Lowest energy consumption of unmanned aerial vehicle wireless rechargeable sensor network charging method and system

Abstract

The application discloses a WRSN charging method and system based on the lowest energy consumption of a UAV, and the method comprises the following steps: S1, a flight-hanging-based charging problem is constructed with the aim of minimizing the total energy consumption of the UAV, and the problem is decomposed into a flight energy consumption minimization problem and a charging energy consumption minimization problem; S2, for the charging energy consumption minimization problem, an adaptive charging strategy is proposed based on the flight and hanging charging modes, and the charging mode, flight speed and charging coverage radius of the UAV during the execution of the charging task are jointly optimized; S3, for the flight energy consumption minimization problem, the problem is decomposed into an optimal non-charging flight speed determination problem and a flight distance minimization problem through function decomposition; the optimal non-charging flight speed is obtained through the tangent method, and the flight distance minimization problem is solved through a genetic algorithm. The application can effectively reduce the total energy consumption of the UAV during the execution of the charging task, and has a relatively low time complexity.

Description

Technical Field

[0001] This invention belongs to the field of wireless rechargeable sensor network (WRSN) technology, specifically relating to a WRSN charging method and system for drones with the lowest energy consumption based on joint optimization of drone charging mode, flight speed and charging range. Background Technology

[0002] In recent years, wireless sensor networks have been widely used in many fields such as the Internet of Things (IoT), intelligent transportation systems, and field data acquisition. Due to limited hardware resources, these networks often face energy constraints. Furthermore, sensor nodes are typically located in inaccessible areas, making battery replacement difficult. To extend network lifespan, engineers have proposed many energy-saving strategies. However, these methods do not fundamentally solve the problem; sensor nodes eventually run out of power. While introducing environmental energy harvesting technologies such as solar and wind power can address the energy depletion issue, these methods rely on the surrounding natural environment, and the performance of energy harvesting is unpredictable. Therefore, how to effectively and reliably replenish the power of wireless sensor networks remains a highly challenging problem.

[0003] In recent years, drone-based power supply for sensor nodes has been applied to WRSN (Wireless Response System) due to its unique advantages. Regarding node charging, drone charging solutions are generally divided into two main categories: one-to-one charging and one-to-many charging. In the former, the drone charges only one sensor node at a time; in the latter, the drone charges multiple sensor nodes simultaneously. During charging, there are two drone charging modes: hovering charging, where the drone hovers in a fixed position to charge the node, typically directly above it; and flight charging, where the drone charges the node while in flight. Current technology typically assumes that the mobile charger has unlimited energy. However, this is impractical, especially for battery-powered drones, which also face energy limitations. Furthermore, the energy consumption of the drone during flight is not negligible. Therefore, balancing the charging efficiency between the drone and sensor nodes with the drone's flight energy consumption is crucial. Summary of the Invention

[0004] To address the issue of minimizing energy consumption for charging sensor nodes using wireless power transfer (WPT) technology in non-dense WRSNs by UAVs, this invention proposes a WRSN charging method and system with the lowest UAV energy consumption by jointly optimizing UAV charging mode, flight speed, and charging coverage radius, through charging energy consumption modeling and flight energy consumption modeling under different UAV charging modes, with the goal of minimizing the overall energy consumption of the UAV.

[0005] This invention addresses a non-dense WRSN application scenario deployed in a two-dimensional area, comprising a charging base station (CBS), a rotary-wing UAV, and N sensor nodes. The UAV departs from the CBS, charging the sensor nodes sequentially at altitude H according to a charging strategy, and finally returns to the CBS with the goal of minimizing the UAV's total energy consumption. In each charging cycle, it is assumed that the CBS knows the coordinates and remaining energy of each sensor node. If the node's charging strategy is hybrid charging, the UAV decelerates to a certain speed within the charging coverage radius and flies at that speed to charge; upon reaching directly above a node, the UAV begins hovering to charge, and after leaving the node, continues flying at the same speed to charge until charging is complete and it flies to the next node. If the charging strategy is flight charging, the UAV does not need to hover directly above the node to charge; otherwise, it operates the same as hybrid charging. Non-dense WRSN refers to a sparsely distributed sensor node network, where the distance between sensor nodes is greater than the UAV's charging coverage radius, meaning the UAV can 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 lowest-power wireless rechargeable sensor network charging method for drones includes the following steps:

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

[0009] S2 addresses the issue of minimizing charging energy consumption by proposing an adaptive charging strategy (ACS) based on flight and hovering charging modes. This strategy jointly optimizes the charging mode, flight speed, and charging coverage radius of the UAV when performing charging tasks.

