Unmanned aerial vehicle assisted mixed data acquisition method

Abstract

The invention relates to an unmanned aerial vehicle assisted mixed data acquisition method, and belongs to the field of unmanned aerial vehicle assisted wireless communication. According to the method, the capability of communication between the base station and the nearby ground nodes is fully considered, the unmanned aerial vehicle is used as the mobile base station, and the unmanned aerial vehicle and base station mixed data acquisition method is constructed. The method comprises the following steps: firstly, decomposing a mixed data acquisition system into an unmanned aerial vehicle assisted data acquisition subsystem and a base station direct acquisition subsystem; secondly, by taking minimization of the maximum task completion time of the two subsystems as a target, establishing an optimization problem of the system, and solving the optimization problem; and for a data acquisition subsystem assisted by the unmanned aerial vehicle, the flight speed, trajectory and ground node transmitting power of the unmanned aerial vehicle are jointly optimized, so that the overall task completion time of the unmanned aerial vehicle is minimized, and the data acquisition efficiency is improved. Various communication resources are fully utilized, the method is practical and efficient, field data can be timely and effectively collected, and the method is suitable for occasions such as fire monitoring and emergency rescue.

Description

Technical Field

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

[0002] In recent years, wireless sensor systems have been widely used in smart grids, including but not limited to power quality monitoring, power outage detection, overhead transmission line monitoring, fault detection and location, equipment fault diagnosis, and underground cable system monitoring. Traditional wireless sensor network data collection is transmitted to the aggregation node in a single hop or multi-hop manner, and then sent to the base station by the aggregation node. This method will accelerate the end of the life of nodes with large data transfer volume, thereby shortening the life of the network; currently, in order to address this problem, drones are introduced as mobile data collectors to form heterogeneous wireless sensor networks, which can extend the life of the system; at the same time, drones can communicate with nodes at a closer distance without obstruction, which can also improve the communication quality.

[0003] Although using drones for data collection has many advantages, it still faces the problem of limited energy and the collection speed is not as fast as the traditional system in a multi-hop manner. At present, most of the research work has done a lot of very meaningful research on the path planning method of drone-assisted sensor network data collection. However, these studies ignore that nodes within the coverage of the base station can spontaneously transmit data to the data center, thereby reducing the workload of the drone; at the same time, these studies do not fully consider the impact of the drone's flight speed on the trajectory and collection tasks, and the limited energy of sensor nodes in actual scenarios. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a hybrid data collection method assisted by a drone. In view of the fact that the base station can also perform sensor node collection tasks when idle, a data collection system that cooperates with the base station and the drone is designed to reduce the collection tasks of the drone. At the same time, in order to further complete the collection tasks efficiently; in the drone collection system, the limited energy of the node is considered and the node data is guaranteed to be fully transmitted. The trajectory, flight speed and transmission power of the drone are adjusted to minimize 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: UAVs, base stations and sensor nodes constitute a hybrid data collection system. The UAV acts as a mobile collector and completes the data collection of all sensor nodes in the area together with the base station. In order to improve the collection efficiency of the hybrid data collection system, according to the base station bandwidth, basic information of sensor nodes (quantity, location, energy), etc., the hybrid collection system is decomposed into two subsystems: UAV-assisted data collection subsystem A and base station direct 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 in 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 collection 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 established. 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, and the specific contents are as follows:

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

[0013] S11: Due to the limited bandwidth of the base station, it is assumed that the base station can communicate with a maximum of M (1≤M≤K) sensor nodes. For the base station direct data collection subsystem B, because the speed of the base station directly collecting data is much faster than the speed of the drone flight collection, 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 collection 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 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 Separate statistics set S A and S B The number of nodes is denoted as N A and N B ; if N B ≤M, then the set S B All the nodes in are collected by the base station, that is, On the contrary, for the set S B The nodes in are screened, and the screening steps are as follows:

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

[0024] b. Combine each combination obtained in the previous step with the set S A The nodes in (a total of N 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 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: In subsystem B, orthogonal frequency division multiple access communication technology is used for node-to-base station data transmission. This technology divides the bandwidth resources into M subchannels, each of which is allocated to a node. The communication bandwidth W of the jth node in j for:

[0027]

[0028] Wherein, 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 collection time of each node, the data collection 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; convert the flight path shortest problem into a traveling salesman problem, and solve it through a hybrid particle swarm algorithm to obtain the collection order;

[0036] S42: Within the flight speed range of the drone, the maximum data volume problem of the drone in the two acquisition modes of flight acquisition and flight hovering is established, and the maximum data volume of the two modes at different flight speeds is solved by the sequential least squares method;

