Transformer substation unmanned aerial vehicle inspection path planning method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a transformer substation unmanned aerial vehicle routing inspection path planning method, device and equipment and a storage medium, and is applied to the technical field of power equipment routing inspection. The method comprises the following steps: establishing an unmanned aerial vehicle energy consumption model based on horizontal and vertical movement and hovering actions of an unmanned aerial vehicle; based on the starting point and the ending point of the to-be-inspected area and the power equipment of the to-be-inspected area, determining an inspection path considering obstacle avoidance and an electromagnetic forbidden area; and optimizing the inspection path of the unmanned aerial vehicle based on the unmanned aerial vehicle energy consumption model and the inspection path considering the obstacle avoidance and the electromagnetic forbidden zone to obtain an optimized unmanned aerial vehicle inspection path. According to the invention, path selection is carried out by considering multiple factors such as energy consumption, path length, obstacle avoidance and the like, and obstacle and electromagnetic interference can be avoided under the condition of efficient inspection.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of power equipment inspection, and particularly relates to a substation unmanned aerial vehicle (UAV) inspection path planning method and device, equipment and a storage medium. BACKGROUND

[0002] Traditional manual inspection of substations has high safety risks, personnel need to be in close contact with high-voltage equipment, and there are safety risks such as electric shock and falling, especially in adverse weather, at night, after disasters, and the like. Limited by human walking, direct recording and the like, the inspection takes a long time and has low efficiency.

[0003] Using a UAV for inspection can complete a wide range of inspection in a short time, and the efficiency is much higher than that of manual inspection; it can reduce personnel operation in dangerous environments such as high voltage, strong electromagnetic field and narrow space, and avoid safety risks such as electric shock and falling; the UAV can carry multiple sensors and support automatic uploading of data, thereby improving the inspection quality and intelligent level.

[0004] However, UAV inspection path planning also faces many problems: first, power equipment in substations is dense and has a complex structure, and equipment heights are different; second, there are multiple high-voltage live bodies that need to be avoided from the electromagnetic interference exclusion zone; and finally, the flight time of the UAV is still limited by the capacity of the on-board battery, which often leads to incomplete tasks.

[0005] Therefore, in the existing UAV inspection process, there is a problem that obstacles and electromagnetic interference cannot be avoided in efficient inspection. SUMMARY

[0006] The present disclosure provides a substation UAV inspection path planning method, device, equipment and storage medium.

[0007] According to a first aspect of the present disclosure, a substation UAV inspection path planning method is provided, which comprises:

[0008] A UAV energy consumption model is established based on horizontal, vertical motion and hovering actions of the UAV;

[0009] A consideration-avoidance-and-electromagnetic-exclusion-zone inspection path is determined based on a starting point and an ending point of a region to be inspected and power equipment in the region to be inspected;

[0010] The inspection path of the UAV is optimized based on the UAV energy consumption model and the consideration-avoidance-and-electromagnetic-exclusion-zone inspection path, to obtain an optimized UAV inspection path.

[0011] In some implementations of the first aspect, the UAV energy consumption model is established based on horizontal, vertical motion and hovering actions of the UAV, comprising:

[0012] A UAV energy consumption model is established based on the UAV's climbing energy consumption, descending energy consumption, hovering energy consumption, and horizontal flight energy consumption.

[0013] In some implementations of the first aspect, determining the inspection path that takes into account obstacle avoidance and electromagnetic exclusion zones based on the starting point and end point of the area to be inspected and the power equipment in the area to be inspected includes:

[0014] Determine the flight path based on the starting and ending points of the area to be inspected;

[0015] For various power equipment in the substation, a hemispherical model is used to perform equivalence and determine obstacles;

[0016] For charged bodies in power equipment, a spherical model is used to obtain an electromagnetic restricted area.

[0017] Based on the flight path, obstacles, and electromagnetic restricted areas, an inspection path that takes obstacle avoidance and electromagnetic restricted areas into consideration is determined.

