Optimal charging distribution method, device, electronic equipment and medium for drone airport

Abstract

The present invention provides an optimal charging allocation method, device, electronic device and medium for drone airports, including: constructing a mission scenario; wherein the mission scenario includes: a smart airport and multiple drones, and the smart airport provides charging services for the drones; constructing a smart airport discount benefit maximization model for the mission scenario; wherein the smart airport discount benefit maximization model is constrained by satisfying the incentive compatibility of dominant strategies and computational efficiency, takes the maximization of the discount benefit of the smart airport as the goal, and takes the allocation mechanism and the payment mechanism as decision variables; based on the smart airport discount benefit maximization model, the optimal charging time allocation mechanism and payment mechanism of the smart airport are determined. The present invention improves the problem in the prior art of lacking the use of shared airports located on the ground to provide charging services for multiple drones.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an optimal charging distribution method, device, electronic equipment and medium for unmanned aerial vehicle airports. Background Art

[0002] Drones are widely used in various fields such as agriculture, construction, delivery, entertainment, news, military, photography, research and development, and surveillance. In agriculture, they are used for crop monitoring, irrigation management, and soil analysis; in construction, they are used to survey sites, inspect structures, and monitor progress; in delivery, drones are used to deliver packages and medical supplies in remote areas; in entertainment, drones are used for light shows and aerial photography; in news, drones are used to capture footage of events and natural disasters; in military applications, drones are used for reconnaissance missions and targeted strikes; in photography, drones are used to capture aerial footage of landscapes and cityscapes; in research and development, drones are used to test new technologies and conduct experiments; in surveillance applications, drones are used to monitor traffic patterns and track wildlife populations.

[0003] The flight time or power limit of drones is the most important limiting factor for their large-scale application in various fields. When standardized charging devices are used, shared smart airports located on the ground can be used to provide charging services for multiple drones. However, there is currently a lack of research on scenarios where shared airports located on the ground can be used to provide charging services for multiple drones. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an optimal charging distribution method, device, electronic device and medium for drone airports, so as to improve the problem in the prior art of lack of charging services for multiple drones using shared airports located on the ground.

[0005] In order to achieve the above purpose, the technical solution adopted by the embodiment of the present invention is as follows:

[0006] In a first aspect, an embodiment of the present invention provides an optimal charging allocation method for an unmanned aerial vehicle airport, comprising: constructing a mission scenario; wherein the mission scenario includes: an intelligent airport and multiple unmanned aerial vehicles, and the intelligent airport provides charging services for the unmanned aerial vehicles; constructing an intelligent airport discounted benefit maximization model for the mission scenario; wherein the intelligent airport discounted benefit maximization model is constrained by satisfying the incentive compatibility of dominant strategies and computational efficiency, takes maximizing the discounted benefit of the intelligent airport as a goal, and takes allocation mechanism and payment mechanism as decision variables; based on the intelligent airport discounted benefit maximization model, the optimal charging time allocation mechanism and payment mechanism of the intelligent airport are determined.

[0007] In one embodiment, a smart airport discounted revenue maximization model is constructed for a mission scenario, including: determining the number of unmanned aerial vehicles to be charged at the beginning of the first auction cycle, as well as the charging time vector, charging quotation vector, charging revenue cumulative distribution function vector, and charging revenue probability density function vector of the unmanned aerial vehicles to be charged at the beginning of the first auction cycle; determining the allocation mechanism function vector and payment mechanism function vector of the smart airport at the beginning of the first auction cycle; determining the value constraints and charging constraints of the allocation mechanism function vector, as well as the value constraints of the payment mechanism function vector; determining the charging revenue valuation vector of the unmanned aerial vehicles to be charged at the beginning of the first auction cycle; based on the charging revenue valuation vector of the unmanned aerial vehicles to be charged, the charging quotation vector, and the allocation mechanism function vector of the smart airport, determining the truthfulness constraints and non-negative utility constraints in the incentive compatibility of the dominant strategy; determining the smart airport revenue function at the first auction cycle and the recursive form of the maximum discounted revenue of the smart airport.

[0008] In one implementation, the revenue function of the smart airport in the first auction cycle is:

[0009]

[0010] Among them, r1(N,w,X(w,b),p(w,b)) represents the revenue of the smart airport in the first auction cycle, N represents the number of drones to be charged, w represents the charging time vector of the drones to be charged, X(w,b) represents the allocation mechanism function vector, and p(w,b) represents the payment mechanism function vector.

[0011] In one embodiment, the recursive form of the maximum discounted benefit of the smart airport is:

[0012]

[0013] Among them, r * represents the maximum discounted benefit of the smart airport, and α represents the benefit discount rate of the smart airport.

[0014] In one embodiment, the optimal charging time allocation mechanism and payment mechanism of the smart airport are determined based on the smart airport discounted benefit maximization model, including: calculating the virtual valuation of the drone to be charged based on the smart airport discounted benefit maximization model, and calculating the revised virtual valuation of the drone to be charged; determining the charging time allocation and payment mechanism of the smart airport based on the revised virtual valuation; calculating the smart airport revenue in the first auction cycle under the charging time allocation and payment mechanism of the smart airport; calculating the maximum discounted benefit of the smart airport based on the recursive form of the smart airport revenue and the maximum discounted benefit of the smart airport; determining the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the maximum discounted benefit of the smart airport and the revised virtual valuation.

