Route planning method, system, terminal equipment and medium

Abstract

The present invention discloses a route planning method, system, terminal device and computer-readable storage medium, the route planning method comprising: determining an aerial photography area; obtaining the aerial photography process parameters and battery parameters of the drone, the aerial photography process parameters at least including the drone flight parameters; determining the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters; constructing a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone, the drone aerial photography model at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters; and performing route planning based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model to obtain a flight route based on the principle of maximizing battery utilization. The present invention can realize efficient drone route planning based on the principle of maximizing battery utilization.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle technology, and in particular to a route planning method, system, terminal equipment and computer-readable storage medium. Background Art

[0002] With the rapid development of drone technology, drones are gradually being widely used in the field of surveying. When using drones for surveying, such as terrain surveying, how to design the flight path of the drone is a question that has attracted much attention in the industry. Currently, most drones on the market are equipped with corresponding aerial photography software, which generally supports automatic path planning.

[0003] However, when using a battery-powered drone for aerial photography, the battery life is often insufficient. In the related technologies, the return path planning provided by the aerial photography software is Figure 1 As shown, after the user inputs the aerial photography area and flight parameters, the UAV flight route will be automatically generated. The advantage of this route generation method is that the logic is simple. The UAV can perform the flight mission according to the route generated for the first time. If the battery is detected to be low during the flight and the remaining power reaches the preset threshold, it will automatically return to the base, wait for the battery to be replaced, and fly back to the previous position to continue the mission. The disadvantages are too many invalid flights and low battery utilization.

[0004] In general, in existing drone aerial photography technologies, the ineffective flight paths of drones are too long and the drone battery utilization efficiency is inefficient. Summary of the invention

[0005] The main purpose of the present invention is to provide a route planning method, system, terminal device and computer-readable storage medium, aiming to achieve efficient UAV flight route planning based on the principle of maximizing battery utilization.

[0006] To achieve the above object, the present invention provides a route planning method, wherein the route planning comprises:

[0007] Determine the aerial photography area;

[0008] Acquire the aerial photography process parameters and battery parameters of the UAV, wherein the aerial photography process parameters at least include the flight parameters of the UAV;

[0009] Determine the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters;

[0010] Building a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters;

[0011] According to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, a flight route is obtained by performing route planning based on the principle of maximizing battery utilization.

[0012] Optionally, after the step of performing route planning based on the battery utilization maximization principle to obtain a flight route, the method further includes:

[0013] When performing the aerial photography mission according to the flight route, detecting the battery parameters in real time, and determining whether to adjust the flight route based on the detected battery parameters;

[0014] If so, based on the detected battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, the route planning is re-performed based on the principle of maximizing battery utilization to adjust the flight route.

[0015] Optionally, the step of determining the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters includes:

[0016] Determine the flight path height and flight path spacing according to the aerial photography process parameters;

[0017] An aerial photography grid including the aerial photography area is generated based on the aerial photography area, the route height, and the route spacing, and the aerial photography point and the aerial photography point parameters are determined based on the aerial photography grid.

[0018] Optionally, the historical battery parameters include historical battery power consumption, and the step of constructing the drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone includes:

[0019] Obtaining the historical aerial photography process parameters and the historical battery power consumption of the drone;

[0020] Based on the acquired historical aerial photography process parameters and the historical battery power consumption, the UAV aerial photography model is constructed using a machine learning algorithm, and the UAV aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery power consumption.

[0021] Optionally, the step of performing route planning to obtain a flight route based on the principle of maximizing battery utilization according to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model includes:

[0022] Inputting the aerial photography process parameters into the drone aerial photography model to obtain an estimated battery power consumption;

[0023] Based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the estimated battery power consumption, the flight route is obtained by optimizing using a heuristic algorithm with the goal of maximizing battery utilization.

[0024] Optionally, the step of detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters includes:

[0025] When performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0026] If the detected current remaining battery power reaches a preset remaining power threshold, it is determined whether to adjust the flight route based on whether the increment of the current remaining battery power relative to the pre-planned remaining power reaches the preset battery increment threshold.

[0027] Optionally, the step of detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters includes:

[0028] When performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0029] Determine whether the aerial photography mission can be completed based on the detected current remaining battery power, and determine whether to adjust the flight route based on the determination result.

[0030] Optionally, the step of detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters includes:

[0031] When performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0032] The method further comprises determining an increment of the current remaining cruising range of the drone relative to the pre-planned remaining cruising range based on the detected current remaining battery power, and determining whether to adjust the flight route based on whether the increment reaches a preset mileage increment threshold.

[0033] To achieve the above object, the present invention further provides a route planning system, the route planning system comprising:

[0034] The first determination module is used to determine the aerial photography area;

[0035] An acquisition module, used to acquire the aerial photography process parameters and battery parameters of the drone, wherein the aerial photography process parameters at least include the flight parameters of the drone;

[0036] A second determination module is used to determine the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters;

[0037] A construction module, used to construct a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters;

[0038] The route planning module is used to plan the flight route based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model and based on the principle of maximizing battery utilization.

[0039] Among them, each functional module of the route planning system of the present invention implements the steps of the route planning method described above when running.

[0040] To achieve the above objectives, the present invention also provides a terminal device, which includes: a memory, a processor, and a route planning program stored in the memory and executable on the processor, wherein the route planning program implements the steps of the route planning method described above when executed by the processor.

