A method for deploying a charging station for unmanned aerial vehicles based on data freshness

Abstract

The application provides a method for deploying a charging station for unmanned aerial vehicles based on data collection freshness, comprising the following steps: S1, initialization; S2, node and system information freshness index representation; S3, two-dimensional Markov modeling for unmanned aerial vehicle state modeling; S4, charging station deployment problem modeling; S5, problem optimization; S6, problem conversion; S7, charging station deployment: based on the Greedy algorithm, the optimal state probability and the optimal state transition probability are used to select a position with the minimum peak value from a charging station position set to deploy the charging station. The method has the beneficial effect that the two-dimensional Markov chain is used to model the position and energy of the unmanned aerial vehicle, the relationship between the information freshness and the state probability in the stable state is represented, then a charging station set satisfying the budget is selected from multiple optional charging station positions on the basis of meeting the charging station deployment budget limit, and the transition probability between the unmanned aerial vehicle states is optimized to ensure the information freshness.

Description

Technical Field

[0001] This invention relates to the technical field of drones, and more specifically, to a method for deploying drone charging stations based on the freshness of data collection. Background Technology

[0002] A drone sensor network consists of many sensors deployed in space. These sensors can monitor environmental changes in the application scenario in real time and generate effective monitoring data. Drone-related technologies are developing rapidly. Due to their high mobility, flexible deployment, and low operating costs, drones are widely used in scenarios such as environmental monitoring, aerial base stations, and agricultural plant protection.

[0003] In wireless sensor networks, drones act as relays. They first collect information from sensor nodes and then forward the received data to the data center. Compared to sensors directly transmitting information to the data center, this method extends the lifespan of sensors and significantly improves channel conditions. The drones fly back and forth between sensor network nodes and transmit information, which consumes a lot of energy. However, the drones themselves have limited energy. To maintain the sustainability of information collection tasks, drones need timely energy replenishment, which necessitates the deployment of charging stations for drones.

[0004] Currently, the main methods for drone-assisted data collection or drone charging station deployment are:

[0005] (1) Complete the specified data collection task and minimize energy consumption by optimizing the power control, trajectory and communication scheduling of the UAV;

[0006] (2) The UAV is used as a relay to collect sensor information. Different communication scheduling strategies are designed to minimize the information age.

[0007] (3) Use fixed-location charging stations to charge drones. Under the condition of energy supply, coordinate communication and charging scheduling strategies to minimize information age.

[0008] However, the above data collection methods or drone charging station deployment methods still have the following problems:

[0009] (1) Without the power supply of the drone, high-efficiency communication scheduling and drone power control still cannot keep the drone in a working state.

[0010] (2) In the case of charging stations, the selection of charging station locations and the selection of drone charging strategies at different times are still issues worth considering. Summary of the Invention

[0011] The problem this invention aims to solve is to provide a drone charging station deployment method based on data acquisition freshness, which ensures that drones can always be in a working state and can reasonably configure charging station locations.

[0012] To address the above problems, this invention provides a method for deploying drone charging stations based on data acquisition freshness, comprising:

[0013] Step S1, Initialization: In the UAV-assisted sensor network, M sensor nodes, one rotary-wing UAV, one data center, and J candidate charging stations are randomly distributed. The sensor network is divided into time slots, and each time slot is T in length. s The flight time and hovering time of the rotary-wing UAV are set to T, respectively. f and T h ;

[0014] Step S2, Node and System Information Freshness Index Representation: The peak values ​​of the sensor node and the system are obtained using the law of large numbers, and for each sensor node within the same time slot, the information freshness index corresponding to the sensor node is decreased when the sensor node is accessed by the rotary-wing UAV; and the information freshness index corresponding to the sensor node is increased when the sensor node is not accessed by the rotary-wing UAV.

[0015] Step S3, Two-dimensional Markov modeling for UAV state modeling: Let the set of charging station locations be V. The graph representation of the sensor node set N and the charging station location set V is G = (V', A), where V' = N∪V, and A represents an existing path. Each path represents an available positive probability state transition. The state of the rotorcraft UAV at time t is represented as s(t) = (q(t), E(t)) using a two-dimensional Markov chain, and the UAV's state probability transition matrix is ​​represented as P. Y×Y =[P r,l ](r,l∈{1,2,….,Y}), where S r S indicates the current status of the drone. l P indicates the next state of the drone. r,l Indicates starting from the current state S r Transition to the next state S l The probability, and

[0016] Step S4, Modeling the charging station deployment problem: Establish the first optimization problem based on the state probability transition matrix and the constraint of the number of charging stations deployed;

[0017] Step S5, Problem optimization: Optimize the first optimization problem according to the constraints of the state transition probability to obtain the second optimization problem;

[0018] Step S6, Problem Transformation: The second optimization problem is transformed into a convex problem, and the optimal state probability and optimal state transition probability are obtained by using mature convex optimization.

