Self-driving deployment scheduling method and system for electric power inspection unmanned aerial vehicle

Abstract

The invention provides a self-driving deployment scheduling method and system for an electric power inspection unmanned aerial vehicle. According to the implementation scheme, under the condition that the salt mist concentration of a first height layer where a first unmanned aerial vehicle is located in a mud flat power grid area exceeds a first concentration threshold value, based on a second height layer, the first unmanned aerial vehicle is controlled to fly up within a first time period, and meteorological information is detected; on the basis of meteorological information detected by the first unmanned aerial vehicle in the flight process of the first time period, predicting second salt mist concentration distribution of a flight space corresponding to the mud flat power grid region in a second time period; and based on the second salt mist concentration distribution, re-planning the flight path of each second unmanned aerial vehicle flying in the flight space corresponding to the mud flat power grid region in the second time period to obtain a third predicted flight path of each second unmanned aerial vehicle, and controlling each second unmanned aerial vehicle to fly in the second time period according to each path. Therefore, the unmanned aerial vehicle is prevented from being attacked by salt mist.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle control technology, and in particular to a self-driving deployment and scheduling method and system for electric power inspection unmanned aerial vehicles. Background Art

[0002] As the power grid continues to expand, corresponding power lines are also installed in the tidal flats. However, the tidal flats are significantly affected by the tidal cycle. During high tide, the seawater floods the area, and after low tide, the salt spray concentration in the humid air remains high, causing serious corrosion to power facilities. Since the salt spray concentration varies with altitude, the salt spray concentration varies greatly at different altitudes, and the corrosion effect on drones is also different.

[0003] Therefore, how to obtain monitoring images of power lines at different heights while avoiding improper drone flight altitude, such as when shooting close to the ground, which may cause key components to be corroded by salt spray or seawater splash, has become a technical difficulty. Summary of the invention

[0004] The present invention provides a method and system for self-driving deployment and scheduling of electric power inspection drones, which can solve at least one of the above technical problems.

[0005] According to one aspect of the present invention, a method for self-driving deployment and scheduling of a power inspection drone is provided, comprising: Based on the situation that the salt spray concentration at the first altitude layer where the first UAV is located in the tidal flat power grid area exceeds the first concentration threshold, determining the second altitude layer based on the first salt spray concentration distribution in the flight space where the first predicted flight trajectory of the first UAV in the first time period is located; Based on the second altitude layer, adjusting the first predicted flight trajectory to obtain a second predicted flight trajectory of the first UAV; Based on the second predicted flight trajectory, controlling the first UAV to fly within the first time period and detecting meteorological information; Based on the meteorological information detected by the first UAV during the flight of the first time period, predicting the second salt spray concentration distribution of the flight space corresponding to the tidal flat power grid area in the second time period, wherein the first time period is earlier than the second time period; Based on the second salt spray concentration distribution, replanning the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area in the second time period to obtain a third predicted flight trajectory of each second UAV; Based on the third predicted flight trajectory of each of the second UAVs, each of the second UAVs is controlled to fly within the second time period.

[0006] According to another aspect of the present invention, a self-driving deployment and scheduling device for a power inspection drone is provided, the method comprising: An altitude layer determination module is used to determine a second altitude layer based on a first salt spray concentration distribution in a flight space where a first predicted flight trajectory of the first drone is located within a first time period, when the salt spray concentration at the first altitude layer where the first drone is located in the tidal flat power grid area exceeds a first concentration threshold; A first trajectory determination module, configured to adjust the first predicted flight trajectory based on the second altitude layer to obtain a second predicted flight trajectory of the first UAV; a first flight control module, configured to control the first UAV to fly and detect meteorological information within the first time period based on the second predicted flight trajectory; A concentration distribution prediction module, configured to predict a second salt spray concentration distribution in a flight space corresponding to the tidal flat power grid area in a second time period based on meteorological information detected by the first UAV during the flight of the first time period, wherein the first time period is earlier than the second time period; A second trajectory determination module is used to replan the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area within a second time period based on the second salt spray concentration distribution, so as to obtain a third predicted flight trajectory of each second UAV; The second flight control module is used to control each of the second UAVs to fly within the second time period based on the third predicted flight trajectory of each of the second UAVs.

