Internet-of-things-assisted indoor unmanned aerial vehicle navigation control system and method for transformer substation and electronic equipment

Abstract

The invention discloses an internet-of-things-assisted indoor unmanned aerial vehicle navigation control system and method for a transformer substation and electronic equipment. The system is characterized in that an active Internet of Things terminal is arranged in a flight area corresponding to a flight route of an unmanned aerial vehicle, a passive Internet of Things terminal is arranged at a fixed position in a transformer substation, and the active Internet of Things terminal is used for actively sending a terminal data signal to the unmanned aerial vehicle; the passive Internet of Things terminal is used for sending the environment data signal of the transformer substation to the unmanned aerial vehicle; the unmanned aerial vehicle is used for sending the received terminal data signal and the environment data signal to the edge computing server in the flight process along the flight path; and the edge computing server is used for performing complex signal field verification on the flight route according to the terminal data signal and the environment data signal, and determining a planned route for the unmanned aerial vehicle to fly. The technical problems of poor anti-interference capability and low accuracy of unmanned aerial vehicle navigation control in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the field of navigation control technology, and more specifically, to an Internet of Things-assisted substation indoor drone navigation control system, method and electronic equipment. Background Art

[0002] The inspection mission of drone flying in substations is an important way of modern power facility operation and maintenance, using automation and drone technology to inspect and monitor the equipment status and environmental conditions of substations. The accuracy of drone flying in substation inspection missions mainly depends on the design of drone navigation routes. Therefore, ensuring the accuracy of drone navigation routes is crucial for drone substation inspections.

[0003] In the related art, the navigation route of the drone is usually formulated only by relying on the data collected by the drone's own sensors as the input data of the drone's navigation system. However, since the design of the drone's navigation route is restricted by many factors, such as the environmental parameters of the substation, the flight parameters of the drone, and the parameters of the drone's communication link, the navigation route formulated only by relying on the data collected by the drone's own sensors has poor anti-interference ability and low accuracy.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide an IoT-assisted substation indoor drone navigation control system, method, and electronic equipment to at least solve the technical problems of poor anti-interference capability and low accuracy of drone navigation control in related technologies.

[0006] According to one aspect of an embodiment of the present application, an IoT-assisted UAV navigation and control system is provided, including: an active IoT terminal, a passive IoT terminal, a UAV and an edge computing server, wherein the active IoT terminal is arranged in a flight area corresponding to the flight route of the UAV, and the passive IoT terminal is arranged in a fixed position in the substation, wherein the active IoT terminal is used to actively send a terminal data signal to the UAV; the passive IoT terminal is used to send an environmental data signal of the substation to the UAV; the UAV is used to send the received terminal data signal and environmental data signal to the edge computing server during the flight along the flight route; the edge computing server is used to perform a complex signal field check on the flight route according to the terminal data signal and the environmental data signal, and determine the planned route for the UAV to fly.

[0007] Optionally, the drone includes: a laser radar, a camera and an Internet of Things information collection component, wherein the Internet of Things information collection component is used to receive terminal data signals and environmental data signals, and determine the signal strength of the terminal data signal.

[0008] Optionally, the edge computing server is also used to construct a three-dimensional complex signal field model based on the signal attenuation characteristics of multiple IoT terminals corresponding to the flight route, wherein the multiple IoT terminals include IoT terminals whose signals cover the flight route.

[0009] Optionally, the multiple Internet of Things terminals include: at least one active Internet of Things terminal and / or at least one passive Internet of Things terminal.

[0010] Optionally, the edge computing server is also used to perform signal verification on the terminal data signal and the environmental data signal using a three-dimensional complex signal field model, obtain a signal verification result, and generate a planned route based on the signal verification result.

[0011] Optionally, the edge computing server is used to send the planned route to the drone, or the edge computing server is used to send the difference information between the planned route and the flight route to the drone.

[0012] Optionally, the UAV is used to adjust the flight action according to the planned route or difference information to complete the flight inspection mission of the substation.

