Method for optimizing path of UHV (extra-high voltage) power transmission line unmanned aerial vehicle entering and exiting electric field

Abstract

The invention relates to the technical field of electric field entering and exiting path planning, in particular to an ultra-high voltage transmission line unmanned aerial vehicle electric field entering and exiting path optimization method which comprises the steps that electric field intensity distribution and potential distribution in an unmanned aerial vehicle operation area are calculated based on an ultra-high voltage transmission line; generating an incidence relation model according to the electric field intensity distribution and the potential distribution; calculating the minimum distance from each discrete space node in the unmanned aerial vehicle operation area to the surface of the obstacle; and establishing a path comprehensive optimization objective function based on the association relationship model and the minimum distance, and generating an optimal path for the unmanned aerial vehicle to enter and exit the electric field according to the path comprehensive optimization objective function by adopting an E-PRGO algorithm. According to the invention, an improved plant rhizome growth algorithm is adopted for optimization, and the optimal path planning of the unmanned aerial vehicle from the starting point to the operation target area is realized.

Description

Technical Field

[0001] This application relates to the field of electric field entry and exit path planning technology, and in particular to a method for optimizing the entry and exit path of UAVs for ultra-high voltage transmission lines. Background Technology

[0002] In recent years, with the continuous expansion of the construction scale of ultra-high voltage transmission lines, these lines often cross complex terrains such as high mountains and valleys, resulting in extremely harsh operating environments. Traditional manual inspection methods are limited by terrain and climate, resulting in limited inspection coverage and long operation cycles, making it difficult to meet the maintenance needs of large-scale lines. Drones, on the other hand, are widely used for the inspection and testing of transmission lines due to their advantages such as strong high-altitude operation capabilities, high spatial mobility, remote control, and wide field of view.

[0003] However, in the electromagnetic environment of high-voltage transmission lines, drones experience charge accumulation and electromagnetic interference on their surfaces due to complex electric fields, which can easily lead to safety hazards such as partial discharge and electromagnetic interference. Existing research mainly focuses on the distribution characteristics of the electric field around transmission lines, while lacking systematic analysis of the potential changes and safe path planning of drones in the electric field. This makes it difficult to accurately determine the safety boundaries and operating paths when drones are near charged bodies.

[0004] To ensure the safe operation of drones in ultra-high voltage (UHV) power fields, their entry and exit paths need to be precisely optimized. This optimization problem is essentially a nonlinear programming problem with multiple constraints, requiring simultaneous satisfaction of constraints such as electric field strength limits, safe distance guarantees, and obstacle avoidance. In the high-dimensional nonlinear space formed by uneven electric field distribution and the interaction of constraints, the path optimization process is prone to convergence instability and getting trapped in local optima. This results in paths that fail to balance safety and feasibility, thus limiting their practical application in UHV live-line work. Summary of the Invention

[0005] Based on this, the present invention proposes a method for optimizing the path of UAVs entering and exiting the electric field of UHV transmission lines. The method uses the position vector of the UAV in the electric field space as the optimization variable, and constructs a multi-objective optimization function by combining the electric field intensity distribution, obstacle potential field and the target area of ​​the operation. The improved plant root and stem growth algorithm (E-PRGO) is used for optimization to achieve the optimal path planning of the UAV from the starting point to the target area of ​​the operation.

[0006] The technical solution of this invention is as follows: A method for optimizing the path of unmanned aerial vehicles (UAVs) entering and exiting the electric field of ultra-high voltage transmission lines, the method comprising: Based on ultra-high voltage transmission lines, the electric field intensity distribution and electric potential distribution in the UAV operation area are calculated; A correlation model is generated based on the electric field intensity distribution and electric potential distribution described above; Calculate the minimum distance from each discrete spatial node in the UAV's operating area to the surface of an obstacle; Based on the aforementioned correlation model and the minimum distance, a path comprehensive optimization objective function is established, and the E-PRGO algorithm is used to generate the optimal path for the UAV to enter and exit the electric field according to the path comprehensive optimization objective function.

[0007] Optionally, the method for optimizing the path of a UHV transmission line UAV entering and exiting the electric field as described in claim 1 is characterized in that, based on the UHV transmission line, the electric field intensity distribution and electric potential distribution in the UAV's operating area are calculated, including: A three-dimensional electric field finite element calculation model was constructed based on ultra-high voltage transmission lines; Based on the three-dimensional electric field finite element calculation model, the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area are calculated.

[0008] Optionally, a three-dimensional electric field finite element calculation model is constructed based on the ultra-high voltage transmission line, including: A three-dimensional structural model was constructed based on the three-dimensional spatial data of ultra-high voltage transmission lines; Based on the aforementioned three-dimensional structural model, a three-dimensional electric field finite element calculation model for ultra-high voltage transmission lines is constructed.

