Optimal energy supply method for distributed cooperative microwave-driven aerial platforms

Abstract

The present invention proposes a distributed cooperative microwave-driven aerial platform optimal energy supply method. First, by establishing a mathematical relationship between the distribution spacing of ground energy transmission base stations, the initial position coordinates of the UAV, and the total energy consumption of the UAV during the entire flight mission, the total energy consumption of the UAV during the entire flight mission is calculated. Then, based on this mathematical relationship, with the minimum total energy consumption during the entire flight mission as the optimization goal, an intelligent optimization algorithm is used to search for the optimal value of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV based on the Kriging proxy model. Based on the obtained optimal value, a control factor is calculated for the entire flight mission. The obtained control factor is used to correct the output power of the ground energy transmission base station, thus achieving the optimal energy supply method. The present invention can effectively improve the energy utilization rate of microwave-driven UAVs when powered by distributed ground transmitters.

Description

Technical Field

[0001] The present invention relates to the technical field of microwave energy supply for aircraft, and specifically to an optimal energy supply method for a distributed collaborative microwave-driven aerial platform. Background Art

[0002] A microwave-powered drone primarily consists of a flight unit (receiving rectenna), a distributed transmitter, and a high-precision guidance and tracking system for ground-to-ground interconnection. The flight unit utilizes a coplanar waveguide-fed microstrip patch antenna to convert microwave energy from the ground-based distributed transmitter into electrical energy for the aircraft. The distributed transmitter, driven by a servo system, tracks the flight platform, creating a microwave composite coverage zone within the drone's airspace and continuously providing energy. For microwave-powered drones, ignoring component wear and tear and lifespan, ground-based energy can enable unlimited flight.

[0003] Currently, there is little research in China on energy management solutions for microwave drones driven by distributed transmitters. Existing research mainly focuses on optimizing the energy propagation process of distributed transmitters and component optimization angles.

[0004] However, when using distributed ground transmitters for energy supply, there are two main factors affecting the energy utilization efficiency of microwave-driven UAVs. First, the flight posture of the UAV during the mission will affect the receiving projection area of the microwave energy, resulting in low efficiency of microwave wireless energy transmission; second, the distribution spacing of the distributed ground transmitters and the energy distribution of each transmitter will affect the real-time energy transmission efficiency of the UAV. Its flight scenario is as follows: Figure 1 As shown; however, at present, the technical personnel in this field have not paid attention to the energy supply method that takes into account the influence of flight attitude, distributed ground transmitter spacing and energy distribution. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a distributed cooperative microwave-driven aerial platform optimal energy supply method. By establishing a mathematical relationship between the distribution spacing of ground energy transmission base stations, the initial position coordinates of the UAV and the total energy consumption of the UAV during the entire flight mission, the total energy consumption of the UAV during the entire flight mission can be calculated based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Then, based on this mathematical relationship, with the minimum total energy consumption of the entire flight mission as the optimization goal, an intelligent optimization algorithm is used to search for the optimal value based on the Kriging proxy model to obtain the optimal value of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Based on the obtained optimal value, a calculation is performed for the entire flight mission to obtain a control factor, and the obtained control factor is used to correct the output power of the ground energy transmission base station, which is the optimal energy supply method. The present invention optimizes the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV for a set flight mission to obtain a control factor that matches the flight mission, and then corrects the output power of the ground energy transmission base station to obtain a power supply method with the lowest energy consumption for the flight mission, which can effectively improve the energy utilization rate of the UAV in the operating environment of a distributed microwave power supply base station.

[0006] The technical solution of the present invention specifically includes the following steps:

[0007] Step 1: Establish an atmospheric environment model based on the International Standard Atmosphere. For a given UAV, obtain the basic design parameters, initial state parameters, and lift-to-drag ratio relationship of the UAV. Use the aircraft performance estimation method to calculate the climb angle and flight speed boundaries of the UAV under the set flight mission.

[0008] Step 2: Establish a mathematical relationship between the distribution spacing of the ground energy transmission base stations, the initial position coordinates of the UAV, and the total energy consumption of the UAV during the entire flight mission. The mathematical relationship can be used to calculate the total energy consumption of the UAV during the entire flight mission based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0009] Step 3: Based on the given array distribution, the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV are used as design variables. The optimization goal is to minimize the total energy consumption during the entire flight mission. The mathematical relationship established in Step 2 is used as the target evaluation method. An intelligent optimization algorithm is used to find the optimal value based on the Kriging proxy model to obtain the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0010] Step 4: Based on the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV obtained in step 3, return to step 2 to calculate the entire flight mission process. The control factor is calculated by calculating the effective energy receiving area of the UAV for each ground energy transmission base station at each time step. The obtained control factor is multiplied by the initial output power of each ground energy transmission base station, and the result is used as the final output power of each ground energy transmission base station to achieve the optimal energy supply of the distributed collaborative microwave-driven aerial platform.

[0011] Furthermore, the basic design parameters of the drone in step 1 include the gravity of the drone , drone quality , UAV wing area , UAV motor power , UAV zero lift drag , the maximum lift coefficient of the UAV , the maximum capacity of drone batteries , inducing factor ; Maximum receiving energy area of the drone ; Climb angle of attack , cruise angle of attack , cruising altitude ;

[0012] The initial state parameters include the initial time , time step , the initial coordinate position of the drone , initial pitch angle , initial yaw angle , initial roll angle , initial climb angle , initial flight speed ,initial Directional speed ,initial Directional speed ,initial Directional speed , Initial output power of a ground energy transmitting base station , initial battery charge , the distribution form and initial spacing of ground energy transmitting base stations ;

[0013] The lift-to-drag ratio relationship of the UAV refers to the lift-to-drag ratio of the UAV and height , pitch angle , roll angle The mathematical form of the relationship is as follows:

[0014]

[0015] in It is in functional form and is determined by the aerodynamic performance of a given UAV, which is a conventional technique in the field of aircraft design.

