Wireless rechargeable sensor network charging method and system with lowest unmanned aerial vehicle energy consumption

Abstract

The invention discloses a WRSN charging method and system based on the lowest energy consumption of an unmanned aerial vehicle, and the method comprises the following steps: S1, taking the minimization of the total energy consumption of the unmanned aerial vehicle as a target, constructing a flight-hovering-based charging problem, and decomposing the flight-hovering-based charging problem into a minimization flight energy consumption problem and a minimization charging energy consumption problem; s2, aiming at the problem of minimizing charging energy consumption, providing an adaptive charging strategy based on flight and hovering charging modes, and carrying out joint optimization on the charging mode, the flight speed and the charging coverage radius when the unmanned aerial vehicle executes a charging task; s3, for a minimum flight energy consumption problem, decomposing the problem into an optimal non-charging flight speed determination problem and a minimum flight distance problem through a function decomposition mode; the optimal non-charging flight speed is obtained through a tangent method, and the problem of minimum flight distance is solved through a genetic algorithm. According to the invention, the total energy consumption of the unmanned aerial vehicle during the charging task can be effectively reduced, and the time complexity is relatively low.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless rechargeable sensor network (WRSN), and in particular relates to a WRSN charging method and system for a drone with the lowest energy consumption based on joint optimization of a drone charging mode, a flight speed and a charging range. Background Art

[0002] In recent years, wireless sensor networks have been widely used in many fields such as the Internet of Things, intelligent transportation systems, and field data collection. Due to limited hardware resources, these networks often face energy constraints. In addition, sensor nodes are often located in hard-to-reach areas, which makes it difficult to replace batteries. In order to extend the life of the network, technicians have proposed many energy-saving strategies. However, these methods cannot fundamentally solve the problem, and sensor nodes will eventually run out of energy. Although the introduction of environmental energy harvesting technologies such as solar energy and wind energy can solve the problem of energy depletion of sensor nodes, these methods rely on the surrounding natural environment, and the performance of energy harvesting is unpredictable. Therefore, how to effectively and reliably replenish energy for wireless sensor networks remains a very challenging problem.

[0003] In recent years, the use of drones to recharge sensor nodes has been applied to WRSN due to its unique advantages. In view of the node charging problem, drone charging schemes are usually divided into two categories: one-to-one charging mode and one-to-many charging mode. In the former, the drone only charges one sensor node at a time; in the latter, the drone charges multiple sensor nodes at the same time. During the charging process, drones have two charging modes: hovering charging mode, that is, the drone hovers at a fixed position to charge the node, generally hovering directly above the node; flight charging mode, that is, the drone charges the node during flight. Current technology usually assumes that mobile chargers have unlimited energy. However, this is not realistic in practical applications, especially when charging battery-powered drones, which also face energy limitations. In addition, the energy consumption of drones during flight cannot be ignored. Therefore, it is particularly important to balance the charging efficiency between drones and sensor nodes and the flight energy consumption of drones. Summary of the invention

[0004] In order to solve the problem of minimizing the energy consumption of UAVs charging sensor nodes using wireless power transmission (WPT) technology in non-dense WRSNs, the present invention proposes a WRSN charging method and system with the lowest energy consumption for UAVs by jointly optimizing the UAV charging mode, flight speed and charging coverage radius, by modeling the charging energy consumption and flight energy consumption of UAVs under different charging modes with the goal of minimizing the overall energy consumption of UAVs.

[0005] The non-dense WRSN application scenario targeted by the present invention is deployed in a two-dimensional area, including a charging base station (CBS), a rotorcraft drone and N sensor nodes. The drone starts from the CBS, charges the sensor nodes in turn at a height H according to the charging strategy, and finally returns to the CBS with the goal of minimizing the total energy consumption of the drone. In each charging cycle, it is assumed that the CBS knows the coordinates and remaining energy of each sensor node. If the charging strategy of the node is hybrid charging, the drone decelerates to a certain speed at the charging coverage radius and flies at this speed for charging; when it arrives directly above the node, the drone starts hovering charging, and after leaving the node, it continues to fly and charge at the same speed until the charging is completed and flies to the next node. If the charging strategy is flying charging, the drone does not need to hover directly above the node for charging, and the rest is the same as hybrid charging. Non-dense WRSN refers to a situation in which the sensor nodes are sparsely distributed and the distance between the sensor nodes is greater than the charging coverage radius of the drone, which makes the drone only charge each sensor node one-to-one.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] The wireless rechargeable sensor network charging method for drones with the lowest energy consumption comprises the following steps:

[0008] S1, for non-dense WRSN, with the goal of minimizing the total energy consumption of UAVs, constructs a charging problem based on flight-hovering and decomposes it into minimizing charging energy consumption and minimizing flight energy consumption;

[0009] S2, in order to minimize the charging energy consumption, an adaptive charging strategy (ACS) is proposed based on the flight and hovering charging modes to jointly optimize the charging mode, flight speed and charging coverage radius when the UAV performs the charging task;

[0010] S3, for the problem of minimizing flight energy consumption, based on the function decomposition method, it is decomposed into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance. The optimal non-charging flight speed is obtained by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm.