[0009] Optionally, based on the aforementioned three-dimensional structural model, a three-dimensional electric field finite element calculation model under the ultra-high voltage transmission line is constructed, including: The three-dimensional structural model is meshed, and the iteration step size of the finite element calculation is set. The physical properties and potential boundary conditions of the conductors in the ultra-high voltage transmission line are set, and the conductors include wires, overhead ground wires and tower metal structures; The physical properties of the medium surrounding the ultra-high voltage transmission line are defined, and the medium is mainly air; Set the outer environmental boundary conditions far away from the ultra-high voltage transmission line and the simulation solution range covering the predetermined operating space of the UAV, wherein the outer environmental boundary conditions are used to simulate the attenuation of the electric field at infinity; Based on the iteration step size, the physical properties and potential boundary conditions of the conductor, the physical properties of the medium, the boundary strips of the outer environment, and the simulation solution range, a three-dimensional electric field finite element calculation model is established.

[0010] Optionally, a correlation model is generated based on the electric field intensity distribution and electric potential distribution, including: Based on the potential distribution of each discrete spatial node, the electric field intensity gradient of each discrete spatial node is calculated. A model is fitted to show the correlation between the electric field intensity distribution and the electric field intensity gradient.

[0011] The method for optimizing the path of UAVs entering and exiting the electric field of ultra-high voltage transmission lines according to claim 1 is characterized in that calculating the minimum distance from each discrete spatial node in the UAV's operating area to the surface of the obstacle includes: Obtain the coordinates of each discrete spatial node in the UAV's operating area; Calculate the shortest Euclidean distance from each of the discrete spatial nodes to any point on the surface of the obstacle in the surface geometry model, and set the shortest Euclidean distance as the minimum distance. The surface geometry model is constructed based on the three-dimensional structural model of the UHV transmission line, and the obstacles include conductors, towers, and ground wires.

[0012] Optionally, a path integration optimization objective function is established, including: Using the position of the UAV in the three-dimensional electric field space as the optimization variable, a set of candidate paths for the UAV from the starting point to the target point is constructed, wherein the set of candidate paths includes multiple candidate paths; Construct a discrete path sequence containing discrete path points of each candidate path; Based on the discrete path sequence, the correlation model, and the minimum distance, a path comprehensive optimization objective function is established.

[0013] Optionally, the E-PRGO algorithm is used to generate the optimal path for the UAV to enter and exit the electric field based on the path comprehensive optimization objective function, including: When using the E-PRGO algorithm, the objective function for path integration optimization is set as the fitness function; Set the constraints when using the E-PRGO algorithm, wherein the constraints include electric field safety constraints, obstacle distance constraints, and spatial boundary constraints; The E-PRGO algorithm is used to generate the optimal path for the UAV to enter and exit the electric field based on the fitness function and the constraints.

[0014] Optionally, when using the E-PRGO algorithm, the method further includes: Multiple candidate paths are randomly generated within the drone's operating area as rootstock samples; The initial growth position is randomly assigned to each of the candidate paths.

[0015] Optionally, a path optimization system for UAVs entering and exiting electric fields along ultra-high voltage transmission lines is also provided, the system comprising: The line electric field calculation module is used to calculate the electric field intensity distribution and electric potential distribution in the UAV operation area based on ultra-high voltage transmission lines. The relation model generation module is used to generate a correlation model based on the electric field intensity distribution and electric potential distribution of each of the above-mentioned modules. The minimum distance generation module is used to calculate the minimum distance from each discrete spatial node in the UAV's operating area to the surface of an obstacle. The optimal path generation module is used to establish a path comprehensive optimization objective function based on the correlation model and the minimum distance, and to generate the optimal path for the UAV to enter and exit the electric field using the E-PRGO algorithm according to the path comprehensive optimization objective function.

[0016] Optionally, the line electric field calculation module is also used to: construct a three-dimensional electric field finite element calculation model based on the UHV transmission line; and calculate the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area according to the three-dimensional electric field finite element calculation model.

[0017] Optionally, the line electric field calculation module is further used to: construct a three-dimensional structural model based on the three-dimensional spatial data of the UHV transmission line; and construct a three-dimensional electric field finite element calculation model under the UHV transmission line according to the three-dimensional structural model.

[0018] Optionally, the line electric field calculation module is further configured to: mesh the three-dimensional structural model and set the iteration step size for the finite element calculation; set the physical properties and potential boundary conditions of the conductors in the UHV transmission line, wherein the conductors include wires, overhead ground wires, and tower metal structures; set the physical properties of the medium surrounding the UHV transmission line, wherein the medium is mainly air; set the outer environmental boundary conditions far from the UHV transmission line and the simulation solution range covering the predetermined operating space of the UAV, wherein the outer environmental boundary conditions are used to simulate the attenuation of the electric field at infinity; and establish a three-dimensional electric field finite element calculation model based on the iteration step size, the physical properties and potential boundary conditions of the conductors, the physical properties of the medium, the outer environmental boundary conditions, and the simulation solution range.

