A method for optimizing a data collection path of a UAV under communication and energy consumption constraints

Abstract

The application relates to the technical field of path optimization, and discloses a UAV data collection path optimization method under communication and energy consumption constraints, which comprises the following steps: step S1, calculating unit horizontal distance energy consumption; step S2, constructing a minimum communicable height field; step S3, calculating a transverse specific energy ridge density field; step S4, calculating edge weight; step S5, calculating a minimum energy consumption section and a path node sequence; step S6, calculating data receiving energy consumption of a collection point; step S7, outputting an optimal visiting sequence; and step S8, generating a three-dimensional flight path and a time label. The minimum communicable height field is constructed by combining the terrain elevation feature and the Fresnel clearance constraint of air-ground communication, and the geometric change of the communication height field is converted into an energy-dimension cost index, so that the communication constraint, the energy consumption constraint and the actual execution demand of the UAV are considered coordinately.

Description

Technical Field

[0001] This invention relates to the field of path optimization technology, and more specifically, to a method for optimizing the data acquisition path of unmanned aerial vehicles (UAVs) under communication and energy consumption constraints. Background Technology

[0002] Drone data acquisition technology is increasingly widely used in fields such as terrain monitoring and environmental perception. Path planning, as a core component, directly determines the efficiency and feasibility of mission execution. Drone battery energy has an inherent upper limit, making energy consumption constraints a key consideration in path planning. Existing technologies mostly optimize paths based on energy consumption during horizontal flight, without fully considering the stringent requirements of air-to-ground communication. This results in planned paths with low energy consumption that are prone to communication interruptions due to terrain obstruction, preventing data transmission.

[0003] The reliability of air-to-ground wireless communication depends on the airspace conditions of the first Fresnel zone. Undulations in terrain elevation can cause obstruction, and intrusion into the Fresnel zone can lead to link loss or even communication interruption. Existing technologies do not combine terrain features to construct an accurate minimum communicable altitude field for the mission area, nor do they convert the altitude changes corresponding to communication constraints into energy consumption costs for UAVs. At the same time, they do not identify the bottleneck locations of communication connectivity, making it impossible to consider communication and energy consumption constraints in the path planning. This can easily lead to situations where excessive increases in flight altitude to meet communication requirements result in a surge in energy consumption.

[0004] Existing path planning technologies suffer from insufficient integration of multiple constraints. They fail to uniformly calculate the energy consumption of UAV flight and the energy consumption of data reception during hovering at data collection points, and they do not optimize the access sequence of data collection points under battery energy constraints, easily leading to situations where total energy consumption exceeds battery supply. Furthermore, most plans only generate two-dimensional paths, failing to combine the communicable altitude field to generate three-dimensional tracks, and do not match flight and hovering time information. This results in planning results that cannot be directly imported into the flight control system, requiring manual secondary processing, increasing mission complexity, and reducing practical application value. Summary of the Invention

[0005] This invention provides a method for optimizing the data acquisition path of unmanned aerial vehicles (UAVs) under communication and energy consumption constraints, thereby solving the technical problems mentioned in the background.

[0006] This invention provides a method for optimizing the data acquisition path of a UAV under communication and energy consumption constraints, comprising the following steps: Step S1: Obtain terrain elevation and grid point set, and calculate energy consumption per unit horizontal distance; Step S2: Construct the minimum communicable height field by utilizing the critical clearance condition between the terrain elevation and the first Fresnel radius; Step S3: Extract the set of connected saddle points from the minimum communicable height field, and calculate the lateral specific energy ridge density field based on the horizontal gradient of the minimum communicable height field; Step S4: Using the set of grid points as nodes, calculate the moving energy consumption from the energy consumption per unit horizontal distance, calculate the structural energy barrier from the lateral specific energy ridge density field at the connected saddle point set, and add the moving energy consumption and the structural energy barrier to obtain the edge weight. Step S5: Calculate the minimum energy consumption segment and path node sequence between key points of the task based on edge weights; Step S6: Determine the hovering height based on the minimum communicable height field, and use the hovering height to calculate the data rate and hovering time to obtain the data reception energy consumption at the acquisition point; Step S7: Calculate the total energy consumption based on the minimum energy consumption segment and the data reception energy consumption at the collection point. Under the constraint of available battery energy, optimize and output the optimal access order. Step S8: Concatenate the path node sequence according to the optimal access order, and combine the hovering time to generate the final 3D track and time label.

[0007] The beneficial effects of this invention are as follows: This invention combines terrain elevation features with Fresnel clearance constraints for air-to-ground communication to construct a minimum communicable altitude field, achieving a spatial quantitative representation of communication constraints. Simultaneously, it transforms the geometric changes of the communication altitude field into a cost index with energy dimensions, enabling quantitative coupling between communication constraints and energy consumption constraints. This ensures that the planned path meets the reliability requirements of air-to-ground communication while avoiding energy waste caused by excessively increasing flight altitude. The invention unifies the UAV's flight energy consumption and the data reception energy consumption at the collection points into a total energy consumption. Under the constraint of available battery energy, it optimizes the access sequence of collection points, ensuring that the overall energy consumption of the mission matches the UAV's energy supply capacity, guaranteeing the feasibility of mission execution. Furthermore, based on the optimal access sequence, it generates a time-annotated 3D track, fusing spatial path information with flight and hovering time information. The planning results can be directly adapted to the UAV flight control system without secondary manual processing, making the path planning results more practical. Overall, it achieves a synergistic consideration of communication constraints, energy consumption constraints, and the actual execution needs of the UAV. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the calculation process of the present invention; Figure 2 This is a schematic diagram of the computational scenario of the present invention. Detailed Implementation

[0009] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0010] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of the present invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in one or more embodiments of the present invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" indicate that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0011] like Figures 1-2 As shown, a method for optimizing the data acquisition path of a UAV under communication and energy consumption constraints includes the following steps: Step S1: Obtain terrain elevation and grid point set, and calculate energy consumption per unit horizontal distance; Step S2: Construct the minimum communicable height field by utilizing the critical clearance condition between the terrain elevation and the first Fresnel radius; Step S3: Extract the set of connected saddle points from the minimum communicable height field, and calculate the lateral specific energy ridge density field based on the horizontal gradient of the minimum communicable height field; Step S4: Using the set of grid points as nodes, calculate the moving energy consumption from the energy consumption per unit horizontal distance, calculate the structural energy barrier from the lateral specific energy ridge density field at the connected saddle point set, and add the moving energy consumption and the structural energy barrier to obtain the edge weight. Step S5: Calculate the minimum energy consumption segment and path node sequence between key points of the task based on edge weights; Step S6: Determine the hovering height based on the minimum communicable height field, and use the hovering height to calculate the data rate and hovering time to obtain the data reception energy consumption at the acquisition point; Step S7: Calculate the total energy consumption based on the minimum energy consumption segment and the data reception energy consumption at the collection point. Under the constraint of available battery energy, optimize and output the optimal access order. Step S8: Concatenate the path node sequence according to the optimal access order, and combine the hovering time to generate the final 3D track and time label.

[0012] In one embodiment of the present invention, obtaining terrain elevation and a set of grid points, and calculating energy consumption per unit horizontal distance includes: Obtain terrain elevation data for the task area and construct a terrain elevation function. ,in The first component of the horizontal coordinate. The second component of the horizontal coordinate. horizontal coordinates The terrain elevation at the location; In relation to terrain elevation function Generate a set of grid points in the same horizontal coordinate system And determine the horizontal distance between adjacent grid points as the grid spacing. ,in The horizontal coordinates of the grid points This refers to the grid spacing; Obtain the drone's cruise power With drone cruising speed Calculate energy consumption per unit horizontal distance The calculation formula is as follows: ;in, For the drone's cruise power, For the drone's cruising speed, Energy consumption per unit horizontal distance.