[0019] Step S7, Deployment of charging stations: Based on the Greedy algorithm, the location with the minimum peak value is selected from the set of charging station locations according to the optimal state probability and the optimal state transition probability to deploy the charging station.

[0020] Preferably, after performing step S1, an energy consumption calculation process is further included, the energy consumption calculation process including:

[0021] Step A1: Obtain the corresponding sensing speed based on the number of rotors, rotor radius, air density, and given thrust of the rotary-wing UAV; and obtain the corresponding flight energy consumption based on the sensing speed, the given thrust, the preset energy conversion efficiency, the preset tilt angle, and the flight time.

[0022] Step A2: Obtain the corresponding hovering energy consumption based on the number of rotors, the rotor radius, the air density, the given thrust, and the hovering time;

[0023] Step A3: Collect the parameter data and environmental data of the rotorcraft UAV itself and input them into the pre-constructed hybrid model of line-of-sight propagation and non-line-of-sight propagation to obtain the corresponding line-of-sight propagation probability and non-line-of-sight propagation probability, and obtain the corresponding communication energy consumption based on the line-of-sight propagation probability and the non-line-of-sight propagation probability.

[0024] Preferably, in step A3, the formula for calculating the channel gain composed of line-of-sight and non-line-of-sight components is as follows:

[0025]

[0026] in,

[0027] β0 is related to the path loss per unit distance (1m) and the carrier frequency;

[0028] k represents the path loss exponent;

[0029] ζ represents the additional attenuation coefficient compared to line-of-sight propagation during non-line-of-sight propagation;

[0030] i represents the i-th sensor node;

[0031] d i (t) represents the distance between the rotary-wing UAV and the i-th sensor node at time t;

[0032] The line-of-sight propagation probability is then expressed as:

[0033]

[0034] in,

[0035] 'a' represents parameter data;

[0036] b represents environmental data;

[0037] H represents the drone's flight altitude;

[0038] The non-line-of-sight propagation probability is then expressed as: P LoS,i =1-P NLoS,i .

[0039] Preferably, in step A3, after collecting the parameter data and environmental data of the rotorcraft itself and inputting them into the pre-constructed hybrid model of line-of-sight (LAS) propagation and non-LAS propagation to obtain the corresponding LAS propagation probability and non-LAS propagation probability, the method further includes:

[0040] The first-stage data transmission rate is obtained based on the preset system bandwidth, the noise power received by the rotorcraft UAV, and the transmission power of the sensor node. The first transmission time is obtained based on the first-stage data transmission rate and the size of a transmission data packet of the sensor. The second transmission time is obtained based on the first transmission time and the hovering time of the rotorcraft UAV. The communication energy consumption is obtained based on the system bandwidth, the noise power, the line-of-sight propagation probability, the non-line-of-sight propagation probability, the non-line-of-sight propagation influence factor, the second transmission time, the size of the transmission data packet, and the path loss index.

[0041] Preferably, the communication energy consumption is obtained using the following formula:

[0042]

[0043] in,

[0044] E tr (t) represents the communication energy consumption;

[0045] σ 2 This represents the noise power;

[0046] B represents the system bandwidth;

[0047] d0(t) represents the distance between the rotary-wing UAV and the data center at time t;

[0048] β0 is related to the path loss per unit distance (1m) and the carrier frequency;

[0049] ζ represents the additional attenuation coefficient compared to line-of-sight propagation during non-line-of-sight propagation;

[0050] k represents the path loss exponent;

[0051] W i This represents the size of the transmitted data packet for the i-th sensor node;

[0052] This indicates the second transmission time.

[0053] Preferably, the first optimization problem is expressed as:

[0054]

[0055]

[0056] in,

[0057] V represents the set of selected charging station locations;

[0058] π r This indicates that the rotary-wing UAV is in state S. r The state probability;

[0059] P r,l Indicates the state S of the rotary-wing UAV r To state S l The state transition probability;

[0060] r represents the preset parameter;

[0061] l represents the preset parameter.

