A multi-unmanned aerial vehicle assisted charging and data acquisition method based on target k-covering

Abstract

The application discloses a kind of based on target k-coverage multi-unmanned plane auxiliary charging and data acquisition method, comprising: obtaining and defining wireless chargeable sensor network area and related parameter set;Formalization based on target k-coverage unmanned plane minimization deployment problem;Using the clustering division algorithm based on coalition formation game, obtain disjoint sensor set family;Using restricted Prim algorithm, obtain candidate charging sensor set;Using the unmanned plane minimization deployment algorithm without neighborhood based on edge weight threshold, obtain the flight trajectory of all unmanned planes, supply power for the sensor of insufficient power, and collect the perception data of data concentrator.Greatly reduce the deployment cost of unmanned plane, ensure the long-term operation of wireless chargeable sensor network, while collecting important perception data for professional data analysis.

Description

Technical Field

[0001] This invention relates to the field of wireless rechargeable sensor network technology, and in particular to a multi-UAV assisted charging and data acquisition method based on target k-coverage. Background Technology

[0002] Breakthroughs in wireless power transmission technology have provided a promising solution for recharging traditional sensors. This technology can ensure continuous power supply to rechargeable devices, such as rechargeable sensors and vehicles, without being sensitive to the surrounding environment. Wireless rechargeable sensor networks consist of wireless rechargeable sensors equipped with wireless receivers. These sensors offer stable power, low deployment costs, and long-term continuous operation, significantly improving the quality and accuracy of target perception in target monitoring scenarios. Many current applications, such as earthquake monitoring, battlefield surveillance, target tracking, and traffic control, utilize wireless rechargeable sensor networks.

[0003] Previous studies have generally treated drones simply as mobile chargers to replenish the power of depleted sensors, ensuring the continuous operation of the sensor network, or as mobile data collectors to gather data stored on the sensors for analysis by personnel. This approach is too simplistic. However, in scenarios such as disaster relief, where the sensor network needs continuous operation to monitor the disaster area, and real-time sensor data is equally crucial for rescue efforts, a service solution that integrates power replenishment and data aggregation is urgently needed. Furthermore, in sensor networks located in mountainous or earthquake-stricken areas, the complex terrain makes deploying static chargers or land-based mobile chargers a significant safety concern, hindering timely data collection for professional analysis. Drones, with their rapid response, flexibility, and convenience, are largely unaffected by terrain and transportation conditions, making them particularly suitable for large-scale data collection and power replenishment. Additionally, sensor networks contain numerous target events, but the range of sensor monitoring is limited, often resembling a fan-shaped area. A single sensor cannot fully reflect the state of a target event, and in mountainous areas, for example, the data perceived by a single sensor due to terrain obstruction is often inaccurate. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a multi-UAV assisted charging and data acquisition method based on target k-coverage to solve the problem that the existing N-1 analysis of medium-voltage distribution networks has a large workload, which seriously limits the calculation speed and leads to low efficiency in practical applications.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution, including:

[0008] Acquire and define the wireless rechargeable sensor network area and related parameter set;

[0009] Based on the relevant parameter set, obtain the data aggregation model of the data aggregator, as well as the drone charging model and data acquisition model;

[0010] Based on the relevant parameter set, obtain the data collection energy consumption of the data aggregator, the flight energy consumption of the UAV, the charging energy consumption, and the data acquisition energy consumption;

[0011] Formalize the drone deployment problem based on target k-coverage;

[0012] A clustering partitioning algorithm based on alliance formation game is used to obtain disjoint sensor sets;

[0013] The restricted Prim algorithm is used to obtain a set of candidate charging sensors;

[0014] A neighborhood-less UAV minimum deployment algorithm based on edge weight thresholds is adopted to obtain the flight trajectories of all UAVs, replenish the power of sensors with low battery, and collect the perception data of the data aggregator.

[0015] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, wherein: the acquisition and definition of the wireless rechargeable sensor network region and related parameter set includes defining the monitoring region of the wireless rechargeable sensor network as a two-dimensional plane Ω;

[0016] The target interest point set is O = {o1, o2, ..., o...} m , ..., o M The data aggregator set is S = {s1, s2, ..., s}. m , ..., s M The rechargeable sensor set is as follows: Let the set of drone flight trajectories be Initial number

[0017] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, the step of obtaining the data aggregation model of the data aggregator, the charging model of the UAV, and the data acquisition model based on the relevant parameter set includes:

[0018] The data aggregation model is represented by sensor v. i Transmit sensed data to data aggregator. m Data transmission rate,

[0019]

[0020] Where A1 represents the channel bandwidth of the data aggregator. Indicates sensor v i The data transmission power, β1 represents the sensor's channel power gain, G1 is a positive constant, and σ represents the noise power. This represents the distance loss index between the sensor and the data aggregator;

[0021] The drone charging model is represented by the drone charging a rechargeable sensor v. i The charging power,

[0022]

[0023] Here, α and β are two parameters determined by the electromagnetic environment and hardware devices. This is a collection of drone flight trajectories. for Chinese UAVs and sensor nodes v i The vertical distance between them, where R is the charging radius of the drone;

[0024] The data acquisition model is represented as follows: Chinese UAVs to data aggregator m Data transmission rate:

[0025]

[0026] Where A2 represents the channel bandwidth of the drone. s m The data transmission power, β2 represents the channel power gain of the UAV at the reference distance, G2 is a positive constant, N0 represents the noise power density, and θ m Represents data aggregator s m Angular deviation from the drone.

[0027] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, the step of obtaining the data collection energy consumption of the data aggregator, the flight energy consumption of the UAV, the charging energy consumption, and the data acquisition energy consumption based on the relevant parameter set includes:

[0028] The energy consumption for data aggregation includes,

[0029] Define sensor v i The amount of data is D i Data aggregator processes D i The number of CPU revolutions required for data of this size is C. i All sensors within the same cluster transmit their sensed data to the cluster's data aggregator, denoted as s. m The total transmission frequency is f m Joined to the same CO cluster m The internal sensors divide the frequency, and the data collector s m Calculation from sensor v i The energy required for the data is expressed as,

[0030] cp i (s m )=η0(f m，i ) λ-1 C i (4)

[0031] Where η0 represents the effective switching capacitance, λ is a positive constant, and f m，i Indicates v i From s m The received frequency;

[0032] sensor v i Transmit sensing data to s m The energy consumption for data transmission is expressed as,

[0033]

[0034] in, s m Data reception power, r i，m Indicates v i Transmit sensing data to s m Data transmission rate;

[0035] Define cost(CO) m ) is a cluster of CO m The sum of the data transmission energy consumption of all sensors and the data computation energy consumption of the corresponding data aggregator yields the clustered CO2. m The total energy consumption within is expressed as,

[0036]

[0037] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, the flight energy consumption of the UAV includes the mobility energy consumption of the UAV flying from the sensor or data collector v to the sensor or data collector v′, expressed as follows:

[0038] w(v, v′)=c2·||v, v′|| (7)

[0039] Where c2 represents the energy consumption per unit distance traveled by the drone, and ||v, v′|| represents the Euclidean distance between v and v′.

