Unmanned aerial vehicle control method, equipment, product and medium

Abstract

The invention discloses an unmanned aerial vehicle control method, equipment, a product and a medium, and relates to the technical field of power inspection unmanned aerial vehicle control. The method comprises the following steps: acquiring electromagnetic field intensity distribution and an air humidity value of a target unmanned aerial vehicle in an operation area, and surface material characteristics, geometric structure data and operation voltage data of a target facility; determining a first danger avoidance area of the operation area according to the air humidity value, the surface material characteristics and the geometric structure data and the operation voltage data of the target facility; determining a second danger avoidance area of the operation area based on the electromagnetic field intensity distribution; determining a comprehensive no-fly envelope area based on the first danger avoidance area and the second danger avoidance area, and generating an inspection route; acquiring real-time wind field vector data, and determining a wind-resistant attitude correction parameter according to the real-time wind field vector data; and generating a flight control instruction based on the inspection route and the wind-resistant attitude correction parameter, and controlling the target unmanned aerial vehicle to fly along the inspection route according to the flight control instruction. And the flight safety of the unmanned aerial vehicle is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power inspection unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle control method, device, product and medium. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology and the continuous expansion of application fields, unmanned aerial vehicles have been widely used in power inspection, infrastructure detection, agricultural plant protection and other fields. In particular, in the power industry, unmanned aerial vehicles are used to inspect high-voltage transmission lines, substations and other power facilities, which has become an important means to improve inspection efficiency and reduce personnel safety risks. Unmanned aerial vehicle inspection not only overcomes the problems of terrain limitations and low efficiency of traditional manual inspection, but also can obtain high-quality detection data in complex environments.

[0003] At present, in the field of unmanned aerial vehicle control technology, a kind of unmanned aerial vehicle control method is disclosed in a Chinese patent with publication number CN117991803A. The method receives the operator's instruction through the user interface, analyzes the received instruction through the pre-set algorithm and converts it into the control signal recognizable by the unmanned aerial vehicle, then sends the control signal to the unmanned aerial vehicle through the wireless signal transmission mode, receives the state information returned by the unmanned aerial vehicle and monitors the flight state in real time, and intervenes when detecting abnormality.

[0004] However, in actual application, when the unmanned aerial vehicle performs the inspection task around the power facility, due to the strong electromagnetic field generated by the high-voltage power facility and the discharge danger, and the complex and changeable meteorological conditions of the working environment, it is difficult to effectively identify and avoid multiple dangerous areas around the power facility by relying only on the basic state information such as position, speed and height for flight control, which may lead to insufficient flight safety of the unmanned aerial vehicle. SUMMARY

[0005] The present application provides an unmanned aerial vehicle control method, device, product and medium, which has the effect of improving the flight safety of the unmanned aerial vehicle.

[0006] In the first aspect of the present application, an unmanned aerial vehicle control method is provided, which specifically comprises: obtaining environmental state data of a target unmanned aerial vehicle in a working area, surface material characteristics of a target facility, geometric structure data and operating voltage data, the working area comprising at least one target facility, the environmental state data comprising electromagnetic field intensity distribution and air humidity value of the working area; calculating electrical insulation characteristic parameters under the working area according to the air humidity value and the surface material characteristics; determining a first danger avoidance area of the working area based on the electrical insulation characteristic parameters in combination with the geometric structure data and the operating voltage data of the target facility; The second hazard avoidance zone of the work area is determined based on the electromagnetic field intensity distribution; The first hazard avoidance zone and the second hazard avoidance zone are spatially merged to generate a comprehensive no-fly envelope area for the operation area; based on the comprehensive no-fly envelope area, inspection routes are generated. Acquire real-time wind field vector data of the work area, and determine wind resistance attitude correction parameters based on the real-time wind field vector data; Based on the inspection route and wind-resistant attitude correction parameters, flight control commands are generated, and the target UAV is controlled to fly along the inspection route according to the flight control commands.

[0007] By employing the aforementioned technical solutions, environmental status data, surface material characteristics, geometric structure data, and operating voltage data of the target UAV in the operating area are acquired, providing comprehensive basic data support for subsequent hazardous area identification. Electrical insulation characteristic parameters are calculated based on air humidity and surface material characteristics, accurately quantifying the electrical breakdown risk threshold under current environmental conditions. Therefore, when determining the first hazard avoidance zone based on electrical insulation characteristic parameters combined with the target facility's geometric structure data and operating voltage data, the electrical discharge hazard range can be precisely delineated, preventing the UAV from entering spaces where electrical breakdown may occur. Simultaneously, a second hazard avoidance zone is determined based on the electromagnetic field intensity distribution, effectively identifying electromagnetic interference hazard zones and preventing strong electromagnetic fields from interfering with the UAV's electronic systems. Spatially merging the first and second hazard avoidance zones generates a comprehensive no-fly envelope, integrating the spatial distribution of electrical discharge and electromagnetic interference risks, ensuring that the generated inspection route can simultaneously avoid multiple hazard sources. By acquiring real-time wind field vector data of the work area and determining wind-resistant attitude correction parameters, the UAV can maintain a stable flight attitude in complex wind field environments, avoiding deviation from the safe flight path due to wind. Finally, based on the inspection route and wind-resistant attitude correction parameters, flight control commands are generated to control the target UAV's flight, improving the flight safety of UAVs in inspection operations around power facilities.

[0008] Optionally, the calculation of electrical insulation characteristic parameters of the work area based on air humidity and surface material properties specifically includes: Based on the air humidity value and the surface material characteristics of each target facility, the equivalent surface conductivity of each target facility surface is determined. Based on the equivalent surface conductivity of each target facility, the electric field distortion enhancement coefficient near the surface of each target facility is determined. The preset benchmark breakdown field strength is corrected by using the electric field distortion enhancement coefficient corresponding to each target facility, and the critical distortion field strength threshold that can induce air breakdown for each target facility is calculated. The set of critical distortion field strength thresholds corresponding to each target facility is determined as the electrical insulation characteristic parameters under the operating area.

[0009] By adopting the above technical solution, the equivalent surface conductivity of each target facility is determined based on the air humidity value and the surface material characteristics of each target facility, which quantifies the differences in conductivity of different materials under the current humidity conditions. Based on the equivalent surface conductivity of each target facility, the electric field distortion enhancement coefficient near the surface of each target facility is determined, which accurately reflects the degree of influence of different facility surfaces on the surrounding electric field distribution. The electric field distortion enhancement coefficient corresponding to each target facility is used to weight and correct the preset benchmark breakdown field strength, and the critical distortion field strength threshold corresponding to each target facility is calculated. Since the combined influence of surface material and humidity on electric field distortion is considered, the calculated critical distortion field strength threshold can truly reflect the actual breakdown risk threshold of each target facility under the current environment. The set of critical distortion field strength thresholds corresponding to each target facility is determined as the electrical insulation characteristic parameters under the working area, realizing a differentiated quantitative characterization of the electrical breakdown risk of different target facilities within the working area.

[0010] Optionally, determining the first hazard avoidance zone of the work area based on electrical insulation characteristic parameters combined with the geometric structure data and operating voltage data of the target facility specifically includes: Extract areas from geometric structure data where the rate of change of the geometric curvature of the target facility surface is greater than a preset threshold, and mark these areas as discharge-sensitive points with a high charge accumulation tendency. Based on the operating voltage data, a virtual spatial electric field intensity distribution field is constructed outward from each discharge sensitive point as the center. From the set of electrical insulation characteristic parameters, extract the critical distortion field strength threshold corresponding to the target facility to which the discharge sensitive point belongs; By comparing the spatial electric field intensity distribution field with the corresponding critical distortion field strength threshold, the spatial range in which the electric field intensity value in the spatial electric field intensity distribution field is higher than the corresponding critical distortion field strength threshold is extracted to generate the breakdown risk envelope surface. Based on the breakdown risk envelope, a preset mechanical positioning tolerance range is superimposed to construct a physical isolation space that can block the electrical breakdown path, and the outer edge of the physical isolation space is defined as the first danger avoidance zone.

[0011] By employing the above technical solution, areas where the rate of change of geometric curvature on the surface of the target facility exceeds a preset threshold are extracted from geometric structure data and marked as discharge-sensitive points, thus identifying high-risk locations prone to charge accumulation. Based on operating voltage data, a virtual spatial electric field intensity distribution field is constructed outward from each discharge-sensitive point, achieving a quantitative description of the spatial distribution of electric field intensity around the discharge-sensitive points. Critical distortion field strength thresholds corresponding to the target facility to which the discharge-sensitive points belong are extracted from the set of electrical insulation characteristic parameters, ensuring that the risk assessment uses the actual breakdown threshold of the target facility under the current environment. By comparing the spatial electric field intensity distribution field with the corresponding critical distortion field strength threshold, the spatial range where the electric field strength value exceeds the critical distortion field strength threshold is extracted to generate a breakdown risk envelope, accurately delineating the spatial area where electrical breakdown may occur. A physical isolation space is constructed by superimposing a preset mechanical positioning tolerance range on the breakdown risk envelope, compensating for potential positional shifts caused by UAV positioning errors. The outer edge of the physical isolation space is defined as the first hazard avoidance zone, completing the accurate delineation of the electrical discharge hazard area.

[0012] Optionally, the second hazard avoidance zone for determining the work area based on the electromagnetic field intensity distribution specifically includes: The electromagnetic field intensity distribution is mapped onto the preset magnetic susceptibility response curve of the target UAV to determine the magnetic interference level of the target UAV at each location point in the operating area; Based on the magnetic interference level, the preset interference-drift mapping table is consulted to determine the maximum attitude uncertainty of the target UAV in the hovering operation state at each location point; Based on the maximum attitude uncertainty, predict the cumulative maximum position drift radius of the target UAV within a preset time period; Based on the maximum position drift radius, an electromagnetic interference buffer space capable of covering potential runaway displacement is constructed in reverse. The boundary of the electromagnetic interference buffer space is expanded by a pre-set anti-magnetic interference safety margin, and the outer contour boundary of the electromagnetic interference buffer space containing the anti-magnetic interference safety margin is used as a second danger avoidance zone that can avoid the risk of collision caused by magnetic interference.

[0013] By employing the aforementioned technical solution, the electromagnetic field intensity distribution is mapped onto the target UAV's preset magnetic susceptibility response curve, determining the magnetic interference level of the target UAV at each location within the operational area, and achieving a quantitative assessment of the impact of electromagnetic interference at different locations. Based on the magnetic interference level, a preset interference-drift mapping table is consulted to determine the maximum attitude uncertainty of the target UAV at each location in a hovering state, establishing a correlation between electromagnetic interference and UAV attitude deviation. Based on the maximum attitude uncertainty, the cumulative maximum position drift radius of the target UAV within a preset time period is predicted, converting the attitude deviation into a quantifiable value of spatial position offset. Using the maximum position drift radius as a basis, an electromagnetic interference buffer space capable of covering potential runaway displacement is constructed, delineating the spatial range within which electromagnetic interference may cause UAV position offset. The boundary of the electromagnetic interference buffer space is expanded by a preset anti-magnetic interference safety margin to compensate for electromagnetic field intensity fluctuations and prediction errors. The outer contour boundary of the electromagnetic interference buffer space, including the anti-magnetic interference safety margin, is designated as a second hazard avoidance zone, completing the accurate delineation of the electromagnetic interference hazard area.