[0015] In one embodiment, the virtual valuation of the drone to be charged is calculated based on the intelligent airport discounted revenue maximization model, and the revised virtual valuation of the drone to be charged is calculated, including: calculating the virtual valuation of the drone to be charged according to the following formula:

[0016]

[0017] in, Indicates that the charging benefit is estimated as v i The virtual valuation of the i-th drone to be charged, and i, w i (v i ) respectively represent the charging time w i The cumulative distribution function and probability density function of the charging income of the i-th drone to be charged;

[0018] The corrected virtual estimate of the drone to be charged is calculated using the following formula:

[0019]

[0020] in, Indicates that the charging benefit is estimated as v i The modified virtual valuation of the i-th drone to be charged is given by the maximum discounted benefit r of the smart airport. * is the threshold benefit, w i represents the charging time of the i-th UAV to be charged, c represents the charging cost per unit time of the smart airport, and α represents the revenue discount rate of the smart airport.

[0021] In one embodiment, the charging time allocation and payment mechanism of the smart airport is determined based on the modified virtual valuation, including: if the maximum value of the modified virtual valuation is less than or equal to zero, the charging time allocation and payment mechanism is determined to not charge the unmanned aerial vehicle to be charged; if the maximum value of the modified virtual valuation is greater than zero, the charging time allocation and payment mechanism is determined to charge the unmanned aerial vehicle to be charged with the largest modified virtual valuation, and the payment of the charging unmanned aerial vehicle is calculated according to the following formula:

[0022]

[0023] in, Indicates the payment for charging the drone,

[0024] In a second aspect, an embodiment of the present invention provides an optimal charging distribution device for an unmanned aerial vehicle airport, including: a scenario construction module, used to construct a mission scenario; wherein the mission scenario includes: an intelligent airport and multiple unmanned aerial vehicles, and the intelligent airport provides charging services for the unmanned aerial vehicles; a model construction module, used to construct an intelligent airport discounted benefit maximization model for the mission scenario; wherein the intelligent airport discounted benefit maximization model is constrained by satisfying the incentive compatibility of dominant strategies and computational efficiency, with maximizing the discounted benefit of the intelligent airport as the goal, and with the allocation mechanism and the payment mechanism as decision variables; a mechanism determination module, used to determine the optimal charging time allocation mechanism and payment mechanism of the intelligent airport based on the intelligent airport discounted benefit maximization model.

[0025] In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the steps of any one of the methods provided in the first aspect above.

[0026] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods provided in the first aspect are executed.

[0027] The embodiments of the present invention bring the following beneficial effects:

[0028] The above-mentioned drone airport optimal charging allocation method, device, electronic device and medium provided by the embodiments of the present invention first construct a mission field (including: an intelligent airport and multiple drones, the intelligent airport provides charging services for the drones); then, a smart airport discounted profit maximization model is constructed for the mission scenario (the smart airport discounted profit maximization model is constrained by satisfying the incentive compatibility of the dominant strategy and computational efficiency, with maximizing the discounted profit of the smart airport as the goal, and with the allocation mechanism and payment mechanism as decision variables); finally, the optimal charging time allocation mechanism and payment mechanism of the smart airport are determined based on the smart airport discounted profit maximization model. The above method constructs a scenario in which an airport is used to provide charging services to multiple drones with their own goals, the drone's valuation of the charging benefits is private information and the charging time is public knowledge, and constructs a smart airport discounted benefit maximization model with constraints of satisfying dominant strategy incentive compatibility and computational efficiency, maximizing the discounted benefits of the smart airport as the goal, and allocation mechanism and payment mechanism as decision variables. In addition, based on the smart airport discounted benefit maximization model, a charging time allocation and payment mechanism for maximizing the discounted benefits of the smart airport that satisfies the dominant strategy incentive compatibility constraints and computational efficiency constraints can be obtained, which improves the problem in the existing technology of lacking the use of shared airports located on the ground to provide charging services for multiple drones.

[0029] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A flowchart of an optimal charging allocation method for a drone airport provided by an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a specific flow chart of optimal charging time and payment mechanism for drone airports based on revenue maximization auction provided by an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a task scenario provided by an embodiment of the present invention;

[0035] Figure 4 A schematic diagram of another task scenario provided by an embodiment of the present invention;

[0036] Figure 5 A schematic diagram of an optimal allocation mechanism and payment mechanism for the first cycle provided by an embodiment of the present invention;

[0037] Figure 6 A schematic diagram of the structure of an optimal charging distribution device for a drone airport provided by an embodiment of the present invention;

[0038] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] At present, the flight time or power constraint of drones is the most important limiting factor for their large-scale application in various fields. When standardized charging devices are used, shared smart airports located on the ground can be used to provide charging services for multiple drones. However, there is currently a lack of research on the scenario of using shared airports located on the ground to provide charging services for multiple drones.

[0041] Based on this, an optimal charging distribution method, device, electronic device and medium for drone airports provided by an embodiment of the present invention improves the problem in the prior art of lacking charging services for multiple drones using shared airports located on the ground.

[0042] To facilitate understanding of this embodiment, firstly, a method for optimal charging allocation for drone airports disclosed in an embodiment of the present invention is introduced in detail, and the method can be executed by an electronic device. Figure 1 The flowchart of the optimal charging distribution method for drone airports shown in FIG. 1 illustrates that the method mainly includes the following steps S101 to S103:

[0043] Step S101: construct a task scenario.

[0044] The mission scenario includes: a smart airport and multiple drones, and the smart airport provides charging services for the drones. In one implementation, a scenario is characterized for using a smart airport to provide charging services for multiple drones with their own goals, and the drones' valuation of the benefits of charging is private information while the charging time is public knowledge, and a mission scenario is constructed.