[0013] It should be noted that terrain elevation data refers to the ground elevation data corresponding to each horizontal coordinate within the mission area, reflecting the basic three-dimensional terrain features of the mission area. This data can be obtained through techniques such as digital elevation model remote sensing mapping, UAV aerial surveying, and ground total station measurement. The first component of the horizontal coordinate reflects the lateral spatial characteristics of the planar position. The second component of the horizontal coordinate reflects the longitudinal spatial characteristics of the planar position. Terrain elevation is the ground elevation value corresponding to a certain horizontal coordinate position within the mission area, reflecting the vertical height characteristics of the terrain at that location. The grid point set is the set of all grid vertices obtained after dividing the horizontal coordinate system of the mission area into a grid, reflecting the distribution characteristics of planar position nodes after discretization of the mission area. The grid spacing is the horizontal distance between adjacent grid points in the grid point set, reflecting the accuracy characteristics of the discretization of the mission area. A preferred value is 1 to 50 meters. This range balances the computational efficiency of path optimization with the accuracy of terrain representation. Smaller spacing is suitable for small-scale mission areas with complex terrain, while larger spacing is suitable for large-scale mission areas with gentle terrain. Cruise power of a drone is its average output power during level flight in cruise mode. It reflects the energy consumption intensity of the drone during level flight and can be obtained through methods such as reading parameters from the drone's flight control system, real-time measurement by a power meter, and consulting the drone's product technical manual. Cruise speed of a drone is its average flight speed during level flight in cruise mode. It reflects the spatial mobility efficiency of the drone during level flight and can be obtained through methods such as GPS speed measurement from the drone's flight control system, airspeed measurement, and consulting the drone's product technical manual. Energy consumption per unit horizontal distance is the energy consumed by the drone per unit distance during level flight, reflecting the energy consumption efficiency of the drone during level flight.

[0014] It should be noted that the specific method for constructing the terrain elevation function is as follows: using the first and second components of the horizontal coordinate as independent variables and terrain elevation as the dependent variable, one of the following interpolation methods is selected: Kriging interpolation, inverse distance weighted interpolation, or bilinear interpolation. The collected discrete terrain elevation data is substituted into the selected interpolation algorithm to construct a continuous terrain elevation function covering the entire task area, enabling terrain elevation calculation at any horizontal coordinate position. The grid is generated using an orthogonal grid generation method, with the first and second components of the horizontal coordinate as the coordinate axes. The grid is divided at equal intervals according to a set spacing. The accuracy of the grid generation is determined by the grid spacing; the smaller the spacing, the higher the accuracy. During generation, it is necessary to ensure that the grid covers the entire task area. If the boundary of the task area is not an integer multiple of the orthogonal grid, the grid at the boundary is extended. The final set of all vertices of the grid is the set of grid points. The conditions for obtaining the drone's cruise power are that the drone is in a stable horizontal cruise state, without any climb, descent, acceleration, or deceleration, in a standard atmospheric environment with a wind speed of less than 3 meters per second. Measurement methods include connecting a power meter in series with the drone's power supply circuit to collect power data during stable cruise and averaging the results; alternatively, the cruise power calibration value from the drone's flight control system can be directly read. Customized drones can obtain this power through bench testing. The selection of the drone's cruise speed is based on its nominal cruise speed, adjusted in conjunction with the communication and wind conditions of the mission area. The cruise speed should be appropriately reduced in high winds, and the cruise speed range for stable communication should be selected in poor communication environments. The determination method is to control the drone to enter a stable horizontal cruise state under standard flight conditions, continuously collect flight speed data over 5 minutes using the GPS module of the flight control system, and average the data; alternatively, the nominal cruise speed value from the drone's product manual can be directly used.

[0015] It's important to note that the calculation of energy consumption per unit horizontal distance follows the physical relationship of the ratio of the drone's cruise power to its cruise speed. This is based on the fundamental principle of energy calculation: energy equals the product of power and time. The time of horizontal flight for a drone equals the ratio of flight distance to flight speed. Substituting the time calculation formula into the energy calculation formula, the ratio of energy to flight distance is the energy consumption per unit horizontal distance, which can be derived as the ratio of cruise power to cruise speed. For example, if a drone's cruise power is 50 watts and its cruise speed is 5 meters per second, then the energy consumption per unit horizontal distance is 50 watts divided by 5 meters per second, resulting in 10 joules per meter. This means that the drone consumes 10 joules of energy to fly 1 meter horizontally.

[0016] In one embodiment of the present invention, a minimum communicable height field is constructed using the critical clearance condition between terrain elevation and the first Fresnel radius, including: Obtain base station spatial coordinates ,in This is the first component of the base station's horizontal coordinates. This is the second component of the base station's horizontal coordinates. The elevation of the base station antenna; based on a set of grid points. For the domain, for each grid point in the set of grid points Calculate the corresponding minimum communicable height ; At the horizontal position of the base station Horizontal position of grid points The connection lines are spaced by grid spacing. Horizontal sampling is performed to obtain horizontal sampling points. And derived from the terrain elevation function Calculate the terrain elevation of the sampling points ; Dominant shading point topographic elevation The calculation formula is as follows: ; The horizontal coordinates of the dominant occlusion point are as follows ; Calculate the horizontal distance from the base station to the dominant obstruction point. and the horizontal distance from the dominant occlusion point to the grid point The calculation formula is as follows: ; ; Obtain carrier wavelength First Fresnel radius The calculation formula is as follows: ; in, The first Fresnel radius corresponding to the grid point; Set the critical clearance ratio as The height of the line connecting the base station to the grid point at the dominant obstruction point is defined as... The calculation formula is as follows: ; in The altitude of the drone at the grid point; critical clearance conditions are used. The minimum communicable height is obtained by solving the problem. The calculation formula is as follows: ; in, For grid points The minimum communicable height at which the grid points are located is determined by the set of grid points. The minimum communicable height combination of all grid points within the grid constitutes the minimum communicable height field. .

[0017] It should be noted that the base station spatial coordinates are the location coordinates of the base station in three-dimensional space, reflecting the spatial deployment location of the base station. These coordinates can be obtained through techniques such as on-site measurement with a GPS real-time dynamic measuring instrument, connection measurement with known control points using a total station, and consulting base station deployment technical documents. The minimum communicable altitude is the lowest flight altitude at which a grid point can maintain reliable air-to-ground communication with the base station, reflecting the feasible vertical height threshold for communication at that grid point location. The base station horizontal position is the corresponding planar coordinate portion of the base station's spatial coordinates, reflecting the planar deployment location of the base station. Horizontal sampling points are planar points obtained by sampling along the horizontal line connecting the base station and grid points at equal intervals between grid points, reflecting the terrain sampling location distribution along the line connecting the base station and grid points. The terrain elevation of the dominant obstruction point is the maximum value among the terrain elevations corresponding to the horizontal sampling points, reflecting the terrain height that forms a critical obstruction to air-to-ground communication between the base station and grid points. The carrier wavelength is the electromagnetic wave wavelength of the carrier used in air-to-ground communication, reflecting the physical characteristics of the communication carrier. This can be obtained through techniques such as consulting communication system parameter configuration files, calculating after measuring the carrier frequency with a spectrum analyzer, and reading communication module calibration parameters. The first Fresnel radius is the radial dimension of the first Fresnel zone at the dominant obstruction point, reflecting the clearance space scale required to ensure reliable communication at that location. The critical clearance ratio is the proportion of clearance that must be maintained in the first Fresnel zone, reflecting the clearance quantification requirements for reliable air-to-ground communication. A preferred value is 0.6, a commonly used clearance threshold for air-to-ground radio wave propagation in engineering, balancing communication reliability with the practical needs of UAV flight altitude. The base station antenna elevation is the vertical height value in the base station's spatial coordinates. The UAV's altitude at a grid point is the vertical flight altitude of the UAV at that grid point's planar position. The altitude of the line connecting the base station to the grid point at the dominant obstruction point is the vertical height of the communication link between the base station and the UAV passing through the dominant obstruction point, reflecting the theoretical height of the communication link at that obstruction point. The critical clearance condition is the clearance quantification criterion for determining reliable air-to-ground communication, reflecting the clearance judgment requirements for communication feasibility. The minimum communicable altitude field is a spatial surface composed of the minimum communicable altitudes of all grid points, reflecting the distribution characteristics of the lowest feasible altitude for communication throughout the entire mission area.