[0019] Optionally, the relationship model generation module is further configured to: calculate the electric field intensity gradient of each discrete spatial node based on the electric potential distribution of each discrete spatial node; and fit a correlation model between the electric field intensity distribution and the electric field intensity gradient.

[0020] Optionally, the minimum distance generation module is further configured to: obtain the coordinates of each discrete spatial node in the UAV operation area; calculate the shortest Euclidean distance from each discrete spatial node to any point on the obstacle surface of the surface geometry model, and set the shortest Euclidean distance as the minimum distance, wherein the surface geometry model is constructed based on the three-dimensional structural model of the UHV transmission line, and the obstacles include conductors, towers and ground wires.

[0021] Optionally, the optimal path generation module is further configured to: construct a set of candidate paths from the starting point to the target point using the position of the UAV in the three-dimensional electric field space as the optimization variable, wherein the set of candidate paths includes multiple candidate paths; construct a discrete path sequence of discrete path points contained in each candidate path; and establish a path comprehensive optimization objective function based on the discrete path sequence, the correlation model, and the minimum distance.

[0022] Optionally, the optimal path generation module is further configured to: when using the E-PRGO algorithm, set the path comprehensive optimization objective function as a fitness function; set constraints when using the E-PRGO algorithm, wherein the constraints include electric field safety constraints, obstacle distance constraints, and spatial boundary constraints; and use the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field based on the fitness function and the constraints.

[0023] Optionally, the optimal path generation module is further configured to: randomly generate multiple candidate paths as root samples within the UAV operating area; and randomly assign an initial growth position to each candidate path.

[0024] Optionally, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps described in the above-described method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines.

[0025] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines.

[0026] The technical effects achieved by this invention are as follows: 1. The above-mentioned method for optimizing the path of UAVs entering and exiting the electric field of UHV transmission lines involves the following steps: calculating the electric field intensity distribution and electric potential distribution in the UAV's operating area based on the UHV transmission line; generating a correlation model based on the electric field intensity distribution and electric potential distribution; calculating the minimum distance from each discrete spatial node in the UAV's operating area to the surface of the obstacle; establishing a path comprehensive optimization objective function based on the correlation model and the minimum distance; and using the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field based on the path comprehensive optimization objective function. This application constructs a multi-objective optimization function by combining the electric field intensity distribution, obstacle potential field, and operating target area, and uses an improved plant root and stem growth algorithm (E-PRGO) for optimization to achieve the optimal path planning of the UAV from the starting point to the operating target area.

[0027] 2. By combining the global search capability and local convergence characteristics of the E-PRGO algorithm, potential optimal path directions can be quickly found, and candidate paths can be finely adjusted in local areas, improving the computational efficiency and optimization quality of path planning. 3. By sensing changes in the spatial electric field intensity in real time, the UAV is guided to converge quickly toward the target area and its flight direction and search step size are dynamically adjusted, effectively avoiding high electric field areas, improving the convergence speed of the algorithm, and reducing computational costs. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a method for optimizing the path of a UAV entering and exiting an electric field for ultra-high voltage transmission lines, as described in one embodiment. Figure 2 This is a two-dimensional electric field contour map of the central section of the tower in one embodiment; Figure 3 This is a schematic diagram of the path of a drone entering and exiting an electric field in one embodiment. Detailed Implementation

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0031] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0032] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0033] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0035] In one embodiment, a terminal is provided, the terminal being used to: calculate the electric field intensity distribution and electric potential distribution in the UAV operating area based on an ultra-high voltage transmission line; generate a correlation model based on each of the electric field intensity distribution and electric potential distribution; calculate the minimum distance from each discrete spatial node in the UAV operating area to the surface of an obstacle; establish a path comprehensive optimization objective function based on the correlation model and the minimum distance, and use the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field based on the path comprehensive optimization objective function.

[0036] The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0037] In one embodiment, such as Figure 1 As shown, a method for optimizing the path of UAVs entering and exiting the electric field of ultra-high voltage transmission lines is provided. The method includes: Step S100: Based on the UHV transmission line, calculate the electric field intensity distribution and electric potential distribution in the UAV operation area; Step S200: Generate a correlation model based on the electric field intensity distribution and electric potential distribution described above; Step S300: Calculate the minimum distance from each discrete spatial node in the UAV's operating area to the obstacle surface; Step S400: Based on the correlation model and the minimum distance, establish a path comprehensive optimization objective function, and use the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field according to the path comprehensive optimization objective function.

[0038] This application calculates the electric field intensity and potential distribution in the UAV's operating area based on ultra-high voltage transmission lines; generates a correlation model based on the electric field intensity and potential distributions; calculates the minimum distance from each discrete spatial node in the UAV's operating area to the obstacle surface; establishes a path comprehensive optimization objective function based on the correlation model and the minimum distance; and uses the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field according to the path comprehensive optimization objective function. This application constructs a multi-objective optimization function by combining the electric field intensity distribution, obstacle potential field, and operating target area, and uses an improved plant root growth algorithm (E-PRGO) for optimization to achieve optimal path planning for the UAV from the starting point to the operating target area. By combining the global search capability and local convergence characteristics of the E-PRGO algorithm, potential optimal path directions can be quickly found, and candidate paths can be finely adjusted in local areas, improving the computational efficiency and optimization quality of path planning.