[0045] Step S102: Construct a smart airport discounted benefit maximization model for the mission scenario.

[0046] Among them, the discounted revenue maximization model of smart airports is constrained by satisfying the incentive compatibility and computational efficiency of dominant strategies, takes maximizing the discounted revenue of smart airports as the goal, and uses allocation mechanism and payment mechanism as decision variables.

[0047] Step S103: Determine the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the smart airport discounted benefit maximization model.

[0048] In one embodiment, the virtual valuation of each drone is corrected using the unit charging cost and the threshold revenue. The drone with the highest non-negative corrected virtual valuation is charged in each auction, and its payment of a key price is calculated to obtain an optimal charging time allocation mechanism and payment mechanism.

[0049] The above-mentioned drone airport optimal charging allocation method provided by the embodiment of the present invention constructs a scenario in which the airport is used to provide charging services to multiple drones with their own goals, the drone's valuation of charging benefits is private information and the charging time is public knowledge, and constructs a smart airport discounted benefit maximization model with constraints of satisfying dominant strategy incentive compatibility and computational efficiency, maximizing the discounted benefits of the smart airport as the goal, and allocation mechanism and payment mechanism as decision variables. In addition, based on the smart airport discounted benefit maximization model, a charging time allocation and payment mechanism for maximizing the discounted benefits of the smart airport that satisfies the dominant strategy incentive compatibility constraints and computational efficiency constraints can be obtained, thereby improving the problem in the prior art of lacking the use of shared airports located on the ground to provide charging services for multiple drones.

[0050] In one embodiment, the aforementioned step S101, i.e., constructing a task scenario (determining a task scenario representation), mainly includes the following processes:

[0051] (1.1) The number of drones to be charged at any time is a random variable, where the cumulative distribution function of the number of drones to be charged is F and the probability density function is f;

[0052] (1.2) Before the auction at any time, the drone charging time w is a random variable, where the cumulative distribution function of the drone charging time is G and the probability density function is g;

[0053] (1.3) The estimated benefit v of the drone for charging is a random variable with w as parameter, where the cumulative distribution function of the estimated benefit is H w , the probability density function is w;

[0054] (1.4) The charging cost per unit time of the smart airport is c;

[0055] (1.5) The auction is conducted in a sealed manner, where the price offered by the i-th drone for charging is b i ;

[0056] (1.6) The discount rate of the benefits of the smart airport is α;

[0057] (1.7) The auction is conducted with an interval of Δt, that is, the auction occurs at the beginning of each cycle, where the cycle numbers are 1, 2, ..., l, ...;

[0058] (1.8) In each auction, the smart airport allocation mechanism X is a function of the charging time and charging bids of all drones;

[0059] (1.9) In each auction, the payment mechanism p of the smart airport is a function of the charging time and charging bids of all drones.

[0060] In one embodiment, the aforementioned step S102, i.e., constructing a smart airport discounted revenue maximization model for a mission scenario, mainly includes the following processes:

[0061] (2.1) The number of drones to be charged at the beginning of the first auction cycle, that is, the number of drones to be charged at the beginning of the first cycle is N;

[0062] (2.2) Determine the charging time vector of the drones to be charged at the beginning of the first auction cycle, that is, the charging time vector of all drones to be charged at the beginning of the first cycle is:

[0063] w=(w1,w2,...,w i ,...,w N ) (1)

[0064] In formula (1), w is the charging time vector of all drones to be charged, w i is the charging time of the i-th drone;

[0065] (2.3) Determine the charging price vector of the drones to be charged at the beginning of the first auction cycle, that is, the charging price vector of all drones to be charged at the beginning of the first cycle is:

[0066] b=(b1,b2,...,b i ,...,b N ) (2)

[0067] In formula (2), b is the charging quotation vector of all drones to be charged, b i The charging price for the i-th drone;

[0068] (2.4) Determine the cumulative distribution function vector of the charging revenue of the drones to be charged at the beginning of the first auction cycle, that is, the cumulative distribution function vector of the charging revenue of all drones to be charged at the beginning of the first cycle is:

[0069]

[0070] In formula (3), H w is the cumulative distribution function vector of charging benefits of all drones to be charged, The charging time is w i The cumulative distribution function of charging benefits of the i-th drone;

[0071] (2.5) Determine the probability density function vector of the charging income of the drones to be charged at the beginning of the first auction cycle, that is, the probability density function vector of the charging income of all drones to be charged at the beginning of the first cycle is:

[0072]

[0073] In formula (4), h w is the probability density function vector of charging benefits of all drones to be charged, i,w i The charging time is w i The probability density function of charging income of the i-th UAV;

[0074] (2.6) Determine the allocation mechanism function vector of the smart airport at the beginning of the first auction cycle, that is, the allocation mechanism function vector of the smart airport at the beginning of the first cycle is:

[0075] X(w,b)=(X1(w,b),X2(w,b),...,X i (w,b),...,X N (w,b)) (5)

[0076] In formula (5), X(w,b) is the allocation mechanism function vector of the smart airport, X i (w,b) is the allocation decision of the i-th UAV;

[0077] (2.7) Determine the payment mechanism function vector of the smart airport at the beginning of the first auction cycle, that is, the payment mechanism function vector of the smart airport at the beginning of the first cycle is:

[0078] p(w,b)=(p1(w,b),p2(w,b),...,p i (w,b),...,p N (w,b)) (6)

[0079] In formula (5), p(w,b) is the payment mechanism function vector of the smart airport, p i (w,b) is the payment of the i-th drone;