[0016] Furthermore, step 1 specifically includes the following sub-steps:

[0017] Step 1.1: Establish an atmospheric environment model based on the International Standard Atmosphere;

[0018] Specifically: Take the sea level reference temperature = 288.15K, the sea level reference pressure = 101325N / m 2 , standard gravitational acceleration g=9.80665Kg / m 2 , the temperature and pressure at different altitudes are determined by the standard formula in the table below, where is the aircraft's altitude;

[0019] Table 1 Temperature and pressure formulas at different altitudes

[0020]

[0021] After determining the temperature and pressure from the altitude, the local atmospheric density can be obtained from the following formula: :

[0022]

[0023] In the formula, the constant , is the absolute temperature, is the atmospheric pressure;

[0024] Step 1.2: Calculate the flight speed limit of the drone;

[0025] At a given altitude, the available power of the drone Almost unchanged with speed, but the power required As the speed increases, it first decreases and then increases. Using the two intersection points of the required power curve and the available power curve, the maximum speed and minimum speed of the drone can be calculated. Specifically:

[0026] according to = , the mathematical relationship satisfied by the flight speed is as follows:

[0027]

[0028] in Take the intersection value of the required power curve and the available power curve, is the flight speed of the UAV. Solving the above equation, we can get the two speed values corresponding to the two intersection points of the required power curve and the available power curve. , respectively the minimum and maximum speeds during level flight;

[0029] Since the UAV has a stall speed, the minimum speed and the stall speed are compared twice, and the smaller value is taken as the minimum speed for level flight. The stall speed formula is as follows:

[0030]

[0031] The minimum speed ends up being:

[0032]

[0033] Then the velocity boundary of the UAV is obtained as follows:

[0034] ;

[0035] Step 1.3: Calculate the climb angle boundary of the UAV;

[0036] From the ground to the aircraft's cruising altitude The altitude range is divided into a set number of altitude intervals. In each altitude interval, assuming that the climbing angle range remains unchanged within this altitude interval, the left boundary of the altitude interval is taken as the current climbing altitude, and the climbing angle range of the altitude interval is obtained by using a graphical method. , as the climb angle range for this altitude interval;

[0037] Use the graphical method to solve the climbing angle range for each altitude interval The specific process is:

[0038] Assume that the UAV is climbing at the current altitude with a climbing angle Perform steady climb and obtain the following according to the dynamic equation:

[0039]

[0040]

[0041]

[0042] Where, is the pulling force of the drone, For the lift of the drone, For the resistance of the drone, is the gravity of the drone, is the climbing angle of the UAV, For the quality of the drone, is the flight speed of the drone, is the curvature radius of the UAV’s trajectory;

[0043] Combining the above formulas, we can get the climb rate for:

[0044]

[0045] Get the climb angle:

[0046]

[0047] The resistance calculation expression

[0048]

[0049] Substituting into the climb rate Evaluating the expression yields:

[0050]

[0051] Use the graphical method to solve the above equations, that is, Plot for the independent variable The function graph is within the velocity boundary of the UAV obtained in step 1.2, and the The minimum speed on the function graph of The slope of the line connecting the corresponding point and the origin is the minimum climbing angle Considering the actual carrying capacity of the aircraft, the maximum climb angle is limited to 2°, that is, ;

[0052] Then the climbing angle boundary of the UAV is obtained as follows:

[0053] .

[0054] Furthermore, step 2 specifically includes the following sub-steps:

[0055] Step 2.1: First, establish the coordinate transformation relationship from the navigation axis system to the body axis system; the coordinate origin of the body axis system is located at the center of gravity of the aircraft, The axis is located in the plane of symmetry of the aircraft, parallel to the axis of the fuselage, and points forward. The axis lies in the plane of symmetry of the aircraft and is perpendicular to The axis points downward. The axis is perpendicular to the symmetry plane and points to the right, satisfying the right-hand system; the navigation axis system is the ground inertial coordinate system, and the coordinate origin is the absolute coordinate (0,0,0);

[0056] Place the transmitting base station in the center of the array at the origin of the navigation axis coordinate system (0,0,0), and calculate the The corresponding position coordinates of the ground energy transmitting base station ; According to the initial position coordinates of the drone 、 、 , calculate the yaw angle of the drone entering the field ;

[0057] Step 2.2: Starting from the initial moment, calculate the energy consumption of the drone at the current time step based on the position coordinates of the ground energy transmitting base station, the position coordinates of the drone at the current time step, the roll angle, pitch angle, and yaw angle , specifically:

[0058] Based on the positional relationship between the navigation axis system and the body axis system, the Transformation matrix for direction (roll), The transformation matrix of the direction (pitch) and The specific expression of the transformation matrix of direction (yaw) is as follows:

[0059]

[0060]

[0061]

[0062] Where, 、 、 For Transformation matrix for direction (roll), The transformation matrix of the direction (pitch) and The transformation matrix of the orientation (yaw); 、 、 They are the roll angle, pitch angle, and yaw angle of the drone at the current time step, and the roll angle at the initial moment , pitch angle , yaw angle ;

[0063] Based on the coordinates of the drone's location at the current time step and The location coordinates of the ground energy transmitting base station , obtain the normal vector of the energy irradiation surface:

[0064]

[0065] Let the initial axis matrix of the drone be:

[0066]

[0067] Then the body axis matrix after transformation is:

[0068]

[0069] drone body The plane is the receiving surface, and the normal direction of the receiving surface in the body axis system is Direction vector , after transformation, the normal vector of the receiving surface at the current time step is obtained:

[0070]

[0071] According to the calculation principle of plane angle in space geometry, the angle between the receiving plane and the normal vector of the irradiated surface is and The angle between them is calculated using the following formula to obtain the effective energy receiving area of the drone for each ground energy transmitting base station at the current time step. :

[0072]

[0073] Where, is the maximum energy receiving area of the UAV;

[0074] Then the following formula is used to calculate the receiving efficiency of the energy transmitted by each energy transmitting base station at the current time step: :

[0075]

[0076] Where, For the The initial output power of a ground energy transmitting base station;

[0077] Then the following formula is used to calculate the energy consumption of the UAV at the current time step:

[0078]

[0079] Where, The maximum capacity of the drone battery;

[0080] Step 2.3: The energy consumption of the drone at each time step Perform superposition and summation to obtain the total energy consumption of the entire UAV flight mission .

