A method for unmanned aerial vehicle assisted hybrid data collection

Abstract

The present application relates to a kind of unmanned aerial vehicle assisted mixed data acquisition method, belong to unmanned aerial vehicle assisted wireless communication field.The present application fully considers the ability of base station and nearby ground node communication, utilizes unmanned aerial vehicle as mobile base station, constructs a kind of unmanned aerial vehicle and base station mixed acquisition data method.First, mixed data acquisition system is decomposed, and divided into unmanned aerial vehicle assisted data acquisition subsystem and base station direct acquisition subsystem;Second, with the minimum of the maximum task completion time of two subsystems as the goal, the optimization problem of system is established, and solution is solved;For unmanned aerial vehicle assisted data acquisition subsystem, jointly optimize unmanned aerial vehicle flight speed, trajectory and ground node transmission power, realize the minimum of unmanned aerial vehicle overall task completion time, improve data acquisition efficiency.The present application makes full use of various communication resources, method is practical and efficient, can timely and effectively collect field data, applicable to fire monitoring, emergency rescue and the like.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV)-assisted wireless communications and relates to a UAV-assisted hybrid data acquisition method. Background Art

[0002] In recent years, wireless sensor systems have seen a wide range of applications in smart grids, including but not limited to power quality monitoring, outage detection, overhead transmission line monitoring, fault detection and location, equipment fault diagnosis, and underground cable system monitoring. Traditional wireless sensor networks collect data via a single or multi-hop transmission process to a sink node, which then transmits it to a base station. This approach accelerates the lifespan of nodes with high data throughput, shortening the network's lifespan. To address this issue, drones are being introduced as mobile data collectors to form heterogeneous wireless sensor networks, extending system lifespan. Furthermore, drones can communicate with nodes at closer, unobstructed distances, improving communication quality.

[0003] While using drones for data collection offers numerous advantages, they still face challenges such as energy constraints and slower data collection speeds than traditional multi-hop systems. Currently, much research has focused on path planning methods for drone-assisted sensor network data collection. However, these studies overlook the fact that nodes within the base station's coverage area can spontaneously transmit data to the data center, reducing the drone's workload. Furthermore, these studies fail to fully consider the impact of drone flight speed on trajectory and data collection tasks, as well as the limited energy consumption of sensor nodes in real-world scenarios. Summary of the Invention

[0004] In light of this, the present invention aims to provide a drone-assisted hybrid data collection method. To enable base stations to perform sensor node collection tasks even when idle, a data collection system that collaborates between base stations and drones is designed, thereby reducing the number of drone collection tasks. Furthermore, to further efficiently complete collection tasks, the drone collection system adjusts the drone's trajectory, flight speed, and node transmission power, taking into account the limited energy of nodes and ensuring complete transmission of node data, thereby minimizing the total task completion time.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A hybrid data acquisition method assisted by a drone, the method comprising the following steps:

[0007] S1: A hybrid data collection system consisting of drones, base stations, and sensor nodes. The drone acts as a mobile collector and works with the base station to collect data from all sensor nodes in the area. To improve the efficiency of the hybrid data collection system, the system is decomposed into two subsystems based on base station bandwidth and basic sensor node information (number, location, and energy): drone-assisted data collection subsystem A and base station-directed data collection subsystem B.

[0008] S2: Based on the principle of shortest overall acquisition time, the overall goal of establishing a hybrid acquisition system is to minimize the maximum acquisition time of the two subsystems. A , t B denotes the completion time of all data of subsystems A and B respectively, then the overall goal of the hybrid system is expressed as: min(max{t A ,t B}).

[0009] S3: For subsystem B, calculate its data acquisition time t B , which is the completion time of the node with the longest collection time in the system.

[0010] S4: For subsystem A, its data collection time t A The time required for the UAV to collect data from all nodes in the system is calculated. By establishing a time minimization problem, the flight speed, path, and transmission power of the sensor nodes of the UAV are optimized to obtain the minimum task completion time of subsystem A.

