Self-driving Deployment and Scheduling Method and System for Power Inspection UAVs

Abstract

The present invention provides a self-driving deployment and scheduling method and system for a power inspection unmanned aerial vehicle (UAV). The implementation solution is as follows: When the salt mist concentration in the first altitude layer where the first UAV is located in the tidal flat power grid area exceeds the first concentration threshold, based on the second altitude layer, control the first UAV to fly upward and detect meteorological information within the first time period; Based on the meteorological information detected during the flight of the first UAV within the first time period, predict the second salt mist concentration distribution in the flight space corresponding to the tidal flat power grid area within the second time period; Based on the second salt mist concentration distribution, re-plan the flight trajectories of each second UAV flying in the flight space corresponding to the tidal flat power grid area within the second time period to obtain the third predicted flight trajectories of each second UAV, and control each second UAV to fly within the second time period according to each trajectory. Thus, the UAVs are prevented from being invaded by salt mist.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and particularly to a self-driving deployment and scheduling method and system for an unmanned aerial vehicle for power inspection. Background Art

[0002] With the continuous expansion of the power grid, power lines are also set up in the tidal flat area. However, the tidal flat area is significantly affected by the tidal cycle. During high tide, the sea water floods, and after low tide, the salt fog concentration in the humid air remains high, causing serious corrosion to power facilities. Due to the stratified distribution characteristics of the salt fog concentration with height, the difference in salt fog concentration at different heights is huge, and the corrosion effect on the unmanned aerial vehicle is also different.

[0003] Therefore, how to obtain monitoring images of different height positions of power lines while avoiding improper flight height of the unmanned aerial vehicle, such as when shooting near the ground, resulting in corrosion of key devices caused by salt fog or sea water splashing, has become a technical difficulty. Summary of the Invention

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

[0005] According to one aspect of the present invention, there is provided a self-driving deployment and scheduling method for an unmanned aerial vehicle for power inspection, including:

[0006] Based on the situation that the salt fog concentration in the first height layer where the first unmanned aerial vehicle is located in the tidal flat power grid area exceeds the first concentration threshold, determine a second height 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 is located within the first time period;

[0007] Based on the second height layer, adjust the first predicted flight trajectory to obtain a second predicted flight trajectory of the first unmanned aerial vehicle;

[0008] Based on the second predicted flight trajectory, control the first unmanned aerial vehicle to fly within the first time period and detect meteorological information;

[0009] Based on the meteorological information detected during the flight of the first unmanned aerial vehicle within the first time period, predict the second salt fog concentration distribution in the flight space corresponding to the tidal flat power grid area within the second time period, where the first time period is earlier than the second time period;

[0010] Based on the second salt fog concentration distribution, re-plan the flight trajectories of each second unmanned aerial vehicle 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 unmanned aerial vehicle;

[0011] Based on the third predicted flight trajectories of the respective second drones, control the respective second drones to fly during the second time period.

[0012] According to another aspect of the present invention, there is provided a self-driving deployment and scheduling device for a power inspection drone, and the method includes:

[0013] An altitude layer determination module, configured to determine a 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 is located during the first time period when the salt spray concentration in the first altitude layer where the first drone is located in the tidal flat power grid area exceeds a first concentration threshold;

[0014] 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 drone;

[0015] A first flight control module, configured to control the first drone to fly during the first time period and detect meteorological information based on the second predicted flight trajectory;

[0016] A concentration distribution prediction module, configured to predict the second salt spray concentration distribution in the flight space corresponding to the tidal flat power grid area during a second time period based on the meteorological information detected during the flight of the first drone during the first time period, where the first time period is earlier than the second time period;

[0017] A second trajectory determination module, configured to re-plan the flight trajectories of the respective second drones flying in the flight space corresponding to the tidal flat power grid area during the second time period based on the second salt spray concentration distribution to obtain third predicted flight trajectories of the respective second drones;

[0018] A second flight control module, configured to control the respective second drones to fly during the second time period based on the third predicted flight trajectories of the respective second drones.