[0014] Optionally, the step of spatially merging the first hazard avoidance zone and the second hazard avoidance zone to generate a comprehensive no-fly envelope zone specifically includes: Map the first hazard avoidance zone and the second hazard avoidance zone to a unified three-dimensional coordinate system; Perform a spatial Boolean union operation on the mapped first hazard avoidance region and the second hazard avoidance region to generate a preliminary joint space that can simultaneously cover electrical breakdown risk and magnetic interference drift risk; The outer contour surface of the preliminary joint space is extracted, and the curvature of the geometric connection of the outer contour surface is smoothed to obtain the integrated no-fly envelope region.

[0015] By adopting the above technical solution, the first and second hazard avoidance zones are mapped to a unified three-dimensional coordinate system, realizing the expression of different types of hazard zones under the same spatial reference. A spatial Boolean union operation is performed on the mapped first and second hazard avoidance zones to generate a preliminary joint space that simultaneously covers electrical breakdown risk and magnetic interference drift risk, integrating the spatial distribution range of the two types of hazard sources. The outer contour surface of the preliminary joint space is extracted, and the curvature of the geometric connection points of the outer contour surface is smoothed, eliminating sharp protrusions or depressions that may exist at the boundary junctions of different hazard zones. This avoids path discontinuities or excessive detours during inspection route planning, resulting in a comprehensive no-fly envelope area, forming a no-fly space range with smooth boundaries that can fully cover multiple hazard sources.

[0016] Optionally, generating inspection routes based on the comprehensive no-fly zone envelope specifically includes: Using the outer surface of the comprehensive no-fly zone as the reference interface, the normal extension is performed along the direction away from the comprehensive no-fly zone, with the preset optimal observation distance as the distance parameter, to construct a three-dimensional equidistant inspection surface that encloses the comprehensive no-fly zone. The locations of key components to be inspected are pre-marked on the target facility. The locations of the key components are then projected along the normal onto a three-dimensional equidistant inspection surface to obtain multiple key inspection viewpoints located on the three-dimensional equidistant inspection surface. A three-dimensional spatial topology network is constructed using multiple key inspection viewpoints as nodes. The shortest path search algorithm is used to plan a geometric trajectory on a three-dimensional equidistant inspection surface that connects all key inspection viewpoints and has the shortest total path length. The geometric trajectory is then determined as the inspection route.

[0017] By adopting the above technical solution, using the outer surface of the comprehensive no-fly zone as the reference interface, and extending the normal along the direction away from the comprehensive no-fly zone with a preset optimal observation distance as the distance parameter, a three-dimensional equidistant inspection surface is constructed that encloses the comprehensive no-fly zone. This ensures that any position on the inspection surface maintains a constant observation distance from the surface of the comprehensive no-fly zone, meeting both the imaging requirements of the detection equipment and maintaining a safe distance from hazardous areas. The locations of pre-marked key components on the target facility are obtained, and these locations are projected along the normal onto the three-dimensional equidistant inspection surface, resulting in multiple key inspection viewpoints located on the surface. This ensures that each key component corresponds to an observation position within a safe area. A three-dimensional spatial topology network is constructed using these multiple key inspection viewpoints as nodes. A shortest path search algorithm is used to plan a geometric trajectory on the three-dimensional equidistant inspection surface that connects all key inspection viewpoints with the shortest total path length. This reduces the UAV's flight distance and operation time. The geometric trajectory is then used as the inspection route, generating an efficient inspection path that covers all detection targets while avoiding hazardous areas.

[0018] Optionally, determining the wind-resistant attitude correction parameters based on real-time wind field vector data specifically includes: The real-time wind field vector data is decomposed into a longitudinal wind component parallel to the tangent of the inspection route and a lateral wind component perpendicular to the tangent of the inspection route. Based on the magnitude of the crosswind component, the required lateral deflection offset moment to maintain the inspection route is calculated, and the first attitude tilt angle compensation value is generated based on the lateral deflection offset moment. Based on the magnitude of the longitudinal wind component and the preset cruising speed of the target UAV, the thrust compensation value required to maintain a constant ground speed is calculated, and the second attitude pitch angle compensation value is generated based on the thrust compensation value. The first attitude tilt angle compensation value and the second attitude pitch angle compensation value are vector-synthesized to obtain the wind-resistant attitude correction parameters used to correct the flight attitude of the target UAV.

[0019] By employing the above technical solution, real-time wind field vector data is decomposed into a longitudinal wind component parallel to the tangent of the inspection route and a lateral wind component perpendicular to the tangent of the inspection route, thus separating the different effects of the wind field on route deviation and speed changes. Based on the magnitude of the lateral wind component, the required lateral offset torque to maintain the inspection route is calculated, establishing a quantitative relationship between lateral wind force and offset torque. A first attitude tilt angle compensation value is generated based on the lateral offset torque, enabling the UAV to generate a lateral thrust component opposite to the lateral wind force to offset route deviation. Based on the magnitude of the longitudinal wind component and the target UAV's preset cruise speed, the required thrust compensation value to maintain a constant ground speed is calculated, determining the thrust values ​​to increase in headwinds or decrease in tailwinds. A second attitude pitch angle compensation value is generated based on the thrust compensation value, adjusting the UAV's forward thrust output to maintain a constant cruise speed. The first attitude tilt angle compensation value and the second attitude pitch angle compensation value are vector-synthesized to obtain wind-resistant attitude correction parameters, avoiding control conflicts and response delays caused by separately adjusting lateral and longitudinal attitudes.

[0020] In a second aspect, this application provides an electronic device for controlling a drone, the electronic device comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the electronic device for controlling the drone to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on an electronic device of a drone control method, cause the electronic device to perform the method as described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a device controlled by a drone, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the architecture of an unmanned aerial vehicle (UAV) control system provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a drone control method provided in an embodiment of this application; Figure 3 This is an exemplary hardware structure diagram of an electronic device for controlling a drone provided in an embodiment of this application. Detailed Implementation

[0024] Figure 1 An exemplary system architecture for an unmanned aerial vehicle (UAV) control system is shown.

[0025] like Figure 1 As shown, the system architecture may include electronic device 11, network 12, drone 13, and data acquisition device 14. Network 12 serves as the medium for providing communication links between electronic device 11, drone 13, and data acquisition device 14. Network 12 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0026] Users can use electronic device 11 to interact with drone 13 and data acquisition device 14 via network 12 to receive or send control commands, environmental status data, etc. Various drone control applications, such as flight path planning applications and data monitoring applications, can be installed on electronic device 11.

[0027] Electronic device 11 is hardware and can be various electronic devices with a display screen and data processing capabilities, including but not limited to ground control stations, tablet computers, laptop computers and desktop computers.

[0028] The drone 13 can be an aircraft that performs inspection operations, such as a drone that inspects power facilities in a work area. The drone 13 can receive flight control commands sent by the electronic device 11 and fly along the inspection route according to the flight control commands.

[0029] The data acquisition device 14 can be various sensing devices deployed in the work area, including but not limited to electromagnetic field strength sensors, humidity sensors, wind speed and direction sensors, etc. The data acquisition device 14 can collect environmental status data, electromagnetic field strength distribution, air humidity values, real-time wind field vector data, and other information of the work area, and transmit the collected data to the electronic device 11 via the network 12. The electronic device 11, as the executing entity, processes the received data, calculates electrical insulation characteristic parameters, determines hazard avoidance areas, generates a comprehensive no-fly envelope area and inspection route, and generates flight control commands based on the real-time wind field vector data, which are then sent to the drone 13.

[0030] The following detailed explanation uses the electronic device side as an example.

[0031] This embodiment provides a method for controlling an unmanned aerial vehicle (UAV). Figure 2 This is a flowchart illustrating a drone control method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes steps S101 to S107: S101: Acquire environmental status data of the target UAV in the work area, surface material characteristics of the target facility, geometric structure data, and operating voltage data. The work area includes at least one target facility, and the environmental status data includes the electromagnetic field intensity distribution and air humidity value of the work area.

[0032] In this embodiment of the application, the target drone refers to the drone that performs the power facility inspection task; the work area refers to the three-dimensional space range where the power facility to be inspected is located, including the substation area, the transmission line corridor area, the wind power plant area or the photovoltaic power station area.

[0033] Environmental condition data includes electromagnetic field intensity distribution and air humidity values. The electromagnetic field intensity distribution indicates the magnitude and direction of the electromagnetic field intensity at different spatial locations within the work area, while the air humidity value represents the percentage of water vapor in the air within the work area.

[0034] Specifically, electronic equipment collects electromagnetic field intensity distribution and air humidity values ​​through a sensor network deployed in the work area, integrating them into environmental status data; it reads the material properties (such as dielectric constant, moisture absorption performance, etc.) of the target facility from the equipment file database as surface material characteristics; it uses 3D scanning modeling technology or reads BIM models to obtain the 3D coordinates, contour dimensions, and surface curvature distribution of the target facility as geometric structure data; and it reads the current operating voltage and voltage fluctuation range of the target facility in real time through the power grid SCADA system or monitoring terminal as operating voltage data.

[0035] S102: Calculate the electrical insulation characteristics parameters of the work area based on the air humidity value and surface material properties.

[0036] In the embodiments of this application, the electrical insulation characteristic parameter represents a quantitative index of the ability of the air medium in the working area to resist electrical breakdown under specific humidity conditions and the influence of the surface material of the target facility.

[0037] Specifically, for each target facility within the work area, the electronic equipment first extracts the moisture absorption coefficient of its surface material properties, multiplies it by the air humidity value to obtain the surface adsorbed moisture content. Next, it substitutes this surface adsorbed moisture content into a preset conductivity lookup table to obtain the corresponding surface conductivity increment, and adds this increment to the material's basic conductivity to calculate the equivalent surface conductivity of the target facility. Subsequently, the charge relaxation time constant is obtained by dividing the equivalent surface conductivity by the absolute dielectric constant of the material (i.e., the product of the dielectric constant parameter and the vacuum dielectric constant). Combined with the reciprocal of the surface curvature radius, the electric field distortion enhancement coefficient near the surface of each target facility is calculated and determined. The electronic equipment reads the reference breakdown field strength value under standard atmospheric conditions from the preset air breakdown database, and divides the reference breakdown field strength value with the reciprocal of the electric field distortion enhancement coefficient as a correction factor to calculate the critical distortion field strength threshold considering the electric field distortion effect. The electronic equipment sets up the multiple critical distortion field strength thresholds calculated for all target facilities in the work area to form a data set containing the critical distortion field strength threshold corresponding to each target facility. The data set is determined as the electrical insulation characteristic parameter under the work area.

[0038] Based on the above embodiments, as an optional embodiment, the step of calculating the electrical insulation characteristic parameters of the working area according to the air humidity value and surface material characteristics may include S201 to S204: S201: Determine the equivalent surface conductivity of each target facility based on the air humidity value and the surface material characteristics of each target facility.