[0011] Preferably, in step S1:

[0012] S11. UAV power modeling.

[0013] According to existing literature, the propulsion power of the UAV is modeled as:

[0014]

[0015] Where P0 and P1 are two constants, representing the blade profile power and induced power in the hovering state respectively; U tiprepresents the tip speed of the rotor; v0 represents the average rotor induced speed during hovering, d0 and s are the fuselage drag ratio and rotor density, ρ and A represent the air density and rotor disc area, and v is the moving speed of the drone. In actual modeling, all the above parameters except v are known parameters.

[0016] When the drone is wirelessly charging the sensor node, the RF power P received by the sensor r (d) Modeled as:

[0017] P r (d) = P t +G UAV +G SN -20log{f}-20log{d}+147.55dB

[0018] Where P t is the radio frequency power sent by the drone, G UAV represents the transmitting antenna gain of the UAV, G SN represents the sensor receiving antenna gain, and the units of these three quantities are all dB; d is the straight-line distance between the drone and the sensor node, in meters, and f is the RF frequency of the drone, in Hz.

[0019] When hovering, the speed of the drone is 0 and the hovering power is P. h It can be expressed as:

[0020] P h =P(0)=P0+P1

[0021] use Indicates the charge amount during hovering. At this time, the straight-line distance between the drone and the sensor is d = H, and the hovering charging time can be modeled as:

[0022]

[0023] Where P r is the RF power received by the sensor, η is the energy conversion efficiency of the sensor, and h represents hovering.

[0024] When charging in flight, the drone needs to be directly above the sensor node at a speed v i Flight, where the distance to the sensor node is Where H represents the vertical distance between the UAV and the sensor node, v i t represents the horizontal distance between the UAV and the sensor node, and t is the flight time.

[0025] Flight charge It can be modeled as:

[0026]

[0027] In the formula, r i is the horizontal coverage radius of the drone charging the i-th sensor node, P r (d) is the received RF power of the sensor node.

[0028] The relationship between the flight charge and the hover charge can be expressed as:

[0029]

[0030] In the formula, e max represents the upper limit of the charging capacity of the sensor node, e i represents the remaining power of the i-th sensor node. Therefore, e max -e i Represents sensor node s i The amount of charge required.

[0031] S12. Energy consumption modeling of UAVs performing charging tasks.

[0032] For node s i When hovering and charging, the drone's hovering charging energy consumption Modeled as:

[0033]

[0034] Where P h is the hovering power of the UAV, P t The wireless charging transmission power of the drone, the first item is the hovering energy consumption, and the second item is the charging energy consumption.

[0035] For node s i When charging in flight, the drone consumes Modeled as:

[0036]

[0037] In the formula, P(v i ) indicates that the drone is moving at a speed v i The flight power of the flight, the first item is the flight energy consumption, the second item is the charging energy consumption.

[0038] Let D represent the total flight distance of the UAV during the charging mission, and v represent the flight speed when not charging the sensor nodes. The flight energy consumption of the UAV when not charging the sensor nodes can be modeled as:

[0039]

[0040] Where P(v) is the flight power of the UAV when it flies at speed v. For flight time.

[0041] The total energy consumption of the drone can be expressed as:

[0042]

[0043] Where N represents the total number of sensor nodes. Represents the flight charging energy consumption and Represents the hover charging energy consumption.

[0044] S13. In order to minimize the total energy consumption of the UAV, the optimization problem P0 is expressed as:

[0045] P0:

[0046] st(1)

[0047] (2)

[0048] (3)

[0049] (4)

[0050] (5)

[0051] (6)

[0052] (7)

[0053] In the optimization problem P0, the decision variables are X, V, R and v. Among them, X determines the flight trajectory of the drone, V and R are the flight speed and charging coverage radius of the drone when charging each node, and R = {r1,…r N}, V={v1,…v N}, v is the flight speed with the optimal energy consumption when the drone is not charging (denoted as v*).

[0054] Furthermore, in the above constraints:

[0055] Constraints 1-4 restrict the flight trajectory of the drone, ensuring that its starting point and end point are both s0 (i.e., CBS), and each sensor node appears only once in the trajectory. ij}, x ij =1 means the drone is from node s i Fly to s j , otherwise x ij =0;

[0056] Constraint 5 indicates that there is an upper limit on the node charging coverage radius where d maxis the maximum charging distance between the drone and the sensor node;

[0057] Constraint 6 means that the flight speed of the drone also has an upper limit v max ;

[0058] Constraint 7 represents charging the node to meet its demand.

[0059] In addition, the present invention also uses T = {t i ,…t N} represents the drone in each s i The hovering charging time directly above the i If only charging in flight is used, then t i is 0.

[0060] S14. Decompose problem P0 into two sub-problems P1 and P2, P1 is the problem of minimizing charging energy consumption and P2 is the problem of minimizing flight energy consumption.

[0061] Define the problem of minimizing charging energy consumption P1:

[0062] P1:

[0063] This sub-problem is used to solve the UAV's response to each sensor node s i The charging flight speed and charging coverage radius are obtained. After obtaining the charging flight speed and charging coverage radius, the charging strategy and hovering charging time of the drone are obtained.