[0019] According to another aspect of the present invention, there is provided a self-driving deployment and scheduling system for a power inspection drone, including: 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 of the power inspection drone according to any one of the embodiments of the present invention.

[0020] According to another aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the self-driving deployment and scheduling method of the power inspection unmanned aerial vehicle according to any one of the embodiments of the present invention.

[0021] Adopting the technical solution of the present invention, when the salt mist concentration in the first altitude layer where the first unmanned aerial vehicle is located in the tidal flat power grid area exceeds the first concentration threshold, based on the first salt mist concentration distribution in the flight space where the first predicted flight trajectory of the first unmanned aerial vehicle is located within the first time period, a second altitude layer is determined; based on the second altitude layer, the first predicted flight trajectory is adjusted to obtain a second predicted flight trajectory of the first unmanned aerial vehicle; based on the second predicted flight trajectory, the first unmanned aerial vehicle is controlled to fly within the first time period and detect meteorological information. In this way, when the first unmanned aerial vehicle detects that the salt mist concentration exceeds the preset threshold, the flight altitude of the first unmanned aerial vehicle is raised, which can avoid the first unmanned aerial vehicle from being invaded by salt mist and can detect relevant meteorological information. Then, based on the meteorological information detected during the flight of the first unmanned aerial vehicle within the first time period, the second salt mist concentration distribution in the flight space corresponding to the tidal flat power grid area within the second time period is predicted, wherein the first time period is earlier than the second time period; based on the second salt mist concentration distribution, the flight trajectories of each second unmanned aerial vehicle flying in the flight space corresponding to the tidal flat power grid area within the second time period are re-planned to obtain a third predicted flight trajectory of each second unmanned aerial vehicle; based on the third predicted flight trajectory of each second unmanned aerial vehicle, each second unmanned aerial vehicle is controlled to fly within the second time period. Thus, by using the meteorological information detected by the first unmanned aerial vehicle during the period of raising the flight altitude, the salt mist concentration distribution in the future second time period is predicted to accurately plan the flight trajectories of each second unmanned aerial vehicle and avoid the second unmanned aerial vehicle from being invaded by salt mist.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used 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

[0023] The drawings are used to better understand the solution and do not constitute a limitation to the present invention. Among them:

[0024] Figure 1 is a flowchart of the self-driving deployment and scheduling method of the power inspection unmanned aerial vehicle according to an embodiment of the present invention;

[0025] Figure 2 is a structural block diagram of the self-driving deployment and scheduling device of the power inspection unmanned aerial vehicle according to an embodiment of the present invention;

[0026] Figure 3 is a block diagram of an electronic device for implementing the method according to an embodiment of the present invention. Detailed Implementation Manner

[0027] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present invention. Similarly, descriptions of well-known functions and structures are omitted in the following description for clarity and conciseness.

[0028] Figure 1 is a flowchart of a self-driving deployment and scheduling method for a power inspection unmanned aerial vehicle according to an embodiment of the present invention.

[0029] As Figure 1 shown, the self-driving deployment and scheduling method for the power inspection unmanned aerial vehicle may include:

[0030] S110. When the salt mist concentration in the first altitude layer where the first unmanned aerial vehicle is located in the tidal flat power grid area exceeds the first concentration threshold, determine a second altitude layer based on the first salt mist concentration distribution in the flight space where the first predicted flight trajectory of the first unmanned aerial vehicle is located within the first time period;

[0031] S120. Adjust the first predicted flight trajectory based on the second altitude layer to obtain a second predicted flight trajectory of the first unmanned aerial vehicle;

[0032] S130. Control the first unmanned aerial vehicle to fly within the first time period and detect meteorological information based on the second predicted flight trajectory;

[0033] S140. Predict the second salt mist concentration distribution in the flight space corresponding to the tidal flat power grid area within the second time period based on the meteorological information detected during the flight of the first unmanned aerial vehicle within the first time period, where the first time period is earlier than the second time period;

[0034] S150. Re-plan the flight trajectories of each second unmanned aerial vehicle flying in the flight space corresponding to the tidal flat power grid area within the second time period based on the second salt mist concentration distribution to obtain third predicted flight trajectories of each second unmanned aerial vehicle;

[0035] S160. Control each second unmanned aerial vehicle to fly within the second time period based on the third predicted flight trajectories of each second unmanned aerial vehicle.