[0040] The charging energy consumption includes the drone's consumption of sensor v i The charging energy consumption is expressed as,

[0041]

[0042] Among them, e U e represents the battery capacity of the sensor node. i c1 represents the current remaining battery power, c1 represents the energy consumption per unit of energy used to charge the drone, and η1 represents the hovering power of the drone.

[0043] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, wherein: the data acquisition energy consumption includes the energy consumed by the UAV at the data collector s m The energy consumption for data acquisition is expressed as,

[0044]

[0045] Where η1 is the hovering power of the drone, and η2 is the data acquisition power of the drone;

[0046] This also includes the flight trajectory of the lth drone. Where q l Indicates in P l The number of nodes on the [a], where 1 ≤ q l If ≤ N+M, the total energy consumption of the l-th drone can be obtained, expressed as:

[0047]

[0048] Since each drone has a battery capacity of B, then for P l In this regard, the drone's battery capacity constraint needs to be met, i.e., ω(P) l )≤B.

[0049] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, wherein: the formalized UAV minimization deployment problem based on target k-coverage includes,

[0050] To address the need to cluster all sensor nodes around a target point of interest, and to minimize the energy consumption of all clusters so that each sensor monitors only one target and transmits the data to the data aggregator, this can be formally represented as follows:

[0051]

[0052]

[0053]

[0054] Among them, constraint (11-1) ensures that all sensors are assigned to clusters, and constraint (11-2) ensures that each sensor is assigned to only one cluster;

[0055] Let each cluster CO m The set of sensors whose remaining battery power is below the operating battery power threshold is denoted as F. m ={v i |e i ≤e f v i ∈CO m};

[0056] For a complete undirected subgraph of G Since the selection criteria for nodes to be charged are consistent within each cluster, let F be... m ∪{s m The corresponding edge set is E. m ,but It satisfies the triangle inequality;

[0057] Define decision variable x v The decision variable x represents whether sensor node v is selected for charging. e Indicates whether edge e is selected for traversal; the objective is to minimize the sum of charging and flight energy consumption of the UAV within each cluster that satisfies objective k-coverage, formally represented as:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] Among them, constraint (12-1) ensures that the power in each cluster meets the target k-coverage, and constraint (12-2) ensures that each data aggregator will be accessed;

[0064] Define z il Indicates sensor v i Whether it is charged in trajectory Pl, z ml Represents data aggregator s m Is it on trajectory P? l The data is collected from the above; the goal is to determine the minimum number of drones required to charge all selected sensors and collect perception data from all data aggregators. Output a set of non-intersecting closed flight trajectories. Formal representation:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Equation (13) represents the minimized set The number of flight trajectories in T, Equation (13-1) represents the power constraint of the UAV, Equation (13-2) represents that all sensors in T are charged, Equation (13-3) represents that the data collector in S must be collected, and Equation (13-4) represents that the flight trajectories of any two UAVs do not intersect.

[0073] As a preferred embodiment of the target k-coverage-based multi-UAV assisted charging and data acquisition method of the present invention, wherein: the clustering partitioning algorithm based on alliance formation game theory is used to obtain disjoint sensor families, including,

[0074] A1: Initialize the initial allocation strategy for all sensors, and select the nearest data collector for subsequent transmission of sensed data;

[0075] A2: Select any sensor v i Given the strategy selection z of other sensors -i Execute v iBehavioral strategy z i Update;

[0076] A3: Select the behavioral strategy with the greatest utility from the set of feasible strategies, denoted as z. i ′, sensor v i According to z i 'Leave the existing alliance and join the new alliance; if this condition is not met, repeat step A2;'

[0077] A4: Calculate the choice z i The utility after ′ i ′, Update sensor v i The rules for this behavior are as follows:

[0078]

[0079] Among them, z i (k+1) represents the sensor v i The k-th iteration;

[0080] A5: Repeat steps A2 to A4 until the utility of all sensors remains constant and the alliance structure is stable.

[0081] As a preferred embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage described in this invention, wherein: the restricted Prim algorithm is used to obtain a set of candidate charging sensors, including,

[0082] B1: Let the final candidate sensor node set T be initialized as follows:

[0083] B2: Obtain a Hamiltonian path with minimum flight energy consumption for the UAV across all data aggregators using the nearest neighbor algorithm. Renumber this access sequence as Seq = {s1, s2, ..., s} m , ..., s M};

[0084] B3: Let T be the set of the m-th candidate sensors. m Initially {s m}, cluster CO m The sum of the current battery levels of all sensors within the device, Sum, is...

[0085] B4: From CO m The set of sensors F that initiate charging requests m Select the sensor node v that minimizes the sum of the drone's flight energy consumption and charging energy consumption. j ;

[0086] B5: Move sensor node v j Add to Tm and transfer the sensor from F m Remove from the middle, update the value of Sum to Sum+(e) U -e j );

[0087] B6: Repeat steps B4 to B4 until cluster CO. m The sum of the power of all internal sensors (Sum) and the total power required to be replenished by the drone meet the power requirements for target k-coverage.

[0088] B7: Update the final candidate charging sensor set T, i.e., T = T∪T m ;

[0089] B8: Repeat steps B3 to B7 until all data aggregators in Seq have been selected;

[0090] B9: Returns the final set of candidate charging sensor nodes T.

[0091] As a preferred embodiment of the target k-coverage-based multi-UAV assisted charging and data acquisition method of the present invention, wherein: the method employs a neighborhood-less UAV minimum deployment algorithm based on edge weight thresholds to obtain the flight trajectories of all UAVs, including:

[0092] C1: Construct an auxiliary graph G″ of T∪S, transforming the complete undirected graph with vertex and edge weights into a complete undirected graph with only edge weights, and initialize the set of UAV flight trajectories.

[0093] C2: For any i∈{2, 3, ..., |T∪S|}, let the edge weight threshold be...

[0094] C3: Remove edge weights exceeding δ from G″ i Given all edges, assuming we obtain q connected components PC1, PC2, ..., PC... q ;

[0095] C4: For each connected component PC q PT is obtained using the minimum spanning tree algorithm. q Let the set of odd-degree nodes in all spanning trees be denoted as .