[0039] In one embodiment, step S100: Based on the UHV transmission line, calculate the electric field intensity distribution and electric potential distribution in the UAV operating area, including: Step S110: Construct a three-dimensional electric field finite element calculation model based on the ultra-high voltage transmission line; Step S120: Based on the three-dimensional electric field finite element calculation model, calculate the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area.

[0040] In this embodiment, a three-dimensional electric field finite element calculation model is constructed based on ultra-high voltage transmission lines. According to the three-dimensional electric field finite element calculation model, the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area are calculated. This enables the UAV to perceive changes in the spatial electric field intensity in real time, guide the UAV to converge quickly to the target area, and dynamically adjust the flight direction and search step size, effectively avoiding high electric field areas, improving the convergence speed of the algorithm, and reducing the computational cost.

[0041] In one embodiment, step S110: Constructing a three-dimensional electric field finite element calculation model based on the UHV transmission line includes: Step S111: Construct a three-dimensional structural model based on the three-dimensional spatial data of the UHV transmission line; In this step, a finite element simulation software (COMSOL) is used to manually construct a model based on typical line parameters. Three-dimensional structural model of DC transmission line.

[0042] Step S112: Based on the three-dimensional structural model, construct a three-dimensional electric field finite element calculation model under the ultra-high voltage transmission line.

[0043] In this embodiment, a three-dimensional structural model is constructed based on the three-dimensional spatial data of the UHV transmission line; and based on the three-dimensional structural model, a three-dimensional electric field finite element calculation model under the UHV transmission line is constructed to provide data support for subsequent calculation of the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area.

[0044] In one embodiment, step S112: Based on the three-dimensional structural model, constructing a three-dimensional electric field finite element calculation model under the ultra-high voltage transmission line includes: Step S1121: Mesh the three-dimensional structural model and set the iteration step size for the finite element calculation; Step S1122: Set the physical properties and potential boundary conditions of the conductors in the UHV transmission line, wherein the conductors include wires, overhead ground wires and tower metal structures; Step S1123: Set the physical properties of the medium surrounding the ultra-high voltage transmission line, wherein the medium is mainly air; Step S1124: Set the outer environmental boundary conditions far away from the UHV transmission line and the simulation solution range covering the predetermined operating space of the UAV, wherein the outer environmental boundary conditions are used to simulate the attenuation of the electric field at infinity; Step S1125: Based on the iteration step size, the physical properties and potential boundary conditions of the conductor, the physical properties of the medium, the outer environment boundary strip, and the simulation solution range, establish a three-dimensional electric field finite element calculation model.

[0045] In this embodiment, by precisely setting the physical properties and potential boundary conditions of conductors such as wires, ground wires, and towers, and accurately setting the properties of the air medium, the correctness of the electric field simulation in its physical essence is ensured. At the same time, by setting the boundary conditions of the outer environment far away from the line, the attenuation of the electric field at infinity is effectively simulated, avoiding simulation distortion caused by improper boundary settings, thereby significantly improving the calculation accuracy of the spatial electric field distribution.

[0046] On the other hand, the simulation solution scope clearly covers the UAV's predetermined operating space, allowing computing resources to be concentrated on the target area. The generated electric field distribution data is highly correlated with the actual flight airspace of the UAV, avoiding unnecessary global calculations.

[0047] like Figure 2 The figure shows a two-dimensional electric field contour plot of the central section of the tower (x=0 plane).

[0048] In one embodiment, step S200, generating a correlation model based on each of the electric field intensity distributions and potential distributions, includes: Step S211: Calculate the electric field intensity gradient of each discrete spatial node based on the electric potential distribution of each discrete spatial node; Step S212: Fit the correlation model between the electric field intensity distribution and the electric field intensity gradient.

[0049] In this embodiment, the electric field intensity gradient of each discrete spatial node is calculated based on the electric potential distribution of each discrete spatial node; and the correlation model between the electric field intensity distribution and the electric field intensity gradient is fitted. The calculated electric field and electric potential distribution are indispensable and reliable inputs for subsequent electric field gradient fitting, obstacle potential field construction and path multi-objective optimization.

[0050] In one embodiment, step S300: calculating the minimum distance from each discrete spatial node in the UAV operating area to the obstacle surface includes: Step S310: Obtain the coordinates of each discrete spatial node in the UAV's operating area; Step S320: Calculate the shortest Euclidean distance from each of the discrete spatial nodes to any point on the obstacle surface of the surface geometry model, and set the shortest Euclidean distance as the minimum distance. The surface geometry model is constructed based on the three-dimensional structural model of the UHV transmission line, and the obstacles include conductors, towers and ground wires.