[0039] In the embodiments of this application, the equivalent surface conductivity represents the equivalent electrical conductivity formed by the surface material of the target facility after absorbing moisture from the air. The equivalent surface conductivity is affected by both the hygroscopic properties of the surface material and the air humidity value. The larger the equivalent surface conductivity value, the stronger the conductivity of the target facility surface.

[0040] Specifically, the electronic device extracts moisture absorption performance parameters from the surface material characteristics of the current target facility. It then queries a pre-set material moisture absorption characteristic database to convert these parameters into corresponding moisture absorption coefficient values. The device multiplies the air humidity value with the moisture absorption coefficient value to obtain the surface adsorbed moisture content of the current target facility. The electronic device then substitutes this surface adsorbed moisture content into a pre-set conductivity lookup table, which stores surface conductivity increment values ​​corresponding to different surface adsorbed moisture contents. The device then obtains the surface conductivity increment matching the current surface adsorbed moisture content. Finally, the electronic device extracts the basic conductivity value of the material itself from the surface material characteristics of the current target facility. It adds the surface conductivity increment to the basic conductivity value to calculate the equivalent surface conductivity of the current target facility after absorbing air moisture. The electronic device repeats the above determination process for all target facilities within the operating area to obtain the equivalent surface conductivity for each target facility.

[0041] S202: Based on the equivalent surface conductivity of each target facility, determine the electric field distortion enhancement coefficient near the surface of each target facility.

[0042] In this embodiment, the electric field distortion enhancement coefficient represents the amplification factor of the actual electric field intensity near the surface of the target facility relative to the uniform electric field intensity. The larger the value of the electric field distortion enhancement coefficient, the higher the concentration of the electric field intensity near the surface of the target facility.

[0043] Specifically, the electronic equipment performs a process to determine the electric field distortion enhancement coefficient for each target facility within the work area. First, it obtains the equivalent surface conductivity of the current target facility. Then, it extracts the dielectric constant parameter from the surface material properties of the current target facility. The dielectric constant parameter is multiplied by the vacuum dielectric constant to obtain the absolute dielectric constant of the material. The absolute dielectric constant is then divided by the equivalent surface conductivity to obtain the charge relaxation time constant of the current target facility. Next, it extracts the surface curvature radius value from the geometric data of the current target facility. The reciprocal of the surface curvature radius value is used to calculate the curvature value. The charge relaxation time constant is multiplied by the curvature value to obtain the charge accumulation intensity index. Finally, it reads the basic distortion factor corresponding to the charge accumulation intensity index from a preset distortion coefficient database. The basic distortion factor is multiplied by a preset amplification weighting coefficient to obtain the distortion increment value. The distortion increment value is then added to the initial distortion reference value to obtain the electric field distortion enhancement coefficient near the surface of the current target facility. The electronic equipment repeats the above determination process for all target facilities within the work area to obtain the electric field distortion enhancement coefficient for each target facility. Among them, the charge relaxation time constant represents the time required for the charge to redistribute on the surface of the target facility to reach an equilibrium state; the surface curvature radius represents the degree of curvature of the surface geometry of the target facility; and the charge accumulation density represents the amount of charge accumulated per unit area.

[0044] S203: The preset benchmark breakdown field strength is corrected by using the electric field distortion enhancement coefficient corresponding to each target facility, and the critical distortion field strength threshold that can induce air breakdown for each target facility is calculated.

[0045] In the embodiments of this application, the reference breakdown field strength represents the minimum electric field strength required for the air medium to undergo breakdown discharge in a uniform electric field under standard atmospheric conditions; the critical distortion field strength threshold represents the actual electric field strength that can induce air breakdown after considering the electric field distortion effect on the surface of the target facility.

[0046] Specifically, the electronic equipment reads the baseline breakdown field strength value under standard atmospheric conditions from the preset air breakdown database, and performs the critical distortion field strength threshold calculation process for each target facility in the work area. The electronic equipment obtains the electric field distortion enhancement coefficient corresponding to the current target facility, and performs a reciprocal operation on the electric field distortion enhancement coefficient to obtain a weighted correction coefficient. The electronic equipment multiplies the baseline breakdown field strength value and the weighted correction coefficient to obtain the corrected field strength value, and determines the corrected field strength value as the critical distortion field strength threshold of the current target facility. The electronic equipment repeats the above calculation process for all target facilities in the work area to obtain the critical distortion field strength threshold corresponding to each target facility.

[0047] S204: The set of critical distortion field strength thresholds corresponding to each target facility is determined as the electrical insulation characteristic parameters under the operating area.

[0048] Specifically, the electronic device acquires the critical distortion field strength threshold values ​​corresponding to all target facilities within the operating area, extracts the facility identification information of each target facility, and establishes an association mapping relationship between the facility identification information and the corresponding critical distortion field strength threshold. The electronic device organizes all critical distortion field strength thresholds according to preset set organization rules, creating an empty set data structure as the initial container for the critical field strength set. It iterates through the critical distortion field strength thresholds of all target facilities, sequentially adding the facility identification information and the critical distortion field strength threshold as key-value pairs to the critical field strength set. The electronic device then performs an integrity verification operation on the critical field strength set. The system checks whether the number of elements in the critical field strength set matches the number of target facilities in the work area, whether there are duplicate facility identification information or missing critical distortion field strength thresholds in the critical field strength set, and confirms the rationality of all critical distortion field strength threshold values ​​in the critical field strength set. After the electronic equipment passes the integrity verification, the completed critical field strength set is determined as the electrical insulation characteristic parameter under the work area. The electrical insulation characteristic parameter is stored in the preset environmental characteristic database, and timestamp markers and work area identification information are added to the electrical insulation characteristic parameter. The system establishes an association index relationship between the electrical insulation characteristic parameter and the environmental status data of the work area.

[0049] S103: Determine the first hazard avoidance zone of the work area based on electrical insulation characteristic parameters combined with the geometric structure data and operating voltage data of the target facility.

[0050] In this embodiment of the application, the first danger avoidance zone represents the dangerous space range within the work area where air breakdown discharge may occur due to the electric field strength exceeding the critical distortion field strength threshold.

[0051] Specifically, the electronic equipment extracts the critical distortion field strength threshold corresponding to each target facility within the work area from the electrical insulation characteristic parameters. For each target facility within the work area, it performs the process of determining the first hazard avoidance zone. The electronic equipment acquires the geometric structure data of the current target facility, extracts the main dimensional parameters of the target facility from the geometric structure data, including length, width, height, and surface curvature radius, and queries the corresponding geometric shape coefficient value from a preset shape coefficient database according to the geometric structure shape type. The electronic equipment acquires the operating voltage data of the current target facility, multiplies the operating voltage value with the geometric shape coefficient value to obtain a voltage shape correction value, and divides the voltage shape correction value with the characteristic length in the main dimensions of the target facility to obtain the surface electric field strength reference value. The electronic equipment multiplies the surface electric field strength reference value with the electric field distortion enhancement coefficient of the current target facility to obtain the maximum actual electric field strength at the surface position of the target facility. A series of spatial sampling points at different distances are established outward from the surface of the target facility, with the sampling point distance starting from zero and increasing in preset steps until reaching the preset maximum search radius. The electronic equipment calculates the maximum actual electric field strength at each spatial sampling point. The actual electric field strength at the location is calculated. During the calculation, the distance attenuation factor is obtained by dividing the reference value of the surface electric field strength by the square of the distance to the sampling point. The actual electric field strength at the sampling point is obtained by multiplying the maximum actual electric field strength of the surface by the distance attenuation factor. The electronic device compares the actual electric field strength of each sampling point with the critical distortion field strength threshold of the current target facility. When the actual electric field strength of a sampling point is less than or equal to the critical distortion field strength threshold for the first time, the distance from the sampling point to the surface of the target facility is determined as the hazard judgment distance. The electronic device extends the hazard judgment distance outward from the surface of the target facility along the direction perpendicular to the surface, and the spatial area covered by the extension range is determined as the first hazard avoidance sub-region corresponding to the current target facility. The electronic device repeats the above determination process for all target facilities in the work area to obtain the first hazard avoidance sub-region corresponding to each target facility. All first hazard avoidance sub-regions are spatially superimposed and merged. The union range of each first hazard avoidance sub-region in three-dimensional space is calculated. The merged spatial area is determined as the first hazard avoidance region of the work area.

[0052] Based on the above embodiments, as an optional embodiment, the step of determining the first hazard avoidance zone of the work area based on electrical insulation characteristic parameters combined with the geometric structure data and operating voltage data of the target facility may include S301 to S305: S301: Extract areas from geometric structure data where the rate of change of the geometric curvature of the target facility surface is greater than a preset threshold, and mark these areas as discharge-sensitive points with a high charge accumulation tendency. In the embodiments of this application, the discharge sensitive point refers to a special location area on the surface of the target facility where charge is easily concentrated due to abrupt changes in geometry, making it highly susceptible to local discharge.

[0053] Specifically, the electronic device traverses the set of three-dimensional coordinate points on the surface of the target facility, calculates the principal curvature values ​​and spatial gradients of each point, and obtains the rate of change of geometric curvature. Regions with a rate of change greater than a preset threshold are marked as candidate regions, and adjacent candidate regions are spatially clustered and merged. The coordinates and coverage area of ​​the cluster center are marked as discharge sensitive points with a high charge accumulation tendency.

[0054] The preset threshold for the rate of change of geometric curvature is determined based on the Rogowski electrode theory and historical discharge experience data. Its value follows a negative correlation between higher voltage levels and lower tolerance for geometric sharpness, accurately identifying high-risk locations prone to corona discharge, such as bolt ends and hardware attachment points. In practice, this threshold is set to different ranges for different voltage levels: for example, for 110kV facilities, it is typically set between 0.5 and 0.8 per meter; for 220kV facilities with higher electric field sensitivity, it is tightened to between 0.2 and 0.4 per meter; and for ultra-high voltage facilities of 500kV and above, it needs to be further set below 0.1 per meter. Furthermore, this preset threshold can be fine-tuned by incorporating a material correction coefficient. For example, the original threshold is maintained for metal hardware materials, while the threshold is appropriately relaxed for composite insulation materials, ensuring both computational efficiency and that no discharge-sensitive locations with a high charge accumulation tendency are overlooked.