[0011] Furthermore, the hybrid data acquisition system is decomposed in S1, specifically as follows:

[0012] Let the total number of sensor nodes that need to be collected by the hybrid system be K, establish the sensor node set S = {k, k = 1, 2, 3, ..., K}, and classify the sensor nodes into subsystems according to the following steps:

[0013] S11: Due to the limited bandwidth of the base station, assume that the base station can communicate with a maximum of M (1≤M≤K) sensor nodes. For the base station direct data acquisition subsystem B, because the base station's direct data acquisition speed is much faster than the drone's flight data acquisition speed, when constructing the subsystem, try to maximize the number of sensor nodes served by the base station, that is, allocate M nodes to the base station acquisition subsystem.

[0014] S12: When allocating, the remaining energy E of sensor node k must also be considered k Can it support the upload of all data? Define the energy consumed by the kth sensor node to upload data for:

[0015]

[0016] in, With P gc denote the transmission power and signal processing power of the kth node respectively; t k represents the data collection time of node k. It means that it can upload data to the base station. At this time, it is added to the set S B Otherwise, put it into the set S A .

[0017] Sensor node k collection time t k Calculated by the following formula:

[0018]

[0019] Among them, Q k is the amount of data to be uploaded by node k, R k It represents the transmission speed of the node, which is related to the node's transmission power and path loss. According to Shannon's theorem, the node's transmission speed is calculated as follows:

[0020]

[0021] Where W represents the channel bandwidth; h k represents the channel gain between node k and the base station; σ represents the noise power.

[0022] S13: The sensor node sets of subsystem A and subsystem B are Statistical set S A and S B The number of nodes is N A and N B ; if N B ≤M, then the set S B All the nodes in the data are collected by the base station, that is, On the contrary, for the set S B The nodes in are filtered, and the filtering steps are as follows:

[0023] a. Combine, from set S B Random selection (N B -M) nodes, a total of Combination of species;

[0024] b. Combine each combination obtained in the previous step with the set S A Nodes in (N in total A +N B -M nodes) constitute the traveling salesman problem, and solve the problem through the hybrid particle swarm algorithm, select the combination with the shortest path, and obtain the sensor node set of subsystem A Set S BThe remaining nodes in the subsystem are added to the sensor node set of subsystem B.

[0025] Furthermore, S3 calculates the data collection time of subsystem B, that is, obtains the completion time of the node with the longest collection time in the system, as follows:

[0026] S31: Orthogonal frequency division multiple access (OFDMA) technology is used in subsystem B to transmit data from the node to the base station. This technology divides the bandwidth resources into M sub-channels, each of which is allocated to one node. The communication bandwidth W of the jth node in j for:

[0027]

[0028] Where M represents the maximum number of communication sub-channels that the base station can accommodate; W represents the channel bandwidth.

[0029] The collection time t of node j j for:

[0030]

[0031] S32: After calculating the acquisition time of each node, the data acquisition time t of subsystem B can be obtained. B for:

[0032]

[0033] in, Represents the number of sensor nodes in subsystem B.

[0034] Furthermore, in S4, the minimum task completion time of subsystem A is obtained by optimizing the flight speed, path, and transmission power of the UAV, as follows:

[0035] S41: Obtain the initial flight path of the UAV, that is, determine the node collection order. Here, we do not consider the data collection task of the UAV, but only focus on how to obtain the shortest path. The flight path shortesting problem is converted into a traveling salesman problem, and the collection order is obtained by solving it using a hybrid particle swarm algorithm.

[0036] S42: Within the UAV flight speed range, establish the maximum data volume problem of the UAV in two acquisition modes: flight acquisition and flight hovering. Use the sequential least squares method to solve the maximum data volume of the two modes at different flight speeds;

[0037] S43: Based on the amount of data to be uploaded by the node and the obtained threshold, determine whether each node can be collected; if so, determine the collection mode of the drone and the initial flight speed of the drone for the node; if not, the drone cannot collect the node;

[0038] S44: Further optimize the speed of the drone, establish the problem of minimizing the acquisition time of a single node, and solve it through the least sequence square method and ergodic method to obtain the optimal flight path, flight speed and transmission power of each node. Assume that for the set The optimized data collection time obtained by the i-th node in is Then, the final task completion time in subsystem A is the time it takes for the UAV to complete the collection of all nodes by performing variable speed flight along the optimized path, that is:

[0039]

[0040] in Represents the number of sensor nodes in subsystem A.