[0051] In this step, the triangular mesh data of the outer surface of the conductors, towers and ground wires in the three-dimensional structural model are extracted to form the obstacle surface geometric model described in this method.

[0052] In this embodiment, by acquiring the surface geometric model of obstacles such as conductors, towers, and ground wires based on a three-dimensional structural model, and calculating the shortest Euclidean distance from each discrete node to its surface, a characterization calculation of the spatial distribution of obstacles in a complex transmission line environment is achieved. This provides reliable input data for setting obstacle constraints and constructing obstacle potential field functions in subsequent path optimization.

[0053] In one embodiment, step S400 involves establishing a path integration optimization objective function, including: Step S411: Using the position of the UAV in the three-dimensional electric field space as the optimization variable, construct a set of candidate paths for the UAV from the starting point to the target point, wherein the set of candidate paths includes multiple candidate paths; Step S412: Construct a discrete path sequence of discrete path points contained in each of the candidate paths; Step S413: Establish a path comprehensive optimization objective function based on the discrete path sequence, the correlation model, and the minimum distance.

[0054] In summary, step S400 employs the E-PRGO algorithm, using the UAV's position as the optimization variable, to establish a path integration optimization objective function. ,by Get the minimum value To optimize the objective, the path of the UAV entering and exiting the electric field is planned to obtain the optimal path scheme.

[0055] Specifically, optimization objectives and variables are set, with the drone's position as the optimization variable, and a path comprehensive optimization objective function is established. to obtain To optimize the objective, the UAV's path in three-dimensional space is determined. for: in, This represents the total number of paths.

[0056] No. Path Depend on The discrete path points are structured as follows: in, ; For the first The number of discrete points along a path is a variable parameter, and its value varies with the path length or sampling interval.

[0057] No. The first path The spatial coordinates of the points are as follows: in, .

[0058] No. The objective function for comprehensive optimization of the path is: in, The weighting coefficient for the path length; Inducing electric charge on the surface of the drone; The weighting coefficient of the electric field; Weighting coefficients for obstacle avoidance; For the first Path number Electric field strength at each path point; Let be the potential field function of the obstacle. When the path point is very close to the obstacle, The distance is large when the waypoint is far from the obstacle. Approaching 0.

[0059] Weighting coefficients This reflects different focuses during the optimization process: improving The guided algorithm tends to generate the path with the shortest total length; increasing This enables optimized paths to significantly avoid regions with high electric field strength, ensuring the electrical safety of drones when entering and exiting electric fields; and increases... This allows the optimized path to focus more on the safe distance from obstacles, enhancing the reliability of the path and its obstacle avoidance performance.

[0060] In one embodiment, to compare the results under different optimization focuses, two operating conditions were set for comparative analysis. Operating Condition 1: The weight coefficients in the optimization objective function are set to... Working condition 2: The weight coefficients in the objective function are set to... .

[0061] The formula for calculating the potential field function of an obstacle is: in, For the first Path number Coordinates of points on the surface of the obstacle; , This represents the total number of obstacle points; This is a control factor, taking positive values ​​of 0.5-3; This is the radius of the area affected by the obstacle, and it is taken as a positive value of 0.4-0.8 in the drone operation environment.

[0062] In one embodiment, step S400 involves using the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field based on the path synthesis optimization objective function, including: Step S421: When using the E-PRGO algorithm, set the path integration optimization objective function as the fitness function; In this step, when setting the fitness function, considering that the fitness function can effectively reflect the growth direction and fitness level of each individual (each path) in the rhizome population, this invention sets the fitness function as the objective function for path optimization of the UAV. .

[0063] Step S422: Set the constraints when using the E-PRGO algorithm, wherein the constraints include electric field safety constraints, obstacle distance constraints, and spatial boundary constraints; Step S423: Using the E-PRGO algorithm, the optimal path for the UAV to enter and exit the electric field is generated based on the fitness function and the constraints.

[0064] In this embodiment, when determining the constraints of the optimization variables, the UAV's flight position in space must simultaneously satisfy the following constraints: Electric field safety constraints: in, This is the electric field safety threshold.

[0065] Obstacles and constraints: in, This refers to the minimum permissible distance between the drone and the obstacle. For the first Path number The minimum distance between a path point and the surface of an obstacle.

[0066] Spatial boundary constraints: in, , These are the upper and lower bounds of the electric field simulation space, respectively. The starting and target points of the UAV must be fixed within the feasible area.

[0067] In one embodiment, when employing the E-PRGO algorithm, the method further includes: Multiple candidate paths are randomly generated within the drone's operating area as rootstock samples; The initial growth position is randomly assigned to each of the candidate paths.

[0068] In this embodiment, the main focus is on determining the size of the rhizome population and initializing it. The number of sample points generated during the rhizome population initialization phase... This refers to the number of candidate paths randomly generated within the drone's operating area; and the initial growth position of each path is randomly assigned.