[0064] Define the problem of minimizing flight energy consumption P2:

[0065] P2:

[0066] Where D is the total flight distance of the UAV. This subproblem is used to solve the flight trajectory of the UAV and the optimal flight speed when not charging.

[0067] Preferably, step S2 is specifically as follows:

[0068] S21. According to the UAV power-speed curve, determine the flight speed with the lowest energy consumption of the UAV. Specifically, on the Pv plane (P is the flight power, v is the flight speed), the speed corresponding to the tangent line of the power curve P(v) from the origin is the flight speed v* with the lowest energy consumption of the UAV. This speed is used as the upper limit of the flight speed of the UAV for flight charging.

[0069] S22. Calculate the maximum amount of electricity that the drone can replenish for the node by charging during flight according to the following formula:

[0070]

[0071] S23. Determine the charging strategy, flight speed, and charging coverage radius of the drone according to the following strategy:

[0072] Strategy 1: When the drone sends the node s i The charging strategy is to charge during flight, and the flight speed is v i =v * , charging coverage radius r i =r max , hover charging time t i =0;

[0073] Strategy 2: When the drone sends the node s i The charging strategy is to charge during flight, and the flight speed is v i =v * , hover charging time t i =0, charging coverage radius r i satisfy:

[0074]

[0075] Strategy 3: hour,

[0076] First, fix r i =r max , t i =0, by adjusting v i , so that it satisfies

[0077]

[0078] In getting <r i ,v i ,t i >After that, calculate the energy consumption E1 of the drone at this time.

[0079] Secondly, use the flight charging mode to charge That is r i =r max , v i =v * The insufficient part is supplemented by hovering charging, that is, a hybrid charging strategy is adopted to charge s i In this strategy, the amount of charge in the flight charging part is Then the charge amount of the hover charging part is Hover charging time t i for:

[0080]

[0081] In getting <ri ,v i ,t i >After that, calculate the energy consumption E2 of the drone at this time.

[0082] Finally, compare the sizes of E1 and E2 and select the smaller one as node s i charging strategy.

[0083] Preferably, step S3 is specifically as follows:

[0084] S31. Rewrite the P2 problem as It can be seen that E f is the product of two terms, the first term is a function of v, the second term is a function of distance D, and there is no coupling between the two terms. Therefore, E can be achieved by minimizing these two terms separately. f Minimal goal.

[0085] S32. For item 1, according to the power-speed curve, by drawing a tangent line from the origin to the power curve P(v), the speed corresponding to the tangent point is the optimal v*.

[0086] S33. For item 2, r i As already obtained in S2, the key is to minimize the total flight distance D, which is defined as:

[0087]

[0088] In the formula, Representative nodes i and j The straight-line distance between .

[0089] S34. The flight trajectory X of the UAV can be obtained by solving S33 using the genetic algorithm.

[0090] The present invention also discloses a wireless rechargeable sensor network charging system for drones with the lowest energy consumption, which is used to execute the above method and includes the following modules:

[0091] Charging problem construction and decomposition module: With the goal of minimizing the total energy consumption of the drone, a charging problem based on flight-hovering is constructed and decomposed into the problem of minimizing charging energy consumption and the problem of minimizing flight energy consumption;

[0092] Minimizing charging energy consumption problem solving module: To minimize the charging energy consumption problem, three charging strategies are proposed based on the flight and hovering charging modes, and the charging mode, flight speed, charging coverage radius, and hovering charging time of the UAV are jointly optimized when performing charging tasks;

[0093] Minimizing flight energy consumption problem solving module: In order to minimize the flight energy consumption problem, based on the function decomposition method, it is decomposed into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance. The optimal non-charging flight speed determination problem is solved by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm.

[0094] Compared with the prior art, the technical effects of the present invention are embodied in the following aspects:

[0095] (1) The existing one-to-one charging mode completely relies on the drone to hover on the node for charging, resulting in high energy consumption and long charging time for the drone. Although the existing one-to-many charging mode can improve the charging efficiency of dense WRSN networks, it is not suitable for sparse WRSN networks. In response to the problems of these two charging modes, the present invention proposes a drone adaptive hybrid mode charging strategy (ACS) based on a one-to-one charging model, which combines flight charging with hovering charging, and jointly optimizes the charging mode, flight speed and charging coverage radius of the drone when performing charging tasks, thereby reducing the total energy consumption of the drone when performing charging tasks and having lower time complexity.

