Power equipment inspection method and system using unmanned aerial vehicle

Abstract

The invention provides a power equipment inspection method and system using an unmanned aerial vehicle, and the method comprises the steps: enabling the unmanned aerial vehicle to receive a first inspection route drawn by a worker, and shooting the image data of each power equipment on the first inspection route after the unmanned aerial vehicle lifts off, identifying equipment attribute information of each power equipment based on the shot image data, and predicting electromagnetic field intensity distribution information of the corresponding power equipment according to the equipment attribute information; predicting the influence degree of the corresponding power equipment on an electronic compass and a communicator of the unmanned aerial vehicle based on the electromagnetic field intensity distribution information, and performing optimization processing on the first inspection route according to the influence degree to obtain a second inspection route; and the unmanned aerial vehicle inspects according to the second inspection route. According to the method, the influence degree of the electromagnetic field of the power equipment on the electronic compass and the communicator of the unmanned aerial vehicle is evaluated, so that each predetermined inspection point location is optimized and adjusted, and the probability that the unmanned aerial vehicle breaks down due to electromagnetic interference is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power inspection, and in particular to an electric power equipment inspection method and system using an unmanned aerial vehicle. Background Art

[0002] The application of drones in power equipment inspection has become more and more widespread. It has brought revolutionary changes to the power industry by improving inspection efficiency, reducing labor costs and improving safety. However, some power equipment has a strong magnetic field. The interference of the strong magnetic field will affect the electronic compass and communicator of the drone, making it unable to accurately judge the heading, causing the drone's hovering position to shift, increasing the risk of flight. How to inspect power equipment while avoiding the interference of strong magnetic fields is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method, system, electronic equipment, computer storage medium and computer program product for inspecting electric power equipment using a drone.

[0004] The present invention provides a method for inspecting electric power equipment using an unmanned aerial vehicle, the method comprising the following steps: The drone receives a first inspection route drawn by a staff member, and after the drone is launched into the air, it captures image data of each power device located on the first inspection route, identifies device attribute information of each power device based on the captured image data, and predicts electromagnetic field intensity distribution information of the corresponding power device based on the device attribute information; Based on the electromagnetic field intensity distribution information, the influence degree of the corresponding power equipment on the electronic compass and the communicator of the drone is predicted, and the first inspection route is optimized according to the influence degree to obtain the second inspection route; The drone performs inspection along the second inspection route.

[0005] Optionally, the first inspection route is drawn in the following manner: The staff selects several power equipment that need to be inspected in the high-precision map, as well as the inspection sequence number of each power equipment; The first inspection route is automatically generated based on the location information of each power device and the corresponding inspection number.

[0006] Optionally, the device attribute information includes a first device type, a voltage level, and a wire material type; and the electromagnetic field intensity distribution information of the corresponding power device predicted according to the device attribute information includes: The first device type, the voltage level and the conductor material type are input into a magnetic field strength depth prediction model, and the magnetic field strength depth prediction model outputs the electromagnetic field strength distribution information of the corresponding power equipment; wherein the electromagnetic field strength distribution information is in three-dimensional form.

[0007] Optionally, the identifying device attribute information of each electric device based on the captured image data includes: Extracting a multi-angle image, a nameplate image, an input wire image and / or an output wire image of the electric power equipment based on the captured image data; Performing image comparison on the multi-angle images to determine the first device type of the power equipment; and extracting character information from the nameplate image to determine the preliminary voltage level of the power equipment; and performing image comparison on the input wire image and / or the output wire image to determine the wire material type of the power equipment; Based on the captured image data, working status data of each associated power equipment that is electrically connected to the power equipment is extracted, a correction coefficient is determined based on each working status data, and the preliminary voltage level is corrected using the correction coefficient to obtain the voltage level of the power equipment; wherein the correction coefficient is less than 1.

[0008] Optionally, determining the correction coefficient according to each of the working status data includes: Determining the number of associated power devices in a working state according to the working state data of each associated power device, and extracting the second device type of each associated power device from the working state data; A load prediction model based on a large model is used to predict the load level of the power equipment according to the quantity and the second equipment type of each related power equipment, and the correction coefficient is obtained by looking up the table according to the load level.

[0009] Optionally, predicting the influence of the corresponding power equipment on the electronic compass and communicator of the drone based on the electromagnetic field intensity distribution information includes: Determine a first inspection point corresponding to the electric equipment from the first inspection route, and predict a first impact score and a second impact score of the corresponding electric equipment on the electronic compass and the communicator of the drone according to the distance between the first inspection point and the electric equipment and the electromagnetic field intensity distribution information; A comprehensive impact score is calculated based on the fusion of the first impact score and the second impact score.