[0037] S43: judging whether each node can be collected according to the amount of data to be uploaded by the node and the obtained threshold value; if it can be collected, judging the collection mode of the drone and the initial flight speed of the drone for the node; if it cannot be collected, 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 traversal 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 task completion time in the final subsystem A is the time it takes for the drone 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] Further, 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 the smallest only when the drone flies directly above the node, so the maximum data transmission rate can be obtained, which is the best hovering collection position; therefore, the problem of maximizing the amount of data collected by the drone at node i in the non-overlapping mode is established:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] R i (t) = Wlog2(1+γ i (t))

[0049]

[0050]

[0051] In the formula, A i (t) represents the trajectory of the drone within the node coverage range at time slot t, where the starting point Entry point to communicate with the node The drone's hovering point and exit coverage point Connected Indicates the exact center of the node’s coverage area; and P tmax Respectively represent the minimum and maximum transmit power of the node; and P gc They represent the transmission power and signal processing power consumed by the node respectively; 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 drone 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 flight data at different 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 the 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, according to 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 lock the optimal range of the drone's flight speed as quickly as possible.

[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 the farthest that node i can cover under this power is the starting point F spi ;

[0059] b. If E i (t+1)>0 and The UAV selects the overlapping flight collection 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 data volume of the i-th node is too large, and its storage energy cannot support the node data to complete the 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 collection 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 drone gives up data collection at this node;

[0061] Further, 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 task time. According to the initial flight speed V of the UAV obtained by node i i start And the collection mode, establish the collection time minimization problem of node i:

[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 drone 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 UAV, reducing the tasks of the UAV acquisition system; at the same time, in the subsystem, the UAV 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 UAV acquisition efficiency.

[0075] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can 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 in conjunction with the accompanying drawings, wherein:

[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 drone for collecting some nodes. DETAILED DESCRIPTION

[0080] The following describes the embodiments of the present invention by 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 only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

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

[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 the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position 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 Figure 1 to Figure 3 , is a drone-assisted hybrid data collection method, specifically:

[0084] Step 1: Assuming that the total number of sensor nodes that the hybrid system needs to collect is K, establish the sensor node set S = {k, k = 1, 2, 3, ..., K}, and record the drone-assisted data collection subsystem as subsystem A and the base station direct collection subsystem as subsystem B. Decompose the hybrid data collection system, the specific content is:

[0085] 1) Due to the limited bandwidth of the base station, it is assumed that the base station can communicate with a maximum of M (1≤M≤K) sensor nodes. For subsystem B, since the speed at which the base station directly collects data is much faster than the speed at which the drone flies, when constructing the subsystem, the number of sensor nodes served by the base station should be maximized, that is, M nodes should be allocated 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 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 Separate statistics set S A and S B The number of nodes is denoted as N A and N B ; if N B ≤M, then the set S B All the nodes in are collected by the base station, that is, On the contrary, for the set S B The nodes in are screened, and the screening steps are as follows:

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

[0096] b. Combine each combination obtained in the previous step with the set S A The nodes in (a total of N 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 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) In subsystem B, orthogonal frequency division multiple access communication technology is used for data transmission from node to base station. This technology will divide the bandwidth resources into M subchannels, each of which is allocated to one node. Therefore, for the set The communication bandwidth W of the jth node in j for:

[0100]

[0101] Wherein, 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 collection time of each node, the data collection 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 flight speed, path and transmission power of the UAV and obtain the minimum task completion time of subsystem A:

[0108] 1) Obtain the initial flight path of the drone, that is, determine the node collection order. Here, we do not consider the data collection task of the drone, but only focus on how to obtain the shortest path. The flight path shortest problem is converted into a traveling salesman problem, and the collection order is obtained by solving it through a hybrid particle swarm algorithm;

[0109] 2) Within the range of the UAV’s flight speed, 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 the non-overlapping mode of the i-th node in is:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] R i (t) = Wlog2(1+γ i (t))

[0118]

[0119]

[0120] In the formula, A i (t) represents the trajectory of the drone within the node coverage range at time slot t, where the starting point Entry point to communicate with the node The drone's hovering point and exit coverage point Connected Indicates the exact center of the node’s coverage area; and P tmax Respectively represent the minimum and maximum transmit power of the node; and P gc They represent the transmission power and signal processing power consumed by the node respectively; 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 drone when it flies to collect 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 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 flight data at different 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 the 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) According to the amount of data to be uploaded by the node and the obtained threshold, it is judged whether each node can be collected; if it can, the collection mode of the drone and the initial flight speed of the drone are judged for the node; if not, 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 according to the amount of data at node i. i start The purpose of designing and selecting the initial speed of the drone is to lock the optimal range of the drone's flight speed as quickly as possible.