[0062] Preferably, the second optimization problem is expressed as:

[0063]

[0064]

[0065] in,

[0066] V represents the set of selected charging station locations;

[0067] π r This indicates that the rotary-wing UAV is in state S. r The probability of;

[0068] P r,l Indicates the state S of the rotary-wing UAV r To state S l The state transition probability;

[0069] r represents the preset parameter;

[0070] l represents the preset parameter.

[0071] Preferably, the convex problem is represented as:

[0072]

[0073]

[0074] in,

[0075] y r,l Represents the state probability π r and state transition probability P r,l The product of;

[0076] r represents the preset parameter;

[0077] l represents the preset parameter.

[0078] The above technical solution has the following advantages or beneficial effects: This invention uses a two-dimensional Markov chain to model the position and energy of the UAV, representing the relationship between information freshness and state probability in a stable state. Then, based on the budget constraints of charging station deployment, a set of charging stations that meet the budget is selected from multiple optional charging station locations. By optimizing the transition probability between UAV states, information freshness is guaranteed. Under budget constraints, charging piles in suitable locations can be selected to charge the UAV. During the operation of the UAV, the energy consumption of two steps is considered to prevent the UAV from stopping work due to insufficient energy. A high-yield data collection and charging strategy is adopted to minimize the information age of the information collected and forwarded by the UAV. Attached Figure Description

[0079] Figure 1 This is a flowchart of the energy consumption calculation process of the present invention;

[0080] Figure 2 This is a graph showing the variation of the peak AOI of the system with the number of deployable charging stations under different charging station deployment schemes in Embodiment 1 of the present invention.

[0081] Figure 3 This is a graph showing the change of the peak AOI of the system under different charging station deployment schemes in Embodiment 1 of the present invention as the number of candidate charging stations (the set of elements increases continuously). Detailed Implementation

[0082] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0083] In a preferred embodiment of the present invention, based on the above-mentioned problems existing in the prior art, a method for deploying drone charging stations based on data acquisition freshness is provided, comprising:

[0084] Step S1, Initialization: In the UAV-assisted sensor network, M sensor nodes, one rotary-wing UAV, one data center, and J candidate charging stations are randomly distributed. The sensor network is divided into time slots, and each time slot is T in length. s The flight time and hovering time of the rotary-wing drone were set to T, respectively. f and T h ;

[0085] Step S2, Node and System Information Freshness Index Representation: The peak values ​​of sensor nodes and the system are obtained using the law of large numbers. For each sensor node in the same time slot, the information freshness index corresponding to the sensor node is decreased when the sensor node is accessed by the rotary-wing UAV; and the information freshness index corresponding to the sensor node is increased when the sensor node is not accessed by the rotary-wing UAV.

[0086] Step S3, Two-dimensional Markov modeling for UAV state modeling: Let the set of charging station locations be V. The graph representation of the sensor node set N and the charging station location set V is G = (V', A), where V' = N∪V, A represents an existing path, and each path represents an available positive probability state transition. The state of the rotorcraft UAV at time t is represented as s(t) = (q(t), E(t)) using a two-dimensional Markov chain, and the UAV's state probability transition matrix is ​​represented as P. Y×Y =[P r,l ](r,l∈{1,2,...,Y}), where S r S indicates the current status of the drone. l P indicates the next state of the drone. r,l Indicates starting from the current state S r Transition to the next state S l The probability, and

[0087] Step S4, Modeling the charging station deployment problem: Establish the first optimization problem based on the state probability transition matrix and the constraint of the number of charging stations deployed;

[0088] Step S5, Problem Optimization: Optimize the first optimization problem according to the constraints of the state transition probability to obtain the second optimization problem;

[0089] Step S6, Problem Transformation: Transform the second optimization problem into a convex problem, and use mature convex optimization to obtain the optimal state probability and the optimal state transition probability;

[0090] Step S7, Deployment of charging stations: Based on the Greedy algorithm, the location with the minimum peak value is selected from the set of charging station locations according to the optimal state probability and the optimal state transition probability to deploy the charging station.