[0041] In addition, to achieve the above-mentioned purpose, the present invention also proposes a computer-readable storage medium, on which a route planning program is stored, and when the route planning program is executed by a processor, the steps of the route planning method described above are implemented.

[0042] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the route planning method described above are implemented.

[0043] The present invention provides a route planning, system, terminal equipment, computer-readable storage medium and computer program product, which determine an aerial photography area; obtain aerial photography process parameters and battery parameters of the unmanned aerial vehicle, wherein the aerial photography process parameters at least include the flight parameters of the unmanned aerial vehicle; determine the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters; construct an unmanned aerial vehicle aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the unmanned aerial vehicle, wherein the unmanned aerial vehicle aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters; and according to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the unmanned aerial vehicle aerial photography model, perform route planning based on the principle of maximizing battery utilization to obtain a flight route.

[0044] Compared with the return path planning method of the UAV in the prior art, in the present invention, the UAV route planning is performed based on the principle of maximizing battery utilization in combination with battery parameters, aerial photography process parameters, aerial photography point parameters and UAV aerial photography model to obtain a flight route, and aerial photography is performed according to the flight route. Therefore, in the scenario where the UAV battery needs to be replaced, the present invention can significantly improve the effective battery utilization rate, increase the effective flight time of the UAV, and thus improve the execution efficiency of the aerial photography task under limited battery capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of automatic path planning by shooting software involved in the background technology of the present invention;

[0046] Figure 2 A schematic diagram of the structure of the hardware operating environment involved in the embodiment of the present invention;

[0047] Figure 3 A first flow chart of an embodiment of a route planning method of the present invention;

[0048] Figure 4 A second flow chart of an embodiment of a route planning method of the present invention;

[0049] Figure 5 A schematic diagram of aerial photography points in an aerial photography area according to an embodiment of a route planning method of the present invention;

[0050] Figure 6-1 A first schematic diagram of an actual aerial photography path before improvement of an embodiment of a route planning method of the present invention;

[0051] Figure 6-2 A first schematic diagram of an improved aerial photography path according to an embodiment of a route planning method of the present invention;

[0052] Figure 7-1 A second schematic diagram of an actual aerial photography path before improvement of an embodiment of a route planning method of the present invention;

[0053] Figure 7-2 A second schematic diagram of an improved aerial photography path according to an embodiment of a route planning method of the present invention;

[0054] Figure 8-1 A third schematic diagram of an actual aerial photography path before improvement of an embodiment of a route planning method of the present invention;

[0055] Figure 8-2 A third schematic diagram of an improved aerial photography path according to an embodiment of a route planning method of the present invention;

[0056] Figure 9-1 A third schematic diagram of an actual aerial photography path before improvement of an embodiment of a route planning method of the present invention;

[0057] Figure 9-2 A third schematic diagram of an improved aerial photography path according to an embodiment of a route planning method of the present invention;

[0058] Figure 9-3 A fourth schematic diagram of an improved aerial photography path according to an embodiment of a route planning method of the present invention;

[0059] Fig.10 Schematic diagram of functional modules of an embodiment of a route planning system of the present invention.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0062] like Figure 2 As shown, Figure 2 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0063] It should be noted that the terminal device of the embodiment of the present invention may be a terminal device for extracting data for implementing UAV route planning, and the terminal device may specifically be a UAV, etc.

[0064] like Figure 2 As shown, the device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or it may be a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0065] Those skilled in the art will understand that Figure 2 The device structure shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0066] like Figure 2As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a route planning program. The operating system is a program that manages and controls the hardware and software resources of the device and supports the operation of the route planning program and other software or programs. Figure 2 In the device shown, the user interface 1003 is mainly used for data communication with the client; the network interface 1004 is mainly used for establishing a communication connection with the server; and the processor 1001 can be used to call the route planning program stored in the memory 1005 and perform the following operations:

[0067] Determine the aerial photography area;

[0068] Acquire the aerial photography process parameters and battery parameters of the UAV, wherein the aerial photography process parameters at least include the flight parameters of the UAV;

[0069] Determine the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters;

[0070] Building a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters;

[0071] According to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, a flight route is obtained by performing route planning based on the principle of maximizing battery utilization.

[0072] Further, after the step of performing route planning based on the principle of maximizing battery utilization to obtain a flight route, the processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0073] When performing the aerial photography mission according to the flight route, detecting the battery parameters in real time, and determining whether to adjust the flight route based on the detected battery parameters;

[0074] If so, based on the detected battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, the route planning is re-performed based on the principle of maximizing battery utilization to adjust the flight route.

[0075] Furthermore, the processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0076] Determine the flight path height and flight path spacing according to the aerial photography process parameters;

[0077] An aerial photography grid including the aerial photography area is generated based on the aerial photography area, the route height, and the route spacing, and the aerial photography point and the aerial photography point parameters are determined based on the aerial photography grid.

[0078] Furthermore, the historical battery parameters include historical battery power consumption. The processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0079] Obtaining the historical aerial photography process parameters and the historical battery power consumption of the drone;

[0080] Based on the acquired historical aerial photography process parameters and the historical battery power consumption, the UAV aerial photography model is constructed using a machine learning algorithm, and the UAV aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery power consumption.

[0081] Furthermore, the processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0082] Inputting the aerial photography process parameters into the drone aerial photography model to obtain an estimated battery power consumption;

[0083] Based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the estimated battery power consumption, the flight route is obtained by optimizing using a heuristic algorithm with the goal of maximizing battery utilization.