[0036] Exemplarily, the above method can be applied to the first unmanned aerial vehicle, which performs self-driving scheduling and flight scheduling for other second unmanned aerial vehicles.

[0037] Exemplarily, a plurality of interconnected power lines are erected in the tidal flat power grid area.

[0038] Understandably, the first height layer and the second height layer can be adjacent or overlapping. The first height layer is located below the second height layer. The height of the first height layer can be less than the height of the second height layer. The height of the first height layer can be the same as the height of the second height layer.

[0039] Exemplarily, the average salt spray concentration of the first height layer is higher than that of the second height layer. The range of the salt spray concentration of the first height layer is higher than the range of the salt spray concentration of the second height layer.

[0040] Exemplarily, the first salt spray concentration distribution includes the salt spray concentration corresponding to each three-dimensional coordinate position in the flight space.

[0041] Exemplarily, in the first salt spray concentration distribution, determine the height layer with a concentration lower than the first height layer, and determine the second height layer in this height layer. For example, select the height layer with a lower concentration change rate as the second height layer.

[0042] Exemplarily, based on the meteorological information detected during the flight of the first unmanned aerial vehicle (UAV) in the first time period and the change in salt spray concentration, determine the salt spray concentration distribution in the flight area of the first time period. Then, based on the salt spray concentration distribution in the flight area of the first time period, perform an extended prediction to obtain the second salt spray concentration distribution in the flight space corresponding to the beach power grid area in the second time period.

[0043] Exemplarily, a neural network model can also be used for concentration distribution prediction. Based on the meteorological information detected during the flight of the first UAV in the first time period, predict the predicted meteorological information in the flight space corresponding to the beach power grid area in the second time period. Input the predicted meteorological information into the trained neural network model, and the second salt spray concentration distribution in the flight space corresponding to the beach power grid area in the second time period can be predicted.

[0044] Exemplarily, the first time period is connected to the second time period, and the duration of the first time period is less than the duration of the second time period.

[0045] Exemplarily, based on the second salt spray concentration distribution, an abnormal area can be determined, and the flight trajectories of each second UAV flying in the abnormal area can be re-planned, while the flight trajectories in other areas can be not re-planned.

[0046] Exemplarily, the first UAV can be one of multiple second UAVs.

[0047] Exemplarily, while controlling the UAV to fly, control the UAV to perform operations such as image shooting, infrared detection, temperature detection, or meteorological information detection on the power lines on the ground.

[0048] Exemplarily, the third predicted flight trajectories of the respective second drones are sent to the corresponding second drones, so that each second drone flies according to the third predicted flight trajectory within the second time period.

[0049] According to the above embodiment, when the salt mist concentration in the first altitude layer where the first drone is located in the tidal flat power grid area exceeds the first concentration threshold, based on the first salt mist concentration distribution in the flight space where the first predicted flight trajectory of the first drone is located within the first time period, the second altitude layer is determined; based on the second altitude layer, the first predicted flight trajectory is adjusted to obtain the second predicted flight trajectory of the first drone; based on the second predicted flight trajectory, the first drone is controlled to fly within the first time period and the meteorological information is detected. In this way, when the first drone detects that the salt mist concentration exceeds the preset threshold, the flight altitude of the first drone is raised, which can avoid the first drone from being invaded by salt mist and can detect relevant meteorological information. Then, based on the meteorological information detected during the flight of the first drone within the first time period, the second salt mist concentration distribution in the flight space corresponding to the tidal flat power grid area within the second time period is predicted, where the first time period is earlier than the second time period; based on the second salt mist concentration distribution, the flight trajectories of the respective second drones flying in the flight space corresponding to the tidal flat power grid area within the second time period are replanned to obtain the third predicted flight trajectories of the respective second drones; based on the third predicted flight trajectories of the respective second drones, the respective second drones are controlled to fly within the second time period. Thus, the salt mist concentration distribution in the future second time period is predicted by using the meteorological information detected by the first drone during the period of raising the flight altitude, so as to accurately plan the flight trajectories of the respective second drones and avoid the second drones from being invaded by salt mist.