[0041] Furthermore, in S42, when solving the problem of maximizing the amount of data collected by the drone at different speeds, it should be noted that the distance between the drone and the node is minimized only when the drone flies directly above the node, so the maximum data transmission rate can be obtained, which is the optimal hovering collection position. Therefore, the problem of maximizing the amount of data collected by the drone at node i in non-overlapping mode is established:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] R i (t) = Wlog2(1+γ i (t))

[0049]

[0050]

[0051] Where A i (t) represents the trajectory of the UAV within the coverage range of the node at time slot t, where the starting point Entry point to communicate with the node The drone's hovering point and exit coverage points connected; Indicates the exact center of the node's coverage area; and P tmax Represent the minimum and maximum transmit power of the node respectively; and P gc Respectively represent the node's transmission power and signal processing power consumption; and They represent the UAV’s flight collection time, hovering collection time, and flight time from the starting point to the boundary of the coverage range of node i; R i (t) represents the transmission rate of the UAV in time slot t; W represents the channel bandwidth; γ i (t) and They represent the signal-to-noise ratio and signal-to-noise ratio threshold between the UAV and the node respectively; h i (t) represents the channel gain at time t; σ represents the noise power; V i Indicates the speed at which the UAV flies to collect data from node i, where V min ≤V i ≤V max ; δ i Indicates the maximum flight speed V max to V i Or from V i to V max The difference in accelerated flight time, Where a is the acceleration of the drone.

[0052] The sequential least squares method is used to solve the above problem and obtain the i The maximum amount of data collected by the UAV flight at the time of , that is, the maximum amount of data in non-overlapping mode To obtain the maximum amount of data collected at different flight speeds, you only need to set different speed constraints, that is, change V i At the same time, to calculate the maximum amount of data in flight collection mode, you only need to set the hovering collection time. Solve the problem in the same way to obtain the maximum amount of flight data collected in non-overlapping acquisition mode

[0053] Furthermore, in S43, based on the amount of data to be uploaded by the node and the obtained threshold, the collection mode of each node and the initial flight collection speed of the drone need to be judged, specifically:

[0054] 1) First determine the initial flight collection speed of the drone. Specifically, different speeds will obtain the maximum amount of data at that speed. You only need to select the speed V corresponding to the amount of data close to the amount of data at node i. i start The purpose of designing and selecting the initial speed of the drone is to quickly lock the optimal range of the drone's flight speed.

[0055] 2) According to the amount of data to be uploaded by node i, Q i 、Current remaining energy E i (t), the initial flight acquisition speed V i start and at speed V i start The maximum amount of data collected under and the maximum amount of data collected during flight The constraints determine whether this node is collected and the specific collection mode:

[0056] a. Calculate the remaining energy E of node i after it is collected i (t+1):

[0057]

[0058] in Indicates that node i can only reach the starting point F at the farthest under this power spi ;

[0059] b. If E i (t+1)>0 and The UAV selects the overlapping flight acquisition mode; if E i (t+1)>0 and The drone selects the overlapping flight hovering acquisition mode; if E i (t+1)>0 and This means that the amount of data at the i-th node is too large, and its storage energy cannot support the completion of the node data transmission, so the data collection of this node is abandoned;

[0060] c. If E i (t+1)≤0 and The UAV performs non-overlapping flight acquisition mode; if E i (t+1)≤0 and The UAV performs non-overlapping flight hovering acquisition mode; if E i (t+1)≤0 and This means that the amount of data to be uploaded at node i is too large, and the node storage energy is insufficient, so the UAV gives up data collection at this node;