[0055] S302: Based on the operating voltage data, construct a virtual spatial electric field intensity distribution field centered on each discharge sensitive point and extending outwards into the external space. Specifically, the electronic equipment performs a process of constructing the spatial electric field intensity distribution field for each discharge-sensitive point of all target facilities within the operating area. The electronic equipment acquires the operating voltage data of the target facility to which the current discharge-sensitive point belongs, reads the average geometric curvature change rate value from the marking information of the discharge-sensitive point, multiplies the operating voltage value with the average geometric curvature change rate value to obtain the voltage curvature product value, and multiplies the voltage curvature product value with a preset charge conversion coefficient to obtain the equivalent charge amount of the current discharge-sensitive point. The electronic equipment reads the spatial sampling step size from a preset distribution field parameter configuration table. The maximum computational radius and the spatial sampling step size determine the interval between electric field intensity calculation points in space, while the maximum computational radius determines the outer boundary of the spatial electric field intensity distribution field. The electronic device establishes a three-dimensional Cartesian coordinate system with the center coordinates of the current discharge sensitive point as the origin. Within this system, a spatial sampling point grid is created according to the spatial sampling step size. This grid extends from the origin along the X, Y, and Z axes to the maximum computational radius, forming a uniformly distributed three-dimensional sampling point array. The electronic device traverses each sampling point in the spatial sampling point array and calculates the value for the current sampling point. The linear distance from the center coordinates of the discharge sensitive point is recorded as the radial distance. The electronic device calculates the electric field intensity at each sampling point according to the point charge electric field distribution calculation formula. The equivalent charge is divided by the vacuum dielectric constant to obtain the charge-to-dielectric ratio. The charge-to-dielectric ratio is then divided by the square of the radial distance to obtain the electric field intensity value at the current sampling point. The dimension of the electric field intensity value is volts per meter. The electronic device constructs a spatial electric field intensity distribution data matrix by combining the coordinates of all sampling points with the corresponding electric field intensity values. Each point in the spatial electric field intensity distribution data matrix... Each line records the X, Y, and Z coordinates and the electric field intensity value of a sampling point. The spatial electric field intensity distribution data matrix completely describes the spatial electric field intensity distribution field centered on the discharge sensitive point. The electronic device performs data smoothing processing on the spatial electric field intensity distribution data matrix, performs a weighted average calculation on the electric field intensity values ​​of adjacent sampling points, eliminates the numerical abruptness caused by discrete sampling, and obtains a smooth and continuous spatial electric field intensity distribution field. The electronic device repeats the above construction process for all discharge sensitive points in the working area to obtain the spatial electric field intensity distribution field corresponding to each discharge sensitive point.

[0056] S303: Extract the critical distortion field strength threshold corresponding to the target facility to which the discharge sensitive point belongs from the set of electrical insulation characteristic parameters. Specifically, the electronic equipment reads the complete critical distortion field strength threshold set data from the storage location of electrical insulation characteristic parameters. This data is organized in key-value pairs, where the key in each pair is the unique identifier of the target facility, and the value is the critical distortion field strength threshold value for that facility. The electronic equipment then performs a critical distortion field strength threshold extraction operation on all discharge-sensitive points within the operating area. It obtains the marking information of the current discharge-sensitive point and reads the unique identifier of the target facility to which it belongs. This unique identifier includes identification fields such as the target facility's number or name. Using the read unique identifier as a search keyword, the electronic equipment performs a key-value matching search operation within the critical distortion field strength threshold set data. It iterates through all key-value pairs in the set, matching the key of each pair with the search keyword. The system performs string or numerical comparisons. When a key-value pair is found to be exactly the same as the search keyword, the corresponding value, i.e., the critical distortion field strength threshold value, is extracted. The electronic device establishes an association between the extracted critical distortion field strength threshold value and the location number of the current discharge sensitive point. A critical distortion field strength threshold field is added to the data record of the discharge sensitive point, and the extracted critical distortion field strength threshold value is filled into this field for storage. The electronic device performs an integrity verification check on the extraction operation to confirm that the corresponding critical distortion field strength threshold has been successfully extracted for each discharge sensitive point. It checks whether there are any discharge sensitive points that cannot be matched with the target facility identifier and whether the extracted critical distortion field strength threshold value is within a reasonable range. The electronic device repeats the above extraction operation for all discharge sensitive points in the working area to obtain the critical distortion field strength threshold value corresponding to each discharge sensitive point.

[0057] S304: Compare the spatial electric field intensity distribution field with the corresponding critical distortion field strength threshold, extract the spatial range in the spatial electric field intensity distribution field where the electric field intensity value is higher than the corresponding critical distortion field strength threshold, and generate the breakdown risk envelope surface. In this embodiment of the application, the breakdown risk envelope surface represents the boundary of a three-dimensional spatial curved surface where the electric field strength formed around the discharge sensitive point exceeds the critical distortion field strength threshold. There is a potential risk of air dielectric breakdown leading to discharge at any position inside the breakdown risk envelope surface.

[0058] Specifically, the electronic equipment performs the process of generating a breakdown risk envelope for each discharge-sensitive point within the work area. It acquires the spatial electric field intensity distribution data matrix corresponding to the current discharge-sensitive point and reads real-time environmental parameter data of the work area from the environmental monitoring system. This environmental parameter data includes ambient temperature, atmospheric pressure, relative humidity, and air pollution level. Based on the environmental parameter data, the electronic equipment queries a preset critical distortion field strength calculation model, substituting the ambient temperature, atmospheric pressure, relative humidity, and air pollution level into the model for calculation. The distortion field strength calculation model is established based on Paschen's law and a humidity correction factor. The critical distortion field strength threshold value under current environmental conditions is calculated. The electronic device performs a threshold comparison operation on the spatial electric field strength distribution data matrix, traversing each row of sampling points in the data matrix, extracting the electric field strength value of that sampling point, and comparing the electric field strength value with the critical distortion field strength threshold. If the electric field strength value is greater than the critical distortion field strength threshold, the sampling point is marked as a high-risk sampling point; if the electric field strength value is less than or equal to the critical distortion field strength threshold, the sampling point is marked as a low-risk sampling point. The electronic device collects... The 3D coordinate data of all high-risk sampling points are input into the isosurface extraction algorithm module. This module uses a moving cube algorithm to construct a triangular mesh surface at the boundary between high-risk and low-risk sampling points. The electric field strength at each vertex of the triangular mesh surface is exactly equal to the critical distortion field strength threshold, forming a closed breakdown risk envelope. The electronic device then performs mesh optimization processing on the breakdown risk envelope, subdividing and smoothing the triangular mesh to eliminate sharp edges and mesh distortion, resulting in a geometrically regular breakdown risk surface. Envelope model; the electronic device calculates the three-dimensional volume enclosed by the breakdown risk envelope, uses a volume integration algorithm to calculate the volume of the space inside the envelope, and records the volume calculation result as the volume value of the risk area; the electronic device saves the triangular mesh vertex coordinate data, mesh topology data, and risk area volume value of the breakdown risk envelope as a breakdown risk envelope data file, and associates the breakdown risk envelope data file with the corresponding discharge sensitive point number; the electronic device repeats the above generation process for all discharge sensitive points in the working area to obtain the breakdown risk envelope corresponding to each discharge sensitive point.

[0059] S305: Based on the breakdown risk envelope, a preset mechanical positioning tolerance range is superimposed to construct a physical isolation space that can block the electrical breakdown path, and the outer edge of the physical isolation space is defined as the first danger avoidance zone.

[0060] In this embodiment, the mechanical positioning tolerance range represents the maximum permissible deviation between the actual position and the target position of the UAV during spatial positioning and flight control due to factors such as sensor accuracy limitations, airflow disturbances, GPS signal drift, and control algorithm errors. The mechanical positioning tolerance range ensures that the UAV will not intrude into dangerous areas even under the most unfavorable positioning error conditions.

[0061] Specifically, the electronic equipment performs a physical isolation space construction process for the breakdown risk envelope of all discharge-sensitive points within the operating area. The electronic equipment reads the mechanical positioning tolerance range value from the UAV's technical specification parameter database. The mechanical positioning tolerance range value comprehensively considers multiple error sources such as the accuracy of the UAV's visual positioning system, GPS positioning accuracy, cumulative error of the inertial navigation system, position offset caused by airflow disturbance, and attitude control jitter amplitude. The electronic equipment obtains the breakdown risk envelope data file corresponding to the current discharge-sensitive point and reads the triangular mesh vertex coordinate data and mesh topology relationship data of the breakdown risk envelope from the data file. The electronic device performs a surface expansion algorithm operation on the breakdown risk envelope. This algorithm traverses each triangular mesh patch on the breakdown risk envelope, calculating the unit normal vector for each patch, with the direction of the unit normal vector pointing outwards from the breakdown risk envelope. The electronic device then translates the coordinates of the three vertices of the current mesh patch along the direction of the unit normal vector by a mechanical positioning tolerance distance, resulting in three new vertex coordinates. These three new vertices form the expanded triangular mesh patch. The electronic device repeats the expansion operation on all triangular mesh patches on the breakdown risk envelope, combining all the expanded triangular mesh patches to form a new closed loop. The new closed surface completely surrounds the original breakdown risk envelope and maintains a uniform spacing distance within the mechanical positioning tolerance range on the outside. The electronic device performs a mesh topology repair operation on the expanded new closed surface to detect and repair topological defects such as mesh self-intersections, holes, and hanging edges that may occur during the surface expansion process, ensuring the geometric integrity and topological consistency of the new closed surface. The electronic device defines the topology-repaired new closed surface as the outer boundary of the physical isolation space. The physical isolation space contains a spatial layer with a thickness of the breakdown risk envelope and the mechanical positioning tolerance range on its outside. The physical isolation space can effectively block electrical shocks between the UAV and the discharge-sensitive point. The electronic device marks the outer boundary surface of the physical isolation space as the first hazard avoidance zone, assigns a unique area identifier number to the first hazard avoidance zone, and records the triangular mesh vertex coordinate data, mesh topology data, and discharge sensitive point number of the first hazard avoidance zone; the electronic device calculates the total volume value enclosed by the first hazard avoidance zone, performs a difference operation between the total volume value and the risk area volume value of the breakdown risk envelope surface, and obtains the volume value of the physical isolation space layer; the electronic device repeats the above construction process for all discharge sensitive points in the working area to obtain the first hazard avoidance zone corresponding to each discharge sensitive point.

[0062] S104: The second hazard avoidance zone for determining the work area based on the electromagnetic field intensity distribution.

[0063] In this embodiment of the application, the second danger avoidance zone refers to the spatial range in which the target UAV is at risk of attitude loss and position drift due to electromagnetic interference, and is used to mark the no-fly zone boundary where collision accidents may be caused by magnetic field interference.

[0064] Specifically, the electronic equipment extracts the magnetic field strength values ​​at each spatial location point in the electromagnetic field intensity distribution. It then compares these extracted magnetic field strength values ​​with the magnetic field strength thresholds in the target UAV's preset magnetic susceptibility response curve. Based on the comparison results, each location point is classified into different magnetic interference levels, completing the magnetic interference level calibration for all locations within the operational area. For each calibrated magnetic interference level location point, the corresponding maximum attitude uncertainty value is extracted from a preset interference drift mapping table. This mapping table, obtained through pre-testing and storing the numerical ranges of pitch, roll, and yaw angle deviations of the target UAV under different magnetic interference levels, is used to calculate the maximum attitude uncertainty. The cumulative maximum position drift radius is calculated by multiplying the certainty value by the preset duration and combining it with the maximum flight speed of the target UAV. The position drift radius reflects the spatial displacement caused by the cumulative attitude angle deviation over time in three spatial dimensions. A spherical space with a radius equal to the corresponding cumulative maximum position drift radius is constructed with each position point as the center. The spherical spaces corresponding to all position points in the operation area are spatially superimposed and merged to form a continuously distributed electromagnetic interference buffer space. The boundary surface of the electromagnetic interference buffer space is identified and the preset anti-magnetic interference safety margin distance is extended outward along the normal direction of the boundary surface. The new boundary surface formed after the extension is the spatial outline of the second danger avoidance area.