[0096] C5: Construct the complete graph G o =(Φ o E o ;w o E o →R ≥0 ), in G o Find the minimum weight best matching H;

[0097] C6: Merge the subgraphs obtained in the first two steps, i.e. Suppose that there are q′ (≤q) connected components PC1′, PC2′, ..., PC′ in G″. q′ ;

[0098] C7: Initialize the set of flight trajectories of the i-th type of UAV P i Empty;

[0099] C8: For each j∈{1,2,...,q′}, delete the connected component PC. j An Euler circuit The repeated points within the loop form a closed loop P. j ′, P j Divide into n j Sub-circuits with energy consumption not exceeding B make

[0100] C9: If Then update For P i ;

[0101] C10: Repeat steps C2 to C9 until all i values ​​are selected;

[0102] C11: Returns the final set of flight paths for all deployed drones.

[0103] Compared with existing technologies, the beneficial effects of this invention are as follows: First, this invention proposes a clustering partitioning algorithm based on alliance formation game theory to obtain a family of disjoint sensor sets, and proves that it eventually converges to a stable alliance partitioning structure; it proposes a restricted Prim algorithm to obtain a set of candidate charging sensors in polynomial time with a time complexity of 0.5; it adopts a neighborhood-less UAV minimization deployment algorithm based on edge weight thresholds with an approximation ratio of 4, and obtains the flight trajectories of all UAVs charging sensors and collecting important sensing data from the data aggregator in polynomial time, which can greatly reduce the deployment cost of UAVs, ensure the long-term operation of wireless rechargeable sensor networks, and collect important sensing data for professionals to perform data analysis. Attached Figure Description

[0104] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0105] Figure 1This is a schematic diagram of the overall process of a multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention;

[0106] Figure 2 This is a two-dimensional distribution diagram of sensors and data aggregators in a wireless rechargeable sensor network, as described in an embodiment of the multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention.

[0107] Figure 3 This is a flowchart of the clustering algorithm based on alliance forming game in the multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention;

[0108] Figure 4 This is a schematic diagram of non-overlapping cluster partitioning in a multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention;

[0109] Figure 5 This is a flowchart of the candidate sensor node selection algorithm based on constrained primum in a multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention.

[0110] Figure 6 This is a schematic diagram of the selection of candidate charging sensor nodes within a cluster in a multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention;

[0111] Figure 7 This is a flowchart of the neighborhood-less UAV minimum deployment algorithm based on edge weight threshold in the multi-UAV assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention;

[0112] Figure 8 This is the output of the number of drones and their flight trajectory scheme in the multi-drone assisted charging and data acquisition method based on target k-coverage according to an embodiment of the present invention. Detailed Implementation

[0113] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0114] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0115] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0116] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0117] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0118] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0119] Example 1

[0120] Reference Figure 1-7 As an embodiment of the present invention, a multi-UAV assisted charging and data acquisition method based on target k-coverage is provided, comprising:

[0121] S1: Acquire and define the wireless rechargeable sensor network region and related parameter set;

[0122] Furthermore, the wireless rechargeable sensor network region and related parameter set are acquired and defined, including defining the monitoring region of the wireless rechargeable sensor network as a two-dimensional plane Ω;

[0123] The target interest point set is O = {o1, o2, ..., o...} m , ..., o M};

[0124] The data aggregator set is S = {s1, s2, ..., s} m , ..., s M The rechargeable sensor set is as follows: Let the set of drone flight trajectories be Initial number

[0125] S2: Obtain the data collection model of the data aggregator, as well as the drone charging model and data acquisition model based on the relevant parameter set;

[0126] Furthermore, based on the relevant parameter set, the data aggregation model of the data aggregator, as well as the charging model and data acquisition model of the drone, are obtained, including,

[0127] The data aggregation model is represented by sensor v. i Transmit sensed data to data aggregator. m Data transmission rate,

[0128]

[0129] Where A1 represents the channel bandwidth of the data aggregator. Indicates sensor v i The data transmission power, β1 represents the sensor's channel power gain, G1 is a positive constant, and σ represents the noise power. This represents the distance loss index between the sensor and the data aggregator;

[0130] The drone charging model is represented by the drone charging a rechargeable sensor v. i The charging power,

[0131]

[0132] Here, α and β are two parameters determined by the electromagnetic environment and hardware devices. This is a collection of drone flight trajectories. for Chinese UAVs and sensor nodes v i The vertical distance between them, where R is the charging radius of the drone;

[0133] The data acquisition model is represented as follows: Chinese UAVs to data aggregator m Data transmission rate:

[0134]

[0135] Where A2 represents the channel bandwidth of the drone. s m The data transmission power, β2 represents the channel power gain of the UAV at the reference distance, G2 is a positive constant, N0 represents the noise power density, and θ m Represents data aggregator s m Angular deviation from the drone.

[0136] S3: Based on the relevant parameter set, obtain the data collection energy consumption of the data aggregator, the flight energy consumption of the UAV, the charging energy consumption, and the data acquisition energy consumption;

[0137] Furthermore, based on the relevant parameter set, the energy consumption of the data aggregator, the flight energy consumption of the drone, the charging energy consumption, and the data acquisition energy consumption are obtained, including:

[0138] Data collection energy consumption includes,

[0139] Define sensor v i The amount of data is D i Data aggregator processes D i The number of CPU revolutions required for data of this size is C. i All sensors within the same cluster transmit their sensed data to the cluster's data aggregator, denoted as s. m The total transmission frequency is f m Joined to the same CO cluster m The internal sensors divide the frequency, and the data collector s m Calculation from sensor v i The energy required for the data is expressed as,

[0140] cp i (s m )=η0(f m，i ) λ-1 C i (4)

[0141] Where η0 represents the effective switching capacitance, λ is a positive constant, and f m，i Indicates v i From s m The received frequency;

[0142] sensor v i Transmit sensing data to s m The energy consumption for data transmission is expressed as,

[0143]

[0144] in, s m Data reception power, r i，m Indicates v i Transmit sensing data to s m Data transmission rate;

[0145] Define cost(CO) m ) is a cluster of CO m The sum of the data transmission energy consumption of all sensors and the data computation energy consumption of the corresponding data aggregator yields the clustered CO2. m The total energy consumption within is expressed as,

[0146]

[0147] Furthermore, the flight energy consumption of the drone includes the mobility energy consumption of the drone as it flies from the sensor or data collector v to the sensor or data collector v′, expressed as follows:

[0148] w(v, v′)=c2·||v, v′|| (7)

[0149] Where c2 represents the energy consumption per unit distance traveled by the drone, and ||v, v′|| represents the Euclidean distance between v and v′.

[0150] Charging energy consumption includes the drone's energy consumption for sensors. i The charging energy consumption is expressed as,

[0151]

[0152] Among them, e U e represents the battery capacity of the sensor node. i c1 represents the current remaining battery power, c1 represents the energy consumption per unit of energy used to charge the drone, and η1 represents the hovering power of the drone.