[0018] It should be noted that the minimum communicable altitude field can be constructed using the critical clearance condition of terrain elevation and the first Fresnel radius. Air-to-ground radio wave propagation is not along a single straight line; the clearance state of the first Fresnel zone directly determines link loss. Terrain elevation can create obstructions and intrude into the Fresnel zone. The minimum flight altitude required for communication at each grid point can be determined through the critical clearance condition. By interpolating the altitude values ​​of discrete grid points into a continuous surface, the minimum communicable altitude field covering the mission area can be obtained, achieving a spatial quantitative representation of communication constraints. The calculation of the first Fresnel radius requires consideration of the carrier wavelength, the horizontal distance from the base station to the dominant obstruction point, and the horizontal distance from the dominant obstruction point to the grid point. The first Fresnel radius is an inherent physical characteristic of radio wave propagation; its magnitude is positively correlated with the carrier wavelength, positively correlated with the square root of the product of the horizontal distances from the base station to the obstruction point and from the obstruction point to the grid point, and negatively correlated with the square root of the sum of the two horizontal distances. This is the fundamental computational physical law of the Fresnel zone. The critical clearance condition is that the difference between the height of the connection from the base station to the grid point at the dominant obstruction point and the terrain elevation of the dominant obstruction point is equal to the product of the critical clearance ratio and the first Fresnel radius. This difference is the actual clearance height at the dominant obstruction point. Only when the actual clearance height reaches the value of the critical clearance ratio multiplied by the first Fresnel radius can the first Fresnel area retain sufficient clearance to avoid sudden deterioration of link loss due to terrain obstruction. For example, if the critical clearance ratio is 0.6 and the first Fresnel radius at the dominant obstruction point is 10 meters, then the actual clearance height needs to reach 6 meters, that is, the height of the connection from the base station to the grid point at the obstruction point needs to be 6 meters higher than the terrain elevation of the obstruction point.

[0019] It should be noted that the specific methods for obtaining the spatial coordinates of base stations are as follows: For fixed base stations, real-time dynamic measurement instruments using the Global Positioning System (GPS) can be used to obtain three-dimensional coordinates with centimeter-level accuracy; for temporarily deployed base stations, coordinates can be obtained by connecting a total station with known control points within the mission area; for base stations that have already been deployed, their construction technical documents can be consulted to extract the calibrated spatial coordinate parameters. The specific setting of the critical clearance ratio is based on a comprehensive consideration of the link loss requirements for air-to-ground communication and the altitude restrictions of UAV flights, with 0.6 being a general engineering threshold; if the communication link has strong resistance to loss, the ratio can be appropriately reduced to 0.4 to 0.5; if there are no strict restrictions on the flight altitude of UAVs, the ratio can be appropriately increased to 0.7 to 0.8 to further improve communication reliability. The specific method for establishing the height of the connection between the base station and the grid point at the dominant obstruction point is as follows: the vertical height of the two ends of the connection is taken as the elevation of the base station antenna and the height of the UAV at the grid point. The proportion of the horizontal distance from the base station to the dominant obstruction point to the total horizontal distance from the base station to the grid point is calculated. The vertical height of the two ends is linearly interpolated according to this proportion. The interpolation result is the theoretical height of the link at the obstruction point.

[0020] It should be noted that this invention is based on the Fresnel zone airspace physics of air-to-ground radio wave propagation, combined with the terrain elevation characteristics of the mission area, to locate the dominant communication obstruction points between the base station and each grid point, calculate the first Fresnel radius at the obstruction point, and then, based on the critical airspace condition, solve for the minimum communicable altitude of each grid point, ultimately constructing a minimum communicable altitude field covering the entire mission area. This invention, by identifying the dominant obstruction points, pinpoints the terrain locations between the base station and grid points that have a key impact on communication, making airspace-related calculations more targeted; by using the critical airspace condition, it transforms the qualitative constraints of communication into quantitative altitude thresholds, achieving a quantitative determination of communication feasibility; the constructed minimum communicable altitude field transforms the altitude thresholds of discrete grid points into continuous spatial surfaces, realizing a spatial representation of communication constraints throughout the mission area, providing a quantitative carrier for subsequently converting communication constraints into energy costs, and also providing a unified standard for determining the minimum communication flight altitude of the UAV at various locations within the mission area, ensuring the effective integration of communication constraints in subsequent path optimization.

[0021] In one embodiment of the present invention, extracting a set of connected saddle points from the minimum communicable height field and calculating the lateral specific energy ridge density field based on the horizontal gradient of the minimum communicable height field includes: In the grid point set Calculate the discrete minimum communicable height for each grid point. The calculation formula is as follows: ; in, For the minimum communicable height field, For grid points The discrete minimum communicable height at the location; For grid points Grid points will be set when the following conditions are met simultaneously. Add to the set of connected saddle points : ; ; in, For a set of connected saddle points, and The discrete minimum communicable height between adjacent grid points; Utilizing grid spacing The formula for calculating the finite difference gradient components is as follows: ; ; Horizontal gradient magnitude The calculation formula is as follows: ; Obtaining drone quality Gravitational acceleration Equivalent efficiency of climbing Discrete values ​​of transverse specific energy ridge density The calculation formula is as follows: ; Depend on Constructing a transverse specific energy ridge density field .

[0022] It should be noted that the discrete minimum communicable altitude is the specific numerical value of the minimum communicable altitude corresponding to each grid point in the grid point set, reflecting the quantitative characteristics of the lowest feasible communication altitude at each discrete grid point. The connected saddle point set is the set of all grid points that satisfy the saddle point determination criteria, reflecting the distribution of connectivity bottlenecks in the communicable altitude field within the task area. Adjacent grid points are grid points in the grid point set that are directly adjacent to the target grid point in the first or second horizontal coordinate component direction, reflecting the local neighborhood spatial distribution characteristics of the grid points. The direction of change of the discrete minimum communicable altitude is the increasing or decreasing trend of the discrete minimum communicable altitude between the target grid point and adjacent grid points, reflecting the local elevation change characteristics of the communicable altitude field. The horizontal gradient magnitude is the magnitude calculated by combining the finite difference gradient components in the two horizontal coordinate component directions, reflecting the overall horizontal change rate of the communicable altitude field at that grid point. The UAV mass is the actual mass of the entire UAV, which can be obtained by directly weighing the entire UAV with an electronic scale, consulting the UAV product technical manual to obtain the calibrated mass, or disassembling and weighing each component and then summing the results. Climb equivalent efficiency is the efficiency coefficient for converting theoretical potential energy into actual energy consumption during the climb of a UAV. It reflects the energy utilization efficiency of the UAV during climb and is preferably taken as 0.5 to 0.8. The climb efficiency of UAV propeller propulsion systems generally falls within this range, balancing the accuracy of energy calculations with engineering practicality. Lateral specific energy ridge density discrete values ​​are the specific values ​​of the lateral specific energy ridge density calculated at each grid point, reflecting the minimum climb energy consumption required for a unit distance of lateral offset at each grid point. The lateral specific energy ridge density field is a spatial field composed of the discrete values ​​of the lateral specific energy ridge density at all grid points, reflecting the climb energy consumption distribution characteristics of lateral offset within the mission area.