[0084] Furthermore, the processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0085] When performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0086] If the detected current remaining battery power reaches a preset remaining power threshold, it is determined whether to adjust the flight route based on whether the increment of the current remaining battery power relative to the pre-planned remaining power reaches the preset battery increment threshold.

[0087] Furthermore, the processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0088] When performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0089] Determine whether the aerial photography mission can be completed based on the detected current remaining battery power, and determine whether to adjust the flight route based on the determination result.

[0090] Furthermore, the processor 1001 may also be used to call a route planning program stored in the memory 1005, and perform the following operations:

[0091] When performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0092] The method further comprises determining an increment of the current remaining cruising range of the drone relative to the pre-planned remaining cruising range based on the detected current remaining battery power, and determining whether to adjust the flight route based on whether the increment reaches a preset mileage increment threshold.

[0093] Reference Figure 3 , Figure 3 Schematic diagram of the flow chart of the first embodiment of the route planning method of the present invention.

[0094] In this embodiment, an embodiment of a route planning method is provided. It should be noted that although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0095] In order to solve the problem of low effective flight time and short battery effective aerial photography time when the drone performs aerial photography according to the automatically planned route, the present embodiment reconstructs the flight route based on the aerial photography point and combines battery parameters (such as battery power) and aerial photography process parameters (such as meteorological data, drone flight parameters, etc.) to optimize the flight route. Compared with the existing return path planning function, the present invention can increase the effective flight time of the drone in the scenario where the battery needs to be replaced, that is, maximize the battery utilization efficiency. Figure 4 As shown, the route planning method in this embodiment may include: inputting the aerial photography process parameters into the background, and the background performing route planning based on the aerial photography process parameters, taking into account the influence of battery and weather conditions during the flight of the UAV, it is necessary to adjust the flight route during the aerial photography process to maximize the effective flight time, and finally name and organize the aerial photography data after the aerial photography mission is completed.

[0096] The route planning method in this embodiment includes the following steps:

[0097] Step S10, determining the aerial photography area;

[0098] Step S20, obtaining the aerial photography process parameters and battery parameters of the drone, wherein the aerial photography process parameters at least include the flight parameters of the drone;

[0099] It should be noted that, in this embodiment, the user may input the drone's aerial photography area, aerial photography process parameters, battery parameters, etc. in advance before using the drone to perform an aerial photography task.

[0100] Furthermore, the terminal device can determine the aerial photography area of ​​the drone, and determine the aerial photography process parameters and battery parameters of the drone. The aerial photography process parameters of the drone may include both the flight parameters of the drone and the take-off and landing points of the drone and the lens parameters of the lens carried by the drone.

[0101] Step S30, determining an aerial photography point and aerial photography point parameters based on the aerial photography area and the aerial photography process parameters;

[0102] After determining the drone's aerial photography area and the aerial photography process parameters including the drone's flight parameters, the terminal device will further determine the aerial photography points within the aerial photography area and the aerial photography point parameters corresponding to each aerial photography point, so as to plan the drone's aerial photography route in combination with the aerial photography point parameters.

[0103] Step S40, constructing a drone aerial photography model based on the historical aerial photography process parameters and the historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters;

[0104] In order to improve the aerial photography efficiency of drones under limited battery capacity, it is possible to consider building a drone aerial photography model based on the drone's historical aerial photography process parameters and historical battery parameters. It is worth noting that since the mapping relationship between the drone's historical aerial photography process parameters and historical battery parameters is constructed in the drone aerial photography model, the drone aerial photography model can be used to determine the battery parameters corresponding to different aerial photography process parameters, and then determine the corresponding aerial photography route.

[0105] Step S50, according to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, a flight route is planned based on the principle of maximizing battery utilization to obtain a flight route.

[0106] After obtaining the drone's battery parameters, aerial photography process parameters, aerial photography point parameters and drone aerial photography model, the terminal device will plan the drone's aerial photography route based on the principle of maximizing battery utilization and according to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model.

[0107] It should be noted that, in this embodiment, the principle of maximizing battery utilization is: the drone completes as many aerial photography tasks as possible under limited battery capacity, that is, the effective flight time of the drone is maximized under limited battery capacity.

[0108] In addition, after the drone completes the aerial photography mission and successfully lands at the preset landing point, in order to facilitate the management of the aerial photography data collected by the drone during the aerial photography process, the aerial photography data can be named according to the aerial photography point parameters of each aerial photography point to facilitate the management of aerial photography data and regional data block processing.

[0109] In this embodiment, the terminal device can determine the aerial photography area of ​​the drone, and determine the aerial photography process parameters and battery parameters of the drone. After determining the aerial photography area of ​​the drone and the aerial photography process parameters including the flight parameters of the drone, the terminal device will further determine the aerial photography points within the aerial photography area and the aerial photography point parameters corresponding to each aerial photography point. In order to improve the aerial photography efficiency of the drone under limited battery capacity, the terminal device will construct a drone aerial photography model based on the drone's historical aerial photography process parameters and historical battery parameters. Furthermore, the terminal device will plan the drone's aerial photography route based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model based on the principle of maximizing battery utilization.

[0110] Compared with the return path planning method of the UAV in the prior art, in the present invention, the flight route is planned based on the principle of maximizing battery utilization according to the battery parameters, aerial photography process parameters, aerial photography point parameters, and the UAV aerial photography model including the mapping relationship between historical aerial photography process parameters and historical battery parameters, aiming to maximize the effective flight of the UAV, that is, maximize the battery utilization efficiency. Therefore, in the scenario where the UAV battery needs to be replaced, the present invention can significantly improve the effective battery utilization rate, increase the effective flight time of the UAV, and thus improve the execution efficiency of the aerial photography task under limited battery capacity.