[0153] Furthermore, data acquisition energy consumption, including the energy consumption of drones on data aggregators. m The energy consumption for data acquisition is expressed as,

[0154]

[0155] Where η1 is the hovering power of the drone, and η2 is the data acquisition power of the drone;

[0156] This also includes the flight trajectory of the lth drone. Where q l This represents the number of nodes on Pl, where 1 ≤ q. l If ≤ N+M, the total energy consumption of the l-th drone can be obtained, expressed as:

[0157]

[0158] Since each drone has a battery capacity of B, then for P l In this regard, the drone's battery capacity constraint needs to be met, i.e., ω(P) l )≤B.

[0159] S4: Formalizing the UAV deployment problem based on target k-coverage;

[0160] Furthermore, the formalized problem of minimizing drone deployment based on target k-coverage includes,

[0161] To address the need to cluster all sensor nodes around a target point of interest, and to minimize the energy consumption of all clusters so that each sensor monitors only one target and transmits the data to the data aggregator, this can be formally represented as follows:

[0162]

[0163]

[0164]

[0165] Among them, constraint (11-1) ensures that all sensors are assigned to clusters, and constraint (11-2) ensures that each sensor is assigned to only one cluster;

[0166] Let each cluster CO m The set of sensors whose remaining battery power is below the operating battery power threshold is denoted as F. m ={v i |e i ≤e f v i ∈CO m};

[0167] For a complete undirected subgraph of G Since the selection criteria for nodes to be charged are consistent within each cluster, let F be... m ∪{s m The corresponding edge set is E. m ,but It satisfies the triangle inequality;

[0168] Define decision variable x v The decision variable x represents whether sensor node v is selected for charging. e Indicates whether edge e is selected for traversal; the objective is to minimize the sum of charging and flight energy consumption of the UAV within each cluster that satisfies objective k-coverage, formally represented as:

[0169]

[0170]

[0171]

[0172]

[0173]

[0174] Among them, constraint (12-1) ensures that the power in each cluster meets the target k-coverage, and constraint (12-2) ensures that each data aggregator will be accessed;

[0175] Define z il Indicates sensor v i Whether it is charged in trajectory Pl, z ml Represents data aggregator s m Is it on trajectory P? l The data is collected from the above; the goal is to determine the minimum number of drones required to charge all selected sensors and collect perception data from all data aggregators. Output a set of non-intersecting closed flight trajectories. Formal representation:

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183] Equation (13) represents the minimized set The number of flight trajectories in T, Equation (13-1) represents the power constraint of the UAV, Equation (13-2) represents that all sensors in T are charged, Equation (13-3) represents that the data collector in S must be collected, and Equation (13-4) represents that the flight trajectories of any two UAVs do not intersect.

[0184] S5: A clustering partitioning algorithm based on alliance formation game is adopted to obtain a family of disjoint sensor sets;

[0185] Furthermore, a clustering partitioning algorithm based on alliance-forming game theory is employed to obtain disjoint sensor families, including:

[0186] A1: Initialize the initial allocation strategy for all sensors, and select the nearest data aggregator for subsequent transmission of sensed data;

[0187] A2: Select any sensor v i Given the strategy selection z of other sensors -i Execute v i Behavioral strategy z i Update;

[0188] A3: Select the behavioral strategy with the greatest utility from the set of feasible strategies, denoted as z. i Sensor v i According to z i 'Leave the existing alliance and join the new alliance; if this condition is not met, repeat step A2;'

[0189] A4: Calculate the choice z i The utility after ′ i ′, Update sensor v i The rules for this behavior are as follows:

[0190]

[0191] Among them, z i (k+1) represents the sensor v i The k-th iteration;

[0192] A5: Repeat steps A2 to A4 until the utility of all sensors remains constant and the alliance structure is stable.

[0193] S6: Use the restricted Prim algorithm to obtain the set of candidate charging sensors;

[0194] Furthermore, the restricted Prim algorithm is used to obtain a set of candidate charging sensors, including:

[0195] B1: Let the final candidate sensor node set T be initialized as follows:

[0196] B2: Obtain a Hamiltonian path with minimum flight energy consumption for the UAV across all data aggregators using the nearest neighbor algorithm. Renumber this access sequence as Seq = {s1, s2, ..., s} m , ..., s M};

[0197] B3: Let T be the set of the m-th candidate sensors. m Initially {s m}, cluster CO m The sum of the current battery levels of all sensors within the device, Sum, is...

[0198] B4: From CO m The set of sensors F that initiate charging requests m Select the sensor node v that minimizes the sum of the drone's flight energy consumption and charging energy consumption. j ;

[0199] B5: Move sensor node v j Add to T m and transfer the sensor from F m Remove from the middle, update the value of Sum to Sum+(e) U -e j );

[0200] B6: Repeat steps B4 to B4 until cluster CO. m The sum of the power of all internal sensors (Sum) and the total power required to be replenished by the drone meet the power requirements for target k-coverage.

[0201] B7: Update the final candidate charging sensor set T, i.e., T = T∪T m ;

[0202] B8: Repeat steps B3 to B7 until all data aggregators in Seq have been selected;

[0203] B9: Returns the final set of candidate charging sensor nodes T.

[0204] S7: Employs a neighborhood-free UAV minimum deployment algorithm based on edge weight thresholds to obtain the flight trajectories of all UAVs, replenish the power of sensors with insufficient power, and collect the perception data of the data aggregator.

[0205] Furthermore, an edge-weighted threshold-based algorithm for minimizing the deployment of no-neighbor drones is employed to obtain the flight trajectories of all drones, including...

[0206] C1: Construct an auxiliary graph G″ of T∪S, transforming the complete undirected graph with vertex and edge weights into a complete undirected graph with only edge weights, and initialize the set of UAV flight trajectories.

[0207] C2: For any i∈{2, 3, ..., |T∪S|}, let the edge weight threshold be...

[0208] C3: Remove edge weights exceeding δ from G″ i Given all edges, assuming we obtain q connected components PC1, PC2, ..., PC... q ;

[0209] C4: For each connected component PCq PT is obtained using the minimum spanning tree algorithm. q Let the set of odd-degree nodes in all spanning trees be denoted as .

[0210] C5: Construct the complete graph G o =(Φ o E o ;w o E o →R ≥0 ), in G o Find the minimum weight best matching H;

[0211] C6: Merge the subgraphs obtained in the first two steps, i.e. Suppose that there are q′ (≤q) connected components PC1′, PC2′, ..., PC′ in G″. q′ ;

[0212] C7: Initialize the set of flight trajectories of the i-th type of UAV P i Empty;

[0213] C8: For each j∈{1,2,...,q′}, delete the connected component PC. j An Euler circuit The repeated points within the loop form a closed loop P. j ′, P j Divide into n j Sub-circuits with energy consumption not exceeding B make

[0214] C9: If Then update For P i ;

[0215] C10: Repeat steps C2 to C9 until all i values ​​are selected;

[0216] C11: Returns the final set of flight paths for all deployed drones.