[0091] Specifically, in this embodiment, the rotary-wing drone is referred to as a drone in the following text, and the locations of the sensor node and the charging station are respectively represented as: L i =[x i ,y i (i = 1, 2, 3, ..., M), C j =[x j ,y j (j=1,2,...,J), the distribution of sensors and candidate charging stations satisfies the parameters λ respectively. G and λ C The Poisson process is divided into time slots, each time slot being T in length. s The drone maintains an altitude of H and a maximum flight speed of V. max When the drone is performing information collection or charging, it first flies to a position H above the sensor or charging station, then hovers to receive and forward information or charge. During this process, the drone's flight time and hovering time are respectively T. f and T h .

[0092] Preferably, during operation, a drone may perform multiple different actions within a single time slot, all of which consume energy. When completing a data forwarding task within a time slot, the drone needs to perform actions such as flying, hovering, and transmitting information.

[0093] Specifically, in this embodiment, when the UAV visits the i-th ground sensor node, i.e., q(t) = i, the sensor information is received and forwarded to the central node. The freshness (AoI) of the information immediately drops to 1. During this time slot, the AoI of other unvisited sensors is incremented by 1. When the UAV charges during a time slot, the AoI of all sensors is incremented by 1. The AoI of the i-th sensor node can be expressed as:

[0094]

[0095] Using the law of large numbers, the peak AoI (PeakAoI) of the sensor node and the system can be expressed as follows:

[0096]

[0097]

[0098] Specifically, in this embodiment, the problem of combining information collection and charging in the system is described as a two-dimensional Markov chain. At time t, the state of the UAV can be represented as s(t)=(q(t), E(t)), where q(t)∈V′, E(t)∈[0,E′]. maxFor ease of explanation, the energy of the drone is divided into m units, and the maximum energy contained in each unit can be expressed as e = E. max / m(Joules), at the end of each time slot, the energy state of the UAV can be represented as: E(t)∈ε=[e,2e,...,m·e], the 0 state is not included, because the UAV can only complete the next action if it has enough energy. The set of all states of the UAV can be represented as: S={(q,E)|q∈V′,E∈ε}.

[0099] Preferably, the set Y is used to represent the sequence number of all states, S y Let y be the state, and the probability transition between states will have P. r,l =0: a) The drone's flight distance and energy are limited within a time slot, and it may not be able to reach some sensor nodes from the current node; b) Between sensors, since energy cannot be replenished, there should be no situation where the energy of the next state is higher than the energy of the current state; c) After the drone completes charging, it should fly to the sensor node in the next moment to complete the task of transmitting information. The distribution of states should also satisfy the equilibrium condition, i.e., π r P r,l =π l P l,r r,l=1,2,...,Y,π s Let represent the probability that state s∈S is in a stable state, and

[0100] In a preferred embodiment of the present invention, after performing step S1, an energy consumption calculation process is further included, the energy consumption calculation process including:

[0101] Step A1: Obtain the corresponding sensing speed based on the number of rotors, rotor radius, air density, and given thrust of the rotary-wing UAV; and obtain the corresponding flight energy consumption based on the sensing speed, given thrust, preset energy conversion efficiency, preset tilt angle, and flight time.

[0102] Step A2: Obtain the corresponding hovering energy consumption based on the number of rotors, rotor radius, air density, given thrust, and hovering time;

[0103] Step A3: Collect the parameter data of the rotorcraft itself and the environmental data and input them into the pre-built hybrid model of line-of-sight propagation and non-line-of-sight propagation to obtain the corresponding line-of-sight propagation probability and non-line-of-sight propagation probability, and obtain the corresponding communication energy consumption based on the line-of-sight propagation probability and non-line-of-sight propagation probability.

[0104] Specifically, in this embodiment, the formula for calculating flight energy consumption is as follows:

[0105]

[0106] Where θ is the tilt angle of the UAV during stable flight, η represents the energy conversion efficiency, and the thrust F is the sum of the thrust generated by the UAV, its own weight, and the drag force f acting on the UAV. d Related, expressed as: F = m u g+f d .

[0107] Preferably, the induced velocity is the induced velocity required for a given driving force F, which can be found by solving the following implicit equation:

[0108]

[0109] Where n, R, and ρ represent the number of rotors, rotor radius, and air density, respectively.

[0110] Preferably, steps A1, A2, and A3 are not in any particular order.