[0065] Based on the above embodiments, as an optional embodiment, the step of determining the second hazard avoidance zone of the work area based on the electromagnetic field intensity distribution may include S401 to S405: S401: Map the electromagnetic field intensity distribution onto the target UAV's preset magnetic susceptibility response curve to determine the magnetic interference level of the target UAV at each location point within the operating area.

[0066] In this embodiment, the magnetic interference level refers to the quantitative classification result of the degree of influence of electromagnetic fields on the flight stability of the target UAV. The magnetic susceptibility response curve represents the response relationship curve of the positioning deviation and attitude error generated by the flight control system, inertial measurement unit and magnetic compass of the target UAV under different magnetic field intensities, wherein multiple magnetic field intensity threshold ranges and corresponding interference levels are pre-calibrated.

[0067] It should be noted that the preset magnetic susceptibility response curve is obtained and stored in advance through offline calibration testing. Specifically, the curve generation process is carried out in a controlled environment such as an electromagnetic shielding room (e.g., a microwave anechoic chamber). First, the target UAV to be calibrated is fixed on a non-magnetic three-axis turntable, and a standard magnetic field environment with controllable uniformity and continuously adjustable intensity (e.g., covering 0 to 1000 microtesla) is generated in space using a three-axis Helmholtz coil. Then, the UAV motor is controlled to be in an unloaded operation state to simulate a real electromagnetic interference background but eliminate the influence of mechanical vibration. As the external magnetic field strength generated by the three-axis Helmholtz coil increases in preset steps, the reading error between the output value of the UAV's onboard magnetic compass and the standard magnetic field, as well as the deviation between the attitude calculated by the inertial measurement unit and the actual attitude of the turntable, are recorded simultaneously. Finally, with the applied external magnetic field strength as the independent variable and the attitude deviation or normalized interference level as the dependent variable, a mapping model is established using a polynomial fitting algorithm such as the least squares method, thereby obtaining a magnetic susceptibility response curve that can reflect the degree of attitude distortion of this specific model of UAV under different magnetic field strengths.

[0068] Specifically, the electronic device traverses each spatial location point in the electromagnetic field intensity distribution, reading its magnetic field intensity value. This value is then compared with multiple pre-defined non-overlapping magnetic field intensity threshold intervals (e.g., 0-50, 50-150 microtesla, etc.) on the horizontal axis of the magnetic susceptibility response curve. Based on the specific interval the value falls into, the corresponding vertical axis level is directly queried to determine the magnetic interference level at that location point.

[0069] S402: Based on the magnetic interference level, query the preset interference-drift mapping table to determine the maximum attitude uncertainty of the target UAV in the hovering operation state at each location point.

[0070] Specifically, the electronic device reads the magnetic field strength values ​​corresponding to each location point from the electromagnetic field strength distribution one by one. The read magnetic field strength values ​​are used as input parameters to locate the corresponding magnetic field strength position on the horizontal axis of the magnetic susceptibility response curve. The horizontal axis of the magnetic susceptibility response curve records the continuous range of magnetic field strength variation, and the vertical axis marks the magnetic interference level classification from level one to level five. The curve divides the horizontal axis magnetic field strength range into multiple non-overlapping magnetic field strength threshold intervals. Each magnetic field strength threshold interval corresponds to a unique magnetic interference level. By determining which magnetic field strength threshold interval the read magnetic field strength value falls into on the curve, the corresponding magnetic interference level is determined. The determined magnetic interference level is then labeled and associated with the location point where the magnetic field strength value was read, thus completing the determination of the magnetic interference level for a single location point. The operations of reading magnetic field strength values, locating magnetic field strength threshold intervals, determining magnetic interference levels, and establishing label associations are repeated until all location points in the work area are processed. All location points and their corresponding magnetic interference levels together constitute the magnetic interference level distribution data for each location point in the work area.

[0071] For example, an electronic device reads the magnetic field strength value of 210 microtesla corresponding to location point A from the electromagnetic field strength distribution one by one. Using this read value as an input parameter, it locates the corresponding magnetic field strength position on the horizontal axis of the magnetic susceptibility response curve. The horizontal axis of the curve records the continuous range of magnetic field strength from 0 to 500 microtesla, while the vertical axis indicates the magnetic interference levels from level one to level five. The curve divides the horizontal magnetic field strength range into five non-overlapping magnetic field strength levels: 0 to 50 microtesla, 50 to 150 microtesla, 150 to 250 microtesla, 250 to 350 microtesla, and 350 to 500 microtesla. The five magnetic field strength threshold ranges correspond to the first, second, third, fourth, and fifth levels of magnetic interference, respectively. By determining whether the magnetic field strength value of 210 microtesla falls within the 150 to 250 microtesla magnetic field strength threshold range of the curve, the corresponding third level of magnetic interference is determined. The determined third level of magnetic interference is then labeled and associated with location point A, thus completing the determination of the magnetic interference level of location point A. The operations of reading magnetic field strength values, locating magnetic field strength threshold ranges, determining magnetic interference levels, and establishing label associations are repeated until all 500 location points in the work area are processed. The 500 location points and their corresponding magnetic interference levels together constitute the magnetic interference level distribution data of each location point in the work area.

[0072] S403: Based on the maximum attitude uncertainty, predict the cumulative maximum position drift radius of the target UAV within a preset time period.

[0073] In this embodiment of the application, the cumulative maximum position drift radius refers to the maximum spatial distance that the target UAV deviates from the predetermined position due to magnetic interference within a preset time period, which is used to quantify the cumulative impact range of magnetic interference on the spatial position control accuracy of the target UAV.

[0074] Specifically, the electronic equipment extracts pitch angle deviation, roll angle deviation, and yaw angle deviation values ​​from the maximum attitude uncertainty. The extracted pitch angle deviation value is multiplied by the target UAV's maximum flight speed to obtain the pitch direction displacement rate per unit time, which represents the change in position per second caused by the pitch angle deviation in the vertical plane. Similarly, the extracted roll angle deviation value is multiplied by the target UAV's maximum flight speed to obtain the roll direction displacement rate per unit time, which represents the change in position per second caused by the roll angle deviation in the horizontal direction. The extracted yaw angle deviation value is multiplied by the target UAV's maximum attitude uncertainty to obtain the roll direction displacement rate per unit time, which represents the change in position per second caused by the roll angle deviation in the horizontal direction. Multiplying the flight speed yields the displacement rate per unit time in the heading direction. The displacement rate per unit time in the heading direction represents the change in position per second caused by the heading angle deviation in the horizontal forward direction. Multiplying the displacement rates per unit time in the pitch, roll, and heading directions by the preset time duration yields the cumulative displacement in the pitch, roll, and heading directions, respectively. Squaring and summing the cumulative displacements in the pitch, roll, and heading directions, and then taking the square root, yields the composite displacement distance in three-dimensional space. This composite displacement distance is the maximum cumulative position drift radius of the target UAV within the preset time duration.

[0075] S404: Based on the maximum position drift radius, construct an electromagnetic interference buffer space that can cover potential runaway displacement.

[0076] In this embodiment, the electromagnetic interference buffer space refers to a three-dimensional spatial envelope that can encompass all potential uncontrolled displacements of the target UAV caused by magnetic interference, and is used to pre-identify the maximum spatial activity range that the target UAV may reach under the influence of magnetic field interference.

[0077] Specifically, the electronic device extracts the cumulative maximum position drift radius value corresponding to each location point within the operation area. Taking the three-dimensional spatial coordinates of each location point as the center point of a sphere, and the cumulative maximum position drift radius value corresponding to that location point as the radius of the sphere, a spherical runaway displacement envelope space centered on the location point is constructed. The surface boundary of the spherical runaway displacement envelope space represents the set of farthest spatial locations that the target UAV may reach within a preset time period due to magnetic interference, starting from that location point. All location points within the operation area are traversed, and a corresponding spherical runaway displacement envelope space is constructed for each location point. A spatial Boolean union operation is performed on the spherical runaway displacement envelope spaces corresponding to all location points within the operation area. The Boolean union operation integrates multiple discrete spherical spaces by identifying the overlapping and independent parts of all spherical spaces and merging them into a single continuous space. The continuous three-dimensional space volume formed after merging is the electromagnetic interference buffer space covering all potential runaway displacements. The outer boundary curved envelope of the electromagnetic interference buffer space encloses all spatial locations that the target UAV at any location point within the operation area may reach under the action of magnetic interference.

[0078] S405: The boundary of the electromagnetic interference buffer space is expanded by a preset anti-magnetic interference safety margin, and the outer contour boundary of the electromagnetic interference buffer space containing the anti-magnetic interference safety margin is used as a second danger avoidance zone that can avoid the risk of collision caused by magnetic interference.

[0079] Specifically, the electronic device reads a pre-set anti-magnetic interference safety margin value. This value is determined comprehensively based on the target UAV's flight speed, attitude control response time, magnetic field strength prediction error range, and safe flight specifications. The electronic device extracts all spatial coordinate points on the boundary surface of the electromagnetic interference buffer space. These spatial coordinate points are obtained by uniformly sampling the outer surface of the electromagnetic interference buffer space, and the three-dimensional coordinate position of each sampling point is recorded. For each spatial coordinate point on the boundary surface of the electromagnetic interference buffer space, the electronic device calculates the vector connecting that coordinate point to the geometric center of the electromagnetic interference buffer space as the outward normal vector of that point. The outward normal vector represents a unit vector pointing away from the center of the electromagnetic interference buffer space from the spatial coordinate point. Each spatial coordinate point is moved along its outward normal vector direction by a distance corresponding to the anti-magnetic interference safety margin value to obtain a new spatial coordinate point. The new spatial coordinate point is offset outward from the electromagnetic interference buffer space relative to the original coordinate point. All the new spatial coordinate points are connected to form a closed three-dimensional surface as the outer contour boundary. The volume of the space enclosed by the outer contour boundary is larger than the volume of the original electromagnetic interference buffer space. The outer contour boundary and all the three-dimensional spaces contained within it are defined as the second danger avoidance zone.

[0080] In this embodiment of the application, the second danger avoidance zone refers to a three-dimensional spatial area that prohibits the target drone from entering after further expanding the anti-magnetic interference safety margin on the basis of the electromagnetic interference buffer space. It is used to ensure that the target drone maintains a sufficient safe distance from the high-voltage line to avoid the collision risk caused by magnetic interference.

[0081] S105: Spatial merging calculations are performed on the first hazard avoidance zone and the second hazard avoidance zone to generate a comprehensive no-fly envelope for the operational area. Inspection routes are then generated based on this comprehensive no-fly envelope.