[0018] In some implementations of the first aspect, the optimization of the drone inspection path based on the drone energy consumption model and the inspection path that takes into account obstacle avoidance and electromagnetic exclusion zones to obtain the optimized drone inspection path includes:

[0019] Based on the UAV energy consumption model E total and the path length L p , determine the objective function F considering energy consumption-path-obstacle avoidance factors obj =λE total +μL p +κR obs , where R obs is the penalty for the overlap between the path and the power equipment and electromagnetic restricted area, λ, μ, κ are adjustable weight factors, λ+μ+κ=1;

[0020] Based on the distance between the i-th waypoint and the j-th power equipment and the distance between the i-th waypoint and the k-th charged object, the objective function F obj Constraints are imposed to obtain the optimized UAV inspection path.

[0021] In some implementations of the first aspect, the above-mentioned drone energy consumption model E total Satisfy the formula E i is the energy consumption of moving from the i-th waypoint to the i+1-th waypoint;

[0022] The path length L p Satisfy the formula (x i ,y i ,z i ) is the coordinate of the i-th waypoint.

[0023] In some implementations of the first aspect, the target function F obj is constrained based on a distance between the i-th waypoint and the j-th power device, satisfying a formula:

[0024]

[0025] where D(i,j) is the distance between the i-th waypoint and the j-th power device, (x i ,y i ,z i ) is the coordinate of the i-th waypoint, (x j ,y j ,z j ) is the center coordinate of the j-th power device, and r j is the radius of the equivalent hemisphere of the j-th power device.

[0026] The target function F obj is constrained based on a distance between the i-th waypoint and the k-th live body, satisfying a formula:

[0027]

[0028] where D(i,k) is the distance between the i-th waypoint and the k-th live body, (x i ,y i ,z i ) is the coordinate of the i-th waypoint, (x k ,y k ,z k ) is the center coordinate of the k-th power device, and r k is the safety distance of the k-th power device.

[0029] In some implementations of the first aspect, the target function F obj is constrained based on the distance between the i-th waypoint and the j-th power device and the distance between the i-th waypoint and the k-th live body, to obtain an optimized UAV inspection path, including:

[0030] The target function F obj is constrained based on the distance between the i-th waypoint and the j-th power device and the distance between the i-th waypoint and the k-th live body using a preset particle swarm optimization algorithm and grey wolf optimization algorithm, to obtain an optimized UAV inspection path.

[0031] According to a second aspect of the present disclosure, a substation UAV inspection path planning device is provided, which includes:

[0032] The energy consumption model determining module is configured to establish a UAV energy consumption model based on horizontal movement, vertical movement and hovering action of the UAV.

[0033] The inspection path determining module is configured to determine an inspection path considering obstacle avoidance and electromagnetic exclusion zone based on the start point and the end point of the to-be-inspected area and the power equipment of the to-be-inspected area.

[0034] The inspection path optimizing module is configured to optimize the inspection path of the UAV based on the UAV energy consumption model and the inspection path considering obstacle avoidance and electromagnetic exclusion zone, to obtain an optimized UAV inspection path.

[0035] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, the memory having a computer program stored thereon, and the processor implementing the method as above when executing the program.

[0036] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, which has a computer program stored thereon, and the program, when executed by a processor, implements the method as above.

[0037] The present disclosure can avoid obstacles and electromagnetic interference while achieving efficient inspection by considering multiple factors such as energy consumption, path length, obstacle avoidance, etc.

[0038] It should be understood that the content described in the summary section is not intended to limit or define key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0039] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent as various embodiments of the present disclosure are disclosed in detail below with reference to the drawings. The drawings provided for a better understanding of the present disclosure and do not limit the present disclosure. In the drawings, the same or similar reference numerals refer to the same or similar elements, and:

[0040] Figure 1 A flowchart of a substation UAV inspection path planning method according to an embodiment of the present disclosure is shown;

[0041] Figure 2 A flowchart of another substation UAV inspection path planning method according to an embodiment of the present disclosure is shown;

[0042] Figure 3 A flowchart of a PSO-GWO algorithm according to an embodiment of the present disclosure is shown;

[0043] Figure 4A block diagram of a substation unmanned aerial vehicle inspection path planning device is shown according to an embodiment of the present disclosure.

[0044] Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0046] In addition, the term "and / or" herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0047] The present disclosure solves the above technical problems by considering multiple factors such as energy consumption, path length, and obstacle avoidance for path selection, which can avoid obstacles and electromagnetic interference while achieving efficient inspection.