[0010] S3, for the problem of minimizing flight energy consumption, is decomposed into two parts based on function decomposition: determining the optimal flight speed without recharging and minimizing the flight distance. The optimal flight speed without recharging is obtained using the tangent method, and the problem of minimizing the flight distance is solved using a genetic algorithm.

[0011] Preferably, in step S1:

[0012] S11. UAV power modeling.

[0013] Based on existing literature, the propulsion power of the UAV is modeled as follows:

[0014]

[0015] In the formula, P0 and P1 are two constants, representing the blade profile power and inductive power in the hovering state, respectively; U tipThe rotor tip velocity is represented by v0; the average rotor induced velocity during hovering is represented by v0; d0 and s are the fuselage drag ratio and rotor compaction, respectively; ρ and A are the air density and rotor disk area, respectively; and v is the UAV's moving speed. In actual modeling, all of the above parameters except v are known parameters.

[0016] When the drone wirelessly charges the sensor node, the radio frequency power P received by the sensor is... r (d) Modeling as follows:

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

[0018] In the formula, P t It is the radio frequency power transmitted by the drone, G UAV G represents the transmit antenna gain of the drone. SN The sensor receiving antenna gain is represented by dB; d is the straight-line distance between the UAV and the sensor node in meters; and f is the UAV's radio frequency in Hz.

[0019] When hovering, the drone's speed is 0, and the hovering power P h It can be represented as:

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

[0021] use This represents the charging 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] In the formula, P r η is the radio frequency power received by the sensor, η is the energy conversion efficiency of the sensor, and h represents hovering.

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

[0025] Flight charging capacity It can be modeled as:

[0026]

[0027] In the formula, r i Let P be the horizontal coverage radius for the drone to charge the i-th sensor node. r (d) represents the radio frequency power received by the sensor node.

[0028] The relationship between flight charge and 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 This represents the remaining power of the i-th sensor node. Therefore, e max -e i Indicates sensor node s i Required amount of charge.

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

[0032] For node s i Energy consumption of drones during hover charging The model is as follows:

[0033]

[0034] In the formula, P h For drone hovering power, P t The wireless charging transmission power for drones is calculated as follows: the first item is hovering energy consumption, and the second item is charging energy consumption.

[0035] For node s i During in-flight charging, the drone consumes energy while charging. The model is as follows:

[0036]

[0037] In the formula, P(v i This indicates that the drone travels at a speed of v. i The flight power is divided into two parts: the first is flight energy consumption, and the second is 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 the sensor nodes are not being charged. The flight energy consumption of the UAV when the sensor nodes are not being charged can be modeled as follows:

[0039]

[0040] In the formula, P(v) represents the flight power of the UAV when it flies at a speed of v. For flight time.

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

[0042]

[0043] In the formula, N represents the total number of sensor nodes. Represents flight charging energy consumption and This represents the energy consumption during hovering charging.

[0044] S13. To minimize the total energy consumption of the drone, 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. X determines the drone's flight trajectory, V and R are the drone's flight speed and charging coverage radius when charging each node, respectively, and R = {r1, ..., r2}. N}, V={v1,…v N}, where v is the optimal flight speed for the drone when it is not charging (denoted as v*).

[0054] Furthermore, in the above constraints:

[0055] Constraints 1-4 restrict the UAV's flight trajectory, ensuring that its start and end points are both s0 (i.e., CBS), and that each sensor node appears exactly once in the trajectory. X = {x} ij}, x ij =1 indicates that the drone is from node s i Flying towards S j Otherwise x ij =0;

[0056] Constraint 5 indicates that there is an upper limit to the node charging coverage radius. Where d maxThe maximum charging distance between the drone and the sensor node;

[0057] Constraint 6 indicates that there is also an upper limit v for the drone's flight speed. max ;

[0058] Constraint 7 means that the amount of charge given to the node meets its needs.