[0081] Furthermore, the calculation method of the position coordinates, roll angle, pitch angle, and yaw angle of the drone at each time step in step 2.2 is as follows:

[0082] ①Calculate the corresponding atmospheric density based on the aircraft's altitude;

[0083] Starting from the initial moment, let time , the altitude of the drone at this time Calculate the atmospheric density corresponding to the current altitude based on the atmospheric environment model in step 1.1 ;

[0084] ②Calculate the pulling force of the drone at the current time step , lift and resistance ;

[0085] Make the flight speed , according to the UAV motor power , calculate the pulling force of the drone at the current time step :

[0086]

[0087] Zero lift drag of the drone based on the initial parameters of the drone state , induction factor , drone weight , UAV wing area Atmospheric density corresponding to the current altitude , flight speed , calculate the drag of the drone at the current time step :

[0088]

[0089] According to the current time of the drone's altitude , pitch angle , roll angle , based on the lift-to-drag ratio relationship of the drone, the calculated lift-to-drag ratio of the drone is , and then calculate the lift of the drone in the current time step :

[0090]

[0091] At the initial moment, the height , pitch angle , roll angle ;

[0092] ③ Based on the force on the drone, calculate the acceleration of the drone at the current time step. Specifically:

[0093] According to Newton's second law, the UAV's current time step is obtained The axial accelerations are:

[0094]

[0095]

[0096]

[0097] Where, Indicates drone Toward acceleration, Indicates drone Toward acceleration, Indicates drone Toward acceleration;

[0098] If the current flight speed exceeds the speed limit of the UAV determined in step 1.2, that is, or When , the acceleration of the UAV in the current time step is regulated as follows:

[0099]

[0100]

[0101]

[0102] , , is the set acceleration control parameter;

[0103] ④ Based on the acceleration of the drone in the current time step, calculate the yaw angle, flight speed, and position coordinates of the drone in the next time step. Specifically:

[0104] Based on the flight speed of the current time step, the yaw angle at the next moment is calculated by the following formula for:

[0105]

[0106] Where, 、 、 are the UAV’s current time step. The velocity of the direction, at the initial moment, , , ;

[0107] Assuming that the drone performs uniform acceleration in a time step, based on the acceleration and velocity of the drone in the current time step, the velocity of the drone in the next time step is calculated by the following formula:

[0108]

[0109]

[0110]

[0111] Where, is the set time step, 、 、 are the UAVs at the next time step. Speed of direction;

[0112] Assuming that the UAV performs uniform acceleration in a time step, based on the acceleration and velocity of the UAV in the current time step, the displacement of the UAV in the current time step is calculated by the following formula:

[0113]

[0114]

[0115]

[0116] Where, 、 、 are the UAV’s current time step. displacement to the direction;

[0117] Then the position coordinates of the drone in the next time step are calculated by the following formula:

[0118]

[0119]

[0120]

[0121] Where, 、 、 They are the position coordinates of the drone at the current time step, and at the initial moment , , ; 、 、 are the position coordinates of the UAV in the next time step, That is, the height of the drone in the next time step;

[0122] ⑤ Energy consumption of the drone based on the current time step Minimum, calculate the roll angle and pitch angle of the drone in the next time step, specifically:

[0123] Based on the roll angle of the current time step and the climb angle boundary calculated in step 1.3, the optional ranges of the pitch angle and roll angle at the next time step are established as follows:

[0124] ϕ range ∈[ϕ-∆ϕ,ϕ+∆ϕ]

[0125] γ range ∈[ γ min , γ max ]

[0126]

[0127] Where, 、 is the optional range of the roll angle and climb angle for the next time step, is the set roll angle change; is the angle of attack of the UAV at the current time step, when hour, ,when hour, , where is the climb angle of attack, is the cruise angle of attack;

[0128] for and , randomly in 、 50 samples were taken from each group to form 50 50, use the method described in step 2.2 to calculate the energy consumption of the drone corresponding to the 2500 combinations at the current time step Calculate and compare, select The minimum value corresponds to and As the roll and pitch angles for the next time step.

[0129] Furthermore, the acceleration control parameter , , Take -2m / s 2 2m / s 2 .

[0130] Furthermore, step 3 specifically includes the following sub-steps:

[0131] Step 3.1: Using the distribution spacing of ground energy transmission base stations and the initial position coordinates of the UAV as design variables and minimizing the total energy consumption throughout the flight mission as the optimization objective, set the value range of the design variables and perform Latin hypercube sampling within the set design variable value range to obtain several sample points. Using the mathematical relationship established in Step 2 as the optimization objective evaluation method, calculate the sample points to obtain the total energy consumption of each sample point throughout the flight mission, thereby obtaining a sample data set consisting of several sample points and their corresponding total energy consumption.

[0132] Step 3.2: Based on the obtained sample data set, a Kriging proxy model is established, and then an intelligent optimization algorithm is used to optimize the Kriging proxy model to obtain the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0133] Furthermore, step 4 specifically includes the following sub-steps:

[0134] Step 4.1: Based on the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV obtained in step 3, return to step 2 to calculate the entire flight mission process and obtain the effective energy receiving area of the UAV for each ground energy transmission base station at each time step. , ;

[0135] Step 4.2: Calculate the control factor using the following formula :

[0136]

[0137]

[0138]

[0139] Where, For the The control factor of a ground energy transmitting base station, For the The average value of the effective receiving energy area of the ground energy transmitting base station under the traversal task time step, It is the sum of the average values of the effective receiving energy area of the ground energy transmitting base station under the traversal task time step;

[0140] Step 4.3: Multiply the control factor obtained in step 4.2 by the initial output power of each ground energy transmitting base station to obtain the final output power of each ground energy transmitting base station:

[0141]

[0142] Where, For the The initial output power of a ground energy transmitting base station, No. The final output power of a ground energy transmitting base station.