[0048] Figure 1 A flowchart of a substation unmanned aerial vehicle inspection path planning method is shown according to an embodiment of the present disclosure, Figure 2 A flowchart of another substation unmanned aerial vehicle inspection path planning method is shown according to an embodiment of the present disclosure, which is combined with Figure 1 and Figure 2 The substation unmanned aerial vehicle inspection path planning method 100 can include:

[0049] S101, a UAV energy consumption model is established based on horizontal, vertical motion and hovering action of the UAV.

[0050] In some embodiments, the UAV energy consumption model established based on the horizontal, vertical motion and hovering action of the UAV can include:

[0051] The UAV energy consumption model is established based on the climb energy consumption, descent energy consumption, hovering energy consumption, and horizontal flight energy consumption of the UAV.

[0052] Specifically, various maneuvers in the UAV inspection process need to be considered, including horizontal, vertical movement and hovering. It is assumed that the movement speed of the UAV is constant, v0.

[0053] Climbing energy consumption:

[0054] Descending energy consumption:

[0055] Hovering energy consumption: E hover = P hover Δt,

[0056] Horizontal flight energy consumption:

[0057] Where, P climb is the climbing power, P descent is the descending power, P hove r is the hovering power, P hor is the horizontal flight power, Δh is the climbing or descending distance, Δt is the hovering time, h dis is the horizontal flight distance.

[0058] Based on the above various energy consumptions, the UAV energy consumption model can be obtained: E = E climb + E descent + E hover + E hor .

[0059] S102, based on the starting point and the ending point of the to-be-inspected area and the power equipment in the to-be-inspected area, determine the inspection path considering obstacles and electromagnetic exclusion zones.

[0060] In some embodiments, the above determination of the inspection path considering obstacles and electromagnetic exclusion zones based on the starting point and the ending point of the to-be-inspected area and the power equipment in the to-be-inspected area can include:

[0061] Determine the flight path based on the starting point and the ending point of the to-be-inspected area;

[0062] For various power equipment in the substation, use a hemisphere model for equivalence to determine obstacles;

[0063] For the live body in the power equipment, use a spherical model for equivalence to obtain an electromagnetic exclusion zone;

[0064] Based on the flight path, obstacles and electromagnetic exclusion zones, determine the inspection path considering obstacles and electromagnetic exclusion zones.

[0065] Specifically, the three-dimensional path of the UAV can be represented by a set of waypoints, which are linked to form a path from the starting point to the ending point, which can be represented as P1(x1, y1, z1), P2(x2, y2, z2) … P n(x n ,y n ,z n ).

[0066] For various power equipment in the substation, an approximate hemisphere model is used for equivalence, i.e. the obstacle can be represented as (x j ,y j ,z j ,r j ), where (x j ,y j ,z j ) is the center coordinate of the power equipment, and r j is the radius of the equivalent hemisphere.

[0067] Part of the power equipment belongs to a live body, and if the unmanned aerial vehicle is too close, it will interfere with the unmanned aerial vehicle and cause danger, so an electromagnetic exclusion zone needs to be set up. During the flight of the unmanned aerial vehicle, the electric field strength in which it is located should be ensured to be not greater than the safe electric field strength thereof. The electromagnetic exclusion zone is equivalent to a spherical model, i.e. the electromagnetic exclusion zone can be represented as (x k ,y k ,z k ,r k ), where (x k ,y k ,z k ) is the center coordinate of the live body, and r k is the safe distance thereof.

[0068] In some specific examples, the safe distance recommended values of common voltage levels of the substation can be given: 35kV should be not less than 0.5m, 110kV should be not less than 1.5m, 220kV should be not less than 2.5m, 500kV should be not less than 4m, and 800-1000kV should be not less than 8m.

[0069] In S103, the inspection path of the unmanned aerial vehicle is optimized based on the energy consumption model of the unmanned aerial vehicle and the inspection path considering the obstacle avoidance and the electromagnetic exclusion zone, to obtain the optimized inspection path of the unmanned aerial vehicle.