[0023] It should be noted that a connected saddle point is a point in the communicable altitude field that exhibits a saddle-shaped characteristic. Its communicable altitude increases in one horizontal direction and decreases in the other. This characteristic makes it a bottleneck connecting different regions in the communicable altitude field. The determination requires that the changes in the two horizontal directions be opposite simultaneously; neither can be omitted. For example, if a grid point's altitude increases on the left and decreases on the right along the first component direction, and decreases above and increases below along the second component direction, with opposite changes in both directions, then this point is a connected saddle point. The horizontal gradient magnitude reflects the climb altitude per unit distance of lateral offset. The product of the UAV's mass and gravitational acceleration is the UAV's gravity. The product of gravity and climb altitude is the theoretical climb potential energy. The climb equivalent efficiency reflects the conversion ratio of theoretical potential energy to actual energy consumption. Therefore, dividing the theoretical potential energy by the climb equivalent efficiency yields the actual minimum climb energy consumption per unit distance of lateral offset. Furthermore, the discrete values ​​of the lateral specific energy ridge density are the quantized characteristic values ​​of each grid point, which are consistent with the construction logic of the minimum communicable height field. By using an interpolation algorithm to transform the discrete grid point values ​​into a continuous spatial field, the lateral specific energy ridge density at any location within the mission area can be characterized, making the spatialization of energy consumption distribution possible.

[0024] It should be noted that the specific setting of the climb equivalent efficiency is based on a comprehensive consideration of the UAV's power system type and flight environment. For multi-rotor UAVs, a climb equivalent efficiency of 0.5 to 0.6 is preferred, while for fixed-wing UAVs, 0.7 to 0.8 is preferred. The efficiency value can be appropriately increased in low-altitude environments and appropriately decreased in high-altitude, low-pressure environments. Calibration corrections should also be made based on actual flight test data of the UAV. The specific method for constructing the lateral energy ridge density field is as follows: using the horizontal coordinates of grid points as the independent variable and the corresponding discrete values ​​of the lateral energy ridge density as the dependent variable, the same bilinear interpolation method as the minimum communicable altitude field is used to interpolate the discrete values, generating a continuous spatial field covering the entire mission area. The lateral energy ridge density at any planar location within the mission area can be queried using the interpolation formula of this field.

[0025] It should be noted that this invention discretizes the constructed minimum communicable altitude field, extracts the set of connectivity saddle points reflecting communication bottlenecks through numerical determination, and then calculates the horizontal gradient of the communicable altitude field using the finite difference method. Combining the UAV's physical parameters and climb efficiency, the geometric changes in the altitude field are transformed into lateral specific energy ridge density in the energy dimension, ultimately constructing a lateral specific energy ridge density field covering the mission area. This invention, by extracting the set of connectivity saddle points, pinpoints the location of communication bottlenecks within the mission area, making the subsequent application of energy costs more targeted; by calculating the horizontal gradient using the finite difference method, it achieves a quantitative characterization of the rate of change of the discrete altitude field; by transforming the geometric gradient into specific energy ridge density in the energy dimension, it achieves a direct mapping from the geometric features of the communication structure to energy costs; and the constructed lateral specific energy ridge density field provides a continuous spatial characterization of the energy gradient distribution within the mission area, providing a core quantitative basis for the subsequent construction of structural energy barriers, ensuring that communication structural risks can be transformed into calculable energy costs.

[0026] In one embodiment of the present invention, using a set of grid points as nodes, the movement energy consumption is calculated from the energy consumption per unit horizontal distance, and the structural energy barrier is calculated from the lateral specific energy ridge density field at the connected saddle point set. The movement energy consumption and the structural energy barrier are added together to obtain the edge weight, including: With grid point set Construct a set of adjacent edges for a set of nodes For each directed edge The horizontal distance of the directed edge is ,in This refers to the grid spacing; Based on the minimum communicable height field Determine the height of the nodes at both ends of the directed edge. and ; Mobile energy consumption The calculation formula is as follows: ; in, Energy consumption for moving along a directed edge. Energy consumption per unit horizontal distance For the quality of drones, It is the acceleration due to gravity. To improve the equivalent efficiency, This refers to the rising portion of the height difference; Constructing a connected saddle point indicator function If node Belongs to the set of connected saddle points ,but ,otherwise Structural energy barrier The calculation formula is as follows: ; in, As a structural energy barrier, and These represent the transverse specific energy ridge density fields at the nodes. With nodes The value at; weight of directed edge The calculation formula is as follows: .

[0027] It should be noted that a directed edge is an edge connecting nodes with a direction of movement in the set of adjacent edges, reflecting the unidirectional movement path characteristics of the UAV between grid nodes. Node height is the altitude value corresponding to the two grid points at the ends of the directed edge in the minimum communicable altitude field, reflecting the minimum flight altitude characteristic for maintaining communication at the node. The increase in altitude difference is the portion of the difference between the height of the endpoint node and the starting node of the directed edge that is greater than zero, reflecting the climb altitude characteristic of the UAV flying along that directed edge. Movement energy consumption is the sum of the horizontal flight energy consumption and climb energy consumption required for the UAV to fly along the directed edge, reflecting the energy consumption characteristic of the UAV moving along that edge. The connectivity saddle point indicator function is a numerical function used to determine whether a grid node belongs to the set of connectivity saddle points, reflecting the attribute characteristic of whether a node is a communication connectivity bottleneck. The structural energy barrier is the additional energy cost that the UAV must incur when traversing directed edges near a connectivity saddle point, reflecting the energy consumption risk characteristic of communication structural bottlenecks. The edge weight is the sum of the movement energy consumption of the directed edge and the structural energy barrier, reflecting the total energy cost characteristic of the UAV moving along that directed edge.

[0028] It should be noted that the principle of determining the node heights at both ends of a directed edge based on the minimum communicable height field is that the minimum communicable height field covers the entire mission area, including the lowest communication flight altitude of all grid points. The two ends of the directed edge are grid nodes. Directly extracting the corresponding height of the node in the height field ensures that the node height meets the communication constraints, thus coupling energy consumption calculation with communication requirements. The principle of calculating the movement energy consumption is based on the energy consumption per unit horizontal distance, the horizontal distance of the directed edge, the UAV's physical parameters, and the increase in node height difference. The energy consumption of the UAV moving along the edge is divided into two parts: horizontal flight and climb. The horizontal energy consumption is obtained by multiplying the energy consumption per unit horizontal distance by the horizontal distance of the edge. The climb energy consumption is calculated by the UAV's gravity, climb altitude, and climb efficiency. Only the increase in height difference is calculated, as there is no additional climb energy consumption when going downhill, which is consistent with the actual energy consumption characteristics of the UAV. The specific method for constructing the adjacent edge set is as follows: Each grid point in the grid point set is used as the starting node, and connections are established with four adjacent grid points in the first and second components of the horizontal coordinate. Each connection corresponds to one edge. The adjacent connection edges of all grid points are summarized, and duplicate edges are removed. The resulting set is the adjacent edge set, ensuring that all adjacent connections of all grid nodes are covered during construction. The specific setting and traversal rules for directed edges are as follows: each undirected edge in the adjacent edge set is converted into a directed edge in two directions: one from node A to node B, and the other from node B to node A. The traversal rule is to traverse each starting node sequentially according to the coordinate order of the grid points, and then traverse all its adjacent nodes to generate the corresponding directed edges, ensuring that all undirected edges are converted into directed edges without omission. Furthermore, the independent variable of the connected saddle point indicator function is a grid node. If the independent variable node belongs to the connected saddle point set, the function is assigned a value of 1; if the independent variable node does not belong to the connected saddle point set, the function is assigned a value of 0. After the function is constructed, the node can be directly substituted to obtain the assigned value, enabling rapid determination of node attributes.

[0029] It should be noted that this invention abstracts the discrete mesh domain into a node-edge model based on graph theory. Mesh points are considered nodes, adjacent connections are considered directed edges, and the node height is determined by the minimum communicable height field. The basic movement energy consumption of directed edges is calculated separately for horizontal and climbing components. Then, the lateral specific energy ridge density of communication bottleneck points is transformed into a structural energy barrier through a connected saddle point indicator function. Finally, the movement energy consumption and the structural energy barrier are superimposed to obtain the edge weight of the directed edge. This invention abstracts the movement behavior in continuous space into edge movement in a discrete graph, providing a standardized graph theory framework for subsequent path search. The calculation of movement energy consumption takes into account both horizontal and climbing flight, aligning with the actual energy consumption characteristics of UAVs. The structural energy barrier is triggered only for connected saddle points, making the application of energy consumption costs structurally targeted. The edge weights achieve coupled quantification of basic movement energy consumption and communication structural risk costs, allowing subsequent shortest path calculations to simultaneously consider energy consumption and communication constraints, providing a unified energy cost judgment standard for path optimization.