[0111] Based on the first embodiment of the route planning method of the present invention, a second embodiment of the route planning method of the present invention is proposed.

[0112] In this embodiment, after the above step S50 of "planning a flight route based on the principle of maximizing battery utilization to obtain a flight route", the following steps may also be included:

[0113] Step S60, detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters;

[0114] Step S70, if yes, then based on the detected battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, the route is re-planned based on the principle of maximizing battery utilization to adjust the flight route.

[0115] It should be noted that in this embodiment, when the UAV is performing an aerial photography mission, the weather conditions may cause the UAV to be insufficient in power or the remaining battery power may exceed the expected estimated remaining power. At this time, the flight route needs to be optimized again to maximize the use of battery power, increase the effective flight time of the UAV, and improve the aerial photography efficiency.

[0116] Specifically, for example, when a drone is performing an aerial photography mission according to a flight route, the drone's battery parameters are detected in real time, where the battery parameters may include: the current remaining battery power. If it is detected that the current remaining battery power of the drone is insufficient to support the completion of the aerial photography mission or the increase in the current remaining battery power relative to the expected remaining battery power reaches a certain threshold, the drone will adjust and optimize the flight route again based on the current remaining battery power, aerial photography process parameters, aerial photography point parameters, and the drone aerial photography model, so as to maximize the use of the remaining battery power and increase the effective flight time of the drone.

[0117] Furthermore, in the above step S30, “determining the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters” may include:

[0118] Step S301, determining the route height and route spacing according to the aerial photography process parameters;

[0119] Step S302, generating an aerial photography grid including the aerial photography area based on the aerial photography area, the route altitude, and the route spacing, and determining the aerial photography point and the aerial photography point parameters based on the aerial photography grid;

[0120] It should be noted that, in this embodiment, the UAV flight parameters in the aerial photography process parameters include the heading overlap rate, the lateral overlap rate, and the flight altitude. In addition to the UAV flight parameters, the aerial photography process parameters also include the lens parameters and lens shooting angle of the UAV-mounted lens.

[0121] Therefore, the terminal device can determine the altitude of the drone's route and the distance between routes in the front, back, left and right directions according to the drone's heading overlap rate, lateral overlap rate, flight altitude, lens parameters, lens shooting angle and other parameters. Furthermore, the aerial photography grid including the aerial photography area can be determined based on the altitude of the route, the distance between the routes and the predetermined aerial photography area. On this basis, the center point of each grid or each intersection between grid lines can be considered as an aerial photography point, and the aerial photography point parameters corresponding to each aerial photography point can be determined, wherein the aerial photography point parameters include the coordinates of the aerial photography point, shooting angle, shooting altitude, etc.

[0122] It should be noted that, in this embodiment, after the aerial photography points are determined, the obtained aerial photography points can be further adjusted, including removing unnecessary aerial photography points at the edge of the aerial photography area, so as to further improve the aerial photography efficiency. Figure 5 A schematic diagram of aerial photography points in an aerial photography area is given.

[0123] Furthermore, the above step S40, “constructing a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone”, may include:

[0124] Step S401, obtaining the historical aerial photography process parameters and the historical battery power consumption of the drone;

[0125] Step S402: Based on the acquired historical aerial photography process parameters and the historical battery power consumption, a machine learning algorithm is used to construct the drone aerial photography model, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery power consumption.

[0126] It should be noted that, in this embodiment, the historical battery parameters of the drone include the historical battery consumption. In addition, the historical aerial photography process parameters include: historical meteorological data (wind direction and wind speed, etc.), historical flight parameters (flight altitude, flight speed, flight attitude (descent, climb, level flight and oblique flight, etc.), aircraft model, number of lenses and flight distance (actual flight path value), etc.

[0127] On this basis, the terminal device can build a relationship model between the above historical aerial photography process parameters and historical battery consumption, i.e., the drone aerial photography model, based on historical meteorological data, historical flight parameters, aircraft model, number of lenses, flight distance and other parameters, combined with the drone's historical battery consumption, using machine learning algorithms such as convolutional neural networks and Tensor Flow (or other machine learning algorithms, not limited). Furthermore, the terminal device can use the drone aerial photography model to quickly calculate the expected battery consumption based on the current aerial photography process parameters.

[0128] Furthermore, in the above step S50, "according to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, a flight route is obtained by performing route planning based on the principle of maximizing battery utilization", which may include:

[0129] Step S501, inputting the aerial photography process parameters into the drone aerial photography model to obtain an estimated battery power consumption;

[0130] Step S502, based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the estimated battery power consumption, with the goal of maximizing battery utilization, a heuristic algorithm is used to optimize the flight route.

[0131] After the terminal device constructs a drone aerial photography model including a mapping relationship between historical aerial photography process parameters and historical battery power consumption, the acquired aerial photography process parameters can be input into the drone aerial photography model to obtain the battery power consumption estimate corresponding to the aerial photography process parameters output by the drone aerial photography model. Then, a heuristic algorithm (such as simulated annealing algorithm, genetic algorithm, ant colony algorithm, artificial neural network, etc., without limitation) is used to perform global path planning for all aerial photography point objects with the purpose of maximizing effective flight time (i.e., maximizing effective battery utilization). It is worth noting that in this embodiment, the flight power consumption information between any two aerial photography points involved in the optimization process can be obtained by the output of the drone aerial photography model.