[0082] Specifically, the electronic device acquires the boundary surface coordinate data of the first hazard avoidance area and the boundary surface coordinate data of the second hazard avoidance area. The boundary surface coordinate data includes the coordinates of all vertices describing the closed surface in three-dimensional space and the surface connection relationships. The electronic device establishes a three-dimensional space coordinate system and maps the boundary surface data of the first and second hazard avoidance areas to the same coordinate system. The electronic device traverses all spatial points in the three-dimensional space to determine whether each spatial point is located inside the first or second hazard avoidance area. The determination method is to calculate the shortest distance from the spatial point to the boundary surface of the area and determine the inside or outside position of the point according to the distance sign. All spatial points located inside the first or second hazard avoidance area are then identified. The spatial points within the two hazard avoidance zones are marked as hazard points. The electronic equipment extracts the outer boundaries of all hazard points to form a new closed three-dimensional surface as the boundary of the comprehensive no-fly zone. The boundary of the comprehensive no-fly zone surrounds the union space of the first and second hazard avoidance zones. The electronic equipment sets the inspection start point and inspection end point within the operation area. The electronic equipment uses a path planning algorithm to search for a flight path from the inspection start point to the inspection end point within the operation area. During the search process, the path planning algorithm sets all spatial points within the comprehensive no-fly zone as impassable nodes. The path planning algorithm prioritizes the flight path with the shortest path length and the largest distance from the boundary of the comprehensive no-fly zone as the inspection route.

[0083] Based on the above embodiments, as an optional embodiment, the step of spatially merging the first hazard avoidance zone and the second hazard avoidance zone to generate a comprehensive no-fly envelope zone may include S501 to S503: S501: Map the first hazard avoidance zone and the second hazard avoidance zone to a unified three-dimensional coordinate system.

[0084] Specifically, the electronic equipment selects a preset fixed reference point within the work area as the origin of a unified three-dimensional coordinate system. The electronic equipment establishes this unified three-dimensional coordinate system by defining due east as the positive X-axis, due north as the positive Y-axis, and the direction perpendicular to the ground upwards as the positive Z-axis. The electronic equipment reads the coordinates of the geometric center of the first hazard avoidance area in the local coordinate system and the coordinates of all vertices on the boundary surface of the first hazard avoidance area in the local coordinate system. The electronic equipment obtains the actual spatial coordinates of the geometric center of the first hazard avoidance area in the unified three-dimensional coordinate system through a global positioning system. The electronic equipment calculates the translation vector from the origin of the local coordinate system of the first hazard avoidance area to the origin of the unified three-dimensional coordinate system. The three components of the quantity represent the offset distances in the X, Y, and Z axes, respectively. The electronic device adds a translation vector to the local coordinates of each vertex on the boundary surface of the first hazard avoidance area to obtain the coordinates of that vertex in a unified three-dimensional coordinate system. The electronic device uses the same method to read the geometric center coordinates and boundary surface vertex coordinates of the second hazard avoidance area and calculates the translation vector from the second hazard avoidance area to the unified three-dimensional coordinate system. The electronic device adds the corresponding translation vector to the local coordinates of each vertex on the boundary surface of the second hazard avoidance area to obtain the coordinates of that vertex in a unified three-dimensional coordinate system. After the mapping is completed, the coordinates of all boundary surface vertices of the first and second hazard avoidance areas are represented in a unified three-dimensional coordinate system.

[0085] S502: Perform a spatial Boolean union operation on the mapped first hazard avoidance region and second hazard avoidance region to generate a preliminary joint space that can simultaneously cover electrical breakdown risk and magnetic interference drift risk.

[0086] Specifically, the electronic device reads the mapped boundary surface data of the first hazard avoidance area and the boundary surface data of the second hazard avoidance area from the memory. The boundary surface data includes the set of vertex coordinates and the topological structure of vertex connections. The electronic device establishes a three-dimensional voxel mesh in a unified three-dimensional coordinate system, dividing the work area space into multiple regularly arranged cubic voxel units. The side length of each cubic voxel unit is a preset spatial resolution value. The electronic device traverses each voxel unit in the three-dimensional voxel mesh and calculates the coordinates of the geometric center point of the voxel unit. The electronic device emits rays from the geometric center point of the voxel unit in any direction and counts the number of intersections between the rays and the boundary surface of the first hazard avoidance area. The electronic device determines whether the geometric center point is located inside the first hazard avoidance area based on the parity of the number of intersections. When the number of intersections is odd, the geometric center point is located inside the first hazard avoidance area. The same ray emission method and parity judgment method are used to determine whether the geometric center point is located inside the second hazard avoidance area. The electronic device marks all voxel units whose geometric center point is located inside the first hazard avoidance area and voxel units whose geometric center point is located inside the second hazard avoidance area as joint voxel units. Joint voxel units include voxel units that belong only to the first hazard avoidance area, voxel units that belong only to the second hazard avoidance area, and voxel units that belong to both areas. The electronic device extracts the three-dimensional spatial region formed by all joint voxel units as the preliminary joint space. The preliminary joint space can simultaneously cover the electrical breakdown risk and the magnetic interference drift risk. The electronic device identifies the outer surface voxel units of the preliminary joint space and extracts the vertex coordinates of the outer surface voxel units to generate the boundary surface data of the preliminary joint space. The boundary surface of the preliminary joint space surrounds the union volume of the first hazard avoidance area and the second hazard avoidance area.

[0087] S503: Extract the outer contour surface of the preliminary joint space, and perform curvature smoothing on the geometric connection of the outer contour surface to obtain the integrated no-fly envelope region.

[0088] In this embodiment, the outer contour surface refers to the outermost boundary surface of the preliminary joint space, which includes all surface portions of the preliminary joint space that come into contact with the external space.

[0089] Specifically, the electronic device extracts the vertex coordinates and triangular facet connection relationships of all outer surface voxel units from the boundary surface data of the preliminary joint space to form an outer contour surface. The outer contour surface includes the exposed boundary surface portions of the first and second hazard avoidance regions. It iterates through all triangular facets of the outer contour surface, calculates the angle between the normal vectors of adjacent facets, and identifies facet connections where the angle exceeds a preset threshold as geometric connection points. These geometric connection points correspond to the splicing positions of the boundary surfaces of the first and second hazard avoidance regions. At the geometric connection points, the vertex coordinates on both sides of the connecting line are selected to construct cubic Bézier curves as transition curves. The starting point of the transition curve is tangentially... The vector is kept consistent with the normal vector of the boundary surface of the first hazard avoidance zone, and the endpoint tangent vector is kept consistent with the normal vector of the boundary surface of the second hazard avoidance zone. A transition surface patch is generated along the transition curve to replace the original triangular surface patch. The transition surface patch makes the curvature at the geometric connection smoothly transition from the curvature value of the boundary surface of the first hazard avoidance zone to the curvature value of the boundary surface of the second hazard avoidance zone. The transition surface patch replacement operation is performed on all geometric connections of the outer contour surface to complete the curvature smoothing process. The outer contour surface after curvature smoothing is used as the boundary to construct a three-dimensional closed space region to obtain the comprehensive no-fly envelope region. The curvature of the boundary surface of the comprehensive no-fly envelope region changes continuously without sharp turns, which is suitable for the obstacle avoidance trajectory planning of the aircraft.

[0090] Based on the above embodiments, as an optional embodiment, the step of generating inspection routes according to the comprehensive no-fly zone envelope may include S601 to S603: S601: Using the outer surface of the comprehensive no-fly zone as the reference interface, extend the normal direction along the direction away from the comprehensive no-fly zone with the preset optimal observation distance as the distance parameter to construct a three-dimensional equidistant inspection surface that encloses the comprehensive no-fly zone.

[0091] Specifically, the electronic device reads the boundary surface data of the integrated no-fly zone, including the vertex coordinate set and the connection relationship of the triangular facets. Using the outer surface of the integrated no-fly zone as the reference interface, it traverses all triangular facets on the reference interface, calculates the coordinates of the geometric center point of each facet, and calculates the outward normal vector direction for each facet. The outward normal vector direction points to the external space of the integrated no-fly zone. It retrieves the preset optimal observation distance from memory as the distance parameter, and performs spatial translation along the outward normal vector direction of each facet using the optimal observation distance as the distance parameter. This translation moves the geometric center point of the facet along the outward normal vector direction to the optimal observation distance. The new geometric center point is obtained by measuring the distance. The three vertices of the triangular facet are translated along the direction of the outward normal vector of the corresponding vertex position to obtain three new vertices. The three new vertices form a new triangular facet. The normal extension operation is performed on all triangular facets on the reference interface to generate multiple new triangular facets. The new triangular facets form a closed surface according to the connection relationship topology of the original triangular facets. The distance between each point on the closed surface and the corresponding point on the reference interface is equal to the optimal observation distance. The closed surface completely encloses the comprehensive no-fly zone to form a three-dimensional equidistant inspection surface. The three-dimensional equidistant inspection surface serves as the trajectory planning surface for the aircraft's inspection flight.

[0092] S602: Obtain the pre-marked locations of key components to be inspected on the target facility, project the locations of the key components along the normal onto the three-dimensional equidistant inspection surface, and obtain multiple key inspection viewpoints located on the three-dimensional equidistant inspection surface.

[0093] In this embodiment of the application, the location of key components refers to the coordinate position in three-dimensional space of the key components on the target facility that need to be inspected and photographed by the aircraft. Key components include core components of power equipment such as transformer insulating bushings, disconnecting switch contacts, and surge arrester porcelain insulators.

[0094] Specifically, the electronic equipment retrieves a pre-calibrated set of coordinates of key components to be tested from the equipment information database of the target facility. This set includes the three-dimensional spatial positions of multiple key components, such as transformer insulating bushings, disconnector contacts, and surge arrester insulators. It iterates through the coordinates of each key component in the set, calculating the line vector connecting the key component's coordinates to the nearest surface point on the outer surface of the comprehensive no-fly zone. The starting point of this line vector is the key component's coordinates, and the ending point is the nearest surface point on the outer surface of the comprehensive no-fly zone. The line vector is then normalized to obtain a unit normal vector, which points from the key component's coordinates to the comprehensive no-fly zone. From the direction of the outer surface, a projection ray is emitted along the opposite direction of the unit normal vector from the location coordinates of the key component. The projection ray extends from the location coordinates of the key component in a direction away from the comprehensive no-fly zone. The coordinates of the intersection point of the projection ray and the three-dimensional equidistant inspection surface are calculated. The intersection point coordinates are the position point where the projection ray first crosses the three-dimensional equidistant inspection surface. The intersection point coordinates are marked as the key inspection viewpoints of the corresponding key component. The normal projection operation is performed on all the location coordinates of the key component in the set of key component location coordinates to generate multiple key inspection viewpoints. Multiple key inspection viewpoints are all located on the three-dimensional equidistant inspection surface and maintain the optimal observation distance from the corresponding key component position. The aircraft flies to multiple key inspection viewpoints in sequence to photograph and inspect the key components.

[0095] S603: Construct a three-dimensional spatial topology network using multiple key inspection viewpoints as nodes, and use the shortest path search algorithm to plan a geometric trajectory on a three-dimensional equidistant inspection surface that connects all key inspection viewpoints and has the shortest total path length. The geometric trajectory is then determined as the inspection route.

[0096] In this embodiment, the three-dimensional spatial topology network refers to a spatial topology structure constructed using multiple key inspection viewpoints as network nodes and the connection paths between key inspection viewpoints on a three-dimensional equidistant inspection surface as network edges. The three-dimensional spatial topology network records the connectivity and spatial distance information between key inspection viewpoints.

