Unmanned aerial vehicle multi-target hovering point multi-round game selection method based on satisfaction function

Abstract

The invention discloses an unmanned aerial vehicle multi-target hovering point multi-round game selection method based on a satisfaction function. The method comprises the steps that the satisfaction function of hovering points is constructed in the form of an arrival time window; constructing a game model, wherein a utility function of a leader in the game model is defined as the total cost of purchasing battery resources at hovering points where the UAV does not hover in each round of game; the utility function of the follower in the game model is defined as the difference between the satisfaction function of the UAV arrival time and the cost paid for purchasing the working resources provided by the UAV battery; the optimal unit resource pricing is obtained by maximizing the utility function of the UAV and the hovering points with the mutual competition relationship to represent respective benefits; and selecting the maximum value in the optimal unit resource pricing as the next target hovering point of the UAV. According to the method, on the basis of optimal unit resource pricing, the unmanned aerial vehicle can reasonably and efficiently carry out approaching flight at a plurality of target hovering points on the premise of limited flight energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV flight control, and relates to a multi-round game selection method for multi-target hover points of a UAV based on a satisfaction function. Background Art

[0002] The multi-target hover technology of unmanned aerial vehicles (UAVs) is an important development direction of modern UAV technology. This technology allows UAVs to stably hover over multiple targets continuously in the air, providing a more flexible and efficient solution for various application scenarios. The basis of UAV multi-target hover technology is UAV hover technology, which relies on the positioning system and flight control system of the UAV. The positioning system realizes high-precision spatial positioning through technologies such as satellite navigation, while the flight control system uses sensors such as inertial measurement units and gyroscopes to monitor and control the attitude, speed and position of the UAV in real time. UAV multi-target hover technology has broad application prospects in fields such as aerial photography, agricultural plant protection, and logistics distribution. For example, in aerial photography, UAVs can hover at multiple designated positions to provide a stable platform and perspective for shooting; in agricultural plant protection, through accurate positioning and static spraying technology, automated operations such as pesticide application can be realized. With the continuous progress and improvement of technology, UAV multi-target hover technology will bring innovation and convenience to more fields. Under the premise of limited flight energy of the UAV, how to select to fly close to multiple target hover points is a key problem that needs to be solved urgently. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-round game selection method for multi-target hover points of a UAV based on a satisfaction function. By maximizing the utility functions that represent the respective interests of the UAV and the hover points with competitive relationships, the optimal unit resource pricing is obtained, so that the UAV can reasonably and efficiently fly close to multiple target hover points under the premise of limited flight energy.

[0004] The technical solution to achieve the purpose of the present invention is as follows:

[0005] A multi-round game selection method for multi-target hover points of a UAV based on a satisfaction function, comprising the following steps:

[0006] S01: Construct a satisfaction function of the hover point in the form of an arrival time window;

[0007] S02: Construct a game model. The utility function of the leader in the game model is defined as the total cost of using the workable resources purchased by the battery for the hover points that the UAV has not hovered in each round of the game for the UAV to fly close to their respective hover points; the utility function of the follower in the game model is defined as the difference between the satisfaction function of the UAV arrival time and the cost of purchasing the workable resources provided by the UAV battery.

[0008] S03: Obtain the optimal unit resource pricing by maximizing the utility functions that represent the respective interests of the UAV and the hovering points with competitive relationships.

[0009] S04: Select the maximum value among the optimal unit resource pricings as the next target hovering point of the UAV.

[0010] In the preferred technical solution, step S01 models the satisfaction function S of the UAV arriving at the hovering point l k,l in the form of a piecewise function, that is:

[0011] When 0 < t k,l ≤ t l , S k,l = λ * max{t l - t k,l , 0} + S l

[0012] When t k,l > t l , S k,l = -log a (t k,l - t l + 1) + S l

[0013] where t k,l represents the total movement time of the UAV from the hovering point k to the hovering point l in the X-axis direction, t l represents the satisfaction time threshold of the hovering point l, λ is the satisfaction coefficient, S l is the satisfaction of the hovering point l when t k,l = t l , and the coefficient a > 0.