[0143] Beneficial effects:

[0144] The present invention proposes an optimal energy supply method for a distributed collaborative microwave-driven aerial platform. By establishing a mathematical relationship between the distribution spacing of ground energy transmission base stations, the initial position coordinates of a UAV, and the total energy consumption of the UAV during the entire flight mission, the total energy consumption of the UAV during the entire flight mission can be calculated based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Then, based on the mathematical relationship, with the minimum total energy consumption during the entire flight mission as the optimization goal, an intelligent optimization algorithm is used to search for the optimal value based on the Kriging proxy model to obtain the optimal value of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Based on the obtained optimal value, a calculation is performed for the entire flight mission to obtain a control factor, and the obtained control factor is used to correct the output power of the ground energy transmission base station, which is the optimal energy supply method. The present invention optimizes the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV for a set flight mission to obtain a control factor that matches the flight mission, and then corrects the output power of the ground energy transmission base station to obtain an energy supply method with the lowest energy consumption for the flight mission, which can effectively improve the energy utilization rate of the UAV in the operating environment of a distributed microwave power supply base station. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] Figure 1 This is a schematic diagram of the flight scenario of the microwave-powered UAV of the present invention;

[0146] Figure 2 This is a flowchart for implementing an embodiment of the present invention;

[0147] Figure 3 This is a schematic diagram of calculating the maximum and minimum speeds based on a graphical method of required power and available power according to an embodiment of the present invention;

[0148] Figure 4 This is a schematic diagram of the aircraft speed boundary according to an embodiment of the present invention;

[0149] Figure 5 A schematic diagram of solving the minimum climb angle based on a graphical method according to an embodiment of the present invention;

[0150] Figure 6 Schematic diagram of the body axis system according to an embodiment of the present invention;

[0151] Figure 7 This is a schematic diagram of a rectangular distribution in an embodiment of the present invention;

[0152] Figure 8 The following is the result of climbing to 8000m after introducing the control factor in the embodiment of the present invention: Process and the control without introducing regulatory factors. DETAILED DESCRIPTION

[0153] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood and to enable those skilled in the art to better understand the solutions of the present invention, the present invention is further described and fully explained below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention.

[0154] like Figure 1 As shown, this embodiment takes a certain type of microwave powered UAV as an example, based on the rectangular distributed array ground energy transmission base station power supply scenario, and adopts the method proposed in the present invention to perform distributed collaborative microwave driven optimal power supply flight planning for this type of UAV under a set flight mission. In this embodiment, the set flight mission is that the UAV takes off from the ground and climbs to a cruising altitude. 18000m, rectangular distributed array includes A ground energy transmitting base station.

[0155] like Figure 2 As shown, the distributed collaborative microwave-driven aerial platform optimal energy supply method of this embodiment includes the following steps:

[0156] Step 1: Establish an atmospheric environment model based on the International Standard Atmosphere. For a given UAV, obtain the basic design parameters, initial state parameters, and lift-to-drag ratio relationship of the UAV. Use the aircraft performance estimation method to calculate the climb angle and flight speed boundaries of the UAV under the set flight mission.

[0157] Step 2: Establish a mathematical relationship between the distribution spacing of the ground energy transmission base stations, the initial position coordinates of the UAV, and the total energy consumption of the UAV during the entire flight mission. The mathematical relationship can be used to calculate the total energy consumption of the UAV during the entire flight mission based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0158] Step 3: Based on the given array distribution, the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV are used as design variables. The optimization goal is to minimize the total energy consumption during the entire flight mission. The mathematical relationship established in Step 2 is used as the target evaluation method. An intelligent optimization algorithm is used to find the optimal value based on the Kriging proxy model to obtain the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0159] Step 4: Based on the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV obtained in step 3, return to step 2 to calculate the entire flight mission process. The control factor is calculated by calculating the effective energy receiving area of the UAV for each ground energy transmission base station at each time step. The obtained control factor is multiplied by the initial output power of each ground energy transmission base station, and the result is used as the final output power of each ground energy transmission base station to achieve the optimal energy supply of the distributed collaborative microwave-driven aerial platform.

[0160] In this embodiment, the basic design parameters of the drone in step 1 include the gravity of the drone , drone quality , UAV wing area , UAV motor power , UAV zero lift drag , the maximum lift coefficient of the UAV , the maximum capacity of drone batteries , inducing factor ; Maximum receiving energy area of the drone ; Climb angle of attack , cruise angle of attack , cruising altitude ;

[0161] Specifically in this embodiment, the drone gravity =3000N, the mass of the drone =306.12kg, UAV wing area =50m 2 , UAV motor power =7.9KW, UAV zero lift resistance =0.0085, the maximum lift coefficient of the UAV =0.725, the maximum capacity of the drone battery =61200000Wh, induction factor =0.05; UAV maximum receiving energy area =17m 2 ; Climb angle of attack =2°, cruise angle of attack =2°, cruising altitude =18000m;

[0162] State initial parameters include initial time =0s, time step =0.3s, initial coordinate position of the drone =200m, =300m, =500m, initial pitch angle =4°, initial yaw angle =35°, initial roll angle =20°, initial climb angle =2°, initial flight speed =10.3m / s, initial Directional speed =9.67m / s, initial Directional speed =3.52m / s, initial Directional speed =0.36m / s, Initial output power of a ground energy transmitting base station =1.7KW, initial battery capacity =1, the distribution of ground energy transmitting base stations is rectangular, and the initial spacing =2000m;

[0163] The lift-to-drag ratio relationship of the UAV refers to the lift-to-drag ratio of the UAV and height , pitch angle , roll angle The mathematical form of the relationship is as follows:

[0164]

[0165] in It is in functional form and is determined by the aerodynamic performance of a given UAV, which is a conventional technique in the field of aircraft design.

[0166] In this embodiment, the relationship between the lift-to-drag ratio of the drone and its altitude, pitch angle, and roll angle is as follows:

[0167] .