[0059] Furthermore, this invention patent also uses T={t i ,…t N} represents the drone in each s i The hovering charging time directly above, if s i If only in-flight charging is used, then t i It is 0.

[0060] S14. Decompose problem P0 into two subproblems P1 and P2, where 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 subproblem is used to solve for the UAV for each sensor node s. i The charging flight speed and charging coverage radius are obtained, and once these are determined, the drone's charging strategy and hovering charging time can be determined as well.

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

[0065] P2:

[0066] In the formula, D is the total flight distance of the UAV. This subproblem is used to solve for the flight trajectory of the UAV and the optimal flight speed when it is not recharged.

[0067] Preferably, step S2 is as follows:

[0068] S21. Determine the flight speed with the lowest energy consumption for the drone based on the drone's power-speed curve. Specifically, on the Pv plane (where P is flight power and v is flight speed), draw a line from the origin to the tangent point of the power curve P(v). The speed corresponding to the point of tangency is the drone's lowest energy consumption flight speed v*. Use this speed as the upper limit of the drone's flight speed for charging.

[0069] S22. Calculate the maximum amount of electricity that the drone's flight charging can replenish to the node according to the following formula.

[0070]

[0071] S23. Determine the drone's charging strategy, flight speed, and charging coverage radius based on the following strategy:

[0072] Strategy 1: At this time, the drone sends a signal to node s. i The charging strategy is to charge during flight, with a flight speed of v. i =v * Charging coverage radius r i =r max Hovering charging time t i =0;

[0073] Strategy 2: At this time, the drone sends a signal to node s. i The charging strategy is to charge during flight, with a flight speed of v. i =v * Hovering 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 To satisfy

[0077]

[0078] In obtaining <r i ,v i ,t i After that, the energy consumption E1 of the drone at this time is calculated.

[0079] Secondly, charging is carried out using the flight charging mode. That is, r i =r max v i =v * The insufficient portion is supplemented by hovering charging, that is, a hybrid charging strategy is used to charge the s i Charging. Under this strategy, the charging capacity of the flight charging section is... The charging amount of the hovering charging section is Hovering charging time t i for:

[0080]

[0081] In obtaining <ri ,v i ,t i After that, the power consumption E2 of the drone at this time was calculated.

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

[0083] Preferably, step S3 is as follows:

[0084] S31. Rewrite the P2 problem as follows: Therefore, E f It is the product of two terms, the first term being a function of v and the second term being a function of the distance D, and these two terms are not coupled. Therefore, E can be achieved by minimizing each of these two terms. f The smallest goal.

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

[0086] S33. For the second term, 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 node s i and s j The straight-line distance between them.

[0089] S34. Solving S33 using a genetic algorithm will yield the UAV flight trajectory X.

[0090] This invention also discloses a wireless rechargeable sensor network charging system for drones with minimal power consumption, used to perform the above method, comprising 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 a problem of minimizing charging energy consumption and a problem of minimizing flight energy consumption;

[0092] Minimize charging energy consumption problem solving module: For the problem of minimizing charging energy consumption, based on flight and hovering charging modes, three charging strategies are proposed to jointly optimize the charging mode, flight speed, charging coverage radius, and hovering charging time of the UAV when performing charging tasks;

[0093] The module for minimizing flight energy consumption solves the problem by decomposing it into two parts: determining the optimal flight speed without recharging and minimizing the flight distance. The optimal flight speed without recharging is solved using the tangent method, while the flight distance minimization problem is solved using a genetic algorithm.

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

[0095] (1) Existing one-to-one charging modes rely entirely on UAVs hovering on nodes for charging, resulting in high UAV energy consumption and long charging times. While existing one-to-many charging modes can improve the charging efficiency of dense WRSN networks, they are not suitable for sparse WRSN networks. To address the problems of these two charging modes, this invention proposes an adaptive hybrid mode charging strategy (ACS) for UAVs based on a one-to-one charging model. This strategy combines flight charging with hovering charging, jointly optimizing the charging mode, flight speed, and charging coverage radius of the UAV when performing charging tasks. This reduces the total energy consumption of the UAV when performing charging tasks and has lower time complexity.