[0007] According to another aspect of the present invention, a self-driving deployment and scheduling system for electric power inspection drones is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the self-driving deployment and scheduling method for electric power inspection drones described in any one of the embodiments of the present invention.

[0008] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the self-driving deployment and scheduling method for a power inspection drone as described in any one of the embodiments of the present invention.

[0009] According to the technical solution of the present invention, when the salt fog concentration of the first altitude layer where the first UAV is located in the tidal flat power grid area exceeds the first concentration threshold, the second altitude layer is determined based on the first salt fog concentration distribution in the flight space where the first predicted flight trajectory of the first UAV is located in the first time period; based on the second altitude layer, the first predicted flight trajectory is adjusted to obtain the second predicted flight trajectory of the first UAV; based on the second predicted flight trajectory, the first UAV is controlled to fly in the first time period and detect meteorological information. In this way, when the first UAV detects that the salt fog concentration exceeds the preset threshold, the flight altitude of the first UAV is raised, which can avoid the first UAV from being attacked by salt fog, and can detect relevant meteorological information. Then, based on the meteorological information detected by the first UAV during the flight process of the first time period, the second salt fog concentration distribution of the flight space corresponding to the tidal flat power grid area in the second time period is predicted, wherein the first time period is earlier than the second time period; based on the second salt fog concentration distribution, the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area in the second time period is replanned to obtain the third predicted flight trajectory of each second UAV; based on the third predicted flight trajectory of each second UAV, each second UAV is controlled to fly in the second time period. Thus, the meteorological information detected by the first UAV during the period of raising the flight altitude is used to predict the salt spray concentration distribution in the future second time period, so as to accurately plan the flight trajectory of each second UAV and prevent the second UAV from being attacked by salt spray.

[0010] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention. Figure 1 It is a flow chart of a method for self-driving deployment and scheduling of a power inspection drone according to an embodiment of the present invention; Figure 2 It is a structural block diagram of a self-driving deployment and dispatching device for a power inspection drone according to an embodiment of the present invention; Figure 3 The block diagram is a block diagram of an electronic device for implementing the method according to the embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0013] Figure 1 It is a flow chart of a method for self-driving deployment and scheduling of a power inspection UAV according to an embodiment of the present invention.

[0014] like Figure 1 As shown, the self-driving deployment and scheduling method of the power inspection drone may include: S110, determining a second altitude layer based on a first salt fog concentration distribution in a flight space where a first predicted flight trajectory of the first drone in a first time period is located, when the salt fog concentration at the first altitude layer where the first drone is located in the tidal flat power grid area exceeds a first concentration threshold; S120, adjusting the first predicted flight trajectory based on the second altitude layer to obtain a second predicted flight trajectory of the first UAV; S130, based on the second predicted flight trajectory, controlling the first UAV to fly within a first time period and detecting meteorological information; S140, predicting a second salt spray concentration distribution in a flight space corresponding to the tidal flat power grid area in a second time period based on meteorological information detected by the first UAV during flight in a first time period, wherein the first time period is earlier than the second time period; S150, based on the second salt spray concentration distribution, replanning the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area within the second time period to obtain a third predicted flight trajectory of each second UAV; S160: Control each second unmanned aerial vehicle to fly within a second time period based on the third predicted flight trajectory of each second unmanned aerial vehicle.

[0015] Exemplarily, the above method can be applied to a first drone, where the first drone performs self-driving scheduling and performs flight scheduling for other second drones.

[0016] Exemplarily, a plurality of interconnected power lines are arranged in the tidal flat power grid area.