[0014] In the preferred technical solution, the utility function of the leader is:

[0015]

[0016] where is the set of optional hovering points, β k,l is the unit battery resource price sold by the UAV from the hovering point k to the hovering point l to the hovering point l, W k,l is the total work done by the UAV from the hovering point k to the hovering point l, β k,l W k,l is the cost paid for providing the resource W k,l by purchasing the UAV battery.

[0017] In the preferred technical solution, the utility function of the follower is:

[0018]

[0019] In the preferred technical solution, in step S03, for the follower in the game model, to maximize its own utility function, there is:

[0020] P1:

[0021] For the leader in the game model, to maximize its own utility function and considering the constraint conditions at the same time, there is:

[0022] P2:

[0023]

[0024] P1 and P2 together form the Stackelberg game, and the optimal unit resource pricing is obtained by solving.

[0025] In the preferred technical solution, in step S04, the next target hovering point selected by the UAV to take off from the hovering point k is:

[0026]

[0027] The next target hovering point l * According to the optimal unit resource pricing The optimal battery resource value is obtained

[0028] The UAV executes the flight process from the hovering point k to the hovering point l * and uses as the energy for attitude adjustment.

[0029] In the preferred technical solution, it also includes: The UAV performs a secondary confirmation on the selected flight target to be flown, including:

[0030] The UAV calculates the remaining available work of the UAV battery after hovering at the planned flight hovering point l * and compares it with the UAV battery return threshold value W0, where W is the work converted from the UAV battery into mechanical energy, and is the work consumed during hovering at the hovering point l for * the hovering period;

[0031] If the planned flight target l * cannot be used as the next flight target; if the planned flight target l * will be used as the next flight target;

[0032] When the planned flight target l *When it cannot be used as the next flight target, it is removed from the set of optional hovering points and the set is selected again with the largest hovering point as the next intended flight target point of the UAV.

[0033] The present invention also discloses a multi-round game selection system for UAV multi-target hovering points based on a satisfaction function, including a processor, and the processor is built-in with the multi-round game selection method for UAV multi-target hovering points based on the satisfaction function.

[0034] The present invention also discloses a UAV, including the multi-round game selection system for UAV multi-target hovering points based on the satisfaction function.

[0035] The present invention also discloses a computer storage medium, on which a computer program is stored, and when the computer program is executed, it implements the above-mentioned multi-round game selection method for UAV multi-target hovering points based on the satisfaction function.

[0036] Compared with the prior art, the present invention has the following remarkable advantages:

[0037] By maximizing the utility functions that represent the respective interests of the UAV and the hovering points with competing relationships, the optimal unit resource pricing is obtained, so that the UAV can efficiently fly close to multiple target hovering points on the premise of limited flight energy. It can be applied in scenarios such as aerial photography, data collection, agricultural plant protection, logistics distribution, and patrol monitoring, physically conforming to the actual application scenarios and being able to be effectively applied to engineering practice.

[0038] In each round of the game, the UAV only selects one hovering point for determination. Therefore, the selection method for multi-target hovering points is a multi-round selection mechanism, and in each round of the game, if the UAV calculates that the intended flight target point cannot ensure a safe return, the set of optional hovering points is updated, effectively reducing the size of the optional set of the subsequent game algorithm in the proposed multi-round game selection method, thereby reducing the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the hovering point satisfaction function of this embodiment;

[0040] Figure 2 It is a flowchart of the multi-round game selection method for UAV multi-target hovering points based on the satisfaction function of this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Embodiment 1:

[0042] A multi-round game selection method for multiple target hovering points of an unmanned aerial vehicle (UAV) based on a satisfaction function, comprising the following steps:

[0043] S01: Construct a satisfaction function for the hovering points in the form of an arrival time window;

[0044] S02: Construct a game model, where the utility function of the leader in the game model is defined as the total cost of using the workable resources purchased for the battery at the hovering points where the UAV has not yet hovered in each round of the game for the UAV to perform a close-range flight to its respective hovering points; the utility function of the follower in the game model is defined as the difference between the satisfaction function of the UAV arrival time and the cost paid for purchasing the workable resources provided by the UAV battery.

