Age minimization joint optimization method for data acquisition information under assistance of multiple unmanned aerial vehicles

Abstract

The invention discloses a data acquisition information age minimization joint optimization method under the assistance of multiple unmanned aerial vehicles, and belongs to the field of wireless communication. The optimization method comprises the following steps: constructing and re-expressing an initial model of the multi-unmanned aerial vehicle auxiliary data acquisition system; initializing an iteration frequency variable and a hovering point corresponding to the ground sensor node; a hovering position # imgabs0 # is given, an initial model of the multi-unmanned-aerial-vehicle auxiliary data acquisition system is expressed as a vehicle path problem with energy constraint to be optimized, optimal multi-unmanned-aerial-vehicle task allocation # imgabs1 # and an access sequence # imgabs2 # are obtained, the initial model of the multi-unmanned-aerial-vehicle auxiliary data acquisition system is optimized in combination with a hovering position # imgabs3 # corresponding to a ground sensor node, and the optimal multi-unmanned-aerial-vehicle task allocation # imgabs1 # and the optimal access sequence # imgabs2 # are obtained. The hovering position # imgabs4 # corresponding to the optimal ground sensor node is obtained; and then, the task allocation # imgabs5 #, the access sequence # imgabs6 # and the hovering position # imgabs7 # are substituted for iterative optimization, and an optimal solution is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communications, and in particular relates to a joint optimization method for minimizing the age of data collection information assisted by multiple unmanned aerial vehicles. Background Art

[0002] The Internet of Things (IoT) integrates the physical world and information space of human beings and has been widely used in transportation, industry and other fields. IoT devices monitor and generate data, which needs to be transmitted to the base station for data analysis and processing. Since some devices are far away from the base station, the data cannot be directly transmitted to the base station. The rapid development of drone technology provides a feasible solution to the above problems.

[0003] According to the number of drones, drone-assisted IoT can be divided into single-drone-assisted and multi-drone-assisted. Although the operation mechanism of a single drone-assisted IoT system is relatively simple, the data collection performance is far from satisfactory. Compared with single-drone-assisted IoT, multi-drone-assisted IoT has outstanding advantages in mission coverage and data collection efficiency, especially in sparse deployment scenarios.

[0004] However, due to the energy limitation of drones and the energy-saving requirements of sensor nodes, multiple drones need to conduct comprehensive collaborative task planning. In the scenario of multi-drone-assisted IoT data collection, traditional methods often independently optimize task allocation, trajectory planning or energy consumption allocation, ignoring the coupling relationship between these factors, resulting in problems such as untimely sensor node information update and low system energy efficiency. Therefore, a joint optimization method for minimizing the age of data collection information assisted by multiple drones is proposed. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a joint optimization method for minimizing the age of data collection information assisted by multiple drones, which solves the problems in the prior art.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The joint optimization method for minimizing the age of data collection information assisted by multiple UAVs includes the following steps: S1, construct the initial model of multi-UAV assisted data collection system based on multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model; S2, determine the expression of energy transfer time and reformulate the initial model of multi-UAV assisted data acquisition system; S3, set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all ground sensor nodes ; S4, the hovering position corresponding to a given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; S5, given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; S6, optimal multi-drone task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into S4 and update the iteration count variable , repeat S4 to S5 until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

[0007] Furthermore, the multi-UAV assisted data collection system includes: a data center with charging facilities, M UAVs and K ground sensor nodes SN; the M UAVs operate on orthogonal frequency channels to collect data, and the channel bandwidth size allocated to each UAV is B; the ground equipment is stationary, and multiple UAVs are connected from the corresponding initial points of the data center. Departure, flying at a fixed altitude H, each UAV visits different hovering points, and hovers at each hovering point, transmits energy to the corresponding SN and collects data, and finally returns to the initial point .

[0008] Furthermore, the energy and data transmission model is expressed as: UAV At the hover point About SN Time of RF energy transmission , the time for SN to transmit data to the drone , SN The amount of data to be transferred, SN With UAV The data transfer rate between .