[0132] In this embodiment, the UAV may be affected by weather conditions during the aerial photography mission, resulting in insufficient power or the remaining battery power exceeding the expected estimated remaining power. At this time, the flight route needs to be optimized again to maximize the use of battery power, increase the effective flight time of the UAV, and improve the efficiency of aerial photography. The terminal device can determine the route height of the UAV and the distance between routes in the front, back, left and right directions according to the UAV's heading overlap rate, lateral overlap rate, flight altitude, lens parameters, lens shooting angle and other parameters. Furthermore, the aerial photography grid including the aerial photography area can be determined according to the route height, the distance between the routes and the predetermined aerial photography area. On this basis, the center point of each grid or each intersection between grid lines can be considered as an aerial photography point, and the aerial photography point parameters corresponding to each aerial photography point can be determined. Aerial photography path planning is then performed based on discrete aerial photography points, and the route is adjusted in time in combination with a variety of process variables to improve the effective flight time and the effective flight utilization rate of the battery. In addition, the terminal device can construct a relationship model between the above historical aerial photography process parameters and the historical battery consumption, i.e., the drone aerial photography model, based on historical meteorological data, historical flight parameters, flight attitude, aircraft model, number of lenses, flight distance and other parameters, and in combination with the historical battery consumption of the drone, using machine learning algorithms such as convolutional neural networks and Tensor Flow (or other machine learning algorithms, not limited). Furthermore, the terminal device can quickly calculate the estimated battery consumption according to the current aerial photography process parameters through the drone aerial photography model. After the terminal device constructs the drone aerial photography model including the mapping relationship between the historical aerial photography process parameters and the historical battery consumption, the acquired aerial photography process parameters can be input into the drone aerial photography model to obtain the battery consumption estimate corresponding to the aerial photography process parameters output by the drone aerial photography model. Furthermore, using a heuristic algorithm, global path planning is performed for all aerial photography point objects with the purpose of maximizing the effective flight time (i.e., maximizing the effective utilization rate of the battery).

[0133] In the present invention, the current battery power consumption is estimated through a drone aerial photography model that includes a mapping relationship between historical aerial photography process parameters and historical battery power consumption, and then the flight route is adjusted according to the estimated battery power consumption and the battery parameters detected in real time when performing aerial photography tasks according to the flight route, thereby realizing route planning based on the principle of maximizing battery utilization. Therefore, the present invention can maximize the effective flight of drones, that is, maximize the battery utilization efficiency. In the scenario where the drone battery is not replaced, the effective battery utilization rate can be significantly improved, the effective flight time of the drone can be increased, and the execution efficiency of the aerial photography task can be improved under limited battery capacity.

[0134] Based on the first embodiment and the second embodiment of the route planning method of the present invention, a third embodiment of the route planning method of the present invention is proposed.

[0135] In this embodiment, in the above step S60, "detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters" may include:

[0136] Step S601, when performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0137] Step S602, if the detected current remaining battery power reaches a preset remaining power threshold, determine whether to adjust the flight route based on whether the increment of the current remaining battery power relative to the pre-planned remaining power reaches a preset battery increment threshold.

[0138] It should be noted that, in this embodiment, the preset remaining power threshold may be set in advance, for example, a preset remaining power threshold such as 70%, 50%, 30%, or 15% may be set.

[0139] Specifically, for example, if it is detected that the remaining battery power of the drone reaches the above-mentioned preset remaining power threshold or the aerial photography mission of part of the aerial photography area is completed, it will automatically evaluate whether the current flight route needs to be adjusted. At this time, if it is evaluated that the increase in the current remaining power relative to the pre-planned remaining power reaches the preset battery increment threshold, such as the battery life has increased by 10% compared to the pre-planned remaining power, it will trigger the flight route adjustment function and adjust and optimize the current flight route according to the current remaining battery power and the aerial photography mission to be completed.

[0140] Furthermore, in the above step S60, “detecting the battery parameters in real time when performing the aerial photography task according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters” may also include:

[0141] Step S603, when performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0142] Step S604, determining whether the aerial photography mission can be completed based on the detected current remaining battery power, and determining whether to adjust the flight route based on the determination result.

[0143] When the drone is performing aerial photography tasks according to the flight route, the current remaining power of the drone battery will be detected in real time. If it is detected that the current remaining power of the battery cannot support the drone to complete the remaining aerial photography tasks due to weather data or the drone battery's own parameters, the drone will re-plan the route near the route of the remaining aerial photography tasks based on the battery interest rate maximization principle, so as to perform more aerial photography tasks as much as possible when the remaining battery power is insufficient, while ensuring the normal take-off and landing of the drone, that is, landing smoothly at the preset landing point.

[0144] Furthermore, in the above step S60, “detecting the battery parameters in real time when performing the aerial photography task according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters” may also include:

[0145] Step S605, when performing the aerial photography mission according to the flight route, detecting the current remaining battery power;

[0146] Step S606, based on the detected current remaining battery power, determine the increment of the current remaining cruising range of the drone relative to the pre-planned remaining cruising range, and determine whether to adjust the flight route based on whether the increment reaches a preset mileage increment threshold.

[0147] When performing aerial photography missions, the drone needs to detect the remaining range in real time. If it is detected that the current remaining range increases relative to the pre-planned remaining range and reaches a preset range increment threshold (for example, 20%), the flight route is replanned to make full use of the remaining battery power to perform more aerial photography missions.