[0030] In one embodiment of the present invention, the sequence of minimum energy consumption segments and path nodes between key points of the edge weight calculation task includes: Obtain the set of key points for the task For any of the key points of the task The grid points obtained by mapping The calculation formula is as follows: ; in, The horizontal coordinates of the grid points For a set of grid points; For any two task key points Define grid points To grid point The edge sequence is a grid path Total energy consumption along the path The calculation formula is as follows: ; Minimum energy consumption segment The calculation formula is as follows: ; in, For border rights; Output and minimum power consumption segment Corresponding path node sequence ,in , And it satisfies the following calculation formula: ; in, For the first in the grid path Each grid point node.

[0031] It should be noted that the task key point set is a collection of all core execution locations in the UAV data acquisition mission, which can be obtained through techniques such as task planning system input, on-site reconnaissance and marking, and extraction from UAV mission requirement documents. A task key point is a single core execution location within the task key point set, reflecting the specific target location characteristics of the UAV mission execution. It can be obtained through techniques such as real-time dynamic measurement using a GPS system, electronic map annotation, and mission planning command issuance. The mapping from task key points to grid points is the association relationship between task key points and the grid point with the smallest horizontal distance in the grid point set. A connecting path is a continuous path between grid points mapped from task key points, consisting of directed edges from adjacent edge sets, reflecting the traversable path characteristics between nodes in the grid discrete domain. The total energy consumption of the path is the energy consumption value obtained by summing the edge weights of all directed edges in the connecting path, reflecting the total energy cost characteristics of moving along this path. The minimum energy consumption segment is the minimum of the total energy consumption of all possible connecting paths between task key points, reflecting the minimum energy cost characteristics of moving between two points. A path node sequence is a sequence of grid nodes that the connecting path passes through in the order of travel when reaching the minimum energy consumption segment, reflecting the node travel trajectory characteristics of the minimum energy consumption movement between two points.

[0032] It should be noted that the key point types are defined according to the requirements of the UAV data acquisition mission. Base stations are fixed communication key points, while acquisition points are information acquisition points deployed according to the mission monitoring requirements. The location information of all base stations and acquisition points is summarized, and points with duplicate coordinates are removed to form the set of mission key points. For small-scale high-precision acquisition missions, acquisition points can be deployed at intervals of 50 to 100 meters, while for large-scale conventional acquisition missions, acquisition points can be deployed at intervals of 200 to 500 meters. The specific method for determining and traversing the connection paths between grid points mapping the mission key points is based on a grid graph constructed from adjacent edge sets. A depth-first search or breadth-first search method is used to traverse all feasible paths from the starting mapping node to the ending mapping node. During the traversal, only paths consisting of continuous directed edges are retained, and duplicate paths containing closed loops are excluded to ensure that each path is a unidirectional continuous path from the starting point to the ending point. The specific method for selecting and determining the minimum energy consumption segment is as follows: First, calculate the total energy consumption of all feasible connection paths between the grid points mapped to the key points of the task. Organize all energy consumption values ​​into a one-dimensional numerical set. Then, select the element with the smallest value from the set. This element is the minimum energy consumption segment between the two points. If the minimum value corresponds to multiple different connection paths, select the energy consumption value corresponding to the path with the fewest grid nodes as the final minimum energy consumption segment.

[0033] It should be noted that this invention maps the actual key points of UAV data acquisition to a pre-constructed grid-based discrete computing domain, transforming path search between key points into shortest path search between mapped nodes in the grid graph. The total energy consumption of each connected path is obtained by accumulating edge weights, and the minimum energy consumption segment is selected and its corresponding path node sequence is extracted. This invention achieves effective integration between the actual task scenario and the discrete computing system through the mapping of key points to grid points, allowing path search to be conducted based on a quantified energy cost grid graph. Traversing all feasible connected paths and calculating the total energy consumption ensures the comprehensiveness of the minimum energy consumption segment selection. The minimum energy consumption segment quantifies the minimum energy cost of movement between any two key points, providing a standardized basic energy consumption unit for subsequent global path optimization. The path node sequence provides the specific node trajectory with minimum energy consumption between two points, providing a discrete node foundation for the final 3D track stitching. This eliminates the need for repeated grid-level path search in subsequent acquisition point access order optimization, effectively reducing the computational complexity of the overall optimization process.

[0034] In one embodiment of the present invention, the hovering height is determined based on the minimum communicable height field, and the data rate and hovering time are calculated using the hovering height to obtain the data reception power consumption at the acquisition point, including: From the set of key points of the task Get collection point set For any collection point hovering height The calculation formula is as follows: ; in, The ground elevation of the data collection point; Calculate the vertical distance from the data collection point to the drone. ; Obtain the transmission power of the acquisition point With link gain parameters Signal-to-noise ratio The calculation formula is as follows:

[0035] ;in, For noise spectral density, For channel bandwidth, The carrier wavelength; Data rate The calculation formula is as follows: ; Acquire the amount of data collected at the collection points Hovering time The calculation formula is as follows: ; Obtain the power consumption of the drone receiver circuit Energy consumption for data reception at collection points The calculation formula is as follows: .

[0036] It should be noted that hovering altitude is the altitude value corresponding to the horizontal coordinates of the acquisition point in the minimum communicable altitude field, reflecting the minimum hovering flight altitude characteristic for maintaining communication at the acquisition point. Ground elevation of the acquisition point is the actual elevation value of the ground corresponding to the horizontal coordinates of the acquisition point, reflecting the vertical elevation characteristics of the terrain at the acquisition point. Transmit power of the acquisition point is the power value of the signal transmitted by the data transmission module of the acquisition point, reflecting the energy intensity characteristics of the signal transmission at the acquisition point. It can be obtained by consulting the technical manual of the acquisition point transmission module, measuring on-site with a power meter, or reading from the acquisition module configuration interface. Link gain parameter is a comprehensive quantitative parameter of various gains and losses in the communication link from the acquisition point to the UAV, reflecting the signal transmission efficiency characteristics of the communication link. It can be obtained by calculating with link simulation software, measuring on-site with a communication tester, or calculating by superimposing the parameters of various components of the link. Noise spectral density is the noise power value per unit frequency in the communication link, reflecting the noise interference level characteristics of the communication link. It can be obtained by measuring on-site with a spectrum analyzer, consulting standard parameters of the communication system, or testing with a noise tester. Channel bandwidth is the width of the available frequency range of the communication link between the data acquisition point and the UAV, reflecting the signal transmission capacity of the communication link. It can be obtained through techniques such as reading the communication module configuration file, measuring with a spectrum analyzer, and extracting mission communication planning parameters. Signal-to-noise ratio (SNR) is the ratio of signal power to noise power in the communication link, reflecting the signal transmission quality. Data rate is the actual data transmission speed from the data acquisition point to the UAV, reflecting the effective data transmission efficiency of the communication link. Data volume at each data acquisition point is the total amount of raw data that each data acquisition point needs to upload to the UAV, reflecting the scale of information acquisition at the data acquisition point. It can be obtained through techniques such as reading the data acquisition point storage module, pre-setting mission acquisition requirements, and agreeing on data acquisition protocols. Hovering time is the time required for the UAV to hover at the data acquisition point and receive all the data, reflecting the time consumption of the data acquisition process. UAV receiver circuit power consumption is the average power consumption of the UAV's data receiver circuit during operation, reflecting the energy consumption intensity of the receiver circuit. It can be obtained through techniques such as consulting the UAV receiver circuit manual, measuring with a power meter in series, and reading flight control system parameters. Data reception energy consumption at the data acquisition point is the total energy consumed by the UAV while hovering at the data acquisition point to receive data, reflecting the energy consumption of the data acquisition activity.