[0017] It is understandable that the first height layer and the second height layer may be adjacent or overlapped. The first height layer is located below the second height layer. The height of the first height layer may be less than the height of the second height layer. The height of the first height layer may be the same as the height of the second height layer.

[0018] Exemplarily, the average salt mist concentration of the first altitude layer is higher than the average salt mist concentration of the second altitude layer. The range of the salt mist concentration of the first altitude layer is higher than the range of the salt mist concentration of the second altitude layer.

[0019] Exemplarily, the first salt spray concentration distribution includes salt spray concentrations corresponding to each three-dimensional coordinate position in the flight space.

[0020] Exemplarily, a height layer having a concentration lower than the first height layer is determined in the first salt fog concentration distribution, and a second height layer is determined in the height layer. For example, a height layer having a lower concentration change rate is selected as the second height layer.

[0021] Exemplarily, based on the meteorological information and salt fog concentration changes detected by the first UAV during the flight of the first time period, the salt fog concentration distribution of the flight area in the first time period is determined, and then, based on the salt fog concentration distribution of the flight area in the first time period, an extended prediction is performed to obtain a second salt fog concentration distribution of the flight space corresponding to the tidal flat power grid area in the second time period.

[0022] For example, a neural network model can also be used to predict concentration distribution. Based on the meteorological information detected by the first UAV during the flight of the first time period, the predicted meteorological information of the flight space corresponding to the tidal flat power grid area in the second time period is predicted. The predicted meteorological information is input into the trained neural network model, and the second salt spray concentration distribution of the flight space corresponding to the tidal flat power grid area in the second time period can be predicted.

[0023] Exemplarily, the first time period is connected to the second time period, and the duration of the first time period is shorter than the duration of the second time period.

[0024] For example, based on the second salt spray concentration distribution, an abnormal area can be determined, and the flight trajectory of each second UAV flying in the abnormal area is replanned, while the flight trajectory of other areas may not be replanned.

[0025] Exemplarily, the first drone may be one of the plurality of second drones.

[0026] For example, while controlling the drone to fly, the drone is controlled to perform operations such as image capture, infrared detection, temperature detection, or meteorological information detection on ground power lines.

[0027] Exemplarily, the third predicted flight trajectory of each second drone is sent to the corresponding second drone, so that each second drone flies according to the third predicted flight trajectory within the second time period.

[0028] According to the above embodiment, when the salt fog concentration of the first altitude layer where the first UAV is located in the tidal flat power grid area exceeds the first concentration threshold, the second altitude layer is determined based on the first salt fog concentration distribution in the flight space where the first predicted flight trajectory of the first UAV is located in the first time period; based on the second altitude layer, the first predicted flight trajectory is adjusted to obtain the second predicted flight trajectory of the first UAV; based on the second predicted flight trajectory, the first UAV is controlled to fly in the first time period and detect meteorological information. In this way, when the first UAV detects that the salt fog concentration exceeds the preset threshold, the flight altitude of the first UAV is raised, which can avoid the first UAV from being attacked by salt fog, and can detect relevant meteorological information. Then, based on the meteorological information detected by the first UAV during the flight process of the first time period, the second salt fog concentration distribution of the flight space corresponding to the tidal flat power grid area in the second time period is predicted, wherein the first time period is earlier than the second time period; based on the second salt fog concentration distribution, the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area in the second time period is replanned to obtain the third predicted flight trajectory of each second UAV; based on the third predicted flight trajectory of each second UAV, each second UAV is controlled to fly in the second time period. Thus, the meteorological information detected by the first UAV during the period of raising the flight altitude is used to predict the salt spray concentration distribution in the future second time period, so as to accurately plan the flight trajectory of each second UAV and prevent the second UAV from being attacked by salt spray.