[0080] (2.8) Determine the value constraints of the allocation mechanism function vector, that is, constrain the value of the allocation mechanism function:

[0081] X i (w,b)∈{0,1},i=1,2,...,N (7)

[0082] (2.9) Determine the charging constraint of the allocation mechanism function vector, that is, after each auction, at most one drone will be charged:

[0083]

[0084] (2.10) Determine the charging revenue valuation vector of the drones to be charged at the beginning of the first auction cycle, that is, the charging revenue valuation vector of all drones at the beginning of the first cycle is:

[0085] v=(v1,v2,...,v i ,...,v N ) (9)

[0086] In formula (9), v is the estimated charging revenue vector of all drones, v i Estimate the charging benefit of the i-th drone;

[0087] (2.11) Determine the value constraints of the payment mechanism function vector, that is, constrain the value of the payment mechanism function:

[0088] p i (w,b)≥0,i=1,2,...,N (10)

[0089] (2.12) Based on the charging revenue valuation vector of the drone to be charged, the charging quotation vector and the allocation mechanism function vector of the smart airport, the truthfulness constraint in the incentive compatibility of the dominant strategy is determined, that is, the truthfulness constraint in the incentive compatibility of the dominant strategy is:

[0090]

[0091] Formula (11) shows that truthfully reporting its own charging benefit valuation is the dominant strategy for each drone, where b -i represents the quotation vector of all drones except the i-th drone;

[0092] (2.13) Based on the charging revenue valuation vector of the drone to be charged, the charging quotation vector and the allocation mechanism function vector of the smart airport, the non-negative utility constraint in the incentive compatibility of the dominant strategy is determined, that is, the non-negative utility constraint in the incentive compatibility of the dominant strategy is:

[0093] v i X i (w,v)-p i (w,v)≥0,i=1,2,..,N (12)

[0094] Formula (12) shows that the utility of a drone that truthfully reports its own charging benefit valuation is non-negative;

[0095] (2.14) Determine the revenue function of the smart airport in the first auction cycle, that is, the revenue of the smart airport in the first auction cycle is:

[0096]

[0097] Among them, r1(N,w,X(w,b),p(w,b)) represents the revenue of the smart airport in the first auction cycle, which is a function of the number of drones to be charged N, the charging time vector w of the drones to be charged, the X(w,b) allocation mechanism function vector X(e,b) and the payment mechanism function vector p(w,b);

[0098] (2.15) The recursive form of the maximum discounted benefit of the smart airport is determined as follows:

[0099]

[0100] In formula (14), r * represents the maximum discounted benefit of the smart airport, and α represents the benefit discount rate of the smart airport.

[0101] (2.16) By summarizing formulas (1) to (14), we can obtain a charging time mechanism model that satisfies the incentive compatibility and computational efficiency of the dominant strategy, takes maximizing the discounted benefits of the smart airport as the goal, and takes the allocation mechanism and payment mechanism as decision variables, namely, the smart airport discounted benefit maximization model.

[0102] In one embodiment, the aforementioned step S103, i.e. determining the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the smart airport discounted revenue maximization model, mainly includes the following processes:

[0103] (3.1) The virtual valuation of the drone to be charged is calculated based on the smart airport discounted benefit maximization model, that is, the virtual valuation of the drone to be charged is calculated according to the following formula:

[0104]

[0105] In formula (15), Indicates that the charging benefit is estimated as v i The virtual valuation of the i-th drone to be charged, and Respectively represent the charging time as w i The cumulative distribution function and probability density function of the charging income of the i-th drone to be charged.

[0106] (3.2) Calculate the corrected virtual estimated value of the UAV to be charged, that is, calculate the corrected virtual estimated value of the UAV to be charged according to the following formula:

[0107]

[0108] In formula (16), Indicates that the charging benefit is estimated as v i The modified virtual valuation of the i-th drone to be charged is given by the maximum discounted benefit r of the smart airport. * is the threshold benefit, w i represents the charging time of the i-th UAV to be charged, c represents the charging cost per unit time of the smart airport, and α represents the revenue discount rate of the smart airport.

[0109] (3.3) Determine the charging time allocation and payment mechanism of the smart airport based on the modified virtual valuation.

[0110] Specifically, if the maximum value of the corrected virtual valuation is less than or equal to zero, the charging time allocation and payment mechanism is determined to not charge the drone to be charged; if the maximum value of the corrected virtual valuation is greater than zero, the charging time allocation and payment mechanism is determined to charge the drone to be charged with the largest corrected virtual valuation, and the payment for the charging drone is calculated.

[0111] In specific implementation, When all X i (w,v) and p i (w,v) are both 0, which means no drone is charged;

[0112] when season but The allocation and payment of the remaining drones are both 0, that is, the drone with the largest modified virtual valuation is charged, and its payment is

[0113] (3.4) Calculate the revenue of the smart airport in the first auction cycle under the charging time allocation and payment mechanism of the smart airport. That is, under the above allocation and payment mechanism, the revenue of the smart airport in the first auction cycle is:

[0114] r1(N,w,X(w,b),p(w,b))=r(N,w,r * ) (17)

[0115] Formula (17) shows that under the above allocation and payment mechanism, the revenue of the smart airport in the first auction cycle is the number of drones to be charged N, the charging time vector of all drones w, and the maximum discounted revenue of the smart airport r * Function of

[0116] (3.5) Based on the recursive form of the smart airport revenue and the maximum discounted revenue of the smart airport, the maximum discounted revenue of the smart airport is calculated. That is, formula (17) is substituted into formula (14) to obtain the maximum discounted revenue r of the smart airport. * .