[0111] Specifically, in this embodiment, the formula for calculating the hovering energy consumption of the drone is as follows:

[0112]

[0113] In a preferred embodiment of the present invention, in step A3, the formula for calculating the channel gain composed of line-of-sight and non-line-of-sight components is as follows:

[0114]

[0115] in,

[0116] β0 is related to the path loss per unit distance (1m) and the carrier frequency;

[0117] k represents the path loss exponent;

[0118] ζ represents the additional attenuation coefficient compared to line-of-sight propagation during non-line-of-sight propagation;

[0119] i represents the i-th sensor node;

[0120] d i (t) represents the distance between the rotary-wing UAV and the i-th sensor node at time t;

[0121] The line-of-sight propagation probability is then expressed as:

[0122]

[0123] in,

[0124] 'a' represents parameter data;

[0125] b represents environmental data;

[0126] H represents the drone's flight altitude;

[0127] The non-line-of-sight propagation probability is expressed as: P LoS,i =1-R NLoS,i .

[0128] In a preferred embodiment of the present invention, after collecting the parameter data and environmental data of the rotary-wing UAV itself and inputting them into a pre-constructed hybrid model of line-of-sight (LAS) and non-LAS propagation to obtain the corresponding LAS and NAS probabilities in step A3, the method further includes:

[0129] The first-stage data transmission rate is obtained based on the preset system bandwidth, the noise power received by the rotorcraft UAV, and the transmission power of the sensor node. The first-stage data transmission rate and the size of the first data packet transmitted by the sensor are used to obtain the corresponding first transmission time. The second-stage transmission time is obtained based on the first transmission time and the hovering time of the rotorcraft UAV. The communication energy consumption is obtained based on the system bandwidth, noise power, line-of-sight propagation probability, non-line-of-sight propagation probability, non-line-of-sight propagation influence factor, second transmission time, data packet size, and path loss index.

[0130] Specifically, in this embodiment, during information transmission, the UAV first receives information from the sensor, and when the i-th sensor node on the ground transmits at power P... i The first-stage data transmission rate can be obtained when transmitting information, and thus the first transmission time used by the i-th ground sensor node to transmit information can be expressed as: After completing the receiving task, the UAV will transmit the received information to the data processing center. The second transmission time required for the UAV to transmit the information of the i-th sensor node can be expressed as: Since the information transmission task needs to be completed within a time slot, that is... The data rate at which the drone relays information from the i-th sensor node can be obtained. Then, based on the channel capacity formula, the transmit power of the UAV is obtained, and the communication energy consumption of the UAV is calculated.

[0131] In a preferred embodiment of the present invention, the communication energy consumption is obtained by the following calculation formula:

[0132]

[0133] in,

[0134] E tr (t) represents the communication energy consumption;

[0135] σ 2 Indicates noise power;

[0136] B represents system bandwidth;

[0137] d0(t) represents the distance between the rotary-wing drone and the data center at time t;

[0138] β0 is related to the path loss per unit distance (1m) and the carrier frequency;

[0139] ζ represents the additional attenuation coefficient compared to line-of-sight propagation during non-line-of-sight propagation;

[0140] k represents the path loss exponent;

[0141] W i This represents the size of the data packet transmitted by the i-th sensor node;

[0142] This indicates the second transmission time.

[0143] Specifically, in this embodiment, the first-stage transmission rate is obtained using the following calculation formula:

[0144]

[0145] in,

[0146] This represents the first transmission time from the i-th sensor node to the drone;

[0147] This represents the second transmission time from the i-th drone to the data center;

[0148] W i This represents the size of the data packet transmitted by the i-th sensor node;

[0149] B represents system bandwidth;

[0150] β0 is related to the path loss per unit distance (1m) and the carrier frequency;

[0151] k represents the path loss exponent;

[0152] P s This indicates the transmit power of the sensor node;

[0153] σ 2 Indicates noise power;

[0154] T h Indicates the hovering time of the drone;

[0155] This indicates the transmission rate in the first stage.

[0156] Specifically, in this embodiment, the division of drones performing information transmission tasks or energy replenishment at different nodes is as follows: the minimum energy consumption in two steps is used as the threshold. The drone travels from its current position to the sensor node to perform one information transmission, and then from the sensor to the charging station to recharge.

[0157] In a preferred embodiment of the present invention, the first optimization problem is expressed as:

[0158]

[0159]

[0160] in,

[0161] V represents the set of selected charging station locations;

[0162] π r This indicates that the rotary-wing drone is in state S. r The state probability;

[0163] P r,l Indicates the state S of the rotary-wing drone r To state S l The state transition probability;

[0164] r represents the preset parameter;

[0165] l represents the preset parameter.