[0029] In one embodiment, the method further includes: based on the flight space where the first predicted flight trajectory is located, obtaining historical three-dimensional point cloud data of the corresponding power line; performing curve fitting on the historical three-dimensional point cloud data to obtain the corresponding line curve space; based on the line curve space, determining the sampling points of the flight space where the first predicted flight trajectory is located; based on the historical salt spray concentration data of the sampling points of the flight space where the first predicted flight trajectory is located, and the predicted meteorological information of the flight space where the first predicted flight trajectory is located, determining the concentration change rate of two adjacent sampling points; based on the concentration change rate of two adjacent sampling points, and the salt spray concentration of the first altitude layer, determining the first salt spray concentration distribution of the flight space where the first predicted flight trajectory is located.

[0030] Exemplarily, the flight space where the first predicted flight trajectory is located is vertically projected onto the ground to determine a vertical projection area, and then historical three-dimensional point cloud data of the power lines falling in the vertical projection area is acquired.

[0031] Exemplarily, the least square method or polynomial function is used to perform curve fitting on the historical three-dimensional point cloud data to obtain the corresponding route curve space, wherein the route curve space includes one or more three-dimensional route curves.

[0032] Exemplarily, each curve in the route curve space is projected into each sub-altitude layer in the flight space where the first predicted flight trajectory is located, and then sampling points are determined on the projected curves in each sub-altitude layer.

[0033] For example, the forecasted meteorological information may include information such as temperature, humidity and wind speed.

[0034] Exemplarily, based on the historical salt spray concentration data of the sampling points in the flight space where the first predicted flight trajectory is located, the historical concentration change rate of two adjacent sampling points is determined, and the change rate can be a statistical value such as an average value or a median. Based on the predicted meteorological information of the flight space where the first predicted flight trajectory is located, the historical concentration change rate of the two adjacent sampling points is adjusted to obtain the concentration change rate of the two adjacent sampling points. For example, based on the difference between the historical meteorological information and the predicted meteorological information corresponding to the historical concentration change rate of the two adjacent sampling points, the historical concentration change rate of the two adjacent sampling points is adjusted using the difference. For example, if the wind speed increases, the concentration change rate also increases; if the temperature increases, the concentration change rate also increases; if the humidity increases, the concentration change rate decreases.

[0035] For example, based on the salt spray concentration and concentration change rate of each sampling point in the first altitude layer, the salt spray concentration of each sampling point in the non-first altitude layer can be derived. The salt spray concentration of each sampling point in the first altitude layer and the salt spray concentration of each sampling point in the non-first altitude layer are fitted and interpolated to obtain the first salt spray concentration distribution of the flight space where the first predicted flight trajectory is located.

[0036] According to the above implementation, the sampling points are obtained by projection using the power lines, and the salt fog concentration at the sampling points can be used to accurately obtain the salt fog concentration distribution of the flight space where the first predicted flight trajectory is located.

[0037] In one embodiment, the above-mentioned determining the second altitude layer based on the first salt fog concentration distribution in the flight space where the first predicted flight trajectory of the first unmanned aerial vehicle in the first future time period is located includes: determining the average concentration change rate of each altitude position along the center of gravity direction based on the first salt fog concentration distribution; determining each peak band based on the average concentration change rate of each altitude position; stratifying the flight space where the first predicted flight trajectory is located based on the altitude position corresponding to the start and end positions in each peak band, and obtaining each altitude layer in the flight space where the first predicted flight trajectory is located; determining the second altitude layer in each altitude layer in the flight space where the first predicted flight trajectory is located based on the salt fog concentration of the first altitude layer and the salt fog concentration of each altitude layer in the flight space where the first predicted flight trajectory is located.

[0038] For example, by averaging the salt spray concentrations at various sampling points in a plane at a certain height position, the average concentration change rate at the height position can be obtained.

[0039] It can be understood that the peak band may be a band in which the average concentration first rises to a peak and then decreases as the height position changes.

[0040] It can be understood that the peak values ​​of different peak bands may be the same or different, and the lengths of different peak bands may be the same or different.