[0013] Optionally, after completing the flight inspection mission, the drone is used to send the flight route, planned route, terminal data signal and environmental data signal corresponding to the flight inspection mission to the edge computing server; the edge computing server is used to update the three-dimensional complex signal field model according to the flight route, planned route, terminal data signal and environmental data signal.

[0014] According to another aspect of the embodiment of the present application, there is also provided an IoT-assisted UAV navigation and control method, including: obtaining terminal data signals and environmental data signals from the UAV, wherein the terminal data signals are collected by an active IoT terminal and actively sent to the UAV, the active IoT terminal is set in a flight area corresponding to the flight route of the UAV, and the environmental data signals are collected by a passive IoT terminal and passively sent to the UAV in response, and the passive IoT terminal is set at a fixed position in the substation; performing complex signal field verification on the flight route according to the terminal data signal and the environmental data signal to determine the planned route for the UAV to fly.

[0015] According to another aspect of an embodiment of the present application, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned drone navigation control method.

[0016] In an embodiment of the present application, an active IoT terminal is set in a flight area corresponding to the flight route of the drone, and a passive IoT terminal is set in a fixed position in the substation, wherein the active IoT terminal is used to actively send a terminal data signal to the drone; the passive IoT terminal is used to send an environmental data signal of the substation to the drone; the drone is used to send the received terminal data signal and environmental data signal to the edge computing server during the flight along the flight route; the edge computing server is used to perform a complex signal field check on the flight route according to the terminal data signal and the environmental data signal, and determine the planned route for the drone to fly. As a result, the present application achieves the purpose of more accurately planning the flight route of the drone, thereby achieving the technical effect of improving the anti-interference ability and accuracy of the drone navigation control, and further solving the technical problems of poor anti-interference ability and low accuracy of the drone navigation control in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a terminal device for an optional IoT-assisted drone navigation control method according to an embodiment of the present application;

[0019] Figure 2 is a structural block diagram of an IoT-assisted drone navigation control system according to an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of an optional Internet of Things-assisted drone navigation control system according to an embodiment of the present application;

[0021] Figure 4 is a flow chart of an optional indoor drone flight navigation according to an embodiment of the present application;

[0022] Figure 5 It is a flow chart of an IoT-assisted drone navigation control method according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to an embodiment of the present application, an embodiment of an IoT-assisted drone navigation control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a hardware structure block diagram of a terminal device for an optional IoT-assisted drone navigation control method according to an embodiment of the present application, such as Figure 1 As shown, the terminal device may include one or more processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Microcontroller Unit, referred to as MCU) or a programmable logic device (Field Programmable Gate Array, referred to as FPGA)), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display device 110, an input / output device 108 (i.e., an I / O device), a Universal Serial Bus (USB) port (which may be included as one of the ports of a computer bus, not shown in the figure), a network interface (not shown in the figure), a power supply (not shown in the figure) and / or a camera (not shown in the figure). It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the above terminal device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] It should be noted that the one or more processors 102 and / or other data processing circuits may be embodied in whole or in part as software, hardware, firmware or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be fully or partially integrated into any of the other components in the terminal device (or mobile device).

[0028] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the Internet of Things-assisted drone navigation control method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned Internet of Things-assisted drone navigation control method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the terminal device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the terminal device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0030] Under the above operating environment, the embodiment of the present application provides the following Figure 2 The IoT-assisted drone navigation control system shown in the figure Figure 2 is a structural block diagram of an IoT-assisted drone navigation control system according to an embodiment of the present application, such as Figure 2As shown, the system includes: an active Internet of Things terminal, a passive Internet of Things terminal, a drone and an edge computing server, the active Internet of Things terminal is set in a flight area corresponding to the flight route of the drone, and the passive Internet of Things terminal is set at a fixed position in the substation, wherein the active Internet of Things terminal is used to actively send a terminal data signal to the drone; the passive Internet of Things terminal is used to send an environmental data signal of the substation to the drone; the drone is used to send the received terminal data signal and environmental data signal to the edge computing server during the flight along the flight route; the edge computing server is used to perform a complex signal field check on the flight route according to the terminal data signal and the environmental data signal, and determine the planned route for the drone to fly.