[0050] In one embodiment, the above method further includes: obtaining the historical three-dimensional point cloud data of the corresponding power line based on the flight space where the first predicted flight trajectory is located; performing curve fitting on the historical three-dimensional point cloud data to obtain the corresponding line curve space; determining the sampling points in the flight space where the first predicted flight trajectory is located based on the line curve space; determining the concentration change rate between two adjacent sampling points based on the historical salt mist concentration data of the sampling points in the flight space where the first predicted flight trajectory is located and the predicted meteorological information in the flight space where the first predicted flight trajectory is located; determining the first salt mist concentration distribution in the flight space where the first predicted flight trajectory is located based on the concentration change rate between two adjacent sampling points and the salt mist concentration in the first altitude layer.

[0051] Exemplarily, the flight space where the first predicted flight trajectory is located is vertically projected onto the ground to determine the vertical projection area, and then the historical three-dimensional point cloud data of the power line falling within the vertical projection area is obtained.

[0052] Exemplarily, using the least squares method or polynomial function to perform curve fitting on historical three-dimensional point cloud data, the corresponding line curve space can be obtained. Among them, the line curve space includes one or more three-dimensional line curves.

[0053] Exemplarily, project each curve in the line curve space onto each sub-height layer in the flight space where the first predicted flight trajectory is located, and then determine sampling points on the projected curves in each sub-height layer.

[0054] Exemplarily, the predicted meteorological information can include information such as temperature, humidity, and wind speed.

[0055] Exemplarily, based on the historical salt fog concentration data of the sampling points in the flight space where the first predicted flight trajectory is located, determine the historical concentration change rate between two adjacent sampling points. This change rate can be statistical values such as the average or median. Based on the predicted meteorological information of the flight space where the first predicted flight trajectory is located, adjust the historical concentration change rate between two adjacent sampling points to obtain the concentration change rate between two adjacent sampling points. For example, based on the difference between the historical meteorological information corresponding to the historical concentration change rate between two adjacent sampling points and the predicted meteorological information, use this difference to adjust the historical concentration change rate between two adjacent sampling points. 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.

[0056] Exemplarily, based on the salt fog concentration and concentration change rate of each sampling point in the first height layer, the salt fog concentration of each sampling point in non-first height layers can be deduced. Fit and interpolate the salt fog concentration of each sampling point in the first height layer and the salt fog concentration of each sampling point in non-first height layers, so as to obtain the first salt fog concentration distribution of the flight space where the first predicted flight trajectory is located.

[0057] According to the above embodiments, use the power line for projection to obtain sampling points, and thus use the salt fog concentration of the sampling points to accurately obtain the salt fog concentration distribution of the flight space where the first predicted flight trajectory is located.

[0058] In one implementation, 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 drone is located within the first future time period includes: determining the average concentration change rate at each altitude position along the centroid direction based on the first salt fog concentration distribution; determining each peak band based on the average concentration change rate at each altitude position; stratifying 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; and determining the second altitude layer among 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 concentrations of each altitude layer in the flight space where the first predicted flight trajectory is located.

[0059] Exemplarily, by averaging the salt fog concentrations of each sampling point in the plane where a certain altitude position is located, the average concentration change rate at this altitude position can be obtained.

[0060] It can be understood that the peak band can be a band where the average concentration change rate first rises to a peak and then decreases as the altitude position changes.

[0061] It can be understood that the peaks of different peak bands can be the same or different, and the lengths of different peak bands can be the same or different.