[0037] It should be noted that the principle of determining the hovering height of the acquisition point by using the value of the minimum communicable height field at the horizontal coordinate of the acquisition point is that the minimum communicable height field is the lowest altitude distribution within the mission area that maintains reliable communication with the base station. Taking the corresponding value of this field at the acquisition point ensures that the UAV still meets the communication constraints with the base station when collecting data, allowing the acquisition behavior to be deeply coupled with the communication requirements, without the need for additional independent setting of the communication altitude. The signal-to-noise ratio (SNR) of the link from the acquisition point to the UAV can be calculated by combining the acquisition point's transmit power, link gain parameters, noise spectral density, channel bandwidth, and carrier wavelength. The principle is that the SNR is the ratio of the effective signal power to the total noise power. The acquisition point's transmit power, after being corrected by the link gain parameters, is the signal power at the UAV's receiving end. The noise spectral density multiplied by the channel bandwidth is the total noise power. The carrier wavelength is used to calculate the free space link loss, which is an inherent physical principle for SNR calculation in air-to-ground wireless communication. The principle behind calculating the data rate from the channel bandwidth and signal-to-noise ratio is that this calculation follows Shannon's formula. Shannon's formula reveals the intrinsic relationship between the maximum transmission rate of a channel and the channel bandwidth and signal-to-noise ratio. It is the fundamental physical law for determining the data transmission capability of a channel in the field of communication and can accurately reflect the effective data rate that the link can actually achieve. For example, if the channel bandwidth is 20 MHz and the signal-to-noise ratio is 1000, the maximum data rate of the link can be calculated to be approximately 200 megabits per second according to Shannon's formula.

[0038] It should be noted that this invention selects a set of collection points from the set of key task points, determines the hovering height of the collection points based on the minimum communicable altitude field to ensure base station communication constraints during data collection, calculates the vertical distance for air-to-ground communication based on the hovering height, calculates the signal-to-noise ratio and data rate by combining various communication link parameters, and then obtains the hovering time of the UAV. Finally, it quantifies the data reception energy consumption of the collection points based on the power consumption of the receiving circuit. This invention deeply couples the hovering height of the collection points with the minimum communicable altitude field, ensuring that data collection always meets communication requirements without the need to set an additional collection communication height; it calculates communication link parameters based on vertical distance, simplifying the calculation process of link loss and aligning with practical engineering applications; it calculates the data rate based on Shannon's formula, accurately reflecting the actual transmission capacity of the link; and it calculates the receiving energy consumption by multiplying power consumption by time, realizing the energy quantification of data collection, making collection energy consumption an important component of total energy consumption, providing complete collection energy consumption data for the subsequent accurate calculation of global total energy consumption, and ensuring the comprehensiveness and accuracy of total energy consumption optimization.

[0039] In one embodiment of the present invention, the total energy consumption is calculated based on the minimum energy consumption segment and the data reception energy consumption at the acquisition point. Under the constraint of available battery energy, the optimal access order is optimized and output, including: With collection point set Construct access order variables for the accessed object ,in , The number of data collection points; from the set of key points of the task. China has identified the base station as ; For any access order Total energy consumption The calculation formula is as follows: ; in, Total energy consumption, For base stations With the first collection point The minimum energy consumption value between the two ranges. This represents the minimum energy consumption segment between adjacent access collection points. For the last collection point With base station The minimum energy consumption value between the two ranges. For collection points Data reception energy consumption; Obtain available battery power Under the constraints Under the given conditions, optimal access order The following calculation formula is satisfied: ; Output the optimal access order. .

[0040] It should be noted that the access order variable is a variable that quantifies the order in which all collection points in the collection point set are accessed, reflecting the sequential arrangement characteristics of the global access to collection points. Total energy consumption is the sum of the flight energy consumption and data reception energy consumption of the UAV in completing the data acquisition task for all collection points, reflecting the overall energy consumption characteristics of the entire collection task. Available battery energy is the maximum usable energy value that the battery can provide when the UAV performs the task, reflecting the upper limit of the UAV's energy supply. It can be obtained through techniques such as consulting the UAV battery technical manual to obtain the rated capacity and converting it, measuring the actual usable energy on-site with a battery tester, and reading the battery's remaining energy calibration value from the flight control system. The optimal access order is the order in which collection points are accessed under the constraint of available battery energy, minimizing the total energy consumption of the entire collection task, reflecting the globally energy-optimal access logic of the collection task.

[0041] It should be noted that the access order variable is constructed and encoded using an integer encoding method. First, each collection point in the collection point set is uniquely numbered, starting from 1 and incrementing sequentially. Then, all collection point numbers are linearly arranged according to the access order, forming a numerical sequence that is the access order variable. The length of the variable is the same as the number of collection points, and each number appears only once in the sequence to ensure no duplicate or missed accesses. The specific method for obtaining available battery energy is as follows: if using a new battery, consult the battery manual to obtain the rated capacity, and then convert it to available energy based on the battery voltage; if using an old battery, measure the actual remaining capacity with a battery tester and convert it to available energy. The verification method is to multiply the converted available energy by a safety factor of 0.9 to obtain the verified available battery energy, avoiding insufficient energy due to reduced actual battery discharge efficiency and ensuring safe task execution. The specific implementation method for optimizing the optimal access order based on available battery energy constraints is to use a genetic algorithm for optimization. The access order variable is treated as a chromosome, and the total energy consumption is used as the fitness function. If the total energy consumption exceeds the available battery energy, the fitness value is set to infinity, and the solution is determined to be infeasible. The algorithm iterates through crossover and mutation operations, retaining the feasible solution with the smallest fitness value in each iteration. After iterating to a preset number of generations, the chromosome corresponding to the final optimal fitness value is selected as the optimal access order. In addition, the specific selection rule for the optimal access order is to select the access order variable with the smallest total energy consumption that satisfies the available battery energy constraint from the optimal solutions obtained by the genetic algorithm iteration. The verification rule is to recalculate the total energy consumption by substituting the selected access order into the total energy consumption calculation formula to confirm that the total energy consumption value is accurate and does not exceed the verified available battery energy. The output rule is to list the actual location names or numbers of the collection points in the order of access, along with the corresponding total energy consumption values, to ensure that the output results are clear and executable.

[0042] It should be noted that this invention constructs a quantified access order variable for the access sequence of collection points. It comprehensively accumulates the minimum energy consumption segments of flight between the base station and collection points, between collection points, and the data reception energy consumption of each collection point to obtain the total energy consumption corresponding to different access sequences. Then, using the available battery energy of the UAV as a constraint, a combinatorial optimization algorithm is used to select the optimal access sequence with the minimum total energy consumption from all feasible access sequences. This invention integrates flight energy consumption and collection energy consumption into the total energy consumption calculation, ensuring the completeness and comprehensiveness of the task energy consumption calculation; using available battery energy as a constraint eliminates infeasible access sequences, ensuring that the optimization result closely matches the actual energy supply capacity of the UAV; using a combinatorial optimization algorithm to find the optimal access sequence minimizes the overall energy consumption of the UAV in completing the entire collection task, aligning with the application characteristics of the UAV's limited battery power; and simultaneously provides a globally optimal collection point access logic for subsequent track stitching, ensuring that the final track possesses the basic characteristics of optimal energy consumption.

[0043] In one embodiment of the present invention, a path node sequence is concatenated according to the optimal access order, and the final three-dimensional track and time annotation are generated by combining the hovering time, including: Obtain the optimal access order ,in The first in the optimal access order One collection point, The number of collection points; and the number of base stations. Construct an ordered sequence of task key point pairs with the optimal access order. ; Read the path node sequence according to the key points of the ordered task. Using sequence concatenation operators By splicing together, a complete two-dimensional node trajectory sequence is formed. The calculation formula is as follows: ; For a complete two-dimensional node trajectory sequence Any grid point node in Its height The calculation formula is as follows: ; in, For the minimum communicable height field; grid point nodes Represented as three-dimensional waypoints To form a three-dimensional track point sequence ; Three-dimensional track point sequence Adjacent 3D track points and Increased travel time The calculation formula is as follows: ; in, For grid spacing, Cruise speed; Read data collection points Hovering time and at the collection point Insert the hovering time period at the corresponding position to generate the final 3D track and time label.