[0097] Specifically, the electronic device reads the coordinates of multiple key inspection viewpoints on a 3D equidistant inspection surface. Each key inspection viewpoint is treated as a network node. The geodesic distance between any two key inspection viewpoints on the 3D equidistant inspection surface is calculated. This geodesic distance represents the shortest path length between the two key inspection viewpoints on the 3D equidistant inspection surface. The geodesic distance between any two key inspection viewpoints is used as the edge weight connecting the two nodes, constructing a fully connected 3D spatial topology network. This 3D spatial topology network contains nodes equal to the number of key inspection viewpoints and weighted edges between nodes. The 3D spatial topology network is then input into a shortest path search algorithm. This shortest path search algorithm uses a genetic algorithm to solve the traveling salesman problem. The initial population contains multiple random access points to all key inspection viewpoints. The path sequence is calculated, and the total length of each path sequence is used as the fitness value. The population is iteratively optimized through genetic operations such as selection, crossover, and mutation. The iteration terminates when the optimal fitness value of the population remains unchanged for a preset number of generations or when the number of iterations reaches a preset upper limit. After the iteration terminates, the path sequence with the smallest fitness value in the population is output as the optimal path. The optimal path connects all key inspection viewpoints and has the shortest total path length. According to the access order of key inspection viewpoints in the optimal path, the geodesic path between adjacent key inspection viewpoints is calculated on the three-dimensional equidistant inspection surface. The geodesic paths between all adjacent key inspection viewpoints are connected end to end to form a continuous geometric trajectory. The geometric trajectory is located on the three-dimensional equidistant inspection surface and connects all key inspection viewpoints. The geometric trajectory is determined as the inspection route for the aircraft to perform inspection tasks.

[0098] S106: Obtain real-time wind field vector data of the work area and determine wind resistance attitude correction parameters based on the real-time wind field vector data.

[0099] In this embodiment, real-time wind field vector data refers to the real-time measurement data of wind speed and direction at the current location of the UAV. This data includes the three-dimensional components of the wind speed vector and wind direction angle information, reflecting the real-time characteristics of the wind field at the UAV's flight location. For example, the real-time wind field vector data at the UAV's current location includes eastward wind speed components, northward wind speed components, vertical wind speed components, and horizontal wind direction and pitch angles. Wind-resistant attitude correction parameters represent the attitude angle corrections and control torque compensations that the UAV needs to adjust to counteract wind field interference and maintain stable flight attitude. These parameters include pitch angle corrections, roll angle corrections, yaw angle corrections, and corresponding propeller speed adjustments. By adjusting its flight attitude according to these parameters, the UAV can effectively counteract wind field interference and maintain a stable flight path. Specifically, the electronic device acquires real-time wind field vector data of the drone's current location via an onboard anemometer. This onboard anemometer is either a three-dimensional ultrasonic anemometer or a hot-wire anemometer mounted on the drone's fuselage. The three-dimensional ultrasonic anemometer calculates the three components of the wind speed vector by measuring the time difference of ultrasonic waves propagating in different directions. The hot-wire anemometer calculates the wind speed magnitude and direction by measuring the cooling rate of the heating wire in the airflow. The electronic device reads the eastward, northward, and vertical wind speed components from the onboard anemometer as real-time wind field vector data. Based on this real-time wind field vector data, it calculates the interference force and torque generated by the wind field on the drone. The specific calculation process is as follows: The electronic device acquires the drone's body parameters, including surface area, mass, moment of inertia, and aerodynamic drag coefficient. It then multiplies the eastward, northward, and vertical wind speed components from the real-time wind field vector data by... The interference force components of the wind field in the UAV's body coordinate system in the forward, lateral, and vertical directions are obtained using the corresponding surface area and aerodynamic drag coefficient. The interference torque is calculated based on the distance between the interference force components and the UAV's center of mass. The product of the forward interference force and the lateral lever arm yields the pitch torque, and the product of the lateral interference force and the forward lever arm yields the roll torque. The eccentric effect of the forward and lateral interference forces generates the yaw torque. The electronic equipment obtains the angular acceleration by dividing the pitch torque, roll torque, and yaw torque by the rotational inertia of the corresponding axis. The angular acceleration is multiplied by the response time to obtain the attitude angle deviation. The electronic equipment determines the pitch angle correction, roll angle correction, and yaw angle correction required to counteract the wind field interference based on the attitude angle deviation. At the same time, the adjustment amount of each propeller speed is calculated based on the relationship between the torque required for wind resistance and the propeller thrust coefficient. The attitude angle correction amount and the propeller speed adjustment amount are used as wind resistance attitude correction parameters.

[0100] Based on the above embodiments, as an optional embodiment, the step of determining the wind resistance attitude correction parameters according to real-time wind field vector data may include S701 to S704: S701: Decomposes real-time wind field vector data into longitudinal wind components parallel to the tangent of the inspection route and lateral wind components perpendicular to the tangent of the inspection route.

[0101] In this embodiment, the longitudinal wind component refers to the projection component of the wind field vector in the tangential direction of the inspection route. The lateral wind component refers to the projection component of the wind field vector in the direction perpendicular to the tangential direction of the inspection route.

[0102] Specifically, the electronic equipment acquires the current inspection route data of the UAV. This data includes the position coordinates of each waypoint along the route and the direction of the flight segments between them. Based on the UAV's current position, the electronic equipment determines the flight segment the UAV is in and calculates the tangential direction of that segment, which is the UAV's flight direction. The tangential direction is represented by a unit vector, pointing from the start point to the end point of the segment. The electronic equipment also acquires real-time wind field vector data measured by the UAV's onboard anemometer. This real-time wind field vector data includes eastward, northward, and vertical wind speed components. The electronic equipment projects this real-time wind field vector data onto the tangential direction of the flight route to obtain the longitudinal wind component. The projection calculation method involves plotting the wind field vector against the tangential unit vector of the flight route. The multiplication operation, specifically the dot product, yields the numerical value of the longitudinal wind component. A positive longitudinal wind component indicates a tailwind, while a negative longitudinal wind component indicates a headwind. The electronic equipment calculates the normal direction perpendicular to the tangential direction of the flight path. The normal direction is the direction perpendicular to the tangential direction in the horizontal plane. The electronic equipment projects the real-time wind field vector data onto the normal direction of the flight path to obtain the lateral wind component. The projection calculation method involves performing a dot product operation between the wind field vector and the normal unit vector of the flight path. The dot product result is the numerical value of the lateral wind component. A positive lateral wind component indicates wind blowing from the left, while a negative lateral wind component indicates wind blowing from the right. The electronic equipment uses the longitudinal and lateral wind components as the basis for subsequent calculations of wind resistance attitude correction parameters.

[0103] S702: Calculate the lateral offset torque required to maintain the inspection route based on the magnitude of the lateral wind component, and generate the first attitude tilt angle compensation value based on the lateral offset torque.

[0104] In this embodiment, the lateral offset torque refers to the control torque that the UAV needs to generate to offset the lateral drift force caused by the crosswind component; the first attitude tilt angle compensation value refers to the body tilt angle that the UAV needs to adjust to generate the lateral offset torque.

[0105] Specifically, the electronic equipment multiplies the crosswind component, air density, lateral projected area, and aerodynamic drag coefficient to obtain the lateral thrust. Multiplying the lateral thrust by the lever arm distance from its point of application to the UAV's center of mass yields the lateral deflection compensation torque. Further, the lateral thrust is divided by the total propeller thrust to obtain the sine of the roll angle, and this sine is then arcsineed to generate the first attitude roll angle compensation value (i.e., the roll angle compensation value) used to compensate for the crosswind effects.

[0106] S703: Based on the magnitude of the longitudinal wind component and the preset cruise speed of the target UAV, calculate the thrust compensation value required to maintain a constant ground speed, and generate a second attitude pitch angle compensation value based on the thrust compensation value.

[0107] In this embodiment, the thrust compensation value refers to the amount of thrust that the UAV needs to increase or decrease in order to counteract the influence of the longitudinal wind component on the flight speed and maintain a constant ground speed; the second attitude pitch angle compensation value refers to the pitch angle of the UAV that needs to be adjusted to generate the thrust compensation value.

[0108] Specifically, the electronic equipment calculates the impact of the wind field on the UAV's ground speed based on the longitudinal wind component and the preset cruise speed. When the longitudinal wind component is positive (tailwind), the actual ground speed equals the preset cruise speed plus the longitudinal wind component. When the longitudinal wind component is negative (headwind), the actual ground speed equals the preset cruise speed minus the absolute value of the longitudinal wind component. To maintain a constant ground speed equal to the preset cruise speed, the electronic equipment needs to calculate a thrust compensation value. The calculation method is to determine the thrust that needs to be increased or decreased based on the impact of the longitudinal wind component on the flight speed. Specifically, the electronic equipment multiplies the longitudinal wind component by the air density, the forward projected area of ​​the UAV, and the aerodynamic drag coefficient to obtain the longitudinal wind drag. When the longitudinal wind component is negative (headwind), the longitudinal wind drag is positive, indicating that thrust needs to be increased to overcome wind resistance, and the thrust compensation value equals the longitudinal wind drag. When the longitudinal wind component is positive (tailwind), the longitudinal wind drag is negative, indicating that thrust needs to be decreased. To avoid excessive ground speed, the thrust compensation value is equal to the negative value of the longitudinal wind resistance. The electronic equipment generates a second attitude pitch angle compensation value based on the thrust compensation value. The generation method is to calculate the required pitch angle compensation value according to the relationship between the UAV's pitch angle and the horizontal component of the propeller thrust. Specifically, the electronic equipment obtains the current total propeller thrust of the UAV, divides the thrust compensation value by the total propeller thrust to obtain the required pitch angle sine value, and performs an arcsine operation on the pitch angle sine value to obtain the pitch angle compensation value. The direction of the pitch angle compensation value is determined according to the direction of the longitudinal wind component. When the longitudinal wind component is negative (i.e., headwind), the pitch angle compensation value is negative, indicating that the nose is tilted down to increase forward thrust. When the longitudinal wind component is positive (i.e., tailwind), the pitch angle compensation value is positive, indicating that the nose is raised to reduce forward thrust. The electronic equipment uses the pitch angle compensation value as the second attitude pitch angle compensation value for subsequent adjustments to the UAV's flight attitude.

[0109] S704: The first attitude tilt angle compensation value and the second attitude pitch angle compensation value are vectorized to obtain the wind-resistant attitude correction parameters used to correct the flight attitude of the target UAV.

[0110] In the embodiments of this application, the wind-resistant attitude correction parameter refers to the multi-dimensional control quantity used to compensate for the influence of wind field disturbance on the flight attitude of the target UAV, including roll angle correction component, pitch angle correction component and yaw angle correction component.

[0111] Specifically, the first attitude tilt angle compensation value acquired by the electronic equipment corresponds to the lateral drift cancellation requirement caused by the lateral wind component, and the second attitude pitch angle compensation value corresponds to the velocity deviation correction requirement caused by the longitudinal wind component. These two compensation values ​​act on different attitude control axes of the target UAV. The first attitude tilt angle compensation value is represented in the body coordinate system as a first vector containing roll and yaw components, while the second attitude pitch angle compensation value is represented in the same body coordinate system as a second vector containing pitch and roll components. These two vectors have projection components along different axes in three-dimensional space. By performing component-wise phase calculations on the first and second vectors... The vector addition operation yields a composite vector. The roll axis component of the composite vector is equal to the sum of the roll axis components of the first vector and the second vector. The pitch axis component of the composite vector is equal to the sum of the pitch axis components of the first vector and the second vector. The yaw axis component of the composite vector is equal to the sum of the yaw axis components of the first vector and the second vector. The synthesized composite vector is converted into a triplet parameter form containing roll angle correction values, pitch angle correction values, and yaw angle correction values. This is output as the wind-resistant attitude correction parameter. The wind-resistant attitude correction parameter can simultaneously characterize all the attitude adjustments required by the target UAV to counteract crosswind drift and maintain a constant ground speed.