[0096] (2) This 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 there exists an optimal flight speed that minimizes flight energy consumption when the UAV is not performing a charging task. Attached Figure Description

[0097] Figure 1 This is a network model diagram of the preferred embodiment of the wireless rechargeable sensor network charging method for drones with the lowest energy consumption according to the present invention.

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

[0099] Figure 3 The drone flight trajectory diagram provided in the embodiments of the present invention;

[0100] Figure 4 This is a graph showing the impact 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 illustrating the time complexity of a drone performing a task, provided in an embodiment of the present invention;

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

[0103] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0104] like Figure 1 As shown, the preferred embodiment of the present invention relates to a network model for replenishing power to a sensor network using a drone. In this network, the set of nodes is S = {s1, ..., s...} N}, where s i Let s represent the i-th sensor node, and s0 represent the CBS. Let s i The current remaining energy is e i The maximum charging capacity of the node is e max When the drone targets node s i During charging, the charging coverage radius is denoted as r. i That is, the charging coverage range is 2 hours. i Flight speed is denoted as v i When the drone is not charging, its flight speed remains constant at v. Furthermore, since this embodiment considers a 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 drones.

[0105] Specific node charging models are as follows: Figure 2 As shown. If s i If the charging strategy is hybrid charging, then the drone will have a charging coverage radius r. i The velocity decreases from v to v i It flies at that speed to recharge; it reaches node s. i After hovering directly above, the drone charges. i Seconds; then leave the node and continue at the same speed v i Recharge in flight, fly away from coverage radius r i After charging ends; finally, it flies to the next node s at speed v. j If s i If the charging strategy is in-flight charging, then the drone does not need to hover directly above the node to charge (i.e., hovering charging time t). i =0 seconds), everything else is the same as hybrid charging.

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

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

[0108] S11. Drone Power Modeling:

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

[0110]

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

[0112] When the drone wirelessly charges the sensor node, the radio frequency power P received by the sensor is... r (d) Modeling as follows:

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

[0114] In the formula, P t It is the radio frequency power transmitted by the drone, G UAV G represents the transmit antenna gain of the drone. SN The sensor receiving antenna gain is represented by dB; d is the straight-line distance between the UAV and the sensor node in meters; and f is the UAV's radio frequency in Hz.

[0115] When hovering, the drone's speed is 0, and the hovering power P h It can be represented as:

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

[0117] use This represents the charging 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] In the formula, P r η is the radio frequency power received by the sensor, η is the energy conversion efficiency of the sensor, and h represents hovering.

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

[0121] Flight charging capacity It can be modeled as:

[0122]

[0123] In the formula, r i Let P be the horizontal coverage radius for the drone to charge the i-th sensor node. r (d) represents the radio frequency power received by the sensor node.

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

[0125]

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

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

[0128] For node s i Energy consumption of drones during hover charging The model is as follows:

[0129]

[0130] In the formula, P h For drone hovering power, P t The wireless charging transmission power for drones is calculated as follows: the first item is hovering energy consumption, and the second item is charging energy consumption.

[0131] For node s i During in-flight charging, the drone consumes energy while charging. The model is as follows:

[0132]

[0133] In the formula, P(v i This indicates that the drone travels at a speed of v. i The flight power is divided into two parts: the first is flight energy consumption, and the second is 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 the sensor nodes are not being charged. The flight energy consumption of the UAV when the sensor nodes are not being charged can be modeled as follows:

[0135]

[0136] In the formula, P(v) represents the flight power of the UAV when it flies at a speed of v. For flight time.

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

[0138]

[0139] In the formula, N represents the total number of sensor nodes. Represents flight charging energy consumption and This represents the energy consumption during hovering charging.

[0140] S13. To minimize the total energy consumption of the drone, 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; where X represents the drone's flight trajectory, V and R represent the drone's flight speed and charging coverage radius when charging each node, and v is the optimal non-charging flight speed, denoted as v*; where R = {r1, ..., r2} N}, V={v1,…v N}, T={t i ,…t N}; if s i If only in-flight charging is used, then the hovering charging time t i =0; X = {x ij}, where x ij =1 indicates that the drone is from node s i Flying towards S j Otherwise x ij =0; r max For the maximum charging coverage radius of the drone, according to Settings, d max The maximum charging distance between the drone and the sensor node; v max This indicates the maximum flight speed of the drone.