[0044] It should be noted that the ordered task key point pair sequence is a continuous set of point pairs composed of base stations and collection points in the optimal access order. The splicing order of the path node sequence is determined by the path node sequence combination order based on the ordered task key point pair sequence. The complete two-dimensional node trajectory sequence is the global grid node planar trajectory formed by splicing all path node sequences in the splicing order. The height value of the grid node is the corresponding height value of each grid point in the minimum communicable height field in the complete two-dimensional node trajectory sequence, reflecting the minimum flight altitude for maintaining communication at each horizontal node. The three-dimensional waypoint sequence is a three-dimensional spatial point sequence formed by combining the horizontal coordinates and corresponding height values ​​of the complete two-dimensional node trajectory sequence, reflecting the UAV's travel point characteristics in three-dimensional space. The travel time increment is the time consumed by the UAV to fly between two adjacent waypoints in the three-dimensional waypoint sequence, reflecting the flight time characteristics between adjacent points of the UAV. The hovering time period is the hovering duration of the UAV at the waypoint position corresponding to the collection point. The final three-dimensional trajectory is the complete three-dimensional flight trajectory of the UAV formed by continuously connecting the three-dimensional waypoint sequences, reflecting the global three-dimensional travel path of the UAV in performing the mission. Time annotation is a set of time information that marks the flight time increment and hovering time period at the corresponding position of the final three-dimensional track, reflecting the time consumption distribution of the UAV in each segment of the track.

[0045] It should be noted that the specific construction method of the ordered task key point pair sequence is as follows: taking the base station as the task starting point, the collection points are arranged in the optimal access order, and the previous task key point is paired with the next task key point to form a point pair. Finally, the last collection point in the optimal access order is paired with the base station. All point pairs are arranged in the progression order, and the resulting set is the ordered task key point pair sequence. Each point pair is a directed point pair, consistent with the UAV's direction of travel. The specific rule for determining the splicing order of the path node sequence is that the splicing order of the path node sequence is completely consistent with the arrangement order of the ordered task key point pair sequence. Each ordered task key point pair corresponds to a unique path node sequence. The splicing order of each path node sequence is determined according to the order of the point pairs to ensure that the spliced ​​trajectory is consistent with the UAV's direction of travel. The sequence splicing operator is specifically defined as a continuous connection operation rule. During the operation, all nodes of the previous path node sequence are retained, and nodes that are repeated at the end of the previous sequence are removed from the next path node sequence. The remaining nodes of the next sequence are then appended to the end of the previous sequence. In practice, this operation is performed sequentially on adjacent path node sequences according to the splicing order until all sequences are successfully connected. The specific splicing and redundant node cleanup method for the complete two-dimensional node trajectory sequence is as follows: All path node sequences are spliced ​​segment by segment using the sequence splicing operator according to the splicing order to obtain the initial two-dimensional node trajectory sequence. During cleanup, the initial sequence is traversed, and consecutively occurring duplicate grid point nodes are removed, retaining only single nodes to ensure the continuity and uniqueness of nodes in the sequence, ultimately yielding the complete two-dimensional node trajectory sequence. In the specific construction of the 3D waypoint sequence, the horizontal coordinates of each grid point in the complete 2D node trajectory sequence are combined with the matched altitude value in the order of the first horizontal component, the second horizontal component, and the altitude value to form a single 3D waypoint. The arrangement rule is that the arrangement order of the 3D waypoints is completely consistent with the node arrangement order of the original 2D node trajectory sequence, ensuring that the direction of travel of the 3D waypoint remains unchanged. In addition, the specific generation process of the final 3D waypoint and time annotation is as follows: the 3D waypoint sequence is continuously connected in sequence to form a 3D spatial curve, which is the final 3D waypoint; the corresponding travel time increment is marked between adjacent waypoints of the waypoint, and the hovering time period is marked at the waypoint corresponding to the acquisition point to complete the time annotation. The output format is that the 3D waypoint is output in the form of a spatial coordinate sequence, and the time annotation is output in the form of a time information table corresponding to each waypoint. At the same time, the visual text of the relationship between the waypoint and time is also output to ensure that the output results can be directly recognized by the UAV flight control system.

[0046] It should be noted that three basic preparatory tasks need to be completed before deployment: First, acquire relevant data for the mission area by obtaining terrain elevation data through satellite remote sensing, UAV aerial surveying, or ground surveying to clarify the scope and terrain features of the mission area; second, determine key point parameters by using a real-time dynamic measuring instrument to measure the spatial coordinates (including antenna elevation) of the base station on-site, deploying collection points in the mission area according to data acquisition requirements, measuring the ground elevation and horizontal coordinates of each collection point, and determining the amount of data to be uploaded by each collection point; third, configure UAV parameters by consulting the UAV product manual or obtaining parameters such as cruise power, cruise speed, mass, and receiver circuit power consumption through actual measurements, and determining engineering parameters such as climb equivalent efficiency. After terrain data acquisition, it is converted into a continuous terrain elevation function using geographic information processing software; during base station and collection point parameter acquisition, the transmit power of the collection point is measured using a power meter, and communication parameters such as carrier wavelength and noise spectral density are measured using a spectrum analyzer, recording configuration information such as link gain parameters and channel bandwidth; after UAV parameter acquisition, it is organized into a standardized parameter table to ensure that all parameters required for calculation are complete and accurate. Finally, the final three-dimensional track and time annotation are calculated and generated according to this invention.

[0047] For example, the optimal access order is base station, collection point A, collection point C, collection point B, base station. The 3D track point sequence contains 110 consecutive points, and the key point information is as follows: Waypoint 1: Horizontal coordinate first component 500 meters, horizontal coordinate second component 300 meters, altitude 32 meters; Waypoint 2: Horizontal coordinate first component 505 meters, horizontal coordinate second component 300 meters, altitude 32 meters; Track point 28: Horizontal coordinate first component 620 meters, horizontal coordinate second component 380 meters, altitude value 35 meters. This point is the track point corresponding to data collection point A. Track point 56: Horizontal coordinate first component 750 meters, horizontal coordinate second component 420 meters, altitude value 34 meters. This point is the track point corresponding to data collection point C. Track point 83: Horizontal coordinate first component 680 meters, horizontal coordinate second component 350 meters, altitude value 36 meters. This point is the track point corresponding to data collection point B. Track point 110: Horizontal coordinate first component 500 meters, horizontal coordinate second component 300 meters, altitude value 32 meters. This point is the final track point for returning to the base station. The remaining waypoints are distributed sequentially according to the grid spacing, and the horizontal coordinates and altitude values ​​transition smoothly with the terrain and communication constraints to form a continuous three-dimensional path.

[0048] The time markers correspond one-to-one with the 3D trackpoint sequence, as detailed below: The flight segment from the base station to collection point A covers track points 1 to 27, with a travel time increment of 1 second between adjacent track points; The hovering time for track point 28, corresponding to data collection point A, is 5 seconds. The flight segment from collection point A to collection point C covers waypoints 29 to 55, with a travel time increment of 1 second between adjacent waypoints; The hovering time for track point C, corresponding to track point 56, is 4 seconds. The flight segment from collection point C to collection point B covers waypoints 57 to 82, with a travel time increment of 1 second between adjacent waypoints; The hovering time for track point B, corresponding to track point 83, is 6 seconds. The flight segment from point B to the base station covers track points 84 to 110, with a travel time increment of 1 second between adjacent track points; All time information clearly presents the duration allocation of flight and hovering, providing accurate time references for drone mission execution, which will not be elaborated upon here.