[0031] The above-mentioned flight area may be a specific geographical or physical space range covered by the drone when performing a mission. The above-mentioned terminal data signal may include terminal location information, terminal type, drone location information, and equipment status information in the substation. The above-mentioned fixed position in the substation may include but is not limited to the wall in the substation, the ceiling in the substation, or around the equipment in the substation. The above-mentioned environmental data signal may include information such as temperature, humidity, and electrical parameters. The above-mentioned complex signal field may be an electromagnetic environment with complex signal strength and distribution formed in the environment of the substation due to the presence of multiple signal sources (such as Internet of Things terminals, wireless communication equipment, power facilities, etc.).

[0032] Specifically, an active IoT terminal is set in a flight area corresponding to the flight route of the UAV, and a passive IoT terminal is set at a fixed position in the substation; further, the active IoT terminal is used to actively send a terminal data signal to the UAV, and the terminal data signal actively sent by the active IoT terminal can reduce the signal search time of the UAV and improve navigation efficiency and accuracy; using a passive IoT terminal set at a fixed position in the substation, when the UAV approaches the passive IoT terminal, the passive terminal responds by receiving the query signal sent by the UAV and sends the environmental data signal of the substation; further, the UAV receives the terminal data signal and the environmental data signal while flying along the flight route, and sends the received terminal data signal and the environmental data signal to the edge computing server; further, using the edge computing server, the flight route is checked for complex signal fields based on the terminal data signal and the environmental data signal, the signal strength changes are identified, and the planned route of the UAV to be flown is corrected and optimized.

[0033] It is easy to understand that, through the above-mentioned IoT-assisted drone navigation and control system, on the one hand, in the embodiment of the present application, by utilizing the IoT terminal signal in the substation, the drone can more accurately determine its own position. Even if the position of the equipment in the substation is adjusted or the environment changes, the navigation and control system can also be adjusted adaptively, avoiding navigation deviations caused by electromagnetic interference or visual occlusion, and having strong anti-interference capabilities; on the other hand, in the embodiment of the present application, by integrating multiple signal sources, the flight route can be formulated more stably and accurately. Therefore, in the embodiment of the present application, the drone navigation and control system has higher accuracy and stronger anti-interference capabilities.

[0034] The following is a further introduction to the above-mentioned Internet of Things-assisted drone navigation and control system in an embodiment of the present application.

[0035] Optionally, the drone includes: a laser radar, a camera and an Internet of Things information collection component, wherein the Internet of Things information collection component is used to receive terminal data signals and environmental data signals, and determine the signal strength of the terminal data signal.

[0036] The above-mentioned laser radar can be used to measure the distance between the drone and its surrounding environment. The laser radar can also be used for environmental perception, obstacle avoidance, and determining the precise position and posture of the drone. The above-mentioned camera can be used to capture visual images. The above-mentioned visual images can be used to assist the drone in identifying landmarks, obstacles or performing visual positioning. The above-mentioned signal strength can represent the distance information between the drone and the signal source, and the signal strength can also be used to assist the drone in correcting its flight path.

[0037] Specifically, the drone is equipped with a laser radar, a camera and an Internet of Things information collection component. By utilizing the laser radar and the camera, positioning information can be provided for the drone. By utilizing the Internet of Things information collection component, terminal data signals from active Internet of Things terminals and environmental data signals from passive Internet of Things terminals can be received. In addition, the Internet of Things information collection component can also be used to analyze the signal strength of the terminal data signal to provide additional positioning information for the drone.

[0038] It is easy to understand that through the above-mentioned IoT-assisted UAV navigation and control system, in the embodiment of the present application, the signal strength received by the IoT information collection component is used for positioning, and combined with the data from the lidar and camera, the IoT-assisted UAV navigation and control navigation system can better cope with the influence of electromagnetic interference, obstacle obstruction and other factors, enhance the robustness and stability of the UAV navigation system, and improve the accuracy of UAV positioning in the complex electromagnetic environment of the substation room.

[0039] Optionally, the edge computing server is also used to construct a three-dimensional complex signal field model based on the signal attenuation characteristics of multiple IoT terminals corresponding to the flight route, wherein the multiple IoT terminals include IoT terminals whose signals cover the flight route.