[0096] (2) The present invention aims to minimize the total energy consumption of the UAV, decomposes the problem into minimizing charging energy consumption and minimizing flight energy consumption, gives an approximate optimal solution, and theoretically proves that when the UAV is not performing a charging task, there is an optimal flight speed that minimizes the flight energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 A network model diagram of a wireless rechargeable sensor network charging method for a drone with the lowest energy consumption according to a preferred embodiment of the present invention;

[0098] Figure 2 A schematic diagram of a node charging strategy provided by a preferred embodiment of the present invention;

[0099] Figure 3 A UAV flight trajectory diagram provided by an embodiment of the present invention;

[0100] Figure 4 A result diagram showing the effect of different numbers of nodes on the energy consumption of a drone provided by an embodiment of the present invention;

[0101] Figure 5 A result diagram of the time complexity of a UAV executing a task provided by an embodiment of the present invention;

[0102] Figure 6 This is a block diagram of a wireless rechargeable sensor network charging method for a drone with the lowest energy consumption according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0103] The following describes the embodiments of the present invention through specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0104] like Figure 1 As shown, the preferred embodiment of the present invention relates to a network model based on a drone replenishing power to a sensor network. In the network, the set of nodes is S = {s1, ..., s N}, where s i represents the i-th sensor node, s0 represents CBS. i The current remaining energy is e i , the maximum charging capacity of the node is e max When the drone is on node s i When charging, the charging coverage radius is recorded as r i , that is, the charging coverage is 2r i , the flight speed is recorded as v i When the drone is not charging, its flight speed remains constant at v. In addition, since this embodiment considers sparse WRSN, it is assumed that the distance between any two sensor nodes is greater than 2r max , where r max The maximum charging coverage radius for the drone.

[0105] The specific node charging model is as follows: Figure 2 As shown. If s i The charging strategy is hybrid charging, then the UAV is within the charging coverage radius r i decelerates from speed v to speed v i and fly at this speed to charge; arrive at node s i After it is directly above, the drone hovers and charges. i seconds; then leave the node and continue at the same speed v i Flying charging, flying away from the coverage radius r i After charging is completed; finally fly to the next node s at speed v j If s i If the charging strategy is flight charging, the drone does not need to hover directly above the node for charging (i.e., the hovering charging time t i = 0 seconds), and the rest are the same as hybrid charging.

[0106] Specifically, the wireless rechargeable sensor network charging method for drones with the lowest energy consumption in this embodiment has the following detailed steps:

[0107] S1, for non-dense WRSN, with the goal of minimizing the total energy consumption of UAVs, constructs the charging problem P0 based on flight-hovering, and decomposes it into the problem of minimizing charging energy consumption P1 and the problem of minimizing flight energy consumption P2, and gives an approximate optimal solution;

[0108] S11. UAV power modeling:

[0109] The propulsion power of the UAV is modeled as:

[0110]

[0111] Where P0 and P1 are two constants, representing the blade profile power and induced power in the hovering state respectively; U tip represents the tip speed of the rotor; v0 represents the average rotor induced speed during hovering, d0 and s are the fuselage drag ratio and rotor density, respectively, ρ and A represent the air density and rotor disc area, and v is the moving speed of the UAV; in actual modeling, all the above parameters except v are known parameters.

[0112] When the drone is wirelessly charging the sensor node, the RF power P received by the sensor r (d) Modeled as:

[0113] P r (d) = P t +G UAV +G SN -20log{f}-20log{d}+147.55dB

[0114] Where P t is the radio frequency power sent by the drone, G UAV represents the transmitting antenna gain of the UAV, G SN represents the sensor receiving antenna gain, and the units of these three quantities are all dB; d is the straight-line distance between the drone and the sensor node, in meters, and f is the RF frequency of the drone, in Hz;

[0115] When hovering, the speed of the drone is 0 and the hovering power is P. h It can be expressed as:

[0116] P h =P(0)=P0+P1

[0117] use Indicates the charge amount during hovering. At this time, the straight-line distance between the drone and the sensor is d = H, and the hovering charging time can be modeled as:

[0118]

[0119] Where P r is the RF power received by the sensor, η is the energy conversion efficiency of the sensor, and h represents hovering.

[0120] When charging in flight, the drone needs to be directly above the sensor node at a speed v i Flight, where the distance to the sensor node is Where H represents the vertical distance between the UAV and the sensor node, v i t represents the horizontal distance between the UAV and the sensor node, and t is the flight time.

[0121] Flight charge It can be modeled as:

[0122]

[0123] In the formula, r i is the horizontal coverage radius of the UAV charging the i-th sensor node, P r (d) is the received RF power of the sensor node.

[0124] The relationship between the flight charge and the hover charge can be expressed as:

[0125]

[0126] In the formula, e max represents the upper limit of the charging capacity of the sensor node, e i represents the remaining power of the i-th sensor node. Therefore, e max -e i Represents sensor node s i The amount of charge required.

[0127] S12. Energy consumption modeling of UAVs performing charging tasks.

[0128] For node s i When hovering and charging, the drone's hovering charging energy consumption Modeled as:

[0129]

[0130] Where P h is the hovering power of the UAV, P t The wireless charging transmission power of the drone, the first item is the hovering energy consumption, and the second item is the charging energy consumption.

[0131] For node s i When charging in flight, the drone consumes Modeled as:

[0132]

[0133] In the formula, P(v i ) indicates that the drone is moving at a speed v i The flight power of the flight, the first item is the flight energy consumption, the second item is the charging energy consumption.

[0134] Let D represent the total flight distance of the UAV during the charging mission, and v represent the flight speed when not charging the sensor nodes. The flight energy consumption of the UAV when not charging the sensor nodes can be modeled as:

[0135]

[0136] Where P(v) is the flight power of the UAV when it flies at speed v. For flight time.