[0217] Example 2

[0218] Reference Figure 1-8 Tables 1-3 illustrate an embodiment of the present invention, which provides a calculation and analysis process for a multi-UAV assisted charging and data acquisition method based on target k-coverage in a real-world application scenario, according to the previous embodiment.

[0219] The set of target points of interest is defined as O = {o1, o2, o3}, distributed in a 500m × 500m two-dimensional plane. The set of data collectors is S = {s1, s2, s3}, deployed near the corresponding target points of interest. The set of rechargeable sensors is... Randomly and uniformly deployed in this two-dimensional plane, forming a wireless rechargeable sensor network, the drones to be deployed fly at an altitude of 15m and a speed of [missing information]. Since the flight altitude and speed of the UAV are specified to be constant, the coordinates of the target interest point, sensor, and data collector in this implementation scheme are in two-dimensional coordinates. The information of each target interest point, data collector, and sensor is shown in Tables 1, 2, and 3, respectively, where Ω takes the lower left corner as the origin, that is, the coordinates of the lower left corner are (0, 0), and the coordinates of the upper right corner are (500m, 500m).

[0220] Table 1 Target Interest Point Information Table

[0221] serial number x-axis ordinate <![CDATA[o1]]> 155 375 <![CDATA[o2]]> 380 275 <![CDATA[o3]]> 280 125

[0222] Table 2 Data Aggregator Information Table

[0223] serial number x-axis ordinate <![CDATA[s1]]> 150 375 <![CDATA[s2]]> 375 275 <![CDATA[s3]]> 275 125

[0224] Table 3 Information on Rechargeable Sensors

[0225] serial number x-axis ordinate Current battery level (kJ) Data volume (MB) Energy to be replenished (kJ) <![CDATA[v1]]> 130 450 0 5 10.8 <![CDATA[v2]]> 100 430 0.8 6 10 <![CDATA[v3]]> 125 425 0.8 5 10 <![CDATA[v4]]> 275 470 0 7 10.8 <![CDATA[v5]]> 225 420 0.8 10 10 <![CDATA[v6]]> 300 425 10.8 5 0 v7 290 385 0 8 10.8 <![CDATA[v8]]> 120 375 0.8 9 10 <![CDATA[v9]]> 350 380 0.8 10 10 <![CDATA[v 10 ]]> 135 310 0.8 5 10 <![CDATA[v 11 ]]> 380 325 0.8 5 10.8 <![CDATA[v 12 ]]> 95 270 0.8 5 10 <![CDATA[v 13 ]]> 460 320 0.8 10 10.8 <![CDATA[v 14 ]]> 300 315 0.8 10 10.8 <![CDATA[v 15 ]]> 225 300 5 9 10 <![CDATA[v 16 ]]> 400 265 0.8 5 10 <![CDATA[v 17 ]]> 295 270 0 8 10.8 <![CDATA[v 18 ]]> 160 250 0.8 10 10 <![CDATA[v 19 ]]> 390 185 0.8 8 10.8 <![CDATA[v 20 ]]> 295 175 2 5 10 <![CDATA[v 21 ]]> 200 185 0 6 10.8 v22 110 200 0.8 7 10 <![CDATA[v 23 ]]> 105 150 0 5 10.8 <![CDATA[v 24 ]]> 135 125 0.8 10 10 <![CDATA[v 25 ]]> 100 115 0.8 6 10 <![CDATA[v 26 ]]> 140 110 0 5 10.8 <![CDATA[v 27 ]]> 385 125 0.8 5 10 <![CDATA[v 28 ]]> 340 60 0 8 10.8 <![CDATA[v 29 ]]> 230 55 0 5 10.8 <![CDATA[v 30 ]]> 370 45 0.8 10 10

[0226] Furthermore, S2: Set the channel bandwidth of the data collector to A1 = 1MHz, and the data transmission power of the sensor to... The channel power gain of the sensor is β1 = 2 × 10 -3 The noise power is σ = 10 -10 dBm, the loss exponent between the sensor and the data collector is The positive constant is G1 = 3. Therefore, substituting the parameter into formula (1), we get:

[0227]

[0228] With α = 10000, β = 15, and the maximum charging distance R = 30m, since the UAV's flight altitude is constant, substituting the parameters into formula (2) yields:

[0229]

[0230] The channel bandwidth of the UAV is set to A2 = 2MHz, and the data collector s m The data transmission power is The channel power gain of the UAV at the reference range is β2 = 4 × 10 -3The noise power density is s m Angular deviation from the drone Since the positive constant is G2 = 6, substituting the parameter into formula (3) yields:

[0231]

[0232] Let CO = {CO1, CO2, CO3} be three disjoint monitoring target interest points. m Sensor clusters, thus enabling drones to access data from data aggregators. m The time required to collect data is

[0233] Furthermore, S3: Set the effective conversion capacitor to η0 = 10. -25 The normal value is λ = 3, and the data collector processes sensor v. i The number of CPU revolutions required for the data is C. i =16M, data aggregator s m The total transmission frequency is f m =8GHz, s m Data receiving power is Added to the same CO cluster m Internal sensor data aggregator m Frequency, therefore, substituting the parameters into formulas (4) and (5), we get:

[0234]

[0235]

[0236] Let cost(CO) m ) is a cluster CO m The sum of the data transmission energy consumption of all sensors and the data computation energy consumption of the corresponding data aggregator is given by equation (6). Then, the data aggregator s can be obtained from equation (6). m Data aggregation costs

[0237]

[0238] Set the energy consumption per unit distance traveled by the drone as follows: Therefore, substituting the parameters into formula (7), we get:

[0239] w(v, v′)=10×||v, v′|| (7)

[0240] The remaining battery power information of the sensors is shown in Table 1. The energy consumption of the drone for charging per unit time is set to... The hovering power of the drone is Therefore, substituting the parameters into formula (8), we get:

[0241]

[0242] The sensor data volume is shown in Table 1. The data acquisition power of the UAV is set to [value missing]. Therefore, substituting the parameters into formula (9), we get:

[0243]

[0244] The drone's battery capacity is set to 210kJ. For the flight trajectory of the l-th drone... Where q l This represents the number of nodes on Pl, where 1 ≤ q. l ≤33, therefore substituting the parameter into formula (10), we get:

[0245]

[0246] Furthermore, S4: Divide all sensors into different clusters, ensuring that each sensor monitors only one target, and transmit the data to the corresponding data aggregator, thereby minimizing the energy consumption of all clusters. This is formalized as follows:

[0247]

[0248]

[0249]

[0250] Where the number of target interest points is M=3, then m=1,2,3;