[0069] Next, the fitness values ​​of all individuals are calculated. And calculate the group mean. Select the globally optimal individual Locally optimal individuals and random reference individuals To ensure that different paths have the same dimensionality matching during the operator update process, before each iteration begins, all individuals participating in the update (including the current individual) are compared. Global optimal individual Random reference individuals Interpolation is performed on the path points (e.g., to unify the number of path points to a fixed value). .

[0070] Then, four different types of operators are defined and invoked to simulate the environmental perception and dynamic expansion mechanism of plant roots and stems during natural growth, achieving a balance between global search and local optimization in the path search process. The generation rules for the four types of operators are as follows: The total number of iterations is set to T, with a value ranging from 800 to 1500. ; In equation (1), For the first The second iteration Path number The point is accessed by the principal root operator ( The updated location is used by drones to explore the optimal direction; In order to be in During the nth iteration Path number The location of each point; This is a control factor, a random number with a value between 0.01 and 0.2; In order to be in The global optimal path at the nth iteration The location of each point; In order to be in The reference path randomly selected in the nth iteration The location of each point.

[0071] In equation (2), For the first The second iteration Path number The points are accessed by the lateral root operator ( The updated location is used to simulate cooperative obstacle avoidance and path branching. For the first During the next iteration, the population... The average position of the points; This is a synergistic growth factor with a value of 0.05-0.3 (random number). In order to be in Another reference path randomly selected in the next iteration The location of each point.

[0072] In equation (3), for The second iteration Path number Each point is corrected by the boundary correction operator ( The updated position is used to expand the search range and avoid getting trapped in local optima; This is a boundary constraint factor, a random number between 0 and 1; For the entire search space. In equation (4), For the first The second iteration Path number The point spreads through the root spread operator ( The updated position is used to balance global and local searches and maintain the stability of population convergence. This is the local center point of the current path point; For the first The two reference paths randomly selected in the second iteration The location of each point; This is a diffusion regulation factor, a random number with a value of 0 or 1; For the first The local optimal path at the nth iteration The location of each point.

[0073] The next step is to update the above four types of operators, as follows: Traditional operator update mechanisms do not consider spatial gradient changes and strong coupling effects between conductors in the complex electric field environment of UHV transmission lines, and rely too much on random individual difference generation to generate update directions, resulting in large deviations between path search results and actual safe area distribution, and unstable convergence directions.

[0074] This invention introduces electric field gradient, barrier potential field gradient, and dynamic random perturbation into the traditional algorithm, and replaces the random difference factor in the operator update process with a fixed parameter. The calculation formula is as follows: In equation (5), For the first The second iteration Path number The electric field intensity gradient at each point. During the iteration of this optimization algorithm, in order to obtain the electric field intensity gradient value at any path point in real time... Instead of recalculating using the finite element method, the model is obtained by querying the correlation model generated in step S200 based on the electric field intensity distribution and electric potential distribution.

[0075] This is the electric field gradient adjustment coefficient, set to a small positive number, with a value range of 0.01-0.2; This indicates that an individual is moving from a region of high electric field intensity to a region of low electric field intensity.

[0076] The electric field gradient is calculated using the central difference method, and the formula is as follows: In equation (6), For the first During the next iteration, the population... The gradient of the obstacle potential field around the average position of each point; The obstacle avoidance weight coefficient is set to a small positive number, ranging from 0.05 to 0.3; This indicates that the individual avoids obstacles or areas of high field strength, achieving safe obstacle avoidance and constraint maintenance. The formula for calculating the obstacle potential field gradient is: in, For the first During the next iteration, the average position of the population is around the [number]th ... The coordinates of a point on the surface of an obstacle.

[0077] In equation (8), This is a scaling factor that controls the degree to which the global optimal position affects particle movement; it is set to a positive number between 0.3 and 0.6. The random disturbance term follows a Gaussian distribution. ; The disturbance factor is set to a positive value of 0.1-0.8; This is used for random perturbation adjustment, ensuring population diversity and enabling individuals to achieve stable aggregation in the later stages of iteration, thus guaranteeing the stability and accuracy of the path search process.

[0078] Then, generate a random number for each path point. ,when When, execute formula (5); when When, execute formula (6); when When, execute formula (7); when At that time, execute equation (8) to obtain the result of each path point at the 1st epoch. Position of the next iteration To form a new path This constitutes the updated path group. .

[0079] Next, fitness values ​​are calculated for all updated paths. This refers to the numerical value of the path optimization objective function. Based on the fitness results, the globally optimal individual in the current iteration is selected. Locally optimal individuals average position of the group .like If the new individual is found to be the correct one, the old individual will be replaced; otherwise, the old individual will be retained.

[0080] The change in the global optimal fitness over several consecutive iterations Less than the set threshold ( Typically 10⁻⁶), or reaching the maximum number of iterations. If the algorithm has converged, then the algorithm has converged.

[0081] Finally, when the iteration stopping condition is met, the global optimal solution is output. This refers to the optimal path for the drone to enter and exit the electric field.