[0041] Exemplarily, the length of the peak band is the same as the thickness of its corresponding altitude layer.

[0042] Exemplarily, based on the salt fog concentration of a height layer and the salt fog concentration of each height layer in the flight space where the first predicted flight track is located, the concentration difference between the first height layer and each height layer is determined; based on the concentration difference between the first height layer and each height layer and the difference distance between the first height layer and each height layer, the second height layer is determined in each height layer in the flight space where the first predicted flight track is located. For example, a height layer with a larger concentration difference but a larger difference distance is selected as the second height layer.

[0043] According to the above implementation, the first salt fog concentration distribution in the flight space where the first predicted flight trajectory is located can be used to determine a suitable second altitude layer, so that the drone can quickly fly to a suitable altitude layer to avoid salt fog invasion.

[0044] In one embodiment, the above-mentioned adjustment of the first predicted flight trajectory based on the second altitude layer to obtain the second predicted flight trajectory of the first UAV includes: updating the terminal position of the first predicted flight trajectory based on the second altitude layer; replanning the flight trajectory of the first UAV based on the current flight speed of the first UAV and the updated terminal position to obtain the second predicted flight trajectory of the first UAV.

[0045] Exemplarily, each curve in the line curve space is projected to the second altitude layer, and the updated terminal position in the first predicted flight trajectory is determined using the sampling points of the projected curve.

[0046] Exemplarily, based on the current flight speed of the first drone, the flight trajectory of the first drone is replanned with the current flight position of the first drone as the starting point and the updated terminal position as the end point to obtain the second predicted flight trajectory of the first drone.

[0047] According to the above-mentioned implementation, the predicted flight trajectory of the UAV can be accurately planned.

[0048] In one embodiment, the above-mentioned prediction of the second salt fog concentration distribution in the second time period of the flight space corresponding to the tidal flat power grid area based on the meteorological information detected by the first unmanned aerial vehicle during the flight of the first time period includes: based on the temperature distribution information, humidity distribution information and wind speed distribution information in the meteorological information detected by the first unmanned aerial vehicle during the flight of the first time period, predicting the predicted temperature distribution information, predicted humidity distribution information and predicted wind speed distribution information of the flight space corresponding to the tidal flat power grid area in the second time period; inputting the predicted temperature distribution information, predicted humidity distribution information and predicted wind speed distribution information into the salt fog concentration distribution prediction model to obtain the second salt fog concentration distribution output by the salt fog concentration distribution prediction model.

[0049] Exemplarily, the salt spray concentration distribution prediction model can be generated by training historical meteorological data and historical salt spray concentration distribution using a neural network model. For example, the neural network model can be a random forest model.

[0050] According to the above implementation, artificial intelligence learning can be used to use meteorological information to predict salt spray concentration distribution and improve prediction accuracy.

[0051] In one embodiment, the above-mentioned re-planning of the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area within the second time period based on the second salt fog concentration distribution to obtain the third predicted flight trajectory of each second UAV includes: determining the salt fog concentration fluctuation abnormal space in the flight space corresponding to the tidal flat power grid area based on the second salt fog concentration distribution; re-planning the flight trajectory of each second UAV flying in the salt fog concentration fluctuation abnormal space within the second time period based on the third salt fog concentration distribution in the salt fog concentration fluctuation abnormal space to obtain the third predicted flight trajectory of each second UAV.

[0052] Exemplarily, the power curve corresponding to the salt fog concentration fluctuation abnormal space is projected into the abnormal space to obtain a projection curve, and the salt fog concentration of the sampling point in the projection curve is used to determine the target sampling point where the salt fog concentration is lower than the preset threshold. Based on the three-dimensional position information of the target sampling point, the flight trajectory of each second UAV flying in the salt fog concentration fluctuation abnormal space in the second time period is replanned to obtain a third predicted flight trajectory of each second UAV.

[0053] According to the above implementation, the predicted flight trajectory of the UAV can be accurately planned to avoid salt spray invasion.