[0010] Optionally, the optimizing the first inspection route according to the impact degree to obtain a second inspection route includes: If the comprehensive impact score is higher than the target impact score, a second inspection point further away from the power equipment is determined around the first inspection point, and the first inspection route is partially adjusted based on the first inspection point and the second inspection point to obtain the second inspection route.

[0011] The present invention also provides a power equipment inspection system using a drone, the system comprising a processor and a memory, the memory storing an executable program code; the processor calls the executable program code stored in the memory to implement the following steps: The drone receives a first inspection route drawn by a staff member, and after the drone is launched into the air, it captures image data of each power device located on the first inspection route, identifies device attribute information of each power device based on the captured image data, and predicts electromagnetic field intensity distribution information of the corresponding power device based on the device attribute information; Based on the electromagnetic field intensity distribution information, the influence degree of the corresponding power equipment on the electronic compass and the communicator of the drone is predicted, and the first inspection route is optimized according to the influence degree to obtain the second inspection route; The drone performs inspection along the second inspection route.

[0012] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute any of the methods described above.

[0013] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above methods is executed.

[0014] The present invention also provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, it implements any of the methods described above.

[0015] The present invention can predict the electromagnetic field strength distribution information of the power equipment based on the equipment properties, and then evaluate the influence of the electromagnetic field of the power equipment on the electronic compass and communicator of the drone based on the electromagnetic field strength distribution information, so as to decide whether to optimize and adjust the predetermined inspection points to reduce the probability of the drone malfunctioning due to electromagnetic interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a schematic diagram of a method for inspecting electric power equipment using a drone disclosed in an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram of a power equipment inspection system using a drone disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] See also Figure 1 The embodiment of the present invention discloses a method for inspecting electric power equipment using a drone, the method comprising the following steps: The drone receives a first inspection route drawn by a staff member, and after the drone is launched into the air, it captures image data of each power device located on the first inspection route, identifies device attribute information of each power device based on the captured image data, and predicts electromagnetic field intensity distribution information of the corresponding power device based on the device attribute information; Based on the electromagnetic field intensity distribution information, the influence degree of the corresponding power equipment on the electronic compass and the communicator of the drone is predicted, and the first inspection route is optimized according to the influence degree to obtain the second inspection route; The drone performs inspection along the second inspection route.

[0021] In the scheme of the present invention, the staff draws a first inspection route for the drone in advance, and the first inspection route includes a plurality of power equipment connected in sequence. The drone can inspect each power equipment one by one by traveling along the first inspection route. After receiving the first inspection route, the drone takes off and then shoots each power equipment on the first inspection route, and identifies the equipment attribute information of each power equipment from the captured image data. The equipment attribute information includes but is not limited to transformers, circuit breakers, generators, reactors, etc. Then, the electromagnetic field intensity distribution information of the corresponding power equipment is predicted based on the equipment attribute information of the power equipment. The drone can predict the influence of the corresponding power equipment on the electronic compass and communicator of the drone based on the determined electromagnetic field intensity distribution information. If the influence is too large, it is necessary to select other better inspection points near the original inspection points of the first inspection route, so as to optimize the first inspection route and obtain a more reasonable second inspection route. Therefore, by inspecting each power equipment according to the second inspection route, it is possible to avoid the strong magnetic field of the power equipment from having an adverse effect on the electronic compass and communicator of the drone, thereby ensuring the smoothness and safety of the drone inspection.

[0022] The present invention can predict the electromagnetic field strength distribution information of the power equipment based on the equipment properties, and then evaluate the influence of the electromagnetic field of the power equipment on the electronic compass and communicator of the drone based on the electromagnetic field strength distribution information, so as to decide whether to optimize and adjust the predetermined inspection points to reduce the probability of the drone malfunctioning due to electromagnetic interference.

[0023] Optionally, the first inspection route is drawn in the following manner: The staff selects several power equipment that need to be inspected in the high-precision map, as well as the inspection sequence number of each power equipment; The first inspection route is automatically generated based on the location information of each power device and the corresponding inspection number.

[0024] In this embodiment, the staff holds a ground device such as a computer or mobile device, which has a high-precision map built in it. The coordinates of each power device in the area are entered in advance in the high-precision map. The staff selects several power devices that need to be inspected in the high-precision map, and the ground device can determine the coordinates of each power device. The staff also specifies an inspection number for each power device, and the ground device can automatically generate the first inspection route based on the location information of each power device and the corresponding inspection number. The ground device then sends the first inspection route to the drone in a wired or wireless manner.