[0137] The total energy consumption of the drone can be expressed as:

[0138]

[0139] Where N represents the total number of sensor nodes. Represents the flight charging energy consumption and Represents the hover charging energy consumption.

[0140] S13. In order to minimize the total energy consumption of the UAV, the optimization problem P0 is expressed as:

[0141] P0:

[0142] st(1)

[0143] (2)

[0144] (3)

[0145] (4)

[0146] (5)

[0147] (6)

[0148] (7)

[0149] In problem P0, the decision variables are X, V, R and v; X represents the flight trajectory of the drone, V and R represent the flight speed and charging coverage radius of the drone when charging each node, and v is the optimal non-charging flight speed, denoted as v*; R = {r1,…r N}, V={v1,…v N}, T = {t i ,…t N}; if s i If only flight charging is used, the hovering charging time t i =0; X = {x ij}, where x ij =1 means the drone is from node s i Fly to s j ; Otherwise x ij =0; r max The maximum charging coverage radius of the drone is Set, d max is the maximum charging distance between the drone and the sensor node; v max Indicates the maximum flight speed of the drone.

[0150] S14. Decompose problem P0 into two sub-problems P1 and P2, P1 is the problem of minimizing charging energy consumption and P2 is the problem of minimizing flight energy consumption.

[0151] Define the problem of minimizing charging energy consumption P1:

[0152] P1:

[0153] This sub-problem is used to solve the UAV's response to each sensor node s i The charging flight speed and charging coverage radius are obtained. After obtaining the charging flight speed and charging coverage radius, the charging strategy and hovering charging time of the drone are obtained.

[0154] Define the problem of minimizing flight energy consumption P2:

[0155] P2:

[0156] Where D is the total flight distance of the UAV. This subproblem is used to solve the flight trajectory of the UAV and the optimal flight speed when not charging.

[0157] S2, for the problem P1 of minimizing charging energy consumption, proposes an adaptive charging strategy (ACS) based on the flight and hovering charging modes, and jointly optimizes the charging mode, flight speed and charging coverage radius when the UAV performs charging tasks. Specifically:

[0158] S21. According to the UAV power-speed curve, determine the flight speed with the lowest energy consumption of the UAV. Specifically, on the Pv plane (P is the flight power, v is the flight speed), the speed corresponding to the tangent line of the power curve P(v) from the origin is the flight speed v* with the lowest energy consumption of the UAV. This speed is used as the upper limit of the flight speed of the UAV for flight charging.

[0159] S22. Calculate the maximum amount of electricity that the drone can replenish for the node by charging during flight according to the following formula:

[0160]

[0161] S23. Determine the charging strategy, flight speed, and charging coverage radius of the drone according to the following strategy:

[0162] Strategy 1: When the drone sends the node s i The charging strategy is to charge during flight, and the flight speed is v i =v * , charging coverage radius r i =r max , hover charging time t i =0;

[0163] Strategy 2: When the drone sends the node s i The charging strategy is to charge during flight, and the flight speed is v i =v * , hover charging time t i =0, charging coverage radius r i satisfy:

[0164]

[0165] Strategy 3: hour,

[0166] First, fix r i =r max , t i = 0, by setting v i Adjust to meet

[0167]

[0168] In getting <r i ,v i ,t i >After that, calculate the energy consumption E1 of the drone at this time.

[0169] Secondly, use the flight charging mode to charge That is r i =r max , v i =v * The insufficient part is supplemented by hovering charging, that is, a hybrid charging strategy is adopted to charge s i In this strategy, the amount of charge in the flight charging part is Then the charge amount of the hover charging part is Hover charging time t i for:

[0170]

[0171] In getting <r i ,v i ,t i >After that, calculate the energy consumption E2 of the drone at this time.

[0172] Finally, compare the sizes of E1 and E2 and select the smaller one as node s i charging strategy.

[0173] S3, for the problem of minimizing flight energy consumption, based on the function decomposition method, it is decomposed into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance. The optimal non-charging flight speed is obtained by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm. The details are as follows:

[0174] S31. Rewrite the P2 problem as It can be seen that E f is the product of two terms, the first term is a function of v, the second term is a function of distance D, and there is no coupling between the two terms. Therefore, E can be achieved by minimizing these two terms separately. f Minimal goal.

[0175] S32. For item 1, according to the power-speed curve, by drawing a tangent line from the origin to the power curve P(v), the speed corresponding to the tangent point is the optimal v*.

[0176] S33. For item 2, r i As already obtained in S2, the key is to minimize the distance D, which is defined as:

[0177]

[0178] In the formula, Representative nodes i and j The straight-line distance between .

[0179] S34. Use genetic algorithm to solve S33 to get the UAV flight trajectory X. The specific steps are as follows:

[0180] (1) Initialize the population

[0181] The UAV charging path planning problem of the present invention is a combinatorial optimization problem. This combinatorial optimization problem is based on order. Although binary coding is simple to operate and easy to genetically operate, its application to this problem will produce invalid solutions, and the efficiency of global search is relatively low. Therefore, the present invention uses natural number coding to encode nodes to deal with planning problems. Nodes represent chromosomes, and the population size (M) represents the number of UAV flight paths. The nodes in WRSN are numbered and expressed as {1, 2, 3...n}, and then randomly shuffled to obtain M groups of sequences, that is, M initialized populations.