[0150] S14. Decompose problem P0 into two subproblems P1 and P2, where 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 subproblem is used to solve for the UAV for each sensor node s. i The charging flight speed and charging coverage radius are obtained, and once these are determined, the drone's charging strategy and hovering charging time can be determined as well.

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

[0155] P2:

[0156] In the formula, D is the total flight distance of the UAV. This subproblem is used to solve for the flight trajectory of the UAV and the optimal flight speed when it is not recharged.

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

[0158] S21. Determine the flight speed with the lowest energy consumption for the drone based on the drone's power-speed curve. Specifically, on the Pv plane (where P is flight power and v is flight speed), draw a line from the origin to the tangent point of the power curve P(v). The speed corresponding to the point of tangency is the drone's lowest energy consumption flight speed v*. Use this speed as the upper limit of the drone's flight speed for charging.

[0159] S22. Calculate the maximum amount of electricity that the drone's flight charging can replenish to the node according to the following formula.

[0160]

[0161] S23. Determine the drone's charging strategy, flight speed, and charging coverage radius based on the following strategy:

[0162] Strategy 1: At this time, the drone sends a signal to node s. i The charging strategy is to charge during flight, with a flight speed of v. i =v * Charging coverage radius r i =r max Hovering charging time t i =0;

[0163] Strategy 2: At this time, the drone sends a signal to node s. i The charging strategy is to charge during flight, with a flight speed of v. i =v * Hovering 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 v i Adjust to meet its needs.

[0167]

[0168] In obtaining <r i ,v i ,t i After that, the energy consumption E1 of the drone at this time is calculated.

[0169] Secondly, charging is carried out using the flight charging mode. That is, r i =r max v i =v * The insufficient portion is supplemented by hovering charging, that is, a hybrid charging strategy is used to charge the s i Charging. Under this strategy, the charging capacity of the flight charging section is... The charging amount of the hovering charging section is Hovering charging time t i for:

[0170]

[0171] In obtaining <r i ,v i ,t i After that, the power consumption E2 of the drone at this time was calculated.

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

[0173] S3, for the problem of minimizing flight energy consumption, based on function decomposition, it is decomposed into the problem of determining the optimal flight speed without recharging and the problem of minimizing flight distance. The optimal flight speed without recharging is obtained through the tangent method, and the problem of minimizing flight distance is solved using a genetic algorithm. Details are as follows:

[0174] S31. Rewrite the P2 problem as follows: Therefore, E f It is the product of two terms, the first term being a function of v and the second term being a function of the distance D, and these two terms are not coupled. Therefore, E can be achieved by minimizing each of these two terms. f The smallest goal.

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

[0176] S33. For the second term, 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 node s i and s j The straight-line distance between them.

[0179] S34. Solving S33 using a genetic algorithm yields the UAV flight trajectory X. The specific steps are as follows:

[0180] (1) Initialize the population

[0181] The drone charging path planning problem of this invention is a combinatorial optimization problem. This combinatorial optimization problem is order-based. Although binary encoding is simple to operate and easy to use with genetic operations, it will produce invalid solutions when applied to this problem, and the efficiency of global search is relatively low. Therefore, this invention uses natural number encoding to encode nodes to handle the planning problem. Nodes represent chromosomes, and the population size (M) represents the number of drone flight paths. The nodes in the WRSN are numbered as {1,2,3…n}, and then randomly shuffled to obtain M sets of sequences, i.e., M initialized populations.

[0182] (2) Calculate individual fitness

[0183] This invention calculates the fitness of each path (individual) in the population based on its length, and the fitness is expressed by the function f(n). i It is usually expressed as the reciprocal of the distance, which can be represented as:

[0184]

[0185] (3) Selection

[0186] This invention employs a roulette wheel selection method for the selection operation of a genetic algorithm, selecting some individuals from the parent generation to inherit to the next generation, thereby improving computational efficiency and global convergence. Each individual has a different fitness level and a different proportion of the overall fitness of the entire roulette wheel.