[0025] Optionally, the device attribute information includes a first device type, a voltage level, and a wire material type; and the electromagnetic field intensity distribution information of the corresponding power device predicted according to the device attribute information includes: The first device type, the voltage level and the conductor material type are input into a magnetic field strength depth prediction model, and the magnetic field strength depth prediction model outputs the electromagnetic field strength distribution information of the corresponding power equipment, wherein the electromagnetic field strength distribution information is in three-dimensional form.

[0026] In this embodiment, the present invention constructs a magnetic field strength deep prediction model, which can conduct a comprehensive and in-depth analysis of the first device type, voltage level and wire material type in the device attribute information of the power equipment, so as to predict the electromagnetic field strength distribution information of each power equipment. Among them, the electromagnetic field strength distribution information is in a three-dimensional form, that is, with the power equipment as the center, the three-dimensional electromagnetic field strength changes from the inside to the outside are drawn separately. Obviously, the farther away from the power equipment itself, the weaker the corresponding electromagnetic field strength, and vice versa. The magnetic field strength deep prediction model is preferably constructed by a graph convolutional neural network or a Transformer.

[0027] Among them, different types of power equipment have different distribution ranges of electromagnetic field strength, and the electromagnetic field strength is also directly related to the voltage level of the power equipment. The higher the voltage level, the greater the corresponding electromagnetic field strength. In addition, the input and output wire materials of the power equipment are directly related to the tolerable current intensity, and currents of different intensities will also cause the electromagnetic field strength of the power equipment to change in strength. Therefore, the present invention analyzes the electromagnetic field strength distribution information of the power equipment by combining the first device type, voltage level and wire material type.

[0028] Optionally, the identifying device attribute information of each electric device based on the captured image data includes: Extracting a multi-angle image, a nameplate image, an input wire image and / or an output wire image of the electric power equipment based on the captured image data; Performing image comparison on the multi-angle images to determine the first device type of the power equipment; and extracting character information from the nameplate image to determine the preliminary voltage level of the power equipment; and performing image comparison on the input wire image and / or the output wire image to determine the wire material type of the power equipment; Based on the captured image data, working status data of each associated power equipment that is electrically connected to the power equipment is extracted, a correction coefficient is determined based on each working status data, and the preliminary voltage level is corrected using the correction coefficient to obtain the voltage level of the power equipment; wherein the correction coefficient is less than 1.

[0029] In this embodiment, the multi-angle image of the power device itself, the nameplate image of the power device, the input wire image and / or the output wire image of the power device can be respectively captured from the captured image data. The multi-angle image of the power device is compared with the external images of various types of power devices pre-stored in the database, so as to determine the first device type of the power device. The input wire image and / or the output wire image of the power device are compared with the external images of various types of wires pre-stored in the database, so as to determine the wire material type of the power device.

[0030] Also, by extracting character information from the nameplate image of the power equipment, the preliminary voltage level of the power equipment can be directly extracted. The preliminary voltage level is generally the highest voltage level of the power equipment, i.e., the rated voltage, but the actual voltage level of the power equipment will be lower than the preliminary voltage level. In this regard, the present invention further extracts the working status data of each related power equipment that is electrically connected to the power equipment based on the captured image data, determines the corresponding correction coefficient based on the working status data of each related power equipment, and uses the correction coefficient less than 1 to appropriately lower the preliminary voltage level, that is, obtains a voltage level that is closer to the current actual situation of the power equipment.

[0031] Optionally, determining the correction coefficient according to each of the working status data includes: Determining the number of the power devices in working state according to the working state data of each associated power device, and extracting the second device type of each associated power device from the working state data; A load prediction model based on a large model is used to predict the load level of the power equipment according to the quantity and the second equipment type of each related power equipment, and the correction coefficient is obtained by looking up the table according to the load level.

[0032] In this embodiment, the working status data of the associated power equipment will affect the load level of the power equipment electrically connected thereto, and further affect the actual voltage level of the power equipment. In this regard, the present invention first determines the number of power equipment in working state according to the working status data of each associated power equipment, and extracts the second equipment type of each associated power equipment from the working status data. The second equipment type, which is the same as the first equipment type, also includes but is not limited to transformers, circuit breakers, generators, reactors, etc. Then, the load prediction model based on the large model is used to predict the load level of the power equipment for the obtained number and each second equipment type, and the correction coefficient is obtained by looking up the table according to the load level. Among them, the comparison table corresponding to the table lookup operation contains multiple groups of comparison relationships between correction coefficients of different sizes and load levels of different heights. Specifically, the higher the load level, the larger the corresponding correction coefficient, that is, the closer it is to 1 (for example, 0.9), and vice versa, the smaller it is to 1 (for example, 0.6).