[0148] In this embodiment, if it is detected that the remaining power of the drone battery reaches the above-mentioned preset remaining power threshold or completes part of the tasks relative to the aerial photography area, it will automatically evaluate whether the current flight route needs to be adjusted. At this time, if it is evaluated that the increment of the current remaining power relative to the pre-planned remaining power reaches the preset battery increment threshold, such as the endurance increased by 10% compared with the pre-planned remaining power, the flight route adjustment function will be triggered, and the current flight route will be adjusted and optimized according to the current remaining power and the aerial photography tasks to be completed. In the process of performing aerial photography tasks according to the flight route, the drone will detect the current remaining power of the drone battery in real time. If it is detected that the current remaining power of the battery cannot support the drone to complete the remaining aerial photography tasks due to weather and meteorological data or the drone battery's own parameters, the drone will re-plan the route near the route of the remaining aerial photography tasks based on the battery interest rate and the principle of maximization, so as to perform more aerial photography tasks as much as possible when the remaining battery power is insufficient, while ensuring the normal take-off and landing of the drone, that is, landing smoothly at the preset landing point. When performing aerial photography missions, the drone needs to detect the remaining range in real time. If it is detected that the current remaining range increases relative to the pre-planned remaining range and reaches a preset range increment threshold (for example, 20%), the flight route is replanned to make full use of the remaining battery power to perform more aerial photography missions.

[0149] In the present invention, the current flight route is adjusted according to the remaining battery power and the remaining aerial photography tasks, so that when the remaining battery power is insufficient or when the battery power has surplus power compared to the expected planned power, the current flight route can be adjusted in time to complete more aerial photography tasks based on the current remaining power, thereby maximizing the battery utilization efficiency and maximizing the effective flight time of the drone.

[0150] Based on the first embodiment, the second embodiment and the third embodiment of the route planning method of the present invention, a fourth embodiment of the route planning method of the present invention is proposed.

[0151] In this embodiment, the route planning method of the present invention is applied to various scenarios to further explain the route planning method of the present invention. It should be noted that in this embodiment, each discrete aerial photography point is a known input, and in this embodiment, flight route planning needs to be performed based on discrete aerial photography points. In addition, in order to expand the area of ​​the flight aerial photography area as much as possible, it is generally required that the take-off and landing points of the drone are located as close to the center of the aerial photography area as possible (drone communication has distance limitations). Therefore, the take-off and landing points of the drone in this embodiment are approximately near the center of the aerial photography area, and in actual operation, it is not necessary to take off and land near the center of the aerial photography area.

[0152] In this embodiment, when all aerial photography tasks can be completed in one flight, the optimal planned route is automatically found based on the maximization of effective flight time (maximization of battery utilization efficiency) based on the aerial photography area, the weather data on the day of the flight aerial photography task, the flight parameters of the drone, the battery parameters, the lens parameters, and the flight take-off and landing points. If the drone's own software path planning function is used, the planned route path is as follows: Figure 2 As shown in the figure, assuming that the take-off and landing point of the drone is located in the center of the aerial photography area, the path automatically planned by calling the drone flight software is as follows Figure 6-1 As shown, the aerial photography path generated by the route planning method of the present invention can be Figure 6-2 For the convenience of calculation, in this embodiment, all scenes assume that the horizontal and vertical distances between each aerial point are a. Figure 6-1 In the figure, the effective flight distance of aerial photography is 119a, the invalid flight distance (dashed line part) is 14.23a, and the effective flight distance ratio is 89.32%. Figure 6-2 In the data, the effective flight distance of aerial photography is 119a, the invalid flight distance is 2.36a, and the effective flight distance ratio is 98.06%. From the two sets of data, it can be seen that the effective flight distance has increased by about 8.74%.

[0153] In another embodiment, based on the regular area, two flights can complete all aerial photography tasks. If the drone's own software is used for aerial photography, it will continue to shoot until the software battery reaches the threshold set by the user, such as 30%, and the drone will automatically return home. The specific return point is unknown before the flight. Figure 7-1 is a possible actual flight path. The effective flight distance in the aerial photography is 119a, the invalid flight distance is 24.08a, and the effective flight distance ratio is 83.17%; Figure 7-2 This is a possible flight path generated by the route planning method of the present invention. In the figure, the effective flight distance of aerial photography is: 119a, the invalid flight distance is: 3.86a, and the effective flight distance ratio is: 96.86%. It can be seen from the two sets of data that the effective flight distance is increased by about 13.69%, which can ensure that the power utilization rate of the first battery is maximized while ensuring the safety of the aircraft.

[0154] In another embodiment, based on a regular area, three flights can complete all photographing tasks. Figure 8-1 is a possible actual flight path. The effective flight distance in the aerial photography is 119a, the invalid flight distance is 36.23a, and the effective flight distance ratio is 76.66%. Figure 8-2This is a possible flight path generated by the route planning method of the present invention. The effective flight distance of the aerial photography in the figure is: 119a, the invalid flight distance is: 6.42a, and the effective flight distance ratio is: 94.88%. It can be seen from the two sets of data that the effective flight distance is increased by about 18.22%. Under the condition of aircraft safety, it can ensure that the power utilization rate of the first two batteries is maximized.