[0187] (4) Cross

[0188] Two individuals are randomly selected from the population. There will be an overlap between these two individuals. In this overlap, the two individuals will exchange parts of their chromosomes. Then, the original points are traversed in sequence. If there is no overlap with the point in the overlap, the point can be copied. Otherwise, the traversal continues until a point without conflict is encountered, and then the copying operation is performed. Finally, two new individuals are obtained.

[0189] (5) Variation

[0190] Crossover is a step that determines the global search capability of a genetic algorithm, while mutation determines its local search capability. By using a small mutation probability, a few individuals are randomly selected from the previous generation population, and their partial composition structure is changed to generate new offspring for the next generation.

[0191] Furthermore, during population processing, node 0 is inserted into the first and last positions of the aforementioned individuals. The position of node 0 will not change with crossover or mutation operations, so as to indicate that the start and end points of each path are CBS nodes.

[0192] 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, as shown in the diagram. Figure 3 As shown, the node needs to be recharged to 3J. An optimal flight trajectory for the drone was obtained based on a genetic algorithm, as follows: Figure 3 As shown in Table 1, under the parameter settings, the optimal flight speed of the UAV is obtained by the tangent method as v. * =15.935m / s.

[0193] Table 1

[0194]

[0195]

[0196] Based on the proposed Adaptive Hybrid Mode Charging Algorithm (ACS), the charging strategy for each node is obtained, as shown in Table 2 below (a table of charging strategies for each node provided in this embodiment of the invention). It can be seen that the difference in the power of the nodes leads to different charging strategies.

[0197] Table 2

[0198]

[0199] For example, node 1 has a charge of 0.5J and needs to be charged with 2.5J. This node uses a hybrid charging method. Within a 5m charging coverage radius, the drone flies towards the node at a speed of 15.935m / s to begin charging. When the drone reaches directly above the node, it hovers for 1.941 seconds to charge the node. Subsequently, the drone continues to fly within the charging coverage radius at a speed of 15.935m / s while simultaneously charging the node. After leaving the charging coverage radius, the drone flies at a speed of v * The drone flies to the next node at a speed of 15.935 m / s. With zero hover charging time, the drone only charges during flight. This typically occurs when a node requires a small amount of charging, which can only be met through flight charging, as in node 4.

[0200] To validate the proposed ACS algorithm, it was compared with other benchmark algorithms. FSC is a full-search charging method, essentially based on exhaustive search, representing the optimal performance upper limit. HC is hover charging, where the drone hovers directly above the node to charge. MRA involves the drone hovering within the charging coverage radius and selecting the point closest to the base station as its docking point to charge the node. Nodes were randomly deployed in a 250m × 250m area. The average total energy consumption of the drone for each scenario was calculated based on 50 experimental runs. Furthermore, all schemes used a genetic algorithm for trajectory planning, with the same parameter settings and optimal flight speed v.* .like Figure 4 As shown, the total energy consumption of the UAV varies from 10 to 50 nodes with a step size of 10. It can be seen that, for different numbers of nodes, the charging strategy (ACS) proposed in this invention achieves results closest to FSC, significantly lower than HC and MRA. The optimization effect of ACS becomes more pronounced as the number of nodes increases. This is because ACS adopts an adaptive charging mode. When the energy required by a node is low, a flight charging mode is used to avoid energy consumption while hovering above the node; when the energy required is high, a hybrid charging method is used to reduce the UAV's hovering time. Since hovering energy consumption is greater than flight energy consumption, this strategy reduces hovering energy consumption, thereby reducing the UAV's total energy consumption. Although FSC has the lowest energy consumption, its exhaustive search method has a high time complexity (specific experimental results are shown in Figure 1). Figure 5 (As shown).

[0201] Finally, to demonstrate that ACS has lower time complexity, the running times of ACS and FSC (excluding GA algorithm time) are as follows: Figure 5 As shown, the runtime of FSC increases significantly with the increase in the number of nodes, while the runtime of ACS remains relatively constant. Combined with... Figure 4 and Figure 5 It can be observed that ACS can achieve almost the same total drone energy consumption as FSC, but significantly reduces time complexity.

[0202] like Figure 6 As shown, this embodiment discloses a wireless rechargeable sensor network charging system for drones with the lowest power consumption, 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 minimizing flight energy consumption and minimizing charging energy consumption.