[0033] Among them, the large model based on which the load forecasting model is constructed refers to the GPT large model and the multimodal large model (such as DALL-E, DataCanvas, DingoDB, etc.). These large models need to be fine-tuned with the corresponding training data before they can be used. That is, the large model after fine-tuning training is a load forecasting model capable of performing load level forecasting. Since it belongs to the prior art, the present invention will not repeat the specific training process here.

[0034] Optionally, predicting the influence of the corresponding power equipment on the electronic compass and communicator of the drone based on the electromagnetic field intensity distribution information includes: Determine a first inspection point corresponding to the electric equipment from the first inspection route, and predict a first impact score and a second impact score of the corresponding electric equipment on the electronic compass and the communicator of the drone according to the distance between the first inspection point and the electric equipment and the electromagnetic field intensity distribution information; A comprehensive impact score is calculated based on the fusion of the first impact score and the second impact score.

[0035] In this embodiment, the first inspection point corresponding to the power equipment predetermined by the staff can be determined from the first inspection route, that is, the predetermined drone is to inspect the power equipment at the first inspection point. Since the coordinates of the power equipment are known, the distance between the inspection point and the power equipment can be calculated. According to the distance and the electromagnetic field strength distribution information, the first impact score and the second impact score of the corresponding power equipment on the electronic compass and the communicator of the drone can be predicted respectively. Among them, the failure critical distance value corresponding to a certain electromagnetic field source (the critical distance value of the electronic compass and the communicator of the drone) can be measured for the electronic compass and the communicator respectively, and the failure critical distance value is re-measured after changing the electromagnetic field strength of the electromagnetic field source, that is, the corresponding relationship between the failure critical distance value and the distance of the two sets of electronic compasses and communicators is obtained. Therefore, according to the actual distance between the drone and the power equipment and the electromagnetic field strength at the actual distance, the first impact score of the electronic compass affected by the electromagnetic field and the second impact score of the communicator affected by the electromagnetic field can be determined. Obviously, when the actual distance is lower than the critical distance value, as the absolute value of the difference with the critical distance value increases, the impact score becomes larger, and vice versa. In addition, when the actual distance is higher than the critical distance value, the impact score is set to 0.

[0036] Finally, the first impact score and the second impact score can be subjected to weighted fusion calculation, for example, to obtain a comprehensive impact score, which is used to determine the comprehensive impact of the electromagnetic field strength of the power equipment on the drone inspecting at the first inspection point. The present invention preferably sets the weighted weight of the first impact score to be greater than the weighted weight of the second impact score, because the electronic compass is more susceptible to failure due to electromagnetic interference.

[0037] Optionally, the optimizing the first inspection route according to the impact degree to obtain a second inspection route includes: If the comprehensive impact score is higher than the target impact score, a second inspection point further away from the power equipment is determined around the first inspection point, and the first inspection route is partially adjusted based on the first inspection point and the second inspection point to obtain the second inspection route.

[0038] In this embodiment, if the comprehensive impact score at some of the first inspection points in the first inspection route is higher than the target impact score, it means that the first inspection point is unreasonable, and the drone will be disturbed by the strong magnetic field when conducting inspections here, which greatly increases the probability of failure. At this time, a second inspection point that is farther away from the power equipment is determined around these unreasonable first inspection points, and the aforementioned comprehensive impact score evaluation process can be performed again on the second inspection point to analyze its rationality. If the comprehensive impact score is still higher than the target impact score, a new second inspection point is further away, and the cycle is repeated until the comprehensive impact score is no higher than the target impact score. Finally, the first inspection route is partially adjusted based on the first inspection point and the second inspection point to obtain the second inspection route.

[0039] See also Figure 2 As shown, an embodiment of the present invention further provides a power equipment inspection system using a drone, the system comprising a processor and a memory, the memory storing an executable program code; the processor calls the executable program code stored in the memory to implement the following steps: The drone receives a first inspection route drawn by a staff member, and after the drone is launched into the air, it captures image data of each power device located on the first inspection route, identifies device attribute information of each power device based on the captured image data, and predicts electromagnetic field intensity distribution information of the corresponding power device based on the device attribute information; Based on the electromagnetic field intensity distribution information, the influence degree of the corresponding power equipment on the electronic compass and the communicator of the drone is predicted, and the first inspection route is optimized according to the influence degree to obtain the second inspection route; The drone performs inspection along the second inspection route.

