum distance, and the size of each barrier is determined by the shape and size of the mountains, trees, buildings and the multi-target photovoltaic power station to be inspected, which are analyzed by the environment; k is a radical of1A scale factor that is an attraction potential; k is a radical of2Scale factor, k, for repulsive potential bits1And k2Are all larger than 0;
and the constraint model building module 404 is used for building an electric quantity screening mathematical model, after the routing inspection unmanned aerial vehicle performs autonomous route planning, performing electric quantity suitable screening on the planned autonomous route, and screening the planned autonomous route as a final routing inspection autonomous route of the routing inspection unmanned aerial vehicle under the condition of the suitable preset electric quantity.
Referring to fig. 5 in detail, fig. 5 is a schematic structural diagram of the constraint model building module in the embodiment of the present application, where the constraint model building module 404 includes: a time-of-flight constraint unit 404a and a discharge time constraint unit 404b, wherein:
in this embodiment of the application, the time-of-flight constraint unit 404a in the constraint model building module presets a time-of-flight algorithm formula:
Figure BDA0003251193090000174
s represents the actual flying distance from the flying starting position to the flying end point of the patrol flying unmanned aerial vehicle, v represents the average flying speed when the patrol flying unmanned aerial vehicle flies from the flying starting position to the flying end point, and T represents the flying speed0Indicating the elapsed time, T, of the patrol flying unmanned aerial vehicle during operation0≥0。
In this embodiment of the application, the discharge time constraint unit 404b in the constraint model building module presets a power discharge time algorithm formula of the unmanned aerial vehicle:
Figure BDA0003251193090000181
wherein q is1Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the flying process1Representing the discharge coefficient of the power supply of the inspection flying unmanned aerial vehicle in unit time in the flying process, q2Presentation instrumentTotal discharge amount xi of power supply of inspection flying unmanned aerial vehicle in operation process2The discharge coefficient of the power supply in unit time in the operation process of the inspection flying unmanned aerial vehicle is shown, and Q is Q1+q2And Q represents the total electric energy storage amount of the patrol flying unmanned aerial vehicle.
The embodiment of the application autonomous navigation planning device based on flying unmanned aerial vehicle patrols and examines photovoltaic power plant, through establishing three-dimensional map storehouse and restraint model, acquire multi-target photovoltaic power plant's position, carry out autonomic course planning to cruising flying unmanned aerial vehicle, through the quantity of flying unmanned aerial vehicle through the barrier, the quantity of barrier-free unit body, artifical resultant field force minimum, flight time restraint and power supply restraint, carry out autonomic course planning for flying unmanned aerial vehicle based on the mode of circulation, autonomic planning's scientificity and suitability have been guaranteed.
To solve the above technical problem, an embodiment of the present application further provides a computer device, and specifically refer to fig. 6, where fig. 6 is a block diagram of a basic structure of the computer device according to the embodiment.
The computer device 6 comprises a memory 6a, a processor 6b, a network interface 6c communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 6a-6c is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 6a includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 6a may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 6a may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 6a may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 6a is generally used to store an operating system and various application software installed in the computer device 6, for example, a program code of an autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying drone. The memory 6a may also be used to temporarily store various types of data that have been output or are to be output.
The processor 6b may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data Processing chip in some embodiments. The processor 6b is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 6b is configured to run the program code stored in the memory 6a or process data, for example, run the program code of the autonomous navigation planning method for routing inspection of the photovoltaic power station based on the flying drone.