[0070] In some embodiments, the above-mentioned optimization of the inspection path of the unmanned aerial vehicle based on the energy consumption model of the unmanned aerial vehicle and the inspection path considering the obstacle avoidance and the electromagnetic exclusion zone, to obtain the optimized inspection path of the unmanned aerial vehicle, can include:

[0071] Based on the energy consumption model E total of the unmanned aerial vehicle and the path length L p , a target function F obj considering the energy consumption-path-obstacle avoidance factor is determined, F total =λE p +μL obs +κR obs , where R obsλ, μ, κ are adjustable weight factors, λ + μ + κ = 1, for the penalty of the coincidence of the path and the power equipment, the electromagnetic forbidden zone;

[0072] Based on the distance between the ith waypoint and the jth power equipment and the distance between the ith waypoint and the kth charged body, the target function F obj is constrained to obtain the optimized unmanned aerial vehicle inspection path.

[0073] In some embodiments, the unmanned aerial vehicle energy consumption model E total satisfies the formula E i is the energy consumption of moving from the ith waypoint to the i+1th waypoint;

[0074] The path length L p satisfies the formula (x i ,y i ,z i ) is the coordinate of the ith waypoint.

[0075] In some embodiments, based on the distance between the ith waypoint and the jth power equipment, the target function F obj is constrained to satisfy the formula:

[0076]

[0077] Wherein, D(i,j) is the distance between the ith waypoint and the jth power equipment, (x i ,y i ,z i ) is the coordinate of the ith waypoint, (x j ,y j ,z j ) is the center coordinate of the jth power equipment, and r j is the radius of the equivalent hemisphere of the jth power equipment;

[0078] Based on the distance between the ith waypoint and the kth charged body, the target function F obj is constrained to satisfy the formula:

[0079]

[0080] Wherein, D(i,k) is the distance between the ith waypoint and the kth charged body, (x i ,y i ,z i ) is the coordinate of the ith waypoint, (x k ,y k ,z k ) is the center coordinate of the kth power equipment, and r kis the safety distance of the kth electrical equipment.

[0081] In some embodiments, the objective function F is calculated based on the distance between the i-th waypoint and the j-th electric device and the distance between the i-th waypoint and the k-th charged object. obj Constraints are applied to obtain the optimized UAV inspection path, which may include:

[0082] Using the preset particle swarm optimization algorithm and the gray wolf optimization algorithm, the objective function F is calculated based on the distance between the i-th waypoint and the j-th power equipment and the distance between the i-th waypoint and the k-th charged object. obj Constraints are imposed to obtain the optimized UAV inspection path.

[0083] Specifically, UAV path planning can be transformed into an optimization problem, comprehensively considering energy consumption, path length and electromagnetic area proximity risk.

[0084] The total energy consumption of each UAV path is

[0085] E i is the energy consumption of moving from the i-th waypoint to the i+1-th waypoint.

[0086] The path length is

[0087] The objective function considering multiple factors of energy consumption, path and obstacle avoidance is F obj =λE total +μL p +κR obs ,

[0088] Among them, R obs is the penalty for the overlap of the path with power equipment and electromagnetic restricted areas. λ, μ, and κ are adjustable weight factors, where λ+μ+κ=1.

[0089] Waypoints cannot be located in power equipment or electromagnetic restricted areas. The constraint can be expressed as follows:

[0090] Constraint 1:

[0091]

[0092] Where D(i,j) is the distance between the i-th waypoint and the j-th power equipment, (x i ,y i ,z i ) is the coordinate of the i-th waypoint, (x j ,y j ,z j ) is the center coordinate of the jth power equipment, r jR(i) = r(i) + r(i) * ln(i) where r(i) is the radius of the equivalent hemisphere for the ith waypoint.

[0093] Constraint 2:

[0094]

[0095] where D(i, k) is the distance between the ith waypoint and the kth electrically charged body, (x i ,y i ,z i ) is the coordinate of the ith waypoint, (x k ,y k ,z k ) is the coordinate of the center of the kth electric power device, and r k is its safety distance.

[0096] The penalty R obs for the overlap of the path with the electric power device and the electromagnetic exclusion zone can be expressed as:

[0097]

[0098] where η is a large penalty factor.

[0099] When the path passes through the electric power device or the electromagnetic exclusion zone, this large penalty factor significantly increases the path cost, resulting in its removal from the desired path.

[0100] The path optimization can then be performed using the PSO-GWO algorithm, Figure 3 a flowchart of a PSO-GWO algorithm process according to an embodiment of the present disclosure is shown, which is combined Figure 3 to further describe the path optimization.