[0045] S03: Obtain the optimal unit resource pricing by maximizing the utility functions representing the respective interests of the UAV and the hovering points with competitive relationships;

[0046] S04: Select the maximum value among the optimal unit resource pricings as the next target hovering point of the UAV.

[0047] Specifically, in combination with Figure 1 and Figure 2 shown, a further specific analysis and description of the design of the solution of the present invention is made.

[0048] In the network described in the design of the solution of the present invention, the UAV is at a charging point, providing services such as charging and maintenance for the UAV. In application scenarios such as aerial photography, data collection, agricultural plant protection, logistics distribution, and patrol monitoring, the UAV needs to hover at multiple points to obtain corresponding data. Assume that when the UAV receives a takeoff command, there are a total of K hovering points and one charging point in the network, and their set is represented as Specifically, k = 0 is the number of the UAV charging point. The coordinates of the UAV charging point are set as the origin O point (x0, y0, z0) of the three-dimensional coordinate system, where x0 = y0 = z0 = 0. The th hovering point has position coordinates (x k , y k , z k ). The work done by the UAV battery converted into mechanical energy is W, and this work can adjust the flight attitude of the UAV during flight. Specifically, when the remaining energy of the UAV battery converted into mechanical energy is relatively low, the UAV needs to perform a return flight operation to the charging point for charging before it can perform subsequent flight tasks, and the battery return threshold is W0.

[0049] When the UAV flies from hovering point k to hovering point l, it needs to adjust its speeds and decelerations on the X-axis, Y-axis, and Z-axis by calling its own flight power, so as to achieve precise control of the UAV's speed and position. Therefore, it is necessary to describe and analyze the motion states of the UAV in the horizontal direction (X-axis and Y-axis) and the vertical direction (Z-axis).

[0050] 1. The motion analysis of the UAV in the X-axis direction from hovering point to hovering point :

[0051] Since the UAV has no speed at both hovering point k and hovering point l, if the UAV wants to reach the position (x l , y l , z l ) of hovering point l, it needs to experience acceleration and deceleration motions in the X-axis direction. Therefore, according to Newton's laws of motion, we have:

[0052] x k,l,1 + x k,l.2 = x l - x k

[0053] t k,l,1 + t k,l.2 = t k,l

[0054] v k,l = a x,k,l t k,l,1

[0055] 0 = v k,l - a x,k,l t k,l.2

[0056]

[0057] where x k,l,1 and x k,l.2 represent the displacement of the UAV's acceleration motion and the displacement of the deceleration motion in the X-axis direction from hovering point k to hovering point l, respectively; t k,l,1 and t k,l.2 represent the time of the UAV's acceleration motion and the time of the deceleration motion in the X-axis direction from hovering point k to hovering point l, respectively; v k,l represents the maximum speed of the UAV in the X-axis direction from hovering point k to hovering point l; a x,k,l represents the acceleration of the UAV in the X-axis from hovering point k to hovering point l; t k,l represents the total motion time of the UAV in the X-axis direction from hovering point k to hovering point l.

[0058] Since the UAV is flying in the air, the instantaneous power of the UAV will change at different speeds. The flight time of the UAV can be discretely divided into time intervals of Δt. Since Δt is small enough, it can be assumed that the instantaneous power of the UAV is constant within each time interval. Combining the above equations, the instantaneous flight power of the UAV in the nth time interval of the X-axis from hovering point k to hovering point l is as follows:

[0059] When time

[0060]

[0061] Among them, m is the mass of the UAV.