[0062] Exemplarily, the length of the peak band is the same as the thickness of the corresponding altitude layer.

[0063] Exemplarily, based on the salt fog concentration of one altitude layer and the salt fog concentrations of each altitude layer in the flight space where the first predicted flight trajectory is located, determine the degree of concentration difference between the first altitude layer and each altitude layer; based on the degree of concentration difference between the first altitude layer and each altitude layer and the difference distance between the first altitude layer and each altitude layer, determine the second altitude layer among each altitude layer in the flight space where the first predicted flight trajectory is located. For example, select the altitude layer with a relatively large degree of concentration difference but a relatively large difference distance as the second altitude layer.

[0064] 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, enabling the drone to quickly fly to a suitable altitude layer and avoid salt fog intrusion.

[0065] In one embodiment, adjusting the first predicted flight trajectory based on the second altitude layer to obtain the second predicted flight trajectory of the first unmanned aerial vehicle (UAV) includes: updating the end position of the first predicted flight trajectory based on the second altitude layer; and replanning the flight trajectory of the first UAV based on the current flight speed of the first UAV and the updated end position to obtain the second predicted flight trajectory of the first UAV.

[0066] Exemplarily, project each curve in the line curve space onto the second altitude layer, and use the sampling points of the projected curve to determine the updated end position in the first predicted flight trajectory.

[0067] Exemplarily, based on the current flight speed of the first UAV, with the current flight position of the first UAV as the starting point and the updated end position as the ending point, replan the flight trajectory of the first UAV to obtain the second predicted flight trajectory of the first UAV.

[0068] According to the above embodiment, the predicted flight trajectory of the UAV can be accurately planned.

[0069] In one embodiment, predicting the second salt fog concentration distribution in the flight space corresponding to the beach power grid area during a second time period based on the meteorological information detected during the flight of the first UAV in a first time period includes: predicting the predicted temperature distribution information, predicted humidity distribution information, and predicted wind speed distribution information in the flight space corresponding to the beach power grid area during the second time period based on the temperature distribution information, humidity distribution information, and wind speed distribution information in the meteorological information detected during the flight of the first UAV in the first time period; and inputting the predicted temperature distribution information, predicted humidity distribution information, and 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.

[0070] Exemplarily, the salt fog concentration distribution prediction model can be a neural network model trained using historical meteorological data and historical salt fog concentration distributions. For example, the neural network model can be a random forest model.

[0071] According to the above embodiment, the salt fog concentration distribution can be predicted using meteorological information in an artificial intelligence learning manner, improving the prediction accuracy.

[0072] In one embodiment, based on the second salt spray concentration distribution, the flight trajectories of each second unmanned aerial vehicle (UAV) flying in the flight space corresponding to the tidal flat power grid area during the second time period are re-planned to obtain the third predicted flight trajectories of each second UAV, including: determining the space with abnormal salt spray concentration fluctuations in the flight space corresponding to the tidal flat power grid area based on the second salt spray concentration distribution; and re-planning the flight trajectories of each second UAV flying in the space with abnormal salt spray concentration fluctuations during the second time period based on the third salt spray concentration distribution in the space with abnormal salt spray concentration fluctuations to obtain the third predicted flight trajectories of each second UAV.

[0073] Exemplarily, project the power curve corresponding to the space with abnormal salt spray concentration fluctuations onto the abnormal space to obtain a projection curve, and determine the target sampling points with salt spray concentration lower than the preset threshold using the salt spray concentration of the sampling points in the projection curve. Based on the three-dimensional position information of the target sampling points, re-plan the flight trajectories of each second UAV flying in the space with abnormal salt spray concentration fluctuations during the second time period to obtain the third predicted flight trajectories of each second UAV.

[0074] According to the above embodiment, the predicted flight trajectories of the UAVs can be accurately planned to avoid salt spray intrusion.

[0075] Figure 2 It is a structural block diagram of a self-driving deployment and scheduling device for a power inspection UAV according to an embodiment of the present invention.