[0061] Furthermore, in S44, the optimal UAV flight acquisition speed is found and the flight trajectory optimization is completed. Specifically, after obtaining the initial speed of the UAV flight, the speed of the node needs to be further optimized. The difference from the design of the initial speed is that the speed change in this step will be smaller, which can better reflect the impact of the speed change on the mission time. According to the initial flight speed V of the UAV obtained by node i i start As well as the acquisition mode, the acquisition time minimization problem of node i is established:

[0062]

[0063]

[0064] V min ≤V i ≤V max

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] R i (t) = Wlog2(1+γ i (t))

[0071]

[0072]

[0073] By traversing and solving, we can find the optimal speed of node i and solve the optimal node transmission power and the flight path of the UAV under this speed through the sequence least square method; and obtain the mission time of node i.

[0074] The beneficial effects of the present invention are as follows: the present invention can well decompose the hybrid acquisition system of the base station and the drone, reducing the tasks of the drone acquisition system; at the same time, in the subsystem, the drone speed and the sensor node transmission power are adjusted on the premise of ensuring that data acquisition is completed before the node energy is exhausted, thereby improving the drone's acquisition efficiency.

[0075] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0077] Figure 1 This is a schematic diagram of the hybrid data collection system model of drones and base stations;

[0078] Figure 2 The flight trajectory diagram corresponding to different speeds when collecting single node data for the UAV;

[0079] Figure 3 This is a graph of the expected flight path of the UAV for collecting some nodes. DETAILED DESCRIPTION

[0080] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0081] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0082] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0083] See also Figures 1 to 3 , is a drone-assisted hybrid data collection method, specifically:

[0084] Step 1: Assuming that the total number of sensor nodes required for the hybrid system is K, establish a sensor node set S = {k, k = 1, 2, 3, ..., K}. The drone-assisted data acquisition subsystem is referred to as subsystem A, and the base station direct data acquisition subsystem is referred to as subsystem B. Decompose the hybrid data acquisition system as follows:

[0085] 1) Due to the limited bandwidth of the base station, assume that the base station can communicate with a maximum of M (1≤M≤K) sensor nodes. For subsystem B, because the base station's direct data collection speed is much faster than the drone's flight data collection speed, when constructing the subsystem, we try to maximize the number of sensor nodes served by the base station, that is, assign M nodes to subsystem B.

[0086] 2) When allocating, the remaining energy E of sensor node k must also be considered k Can it support the upload of all data? Define the energy consumed by the kth sensor node to upload data for:

[0087]

[0088] in, With P gc denote the transmission power and signal processing power of the kth node respectively; t k represents the data collection time of node k. It means that it can upload data to the base station. At this time, it is added to the set S B Otherwise, put it into the set S A .

[0089] Sensor node k collection time t k Calculated by the following formula:

[0090]

[0091] Among them, Q k is the amount of data to be uploaded by node k, R k It represents the transmission speed of the node, which is related to the node's transmission power and path loss. According to Shannon's theorem, the node's transmission speed is calculated as follows:

[0092]

[0093] Where W represents the channel bandwidth; h k represents the channel gain between node k and the base station; σ represents the noise power.

[0094] 3) The sensor node sets of subsystem A and subsystem B are respectively Statistical set S A and S B The number of nodes is N A and N B ; if N B ≤M, then the set S B All the nodes in the data are collected by the base station, that is, On the contrary, for the set S B The nodes in are filtered, and the filtering steps are as follows:

[0095] a. Combine, from set S B Random selection (N B -M) nodes, a total of Combination of species;

[0096] b. Combine each combination obtained in the previous step with the set S A Nodes in (N in total A +N B -M nodes) constitute the traveling salesman problem, and solve the problem through the hybrid particle swarm algorithm, select the combination with the shortest path, and obtain the sensor node set of subsystem A Set S B The remaining nodes in the subsystem are added to the sensor node set of subsystem B.