[0112] S107: Based on the inspection route and wind resistance attitude correction parameters, generate flight control commands and control the target UAV to fly along the inspection route according to the flight control commands.

[0113] In this embodiment of the application, flight control commands refer to a set of comprehensive control commands generated by the electronic device based on the inspection route and wind resistance attitude correction parameters, used to control the flight attitude, speed, and direction of the UAV.

[0114] Specifically, the electronic equipment acquires inspection route data and wind-resistant attitude correction parameters. The inspection route data includes information such as the coordinates of each waypoint, flight segment direction, flight segment length, flight altitude, and preset cruise speed. The wind-resistant attitude correction parameters include the first attitude roll angle compensation value (i.e., roll angle compensation value) and the second attitude pitch angle compensation value (i.e., pitch angle compensation value). Based on the inspection route data, the electronic equipment determines the coordinates of the target waypoint and the flight altitude of the target waypoint to which the UAV should currently fly. Based on the current position of the UAV and the target waypoint position, the electronic equipment calculates the heading angle that the UAV should maintain. The heading angle is calculated by taking the azimuth angle between the current position coordinates and the target waypoint position coordinates as the heading angle. The electronic equipment then performs wind-resistant attitude correction... The parameters generate attitude control commands, which include roll angle, pitch angle, and yaw angle settings. The roll angle setting equals the current roll angle of the UAV plus the first attitude tilt angle compensation value, i.e., the roll angle compensation value. The pitch angle setting equals the current pitch angle of the UAV plus the second attitude pitch angle compensation value, i.e., the pitch angle compensation value. The yaw angle setting equals the calculated yaw angle. The electronic equipment generates speed control commands based on the preset cruise speed and longitudinal wind component. The speed control commands include propeller speed setting or throttle opening setting. When the longitudinal wind component is negative (headwind), the propeller speed setting is increased to provide greater thrust to maintain ground speed. When the longitudinal wind component is positive (tailwind), the propeller speed setting is decreased to avoid ground speed. If the speed is too fast, the electronic equipment generates altitude control commands based on the altitude information of the inspection route. These commands include a target altitude setpoint, which is equal to the preset flight altitude for the current segment. The electronic equipment integrates attitude control commands, speed control commands, heading control commands, and altitude control commands into a complete flight control command. This command is then transmitted wirelessly to the target UAV's flight control system. Upon receiving the commands, the target UAV's flight control system parses the control parameters of each sub-command. Based on the roll angle setpoint, the flight control system adjusts the UAV's roll control surfaces or differential propeller speed to achieve the target roll angle. Similarly, based on the pitch angle setpoint, the flight control system adjusts the UAV's pitch control... The control surfaces or propeller speeds allow the UAV to reach the target pitch angle. The flight control system adjusts the yaw control surfaces or propeller speeds according to the yaw angle setpoint to achieve the target heading angle. The flight control system adjusts the speeds of each propeller motor according to the propeller speed setpoint to generate the required thrust. The flight control system adjusts the total thrust of the UAV according to the target altitude setpoint to climb or descend to the target altitude. Under the control of the flight control system, the target UAV adjusts its flight attitude and speed according to flight control commands. The roll angle adjustment counteracts the lateral drift caused by the crosswind component, the pitch angle adjustment counteracts the effect of the longitudinal wind component on ground speed, and the yaw angle adjustment keeps the UAV in the correct flight direction.The drone's thrust is adjusted to maintain a constant ground speed and flight altitude. Electronic equipment continuously monitors the drone's real-time position, attitude, and speed, comparing this data with preset parameters for the inspection route. When the drone deviates from the inspection route by more than a set threshold, the electronic equipment recalculates wind-resistant attitude correction parameters and generates new flight control commands to correct the deviation. Through continuous closed-loop control, the target drone accurately flies along the inspection route to complete the power line inspection task.

[0115] The following describes an exemplary unmanned aerial vehicle (UAV) control electronic device provided in the embodiments of this application. Figure 3 This is an exemplary hardware structure diagram of an electronic device for controlling a drone provided in an embodiment of this application.

[0116] In some embodiments, the electronic device controlled by the drone is a computer device or includes a computer device in the electronic device controlled by the drone. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0117] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

Claims

1. A method for controlling an unmanned aerial vehicle (UAV), characterized in that, The method includes: Acquire environmental status data of the target UAV in the operation area, surface material characteristics of the target facility, geometric structure data, and operating voltage data. The operation area includes at least one target facility, and the environmental status data includes the electromagnetic field intensity distribution and air humidity value of the operation area. Calculate the electrical insulation characteristics of the work area based on the air humidity value and surface material properties; The first hazard avoidance zone of the work area is determined based on electrical insulation characteristic parameters combined with the geometric structure data and operating voltage data of the target facility; The second hazard avoidance zone of the work area is determined based on the electromagnetic field intensity distribution; The first hazard avoidance zone and the second hazard avoidance zone are spatially merged to generate a comprehensive no-fly envelope area for the operation area; based on the comprehensive no-fly envelope area, inspection routes are generated. Acquire real-time wind field vector data of the work area, and determine wind resistance attitude correction parameters based on the real-time wind field vector data; Based on the inspection route and wind-resistant attitude correction parameters, flight control commands are generated, and the target UAV is controlled to fly along the inspection route according to the flight control commands.

2. The UAV control method according to claim 1, characterized in that, The calculation of electrical insulation characteristic parameters of the work area based on air humidity and surface material properties specifically includes: Based on the air humidity value and the surface material characteristics of each target facility, the equivalent surface conductivity of each target facility surface is determined. Based on the equivalent surface conductivity of each target facility, the electric field distortion enhancement coefficient near the surface of each target facility is determined. The preset benchmark breakdown field strength is corrected by using the electric field distortion enhancement coefficient corresponding to each target facility, and the critical distortion field strength threshold that can induce air breakdown for each target facility is calculated. The set of critical distortion field strength thresholds corresponding to each target facility is determined as the electrical insulation characteristic parameters under the operating area.

3. The UAV control method according to claim 2, characterized in that, The determination of the first hazard avoidance zone in the work area based on electrical insulation characteristic parameters combined with the geometric structure data and operating voltage data of the target facility specifically includes: Extract areas from geometric structure data where the rate of change of the geometric curvature of the target facility surface is greater than a preset threshold, and mark these areas as discharge-sensitive points with a high charge accumulation tendency. Based on the operating voltage data, a virtual spatial electric field intensity distribution field is constructed outward from each discharge sensitive point as the center. From the set of electrical insulation characteristic parameters, extract the critical distortion field strength threshold corresponding to the target facility to which the discharge sensitive point belongs; By comparing the spatial electric field intensity distribution field with the corresponding critical distortion field strength threshold, the spatial range in which the electric field intensity value in the spatial electric field intensity distribution field is higher than the corresponding critical distortion field strength threshold is extracted to generate the breakdown risk envelope surface. Based on the breakdown risk envelope, a preset mechanical positioning tolerance range is superimposed to construct a physical isolation space that can block the electrical breakdown path, and the outer edge of the physical isolation space is defined as the first danger avoidance zone.

4. The UAV control method according to claim 1, characterized in that, The second hazard avoidance zone, which is determined based on the electromagnetic field intensity distribution, specifically includes: The electromagnetic field intensity distribution is mapped onto the preset magnetic susceptibility response curve of the target UAV to determine the magnetic interference level of the target UAV at each location point in the operating area; Based on the magnetic interference level, the preset interference-drift mapping table is consulted to determine the maximum attitude uncertainty of the target UAV in the hovering operation state at each location point; Based on the maximum attitude uncertainty, predict the cumulative maximum position drift radius of the target UAV within a preset time period; Based on the maximum position drift radius, an electromagnetic interference buffer space capable of covering potential runaway displacement is constructed in reverse. The boundary of the electromagnetic interference buffer space is expanded by a pre-set anti-magnetic interference safety margin, and the outer contour boundary of the electromagnetic interference buffer space containing the anti-magnetic interference safety margin is used as a second danger avoidance zone that can avoid the risk of collision caused by magnetic interference.

5. The UAV control method according to claim 1, characterized in that, The step of spatially merging the first hazard avoidance zone and the second hazard avoidance zone to generate a comprehensive no-fly envelope zone specifically includes: Map the first hazard avoidance zone and the second hazard avoidance zone to a unified three-dimensional coordinate system; Perform a spatial Boolean union operation on the mapped first hazard avoidance region and the second hazard avoidance region to generate a preliminary joint space that can simultaneously cover electrical breakdown risk and magnetic interference drift risk; The outer contour surface of the preliminary joint space is extracted, and the curvature of the geometric connection of the outer contour surface is smoothed to obtain the integrated no-fly envelope region.

6. The UAV control method according to claim 1, characterized in that, The process of generating inspection routes based on the comprehensive no-fly zone envelope specifically includes: Using the outer surface of the comprehensive no-fly zone as the reference interface, the normal extension is performed along the direction away from the comprehensive no-fly zone, with the preset optimal observation distance as the distance parameter, to construct a three-dimensional equidistant inspection surface that encloses the comprehensive no-fly zone. The locations of key components to be inspected are pre-marked on the target facility. The locations of the key components are then projected along the normal onto a three-dimensional equidistant inspection surface to obtain multiple key inspection viewpoints located on the three-dimensional equidistant inspection surface. A three-dimensional spatial topology network is constructed using multiple key inspection viewpoints as nodes. The shortest path search algorithm is used to plan a geometric trajectory on a three-dimensional equidistant inspection surface that connects all key inspection viewpoints and has the shortest total path length. The geometric trajectory is then determined as the inspection route.

7. The UAV control method according to claim 1, characterized in that, The determination of wind resistance attitude correction parameters based on real-time wind field vector data specifically includes: The real-time wind field vector data is decomposed into a longitudinal wind component parallel to the tangent of the inspection route and a lateral wind component perpendicular to the tangent of the inspection route. Based on the magnitude of the crosswind component, the required lateral deflection offset moment to maintain the inspection route is calculated, and the first attitude tilt angle compensation value is generated based on the lateral deflection offset moment. Based on the magnitude of the longitudinal wind component and the preset cruising speed of the target UAV, the thrust compensation value required to maintain a constant ground speed is calculated, and the second attitude pitch angle compensation value is generated based on the thrust compensation value. The first attitude tilt angle compensation value and the second attitude pitch angle compensation value are vector-synthesized to obtain the wind-resistant attitude correction parameters used to correct the flight attitude of the target UAV.

8. An electronic device for controlling a drone, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on an electronic device controlled by a drone, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device controlled by a drone, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

Images