[0062] When time

[0063]

[0064] Among them,

[0065] Therefore, the work that the UAV needs to do on the X-axis from hovering point k to hovering point l is:

[0066]

[0067] 2. Motion analysis of the UAV from hovering point to hovering point in the Y-axis direction:

[0068] Similarly, the instantaneous flight power of the UAV in the nth time interval of the Y-axis from hovering point k to hovering point l can be obtained as follows:

[0069] When time

[0070]

[0071] Among them,

[0072] When time

[0073]

[0074] Among them,

[0075] Therefore, the work that the UAV needs to do on the Y-axis from hovering point k to hovering point l is:

[0076]

[0077] 3. The UAV from hovering point To the hovering point Motion analysis in the Z-axis direction:

[0078] Since the UAV has no velocity at both the hovering point k and the hovering point l, in order for the UAV to reach the position (x l , y l , z l ) of the hovering point l, it also needs to experience acceleration and deceleration motions in the Z-axis direction. Therefore, according to Newton's laws of motion, the instantaneous flight power of the UAV in the Z-axis during the nth time interval from the hovering point k to the hovering point l can be obtained as follows:

[0079] When time

[0080]

[0081] Among them, g is the acceleration due to gravity.

[0082] When time

[0083]

[0084] Among them,

[0085] Therefore, the work that the UAV needs to do in the Z-axis from the hovering point k to the hovering point l is:

[0086]

[0087] Therefore, the total work that the UAV needs to do from the hovering point k to the hovering point l is:

[0088] W k,l = W x,k,l + W y,k,l + W z,k,l

[0089] In the UAV multi-objective hovering point selection method involved in the present invention, each hovering point needs to compete with each other through a game mechanism to win the UAV's approach flight. Also, since the UAV can only select one of the hovering points for approach flight and hovering each time, the arrival time of the UAV is one of the key factors for measuring the satisfaction of each hovering point. The present invention uses the form of an arrival time window to define the satisfaction function of the hovering point. As Figure 1 shown, the satisfaction of the UAV arriving at the hovering point l is modeled in the form of a piecewise function, that is:

[0090] When 0 < t k,l ≤ t l time, there is:

[0091] S k,l =λ*max{t l -t k,l ,0}+S l

[0092] When t k,l >t l When:

[0093] S k,l =-log a (t k,l -t l +1)+S l

[0094] Among them, t l represents the satisfaction time threshold of the hovering point l, that is, when 0 <t k,l ≤t l When the hovering point l has a high satisfaction, there is a linear satisfaction and the satisfaction coefficient is λ to control the change of satisfaction, S l t k,l =t l The satisfaction of the hovering point l when t k,l >t l When , the satisfaction of the hovering point l decreases nonlinearly (determined by the coefficient a>0).

[0095] In the present invention, since the work that the UAV battery can do is limited by the battery capacity, how to reasonably select multiple target hovering points for close flight is a key issue that needs to be solved urgently. Considering that each hovering point hopes to purchase the resources provided by the UAV battery so that the UAV can fly to the hovering point, in the selection scheme of the multi-target hovering point of the UAV given by the present invention, each hovering point has a competitive relationship in the issue of purchasing the resources provided by the UAV battery.

[0096] In the Stackelberg game, the strategy selection of the leader and the follower is the core of the game. The leader's strategy is to set a price so that the follower cannot change the strategy to obtain higher profits after observing this strategy. The leader needs to consider the follower's reaction function, that is, the optimal price of the follower given the leader's output. By predicting the behavior of the follower, the leader can formulate a strategy to maximize its own profits. The follower's strategy is to choose the optimal output to maximize its own profits after observing the leader's decision. The follower needs to consider the leader's strategy as well as factors such as market demand and cost to formulate an optimal response strategy. In the present invention, since the UAV has global information and resources provided by the battery, the UAV is used as the leader of the game model, and the hovering point that only has partial information acts as a follower.