[0117] (3.6) Based on the maximum discounted benefit and modified virtual valuation of the smart airport, the optimal charging time allocation mechanism and payment mechanism of the smart airport are determined. That is, after substituting the above maximum discounted benefit into formula (16), the optimal allocation mechanism and payment mechanism can be further obtained.

[0118] For ease of understanding, the embodiment of the present invention also provides a specific flowchart of the optimal charging time and payment mechanism of the drone airport based on the revenue maximization auction, see Figure 2 As shown, it mainly includes the following steps 1 to 3:

[0119] Step 1: Task scenario representation.

[0120] For the task scenario in this embodiment, see Figure 3 As shown in the figure, the scenario of using smart airports to provide charging services to multiple drones with their own goals, where the drones’ valuation of the benefits of charging is private information and the charging time is public knowledge is characterized. It mainly includes the following processes:

[0121] 1.1. The number of drones to be charged at any time is a random variable, where the cumulative distribution function of the number of drones to be charged is F and the probability density function is f. Figure 4 The circle in the figure indicates that the number of drones is uniformly distributed on [3,10].

[0122] 1.2. Before the auction at any time, the charging time w of the drone is a random variable, where the cumulative distribution function of the drone charging time is G and the probability density function is g. In this embodiment, the charging time of the drone is uniformly distributed on [1,2].

[0123] 1.3. The estimated benefit v of the drone for charging is a random variable with w as parameter, where the cumulative distribution function of the estimated benefit is H w , the probability density function is w. In this embodiment, the revenue estimation of the drone is uniformly distributed on [w,2w].

[0124] 1.4. The charging cost per unit time of the smart airport is c. In this embodiment, the smart airport consists of Figure 4 The square in the figure indicates that c = 0.1.

[0125] 1.5. The auction is conducted in a sealed manner, where the price offered by the i-th drone for charging is b i In this embodiment, Figure 4 The dotted arrow between the circle and the square indicates the charging time quoted by the drone to the smart airport.

[0126] 1.6. The discount rate of the revenue of the smart airport is α. In this embodiment, α=0.98.

[0127] 1.7. The auction is conducted with Δt as the interval period, that is, the auction occurs at the beginning of each period, wherein the period sequence is 1, 2, ..., 1, .... In this embodiment, Δt = 0.2.

[0128] 1.8. In each auction, the smart airport allocation mechanism X is a function of the charging time and charging bids of all drones.

[0129] 1.9. In each auction, the payment mechanism p of the smart airport is a function of the charging time and charging quotes of all drones.

[0130] Step 2: Construction of the discounted benefit maximization model for smart airports.

[0131] The model is built with the constraints of satisfying the incentive compatibility and computational efficiency of the dominant strategy, the goal of maximizing the discounted benefits of the smart airport, and the allocation mechanism and payment mechanism as decision variables, including the following process:

[0132] 2.1. The number of drones to be charged at the beginning of the first cycle is N. In this embodiment, see Figure 5 As shown, N=5, and the serial number of the drone is marked in the circle.

[0133] 2.2. At the beginning of the first cycle, the charging time vector of all drones to be charged is:

[0134] w=(w1,w2,...,w i ,...,w N ) (18)

[0135] In formula (18), w is the charging time vector of all drones to be charged, w i is the charging time of the i-th drone. In this embodiment, w=(1.327, 1.287, 1.400, 1.573, 1.647). Figure 5 The charging time of the corresponding drone is indicated above each circle.

[0136] 2.3. At the beginning of the first cycle, the charging quotation vector of all drones to be charged is:

[0137] b=(b1,b2,...,b i ,...,b N ) (19)

[0138] In formula (19), b is the charging quotation vector of all drones to be charged, b i The charging quotation for the i-th drone.

[0139] 2.4. At the beginning of the first cycle, the cumulative distribution function vector of the charging benefits of all drones to be charged is:

[0140]

[0141] In formula (20), H w is the cumulative distribution function vector of charging benefits of all drones to be charged, The charging time is w i The cumulative distribution function of the charging revenue of the i-th drone.

[0142] 2.5. At the beginning of the first cycle, the probability density function vector of the charging income of all drones to be charged is:

[0143]

[0144] In formula (21), h w is the probability density function vector of charging benefits of all drones to be charged, i,w i The charging time is w i The probability density function of the charging income of the i-th drone.

[0145] 2.6. The allocation mechanism function vector of the smart airport at the beginning of the first cycle is:

[0146] X(w,b)=(X1(w,b),X2(w,b),...,X i (w,b),...,X N (w,b)) (22)

[0147] In formula (22), X(w,b) is the allocation mechanism function vector of the smart airport, X i (w,b) is the allocation decision of the i-th UAV.

[0148] 2.7. At the beginning of the first cycle, the payment mechanism function vector of the smart airport is:

[0149] p(w,b)=(p1(w,b),p2(w,b),...,p i (w,b),...,p N (w,b)) (23)

[0150] In formula (23), p(w,b) is the payment mechanism function vector of the smart airport, p i (w,b) is the payment of the i-th drone;

[0151] 2.8. Constrain the value of the allocation mechanism function:

[0152] X i(w,b)∈{0,1},i=1,2,...,N (24)

[0153] 2.9. After each auction, only one drone can be charged:

[0154]

[0155] 2.10. The estimated charging revenue vector of all drones at the beginning of the first cycle is:

[0156] v=(v1,v2,...,v i ,...,v N ) (26)

[0157] In formula (26), v is the estimated charging revenue vector of all drones, v i is the estimated charging income of the i-th drone. In this embodiment, v = (2.225, 1.755, 2.039, 1.908, 2.037). Figure 5 The estimated charging revenue of the corresponding drone is indicated under each circle.