[0168] In this embodiment, the process of calculating the climb angle boundary and the flight speed boundary of the UAV under the set flight mission using the aircraft performance estimation method in step 1 specifically includes the following sub-steps:

[0169] Step 1.1: Establish an atmospheric environment model based on the International Standard Atmosphere;

[0170] Specifically: Take the sea level reference temperature = 288.15K, the sea level reference pressure = 101325N / m 2 , standard gravitational acceleration g=9.80665Kg / m 2 , the temperature and pressure at different altitudes are determined by the standard formula in the table below, where is the aircraft's altitude;

[0171] Table 2 Temperature and pressure formulas at different altitudes

[0172]

[0173] After determining the temperature and pressure from the altitude, the local atmospheric density can be obtained from the following formula: :

[0174]

[0175] In the formula, the constant , is the absolute temperature, is the atmospheric pressure;

[0176] Step 1.2: Calculate the velocity boundary of the UAV;

[0177] At a given altitude, the aircraft's available power Almost unchanged with speed, but the power required As the speed increases, it first decreases and then increases. Using the two intersection points of the required power curve and the available power curve, as shown in Figure 3 As shown, find the maximum and minimum speeds of the aircraft, specifically:

[0178] according to = , the mathematical relationship satisfied by the flight speed is as follows:

[0179]

[0180] in Take the intersection value of the required power curve and the available power curve, is the flight speed of the UAV. Solving the above equation, we can get the two speed values corresponding to the two intersection points of the required power curve and the available power curve. , respectively the minimum and maximum speeds during level flight;

[0181] Since the UAV has a stall speed, the minimum speed and the stall speed are compared twice, and the smaller value is taken as the minimum speed for level flight. The stall speed formula is as follows:

[0182]

[0183] The minimum speed ends up being:

[0184]

[0185] Then the velocity boundary of the UAV is obtained as follows:

[0186] ;

[0187] In this embodiment, the calculated speed boundary of the UAV is as follows: Figure 4 As shown, the aerodynamic boundary shown in the figure is the calculated stall speed .

[0188] Step 1.3: Calculate the climb angle boundary of the UAV;

[0189] From the ground to the aircraft's cruising altitude The altitude range is divided into a set number of altitude intervals. In each altitude interval, assuming that the climbing angle range remains unchanged within this altitude interval, the left boundary of the altitude interval is taken as the current climbing altitude, and the climbing angle range of the altitude interval is obtained by using a graphical method. , as the climbing angle range of the altitude interval; the climbing angle range of each altitude interval is obtained by using the graphical method The specific process is:

[0190] Assume that the UAV is climbing at the current altitude with a climbing angle Perform steady climb and obtain according to the dynamic equation:

[0191]

[0192]

[0193]

[0194] Where, is the pulling force of the drone, For the lift of the drone, For the resistance of the drone, is the gravity of the drone, is the climbing angle of the UAV, For the quality of the drone, is the flight speed of the drone, is the curvature radius of the UAV’s trajectory;

[0195] Combining the above formulas, we can get the climb rate for:

[0196]

[0197] Get the climb angle:

[0198]

[0199] The resistance calculation expression

[0200]

[0201] Substitute into the climb rate Evaluating the expression yields:

[0202]

[0203] Use the graphical method to solve the above equations, that is, Plot for the independent variable The function graph is within the velocity boundary of the UAV obtained in step 1.2, and the The minimum speed on the function graph of The slope of the line connecting the corresponding point and the origin is the minimum climbing angle ,like Figure 5 As shown; Considering the actual carrying capacity of the aircraft, the maximum value of the climb angle is limited to 2°, that is, ;

[0204] Then the climbing angle boundary of the UAV is obtained as follows:

[0205] .

[0206] In this embodiment, the distance from the ground to the aircraft cruising altitude is The altitude range is divided into 5 altitude intervals. In each altitude interval, the calculated climb angle boundaries of the drone are as follows:

[0207] The altitude is between 0-1500m, and the climbing angle range is 0.0533°-2°;

[0208] The altitude is between 1500-4500m, and the climbing angle range is 0.0508°-2°;

[0209] The altitude is between 4500-7500m, and the climbing angle range is 0.1619°-2°;

[0210] The altitude is between 7500-13500m, and the climbing angle range is 0.0565°-2°;

[0211] The altitude is between 13500-18000m, and the climbing angle range is 0.0391°-2°;

[0212] In this embodiment, step 2 specifically includes the following sub-steps:

[0213] Step 2.1: First, establish the coordinate transformation relationship from the navigation axis system to the body axis system; the coordinate origin of the body axis system is located at the center of gravity of the aircraft, The axis is located in the plane of symmetry of the aircraft, parallel to the axis of the fuselage, and points forward. The axis lies in the plane of symmetry of the aircraft and is perpendicular to The axis points downward. The axis is perpendicular to the symmetry plane and points to the right, satisfying the right-hand system; the navigation axis system is the ground inertial coordinate system, and the coordinate origin is the absolute coordinate (0,0,0);

[0214] Place the transmitting base station in the center of the array at the origin of the navigation axis coordinate system (0,0,0), and calculate the The corresponding position coordinates of the ground energy transmitting base station ; According to the initial position coordinates of the drone 、 、 , calculate the yaw angle of the drone entering the field ;

[0215] Step 2.2: Starting from the initial moment, calculate the energy consumption of the drone at each time step based on the position coordinates of the ground energy transmitting base station, the current time step position coordinates of the drone, the roll angle, pitch angle, and yaw angle , specifically:

[0216] Based on the positional relationship between the navigation axis system and the body axis system, the Transformation matrix for direction (roll), The transformation matrix of the direction (pitch) and The specific expression of the transformation matrix of direction (yaw) is as follows:

[0217]

[0218]

[0219]

[0220] Where, 、 、 For Transformation matrix for direction (roll), The transformation matrix of the direction (pitch) and The transformation matrix of the orientation (yaw); 、 、 They are the roll angle, pitch angle, and yaw angle of the drone at the current time step, and the roll angle at the initial moment , pitch angle , yaw angle ;

[0221] Based on the coordinates of the drone's location at the current time step and The location coordinates of the ground energy transmitting base station , obtain the normal vector of the energy irradiation surface:

[0222]

[0223] Let the initial axis matrix of the drone be:

[0224]

[0225] Then the body axis matrix after transformation is:

[0226]

[0227] drone body The plane is the receiving surface, and the normal direction of the receiving surface in the body axis system is Direction vector , after transformation, the normal vector of the receiving surface at the current time step is obtained:

[0228]

[0229] According to the calculation principle of plane angle in space geometry, the angle between the receiving plane and the normal vector of the irradiated surface is and The angle between them is calculated using the following formula to obtain the effective energy receiving area of the drone for each ground energy transmitting base station at the current time step. :

[0230]

[0231] Where, is the maximum energy receiving area of the UAV;

[0232] Then the following formula is used to calculate the receiving efficiency of the energy emitted by each energy station at the current time step: :

[0233]

[0234] Where, For the The initial output power of a ground energy transmitting base station;

[0235] Then the following formula is used to calculate the energy consumption of the UAV at the current time step:

[0236]

[0237] Where, The maximum capacity of the drone battery;

[0238] Step 2.3: The energy consumption of the drone at each time step Perform superposition and summation to obtain the total energy consumption of the entire UAV flight mission .