[0097] In the present invention, each hovering point has its own requirement for the satisfaction of the approaching flight time. Therefore, the purpose of the hovering point l (the follower) purchasing the resources provided by the UAV battery in the game mechanism is to increase the satisfaction function value of the UAV arrival time. In addition, considering the situation of competition among a large number of hovering points, as a follower in the game, it needs to pay a corresponding price for its behavior of purchasing the resources provided by the UAV battery. Therefore, the utility function of each hovering point consists of two parts, namely, the satisfaction function of the UAV arrival time and the price paid for purchasing the resources provided by the UAV battery, that is:

[0098]

[0099] where β k,l is the unit battery resource price sold by the UAV from the hovering point k to the hovering point l to the hovering point l, and β k,l W k,l is the price paid for purchasing the resources W provided by the UAV battery k,l and paid.

[0100] As the hovering point l purchases more resources, the obtained t k,l is smaller, and thus the satisfaction of the approaching flight time obtained by it is greater, but its cost function will also increase. For the follower in the game model, its goal is to obtain the highest benefit at the smallest cost in the game behavior, that is, to maximize its own utility function, and there is:

[0101] P1:

[0102] For the seller UAV in the game relationship, selling limited battery resources to multiple competing hovering points for adjusting the flight attitude of the UAV approaching it, so its objective function is defined as the total price paid by all hovering points for purchasing battery resources, which is:

[0103]

[0104] Also, due to the limited battery resources of the UAV, there is a constraint at the leader in the Stackelberg game relationship:

[0105]

[0106] In the game, the UAV, as the leader, aims to obtain benefits by selling limited battery resources. Therefore, the goal of the seller in the selling behavior is to maximize its own utility function while considering the constraint conditions. Therefore, there is:

[0107] P2:

[0108]

[0109] P1 and P2 together form a Stackelberg game. Through the game actions of the leader (UAV) and the follower (hovering point) of both sides of the game according to certain rules, the final Stackelberg equilibrium can be obtained, that is, the optimal unit resource pricing is obtained by maximizing the utility functions that characterize the respective interests of the UAV and the hovering points with competitive relationships.

[0110] It should be noted that the UAV can only select one of the hovering points for close-range flight in one game. At the same time, in the game, the leader UAV gives the optimal price for each hovering point according to the values of the utility functions of different followers. And it can be known from game theory The larger it is, the higher the profit that can be obtained when the hovering point l consumes the same resources, the stronger its willingness to purchase UAV battery resources, and at the same time, the UAV can obtain higher resource sales revenue.

[0111] Based on the above analysis, it can be known that the next target hovering point selected by the UAV taking off from the hovering point k is:

[0112]

[0113] Subsequently, the buyer l * According to the optimal pricing of the seller Obtain the optimal battery resource value (From the optimal in P1 is obtained) is:

[0114]

[0115] Next, the UAV executes the flight process from the hovering point k to the hovering point l * and uses as the energy for attitude adjustment.

[0116] Since the UAV needs to hover at the hovering point l * The work consumed during hovering can be calculated according to the hovering time requirements of different hovering points as:

[0117]

[0118] Among them, ρ is the air density; ζ and ε are the rotor area and the number of rotors respectively; is the hovering time of the hovering point l * ; g is the acceleration due to gravity.

[0119] Based on the above principle analysis, as Figure 2 shown, the following gives a multi-round game selection method, including the following steps:

[0120] Step 1: The UAV is charged at the charging point and receives an instruction on whether to hover at multiple target points. If an instruction is obtained, the mass m of the UAV, the gravitational acceleration g of the current flight area, the position information of the charging point and K hovering points, the work W converted from the UAV battery into mechanical energy, and the return threshold value W0 of the UAV battery need to be obtained. The coordinates of the UAV warehouse are set as the origin O point (x0, y0, z0) of the three-dimensional coordinate system, where x0 = y0 = z0 = 0. The position coordinates of the k-th k hovering point are (x k , y k ).