[0082] When the iteration stopping condition is met, the global optimal solution is output, which is the optimal path for the UAV to enter and exit the electric field. Optimal path result. Figure 3 As shown, the black path is the result diagram for working condition 1, and the red path is the result diagram for working condition 2.

[0083] In one embodiment, a path optimization system for UHV transmission line drones entering and exiting the electric field is also provided, the system comprising: The line electric field calculation module is used to calculate the electric field intensity distribution and electric potential distribution in the UAV operation area based on ultra-high voltage transmission lines. The relation model generation module is used to generate a correlation model based on the electric field intensity distribution and electric potential distribution of each of the above-mentioned modules. The minimum distance generation module is used to calculate the minimum distance from each discrete spatial node in the UAV's operating area to the surface of an obstacle. The optimal path generation module is used to establish a path comprehensive optimization objective function based on the correlation model and the minimum distance, and to generate the optimal path for the UAV to enter and exit the electric field using the E-PRGO algorithm according to the path comprehensive optimization objective function.

[0084] In one embodiment, the line electric field calculation module is further used to: construct a three-dimensional electric field finite element calculation model based on the ultra-high voltage transmission line; and calculate the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area according to the three-dimensional electric field finite element calculation model.

[0085] In one embodiment, the line electric field calculation module is further used to: construct a three-dimensional structural model based on the three-dimensional spatial data of the UHV transmission line; and construct a three-dimensional electric field finite element calculation model under the UHV transmission line based on the three-dimensional structural model.

[0086] In one embodiment, the line electric field calculation module is further configured to: mesh the three-dimensional structural model and set the iteration step size for the finite element calculation; set the physical properties and potential boundary conditions of the conductors in the UHV transmission line, wherein the conductors include wires, overhead ground wires, and tower metal structures; set the physical properties of the medium surrounding the UHV transmission line, wherein the medium is mainly air; set the outer environmental boundary conditions far from the UHV transmission line and the simulation solution range covering the predetermined operating space of the UAV, wherein the outer environmental boundary conditions are used to simulate the attenuation of the electric field at infinity; and establish a three-dimensional electric field finite element calculation model based on the iteration step size, the physical properties and potential boundary conditions of the conductors, the physical properties of the medium, the outer environmental boundary conditions, and the simulation solution range.

[0087] In one embodiment, the relationship model generation module is further configured to: calculate the electric field intensity gradient of each discrete spatial node based on the potential distribution of each discrete spatial node; and fit a correlation model between the electric field intensity distribution and the electric field intensity gradient.

[0088] In one embodiment, the minimum distance generation module is further configured to: obtain the coordinates of each discrete spatial node in the UAV operation area; calculate the shortest Euclidean distance from each discrete spatial node to any point on the obstacle surface of the surface geometry model, and set the shortest Euclidean distance as the minimum distance, wherein the surface geometry model is constructed based on the three-dimensional structural model of the UHV transmission line, and the obstacles include conductors, towers and ground wires.

[0089] In one embodiment, the optimal path generation module is further configured to: construct a set of candidate paths for the UAV from the starting point to the target point using the position of the UAV in the three-dimensional electric field space as the optimization variable, wherein the set of candidate paths includes multiple candidate paths; construct a discrete path sequence of discrete path points contained in each candidate path; and establish a path comprehensive optimization objective function based on the discrete path sequence, the correlation model, and the minimum distance.

[0090] In one embodiment, the optimal path generation module is further configured to: when using the E-PRGO algorithm, set the path comprehensive optimization objective function as a fitness function; set constraints when using the E-PRGO algorithm, wherein the constraints include electric field safety constraints, obstacle distance constraints, and spatial boundary constraints; and use the E-PRGO algorithm to generate the optimal path for the UAV to enter and exit the electric field based on the fitness function and the constraints.

[0091] In one embodiment, the optimal path generation module is further configured to: randomly generate multiple candidate paths as root samples within the UAV operating area; and randomly assign an initial growth position to each candidate path.

[0092] In one embodiment, such as Figure 3 As shown, a computer device is also provided, including a memory and a processor. The memory stores a computer program and an operating system. When the processor executes the computer program, it implements the steps described in the above-mentioned method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines. The computer device also includes a system bus, internal memory, network structure, display screen, and input devices.

[0093] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for optimizing the path of UAVs entering and exiting electric fields for ultra-high voltage transmission lines.

[0094] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0097] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0098] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the various method embodiments above.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0100] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0101] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0105] One embodiment of this application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described methods.

[0106] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above description is an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than described above, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0107] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0108] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0109] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An optimization method for the path of a drone entering and exiting an electric field of an ultra-high voltage transmission line, characterized in that, The method includes: Based on ultra-high voltage transmission lines, the electric field intensity distribution and electric potential distribution in the UAV operation area are calculated; A correlation model is generated based on the electric field intensity distribution and electric potential distribution described above; Calculate the minimum distance from each discrete spatial node in the UAV's operating area to the surface of an obstacle; Based on the aforementioned correlation model and the minimum distance, a path comprehensive optimization objective function is established, and the E-PRGO algorithm is used to generate the optimal path for the UAV to enter and exit the electric field according to the path comprehensive optimization objective function.