[0040] The signal attenuation characteristic may refer to characteristic information corresponding to the phenomenon that the wireless signal gradually weakens as the distance increases during the spatial propagation process. The three-dimensional complex signal field model may be used to predict the signal reception of the drone on the flight route.

[0041] Specifically, the edge computing server is used to determine the relative position relationship between the drone and each IoT terminal, as well as the signal propagation path and intensity changes in space according to the signal attenuation characteristics of multiple IoT terminals corresponding to the flight route, and a three-dimensional complex signal field model is constructed.

[0042] It is easy to understand that through the above-mentioned IoT-assisted drone navigation and control system, in the embodiment of the present application, using the three-dimensional complex signal field model, the edge computing server can analyze the changes in the signal strength received by the drone in real time, and can quickly adjust the model according to real-time data, correct the drone's navigation data, and ensure the accuracy of the drone's flight path in a complex electromagnetic environment.

[0043] Optionally, the multiple Internet of Things terminals include: at least one active Internet of Things terminal and / or at least one passive Internet of Things terminal.

[0044] It is easy to understand that through the above-mentioned IoT-assisted drone navigation and control system, in the embodiment of the present application, multiple IoT terminals may include at least one active IoT terminal and / or at least one passive IoT terminal, which can provide more comprehensive and real-time environmental data, thereby improving the flexibility and adaptability of the drone when performing tasks, and is suitable for a variety of different flight environments and mission requirements.

[0045] Optionally, the edge computing server is also used to perform signal verification on the terminal data signal and the environmental data signal using a three-dimensional complex signal field model, obtain a signal verification result, and generate a planned route based on the signal verification result.

[0046] The above signal verification results can be used to verify the accuracy and completeness of UAV navigation data.

[0047] Specifically, the edge computing server uses a three-dimensional complex signal field model to analyze the terminal data signals and environmental data signals received by the drone, compares the signal strength with the strength in the expected model, and obtains the signal verification result; further, based on the signal verification result, the edge computing server can make a judgment. If the signal verification result indicates that the drone deviates from the planned path, or detects potential obstacles or dangerous signals, the edge computing server will generate a new planned route to ensure that the drone can complete the inspection task safely and accurately.

[0048] It is easy to understand that through the above-mentioned IoT-assisted drone navigation and control system, in the embodiment of the present application, by using a three-dimensional complex signal field model to verify the signal, the edge computing server can generate the flight path of the drone according to the actual situation, avoiding navigation deviations caused by signal interference or equipment failure, and ensuring the accuracy of the drone flight path.

[0049] Optionally, the edge computing server is used to send the planned route to the drone, or the edge computing server is used to send the difference information between the planned route and the flight route to the drone.

[0050] The above-mentioned difference information can be the difference adjustment information generated by the edge computing server comparing the planned route and the actual flight route of the drone.

[0051] Specifically, the edge computing server compares and analyzes the planned route with the flight route. If there is no difference between the planned route and the flight route, the edge computing server sends the planned route to the drone; if there is a difference between the planned route and the flight route, the edge computing server sends the difference information between the planned route and the flight route to the drone.

[0052] It is easy to understand that through the above-mentioned IoT-assisted drone navigation and control system, on the one hand, in the embodiment of the present application, the planned route generated by the edge computing server can be sent to the drone in real time to dynamically adjust the planned route, ensuring that the drone can avoid areas with strong electromagnetic interference and ensure the safety and accuracy of the flight; on the other hand, in the embodiment of the present application, if the edge computing server detects that the drone deviates from the planned route, it can timely generate the difference information between the planned route and the flight route, send the difference information to the drone, and guide the drone to adjust the path. Therefore, in the embodiment of the present application, the drone navigation and control system has higher accuracy and stronger anti-interference ability.

[0053] Optionally, the UAV is used to adjust the flight action according to the planned route or difference information to complete the flight inspection mission of the substation.

[0054] The above-mentioned flight action may include but is not limited to flight attitude, direction and speed. The above-mentioned flight inspection task may be a task that the drone needs to complete to inspect the substation area and equipment, for example, the collection and monitoring of the substation equipment status and environmental parameters.