[0155] In another embodiment, based on an irregular area, two flights can complete all shooting tasks, and the weather and battery etc. may change during the shooting process. Figure 9-1 The aerial photography software comes with a planned path result. In actual operation, it is found that the area selection generated by the built-in software only allows dragging 4 vertices to select the area. This will cause the generated flight area to be larger than the actual required area, resulting in a waste of resources. Figure 9-1 This is a possible aerial photography route diagram. In the diagram, the effective aerial photography flight distance is 121a, the invalid flight distance is 40.37a, and the effective flight distance ratio is 74.98%. Figure 9-2 This is the aerial photography planning map obtained by the route planning method of the present invention. In the map, the effective flight distance is 121a, the invalid flight distance is 3.24a, and the effective flight distance ratio is 97.39%. Figure 9-1 The planned path in is improved by 22.41%. Figure 9-3 During the aerial photography flight, after completing the flight of a small aerial photography area, it is detected that the current remaining power relative to the pre-planned remaining power reaches the preset battery increment threshold, and combined with the weather conditions, the route is automatically re-optimized and the aerial photography route is adjusted to Figure 9-3 The flight route shown in the figure is adjusted to maximize the use of battery power. The effective flight distance of aerial photography in the figure after adjustment is: 121a, the invalid flight distance is: 3.94a, and the effective flight distance ratio is: 96.85%, which is Figure 9-2 Compared with the paths shown, the biggest advantage is that it improves the effective use rate of the battery (the aircraft consumes the most power during the climb phase, so it is possible to reduce the climb process) to prepare for subsequent flight missions.

[0156] In this embodiment, various scenarios to which the route planning method of the present invention is applied are proposed, including the case where the drone can complete the aerial photography task within one, two and three flights in a regular aerial photography area and the case where the drone can complete the aerial photography task within two flights in an irregular aerial photography area, and the effective flight distance ratio of the drone in each scenario is further determined. It can be seen that the flight route determined by the route planning method of the present invention maximizes the battery utilization efficiency of the drone and the effective flight time of the drone, improves the execution efficiency of the aerial photography task under the limited battery capacity of the drone, and is applicable to various flight scenarios.

[0157] In addition, the present invention also provides a route planning system, referring to Fig.10 , Fig.10 FIG. 1 is a schematic diagram of the functional modules of an embodiment of route planning of the present invention. Fig.10 As shown, the route planning system of the present invention comprises:

[0158] A first determination module 10 is used to determine an aerial photography area;

[0159] An acquisition module 20 is used to acquire the aerial photography process parameters and battery parameters of the drone, wherein the aerial photography process parameters at least include the flight parameters of the drone;

[0160] A second determination module 30, configured to determine an aerial photography point and aerial photography point parameters based on the aerial photography area and the aerial photography process parameters;

[0161] A construction module 40 is used to construct a drone aerial photography model based on the historical aerial photography process parameters and the historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters;

[0162] The route planning module 50 is used to plan a flight route based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model and based on the principle of maximizing battery utilization.

[0163] Furthermore, the route planning system further includes:

[0164] A detection module, used for detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters;

[0165] The route adjustment module is used to re-plan the route based on the principle of maximizing battery utilization to adjust the flight route based on the detected battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model.

[0166] Furthermore, the second determining module 30 includes:

[0167] A first determining unit, configured to determine a route height and a route spacing according to the aerial photography process parameters;

[0168] The second determining unit is used to generate an aerial photography grid including the aerial photography area based on the aerial photography area, the route altitude and the route spacing, and determine the aerial photography point and the aerial photography point parameters based on the aerial photography grid.

[0169] Furthermore, the building block 40 comprises

[0170] An acquisition unit, used for acquiring the historical aerial photography process parameters and the historical battery power consumption of the drone;

[0171] A construction unit is used to construct the UAV aerial photography model using a machine learning algorithm based on the acquired historical aerial photography process parameters and the historical battery consumption, wherein the UAV aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery consumption.

[0172] Furthermore, the route planning module 50 includes:

[0173] A battery power consumption estimation value obtaining unit, used for inputting the aerial photography process parameters into the drone aerial photography model to obtain a battery power consumption estimation value;

[0174] The optimization unit is used to optimize the flight route by using a heuristic algorithm based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the estimated battery power consumption, with the goal of maximizing battery utilization.

[0175] Furthermore, the detection module includes:

[0176] A first detection unit, used to detect the current remaining battery power when performing the aerial photography mission according to the flight route;

[0177] The first determination unit is used to determine whether to adjust the flight route if the detected current battery remaining power reaches a preset remaining power threshold, based on whether the increment of the current battery remaining power relative to the pre-planned remaining power reaches a preset battery increment threshold.

[0178] Furthermore, the detection module further includes:

[0179] A second detection unit is used to detect the current remaining battery power when performing the aerial photography mission according to the flight route;

[0180] The second determination unit is used to determine whether the aerial photography task can be completed based on the detected current remaining battery power, and determine whether to adjust the flight route according to the determination result.

[0181] Furthermore, the detection module further includes:

[0182] A third detection unit, used to detect the current remaining battery power when performing the aerial photography mission according to the flight route;

[0183] The third determination unit is used to determine the increment of the current remaining cruising range of the drone relative to the pre-planned remaining cruising range based on the detected current remaining battery power, and determine whether to adjust the flight route according to whether the increment reaches a preset mileage increment threshold.

[0184] The specific implementation of each functional module of the route planning system of the present invention is basically the same as the various embodiments of the route planning method described above, and will not be described in detail here.

[0185] In addition, an embodiment of the present invention further proposes a computer-readable storage medium, on which a route planning program is stored. When the route planning program is executed by a processor, the steps of the route planning method described above are implemented.