[0054] Figure 2 It is a structural block diagram of a self-driving deployment and scheduling device for a power inspection drone according to an embodiment of the present invention.

[0055] like Figure 2As shown, the self-driving deployment and dispatching device of the power inspection drone may include: The altitude layer determination module 210 is used to determine the second altitude layer based on the first salt spray concentration distribution in the flight space where the first predicted flight trajectory of the first drone in the first time period is located, when the salt spray concentration at the first altitude layer where the first drone is located in the tidal flat power grid area exceeds the first concentration threshold; A first trajectory determination module 220, configured to adjust the first predicted flight trajectory based on the second altitude layer to obtain a second predicted flight trajectory of the first UAV; A first flight control module 230, configured to control the first UAV to fly and detect meteorological information within the first time period based on the second predicted flight trajectory; A concentration distribution prediction module 240 is used to predict a second salt spray concentration distribution in a flight space corresponding to the tidal flat power grid area in a second time period based on the meteorological information detected by the first UAV during the flight of the first time period, wherein the first time period is earlier than the second time period; A second trajectory determination module 250 is used to replan the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area within a second time period based on the second salt spray concentration distribution, so as to obtain a third predicted flight trajectory of each second UAV; The second flight control module 260 is used to control each of the second UAVs to fly within the second time period based on the third predicted flight trajectory of each of the second UAVs.

[0056] In one embodiment, the above device further comprises: A point cloud data acquisition module, used to acquire historical three-dimensional point cloud data of the corresponding power line based on the flight space where the first predicted flight trajectory is located; A curve fitting module, used for performing curve fitting on the historical three-dimensional point cloud data to obtain a corresponding line curve space; A sampling point determination module, used to determine the sampling point of the flight space where the first predicted flight trajectory is located based on the route curve space; A concentration change rate determination module, used to determine the concentration change rate of two adjacent sampling points based on the historical salt spray concentration data of the sampling points in the flight space where the first predicted flight trajectory is located and the predicted meteorological information of the flight space where the first predicted flight trajectory is located; The concentration distribution determination module is used to determine the first salt spray concentration distribution of the flight space where the first predicted flight trajectory is located based on the concentration change rate of two adjacent sampling points and the salt spray concentration of the first altitude layer.

[0057] In one embodiment, the altitude layer determination module 210 includes: an average change rate determining unit, configured to determine an average concentration change rate at each height position along a gravity center direction based on the first salt spray concentration distribution; A peak band determination unit, used to determine each peak band based on the average concentration change rate at each height position; A space stratification unit, configured to stratify the flight space where the first predicted flight trajectory is located based on the altitude positions corresponding to the start and end positions in each peak band, to obtain each altitude layer in the flight space where the first predicted flight trajectory is located; The altitude layer determining unit is used to determine the second altitude layer in each altitude layer in the flight space where the first predicted flight trajectory is located based on the salt spray concentration at the first altitude layer and the salt spray concentration at each altitude layer in the flight space where the first predicted flight trajectory is located.

[0058] In one implementation, the first trajectory determination module 220 includes: an endpoint updating unit, configured to update the endpoint position of the first predicted flight trajectory based on the second altitude layer; The first trajectory planning unit is used to re-plan the flight trajectory of the first UAV based on the current flight speed of the first UAV and the updated terminal position to obtain a second predicted flight trajectory of the first UAV.

[0059] In one embodiment, the concentration distribution prediction module 240 includes: a meteorological information prediction unit, configured to predict predicted temperature distribution information, predicted humidity distribution information, and predicted wind speed distribution information of the flight space corresponding to the tidal flat power grid area in a second time period based on the temperature distribution information, humidity distribution information, and wind speed distribution information in the meteorological information detected by the first UAV during the flight of the first time period; The concentration distribution prediction unit is used to input the predicted temperature distribution information, the predicted humidity distribution information and the predicted wind speed distribution information into a salt fog concentration distribution prediction model to obtain the second salt fog concentration distribution output by the salt fog concentration distribution prediction model.