[0204] Joint optimization module: To address the issue of minimizing charging energy consumption, an adaptive charging strategy is proposed based on flight and hovering charging modes to jointly optimize the charging mode, flight speed, and charging coverage radius of the UAV when performing charging tasks;

[0205] The module for minimizing flight energy consumption solves the problem by decomposing it into two parts: determining the optimal flight speed without recharging and minimizing the flight distance. The optimal flight speed without recharging is solved using the tangent method, while the flight distance minimization problem is solved using a genetic algorithm.

[0206] Other aspects of this embodiment can be found in the above method embodiments.

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

[0208] The above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which 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: Includes the following steps: S1, targeting non-dense wireless rechargeable sensor networks, aims to minimize the total energy consumption of drones by constructing a flight-hovering-based charging problem and decomposing it into minimizing flight energy consumption and minimizing charging energy consumption problems; S2 addresses the issue of minimizing charging energy consumption by proposing an adaptive charging strategy based on flight and hovering charging modes. This strategy jointly optimizes the charging mode, flight speed, and charging coverage radius of the UAV when performing charging tasks. S3. For the problem of minimizing flight energy consumption, it is decomposed into the problem of determining the optimal flight speed without recharging and the problem of minimizing flight distance through function decomposition. The optimal flight speed without recharging is obtained by the tangent method, and the problem of minimizing flight distance is solved by the genetic algorithm. In step S1: The propulsion power of the drone is modeled as follows: In the formula, P0 and P1 represent the blade profile power and inductive power in the hovering state, respectively; U tip The blade tip velocity of the rotor is represented; v0 represents the average rotor induced velocity when hovering; d0 and s represent the fuselage drag ratio and rotor compaction, respectively; ρ and A represent the air density and rotor disk area, respectively; and v is the moving speed of the UAV. When the drone wirelessly charges the sensor node, the radio frequency power P received by the sensor is... r (d) Modeling as follows: In the formula, P t It is the radio frequency power transmitted by the drone, G UAV G represents the transmit antenna gain of the drone. SN This represents the sensor receiving antenna gain; d is the straight-line distance between the UAV and the sensor node; and f is the UAV's radio frequency. When hovering, the drone's speed is 0, and the hovering power P h Represented as: use This indicates the charge level while hovering; at this time, the straight-line distance between the drone and the sensor. d = H The hovering charging time is modeled as follows: In the formula, P r It is the radio frequency power received by the sensor. For the energy conversion efficiency of the sensor, h Represents hovering; During flight charging, the drone needs to be directly above the sensor node at a speed v i Flight, where the distance to the sensor node is In the formula, H Represents the vertical distance between the drone and the sensor node. v i t Represents the horizontal distance between the drone and the sensor node. t For flight time; Flight charging capacity The model is as follows: In the formula, r i For drones to give the first i The horizontal coverage radius of each sensor node charging P r ( d () represents the received radio frequency power of the sensor node; The relationship between flight charge and hover charge is expressed as follows: In the formula, e max This represents the upper limit of the charging capacity of the sensor node. e i Representing the i The remaining power of each sensor node; therefore, e max - e i Represents sensor nodes s i Required charging amount; For nodes s i Energy consumption of drones during hover charging The model is as follows: In the formula, P h For drone hovering power, P t The wireless charging transmission power for drones is calculated as follows: the first item is hovering energy consumption, and the second item is charging energy consumption. For nodes s i During in-flight charging, the drone consumes energy while charging. The model is as follows: In the formula, P ( v i ) indicates the drone's speed v i The flight power is divided into two parts: flight energy consumption and charging energy consumption. make D This indicates the total flight distance of the drone during the charging mission. v This represents the flight speed without charging the sensor nodes; the flight energy consumption of the UAV without charging the sensor nodes is modeled as follows: In the formula, P ( v For drones at speed v Flight power during flight is equal to flight time. The total energy consumption of the drone is expressed as follows: In the formula, N Represents the total number of sensor nodes. Represents flight charging energy consumption and This represents the energy consumption during hovering charging. To minimize the total energy consumption of the drone, the optimization problem P0 is expressed as: In the optimization problem P0, the decision variables are X, V, R, and ... v Where X determines the drone's flight trajectory, and V and R are the drone's flight speed and charging coverage radius when charging each node. , , v The optimal flight speed of the drone when not charging is denoted as . v* ; Step S2 is as follows: Based on the drone's power-speed curve, determine the flight speed at which the drone consumes the least energy; Pv On a plane, P For flight power, v For flight speed, the power curve is obtained from the origin. P ( v The speed at the point of tangency to the tangent line is the flight speed at which the drone consumes the least energy. v * This speed is used as the upper limit of the flight speed for drone charging; The maximum amount of electricity that the drone can replenish to the node during flight can be calculated using the following formula. : The following strategies determine the drone's charging strategy, flight speed, and charging coverage radius: Strategy 1: At this time, the drone sends a signal to the node. s i The charging strategy is to charge during flight, and the flight speed... Charging coverage radius r i = r max Hover charging time t i =0; Strategy 2: At this time, the drone sends a signal to the node. s i The charging strategy is to charge during flight, and the flight speed... 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 To satisfy In obtaining < r i , v i , t i > Then, the energy consumption of the drone at this time was calculated. E 1; Secondly, charging is carried out using the flight charging mode. ,Right now r i = r max , The insufficient portion is supplemented by hovering charging, that is, a hybrid charging strategy is used to provide... s i Charging; under this strategy, since the charging capacity of the flight charging section is... Then the charging amount of the hovering charging section is Hover charging time t i for: In obtaining < r i , v i , t i > Then, the energy consumption of the drone at this time was calculated. E 2; Finally, comparison E 1 and E 2. Size: Select the smaller one as the node. s i The charging strategy.