[0055] Specifically, the drone generates corresponding adjustment instructions based on the received planned route or difference information, and adjusts the flight actions (such as turning, accelerating, decelerating, ascending, descending, etc.) according to the adjustment instructions to ensure that the actual flight path is consistent with the planned route.

[0056] It is easy to understand that through the above-mentioned Internet of Things-assisted drone navigation and control system, in the embodiment of the present application, by adjusting the flight action according to the planned route or difference information, the drone can navigate more accurately according to the planned route or difference information, effectively avoiding drone flight deviations caused by electromagnetic interference or environmental changes, and improving the quality and efficiency of drone inspection tasks.

[0057] Optionally, after completing the flight inspection mission, the drone is used to send the flight route, planned route, terminal data signal and environmental data signal corresponding to the flight inspection mission to the edge computing server; the edge computing server is used to update the three-dimensional complex signal field model according to the flight route, planned route, terminal data signal and environmental data signal.

[0058] It is easy to understand that through the above-mentioned Internet of Things-assisted UAV navigation and control system, in the embodiment of the present application, by updating the three-dimensional complex signal field model, the Internet of Things-assisted UAV navigation and control system can automatically optimize the three-dimensional complex signal field model, thereby promoting the self-learning and self-adaptation capabilities of the system, and improving the intelligence level of the inspection tasks. Further, it can optimize the planned route generated by the three-dimensional complex signal field model, thereby improving the accuracy and reliability of the planned route.

[0059] In an exemplary application scenario, Figure 3 is a schematic diagram of an optional Internet of Things-assisted drone navigation control system according to an embodiment of the present application, such as Figure 3 As shown, the IoT-assisted UAV navigation and control system includes a UAV part, an edge computing part, and an IoT terminal part, wherein the UAV part includes a laser radar, a camera, and an IoT information collection component; the IoT terminal part includes an active IoT terminal and a passive IoT terminal.

[0060] Furthermore, the drone part can fuse the data collected by the lidar with the data collected by the camera to obtain the drone navigation data, and the drone part can also send the received terminal data signal and environmental data signal to the edge computing server. The IoT terminal can collect signals and send the collected signals to the drone and / or edge computing server.

[0061] Furthermore, the edge computer server can screen the corresponding IoT terminals on the drone inspection route, obtain the IoT terminal data corresponding to the screened IoT terminals, determine the signal strength of the IoT terminal data signal, and construct a complex signal field model based on the signal attenuation characteristics of multiple IoT terminals corresponding to the flight route.

[0062] Furthermore, the complex signal field model in the edge computer server is used to perform signal verification based on the IoT terminal data, generate a planned path, and compare and analyze the planned path with the drone navigation data. If the planned path is inconsistent with the drone navigation data, the difference information between the planned path and the drone navigation data is generated to correct the navigation data. The edge computer server transmits the planned path / difference information to the drone, and the drone updates the flight route based on the planned path / difference information.

[0063] It should be noted that, if a complex signal field model already exists on the edge computer server before the complex signal field model is constructed, the most recently optimized complex signal field model can be directly used for signal verification.

[0064] In an exemplary application scenario, Figure 4 is a flow chart of an optional indoor drone flight navigation according to an embodiment of the present application, such as Figure 4 As shown, the edge computing server receives the flight route and flight time data of the drone, selects multiple IoT terminals in the substation that participate in data analysis based on the flight route, and constructs a three-dimensional complex signal field model based on the signal attenuation characteristics of multiple IoT terminals.

[0065] Furthermore, during the flight, the drone sends the flight data and IoT terminal signal data of the actual flight process to the edge computing server. The flight data, IoT terminal signal data and the three-dimensional complex signal field model are used to perform signal verification. If the flight data is inconsistent with the IoT terminal signal data, the difference information between the flight data and the IoT terminal signal data is used as optimization data and sent to the drone. The drone adjusts the flight route based on the received optimization data.