[0076] As Figure 2 shown, the self-driving deployment and scheduling device for the power inspection UAV may include:

[0077] An altitude layer determination module 210, configured to determine a 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 is located during the first time period when the salt spray concentration in the first altitude layer where the first UAV is located in the tidal flat power grid area exceeds the first concentration threshold;

[0078] A first trajectory determination module 220, configured to adjust the first predicted flight trajectory based on the second altitude layer to obtain the second predicted flight trajectory of the first UAV;

[0079] A first flight control module 230, configured to control the first UAV to fly during the first time period and detect meteorological information based on the second predicted flight trajectory;

[0080] A concentration distribution prediction module 240, configured to predict a second salt fog concentration distribution in a flight space corresponding to the tidal flat power grid area during a second time period based on meteorological information detected during the flight of the first unmanned aerial vehicle in the first time period, where the first time period is earlier than the second time period;

[0081] A second trajectory determination module 250, configured to re-plan the flight trajectories of each second unmanned aerial vehicle flying in the flight space corresponding to the tidal flat power grid area during the second time period based on the second salt fog concentration distribution, to obtain a third predicted flight trajectory for each of the second unmanned aerial vehicles;

[0082] A second flight control module 260, configured to control each second unmanned aerial vehicle to fly during the second time period based on the third predicted flight trajectory of each second unmanned aerial vehicle.

[0083] In one implementation, the above device further includes:

[0084] A point cloud data acquisition module, configured to acquire historical three-dimensional point cloud data of a corresponding power line based on the flight space where the first predicted flight trajectory is located;

[0085] A curve fitting module, configured to perform curve fitting on the historical three-dimensional point cloud data to obtain a corresponding line curve space;

[0086] A sampling point determination module, configured to determine sampling points in the flight space where the first predicted flight trajectory is located based on the line curve space;

[0087] A concentration change rate determination module, configured to determine a concentration change rate between two adjacent sampling points based on historical salt fog concentration data of the sampling points in the flight space where the first predicted flight trajectory is located, and predicted meteorological information in the flight space where the first predicted flight trajectory is located;

[0088] A concentration distribution determination module, configured to determine a first salt fog concentration distribution in the flight space where the first predicted flight trajectory is located based on the concentration change rate between two adjacent sampling points and the salt fog concentration in the first height layer.

[0089] In one implementation, the height layer determination module 210 includes:

[0090] An average change rate determination unit, configured to determine an average concentration change rate at each height position along the centroid direction based on the first salt fog concentration distribution;

[0091] A peak band determination unit, configured to determine each peak band based on the average concentration change rate at each height position;

[0092] A spatial stratification unit, configured to stratify the flight space where the first predicted flight trajectory is located based on the height positions corresponding to the starting and ending positions in each peak band, so as to obtain each height layer in the flight space where the first predicted flight trajectory is located;

[0093] A height layer determination unit, configured to determine the second height layer among the height layers in the flight space where the first predicted flight trajectory is located based on the salt mist concentration of the first height layer and the salt mist concentrations of the height layers in the flight space where the first predicted flight trajectory is located.

[0094] In one implementation, the first trajectory determination module 220 includes:

[0095] An end point update unit, configured to update the end point position of the first predicted flight trajectory based on the second height layer;

[0096] A first trajectory planning unit, configured to re-plan the flight trajectory of the first unmanned aerial vehicle based on the current flight speed of the first unmanned aerial vehicle and the updated end point position, so as to obtain a second predicted flight trajectory of the first unmanned aerial vehicle.

[0097] In one implementation, the concentration distribution prediction module 240 includes:

[0098] A meteorological information prediction unit, configured to predict the predicted temperature distribution information, predicted humidity distribution information, and predicted wind speed distribution information in the flight space corresponding to the tidal flat power grid area during a second time period based on the temperature distribution information, humidity distribution information, and wind speed distribution information in the meteorological information detected during the flight of the first unmanned aerial vehicle in the first time period;

[0099] A concentration distribution prediction unit, configured to input the predicted temperature distribution information, predicted humidity distribution information, and predicted wind speed distribution information into a salt mist concentration distribution prediction model, so as to obtain the second salt mist concentration distribution output by the salt mist concentration distribution prediction model.