[0158] 2.11. Constrain the value of the payment mechanism function:

[0159] p i (w,b)≥0,i=1,2,...,N (27)

[0160] 2.12. The truthfulness constraint in the incentive compatibility of dominant strategies is:

[0161]

[0162] Formula (28) shows that truthfully reporting its own charging benefit valuation is the dominant strategy for each drone, where b -i represents the quotation vector of all drones except the i-th drone;

[0163] 2.13. The non-negative utility constraint in the incentive compatibility of the dominant strategy is:

[0164] v i X i (w,v)-p i (w,v)≥0,i=1,2,..,N (29)

[0165] Formula (29) shows that the utility of a drone that truthfully reports its own charging benefit valuation is non-negative;

[0166] 2.14. The revenue of smart airports in the first auction cycle is:

[0167]

[0168] In formula (30), r1(N,w,X(w,b),p(w,b)) represents the revenue of the smart airport in the first auction cycle, which is a function of the number of drones to be charged N, the charging time vector w of the drones to be charged, the X(w,b) allocation mechanism function vector X(w,b) and the payment mechanism function vector p(w,b);

[0169] 2.15. The recursive form of the maximum discounted benefit of smart airports is:

[0170]

[0171] In formula (31), r * represents the maximum discounted benefit of the smart airport, and α represents the benefit discount rate of the smart airport.

[0172] 2.16. Formula (18) to formula (31) are summarized to obtain a charging time mechanism model that satisfies the incentive compatibility and computational efficiency of the dominant strategy, takes the maximization of the discounted benefits of the smart airport as the goal, and takes the allocation mechanism and payment mechanism as decision variables, namely, the smart airport discounted benefit maximization model.

[0173] Step 3: Optimal charging time allocation and payment mechanism for smart airports.

[0174] The virtual valuation of each drone is corrected using the unit charging cost and threshold revenue. Each auction charges the drone with the highest non-negative corrected virtual valuation and requires it to pay a certain key price. The calculation of the threshold revenue occurs before all auctions, so it can meet the computational efficiency constraint. It includes the following process:

[0175] 3.1. Calculate the virtual valuation of the i-th drone to be charged:

[0176]

[0177] In formula (32), Indicates that the charging benefit is estimated as v i The virtual valuation of the i-th drone to be charged, and i,w i (v i ) respectively represent the charging time w i The cumulative distribution function and probability density function of the charging income of the i-th drone to be charged.

[0178] 3.2. Calculate the corrected virtual valuation of the i-th drone to be charged:

[0179]

[0180] In formula (33), Indicates that the charging benefit is estimated as v i The modified virtual valuation of the i-th drone to be charged is given by the maximum discounted benefit r of the smart airport. * is the threshold benefit, w i represents the charging time of the i-th UAV to be charged, c represents the charging cost per unit time of the smart airport, and α represents the revenue discount rate of the smart airport.

[0181] 3.3 When When all X i (w,v) and p i (w,v) are both 0, which means no drone is charged;

[0182] 3.4 When season but The allocation and payment of the remaining drones are both 0.

[0183] 3.5. Under the above allocation and payment mechanism, the revenue of smart airports in the first auction cycle is:

[0184] r1(N,w,X(w,b),p(w,b))=r(N,w,r * ) (34)

[0185] Formula (34) shows that under the above allocation and payment mechanism, the revenue of the smart airport in the first auction cycle is the number of drones to be charged N, the charging time vector of all drones w, and the maximum discounted revenue of the smart airport r * Function of

[0186] 3.6. Substitute formula (17) into formula (14) to obtain the maximum discounted benefit r of the smart airport: * .

[0187] As a possible implementation, in this embodiment, the maximum r that minimizes the following objective function is solved * :

[0188]

[0189] As a possible implementation method: 1) randomly select 10 points in the value range [0, 20] as initial values; 2) use the gradient descent method to solve the r that minimizes formula (35) for each initial value. * ; 3) Select the largest r among the above 10 * As the maximum discounted return. In this case, we can solve for r * =9.046.

[0190] 3.7. Substituting the above maximum discounted income into formula (33), we can further obtain the optimal allocation mechanism and payment mechanism. In this embodiment, it can be solved that the drone that is charged in the first cycle is the first drone, and its payment is 1.933. Figure 5 In the figure, the arrows represent the drones that are charged in the first cycle, and the numbers next to the circles represent the payment for each drone.

[0191] The above-mentioned optimal charging time and payment mechanism for drone airports based on revenue maximization auction provided by an embodiment of the present invention is aimed at the scenario where a smart airport is used to provide charging services for multiple drones with their own goals, and the drone’s valuation of charging revenue is private information while the charging time is public knowledge. The above-mentioned problem is modeled using revenue maximization auction to solve the charging time mechanism for maximizing the discounted revenue of the smart airport that satisfies the dominant strategy incentive compatibility constraints and computational efficiency constraints.

[0192] The above method provided by the embodiment of the present invention has the following beneficial effects: 1) For the scenario where an airport is used to provide charging services for multiple drones with their own goals, and the drone's valuation of the charging revenue is private information while the charging time is public knowledge, the airport's discounted revenue maximization problem is studied, which can provide guidance for the overall performance improvement of drones and smart airport systems under multi-agents; 2) A charging time allocation and payment mechanism for maximizing the discounted revenue of smart airports that meets the dominant strategy incentive compatibility constraints and computational efficiency constraints is proposed, which can solve the allocation and payment of each drone in polynomial time.