[0251] Let each cluster CO m The set of sensor nodes whose remaining battery power is below the operating battery power threshold is denoted as F. m ={v i |e i ≤e f v i ∈CO m}; For a complete undirected subgraph G′=(V∪S,E′), Since the selection criteria for nodes to be charged are consistent within each cluster, let F be the criterion. m ∪{s m The corresponding edge set is E. m ,but Satisfy the triangle inequality; define the decision variable x. n The decision variable x represents whether sensor v is selected for charging. eThis indicates whether edge e is selected for traversal; the objective is to minimize the sum of the charging and flight energy consumption of the sensors of the UAV that satisfy the objective k-coverage within each cluster. In this example, objective 8-coverage is used, i.e., k = 8, then we have

[0252]

[0253]

[0254]

[0255]

[0256]

[0257] Given the battery capacity constraint B for each drone, determine the minimum number of drones required to charge all selected sensors and collect perception data from all data aggregators. Output a set of non-intersecting closed flight trajectories. The specific formalization is as follows:

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264]

[0265] Furthermore, S5:

[0266] A1: Initialize the initial allocation strategy for all sensors, selecting only the nearest data aggregator for each sensor. The initial clusters are then as follows:

[0267] CO1={v1, v2, v3, v4, v5, v6, v7, v8, v 10 v 12 v 15 v 18 v 22},

[0268] CO2 = {v9, v} 11 v 13 v 14 v16 v 17 v 19},

[0269] CO3={v 20 v 21 v 23 v 24 v 25 v 26 v 27 v 28 v 29 v 30},

[0270] The data transmission frequency of s1 within the cluster is divided equally into The data transmission frequency of s2 within the cluster is divided into The data transmission frequency of s1 within the cluster is divided into

[0271] A2: Next, let's use v 22 For example, its current strategy choice is z. 22 =1, the strategy selection for other sensors is z -22 Execute z 22 Update;

[0272] A3: From v 22 Choose the behavioral strategy with the greatest utility from the set of feasible strategies, let z′. 22 =3, sensor v 22 He withdrew from the original alliance CO1 and joined the new alliance CO3;

[0273] A4: Calculate v respectively 22 The original effect u 22 And the utility u′ after choosing CO3 22 ,

[0274]

[0275]

[0276] According to the following rules:

[0277]

[0278] Due to u 22 (z 22 , z -22 )>u′ 22 (z 22 , z -22 Therefore, this time v 22 Instead of updating the policy, select the next sensor node and update the policy there.

[0279] A5: Repeat steps A2 to A4 until the utility of all sensors remains constant, the coalition structure is stable, and a stable coalition architecture partition is finally obtained as follows: Figure 4 As shown, they are respectively

[0280] CO1={v1, v2, v3, v4, v5, v6, v7, v8, v 10 v 12},

[0281] CO2 = {v9, v} 11 v 13 v 14 v 15 v 16 v 17 v 18 v 19 v 20},

[0282] CO3={v 21 v 22 v 23 v 24 v 25 v 26 v 27 v 28 v 29 v 30}

[0283] Furthermore, S6:B1: Let the candidate sensor node set T be initialized as...

[0284] B2: Obtain a Hamiltonian cycle with minimum flight energy consumption of UAV with respect to all data aggregators through the nearest neighbor algorithm, renumber the access sequence, and denote the path as Seq={s1,s2,s3};

[0285] B3: Initialize the candidate sensor set in each cluster, i.e., T1 = {s1}, T2 = {s2}, T3 = {s3}, and the sum of the current power of all sensors in each cluster is Sum1 = 15.6kJ, Sum2 = 4kJ, and Sum3 = 4kJ.

[0286] B4: Taking CO1 as an example, Table 1 shows that the set of sensors in CO1 that initiate charging requests is F1 = {v1, v2, v3, v4, v5, v7, v8, v...} 10 v 12}, selecting the sensor node that minimizes the sum of the drone's flight energy consumption and charging energy consumption, with the node within T1 as the root node, calculates the total energy consumption required to reach v3 and charge as 10×58+1.5(1+10 -3 (15+15) 2 )×(1.08×10 4 -0.8×10 3 ) = 29.08 kJ;

[0287] B5: Add sensor node v3 to T1 and remove the sensor from F1, update Sum1 = 25.6kJ;

[0288] B6: Repeat steps B4 to B5 until the sum of the power of all sensors in cluster CO1, Sum1, and the total power that needs to be replenished by the UAV meet the power requirements of the target k-coverage.

[0289] B7: Update the final candidate charging sensor set T, i.e., T = T∪T1. At this time,

[0290] T1={v1, v2, v3, v5, v7, v8, v 10};

[0291] B8: Repeat steps B3 to B7 until all data aggregators in Seq have been selected. At this point:

[0292] T2 = {v9, v} 11 v 13 v 14 v 16 v 17 v 18 v 19},

[0293] T3={v 21 v 23 v 24 v 25 v 26 v 27 v 28 v 29};

[0294] B9: Returns the final set of candidate charging sensor nodes.

[0295] Furthermore, S7:

[0296] C1: Construct an auxiliary graph G″ of T∪S, transforming the complete undirected graph with vertex and edge weights into a complete undirected graph with only edge weights, and initialize the set of UAV flight trajectories.

[0297] C2: For any i∈{2, 3, ..., |T∪S|}, let the edge weight threshold be... Taking i=5 as an example, then δ5=30kJ;

[0298] C3: Remove all edges in G″ with a weight greater than δ5 to obtain 4 connected components PC1, PC2, PC3, and PC4;

[0299] C4: For each connected component PC q (q = 1, 2, 3, 4) PT is obtained using the minimum spanning tree algorithm. q ,like Figure 8 As shown by the black solid line, the set of odd-degree nodes in all spanning trees is denoted as . like Figure 8 As shown by the gray node;

[0300] C5: Construct the complete graph G o =(Φ o E o ;w o E o →R ≥0 ), in G o Find the minimum weight best matching H, such as Figure 8 As shown by the gray dashed line;

[0301] C6: Merge the subgraphs obtained in the first two steps, i.e. At this time, there are 3 connected components PC′1, PC′2, and PC′3 in G″′;

[0302] C7: Initialize the flight trajectory set P5 of the i=5th type of UAV to be empty;

[0303] C8: For each j∈{1,2,3}, delete the connected component PC′. j An Euler circuit The repeated points within the loop form a closed loop P′. j ,like Figure 8 b shows P′1, P′2, P′3, and P′4. P′... j Divide into n j Sub-circuits with energy consumption not exceeding B make like Figure 8 As shown in c, since each component already satisfies the constraint of B, it is not further divided into smaller sub-loops. Therefore, P5 ← {P 1，1 P 2，1 P 3，1 P 4，1};

[0304] C9: If Then update P5;

[0305] C10: Repeat steps C2 to C9 until all i values ​​are selected;

[0306] C11: Returns the set of flight paths of all finally deployed drones. Therefore, the final set of all drone flight trajectories is: The final flight trajectory scheme output in this embodiment is shown in Table 4.