[0166] Specifically, in this embodiment, min means "minimize", st means "constrained by", (a) means the constraint on the number of deployable charging stations, (b) means the sum of probabilities of transitioning to the next state is 1, (c) means the sum of probabilities of all states is 1, (d) the probability of a non-existent state transition is 0, (e) the state transition probability is non-negative, and (f) the state transition satisfies the property of stability.

[0167] In a preferred embodiment of the present invention, the second optimization problem is expressed as:

[0168]

[0169]

[0170] in,

[0171] V represents the set of selected charging station locations;

[0172] π r This indicates that the rotary-wing drone is in state S. r The probability of;

[0173] P r,l Indicates the state S of the rotary-wing drone r To state Sl The state transition probability;

[0174] r represents the preset parameter;

[0175] l represents the preset parameter.

[0176] In a preferred embodiment of the present invention, the convex problem is represented as:

[0177]

[0178]

[0179] in,

[0180] y r,l Represents the state probability π r and state transition probability P r,l The product of;

[0181] r represents the preset parameter;

[0182] l represents the preset parameter.

[0183] Example 1:

[0184] Assume there is a data processing center, five sensor nodes, and seven candidate locations for deploying charging stations in a two-dimensional plane. The data processing center is located at (0,0). The true coordinates of the five sensor nodes and the seven candidate locations for deploying charging stations are generated by a Poisson process, x:(-50,50)m, y:(-50,50)m. Other main parameter settings are as follows: H = 100m, σ 2 = -100dBm, P S =1Watt,T S =2s,E max =10 4 Joule,W x =1Mbits, m u =2.07kgρ=1.225kg / m 3 f d =9.6998N, q=4, r=0.254m, eta=0.7, k=2.3, a=10, b=0.6 and

[0185] The performance of the test method is as follows, under the condition of fixed sensor node and candidate charging station locations: Figure 2 and Figure 3 The figure shows how information freshness (peak AOI) changes with the number of charging stations and the set of deployable charging station locations (with the set of elements continuously increasing):

[0186] ① The information age (AoI) of the method mentioned in this invention has been continuously decreasing and eventually stabilized as the number of deployable charging stations increases, and its performance has been close to that of the exhaustive search method. This shows the effectiveness of the method. Moreover, the time complexity of the Greedy algorithm mentioned in this invention is much smaller than that of exhaustive search.

[0187] ②The method mentioned in this invention performs far better than the nearest distance method for selecting charging locations, while avoiding the problem that the nearest distance method selects charging station locations with similar effects, resulting in a small gain in Information Age (AoI).

[0188] ③The method mentioned in this invention has a continuously decreasing information age (AoI) and better performance than the nearest distance method as the set of charging stations (the set of elements increases continuously) changes.

[0189] While the disclosure is as stated above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of this disclosure, and all such changes and modifications will fall within the protection scope of this invention.

Claims

1. A method for deploying drone charging stations based on data freshness, characterized in that, include: Step S1, Initialization: In the UAV-assisted sensor network, randomly distribute... A sensor node, a rotary-wing drone, a data center, and There are 10 candidate charging station locations, and the sensor network is divided into time slots, with each time slot being 10 ... The flight time and hovering time of the rotary-wing UAV are set as follows: and ; Step S2, Node and System Information Freshness Index Representation: The peak values ​​of the sensor node and the system are obtained using the law of large numbers, and for each sensor node within the same time slot, the information freshness index corresponding to the sensor node is decreased when the sensor node is accessed by the rotary-wing UAV; and the information freshness index corresponding to the sensor node is increased when the sensor node is not accessed by the rotary-wing UAV. Step S3, Two-dimensional Markov modeling for UAV state modeling: Set the charging station location set as... , consisting of a set of sensor nodes Collection of charging station locations The diagram representing the composition is as follows ,in , The path represents an existing path, each path representing a usable positive probability state transition, and is represented in the form of a two-dimensional Markov chain. The state of the rotorcraft UAV at time t is represented as follows: And the state probability transition matrix of the UAV is expressed as ,in Indicates the current status of the drone. Indicates the next state of the drone. Indicates starting from the current state Transition to the next state The probability, and ; Step S4, Modeling the charging station deployment problem: Establish the first optimization problem based on the state probability transition matrix and the constraint of the number of charging stations deployed; Step S5, Problem optimization: Optimize the first optimization problem according to the constraints of the state transition probability to obtain the second optimization problem; Step S6, Problem Transformation: The second optimization problem is transformed into a convex problem, and the optimal state probability and optimal state transition probability are obtained by using mature convex optimization. Step S7, Deployment of charging stations: Based on the Greedy algorithm, the location with the minimum peak value is selected from the set of charging station locations according to the optimal state probability and the optimal state transition probability to deploy the charging station; After performing step S1, an energy consumption calculation process is further included, which includes: Step A1: Obtain the corresponding sensing speed based on the number of rotors, rotor radius, air density, and given thrust of the rotary-wing UAV; and obtain the corresponding flight energy consumption based on the sensing speed, the given thrust, the preset energy conversion efficiency, the preset tilt angle, and the flight time. Step A2: Obtain the corresponding hovering energy consumption based on the number of rotors, the rotor radius, the air density, the given thrust, and the hovering time; Step A3: Collect the parameter data and environmental data of the rotorcraft UAV itself and input them into the pre-constructed hybrid model of line-of-sight propagation and non-line-of-sight propagation to obtain the corresponding line-of-sight propagation probability and non-line-of-sight propagation probability, and obtain the corresponding communication energy consumption based on the line-of-sight propagation probability and the non-line-of-sight propagation probability.