[0239] In this embodiment, the method for calculating the position coordinates, roll angle, pitch angle, and yaw angle of the drone at each time step in step 2.2 is as follows:

[0240] ①Calculate the corresponding atmospheric density based on the aircraft's altitude;

[0241] Starting from the initial moment, let time , the altitude of the drone at this time Calculate the atmospheric density corresponding to the current altitude based on the atmospheric environment model in step 1.1 ;

[0242] ②Calculate the pulling force of the drone at the current time step , lift and resistance ;

[0243] Make the flight speed , according to the UAV motor power , calculate the pulling force of the drone at the current time step :

[0244]

[0245] Zero lift drag of the drone based on the initial parameters of the drone state , induction factor , drone weight , UAV wing area Atmospheric density corresponding to the current altitude , flight speed , calculate the drag of the drone at the current time step :

[0246]

[0247] According to the current time of the drone's altitude , pitch angle , roll angle , based on the lift-to-drag ratio relationship of the drone, the calculated lift-to-drag ratio of the drone is , and then calculate the lift of the drone in the current time step :

[0248]

[0249] At the initial moment, the height , pitch angle , roll angle ;

[0250] ③ Based on the force on the drone, calculate the acceleration of the drone at the current time step. Specifically:

[0251] According to Newton's second law, the UAV's current time step is obtained The axial accelerations are:

[0252]

[0253]

[0254]

[0255] Where, Indicates drone Toward acceleration, Indicates drone Toward acceleration, Indicates drone Toward acceleration;

[0256] If the current flight speed exceeds the speed limit of the UAV determined in step 1.2, that is, or When , the acceleration of the UAV in the current time step is regulated as follows:

[0257]

[0258]

[0259]

[0260] , , is the set acceleration control parameter; in this embodiment, , , Take 1m / s 2 ;

[0261] ④ Based on the acceleration of the drone in the current time step, calculate the yaw angle, flight speed, and position coordinates of the drone in the next time step. Specifically:

[0262] Based on the flight speed of the current time step, the yaw angle at the next moment is calculated by the following formula for:

[0263]

[0264] Where, 、 、 are the UAV’s current time step. The velocity of the direction, at the initial moment, , , ;

[0265] Assuming that the drone performs uniform acceleration in a time step, based on the acceleration and velocity of the drone in the current time step, the velocity of the drone in the next time step is calculated by the following formula:

[0266]

[0267]

[0268]

[0269] Where, is the set time step, 、 、 are the UAVs at the next time step. Speed of direction;

[0270] Assuming that the UAV performs uniform acceleration in a time step, based on the acceleration and velocity of the UAV in the current time step, the displacement of the UAV in the current time step is calculated by the following formula:

[0271]

[0272]

[0273]

[0274] Where, 、 、 are the UAV’s current time step. displacement to the direction;

[0275] Then the position coordinates of the drone in the next time step are calculated by the following formula:

[0276]

[0277]

[0278]

[0279] Where, 、 、 They are the position coordinates of the drone at the current time step, and at the initial moment , , ; 、 、 are the position coordinates of the UAV in the next time step, That is, the height of the drone in the next time step;

[0280] ⑤ Energy consumption of the drone based on the current time step Minimum, calculate the roll angle and pitch angle of the drone in the next time step, specifically:

[0281] Based on the roll angle of the current time step and the climb angle boundary calculated in step 1.3, the optional ranges of the pitch angle and roll angle at the next time step are established as follows:

[0282] ϕ range ∈[ϕ-∆ϕ,ϕ+∆ϕ]

[0283] γ range ϵ[ γ min , γ max ]

[0284]

[0285] Where, 、 is the optional range of the roll angle and climb angle for the next time step, is the set roll angle variation, in this embodiment, ; is the angle of attack of the UAV at the current time step, when hour, ,when hour, , where is the climb angle of attack, is the cruise angle of attack;

[0286] for and , randomly in 、 50 samples were taken from each group to form 50 50, use the method described in step 2.2 to calculate the energy consumption of the drone corresponding to the 2500 combinations at the current time step Calculate and compare, select The minimum value corresponds to and As the roll and pitch angles for the next time step.

[0287] In this embodiment, step 3 specifically includes the following sub-steps:

[0288] Step 3.1: Using the distribution spacing of ground energy transmission base stations and the initial position coordinates of the UAV as design variables and minimizing the total energy consumption throughout the flight mission as the optimization objective, set the value range of the design variables and perform Latin hypercube sampling within the set design variable value range to obtain several sample points. Using the mathematical relationship established in Step 2 as the optimization objective evaluation method, calculate the sample points to obtain the total energy consumption of each sample point throughout the flight mission, thereby obtaining a sample data set consisting of several sample points and their corresponding total energy consumption.

[0289] Step 3.2: Based on the obtained sample data set, a Kriging proxy model is established, and then an intelligent optimization algorithm is used to optimize the Kriging proxy model to obtain the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

[0290] In this embodiment, the range of the variable is set as follows: the base station distance range is 500m 2500m, the pitch angle range is 2° 6°, and the roll angle range is 15° 25°, and the yaw angle range is -180° The flight speed range is between 180° and 180°, and the speed limit of the UAV is determined in step 1.2. 1380 sample points are obtained by sampling. The intelligent optimization algorithm is used to optimize the Kriging proxy model. After optimization, the optimal distribution spacing of the rectangular energy supply array is 1040.16m, and the optimal initial coordinates of the UAV are (435.3, 502.3, 500).