[0097] Step 2: Based on the principle of shortest overall acquisition time, establish the overall goal of the hybrid acquisition system, which is to minimize the maximum acquisition time of the two subsystems. A , t B denotes the completion time of all data of subsystems A and B respectively, then the overall goal of the hybrid system is expressed as: min(max{t A ,t B}).

[0098] Step 3: For subsystem B, calculate its data acquisition time:

[0099] 1) Orthogonal frequency division multiple access (OFDMA) is used in subsystem B to transmit data from node to base station. This technology divides the bandwidth resources into M sub-channels, each of which is allocated to one node. The communication bandwidth W of the jth node in j for:

[0100]

[0101] Where M represents the maximum number of communication sub-channels that the base station can accommodate; W represents the channel bandwidth.

[0102] The collection time t of node j j for:

[0103]

[0104] 2) After calculating the acquisition time of each node, the data acquisition time t of subsystem B can be obtained B for:

[0105]

[0106] in, Represents the number of sensor nodes in subsystem B.

[0107] Step 4: Optimize the UAV flight speed, path, and sensor node transmission power to obtain the minimum task completion time of subsystem A:

[0108] 1) Obtain the initial flight path of the UAV, that is, determine the node collection order. Here, we do not consider the UAV's data collection task, but only focus on how to obtain the shortest path. The flight path minimization problem is converted into a traveling salesman problem and solved using a hybrid particle swarm algorithm to determine the collection order.

[0109] 2) Within the UAV flight speed range, establish the maximum data volume problem of the UAV in the two acquisition modes of flight acquisition and flight hovering, and solve the maximum data volume of the two modes at different flight speeds. Specifically:

[0110] A. Create a collection The problem of maximizing the amount of data collected by the UAV in non-overlapping mode is:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] R i (t) = Wlog2(1+γ i (t))

[0118]

[0119]

[0120] Where A i (t) represents the trajectory of the UAV within the coverage range of the node at time slot t, where the starting point Entry point to communicate with the node The drone's hovering point and exit coverage points connected; Indicates the exact center of the node's coverage area; and P tmax Represent the minimum and maximum transmit power of the node respectively; and P gc Respectively represent the node's transmission power and signal processing power consumption; and They represent the UAV’s flight collection time, hovering collection time, and flight time from the starting point to the boundary of the coverage range of node i; R i (t) represents the transmission rate of the UAV in time slot t; W represents the channel bandwidth; γ i (t) and They represent the signal-to-noise ratio and signal-to-noise ratio threshold between the UAV and the node respectively; h i (t) represents the channel gain at time t; σ represents the noise power; V i Indicates the speed of the UAV when it flies to collect data at node i, where V min ≤V i ≤V max ; δ i Indicates the maximum flight speed V max to V i Or from V i to V max The difference in accelerated flight time, Where a is the acceleration of the drone.

[0121] B. Use the sequential least squares method to solve the above problem and obtain the value of the velocity V i The maximum amount of data collected by the UAV flight at the time of , that is, the maximum amount of data in non-overlapping mode To obtain the maximum amount of data collected at different flight speeds, you only need to set different speed constraints, that is, change V i At the same time, to calculate the maximum amount of data in flight collection mode, you only need to set the hovering collection time. Solve the problem in the same way to obtain the maximum amount of flight data collected in non-overlapping acquisition mode

[0122] 3) Based on the amount of data to be uploaded by the node and the obtained threshold, determine whether each node can be collected; if it can, then determine the drone's collection mode and initial flight speed for the node; if it cannot, the drone cannot collect the node; the details are as follows:

[0123] A. First determine the initial flight collection speed of the drone. Specifically, different speeds will obtain the maximum amount of data at that speed. You only need to select the speed V corresponding to the closest amount of data based on the amount of data at node i. i start The purpose of designing and selecting the initial speed of the drone is to quickly lock the optimal range of the drone's flight speed.