2. The wireless rechargeable sensor network charging method for UAVs with the lowest energy consumption as described in claim 1, characterized in that, Among the above constraints: Constraints 1-4 restrict the flight trajectory of the UAV, ensuring that its starting point and ending point are both zero, and that each sensor node appears exactly once in the trajectory; x ij =1 indicates that the drone started from node s i Flying towards S j Otherwise x ij =0; Constraint 5 indicates that there is an upper limit to the node charging coverage radius. , where d max The maximum charging distance between the drone and the sensor node; Constraint 6 indicates that there is also an upper limit v for the drone's flight speed. max ; Constraint 7 means that the charging point of the node meets its requirements. use Represents each s i The hovering charging time, if s i If only in-flight charging is used, then t i It is 0.

3. The wireless rechargeable sensor network charging method for UAVs with the lowest energy consumption as described in claim 2, 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 for the UAV for each sensor node. s i The charging flight speed and charging coverage radius; Define the minimization of flight energy consumption problem P2: In the formula, D is the flight distance of the drone without charging. This problem is used to solve for the drone's flight trajectory and optimal flight speed without charging.

4. The wireless rechargeable sensor network charging method for UAVs with the lowest energy consumption as described in claim 1, characterized in that, Step S3 is as follows: Rewrite the P2 problem as follows: , E f It is the product of two uncoupled terms; therefore, it can be achieved by minimizing each of these terms individually. E f Minimum goal; For the first item, based on the power-speed curve, by starting from the origin and working along the power curve... P ( v The velocity corresponding to the point of tangency to the tangent line is the optimal velocity. v *; Regarding item 2, r i In S2, the minimum distance is obtained. D Defined as: In the formula, represents a node. s i and s j The straight-line distance between them; The UAV flight trajectory X was obtained by using a genetic algorithm.

5. A wireless rechargeable sensor network charging system for drones with minimal power consumption, for performing the method as described in any one of claims 1-4, 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 UAVs, a charging problem based on flight-hovering is constructed and decomposed into minimizing charging energy consumption problem and minimizing flight energy consumption problem; Minimize charging energy consumption problem solving module: For the problem of minimizing charging energy consumption, based on flight and hovering charging modes, the charging mode, flight speed and charging coverage radius of the drone when performing charging tasks are jointly optimized; Minimize flight energy consumption problem solving module: For the problem of minimizing flight energy consumption, it is decomposed into the problem of determining the optimal flight speed without recharging and the problem of minimizing flight distance through function decomposition; the optimal flight speed without recharging is obtained by the tangent method, and the problem of minimizing flight distance is solved by the genetic algorithm.

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