[0121] Step 2: Let k = 0,

[0122] Step 3: Calculate the optimal unit resource pricing according to the game theory

[0123] Step 4: Give the next target hovering point of the UAV as

[0124] Step 5: According to Calculate the flight time of the UAV from the hovering point k to the hovering point l based on P1 * and the work that the UAV needs to do The UAV calculates the hovering time requirement of the hovering point l The work consumed during hovering at the hovering point l is *

[0125] Step 6: If Update If The UAV returns directly from the hovering point k, otherwise execute Step 4; if Then execute Step 7;

[0126] Step 7: The UAV executes the flight process from the hovering point k to the hovering point l * and uses as the energy for attitude adjustment;

[0127] Step 8: The UAV executes the hovering process at the hovering point l * and uses as the energy for hovering;

[0128] Step 9: Update the remaining work that the UAV battery can do to The set of hovering points is updated to k = l * ​​, the satisfaction time of the remaining hovering points is updated to Proceed to Step 3.

[0129] In Step 4, the UAV only selects the hovering point with the maximum value in the set as the target to be flown to. Then, according to Calculate the flight time of the UAV from hovering point k to hovering point l * and the work that the UAV needs to do Secondly, the UAV calculates the work consumed during hovering according to the hovering time requirement of hovering point l Secondly, the UAV calculates the work consumed during hovering according to the hovering time requirement of hovering point l * Hovering time requirement Calculate the work consumed during hovering as

[0130] In Step 6, the UAV needs to reconfirm the target to be flown to selected in Step 4;

[0131] The UAV needs to calculate the remaining available work of the UAV battery after hovering at the hovering point l to be flown to * Remaining available work of the UAV battery after hovering and compare it with W0. If The target to be flown to l in Step 4 * cannot be used as the next flight target; if The target to be flown to l in Step 4 * will be used as the next flight target;

[0132] When the target to be flown to l * cannot be used as the next flight target, it is deleted from the set of optional hovering points and the hovering point with the maximum in the set needs to be selected again as the next target hovering point of the UAV.

[0133] In Step 6, if the updated it means that the UAV does not have enough battery capacity to return safely after reaching any hovering point and performing the hovering operation. Therefore, the UAV returns directly from the current hovering point k.

[0134] In Step 9, update the remaining available work of the UAV battery to The set of hovering points is updated to k = l * , the satisfaction time of the remaining hovering points is updated to For the game selection method of the next round of target hovering points.

[0135] In each round of the game, the UAV only selects one hovering point for determination. Therefore, the method for selecting multi-objective hovering points is a multi-round selection mechanism. And in each round of the game, if the UAV calculates that the planned flight target point cannot guarantee a safe return, the set of optional hovering points is updated, which effectively reduces the size of the optional set of the subsequent game algorithm in the proposed multi-round game selection method, thereby reducing the computational complexity.

[0136] It should be noted that since the work that the UAV battery can do is limited, when there are still some hovering points in the network that are not activated when the UAV performs the return operation, the UAV can still use the method proposed in the present invention to select the hovering points in sequence after charging at the charging point until all hovering points are activated.

[0137] In another embodiment, a multi-round game selection system for multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function includes a processor, and the processor is built-in with the above-mentioned multi-round game selection method for multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function. This will not be elaborated here.

[0138] In another embodiment, an unmanned aerial vehicle includes the above-mentioned multi-round game selection system for multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function. This will not be elaborated here.

[0139] In another embodiment, a computer storage medium stores a computer program, and when the computer program is executed, it implements the above-mentioned multi-round game selection method for multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function.