[0124] B. According to the amount of data to be uploaded by node i, Q i 、Current remaining energy E i (t), the initial flight acquisition speed V i start and at speed V i start The maximum amount of data collected under and the maximum amount of data collected during flight The constraints determine whether this node is collected and the specific collection mode:

[0125] a. Calculate the remaining energy E of node i after it is collected i (t+1):

[0126]

[0127] in Indicates that node i can only cover the starting point at the farthest under this power

[0128] b. If E i (t+1)>0 and The UAV selects the overlapping flight acquisition mode; if E i (t+1)>0 and The drone selects the overlapping flight hovering acquisition mode; if E i (t+1)>0 and This means that the amount of data at the i-th node is too large, and its storage energy cannot support the completion of the node data transmission, so the data collection of this node is abandoned;

[0129] c. If E i (t+1)≤0 and The UAV performs non-overlapping flight acquisition mode; if E i (t+1)≤0 and The UAV performs non-overlapping flight hovering acquisition mode; if E i (t+1)≤0 and This means that the amount of data to be uploaded at node i is too large, and the node storage energy is insufficient, so the UAV gives up data collection at this node;

[0130] 4) After obtaining the initial flight speed of the drone, the node speed needs to be further optimized. The difference from the initial speed design is that the speed change in this step will be smaller. Therefore, according to the initial flight speed V of the drone obtained by node i i start As well as the acquisition mode, the acquisition time minimization problem of node i is established:

[0131]

[0132]

[0133] V min ≤V i ≤V max

[0134]

[0135]

[0136]

[0137]

[0138]

[0139] R i (t) = Wlog2(1+γ i (t))

[0140]

[0141]

[0142] By traversing and solving, we can find the optimal speed of node i and use the sequence least square method to solve the optimal node transmission power and the flight path of the UAV at this speed, and obtain the mission time of node i.

[0143] The final task completion time of subsystem A is the time it takes for the UAV to complete the acquisition of all nodes by varying the speed of the flight along the optimized path. That is, the task completion time of subsystem A is as follows:

[0144]

[0145] in Representing a collection The number of nodes.

[0146] Step 5: Obtain the nodes and After connecting, you can obtain the flight collection path of the drone, that is, the flight trajectory is: in Indicates the starting and ending points of the drone, and also the location of the base station. The drone adjusts its flight speed and path collection according to the optimization results, and can complete the collection task efficiently. In order to feel the optimized path more intuitively, you can see Figure 3 The red solid line in represents the ideally optimized path, whose path length is significantly smaller than the initial path length.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A drone-assisted hybrid data acquisition method, characterized by: The method comprises the following steps: S1: A hybrid data collection system consisting of drones, base stations, and sensor nodes. The drone acts as a mobile collector and works with the base station to collect data from all sensor nodes in the area. To improve the efficiency of the hybrid data collection system, the hybrid data collection system is decomposed into two subsystems based on the base station bandwidth and basic information of the sensor nodes: drone-assisted data collection subsystem A and base station direct data collection subsystem B. S2: Based on the principle of shortest overall acquisition time, the overall goal of establishing a hybrid acquisition system is to minimize the maximum acquisition time of the two subsystems; if t A , t B denotes the completion time of all data of subsystems A and B respectively, then the overall goal of the hybrid system is expressed as: min(max{t A ,t B }); S3: For subsystem B, calculate its data acquisition time t B , which is the completion time of the node with the longest collection time in the system; S4: For subsystem A, its data collection time t A The time required for the UAV to collect data from all nodes in the system is calculated. By establishing a time minimization problem, the flight speed, path, and transmission power of the sensor nodes of the UAV are optimized to obtain the minimum task completion time of subsystem A.