[0101] Specifically, a particle swarm optimization-grey wolf optimizer (PSO-GWO) algorithm can be used for path optimization. The particle swarm algorithm directs some particles to random positions with smaller likelihoods to avoid local minimum values. The exploration ability of the grey wolf algorithm is utilized by directing the particles to the positions improved by the grey wolf algorithm to avoid these risks instead of directing them to random positions.

[0102] ① Particle swarm optimization algorithm

[0103] The PSO algorithm first randomly generates an initial population (each particle in the population is a path) in the search domain. The optimal position of each particle and the position information of the optimal particle in the group are saved in the memory. The particles in the group update their positions and velocities in each iteration using the following equations:

[0104]

[0105] where the superscript i denotes the particle in the population, n denotes the iteration number, r1, r2 are random numbers in the interval [0, 1], and ω is the inertia weight parameter. c1, c2 are optimization parameters, V is the velocity vector, X is the position vector, P i is the best position obtained by the i-th particle, P g is the best position available in the population.

[0106] 2) Grey Wolf Optimization Algorithm

[0107] There are four types of wolves in the wolf pack leadership structure: alpha wolf, beta wolf, delta wolf, and omega wolf. In the GWO algorithm, the alpha wolf represents the solution with the best result, the beta wolf and the delta wolf represent the second and third solutions in the population, and the omega wolf is the best solution candidate. The GWO algorithm assumes that hunting is performed by the alpha wolf, beta wolf, and delta wolf, and the omega wolf follows these wolves.

[0108] The hunting of the grey wolf includes three steps:

[0109] 1) Tracking, chasing, and approaching the prey;

[0110] 2) Chasing, surrounding, and harassing the prey until the prey stops moving;

[0111] 3) Attacking the prey.

[0112] The mathematical model for surrounding the prey is as follows:

[0113] D = |C x X p (k) - X(k) |,

[0114] X(k+1) = X p (k) - A x D,

[0115] where k is the iteration number, X p is the position of the prey, X is the position of the grey wolf, and A and C are vector coefficients calculated by:

[0116] A = a x (2 x r1 - 1),

[0117] C = 2 x r2,

[0118] where the value of a increases linearly from 0 to 2 as the iteration number increases. r1, r2 are random numbers between [0, 1].

[0119] The other wolves in the population will move according to the positions of the alpha wolf, beta wolf, and delta wolf, which can be represented as:

[0120] D α = |C1 x X α - X(k) |,

[0121] D β = |C2 x X β - X(k) |,

[0122] D δ = |C3 x X δ - X(k) |,

[0123] where X α , X β , X δ represent the positions of the best three wolves in each iteration.

[0124] The new position X p (k+1) of the prey is the average of the positions of the best three wolves in the population, which can be expressed as:

[0125]

[0126] where X1 = |X α - a1D α |, X2 = |X β - a2D β |, X3 = |X δ - a3D δ |.

[0127] The gray wolves complete hunting by attacking the prey, and in order to attack, the gray wolves must be close enough to the prey. When |A| ≥ 1, the existing hunting is abandoned to find a better solution. This method can prevent falling into a local minimum. When the GWO algorithm reaches the desired number of iterations, the search is complete.

[0128] The substation unmanned aerial vehicle inspection path planning method provided by the present disclosure can select a path by considering multiple factors such as energy consumption, path length, and obstacle avoidance, so as to avoid obstacles and electromagnetic interference while achieving efficient inspection.

[0129] The above is an introduction to the method embodiment. The following describes the present disclosure scheme through a device embodiment.

[0130] Figure 4 A block diagram of a substation unmanned aerial vehicle inspection path planning device according to an embodiment of the present disclosure is shown.

[0131] As shown in Figure 4 , the substation unmanned aerial vehicle inspection path planning device 400 can include:

[0132] An energy consumption model determination module 401 is configured to establish an unmanned aerial vehicle energy consumption model based on the horizontal, vertical movement and hovering action of the unmanned aerial vehicle.

[0133] An inspection path determination module 402 is configured to determine an inspection path that takes into account obstacle avoidance and electromagnetic exclusion zones based on the starting point and end point of the area to be inspected and the electrical equipment in the area to be inspected;

[0134] The inspection path optimization module 403 is used to optimize the inspection path of the drone based on the drone energy consumption model and the inspection path considering obstacle avoidance and electromagnetic restricted areas, so as to obtain an optimized drone inspection path.