[0307] Table 4. Flight trajectories of UAVs

[0308] Drone trajectory number Flight trajectory sequence Total energy consumed <![CDATA[P 1，1 ]]> <![CDATA[{v1,v3,s1,v5,v 10 v8, v2, v1]]> 179.4kJ <![CDATA[P 2，1 ]]> <![CDATA[{v 13 ,in 16 ,s2,v 17 ,in 14 ,v7,v9,v 11 ,in 13 }]]> 209.1kJ <![CDATA[P 3，1 ]]> <![CDATA[{v 25 ,v 26 ,v 24 ,v 21 ,v 18 ,v 23 ,v 25 }]]> 181.7kJ <![CDATA[P 4，1 ]]> <![CDATA[{v 29 ,s3,v 19 ,v 27 ,v 28 ,v 29 }]]> 123.2kJ

[0309] Example 3

[0310] This embodiment provides a multi-UAV assisted charging and data acquisition method based on target k-coverage. To verify its beneficial effects, a derivation and verification explanation are provided.

[0311] In S5, the cluster partitioning algorithm based on alliance formation game can eventually converge to a stable alliance structure under the alliance order.

[0312] First, for any sensor v i and any two alliances CO within its monitoring range m CO m′ The alliance order is defined as follows:

[0313]

[0314] sensor v i When deciding to join an alliance, in addition to considering its own benefits, a sensor also considers its contribution to other members of the alliance. Therefore, sensor v i They prefer to join alliances that can reduce their own and their alliance members' energy consumption.

[0315] Secondly, the utility function of the sensor is designed as follows:

[0316]

[0317] Then, design a potential function as

[0318]

[0319] When any sensor The strategy selection from z i Change to z′ i When the potential function changes, it is as follows:

[0320]

[0321] The fourth part of the second equal sign is due to the sensor v. i The change in strategy only involves and Therefore, this part equals 0. After any sensor unilaterally changes its strategy, the resulting change in its utility function is equal to the change in its potential function. Therefore, this game is a precise potential game, and consequently, a precise potential game has at least one pure Nash equilibrium. Thus, the proposed game has at least one pure Nash equilibrium. This means that no sensor can reduce its energy consumption by changing its alliance, i.e., the definition of a stable alliance partition.

[0322] Furthermore, since the number of sensors in this coalition game is finite, and each sensor's policy space only considers points of interest within its limited monitoring range, this implies that the potential function has an upper bound. Since it has been proven that this game is an exact potential game under coalition order, the algorithm will converge to its maximum value, i.e., achieve a stable coalition partition, within a finite number of steps, when no sensor can reduce its energy consumption by changing its actions.

[0323] In S6: The Restricted Prim algorithm is a polynomial-time algorithm that reduces the energy consumption of services between sensors and data aggregators within each cluster of UAVs.

[0324] This algorithm is a modified version of the classic Prim's algorithm, considering both edge and vertex weights. Each time, it selects the node with the minimum sum of the UAV's charging and movement energy consumption among the adjacent remaining sensor nodes in the spanning tree and adds it to the spanning tree until k-coverage is satisfied. This algorithm applies a certain number of CO clusters. m Each of the nodes (m = 1, 2, ..., M) undergoes a charging node selection process, resulting in a total of M disjoint clusters. The number of sensors initiating charging requests within each cluster is |F... m Since the selection of charging sensors within each cluster can be performed synchronously, the time depends on the cluster that selects the most charging sensors. Each time, the sensor node with the lowest drone mobility and charging energy consumption that is not yet added to the current spanning tree needs to be selected, requiring a comparison of O(|F m |) times, so the total time complexity is

[0325] In S7, the neighborhood-less UAV minimization deployment algorithm based on edge weight threshold has an approximation ratio of 4 and can obtain the flight trajectories of all UAVs in polynomial time, saving UAV deployment costs.

[0326] It is easy to see that the optimal number of drones deployed in the auxiliary graph G″ is equal to the optimal number of drones deployed in the original graph G. Assume the optimal number of drones deployed in G″ is K*, i.e. Then |P i | The upper bound is

[0327]

[0328] because The weight in each loop is greater than The number of edges does not exceed (i-1). Let K... * The loops are divided into i groups, denoted as in. when It contains exactly j items with weights greater than When at the edge, Will be added to In the group. Order So From this, we can conclude that... The upper bound of ω′(H),

[0329]

[0330]

[0331] Therefore, there is

[0332]

[0333] Then we can obtain |P i | the upper bound,

[0334]

[0335] When i = 4, we know

[0336]

[0337] in, Therefore, in conclusion,

[0338]

[0339] Therefore, the algorithm for minimizing the deployment of no-neighbor drones based on edge weight thresholds can achieve a 4-approximate solution.

[0340] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-UAV assisted charging and data acquisition method based on target k-coverage, characterized in that, include: Acquire and define the wireless rechargeable sensor network area and related parameter set; Based on the relevant parameter set, obtain the data aggregation model of the data aggregator, as well as the drone charging model and data acquisition model; Based on relevant parameter sets, the data aggregation energy consumption of the data aggregator, the flight energy consumption of the UAV, the charging energy consumption, and the data acquisition energy consumption are obtained. The data aggregation energy consumption includes: Define sensor The amount of data is Data aggregator processing The number of CPU revolutions required for the size of the data is All sensors within the same cluster transmit their sensed data to the cluster's data aggregator, which is called the data aggregator. Total transmission frequency is Join the same cluster Internal sensors share the frequency, data aggregator Calculation from sensor The energy required for the data is expressed as, （4） in, Indicates the effective switching capacitor. For positive integers, express from The received frequency; sensor Transmit sensing data to The energy consumption for data transmission is expressed as, （5） in, express Data reception power, express Transmit sensing data to Data transmission rate; definition For clustering The sum of the data transmission energy consumption of all sensors and the data computation energy consumption of the corresponding data aggregator can be used to obtain the clustering. The total energy consumption within is expressed as, （6） Formalize the drone deployment problem based on target k-coverage; A clustering partitioning algorithm based on alliance formation game is used to obtain disjoint sensor sets; The restricted Prim algorithm is used to obtain a set of candidate charging sensors; A neighborhood-less UAV minimum deployment algorithm based on edge weight thresholds is adopted to obtain the flight trajectories of all UAVs, replenish the power of sensors with low battery, and collect the perception data of the data aggregator.

2. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 1, characterized in that, The process of acquiring and defining the wireless rechargeable sensor network region and related parameter set includes defining the monitoring region of the wireless rechargeable sensor network as a two-dimensional plane. ; The target interest set is The data aggregator set is The rechargeable sensor set is Let the set of UAV flight trajectories be . Initial number .

3. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 2, characterized in that, The process of obtaining the data aggregation model of the data aggregator based on the relevant parameter set, as well as the charging model and data acquisition model of the UAV, includes: Data aggregation model, represented by sensors Transmit sensed data to the data aggregator Data transmission rate, （1） in, This indicates the channel bandwidth of the data aggregator. Indicates sensor Data transmission power, This indicates the channel power gain of the sensor. It is a positive integer. Indicates noise power. This represents the distance loss index between the sensor and the data aggregator; The drone charging model represents the drone's connection to a rechargeable sensor. The charging power, （2） in, and These are two parameters determined by the electromagnetic environment and the hardware equipment. This is a collection of drone flight trajectories. for Chinese UAVs and sensor nodes The vertical distance between them The charging radius for drones; The data acquisition model is represented as follows: Chinese UAV to data collector Data transmission rate: （3） in, This indicates the channel bandwidth of the drone. express Data transmission power, This indicates the channel power gain of the UAV at the reference distance. It is a positive integer. Indicates noise power density. Represents data aggregator Angular deviation from the drone.

4. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 3, characterized in that, The drone's flight energy consumption includes energy consumed by the drone from sensors or data collectors. Fly to sensor or data collector Mobile energy consumption, expressed as, （7） in, This indicates the energy consumption per unit distance traveled by a drone. express and The Euclidean distance between them; The charging energy consumption includes the drone's consumption of sensors. The charging energy consumption is expressed as, （8） in, For the battery capacity of the sensor node, This is the current remaining battery level. Energy consumption per unit of energy for charging drones This refers to the hovering power of the drone.

5. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 4, characterized in that, The energy consumption of data acquisition Including drones to data aggregators The energy consumption for data acquisition is expressed as, （9） in, The hovering power of the drone. For the data acquisition power of the drone; It also includes, for the first Flight trajectory of a drone ,in Indicates in The number of nodes on, and , thus we can obtain the first The total energy consumption of the drone is expressed as, （10） Because the battery capacity of each drone is Therefore, for In other words, it is necessary to meet the battery capacity constraints of the drone, that is... .

6. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 5, characterized in that, The formalized problem of minimizing UAV deployment based on target k-coverage includes, To address the need to cluster all sensor nodes around a target point of interest, and to minimize the energy consumption of all clusters so that each sensor monitors only one target and transmits the data to the data aggregator, this can be formally represented as follows: （P1） （11） （11-1） （11-2） Among them, constraint (11-1) ensures that all sensors are assigned to a cluster, and constraint (11-2) ensures that each sensor is assigned to only one cluster; Assume each cluster The set of sensors whose remaining battery power is below the operating battery power threshold is represented as follows: ； for A complete undirected subgraph , Since the selection criteria for nodes to be charged are consistent within each cluster, let's call them... The corresponding edge set is ,but , satisfying the triangle inequality; Define decision variables Represents sensor nodes Whether to be selected for charging, decision variables Representing an edge Whether to select a drone for traversal; the goal is to minimize the number of drones that satisfy the objective within each cluster. - The sum of the energy consumption for charging and the energy consumption for flight, as formally represented, is: （P2） （12） （12-1） （12-2） （12-3） （12-4） Among them, constraint (12-1) ensures that the power in each cluster meets the target k-coverage, and constraint (12-2) ensures that each data aggregator will be accessed; definition Indicates sensor Is it on the trajectory? It is being charged. Represents data aggregator Is it on the trajectory? The data is collected from the above; the goal is to determine the minimum number of drones required to charge all selected sensors and collect perception data from all data aggregators. Output a set of non-intersecting closed flight trajectories. Formal representation: （P3） （13） （13-1） （13-2） （13-3） （13-4） （13-5） （13-6） Equation (13) represents the minimized set The number of flight paths, Equation (13-1) represents the power constraint of the UAV, and Equation (13-2) represents All the sensors are charged, as shown in equation (13-3). The data from the data aggregator must be collected, and Equation (13-4) indicates that the flight trajectories of any two UAVs do not intersect.

7. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 6, characterized in that, The clustering algorithm based on alliance-forming game theory is used to obtain disjoint sensor families, including: A1: Initialize the initial allocation strategy for all sensors, and select the nearest data collector for subsequent transmission of sensed data; A2: Select any sensor Given other sensor strategy choices ,implement Behavioral strategies Update; A3: Select the behavioral strategy with the greatest utility from the set of feasible strategies, denoted as... ,sensor according to Leave the existing alliance and join a new alliance; if this is not met, repeat step A2. A4: Calculation Selection The effect after Update the sensor The rules for this behavior are as follows: （14） in, Indicates sensor The The next iteration; A5: Repeat steps A2 to A4 until the utility of all sensors remains constant and the alliance structure is stable.

8. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 7, characterized in that, The restricted Prim algorithm is used to obtain a set of candidate charging sensors, including: B1: Let the final candidate sensor node set be... Initialized to ; B2: Obtain a Hamiltonian path with minimum flight energy consumption for the UAV across all data aggregators using the nearest neighbor algorithm, and renumber the access sequence as follows. ; B3: Let the first A set of candidate sensors Initially ,cluster The sum of the current battery levels of all sensors within the device. for ; B4: From The set of sensors that initiate charging requests Select the sensor node that minimizes the sum of the drone's flight energy consumption and charging energy consumption. ; B5: Sensor Node Add to and the sensor from Remove from, update The value is ; B6: Repeat steps B4 to B4 until clusters are formed. The sum of the power of all internal sensors And the total amount of power that needs to be replenished by the drone must meet the power requirements of the target k-coverage; B7: Update the final candidate charging sensor set ,Right now ; B8: Repeat steps B3 through B7 until... All data aggregators have been selected. B9: Returns the final set of candidate charging sensor nodes. .

9. The multi-UAV assisted charging and data acquisition method based on target k-coverage as described in claim 8, characterized in that, The algorithm employs a neighborhood-less UAV minimum deployment based on edge weight thresholds to obtain the flight trajectories of all UAVs. include, C1: Constructing about auxiliary diagram Transform a complete undirected graph with vertex and edge weights into a complete undirected graph with only edge weights, and initialize the set of drone flight trajectories. ; C2: For any Set the edge weight threshold ; C3: Remove Middle side weight exceeds All edges, assuming we get Connected components ; C4: For each connected component Obtained using the minimum spanning tree algorithm Let the set of odd-degree nodes in all spanning trees be denoted as . ; C5: Construct the complete graph ,exist Find the minimum weight best match. ; C6: Merge the subgraphs obtained in the first two steps, i.e. Assuming There exists Connected components ; C7: Initialize the first Collection of drone flight trajectories Empty; C8: For each Delete connected components An Euler circuit The repeated points within the loop form a closed loop. ,Will Divided into Energy consumption not exceeding sub-circuit ,make ; C9: If Then update for ; C10: Repeat steps C2 through C9 until all are completed. The value was selected; C11: Returns the final set of flight paths for all deployed drones. .

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