2. The drone-assisted hybrid data acquisition method according to claim 1, characterized in that: In S1, the hybrid data acquisition system is decomposed as follows: Let the total number of sensor nodes that need to be collected by the hybrid system be K, establish the sensor node set S = {k, k = 1, 2, 3, ..., K}, and classify the sensor nodes into subsystems according to the following steps: S11: Due to the limited bandwidth of the base station, it is assumed that the base station can communicate with a maximum of M sensor nodes, 1≤M≤K; for the base station direct acquisition subsystem B, when constructing the subsystem, try to maximize the number of sensor nodes served by the base station, that is, allocate M nodes to the base station acquisition subsystem; S12: When allocating, the remaining energy E of sensor node k must also be considered k Whether it can support the upload of all data; define the energy consumed by the kth sensor node to upload data for: in, With P gc denote the transmission power and signal processing power of the kth node respectively; t k represents the data collection time of node k; if It means that it can upload data to the base station and add it to the set S B Otherwise, put it into the set S A ; Sensor node k collection time t k Calculated by the following formula: Among them, Q k is the amount of data to be uploaded by node k, R k It represents the transmission speed of the node, which is related to the node's transmission power and path loss. According to Shannon's theorem, the node's transmission speed is calculated as follows: Where W represents the channel bandwidth; h k represents the channel gain between node k and the base station; σ represents the noise power; S13: The sensor node sets of subsystem A and subsystem B are Statistical set S A and S B The number of nodes is N A and N B ; if N B ≤M, then the set S B All the nodes in the data are collected by the base station, that is, On the contrary, for the set S B The nodes in are filtered, and the filtering steps are as follows: a. Combine, from set S B Random selection (N B -M) nodes, a total of Combination of species; b. Total N A +N B -M nodes, each combination obtained in the previous step is combined with the set S A The nodes in the system constitute the traveling salesman problem, and the problem is solved by hybrid particle swarm optimization, the combination with the shortest path is selected, and the sensor node set of subsystem A is obtained. Set S B The remaining nodes in the subsystem are added to the sensor node set of subsystem B.

3. The drone-assisted hybrid data acquisition method according to claim 1, characterized in that: The data collection time of subsystem B is calculated in S3, that is, the completion time of the node with the longest collection time in the system is obtained, as follows: S31: Orthogonal frequency division multiple access communication technology is used in subsystem B to transmit data from the node to the base station. This communication technology divides the bandwidth resources evenly into the maximum communication sub-channels that can be accommodated by M base stations, and each sub-channel is allocated to one node; for the set The communication bandwidth W of the jth node in j for: Where M represents the maximum number of communication sub-channels that the base station can accommodate; W represents the channel bandwidth; The collection time t of node j j for: S32: After calculating the acquisition time of each node, the data acquisition time t of subsystem B is obtained B for: in, Represents the number of sensor nodes in subsystem B.

4. The drone-assisted hybrid data acquisition method according to claim 1, characterized in that: In S4, the minimum task completion time of subsystem A is obtained by optimizing the flight speed, path, and transmission power of the UAV, as follows: S41: Obtain the initial flight path of the UAV, that is, determine the node collection order; do not consider the data collection task of the UAV, only consider how to obtain the shortest path; convert the flight path shortest problem into a traveling salesman problem, and solve it using a hybrid particle swarm algorithm to obtain the collection order; S42: Within the UAV flight speed range, establish the maximum data volume problem of the UAV in two acquisition modes: flight acquisition and flight hovering. Use the sequential least squares method to solve the maximum data volume of the two modes at different flight speeds; S43: Based on the amount of data to be uploaded by the node and the obtained threshold, determine whether each node can be collected; if so, determine the collection mode of the drone and the initial flight speed of the drone for the node; If not, the drone cannot collect the node; S44: Optimize the speed of the drone, establish the problem of minimizing the acquisition time of a single node, and solve it through the least sequence square method and ergodic method to obtain the optimal flight path, flight speed and transmission power of each node; suppose that for the set The optimized data collection time obtained by the i-th node in is The final task completion time in subsystem A is the time it takes for the UAV to complete the collection of all nodes by performing variable speed flight along the optimized path, that is: in Represents the number of sensor nodes in subsystem A.