[0291] In this embodiment, step 4 specifically includes the following sub-steps:

[0292] Step 4.1: Based on the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV obtained in step 3, return to step 2 to calculate the entire flight mission process and obtain the effective energy receiving area of the UAV for each ground energy transmission base station at each time step. , ;

[0293] Step 4.2: Calculate the control factor using the following formula :

[0294]

[0295]

[0296]

[0297] Where, For the The control factor of a ground energy transmitting base station, For the The average value of the effective receiving energy area of the ground energy transmitting base station under the traversal task time step, It is the sum of the average values of the effective receiving energy area of the ground energy transmitting base station under the traversal task time step;

[0298] Step 4.3: Multiply the control factor obtained in step 4.2 by the initial output power of each ground energy transmitting base station to obtain the final output power of each ground energy transmitting base station:

[0299]

[0300] Where, For the The initial output power of a ground energy transmitting base station, No. The final output power of a ground energy transmitting base station.

[0301] In this embodiment, based on the distribution spacing of the ground energy transmission base stations obtained in step 3, the optimal values of the pitch angle, yaw angle, roll angle, and flight speed of the drone, the control factors of the nine ground energy transmission base stations are calculated as shown in the following table:

[0302] Table 3 Control factors of each base station

[0303]

[0304] In this embodiment, based on the rectangular array distribution form, the above-mentioned control factor is introduced to correct the output power of the ground energy transmitting base station. The functional method without the introduction of the control factor is used as a control. The UAV climbs 8000m. Its changing trend is as follows Figure 8 As shown in the figure, it can be seen that the climbing altitude of about 8000m can be reduced The consumption is about 12%, which proves that the energy supply method of the present invention can effectively improve the energy utilization rate of UAVs in the distributed microwave power supply base station operating environment.

[0305] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims

1. An optimal energy supply method for a distributed collaborative microwave-driven aerial platform, characterized by: The steps include: Step 1: Establish an atmospheric environment model based on the International Standard Atmosphere. For a given UAV, obtain the basic design parameters, initial state parameters, and lift-to-drag ratio relationship of the UAV. Use the aircraft performance estimation method to calculate the climb angle and flight speed boundaries of the UAV under the set flight mission. Step 2: Establish a mathematical relationship between the distribution spacing of the ground energy transmission base stations, the initial position coordinates of the UAV, and the total energy consumption of the UAV during the entire flight mission. The mathematical relationship can be used to calculate the total energy consumption of the UAV during the entire flight mission based on the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Step 3: Based on the given array distribution, the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV are used as design variables. The optimization goal is to minimize the total energy consumption during the entire flight mission. The mathematical relationship established in Step 2 is used as the target evaluation method. An intelligent optimization algorithm is used to find the optimal value based on the Kriging proxy model to obtain the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV. Step 4: Based on the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV obtained in step 3, return to step 2 to perform calculations for the entire flight mission. The control factor is calculated by calculating the effective energy receiving area of the UAV for each ground energy transmission base station at each time step. The obtained control factor is multiplied by the initial output power of each ground energy transmission base station, and the result is used as the final output power of each ground energy transmission base station. Achieve optimal energy supply for distributed collaborative microwave-driven aerial platforms.

2. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 1 is characterized by: The basic design parameters of the drone mentioned in step 1 include the gravity of the drone , drone quality , UAV wing area , UAV motor power , UAV zero lift drag , the maximum lift coefficient of the UAV , the maximum capacity of drone batteries , inducing factor ; Maximum energy receiving area of the drone ; Climb angle of attack , cruise angle of attack , cruising altitude ; The initial state parameters include the initial time , time step , the initial coordinate position of the drone , initial pitch angle , initial yaw angle , initial roll angle , initial climb angle , initial flight speed ,initial Directional speed ,initial Directional speed ,initial Directional speed , Initial output power of a ground energy transmitting base station , initial battery charge , the distribution form and initial spacing of ground energy transmitting base stations ; The lift-to-drag ratio relationship of the UAV refers to the lift-to-drag ratio of the UAV and height , pitch angle , roll angle The mathematical form of the relationship is as follows: in It is in functional form and is determined by the aerodynamic performance of a given UAV.

3. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 2 is characterized by: Step 1 specifically includes the following sub-steps: Step 1.1: Establish an atmospheric environment model based on the International Standard Atmosphere; Step 1.2: Calculate the flight speed limit of the drone; According to the two intersection points of the UAV's required power curve and available power curve, use the formula Solve and obtain the two speed values corresponding to the two intersection points of the required power curve and the available power curve , respectively the minimum speed and maximum speed during level flight; is the local atmospheric density determined by the atmospheric environment model, is the flight speed of the drone, Take the intersection value of the required power curve and the available power curve; The minimum speed calculated by the above formula is equal to the stall speed Perform a second comparison and take the smaller value as the minimum speed for level flight: Then the velocity boundary of the UAV is obtained as follows: ； Step 1.3: Calculate the climb angle boundary of the UAV; From the ground to the aircraft's cruising altitude The altitude range is divided into a set number of altitude intervals. In each altitude interval, assuming that the climbing angle range remains unchanged within this altitude interval, the left boundary of the altitude interval is taken as the current climbing altitude, and the climbing angle range of the altitude interval is obtained by using a graphical method. , as the climb angle range for this altitude interval.

4. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 3 is characterized by: Use the graphical method to solve the climbing angle range for each altitude interval The specific process is: Assume that the UAV is climbing at the current climbing height with a climbing angle Perform steady climb and obtain the following according to the dynamic equation: Where, is the pulling force of the drone, For the lift of the drone, For the resistance of the drone, is the gravity of the drone, is the climbing angle of the UAV, For the quality of the drone, is the flight speed of the drone, is the curvature radius of the UAV’s trajectory; Combining the above formulas, we can get the climb rate for: Get the climb angle: The resistance calculation expression Substituting into the climb rate Evaluating the expression yields: Use the graphical method to solve the above equations, that is, Plot for the independent variable The function graph is within the velocity boundary of the UAV obtained in step 1.2, and the The minimum speed on the function graph of The slope of the line connecting the corresponding point and the origin is the minimum climbing angle ; Maximum climb angle Determined according to the actual carrying capacity of the UAV.

5. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 3 is characterized by: Step 2 specifically includes the following sub-steps: Step 2.1: First, establish the coordinate transformation relationship from the navigation axis system to the body axis system; the coordinate origin of the body axis system is located at the center of gravity of the aircraft, The axis is located in the plane of symmetry of the aircraft, parallel to the axis of the fuselage, and points forward. The axis lies in the plane of symmetry of the aircraft and is perpendicular to The axis points downward. The axis is perpendicular to the symmetry plane and points to the right, satisfying the right-hand system; the navigation axis system is the ground inertial coordinate system, and the coordinate origin is the absolute coordinate (0,0,0); Place the center transmitting base station of the array at the coordinate origin (0,0,0), and calculate the The corresponding position coordinates of the ground energy transmitting base station ; According to the initial position coordinates of the drone 、 、 , calculate the yaw angle of the drone entering the field ; Step 2.2: Starting from the initial moment, calculate the energy consumption of the drone at each time step based on the position coordinates of the ground energy transmitting base station, the current time step position coordinates of the drone, the roll angle, pitch angle, and yaw angle , specifically: Based on the coordinates of the drone's location at the current time step and The location coordinates of the ground energy transmitting base station , obtain the normal vector of the energy irradiation surface: Let the initial axis matrix of the drone be: Then the body axis matrix after transformation is: 、 、 They are respectively the navigation axis system and the body axis system for The transformation matrix of the direction, The transformation matrix of the direction and the The transformation matrix of the direction; drone body The plane is the receiving surface, and the normal direction of the receiving surface in the body axis system is Direction vector , after transformation, the normal vector of the receiving surface at the current time step is obtained: The following formula is used to calculate the effective energy receiving area of the drone for each ground energy transmitting base station at the current time step: : Where, is the maximum energy receiving area of the UAV; Then the following formula is used to calculate the receiving efficiency of the energy transmitted by each energy transmitting base station at the current time step: : Where, For the The initial output power of a ground energy transmitting base station; Then the following formula is used to calculate the energy consumption of the UAV at the current time step: Where, The maximum capacity of the drone battery; Step 2.3: The energy consumption of the drone at each time step Perform superposition and summation to obtain the total energy consumption of the entire UAV flight mission .

6. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 5 is characterized by: The calculation method of the position coordinates, roll angle, pitch angle, and yaw angle of the drone at each time step in step 2.2 is as follows: ①According to the altitude of the drone Calculate the corresponding atmospheric density ; ②Calculate the pulling force of the drone at the current time step , lift and resistance ; ③ Based on the force on the drone, calculate the acceleration of the drone at the current time step; ④ Based on the acceleration of the drone in the current time step, calculate the yaw angle, flight speed, and position coordinates of the drone in the next time step; ⑤ Energy consumption of the drone based on the current time step Minimum, calculate the roll angle and pitch angle of the drone in the next time step: Based on the roll angle of the current time step and the climb angle boundary calculated in step 1.3, the optional ranges of the pitch angle and roll angle at the next time step are established as follows: Where, 、 is the optional range of the roll angle and climb angle for the next time step, is the set roll angle change; is the angle of attack of the UAV at the current time step, when hour, ,when hour, , where is the climb angle of attack, is the cruise angle of attack; for and , randomly in 、 Each sample G samples, forming G The matrix of G is converted into G by the method described in step 2.

2. Energy consumption of the drone corresponding to G combinations at the current time step Calculate and compare, select The minimum value corresponds to and As the roll and pitch angles for the next time step.

7. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 6 is characterized by: Calculate the pulling force of the drone at the current time step , lift and resistance The process is: Make the flight speed , according to the UAV motor power , calculate the pulling force of the drone at the current time step : Zero lift drag of the drone based on the initial parameters of the drone state , induction factor , drone weight , UAV wing area Atmospheric density corresponding to the current altitude , flight speed , calculate the drag of the drone at the current time step : According to the current time of the drone's altitude , pitch angle , roll angle , based on the lift-to-drag ratio relationship of the drone, the calculated lift-to-drag ratio of the drone is , and then calculate the lift of the drone in the current time step : At the initial moment, the height , pitch angle , roll angle .

8. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 6 is characterized by: Based on the force on the drone, the process of calculating the acceleration of the drone at the current time step is as follows: According to Newton's second law, the UAV's current time step is obtained The axial accelerations are: Where, Indicates drone Toward acceleration, Indicates drone Toward acceleration, Indicates drone Toward acceleration; If the current flight speed exceeds the speed limit of the UAV determined in step 1.2, that is, or When , the acceleration of the UAV in the current time step is regulated as follows: , , is the set acceleration control parameter.

9. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 1 is characterized by: Step 3 specifically includes the following sub-steps: Step 3.1: Using the distribution spacing of ground energy transmission base stations and the initial position coordinates of the UAV as design variables and minimizing the total energy consumption throughout the flight mission as the optimization objective, set the value range of the design variables and perform Latin hypercube sampling within the set design variable value range to obtain several sample points. Using the mathematical relationship established in Step 2 as the optimization objective evaluation method, calculate the sample points to obtain the total energy consumption of each sample point throughout the flight mission, thereby obtaining a sample data set consisting of several sample points and their corresponding total energy consumption. Step 3.2: Based on the obtained sample data set, a Kriging proxy model is established, and then an intelligent optimization algorithm is used to optimize the Kriging proxy model to obtain the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV.

10. The distributed collaborative microwave-driven aerial platform optimal energy supply method according to claim 1 is characterized by: Step 4 specifically includes the following sub-steps: Step 4.1: Based on the optimal values of the distribution spacing of the ground energy transmission base stations and the initial position coordinates of the UAV obtained in step 3, return to step 2 to calculate the entire flight mission process and obtain the effective energy receiving area of the UAV for each ground energy transmission base station at each time step. , ; Step 4.2: Calculate the control factor using the following formula : Where, For the The control factor of a ground energy transmitting base station, For the The average value of the effective receiving energy area of the ground energy transmitting base station under the traversal task time step, It is the sum of the average values of the effective receiving energy area of the ground energy transmitting base station under the traversal task time step; Step 4.3: Multiply the control factor obtained in step 4.2 by the initial output power of each ground energy transmitting base station to obtain the final output power of each ground energy transmitting base station: Where, For the The initial output power of a ground energy transmitting base station, No. The final output power of a ground energy transmitting base station.

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