[0049] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0050] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A method for optimizing the data acquisition path of a UAV under communication and energy consumption constraints, characterized in that, Includes the following steps: Step S1: Obtain terrain elevation and grid point set, and calculate energy consumption per unit horizontal distance; Step S2: Construct the minimum communicable height field by utilizing the critical clearance condition between the terrain elevation and the first Fresnel radius; Step S3: Extract the set of connected saddle points from the minimum communicable height field, and calculate the lateral specific energy ridge density field based on the horizontal gradient of the minimum communicable height field; Step S4: Using the set of grid points as nodes, calculate the moving energy consumption from the energy consumption per unit horizontal distance, calculate the structural energy barrier from the lateral specific energy ridge density field at the connected saddle point set, and add the moving energy consumption and the structural energy barrier to obtain the edge weight. Step S5: Calculate the minimum energy consumption segment and path node sequence between key points of the task based on edge weights; Step S6: Determine the hovering height based on the minimum communicable height field, and use the hovering height to calculate the data rate and hovering time to obtain the data reception energy consumption at the acquisition point; Step S7: Calculate the total energy consumption based on the minimum energy consumption segment and the data reception energy consumption at the collection point. Under the constraint of available battery energy, optimize and output the optimal access order. Step S8: Concatenate the path node sequence according to the optimal access order, and combine the hovering time to generate the final 3D track and time label.

2. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, Obtain terrain elevation data for the task area, construct the mapping relationship between the first component and the second component of the horizontal coordinate and the terrain elevation, and obtain the terrain elevation function; The grid is divided in the horizontal coordinate system of the terrain elevation function, a set of grid points is generated, and the horizontal distance between adjacent grid points in the set of grid points is determined as the grid spacing; Obtain the drone's cruise power and cruise speed, calculate the ratio of drone cruise power to drone cruise speed, and obtain the energy consumption per unit horizontal distance.

3. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, Obtain the spatial coordinates of the base station, and using the grid point set as the domain, calculate the corresponding minimum communicable height for each grid point in the grid point set; Horizontal sampling points are obtained by horizontally sampling along the line connecting the horizontal position of the base station and the horizontal position of the grid points, according to the grid spacing. The terrain elevation corresponding to the horizontal sampling points is calculated using the terrain elevation function. The maximum value of the terrain elevation corresponding to the horizontal sampling points is selected as the terrain elevation of the dominant occlusion point, and the corresponding horizontal coordinates of the dominant occlusion point are determined. Calculate the horizontal distance from the base station to the horizontal coordinates of the dominant obstruction point, and the horizontal distance from the horizontal coordinates of the dominant obstruction point to the horizontal position of the grid point. Obtain the carrier wavelength and calculate the first Fresnel radius at the dominant obstruction point.

4. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 3, characterized in that, Set a critical clearance ratio, and establish the height of the line connecting the base station to the grid point at the dominant obstruction point based on the base station antenna elevation, the horizontal distance from the base station to the dominant obstruction point, the horizontal distance from the dominant obstruction point to the grid point, and the height of the UAV at the grid point. Based on the critical clearance condition that the difference between the height of the line connecting the base station to the grid point at the dominant obstruction point and the terrain elevation of the dominant obstruction point is equal to the product of the critical clearance ratio and the first Fresnel radius, the minimum communicable height corresponding to the grid point is solved, and the minimum communicable height field is constructed from the minimum communicable heights of all grid points in the grid point set.

5. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, For each grid point in the grid point set, calculate the discrete minimum communicable height. Then, judge the grid points in the grid point set. If the grid point satisfies the opposite direction of the discrete minimum communicable height change between adjacent grid points along the first component of the horizontal coordinate and the opposite direction of the discrete minimum communicable height change between adjacent grid points along the second component of the horizontal coordinate, add the grid point that meets the conditions to the connected saddle point set. The finite difference gradient components of the discrete minimum communicable height in the first and second horizontal coordinate directions are calculated using the grid spacing, and the horizontal gradient magnitude is calculated based on the finite difference gradient components. The drone's mass, gravitational acceleration, and climb efficiency are obtained. The product of the drone's mass, gravitational acceleration, and horizontal gradient modulus is calculated and divided by the climb efficiency to obtain the discrete value of the lateral specific energy ridge density. The lateral specific energy ridge density field is constructed from the discrete value of the lateral specific energy ridge density.

6. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, The set of grid points is used as the set of nodes to construct the set of adjacent edges, and the horizontal distance of each directed edge formed by adjacent grid points is determined as the grid spacing. The heights of the nodes at both ends of the directed edge are determined based on the minimum communicable height field. The mobility energy consumption is calculated by using the energy consumption per unit horizontal distance, the horizontal distance of the directed edge, the mass of the UAV, the gravitational acceleration, and the climbing equivalent efficiency, combined with the increase in the height difference between the nodes at both ends of the directed edge. A connection saddle point indicator function is constructed based on the set of connection saddle points to determine whether a node belongs to the set of connection saddle points; the structural energy barrier is calculated using the values ​​of the transverse specific energy ridge density field at the node, the connection saddle point indicator function, and the grid spacing. The weight of the directed edge is obtained by summing the mobile energy consumption and the structural energy barrier.

7. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, Obtain the set of task key points. For each task key point in the set of task key points, find the grid point with the smallest horizontal distance in the set of grid points to complete the mapping of task key points to grid points. For any two task key points in the set of task key points, the connection path is determined in the set of adjacent edges based on the grid points obtained by mapping. The total energy consumption of the path is obtained by accumulating the edge weight of each edge in the connection path. The minimum total energy consumption of the path among all possible connection paths is selected as the minimum energy consumption segment. The output lists the nodes traversed when the minimum energy consumption segment is reached, thus obtaining the path node sequence.

8. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, The collection point set is obtained from the task key point set, and the hovering height of the collection point is determined by the value of the minimum communicable height field at the horizontal coordinate of the collection point. Calculate the difference between the hovering height and the ground elevation of the data collection point to obtain the vertical distance from the data collection point to the UAV; obtain the transmit power and link gain parameters of the data collection point, and calculate the signal-to-noise ratio of the link from the data collection point to the UAV by combining the noise spectral density, channel bandwidth and carrier wavelength; The data rate is calculated using channel bandwidth and signal-to-noise ratio; the amount of data collected at each point is obtained, and the ratio of the amount of data collected at each point to the data rate is calculated to obtain the hovering time; the power consumption of the UAV receiving circuit is obtained, and the product of the power consumption of the UAV receiving circuit and the hovering time is calculated to obtain the energy consumption for receiving data at each point.

9. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, An access order variable is constructed using the set of collection points as the access object, and the base station is determined from the set of key points of the task. The access order variable determines the access order of each collection point. For any access order, the total energy consumption is calculated by accumulating the minimum energy consumption segment between the base station and the first collection point, the minimum energy consumption segment between adjacent access collection points, the minimum energy consumption segment between the last collection point and the base station, and the data reception energy consumption of all collection points. Given the available battery energy, and under the constraint that the total energy consumption is no greater than the available battery energy, find the access order that minimizes the total energy consumption and output the optimal access order.

10. The method for optimizing UAV data acquisition paths under communication and energy consumption constraints according to claim 1, characterized in that, Obtain the optimal access order, construct an ordered sequence of key task points based on the base station and the optimal access order, and determine the splicing order of the path node sequence. The path node sequence is read according to the ordered task key point sequence, and then spliced ​​sequentially using the sequence splicing operator to form a complete two-dimensional node trajectory sequence. For each grid point node in the complete two-dimensional node trajectory sequence, the height value of the grid point node is determined using the minimum communicable height field, and the horizontal coordinates and height values ​​of the grid point node are combined to form a three-dimensional track point sequence. The travel time increment between two adjacent points in the 3D track point sequence is calculated using grid spacing and cruising speed. The hovering time corresponding to the acquisition point is read, and the hovering time period is inserted at the corresponding position of the acquisition point to generate the final 3D track and time label.

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