[0066] Furthermore, after the UAV flight is completed, the flight data, IoT terminal signal data and navigation optimization data are sent to the edge computing server, and the three-dimensional complex signal field model is optimized based on the flight data, IoT terminal signal data and navigation optimization data.

[0067] In an embodiment of the present application, the active IoT terminal is set in the flight area corresponding to the flight route of the drone, and the passive IoT terminal is set in a fixed position in the substation, wherein the active IoT terminal is used to actively send a terminal data signal to the drone; the passive IoT terminal is used to send the environmental data signal of the substation to the drone; the drone is used to send the received terminal data signal and the environmental data signal to the edge computing server during the flight along the flight route; the edge computing server is used to perform a complex signal field check on the flight route according to the terminal data signal and the environmental data signal, and determine the planned route for the drone to fly. As a result, the present application achieves the purpose of more accurately planning the flight route of the drone, thereby achieving the technical effect of improving the anti-interference ability and accuracy of the drone navigation control, and thus solving the technical problems of poor anti-interference ability and low accuracy of the drone navigation control in the related technology.

[0068] In this embodiment, a method for controlling a drone using the Internet of Things is also provided. Figure 5 is a flow chart of an IoT-assisted drone navigation control method according to an embodiment of the present application, such as Figure 5 As shown, the method includes:

[0069] Step S501, obtaining a terminal data signal and an environmental data signal from the drone, wherein the terminal data signal is collected by an active IoT terminal and actively sent to the drone, the active IoT terminal is set in a flight area corresponding to the flight route of the drone, and the environmental data signal is collected by a passive IoT terminal and passively sent to the drone in response, the passive IoT terminal is set at a fixed position in the substation;

[0070] Step S502, performing complex signal field verification on the flight route according to the terminal data signal and the environmental data signal to determine the planned route for the UAV to fly.

[0071] Optionally, in the above step S502, performing complex signal field verification on the flight route according to the terminal data signal and the environmental data signal to determine the planned route for the UAV to fly may also include the following execution steps:

[0072] Step S521, constructing a three-dimensional complex signal field model according to the signal attenuation characteristics of multiple Internet of Things terminals corresponding to the flight route, wherein the multiple Internet of Things terminals include Internet of Things terminals whose signals cover the flight route.

[0073] Optionally, in the above step S521, performing complex signal field verification on the flight route according to the terminal data signal and the environmental data signal to determine the planned route for the UAV to fly may also include the following execution steps:

[0074] Step S5211, use the three-dimensional complex signal field model to perform signal verification on the terminal data signal and the environmental data signal, obtain the signal verification result, and generate a planned route based on the signal verification result.

[0075] Optionally, the above-mentioned IoT-assisted drone navigation control method may further include the following method steps:

[0076] Step S503: sending the planned route to the drone, or the edge computing server is used to send the difference information between the planned route and the flight route to the drone.

[0077] Optionally, the above-mentioned IoT-assisted drone navigation control method may further include the following method steps:

[0078] Step S504: The UAV adjusts its flight action according to the planned route or the difference information to complete the flight inspection task of the substation.

[0079] Optionally, the above-mentioned IoT-assisted drone navigation control method may further include the following method steps:

[0080] Step S505, after completing the flight inspection task, the drone sends the flight route, planned route, terminal data signal and environmental data signal corresponding to the flight inspection task to the edge computing server;

[0081] Step S506: The edge computing server updates the three-dimensional complex signal field model according to the flight route, planned route, terminal data signal and environmental data signal.

[0082] In an embodiment of the present application, a terminal data signal and an environmental data signal are obtained from a drone, wherein the terminal data signal is collected by an active IoT terminal and actively sent to the drone, the active IoT terminal is set in a flight area corresponding to the flight route of the drone, and the environmental data signal is collected by a passive IoT terminal and passively sent to the drone in response, the passive IoT terminal is set in a fixed position in a substation; the flight route is subjected to a complex signal field check based on the terminal data signal and the environmental data signal, and the planned route of the drone to be flown is determined. Thus, the present application achieves the purpose of more accurately planning the flight route of the drone, thereby achieving the technical effect of improving the anti-interference ability and accuracy of the drone navigation control, and further solving the technical problem of poor anti-interference ability and low accuracy of the drone navigation control in the related art.