[0135] In some embodiments, establishing a drone energy consumption model based on the drone's horizontal and vertical motions and hovering actions may include:

[0136] A UAV energy consumption model is established based on the UAV's climbing energy consumption, descending energy consumption, hovering energy consumption, and horizontal flight energy consumption.

[0137] In some embodiments, determining the inspection path that takes into account obstacle avoidance and electromagnetic exclusion zones based on the starting point and end point of the area to be inspected and the electrical equipment in the area to be inspected includes:

[0138] Determine the flight path based on the starting and ending points of the area to be inspected;

[0139] For various power equipment in the substation, a hemispherical model is used to perform equivalence and determine obstacles;

[0140] For charged bodies in power equipment, a spherical model is used to obtain an electromagnetic restricted area.

[0141] Based on the flight path, obstacles, and electromagnetic restricted areas, an inspection path that takes obstacle avoidance and electromagnetic restricted areas into consideration is determined.

[0142] In some embodiments, the above-mentioned inspection path of the drone is optimized based on the drone energy consumption model and the inspection path considering obstacle avoidance and electromagnetic restricted areas, and the optimized drone inspection path is obtained, including:

[0143] Based on the UAV energy consumption model E total and the path length L p , determine the objective function F considering energy consumption-path-obstacle avoidance factors obj =λE total +μL p +κR obs , where R obs is the penalty for the overlap between the path and the power equipment and electromagnetic restricted area, λ, μ, κ are adjustable weight factors, λ+μ+κ=1;

[0144] Based on the distance between the i-th waypoint and the j-th power equipment and the distance between the i-th waypoint and the k-th charged object, the objective function F obj Constraints are imposed to obtain the optimized UAV inspection path.

[0145] In some embodiments, the energy consumption model E of the UAV described above total satisfies the formula E i is the energy consumption for moving from the ithwaypoint to the (i+1)thwaypoint;

[0146] The path length L p satisfies the formula (x i ,y i ,z i ) is the coordinate of the ithwaypoint.

[0147] In some embodiments, the objective function F obj is constrained based on the distance between the ithwaypoint and the jthpower device, satisfying the formula:

[0148]

[0149] where D(i,j) is the distance between the ithwaypoint and the jthpower device, (x i ,y i ,z i ) is the coordinate of the ithwaypoint, (x j ,y j ,z j ) is the center coordinate of the jthpower device, and r j is the radius of the equivalent hemisphere of the jthpower device;

[0150] The objective function F obj is constrained based on the distance between the ithwaypoint and the kthcharged body, satisfying the formula:

[0151]

[0152] where D(i,k) is the distance between the ithwaypoint and the kthcharged body, (x i ,y i ,z i ) is the coordinate of the ithwaypoint, (x k ,y k ,z k ) is the center coordinate of the kthpower device, and r k is the safety distance of the kthpower device.

[0153] In some embodiments, the objective function F obj is constrained based on the distance between the ithwaypoint and the jthpower device and the distance between the ithwaypoint and the kthcharged body, to obtain an optimized UAV inspection path, including:

[0154] Using a preset particle swarm optimization algorithm and grey wolf optimization algorithm, based on the distance between the ith waypoint and the jth power equipment and the distance between the ith waypoint and the kth live body, the target function F obj is subjected to constraints to obtain an optimized unmanned aerial vehicle inspection path.

[0155] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the action sequence described, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0156] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0157] According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0158] Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the present disclosure described and / or claimed in this document.

[0159] The device 500 includes a computing unit 501 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0160] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through computer networks, such as the Internet, and / or various telecommunication networks.

[0161] The computing unit 501 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method 100 described above can be performed.

[0162] Various implementations of the systems and techniques described above herein can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0163] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package, or entirely on a remote machine or server.

[0164] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0165] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0166] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0167] The computer system can include clients and servers. This relationship can be. The servers are typically remote from the clients with the interactions between them occurring over a communication network. The relationship between client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The servers can be cloud servers, servers of a distributed system, or servers incorporating blockchain.

[0168] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, without departing from the desired results of the technical solutions of the present disclosure, and this is not limited herein.