[0140] The above embodiments are the preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A multi-round game selection method for multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function, characterized in that, It includes the following steps: S01: Construct a satisfaction function for hovering points in the form of an arrival time window; S02: Construct a game model. The utility function of the leader in the game model is defined as the total cost of using the work - capable resources purchased for the batteries of the hovering points where the drones have not hovered in each round of the game for the drones to fly close to their respective hovering points. The utility function of the follower in the game model is defined as the difference between the satisfaction function of the drone arrival time and the cost paid for purchasing the work - capable resources provided by the drone batteries; S03: Obtain the optimal unit resource pricing by maximizing the utility functions representing the respective interests of the drones and the hovering points with competing relationships; S04: Select the maximum value among the optimal unit resource pricings as the next target hovering point of the drone.

2. The multi-round game selection method for the multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function according to claim 1, characterized in that, Step S01 models the satisfaction function S when the drone reaches the hovering point l as a piecewise function, that is: k,l ​ When 0 < t k,l ≤ t l then, S k,l = λ * max{t l - t k,l , 0} + S l When t k,l > t l then S k,l = -log a (t k,l - t l + 1) + S l Among them, t k,l represents the total movement time of the UAV from the hovering point k to the hovering point l in the X-axis direction, and t l represents the satisfaction time threshold of the hovering point l, λ is the satisfaction coefficient, and S l is for t k,l = t l is the satisfaction degree of the hovering point l when t and the coefficient a > 0.

3. The multi-round game selection method for the multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function according to claim 2, wherein The utility function of the leader is: Among them, is a set of optional hover points, β k,l is the unit battery resource price sold by the UAV from hover point k to hover point l to hover point l, W k,l is the total work done by the UAV from hover point k to hover point l, β k,l W k,l is the resource W provided by purchasing the UAV battery k,l and the cost paid.

4. The multi-round game selection method for the multi-objective hovering points of the drone based on the satisfaction function according to claim 2, characterized in that Utility function of the follower is as follows:

5. The method for multi-round game selection of multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function according to claim 2, characterized in that In step S03, for the follower in the game model, by maximizing its own utility function, we have: P1： For the leader in the game model, by maximizing its own utility function and considering the constraint conditions, we have: P2： s.t. P1 and P2 together form a Stackelberg game, and the optimal unit resource pricing is obtained by solving is the set of optional hovering points, β k,l is the unit battery resource price sold by the UAV from hovering point k to hovering point l to hovering point l, W k,l is the total work done by the UAV from hovering point k to hovering point l. W is the work done by the UAV battery converted into mechanical energy.

6. The multi-round game selection method for the multi-objective hovering points of the drone based on the satisfaction function according to claim 5, characterized in that, In step S04, the next target hovering point selected by the drone taking off from hovering point k is: The next target hovering point l * According to the optimal unit resource pricing Obtain the optimal battery resource value The drone performs a flight process from hovering point k to hovering point l * and uses W k,l* as the energy for attitude adjustment.

7. The multi-round game selection method for the multi-objective hovering points of the drone based on the satisfaction function according to claim 6, wherein It also includes: The drone makes a secondary confirmation of the selected flight target, including: The UAV calculates the planned hovering point l * The remaining available work of the UAV battery after hovering ends And compare it with the return threshold value W0 of the UAV battery. W is the work converted from the UAV battery into mechanical energy, and W l* Is at the hovering point l * The work consumed during hovering; If the target flight candidate l * cannot be the next target flight; if the target flight candidate l * will be the next target flight; When the target to be flown l * cannot be used as the next flight target, it is removed from the set of optional hovering points and the hovering point with the maximum in the set is selected again as the next target point for the UAV to fly to.

8. A multi-round game selection system for multi-objective hovering points of an unmanned aerial vehicle based on a satisfaction function, characterized in that, It includes a processor, and the processor is built - in with the method for multi - round game selection of multiple target hovering points of drones based on the satisfaction function according to any one of claims 1 - 7.

9. A drone, characterized in that, It includes the system for multi - round game selection of multiple target hovering points of drones based on the satisfaction function according to claim 8.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for multi - round game selection of multiple target hovering points of drones based on the satisfaction function according to any one of claims 1 - 7.

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