[0193] For the optimal charging allocation method for drone airports provided in the above embodiments, the present invention also provides an optimal charging allocation device for drone airports, see Figure 6 The schematic diagram of the structure of an optimal charging distribution device for a drone airport is shown, indicating that the device mainly includes the following parts:

[0194] The scenario construction module 601 is used to construct a mission scenario; wherein the mission scenario includes: a smart airport and multiple drones, and the smart airport provides charging services for the drones;

[0195] The model building module 602 is used to build a smart airport discounted benefit maximization model for the mission scenario; wherein the smart airport discounted benefit maximization model is constrained by satisfying the incentive compatibility of the dominant strategy and computational efficiency, takes maximizing the discounted benefit of the smart airport as the goal, and takes the allocation mechanism and the payment mechanism as decision variables;

[0196] The mechanism determination module 603 is used to determine the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the smart airport discounted benefit maximization model.

[0197] The above-mentioned drone airport optimal charging allocation device provided by the embodiment of the present invention constructs a scenario in which the airport is used to provide charging services to multiple drones with their own goals, the drone's valuation of charging benefits is private information and the charging time is public knowledge, and constructs a smart airport discounted benefit maximization model with constraints of satisfying dominant strategy incentive compatibility and computational efficiency, maximizing the discounted benefit of the smart airport as the goal, and allocation mechanism and payment mechanism as decision variables. In addition, based on the smart airport discounted benefit maximization model, a charging time allocation and payment mechanism for maximizing the discounted benefit of the smart airport that satisfies the dominant strategy incentive compatibility constraints and computational efficiency constraints can be obtained, thereby improving the problem in the prior art of lacking the use of shared airports located on the ground to provide charging services for multiple drones.

[0198] It should be noted that the implementation principle and technical effects of the device provided in the embodiment of the present invention are the same as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding contents in the aforementioned method embodiment. The specific numerical values ​​provided in the implementation of the present invention are only exemplary and are not limited here.

[0199] An embodiment of the present invention further provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above implementation methods.

[0200] Figure 7 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 70, a memory 71, a bus 72 and a communication interface 73, wherein the processor 70, the communication interface 73 and the memory 71 are connected via the bus 72; the processor 70 is used to execute an executable module stored in the memory 71, such as a computer program.

[0201] The memory 71 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 73 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0202] The bus 72 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0203] Among them, the memory 71 is used to store programs, and the processor 70 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 70 or implemented by the processor 70.

[0204] The processor 70 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 70. The above processor 70 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 71, and the processor 70 reads the information in the memory 71 and completes the steps of the above method in combination with its hardware.

[0205] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0206] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0207] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An optimal charging distribution method for drone airports, characterized in that: include: Constructing a mission scenario; wherein the mission scenario includes: a smart airport and multiple drones, and the smart airport provides charging services for the drones; A smart airport discounted revenue maximization model is constructed for the mission scenario; wherein the smart airport discounted revenue maximization model is constrained by satisfying the incentive compatibility and computational efficiency of the dominant strategy, takes maximizing the discounted revenue of the smart airport as the goal, and takes the allocation mechanism and the payment mechanism as decision variables; Determining the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the smart airport discounted revenue maximization model; Construct a mission scenario, including: the number of drones to be charged at any time n is a random variable, the cumulative distribution function of the number of drones to be charged is F, and the probability density function is f; before the auction at any time, the drone charging time w is a random variable, the cumulative distribution function of the drone charging time is G, and the probability density function is g; the drone's valuation of charging benefits v is a random variable with w as a parameter, and the cumulative distribution function of the benefit valuation is H w , the probability density function is w ; The charging cost per unit time of the smart airport is c; the auction is conducted in a sealed manner, and the price quoted by the i-th drone for charging is b i ; The revenue discount rate of the smart airport is α; The auction is conducted with an interval of Δt, and the auction occurs at the beginning of each cycle. The cycle number is 1, 2, ..., l, ...; At each auction, the allocation mechanism X of the smart airport is a function of the charging time and charging quotes of all drones; At each auction, the payment mechanism p of the smart airport is a function of the charging time and charging quotes of all drones; A smart airport discounted revenue maximization model is constructed for the mission scenario, including: determining the number of drones to be charged at the beginning of the first auction cycle, as well as the charging time vector, charging quotation vector, charging revenue cumulative distribution function vector, and charging revenue probability density function vector of the drones to be charged at the beginning of the first auction cycle; determining the allocation mechanism function vector and payment mechanism function vector of the smart airport at the beginning of the first auction cycle; determining the value constraints and charging constraints of the allocation mechanism function vector, as well as the value constraints of the payment mechanism function vector; determining the charging revenue valuation vector of the drones to be charged at the beginning of the first auction cycle; based on the charging revenue valuation vector of the drones to be charged, the charging quotation vector, and the allocation mechanism function vector of the smart airport, determining the truthfulness constraints and non-negative utility constraints in the incentive compatibility of the dominant strategy; determining the smart airport revenue function at the first auction cycle and the recursive form of the maximum discounted revenue of the smart airport; The revenue function of the smart airport in the first auction cycle is: Among them, r1(N,w,X(w,b),p(w,b)) represents the revenue of the smart airport in the first auction cycle, N represents the number of drones to be charged, w represents the charging time vector of the drones to be charged, X(w,b) represents the allocation mechanism function vector, and p(w,b) represents the payment mechanism function vector; The recursive form of the maximum discounted benefit of the smart airport is: Among them, r * represents the maximum discounted benefit of the smart airport, and α represents the benefit discount rate of the smart airport.