[0124] B. Based on 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 collection 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 data volume of the i-th node is too large, and its storage energy cannot support the node data to complete the 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 collection 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 drone 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 And the collection mode, establish the collection time minimization problem of node i:

[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] The optimal speed of node i is found by traversal and the optimal node transmission power and the flight path of the drone are solved by sequential least squares method under this speed, and the mission time of node i is obtained.

[0143] The final task completion time of subsystem A is the time it takes for the drone to complete the collection of all nodes by performing variable speed 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 point and end point of the drone, and also indicates 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 more intuitively feel the optimized path, it can be seen Figure 3 The solid red 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 solution of the present invention rather than to limit it. 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 solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A drone-assisted hybrid data collection method, characterized in that: The method comprises the following steps: S1: UAVs, base stations and sensor nodes constitute a hybrid data collection system. The UAV acts as a mobile collector and completes the data collection of all sensor nodes in the area together with the base station. In order to improve the collection efficiency of the hybrid data collection system, the hybrid collection system is decomposed into two subsystems according to the base station bandwidth and the basic information of the sensor nodes: UAV-assisted data collection subsystem A and base station direct 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 in 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 }); S3: For subsystem B, calculate its data collection 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 established. 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 hybrid data acquisition method assisted by a drone according to claim 1, characterized in that: In S1, the hybrid data acquisition system is decomposed as follows: The total number of sensor nodes that need to be collected by the hybrid system is K, and the sensor node set S = {k, k = 1, 2, 3, ..., K} is established. The sensor nodes are classified 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 M sensor nodes at most, 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 E consumed by the kth sensor node to upload data SNk 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 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 Separate statistics set S A and S B The number of nodes is denoted as N A and N B ; if N B ≤M, then the set S B All the nodes in are collected by the base station, that is, On the contrary, for the set S B The nodes in are screened, and the screening steps are as follows: a. Combining, from set S B Random selection (N B -M) nodes, a total of Combination of 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 subsystem A constitute a traveling salesman problem, and the problem is solved by a hybrid particle swarm algorithm to select the combination with the shortest path and obtain the sensor node set of subsystem A. Set S B The remaining nodes in are added to the sensor node set of subsystem B.

3. The hybrid data acquisition method assisted by a drone 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, which is specifically as follows: S31: In subsystem B, orthogonal frequency division multiple access communication technology is used to transmit data from the node to the base station. This communication technology divides the bandwidth resources into M sub-channels on average, and each sub-channel is allocated to a 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 collection time of each node, the data collection time t of subsystem B is obtained B for: in, Represents the number of sensor nodes in subsystem B.

4. The hybrid data acquisition method assisted by a drone 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 through a hybrid particle swarm algorithm to obtain the collection order; S42: Within the flight speed range of the drone, the maximum data volume problem of the drone in the two acquisition modes of flight acquisition and flight hovering is established, and the maximum data volume of the two modes at different flight speeds is solved by the sequential least squares method; S43: judging whether each node can be collected according to the amount of data to be uploaded by the node and the obtained threshold value; if so, judging 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 minimum sequence square method and traversal 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 task completion time in the final subsystem A is the time it takes for the drone to complete the collection of all nodes by variable speed flight along the optimized path, that is: in Represents the number of sensor nodes in subsystem A.

5. The hybrid data acquisition method assisted by a drone 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 the smallest only when the drone flies directly above the node, and the maximum data transmission rate is obtained, which is the best 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: In the formula, A i (t) represents the trajectory of the drone within the node coverage range at time slot t, where the starting point Entry point to communicate with the node The drone's hovering point and exit coverage point Connected Indicates the exact center of the node’s coverage area; and P tmax Respectively represent the minimum and maximum transmit power of the node; and P gc They represent the transmission power and signal processing power consumed by the node respectively; 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 drone 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 flight data at different 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 the flight collection mode, you only need to set the hovering collection time Solve the problem in the same way and obtain the maximum amount of flight data collected in non-overlapping acquisition mode 6. The hybrid data acquisition method assisted by a drone according to claim 4, characterized in that: In S43, according to 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 volume of node i, select the speed V corresponding to the closest data volume. 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 after node i is harvested i (t+1): in Indicates that node i can only cover the starting point at the farthest under this power b. If E i (t+1)>0 and The UAV selects the overlapping flight collection 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 data volume of the i-th node is too large, and its storage energy cannot support the node data to complete the transmission, so the data collection of this node is abandoned; c. If E i (t+1)≤0 and The UAV performs non-overlapping flight collection 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 drone gives up data collection at this node; 7. The hybrid data acquisition method assisted by a drone 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 And the collection mode, establish the collection time minimization problem of node i: 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 through the sequence least squares method at this speed; And get the task time of node i

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