[0100] In one implementation, the second flight control module 260 includes:

[0101] An abnormal space determination unit, configured to determine a salt mist concentration fluctuation abnormal space in the flight space corresponding to the tidal flat power grid area based on the second salt mist concentration distribution;

[0102] A second trajectory planning unit, configured to re-plan the flight trajectories of the second unmanned aerial vehicles flying in the salt mist concentration fluctuation abnormal space during the second time period based on the third salt mist concentration distribution in the salt mist concentration fluctuation abnormal space, so as to obtain third predicted flight trajectories of the second unmanned aerial vehicles.

[0103] For the specific functions and examples of each module and sub-module of the system in the embodiments of the present invention, reference may be made to the relevant descriptions of the corresponding steps in the above method embodiments, which will not be elaborated herein.

[0104] In the technical solution of the present invention, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0105] According to an embodiment of the present invention, the present invention also provides a system and a readable storage medium.

[0106] Figure 3 FIG. shows a schematic block diagram of an exemplary electronic device 800 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital assistant, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present invention described herein and / or claimed.

[0107] As Figure 3 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 into 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.

[0108] A plurality 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 magnetic 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.

[0109] The computing unit 801 can be various general-purpose and / or special-purpose 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, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 executes the various methods and processes described above, such as the self-driving deployment scheduling method of the power inspection unmanned aerial vehicle. For example, in some embodiments, the self-driving deployment scheduling method of the power inspection unmanned aerial vehicle can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto 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 unmanned aerial vehicle described above can be executed. Alternatively, in other embodiments, the computing unit 801 can be configured to execute the self-driving deployment scheduling method of the power inspection unmanned aerial vehicle in any other suitable manner (e.g., by means of firmware).

[0110] The various embodiments of the systems and techniques described above in this document 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-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0111] 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 the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0112] In the context of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide for 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, 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, speech input, or tactile input).

[0114] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end, middleware, or front-end components. The components of the system can 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.

[0115] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is generated by computer programs running on the 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.

[0116] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved, and no limitations are imposed herein.

[0117] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the principles of the present invention shall be included within 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, the second altitude layer is determined 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, wherein the first altitude layer is below the second altitude layer, and the average salt spray concentration of the first altitude layer is higher than the average salt spray concentration of the second altitude layer and / or the salt spray concentration extreme difference of the first altitude layer is higher than the salt spray concentration extreme difference of the second altitude layer; 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, and performing curve fitting on the historical three-dimensional point cloud data to obtain a corresponding line curve space; Based on the second altitude layer, adjusting the first predicted flight trajectory to obtain a second predicted flight trajectory of the first UAV, including: projecting each curve in the line curve space to the second altitude layer, determining an updated terminal position in the first predicted flight trajectory using sampling points of the projected curve in the second altitude layer, and 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; 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 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 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.

5. 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.

6. A self-driving deployment and dispatching device for electric power inspection drones, characterized in that: include: The altitude layer determination module 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 of the first altitude layer where the first drone is located exceeds the first concentration threshold in the tidal flat power grid area, wherein the first altitude layer is located below the second altitude layer, and the average salt spray concentration of the first altitude layer is higher than the average salt spray concentration of the second altitude layer and / or the salt spray concentration extreme difference of the first altitude layer is higher than the salt spray concentration extreme difference of the second altitude layer; 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 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, wherein the first trajectory determination module includes: an endpoint updating unit, configured to project each curve in the line curve space to the second altitude layer, and determine an updated endpoint position in the first predicted flight trajectory using sampling points of the projected curve in the second altitude layer; a first trajectory planning unit, configured to re-plan the flight trajectory of the first UAV based on the current flight speed of the first UAV and the updated endpoint position to obtain the 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.

7. The device according to claim 6, 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.

8. 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 5.

9. 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 1-5.

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