The network interface 6c may comprise a wireless network interface or a wired network interface, and the network interface 6c is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The present application further provides another embodiment, which is to provide a non-volatile computer-readable storage medium storing an autonomous navigation planning program for polling a photovoltaic power station based on a flying drone, where the autonomous navigation planning program for polling the photovoltaic power station based on the flying drone is executable by at least one processor, so that the at least one processor performs the steps of the autonomous navigation planning method for polling the photovoltaic power station based on the flying drone.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims (10)

1. An autonomous navigation planning method for routing inspection of a photovoltaic power station based on a flying unmanned aerial vehicle is characterized by comprising the following steps:
constructing a three-dimensional map library based on a multi-target photovoltaic power station to be inspected, and acquiring a three-dimensional environment global map from the three-dimensional map library to serve as a first three-dimensional map;
sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, performing three-dimensional grid planning on the first three-dimensional map based on a grid method, and dividing the first three-dimensional map into a three-dimensional grid map consisting of a plurality of unit bodies by taking a regular hexagonal grid with a preset size as a unit body;
determining the difference numbers of the unit bodies respectively corresponding to the multi-target photovoltaic power stations to be inspected, and using the difference number information to represent the photovoltaic power stations to be inspected corresponding to the multi-target photovoltaic power stations one by one;
determining the flight initial position information of the inspection flight unmanned aerial vehicle, and using A0,0,0Expressing, respectively taking the multi-target photovoltaic power stations to be inspected as flight end points, constructing an artificial potential field algorithm, and carrying out autonomous air route planning on the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, wherein the gravitational potential force from the inspection flying unmanned aerial vehicle to the flight end points is as follows:
Figure FDA0003251193080000011
patrol and examine flight unmanned aerial vehicle extremely the repulsion force when flying terminal point in-process through each barrier does:
Figure FDA0003251193080000012
then the manual resultant force of patrolling and examining the flying unmanned aerial vehicle to the flying terminal point is:
Figure FDA0003251193080000013
wherein L is the number of the obstacles, L is the number information of each obstacle, L is a positive integer, and the value range of L is [1, L]L right to the inspection flying unmanned aerial vehicle to the flying terminalEach barrier that the in-process passes through plays difference identification effect, it is p to patrol and examine the distance of flying unmanned aerial vehicle relative each barrier when flightrlAnd the distance between the patrol flying unmanned aerial vehicle and the flying terminal point is prwThe European distance of the patrol flying unmanned aerial vehicle relative to each obstacle is | luminanceprlThe European distance of the patrol flying unmanned aerial vehicle relative to the terminal point is prw||，k3Providing a minimum distance between the inspection flying unmanned aerial vehicle and each obstacle; k is a radical of1A scale factor that is an attraction potential; k is a radical of2Scale factor, k, for repulsive potential bits1And k2Are all larger than 0;
and constructing an electric quantity screening mathematical model, after the routing inspection flying unmanned aerial vehicle carries out autonomous route planning, carrying out electric quantity suitable screening on the planned autonomous route, and screening out the planned autonomous route as the final routing inspection autonomous route of the routing inspection flying unmanned aerial vehicle under the condition of preset electric quantity suitable.
2. The autonomous navigation planning method for routing inspection of photovoltaic power plants based on flying drones according to claim 1, characterized in that said first relief map is sent to a preset analysis model for the analysis of the stereoscopic environment, said preset analysis model comprising at least:
analyzing mountainous regions, trees, buildings and multi-target photovoltaic power stations to be inspected in the first three-dimensional map, determining spatial position information of the mountainous regions, the trees, the buildings and the multi-target photovoltaic power stations to be inspected in the first three-dimensional map respectively, and marking fixed points by using four different color point values respectively, wherein the four different color point values are used for representing the mountainous regions, the trees, the buildings and the multi-target photovoltaic power stations to be inspected respectively.
3. The autonomous navigation planning method for routing inspection of photovoltaic power plants based on flying drones according to claim 2, characterized in that the stereoscopic grid planning is performed on the first stereoscopic map based on a grid method, and the first stereoscopic map is divided into stereoscopic grid maps composed of a plurality of unit bodies by using orthohexagonal grids of a preset size as unit bodies, further comprising:
after the three-dimensional grid map is divided, three-dimensional modeling and distinguishing numbering are carried out on a plurality of divided unit bodies;
the three-dimensional modeling of the divided unit bodies specifically comprises the following operations:
establishing a three-dimensional coordinate system by taking the length, width and height of the regular hexagonal grid as coordinate units of the three-dimensional coordinate system respectively, and determining three-dimensional coordinate (x, y, z) information of each unit body in the three-dimensional coordinate system;
the distinguishing and numbering of the plurality of divided unit bodies specifically comprises the following operations:
the unit bodies are numbered along the positive direction of the x axis of the three-dimensional coordinate system, I is used for representing, I is a positive integer, the value range of I is [1, I ], and I represents the number of the unit bodies on the x axis under the condition of the same y value and the same z value;
secondly numbering the plurality of unit bodies along the positive direction of the y axis of the three-dimensional coordinate system, and expressing by using J, wherein J is a positive integer, the value range of J is [1, J ], and J expresses the number of the unit bodies on the y axis under the condition of the same x value and the same z value;
thirdly numbering the plurality of unit bodies along the positive direction of the z axis of the three-dimensional coordinate system, and expressing by using K, wherein K is a positive integer, the value range of K is [1, K ], and K expresses the number of the unit bodies on the z axis under the condition of the same x value and the same y value;
after the first number, the second number and the third number are finished, using Ai,j,kAnd as the distinguishing numbers among the unit bodies, distinguishing and identifying the unit bodies.