[0040] An embodiment of the present invention further provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method described in any of the above embodiments.

[0041] An embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any of the above embodiments is executed.

[0042] An embodiment of the present invention further provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, the method described in any of the above embodiments is implemented.

[0043] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0046] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for inspecting electric power equipment using a drone, characterized in that: The method comprises the following steps: The drone receives a first inspection route drawn by a staff member, and after the drone is launched into the air, it captures image data of each power device located on the first inspection route, identifies device attribute information of each power device based on the captured image data, and predicts electromagnetic field intensity distribution information of the corresponding power device based on the device attribute information; Based on the electromagnetic field intensity distribution information, the influence degree of the corresponding power equipment on the electronic compass and the communicator of the drone is predicted, and the first inspection route is optimized according to the influence degree to obtain the second inspection route; The drone performs inspection along the second inspection route.

2. The method for inspecting electric power equipment using a drone according to claim 1, characterized in that: The first inspection route is drawn in the following manner: The staff selects several power equipment that need to be inspected in the high-precision map, as well as the inspection sequence number of each power equipment; The first inspection route is automatically generated based on the location information of each power device and the corresponding inspection number.

3. The method for inspecting electric power equipment using a drone according to claim 2, characterized in that: The device attribute information includes a first device type, a voltage level, and a conductor material type; and the electromagnetic field intensity distribution information of the corresponding power device predicted based on the device attribute information includes: The first device type, the voltage level and the conductor material type are input into a magnetic field strength depth prediction model, and the magnetic field strength depth prediction model outputs the electromagnetic field strength distribution information of the corresponding power equipment; wherein the electromagnetic field strength distribution information is in three-dimensional form.

4. The method for inspecting electric power equipment using a drone according to claim 3, characterized in that: The identifying of device attribute information of each electric device based on the captured image data includes: Extracting a multi-angle image, a nameplate image, an input wire image and / or an output wire image of the electric power equipment based on the captured image data; Performing image comparison on the multi-angle images to determine the first device type of the power equipment; and extracting character information from the nameplate image to determine the preliminary voltage level of the power equipment; and performing image comparison on the input wire image and / or the output wire image to determine the wire material type of the power equipment; Based on the captured image data, working status data of each associated power equipment that is electrically connected to the power equipment is extracted, a correction coefficient is determined based on each working status data, and the preliminary voltage level is corrected using the correction coefficient to obtain the voltage level of the power equipment; wherein the correction coefficient is less than 1.

5. The method for inspecting electric power equipment using a drone according to claim 4, characterized in that: The determining of the correction coefficient according to each of the working status data comprises: Determining the number of associated power devices in a working state according to the working state data of each associated power device, and extracting the second device type of each associated power device from the working state data; A load prediction model based on a large model is used to predict the load level of the power equipment according to the quantity and the second equipment type of each related power equipment, and the correction coefficient is obtained by looking up the table according to the load level.

6. The method for inspecting electric power equipment using a drone according to claim 5, characterized in that: The predicting of the influence of the corresponding power equipment on the electronic compass and communicator of the drone based on the electromagnetic field intensity distribution information includes: Determine a first inspection point corresponding to the electric equipment from the first inspection route, and predict a first impact score and a second impact score of the corresponding electric equipment on the electronic compass and the communicator of the drone according to the distance between the first inspection point and the electric equipment and the electromagnetic field intensity distribution information; A comprehensive impact score is calculated based on the fusion of the first impact score and the second impact score.

7. A power equipment inspection system using a drone, the system comprising a processor and a memory, the memory storing executable program code; characterized in that: The processor calls the executable program code stored in the memory to implement the following steps: The drone receives a first inspection route drawn by a staff member, and after the drone is launched into the air, it captures image data of each power device located on the first inspection route, identifies device attribute information of each power device based on the captured image data, and predicts electromagnetic field intensity distribution information of the corresponding power device based on the device attribute information; Based on the electromagnetic field intensity distribution information, the influence degree of the corresponding power equipment on the electronic compass and the communicator of the drone is predicted, and the first inspection route is optimized according to the influence degree to obtain the second inspection route; The drone performs inspection along the second inspection route.

8. An electronic device comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.

10. A computer program product, comprising a computer program stored in a computer storage medium, characterized in that: When the computer program is executed by a processor of an electronic device, the method according to any one of claims 1 to 6 is implemented.

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