[0182] (2) Calculate individual fitness

[0183] The present invention calculates each adaptability by the length of each path (individual) in the population, and the adaptability is expressed by the function f(n i ), usually taking the inverse of the distance, which can be expressed as:

[0184]

[0185] (3)Select

[0186] The present invention adopts the roulette selection method to perform the selection operation of the genetic algorithm, selects some individuals from the parent generation to be inherited to the next generation, improves the efficiency of calculation and the global convergence, and each individual has a different fitness and a different proportion of the overall fitness of the entire roulette.

[0187] (4) Cross

[0188] Randomly select two individuals from the population. There will be an intersection between the two individuals. In this part, the two individuals will exchange part of their chromosomes with each other, and then traverse the original points in order. If there is no duplication with the intersection point, it can be copied. Otherwise, continue to traverse until a point without conflict is encountered, and then copy the operation, and finally two new individuals can be obtained.

[0189] (5) Variation

[0190] Crossover is a step that determines the global search capability of the genetic algorithm, while mutation determines the local search capability of the algorithm. Using a smaller mutation probability, several individuals are randomly selected from the previous generation population, their partial composition structure is changed, and new sub-individuals are generated for the next generation.

[0191] In addition, during population processing, node 0 is inserted into the first and last positions of the above individuals, and the position of node 0 will not change with the crossover and mutation operations to indicate that the starting point and end point of each path are CBS nodes.

[0192] In order to verify the effectiveness of the method proposed in this invention, Figure 1 The system shown is a model, with 10 nodes randomly deployed in a 100m×100m area. The node positions are as follows: Figure 3 As shown in , the node needs to replenish the power to 3J. Based on the genetic algorithm, the optimal trajectory of the UAV flight is obtained, as shown in Figure 3 As shown in Table 1, under the condition that the parameters are set, the optimal flight speed of the UAV is v obtained by the tangent method. * =15.935m / s.

[0193] Table 1

[0194]

[0195]

[0196] According to the proposed adaptive hybrid mode charging algorithm (ACS), the charging strategy of each node is obtained, as shown in Table 2 below (a table of charging strategies for each node provided in an embodiment of the present invention). It can be seen that the difference in power of the nodes leads to different charging strategies.

[0197] Table 2

[0198]

[0199] For example, the power of node 1 is 0.5J and needs to be charged by 2.5J. This node adopts a hybrid charging method. Within the charging coverage radius of 5m, the drone flies to the node at a speed of 15.935m / s to start charging. When the drone reaches directly above the node, it hovers for 1.941 seconds to charge the node. Then, the drone continues to fly at a speed of 15.935m / s within the charging coverage radius and charges the node at the same time. After leaving the charging coverage radius, the drone hovers at v * =15.935m / s to the next node. In the case of zero hover charging time, the drone is only charged during flight. This usually happens when the node needs a small amount of charging, which can only be satisfied by flying charging, such as node 4.

[0200] In order to verify the proposed ACS algorithm, it is compared with other benchmark algorithms. FSC is a full search charging method, which is essentially a method based on exhaustive search, and it represents the optimal performance upper limit. HC is hovering charging, that is, the drone is hovering directly above the node to charge. MRA is that the drone hovers at the charging coverage radius and selects the point closest to the base station as the docking point to charge the node. The nodes are randomly deployed in a 250m×250m area. For each scenario, the average total energy consumption of the drone is calculated based on 50 experimental runs. In addition, all schemes use genetic algorithms for trajectory planning with the same parameter settings and optimal flight speed v* .like Figure 4 As shown, when the step size is 10, the total energy consumption of the UAV changes from 10 to 50 nodes. It can be seen that under different numbers of nodes, the charging strategy (ACS) proposed in the present invention achieves the closest result to FSC, which is significantly lower than HC and MRA. As the number of nodes increases, the optimization effect of ACS becomes more obvious. This is because ACS adopts an adaptive charging mode. When the energy required by the node is low, the flight charging mode is adopted to avoid the energy consumption of hovering above the node; when the required energy is large, a hybrid charging method is adopted to reduce the hovering time of the UAV. Since the hovering energy consumption is greater than the flight energy consumption, this strategy reduces the hovering energy consumption, thereby reducing the total energy consumption of the UAV. Although FSC has the lowest energy consumption, its exhaustive search method has a high time complexity (specific experimental results are shown in Figure 5 shown).

[0201] Finally, to prove that ACS has lower time complexity, the running time of ACS and FSC (excluding GA algorithm time) is shown as Figure 5 As shown in Figure 2, as the number of nodes increases, the running time of FSC increases significantly, while the running time of ACS remains basically unchanged. Figure 4 and Figure 5 , it can be observed that ACS can achieve almost the same total UAV energy consumption as FSC, but significantly reduces the time complexity.