2. The method for deploying drone charging stations according to claim 1, characterized in that, In step A3, the formula for calculating the channel gain, which is a mixture of line-of-sight and non-line-of-sight components, is as follows: ； in, It is related to the path loss per unit distance of 1m and the carrier frequency; Indicates the path loss index; This represents the additional attenuation coefficient compared to line-of-sight propagation during non-line-of-sight propagation. Indicates the first One sensor node; Indicates in The rotorcraft drone mentioned at the time and the first The distance between sensor nodes; Indicates the line-of-sight channel; Indicates a non-line-of-sight channel; The line-of-sight propagation probability is then expressed as: ； in, Indicates parameter data; Represents environmental data; Indicates the drone's flight altitude; The non-line-of-sight propagation probability is then expressed as: .

3. The method for deploying drone charging stations according to claim 2, characterized in that, In step A3, after collecting the parameter data and environmental data of the rotorcraft itself and inputting them into the pre-constructed hybrid model of line-of-sight (LAS) and non-LAS propagation to obtain the corresponding LAS and NAS probabilities, the method further includes: The first-stage data transmission rate is obtained based on the preset system bandwidth, the noise power received by the rotorcraft UAV, and the transmission power of the sensor node. The first transmission time is obtained based on the first-stage data transmission rate and the size of a transmission data packet of the sensor. The second transmission time is obtained based on the first transmission time and the hovering time of the rotorcraft UAV. The communication energy consumption is obtained based on the system bandwidth, the noise power, the line-of-sight propagation probability, the non-line-of-sight propagation probability, the non-line-of-sight propagation influence factor, the second transmission time, the size of the transmission data packet, and the path loss index.

4. The method for deploying drone charging stations according to claim 3, characterized in that, The communication energy consumption is obtained using the following formula: ； in, This indicates the communication power consumption; This represents the noise power; This indicates the system bandwidth; Indicates in The distance between the rotorcraft drone and the data center at any given time; It is related to the path loss per unit distance of 1m and the carrier frequency; This represents the additional attenuation coefficient compared to line-of-sight propagation during non-line-of-sight propagation. Indicates the path loss index; Indicates the first The size of the transmitted data packets for each sensor node; This indicates the second transmission time.

5. The method for deploying drone charging stations according to claim 1, characterized in that, The first optimization problem is expressed as: ； in, This represents the set of selected charging station locations; This indicates that the rotary-wing UAV is in a certain state. The state probability; Indicates the state of the rotary-wing UAV To state The state transition probability; Indicates preset parameters; This indicates the preset parameters.

6. The method for deploying drone charging stations according to claim 1, characterized in that, The second optimization problem is expressed as: ； in, This represents the set of selected charging station locations; This indicates that the rotary-wing UAV is in a certain state. The probability of; Indicates the state of the rotary-wing UAV To state The state transition probability; Indicates preset parameters; This indicates the preset parameters.

7. The method for deploying drone charging stations according to claim 1, characterized in that, The convex problem is represented as: ； in, Represents state probability and state transition probability The product of; Indicates preset parameters; This indicates the preset parameters.

Images