5. The drone-assisted hybrid data acquisition method according to claim 4, characterized in that: In S42, when solving the problem of maximizing the amount of data collected by the drone at different speeds, the distance between the drone and the node is minimized only when the drone flies directly above the node, achieving the maximum data transmission rate and being the optimal hovering collection position. The problem of maximizing the amount of data collected by the drone at node i in the non-overlapping mode is established: R i (t)=Wlog2(1+γ i (t)) Where A i (t) represents the trajectory of the UAV within the coverage range of the node at time slot t, where the starting point Entry point to communicate with the node The drone's hovering point and exit coverage points connected; Indicates the exact center of the node's coverage area; and P tmax Represent the minimum and maximum transmit power of the node respectively; and P gc Respectively represent the node's transmission power and signal processing power consumption; and They represent the UAV’s flight collection time, hovering collection time, and flight time from the starting point to the boundary of the coverage range of node i; R i (t) represents the transmission rate of the UAV in time slot t; W represents the channel bandwidth; γ i (t) and They represent the signal-to-noise ratio and signal-to-noise ratio threshold between the UAV and the node respectively; h i (t) represents the channel gain at time t; σ represents the noise power; V i Indicates the speed at which the UAV flies to collect data from node i, where V min ≤V i ≤V max ; δ i Indicates the maximum flight speed V max to V i Or from V i to V max The difference in accelerated flight time, Where a is the acceleration of the drone; The sequential least squares method is used to solve the above problem and obtain the i The maximum amount of data collected by the UAV flight at the time of , that is, the maximum amount of data in non-overlapping mode To obtain the maximum amount of data collected at different flight speeds, you only need to set different speed constraints, that is, change V i At the same time, to calculate the maximum amount of data in flight acquisition mode, you only need to set the hovering acquisition time. Solve the problem in the same way and obtain the maximum amount of flight data collected in non-overlapping acquisition mode 6. The drone-assisted hybrid data acquisition method according to claim 4, characterized in that: In S43, based on the amount of data to be uploaded by the node and the obtained threshold, the collection mode of each node and the initial flight collection speed of the drone need to be judged, specifically: 1) First determine the initial flight collection speed of the drone. Specifically, different speeds will obtain the maximum amount of data at that speed. According to the data size of node i, the speed V corresponding to the closest data volume is selected. i start The initial speed of the drone is designed to lock the optimal range of the drone's flight speed as quickly as possible. 2) According to the amount of data to be uploaded by node i, Q i 、Current remaining energy E i (t), the initial flight acquisition speed V i start and at speed V i start The maximum amount of data collected under and the maximum amount of data collected during flight The constraints determine whether this node is collected and the specific collection mode: a. Calculate the remaining energy E of node i after it is collected i (t+1): in Indicates that node i can only cover the starting point at the farthest under this power P gc represents the signal processing power of the kth node; Indicates the exact center of the node's coverage area; b. If E i (t+1)>0 and The UAV selects the overlapping flight acquisition mode; if E i (t+1)>0 and The drone selects the overlapping flight hovering acquisition mode; if E i (t+1)>0 and This means that the amount of data at the i-th node is too large, and its storage energy cannot support the completion of the node data transmission, so the data collection of this node is abandoned; c. If E i (t+1)≤0 and The UAV performs non-overlapping flight acquisition mode; if E i (t+1)≤0 and The UAV performs non-overlapping flight hovering acquisition mode; if E i (t+1)≤0 and This means that the amount of data to be uploaded at node i is too large, and the node storage energy is insufficient, so the UAV gives up data collection at this node.

7. The drone-assisted hybrid data acquisition method according to claim 4, characterized in that: In the above S44, the optimal UAV flight acquisition speed is found and the flight trajectory optimization is completed. Specifically, after obtaining the initial speed of the UAV flight, the speed of the node is further optimized. According to the initial flight speed V of the UAV obtained by node i, i start As well as the acquisition mode, the acquisition time minimization problem of node i is established: In min ≤V i ≤V max R i (t)=Wlog2(1+γ i (t)) The optimal speed of node i is found through traversal and the optimal node transmission power and the flight path of the UAV are solved by sequential least squares method under this speed. And get the task time of node i

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