[0186] The various embodiments of the route planning system and computer-readable storage medium of the present invention may all refer to the various embodiments of the route planning method of the present invention, and will not be described in detail here.

[0187] In addition, an embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the route planning method as described in any embodiment of the route planning method above are implemented.

[0188] The specific embodiments of the computer program product of the present invention are basically the same as the embodiments of the above-mentioned route planning method, and will not be described in detail here.

[0189] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0190] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0191] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or part of the contribution to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including a number of instructions for a terminal device (which can be a drone, etc.) to execute the methods described in each embodiment of the present invention.

[0192] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A route planning method, characterized in that: The route planning method is applied to a UAV, and the route planning method comprises: Determine the aerial photography area; Acquire the aerial photography process parameters and battery parameters of the drone, wherein the aerial photography process parameters at least include drone flight parameters, lens parameters, and lens shooting angles, wherein the drone flight parameters at least include heading overlap rate, lateral overlap rate, and flight altitude; Determine the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters; Building a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters; According to the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, a flight route is obtained by performing route planning based on the principle of maximizing battery utilization, wherein the principle of maximizing battery utilization refers to maximizing the effective flight time of the drone under limited battery capacity; The step of determining the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters comprises: Determine the flight path height and flight path spacing according to the aerial photography process parameters; An aerial photography grid including the aerial photography area is generated based on the aerial photography area, the route height, and the route spacing, and the aerial photography point and the aerial photography point parameters are determined based on the aerial photography grid.

2. The route planning method according to claim 1, characterized in that: After the step of performing route planning based on the battery utilization maximization principle to obtain a flight route, the method further includes: When performing the aerial photography mission according to the flight route, detecting the battery parameters in real time, and determining whether to adjust the flight route based on the detected battery parameters; If so, based on the detected battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model, the route planning is re-performed based on the principle of maximizing battery utilization to adjust the flight route.

3. The route planning method according to claim 1, characterized in that: The historical battery parameters include historical battery power consumption, and the step of constructing the drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone includes: Obtaining the historical aerial photography process parameters and the historical battery power consumption of the drone; Based on the acquired historical aerial photography process parameters and the historical battery power consumption, the UAV aerial photography model is constructed using a machine learning algorithm, and the UAV aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery power consumption.

4. The route planning method according to claim 3, characterized in that: The step of performing route planning to obtain a flight route based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the drone aerial photography model and the principle of maximizing battery utilization includes: Inputting the aerial photography process parameters into the drone aerial photography model to obtain an estimated battery power consumption; Based on the battery parameters, the aerial photography process parameters, the aerial photography point parameters and the estimated battery power consumption, the flight route is obtained by optimizing using a heuristic algorithm with the goal of maximizing battery utilization.

5. The route planning method according to claim 2, characterized in that: The step of detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters, includes: When performing the aerial photography mission according to the flight route, detecting the current remaining battery power; If the detected current remaining battery power reaches a preset remaining power threshold, it is determined whether to adjust the flight route based on whether the increment of the current remaining battery power relative to the pre-planned remaining power reaches the preset battery increment threshold.

6. The route planning method according to claim 2, characterized in that: The step of detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters, includes: When performing the aerial photography mission according to the flight route, detecting the current remaining battery power; Determine whether the aerial photography mission can be completed based on the detected current remaining battery power, and determine whether to adjust the flight route based on the determination result.

7. The route planning method according to claim 2, characterized in that: The step of detecting the battery parameters in real time when performing the aerial photography mission according to the flight route, and determining whether to adjust the flight route based on the detected battery parameters, includes: When performing the aerial photography mission according to the flight route, detecting the current remaining battery power; The method further comprises determining an increment of the current remaining cruising range of the drone relative to the pre-planned remaining cruising range based on the detected current remaining battery power, and determining whether to adjust the flight route based on whether the increment reaches a preset mileage increment threshold.

8. A route planning system, characterized in that: The route planning system comprises: The first determination module is used to determine the aerial photography area; An acquisition module is used to acquire the aerial photography process parameters and battery parameters of the drone, wherein the aerial photography process parameters at least include the drone flight parameters, lens parameters and lens shooting angle, wherein the drone flight parameters at least include the heading overlap rate, the lateral overlap rate and the flight altitude; A second determination module is used to determine the aerial photography point and the aerial photography point parameters based on the aerial photography area and the aerial photography process parameters; A construction module, used to construct a drone aerial photography model based on the historical aerial photography process parameters and historical battery parameters of the drone, wherein the drone aerial photography model is at least used to indicate the relationship between the historical aerial photography process parameters and the historical battery parameters; A route planning module, configured to perform route planning based on the battery parameter, the aerial photography process parameter, the aerial photography point parameter and the drone aerial photography model to obtain a flight route based on the principle of maximizing battery utilization, wherein the principle of maximizing battery utilization refers to maximizing the effective flight time of the drone under limited battery capacity; The second determination module is further used to determine the route height and route spacing according to the aerial photography process parameters; An aerial photography grid including the aerial photography area is generated based on the aerial photography area, the route height, and the route spacing, and the aerial photography point and the aerial photography point parameters are determined based on the aerial photography grid.

9. A terminal device, characterized in that: The terminal device includes a memory, a processor, and a route planning program stored in the memory and executable on the processor. When the route planning program is executed by the processor, the steps of the route planning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a route planning program, and when the route planning program is executed by a processor, the steps of the route planning method according to any one of claims 1 to 7 are implemented.

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