[0083] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the above-mentioned electrical equipment defect location methods.

[0084] Optionally, in this embodiment, the above-mentioned processor can be configured to perform the following steps through a computer program: obtaining terminal data signals and environmental data signals from the drone, wherein the terminal data signals are collected by an active Internet of Things terminal and actively sent to the drone, and the active Internet of Things terminal is set in a flight area corresponding to the flight route of the drone; the environmental data signals are collected by a passive Internet of Things terminal and passively sent to the drone in response, and the passive Internet of Things terminal is set at a fixed position in the substation; the flight route is checked for complex signal fields based on the terminal data signal and the environmental data signal to determine the planned route for the drone to fly.

[0085] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiment and its optional implementation manners, which will not be described in detail here.

[0086] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0087] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words contribute to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, ROM, RAM, mobile hard disks, magnetic disks or optical disks.

[0092] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An IoT-assisted substation indoor drone navigation control system, characterized in that: include: An active IoT terminal, a passive IoT terminal, a drone and an edge computing server, wherein the active IoT terminal is arranged in a flight area corresponding to the flight route of the drone, and the passive IoT terminal is arranged in a fixed position in the substation, wherein: The active Internet of Things terminal is used to actively send a terminal data signal to the drone; The passive Internet of Things terminal is used to send the environmental data signal of the substation to the drone; The drone is used to send the received terminal data signal and the environmental data signal to the edge computing server during the flight along the flight route; The edge computing server is used to perform complex signal field verification on the flight route according to the terminal data signal and the environmental data signal to determine the planned route for the UAV to fly.

2. The unmanned aerial vehicle navigation control system according to claim 1, characterized in that: The drone includes: a laser radar, a camera and an Internet of Things information collection component, wherein the Internet of Things information collection component is used to receive the terminal data signal and the environmental data signal, and determine the signal strength of the terminal data signal.

3. The unmanned aerial vehicle navigation control system according to claim 1, characterized in that: The edge computing server is also used to construct a three-dimensional complex signal field model according to the signal attenuation characteristics of multiple Internet of Things terminals corresponding to the flight route, wherein the multiple Internet of Things terminals include Internet of Things terminals whose signals cover the flight route.

4. The unmanned aerial vehicle navigation control system according to claim 3, characterized in that: The multiple Internet of Things terminals include: at least one active Internet of Things terminal and / or at least one passive Internet of Things terminal.

5. The unmanned aerial vehicle navigation control system according to claim 3, characterized in that: The edge computing server is also used to perform signal verification on the terminal data signal and the environmental data signal using the three-dimensional complex signal field model to obtain a signal verification result, and generate a planned route based on the signal verification result.

6. The unmanned aerial vehicle navigation control system according to claim 1, characterized in that: The edge computing server is used to send the planned route to the drone, or the edge computing server is used to send the difference information between the planned route and the flight route to the drone.

7. The unmanned aerial vehicle navigation control system according to claim 6, characterized in that: The drone is used to adjust the flight action according to the planned route or the difference information to complete the flight inspection task of the substation.

8. The unmanned aerial vehicle navigation control system according to claim 5, characterized in that: The drone is used to send the flight route, the planned route, the terminal data signal and the environmental data signal corresponding to the flight inspection task to the edge computing server after completing the flight inspection task; The edge computing server is used to update the three-dimensional complex signal field model according to the flight route, the planned route, the terminal data signal and the environmental data signal.

9. An IoT-assisted substation indoor drone navigation control method, characterized in that: include: Acquire a terminal data signal and an environmental data signal from the drone, wherein the terminal data signal is collected by an active IoT terminal and actively sent to the drone, the active IoT terminal is set in a flight area corresponding to the flight route of the drone, and the environmental data signal is collected by a passive IoT terminal and passively sent to the drone in response, the passive IoT terminal is set at a fixed position in the substation; The flight route is verified by complex signal fields according to the terminal data signal and the environmental data signal to determine the planned route for the UAV to fly.

10. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the Internet of Things-assisted substation indoor drone navigation control method in claim 9.

Images