[0060] In one embodiment, the second flight control module 260 includes: an abnormal space determination unit, configured to determine, based on the second salt spray concentration distribution, an abnormal space of salt spray concentration fluctuation in the flight space corresponding to the tidal flat power grid area; The second trajectory planning unit is used to replan the flight trajectory of each second UAV flying in the salt fog concentration fluctuation abnormal space within a second time period based on the third salt fog concentration distribution in the salt fog concentration fluctuation abnormal space, so as to obtain a third predicted flight trajectory of each second UAV.

[0061] For the description of specific functions and examples of each module and submodule of the system in the embodiment of the present invention, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0062] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0063] According to an embodiment of the present invention, the present invention also provides a system and a readable storage medium.

[0064] Figure 3 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0065] like Figure 3 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0066] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0067] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as the self-driving deployment scheduling method of the power inspection drone. For example, in some embodiments, the self-driving deployment scheduling method of the power inspection drone may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the self-driving deployment scheduling method of the power inspection drone described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the self-driving deployment scheduling method of the power inspection drone in any other appropriate manner (for example, by means of firmware).

[0068] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0070] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0072] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0073] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0074] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0075] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-driving deployment and scheduling method for electric power inspection drones, characterized in that: include: Based on the situation that the salt spray concentration at the first altitude layer where the first UAV is located in the tidal flat power grid area exceeds the first concentration threshold, determining the second altitude layer based on the first salt spray concentration distribution in the flight space where the first predicted flight trajectory of the first UAV in the first time period is located; Based on the second altitude layer, adjusting the first predicted flight trajectory to obtain a second predicted flight trajectory of the first UAV; Based on the second predicted flight trajectory, controlling the first UAV to fly within the first time period and detecting meteorological information; Based on the meteorological information detected by the first UAV during the flight of the first time period, predicting the second salt spray concentration distribution of the flight space corresponding to the tidal flat power grid area in the second time period, wherein the first time period is earlier than the second time period; Based on the second salt spray concentration distribution, replanning the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area in the second time period to obtain a third predicted flight trajectory of each second UAV; Based on the third predicted flight trajectory of each of the second UAVs, each of the second UAVs is controlled to fly within the second time period.

2. The method according to claim 1, characterized in that Also includes: Based on the flight space where the first predicted flight trajectory is located, acquiring historical three-dimensional point cloud data of the corresponding power line; Performing curve fitting on the historical three-dimensional point cloud data to obtain a corresponding line curve space; Based on the route curve space, determining a sampling point in the flight space where the first predicted flight trajectory is located; Determine the concentration change rate of two adjacent sampling points based on the historical salt spray concentration data of the sampling points in the flight space where the first predicted flight trajectory is located and the predicted meteorological information of the flight space where the first predicted flight trajectory is located; Based on the concentration change rate of two adjacent sampling points and the salt spray concentration of the first altitude layer, a first salt spray concentration distribution of the flight space where the first predicted flight trajectory is located is determined.

3. The method according to claim 2, characterized in that The determining of the second altitude layer based on a first salt spray concentration distribution in a flight space where a first predicted flight trajectory of the first UAV in a first future time period is located includes: Based on the first salt spray concentration distribution, determining the average concentration change rate at each height position along the center of gravity direction; Based on the average concentration change rate at each height position, each peak band is determined; Based on the altitude positions corresponding to the start and end positions in each peak band, the flight space where the first predicted flight trajectory is located is layered to obtain each altitude layer in the flight space where the first predicted flight trajectory is located; Based on the salt spray concentration at the first altitude layer and the salt spray concentration at each altitude layer in the flight space where the first predicted flight trajectory is located, the second altitude layer is determined in each altitude layer in the flight space where the first predicted flight trajectory is located.