[0202] like Figure 6 As shown, this embodiment discloses a wireless rechargeable sensor network charging system for drones with the lowest energy consumption, which is used to execute the above method embodiment and includes the following modules:

[0203] Charging problem construction and decomposition module: With the goal of minimizing the total energy consumption of the drone, a charging problem based on flight-hovering is constructed and decomposed into the problems of minimizing flight energy consumption and minimizing charging energy consumption;

[0204] Joint Optimization Module: To minimize the charging energy consumption, an adaptive charging strategy is proposed based on the flight and hovering charging modes, and the charging mode, flight speed and charging coverage radius of the UAV are jointly optimized when performing charging tasks;

[0205] Minimizing flight energy consumption problem solving module: For the problem of minimizing flight energy consumption, the function decomposition method is used to decompose it into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance. The optimal non-charging flight speed determination problem is solved by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm.

[0206] For other contents of this embodiment, please refer to the above method embodiment.

[0207] The present invention belongs to the field of wireless rechargeable sensor networks, and relates to a WRSN charging technology for drones with the lowest energy consumption based on the joint optimization of drone charging mode, flight speed and charging coverage radius. The implementation process of the method of the present invention includes: proposing a WRSN charging problem with the goal of minimizing the total energy consumption of drones; decomposing the charging problem into two sub-problems of minimizing flight energy consumption and minimizing charging energy consumption. In view of the problem of minimizing charging energy consumption, an adaptive charging strategy (ACS) is proposed to jointly optimize the charging mode, flight speed and charging distance of the drone. In view of the problem of minimizing flight energy consumption, it is decomposed into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance by function decomposition. The problem of determining the optimal non-charging flight speed is solved by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm. The present invention can effectively reduce the total energy consumption of drones when performing charging tasks, and has low time complexity.

[0208] The above are only preferred embodiments of the present invention and the technical principles used. It is understood by those skilled in the art that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A wireless rechargeable sensor network charging method for drones with the lowest energy consumption, characterized by: The steps include: S1, for non-dense wireless rechargeable sensor networks, with the goal of minimizing the total energy consumption of drones, constructs a charging problem based on flight-hovering and decomposes it into minimizing flight energy consumption and minimizing charging energy consumption; S2, in order to minimize the charging energy consumption, proposes an adaptive charging strategy based on the flight and hovering charging modes, and jointly optimizes the charging mode, flight speed and charging coverage radius when the UAV performs the charging task; S3, for the problem of minimizing flight energy consumption, it is decomposed into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance through function decomposition; the optimal non-charging flight speed is obtained by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm.

2. The wireless rechargeable sensor network charging method for drones with the lowest energy consumption as claimed in claim 1, characterized in that the steps In S1: The propulsion power of the UAV is modeled as: Where P0 and P1 represent the blade profile power and induced power in the hovering state respectively; U tip represents the tip speed of the rotor; v0 represents the average rotor induced speed during hovering, d0 and s are the fuselage drag ratio and rotor density, respectively, ρ and A represent the air density and rotor disc area, and v is the moving speed of the UAV; When the drone is wirelessly charging the sensor node, the RF power P received by the sensor r (d) Modeled as: P r (d)=P t +G UAV +G SN -20log{f}-20log{d}+147.55dB Where P t is the radio frequency power sent by the drone, G UAV represents the transmitting antenna gain of the UAV, G SN represents the sensor receiving antenna gain; d is the straight-line distance between the UAV and the sensor node, and f is the radio frequency frequency of the UAV; When hovering, the speed of the drone is 0 and the hovering power is P. h It is expressed as: P h =P(0)=P0+P1 use represents the charge amount during hovering; at this time, the straight-line distance between the drone and the sensor is d = H, and the hovering charging time is modeled as: Where P r is the RF power received by the sensor, η is the energy conversion efficiency of the sensor, and h represents hovering; When charging in flight, the drone needs to be directly above the sensor node at a speed v i Flight, where the distance to the sensor node is Where H represents the vertical distance between the UAV and the sensor node, v i t represents the horizontal distance between the UAV and the sensor node, and t is the flight time; Flight charge Modeled as: In the formula, r i is the horizontal coverage radius of the UAV charging the i-th sensor node, P r (d) is the received RF power of the sensor node; The relationship between the flight charge and the hover charge is expressed as: In the formula, e max represents the upper limit of the charging capacity of the sensor node, e i represents the remaining power of the i-th sensor node; therefore, e max -e i Represents sensor node s i The amount of charge required; For node s i When hovering and charging, the drone's hovering charging energy consumption Modeled as: Where P h is the hovering power of the UAV, P t The wireless charging transmission power of the drone, the first item is the hovering energy consumption, and the second item is the charging energy consumption; For node s i When charging in flight, the drone consumes Modeled as: In the formula, P(v i ) indicates that the drone is moving at a speed v i The flight power of the flight, the first item is the flight energy consumption, the second item is the charging energy consumption; Let D represent the total flight distance of the UAV during the charging mission, and v represent the flight speed when the sensor nodes are not charged; the flight energy consumption of the UAV when the sensor nodes are not charged is modeled as: Where P(v) is the flight power of the UAV when it flies at speed v. is the flight time; The total energy consumption of the drone is expressed as: Where N represents the total number of sensor nodes. Represents the flight charging energy consumption and Represents hover charging energy consumption; In order to minimize the total energy consumption of the UAV, the optimization problem P0 is expressed as: In the optimization problem P0, the decision variables are X, V, R, and v; X determines the flight trajectory of the drone, V and R are the flight speed and charging coverage radius of the drone when charging each node, and R = {r1,…r N }, V={v1,…v N }, v is the optimal non-charging UAV flight speed, denoted as v*.