2. The method according to claim 1, characterized in that Determining the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the smart airport discounted revenue maximization model includes: Calculating the virtual valuation of the UAV to be charged based on the smart airport discounted revenue maximization model, and calculating the revised virtual valuation of the UAV to be charged; determining a charging time allocation and a payment mechanism for the smart airport based on the modified virtual valuation; Calculate the revenue of the smart airport in the first auction cycle under the charging time allocation and payment mechanism of the smart airport; Calculating the maximum discounted benefit of the smart airport based on the smart airport benefit and the recursive form of the maximum discounted benefit of the smart airport; Based on the maximum discounted benefit of the smart airport and the modified virtual valuation, an optimal charging time allocation mechanism and a payment mechanism of the smart airport are determined.

3. The method according to claim 2, characterized in that Calculating the virtual valuation of the unmanned aerial vehicle to be charged based on the intelligent airport discounted revenue maximization model, and calculating the revised virtual valuation of the unmanned aerial vehicle to be charged, including: The virtual estimated value of the drone to be charged is calculated according to the following formula: in, Indicates that the charging benefit is estimated as v i The virtual valuation of the i-th drone to be charged, and i,w i (v i ) respectively represent the charging time w i The cumulative distribution function and probability density function of the charging income of the i-th drone to be charged; The corrected virtual estimate of the drone to be charged is calculated according to the following formula: in, Indicates that the charging benefit is estimated as v i The modified virtual valuation of the i-th drone to be charged is given by the maximum discounted benefit r of the smart airport. * is the threshold benefit, w i represents the charging time of the i-th UAV to be charged, c represents the charging cost per unit time of the smart airport, and α represents the revenue discount rate of the smart airport.

4. The method according to claim 2, characterized in that: Determining a charging time allocation and a payment mechanism for the smart airport based on the modified virtual valuation includes: If the maximum value of the modified virtual valuation is less than or equal to zero, determining the charging time allocation and payment mechanism as not charging the drone to be charged; If the maximum value of the modified virtual valuation is greater than zero, the charging time allocation and payment mechanism is determined to charge the drone to be charged with the largest modified virtual valuation, and the payment of the charging drone is calculated according to the following formula: in, Indicates the payment for charging the drone, 5. An optimal charging distribution device for drone airports, characterized in that: include: A scenario construction module is used to construct a mission scenario; wherein the mission scenario includes: a smart airport and multiple drones, and the smart airport provides charging services for the drones; A model building module is used to build a smart airport discounted benefit maximization model for the mission scenario; wherein the smart airport discounted benefit maximization model is constrained by satisfying the incentive compatibility of dominant strategies and computational efficiency, takes maximizing the discounted benefit of the smart airport as the goal, and takes allocation mechanism and payment mechanism as decision variables; A mechanism determination module, used to determine the optimal charging time allocation mechanism and payment mechanism of the smart airport based on the smart airport discounted benefit maximization model; The scenario construction module is used for the number of drones to be charged at any time n is a random variable, the cumulative distribution function of the number of drones to be charged is F, and the probability density function is f; before the auction at any time, the drone charging time w is a random variable, the cumulative distribution function of the drone charging time is G, and the probability density function is g; the drone's revenue valuation v for charging is a random variable with w as a parameter, and the cumulative distribution function of the revenue valuation is H w , the probability density function is w; the charging cost per unit time of the smart airport is c; the auction is conducted in a sealed manner, and the price quoted by the i-th drone for charging is b i ; The revenue discount rate of the smart airport is α; The auction is conducted with an interval of Δt, and the auction occurs at the beginning of each cycle. The cycle number is 1, 2, ..., l, ...; At each auction, the allocation mechanism X of the smart airport is a function of the charging time and charging quotes of all drones; At each auction, the payment mechanism p of the smart airport is a function of the charging time and charging quotes of all drones; The model building module is used to: determine the number of drones to be charged at the beginning of the first auction cycle, as well as the charging time vector, charging quotation vector, charging income cumulative distribution function vector, and charging income probability density function vector of the drones to be charged at the beginning of the first auction cycle; determine the allocation mechanism function vector and payment mechanism function vector of the smart airport at the beginning of the first auction cycle; determine the value constraints and charging constraints of the allocation mechanism function vector, as well as the value constraints of the payment mechanism function vector; determine the charging income valuation vector of the drones to be charged at the beginning of the first auction cycle; based on the charging income valuation vector of the drones to be charged, the charging quotation vector, and the allocation mechanism function vector of the smart airport, determine the truthfulness constraints and non-negative utility constraints in the incentive compatibility of the dominant strategy; determine the smart airport income function at the first auction cycle and the recursive form of the maximum discounted income of the smart airport; The revenue function of the smart airport in the first auction cycle is: Among them, r1(N,w,X(w,b),p(w,b)) represents the revenue of the smart airport in the first auction cycle, N represents the number of drones to be charged, w represents the charging time vector of the drones to be charged, X(w,b) represents the allocation mechanism function vector, and p(w,b) represents the payment mechanism function vector; The recursive form of the maximum discounted benefit of the smart airport is: Among them, r * represents the maximum discounted benefit of the smart airport, and α represents the benefit discount rate of the smart airport.

6. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are executed.

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