4. The method according to claim 1, characterized in that: The adjusting the first predicted flight trajectory based on the second altitude layer to obtain a second predicted flight trajectory of the first UAV includes: Based on the second altitude layer, updating the terminal position of the first predicted flight trajectory; Based on the current flight speed of the first UAV and the updated terminal position, the flight trajectory of the first UAV is replanned to obtain a second predicted flight trajectory of the first UAV.

5. The method according to claim 1, characterized in that The method of predicting a second salt spray concentration distribution in a second time period in a flight space corresponding to the tidal flat power grid area based on the meteorological information detected by the first UAV during the flight of the first time period includes: Based on the temperature distribution information, humidity distribution information and wind speed distribution information in the meteorological information detected by the first UAV during the flight process of the first time period, predict the predicted temperature distribution information, predicted humidity distribution information and predicted wind speed distribution information of the flight space corresponding to the tidal flat power grid area in the second time period; The predicted temperature distribution information, the predicted humidity distribution information and the predicted wind speed distribution information are input into a salt fog concentration distribution prediction model to obtain the second salt fog concentration distribution output by the salt fog concentration distribution prediction model.

6. The method according to claim 1, characterized in that The method of replanning the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area within the second time period based on the second salt spray concentration distribution to obtain a third predicted flight trajectory of each second UAV includes: Based on the second salt fog concentration distribution, determining the salt fog concentration fluctuation abnormal space in the flight space corresponding to the tidal flat power grid area; Based on the third salt fog concentration distribution in the salt fog concentration fluctuation abnormal space, the flight trajectory of each second UAV flying in the salt fog concentration fluctuation abnormal space in a second time period is replanned to obtain a third predicted flight trajectory of each second UAV.

7. A self-driving deployment and dispatching device for electric power inspection drones, characterized in that: include: An altitude layer determination module is used to determine a second altitude layer based on a first salt spray concentration distribution in a flight space where a first predicted flight trajectory of the first drone is located within a first time period, when the salt spray concentration at the first altitude layer where the first drone is located in the tidal flat power grid area exceeds a first concentration threshold; A first trajectory determination module, configured to adjust the first predicted flight trajectory based on the second altitude layer to obtain a second predicted flight trajectory of the first UAV; a first flight control module, configured to control the first UAV to fly and detect meteorological information within the first time period based on the second predicted flight trajectory; A concentration distribution prediction module, configured to predict a second salt spray concentration distribution in a flight space corresponding to the tidal flat power grid area in a second time period based on meteorological information detected by the first UAV during the flight of the first time period, wherein the first time period is earlier than the second time period; A second trajectory determination module is used to replan the flight trajectory of each second UAV flying in the flight space corresponding to the tidal flat power grid area within a second time period based on the second salt spray concentration distribution, so as to obtain a third predicted flight trajectory of each second UAV; The second flight control module is used to control each of the second UAVs to fly within the second time period based on the third predicted flight trajectory of each of the second UAVs.

8. The device according to claim 7, characterized in that Also includes: A point cloud data acquisition module, used to acquire historical three-dimensional point cloud data of the corresponding power line based on the flight space where the first predicted flight trajectory is located; A curve fitting module, used for performing curve fitting on the historical three-dimensional point cloud data to obtain a corresponding line curve space; A sampling point determination module, used to determine the sampling point of the flight space where the first predicted flight trajectory is located based on the route curve space; A concentration change rate determination module, used to determine the concentration change rate of two adjacent sampling points based on the historical salt spray concentration data of the sampling points in the flight space where the first predicted flight trajectory is located and the predicted meteorological information of the flight space where the first predicted flight trajectory is located; The concentration distribution determination module is used to determine the first salt spray concentration distribution of the flight space where the first predicted flight trajectory is located based on the concentration change rate of two adjacent sampling points and the salt spray concentration of the first altitude layer.

9. A self-driving deployment and dispatching system for a power inspection drone, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-6.

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