[0169] The specific implementation described above does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A substation drone inspection path planning method, characterized in that: The method comprises: Establish a UAV energy consumption model based on the UAV's horizontal, vertical and hovering movements; Determine the inspection path taking into account obstacle avoidance and electromagnetic exclusion zones based on the starting and ending points of the area to be inspected and the power equipment in the area to be inspected; Based on the UAV energy consumption model and the inspection path considering obstacle avoidance and electromagnetic restricted areas, the inspection path of the UAV is optimized to obtain an optimized UAV inspection path.

2. The method according to claim 1, characterized in that The energy consumption model of the drone is established based on the horizontal and vertical motions and hovering actions of the drone, including: A UAV energy consumption model is established based on the UAV's climbing energy consumption, descending energy consumption, hovering energy consumption, and horizontal flight energy consumption.

3. The method according to claim 1, characterized in that The determining of an inspection path taking into account obstacle avoidance and electromagnetic restricted areas based on the starting point and end point of the area to be inspected and the power equipment in the area to be inspected includes: Determine the flight path based on the starting and ending points of the area to be inspected; For various power equipment in the substation, a hemispherical model is used to perform equivalence and determine obstacles; For charged bodies in power equipment, a spherical model is used to obtain an electromagnetic restricted area. Based on the flight path, obstacles, and electromagnetic restricted areas, an inspection path that takes obstacle avoidance and electromagnetic restricted areas into consideration is determined.

4. The method according to claim 1, wherein The method optimizes the inspection path of the drone based on the drone energy consumption model and the inspection path considering obstacle avoidance and electromagnetic restricted areas to obtain the optimized drone inspection path, including: Based on the UAV energy consumption model E total and the path length L p , determine the objective function F considering energy consumption-path-obstacle avoidance factors obj =λE total +μL p +κR obs , where R obs is the penalty for the overlap between the path and the power equipment and electromagnetic restricted area, λ, μ, κ are adjustable weight factors, λ+μ+κ=1; Based on the distance between the i-th waypoint and the j-th power equipment and the distance between the i-th waypoint and the k-th charged object, the objective function F obj Constraints are imposed to obtain the optimized UAV inspection path.

5. The method according to claim 4, characterized in that The UAV energy consumption model E total Satisfy the formula E i is the energy consumption of moving from the i-th waypoint to the i+1-th waypoint; The path length L p Satisfy the formula (x i ,y i ,z i ) is the coordinate of the i-th waypoint.

6. The method according to claim 4, characterized in that Based on the distance between the i-th waypoint and the j-th power equipment, the objective function F obj Constraints are made to satisfy the formula: Where D(i,j) is the distance between the i-th waypoint and the j-th power equipment, (x i ,y i ,z i ) is the coordinate of the i-th waypoint, (x j ,y j ,z j ) is the center coordinate of the jth power equipment, r j is the radius of the equivalent hemisphere of the jth electrical device; Based on the distance between the i-th waypoint and the k-th charged body, the objective function F obj Constraints are made to satisfy the formula: Where D(i,k) is the distance between the i-th waypoint and the k-th charged body, (x i ,y i ,z i ) is the coordinate of the i-th waypoint, (x k ,y k ,z k ) is the center coordinate of the kth power equipment, r k is the safety distance of the kth electrical equipment.

7. The method according to claim 4, characterized in that The objective function F is calculated based on the distance between the i-th waypoint and the j-th power equipment and the distance between the i-th waypoint and the k-th charged object. obj Constraints are applied to obtain the optimized UAV inspection path, including: Using the preset particle swarm optimization algorithm and the gray wolf optimization algorithm, the objective function F is calculated based on the distance between the i-th waypoint and the j-th power equipment and the distance between the i-th waypoint and the k-th charged object. obj Constraints are imposed to obtain the optimized UAV inspection path.

8. A substation drone inspection path planning device, characterized in that: The device comprises: Energy consumption model determination module, used to establish the energy consumption model of the drone based on its horizontal, vertical movement and hovering action; An inspection path determination module is used to determine an inspection path that takes into account obstacle avoidance and electromagnetic restricted areas based on the starting and ending points of the area to be inspected and the power equipment in the area to be inspected; The inspection path optimization module is used to optimize the inspection path of the drone based on the drone energy consumption model and the inspection path considering obstacle avoidance and electromagnetic restricted areas, so as to obtain an optimized drone inspection path.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; It is characterized in that the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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