4. The autonomous navigation planning method for the routing inspection of photovoltaic power stations based on flying unmanned aerial vehicles according to claim 3, characterized in that the autonomous route planning for the flying unmanned aerial vehicles for routing inspection based on the artificial potential field algorithm specifically comprises the following operations:
determining the number L of passing obstacles from a flight starting position to a flight terminal of the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, marking the unit bodies where the passing obstacles are located respectively, and acquiring different number information of the unit bodies corresponding to the passing obstacles respectively;
based on a preset screening model, when the number L of the obstacles is selected as the minimum value, determining a planned path from a flight starting position to a flight terminal point of the inspection flying unmanned aerial vehicle, and taking the planned path as a first alternative autonomous air route of the flying unmanned aerial vehicle;
if the number L of the obstacles passing through in the multiple first alternative autonomous routes is the same, acquiring the number of the obstacle-free unit bodies passing through based on the difference number information of the unit bodies respectively corresponding to the passed obstacles, and selecting a path when the number of the obstacle-free unit bodies passing through is the minimum value as the secondary alternative autonomous route of the flying unmanned aerial vehicle;
and if a plurality of secondary alternative autonomous routes exist, acquiring a corresponding path when the manual potential force from the inspection flying unmanned aerial vehicle to the flying terminal point is the minimum value, and using the path as a tertiary alternative autonomous route.
5. The autonomous navigation planning method for the routing inspection of photovoltaic power plants based on flying drones according to any of claims 1 to 4, characterized in that said obstacles comprise:
patrol and examine flight unmanned aerial vehicle and pass through at the flight in-process mountain region, trees, building, the multi-target photovoltaic power plant that waits to patrol and examine.
6. The autonomous navigation planning method for patrolling photovoltaic power plants based on flying unmanned aerial vehicle of claim 5, characterized in that the electric quantity screening mathematical model comprises:
calculating a first reference time T according to a preset time-of-flight algorithm1(ii) a Preset time of flight calculationFormula of method:
Figure FDA0003251193080000041
wherein S represents the actual flying distance from the flying starting position to the flying end point of the inspection flying unmanned aerial vehicle,
Figure FDA0003251193080000042
representing the average speed, T, of the flight of the patrol flying unmanned aerial vehicle from the flight starting position to the flight end point0Indicating the elapsed time, T, of the patrol flying unmanned aerial vehicle during operation0≥0；
Calculating second reference time T according to power supply discharge time algorithm of preset flying unmanned aerial vehicle2(ii) a Presetting a power supply discharge time algorithm formula of the flying unmanned aerial vehicle:
Figure FDA0003251193080000043
wherein q is1Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the flying process1Representing the discharge coefficient of the power supply of the inspection flying unmanned aerial vehicle in unit time in the flying process, q2Represents the total discharge quantity xi of the power supply of the inspection flying unmanned aerial vehicle in the operation process2The discharge coefficient of the power supply in unit time in the operation process of the inspection flying unmanned aerial vehicle is shown, and Q is Q1+q2Q represents the total electric energy storage quantity of the inspection flying unmanned aerial vehicle;
comparing the calculation results of the preset flight time algorithm and the power supply discharge time algorithm of the preset flight unmanned aerial vehicle, and if T is reached2≥T1And taking the screened autonomous route as a final inspection autonomous route of the inspection aircraft.