3. The wireless rechargeable sensor network charging method with the lowest energy consumption for unmanned aerial vehicles as claimed in claim 2 is characterized in that: In the above constraints: Constraints 1-4 restrict the flight trajectory of the drone, ensuring that its starting point and end point are both s0, and each sensor node appears only once in the trajectory; X = {xij}, x ij =1 means the drone is from node s i Fly to s j , otherwise x ij =0; Constraint 5 indicates that there is an upper limit on the node charging coverage radius Among them, d max is the maximum charging distance between the drone and the sensor node; Constraint 6 means that the flight speed of the drone also has an upper limit v max ; Constraint 7 represents charging nodes to meet their needs; Use T = {t i ,…t N } represents each s i The hovering charging time, if s i If only charging in flight is used, then t i is 0.

4. The wireless rechargeable sensor network charging method with the lowest energy consumption for unmanned aerial vehicles as claimed in claim 3 is characterized in that: In step S1, problem P0 is decomposed into two sub-problems P1 and P2, where P1 is the problem of minimizing charging energy consumption and P2 is the problem of minimizing flight energy consumption; Define the problem of minimizing charging energy consumption P1: This problem is used to solve the problem of the UAV for each sensor node s i The charging flight speed and charging coverage radius; Define the problem of minimizing flight energy consumption P2: Where D is the flight distance of the drone when not charging. This problem is used to solve the flight trajectory of the drone and the optimal flight speed when not charging.

5. The wireless rechargeable sensor network charging method for drones with the lowest energy consumption as claimed in claim 4, characterized in that: Step S2 is specifically as follows: According to the UAV power-speed curve, determine the flight speed with the lowest energy consumption of the UAV; on the Pv plane, P is the flight power, v is the flight speed, and the speed corresponding to the tangent line of the power curve P(v) from the origin is the flight speed v* with the lowest energy consumption of the UAV. This speed is used as the upper limit of the flight speed for UAV flight charging; According to the following formula, calculate the maximum amount of power that the drone can replenish for the node during flight charging: Determine the drone's charging strategy, flight speed, and charging coverage radius based on the following strategies: Strategy 1: When the drone sends the node s i The charging strategy is to charge during flight, and the flight speed is v i =v * , charging coverage radius r i =r max , hover charging time t i =0; Strategy 2: When the drone sends the node s i The charging strategy is to charge during flight, and the flight speed is v i =v * , hover charging time t i =0, charging coverage radius r i satisfy: Strategy 3: hour, First, fix r i =r max , t i =0, by adjusting v i , so that it satisfies In getting <r i ,v i ,t i >After that, calculate the energy consumption E1 of the drone at this time; Secondly, use the flight charging mode to charge That is r i =r max , v i =v * The insufficient part is supplemented by hovering charging, that is, a hybrid charging strategy is adopted to charge s i Charging; Under this strategy, the charging amount of the flight charging part is Then the charge amount of the hover charging part is Hover charging time t i for: In getting <r i ,v i ,t i >After that, calculate the energy consumption E2 of the drone at this time; Finally, compare the sizes of E1 and E2 and select the smaller one as node s i charging strategy.

6. The wireless rechargeable sensor network charging method with the lowest energy consumption for unmanned aerial vehicles as claimed in claim 5 is characterized in that: Step S3 is as follows: Rewrite the P2 problem as E f is the product of two terms with no coupling between them; therefore, E is achieved by minimizing each of these two terms. f Minimal goal; For the first item, according to the power-speed curve, by drawing a tangent line from the origin to the power curve P(v), the speed corresponding to the tangent point is the optimal v*; For the second term, r i In S2, the minimum distance D is defined as: In the formula, Representative nodes i and j The straight-line distance between The flight trajectory X of the UAV is obtained by using genetic algorithm.

7. A wireless rechargeable sensor network charging system for drones with the lowest energy consumption, used to execute the method according to any one of claims 1 to 6, comprising the following modules: Charging problem construction and decomposition module: For non-dense wireless rechargeable sensor networks, with the goal of minimizing the total energy consumption of drones, a charging problem based on flight-hovering is constructed and decomposed into the problem of minimizing charging energy consumption and the problem of minimizing flight energy consumption; Minimizing charging energy consumption problem solving module: To minimize the charging energy consumption problem, based on the flight and hovering charging modes, the charging mode, flight speed, and charging coverage radius of the UAV when performing charging tasks are jointly optimized; Module for solving the problem of minimizing flight energy consumption: In order to solve the problem of minimizing flight energy consumption, the function decomposition method is used to decompose it into the problem of determining the optimal non-charging flight speed and the problem of minimizing the flight distance; the optimal non-charging flight speed is obtained by the tangent method, and the problem of minimizing the flight distance is solved by the genetic algorithm.

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