2. The method of claim 1, wherein, Based on ultra-high voltage transmission lines, the electric field intensity distribution and electric potential distribution in the UAV operating area are calculated, including: A three-dimensional electric field finite element calculation model was constructed based on ultra-high voltage transmission lines; Based on the three-dimensional electric field finite element calculation model, the electric field intensity distribution and electric potential distribution of each discrete spatial node in the UAV operation area are calculated.

3. The method of claim 2, wherein, A three-dimensional electric field finite element calculation model based on ultra-high voltage transmission lines is constructed, including: A three-dimensional structural model was constructed based on the three-dimensional spatial data of ultra-high voltage transmission lines; Based on the aforementioned three-dimensional structural model, a three-dimensional electric field finite element calculation model for ultra-high voltage transmission lines is constructed.

4. The method of claim 2, wherein, Based on the aforementioned three-dimensional structural model, a three-dimensional electric field finite element calculation model for ultra-high voltage transmission lines is constructed, including: The three-dimensional structural model is meshed, and the iteration step size of the finite element calculation is set. The physical properties and potential boundary conditions of the conductors in the ultra-high voltage transmission line are set, and the conductors include wires, overhead ground wires and tower metal structures; The physical properties of the medium surrounding the ultra-high voltage transmission line are defined, and the medium is mainly air; Set the outer environmental boundary conditions far away from the ultra-high voltage transmission line and the simulation solution range covering the predetermined operating space of the UAV, wherein the outer environmental boundary conditions are used to simulate the attenuation of the electric field at infinity; Based on the iteration step size, the physical properties and potential boundary conditions of the conductor, the physical properties of the medium, the boundary strips of the outer environment, and the simulation solution range, a three-dimensional electric field finite element calculation model is established.

5. The method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines according to claim 1, characterized in that, A correlation model is generated based on the electric field intensity distribution and electric potential distribution, including: Based on the potential distribution of each discrete spatial node, the electric field intensity gradient of each discrete spatial node is calculated. A model is fitted to show the correlation between the electric field intensity distribution and the electric field intensity gradient.

6. The method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines according to claim 1, characterized in that, Calculate the minimum distance from each discrete spatial node in the UAV's operational area to the surface of an obstacle, including: Obtain the coordinates of each discrete spatial node in the UAV's operating area; Calculate the shortest Euclidean distance from each of the discrete spatial nodes to any point on the surface of the obstacle in the surface geometry model, and set the shortest Euclidean distance as the minimum distance. The surface geometry model is constructed based on the three-dimensional structural model of the UHV transmission line, and the obstacles include conductors, towers, and ground wires.

7. The method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines according to claim 1, characterized in that, Establish the objective function for path integration optimization, including: Using the position of the UAV in the three-dimensional electric field space as the optimization variable, a set of candidate paths for the UAV from the starting point to the target point is constructed, wherein the set of candidate paths includes multiple candidate paths; Construct a discrete path sequence containing discrete path points of each candidate path; Based on the discrete path sequence, the correlation model, and the minimum distance, a path comprehensive optimization objective function is established.

8. The method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines according to claim 1, characterized in that, The E-PRGO algorithm is used to generate the optimal path for the UAV to enter and exit the electric field based on the path comprehensive optimization objective function, including: When using the E-PRGO algorithm, the objective function for path integration optimization is set as the fitness function; Set the constraints when using the E-PRGO algorithm, wherein the constraints include electric field safety constraints, obstacle distance constraints, and spatial boundary constraints; The E-PRGO algorithm is used to generate the optimal path for the UAV to enter and exit the electric field based on the fitness function and the constraints.

9. The method for optimizing the path of UAVs entering and exiting the electric field for ultra-high voltage transmission lines according to claim 8, characterized in that, When using the E-PRGO algorithm, the method further includes: Multiple candidate paths are randomly generated within the drone's operating area as rootstock samples; The initial growth position is randomly assigned to each of the candidate paths.

10. A path optimization system for UAVs entering and exiting electric fields on ultra-high voltage transmission lines, characterized in that, The system includes: The line electric field calculation module is used to calculate the electric field intensity distribution and electric potential distribution in the UAV operation area based on ultra-high voltage transmission lines. The relation model generation module is used to generate a correlation model based on the electric field intensity distribution and electric potential distribution of each of the above-mentioned modules. The minimum distance generation module is used to calculate the minimum distance from each discrete spatial node in the UAV's operating area to the surface of an obstacle. The optimal path generation module is used to establish a path comprehensive optimization objective function based on the correlation model and the minimum distance, and to generate the optimal path for the UAV to enter and exit the electric field using the E-PRGO algorithm according to the path comprehensive optimization objective function.

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