7. The autonomous navigation planning method for the inspection of photovoltaic plants based on flying drones according to claim 6, characterized in that said number T is2≥T1Taking the screened autonomous route as a final inspection autonomous route of the inspection aircraft, comprising the steps ofThe following:
screening the tertiary alternative autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening condition exist in the tertiary alternative autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft;
if the autonomous routes meeting the screening conditions do not exist in the third standby autonomous routes, screening the secondary standby autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the secondary standby autonomous routes, taking the screened autonomous routes as final inspection autonomous routes of the inspection aircraft;
and if the autonomous routes meeting the screening conditions do not exist in the secondary alternative autonomous routes, screening the primary alternative autonomous routes based on the electric quantity screening mathematical model, and if the autonomous routes meeting the screening conditions exist in the primary alternative autonomous routes, taking the screened autonomous routes as the final inspection autonomous routes of the inspection aircraft.
8. The utility model provides an independently navigate planning device based on unmanned aerial vehicle that flies patrols and examines photovoltaic power plant which characterized in that includes:
the three-dimensional map building module is used for building a three-dimensional map library based on the multi-target photovoltaic power station to be inspected, and acquiring a three-dimensional environment global map from the three-dimensional map library to serve as a first three-dimensional map; sending the first three-dimensional map to a preset analysis model for three-dimensional environment analysis, performing three-dimensional grid planning on the first three-dimensional map based on a grid method, and dividing the first three-dimensional map into a three-dimensional grid map consisting of a plurality of unit bodies by taking a regular hexagonal grid with a preset size as a unit body;
the photovoltaic power station position determining module is used for determining the distinguishing numbers of the unit bodies respectively corresponding to the multi-target photovoltaic power stations to be inspected, and using the distinguishing number information to represent the photovoltaic power stations to be inspected corresponding to the distinguishing numbers one by one;
an autonomous air route simulation planning module for determining the flight initial position information of the inspection flying unmanned aerial vehicle and using A0,0,0Expressing, respectively taking the multi-target photovoltaic power stations to be inspected as flight end points, constructing an artificial potential field algorithm, and carrying out autonomous air route planning on the inspection flying unmanned aerial vehicle based on the artificial potential field algorithm, wherein the gravitational potential force from the inspection flying unmanned aerial vehicle to the flight end points is as follows:
Figure FDA0003251193080000061
patrol and examine flight unmanned aerial vehicle extremely the repulsion force when flying terminal point in-process through each barrier does:
Figure FDA0003251193080000062
then the manual resultant force of patrolling and examining the flying unmanned aerial vehicle to the flying terminal point is:
Figure FDA0003251193080000063
wherein L is the number of the obstacles, L is the number information of each obstacle, L is a positive integer, and the value range of L is [1, L]L is right it reaches to patrol and examine flight unmanned aerial vehicle each barrier that the flight terminal point in-process passed through plays difference identification effect, it is p to patrol and examine the distance of flight unmanned aerial vehicle relative each barrier when flightrlAnd the distance between the patrol flying unmanned aerial vehicle and the flying terminal point is prwThe European distance of the inspection flying unmanned aerial vehicle relative to each obstacle is | | prlThe European distance of the patrol flying unmanned aerial vehicle relative to the terminal point is prw||，k3The minimum distance between the inspection flying unmanned aerial vehicle and each obstacle can be approached, and the size of the minimum distance is determined by the shape and size of the mountainous region, trees, buildings and the multi-target photovoltaic power station to be inspected, which are analyzed by the environment; k is a radical of1A scale factor that is an attraction potential; k is a radical of2Scale factor, k, for repulsive potential bits1And k2Are all larger than 0;
and the constraint model building module is used for building an electric quantity screening mathematical model, after the routing inspection flying unmanned aerial vehicle carries out autonomous route planning, the planned autonomous route is subjected to electric quantity suitable screening, and the planned autonomous route is screened out under the condition of proper preset electric quantity to serve as the final routing inspection autonomous route of the routing inspection flying machine.
9. A computer device comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the autonomous navigation planning method for the inspection of photovoltaic power plants based on flying drones according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the steps of the method for autonomous navigation planning for the inspection of photovoltaic power plants based on flying drones according to any of claims 1 to 7.
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