[0009] Furthermore, the energy consumption model of the UAV is: In the formula, The energy consumption of the drone, is the flight energy consumption, is the hovering energy consumption, is the energy transmission energy consumption; For drones Visited SN The horizontal coordinate of the corresponding hover point, For drones Visited SN The horizontal coordinate of the corresponding hover point, is the flight speed of the drone, so Drones Visited The flight time between the first hovering point and the next hovering point, ; Indicates drone The associated SN set, yes The replacement of SN labels in Indicates drone Service The label of each SN; Indicates the propulsion power consumption of the UAV; Indicates the hovering power consumption of the drone. Indicates the power used by the drone for energy transmission.

[0010] Furthermore, the information age model is: In the formula, For UAV from The average information age of all data collected by SN.

[0011] Furthermore, the initial model of the multi-UAV assisted data acquisition system is: In the formula, is the label of M drones, are the labels of K ground sensor nodes, is an associated variable, indicating that the drone Whether it is connected to the ground sensor node association; Indicates drone The associated SN set, yes The replacement of SN labels in Indicates drone Service The label of each SN; For drones From its associated SN The horizontal coordinates of the hovering position where data was collected; The transmission power of the SN uploading data; is the maximum energy consumption of the drone itself; is the energy transfer efficiency, is the energy transfer constant, which is affected by actual environmental factors. SN The horizontal coordinate of It represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

[0012] Furthermore, the reformulated initial model of the UAV-assisted data acquisition system is: .

[0013] The multi-UAV-assisted data collection information age minimization joint optimization system includes: Initial model building module: Based on the multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model, the initial model of the multi-UAV assisted data collection system is built; Model restatement module: Determine the expression of energy transfer time and restate the initial model of multi-UAV assisted data acquisition system; Initial parameter setting module: set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all sensor nodes ; First optimization module: hovering position corresponding to a given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; Second optimization module: Given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; And, iterative optimization module: optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into the first optimization module and update the number of iterations variable , repeat the first optimization module to the second optimization module until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

[0014] A computer storage medium stores a readable program, which can execute the above-mentioned joint optimization method for minimizing the age of data collection information assisted by multiple unmanned aerial vehicles when the program is running.

[0015] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned joint optimization method for minimizing the age of data collection information assisted by multiple drones.

[0016] Beneficial effects of the present invention: 1. The method proposed in the present invention can adapt to special application scenarios more flexibly and conveniently, realize more efficient collaborative task planning, and effectively improve the freshness of information and the overall performance of the network; the present invention considers multiple drones collecting data from ground equipment based on a joint optimization method, with the goal of minimizing the maximum value of the average information age of sensor nodes collected by each drone in multiple drones while satisfying the minimum ground sensor node energy acquisition and the limited energy consumption of drones, and jointly optimizes the multi-drone task allocation and access sequence, the corresponding hovering point position of each sensor node, and the hovering time.

[0017] 2. Since the proposed optimization problem is non-convex and difficult to solve, the present invention decomposes the problem into two sub-problems: task allocation and access order optimization and hovering point position optimization. Given the hovering point position, an improved genetic algorithm is proposed to optimize the task allocation and the access order of the hovering points associated with each UAV. Given the task allocation and the access order of the hovering points associated with each UAV, SCA is used to optimize the hovering point position. The optimization variables of these two sub-problems are optimized alternately, and the solution obtained in each iteration will be used as the input of the next iteration.

[0018] 3. The legitimacy of the solution obtained by the improved genetic algorithm proposed in the present invention can be guaranteed. The improved chromosome encoding method ensures the legitimacy of task allocation. Each SN is uniquely assigned, and the task allocation is balanced as much as possible. The improved fitness function ensures that the obtained solutions meet the energy constraints. For the chromosome encoding method proposed in this patent, the legitimacy of the chromosome after the crossover cannot be ensured under the existing crossover method. This patent proposes an improved zero-part mapping crossover (NZPMX) method to exchange the genes of the parent chromosome to ensure that all sensor nodes can be included in the chromosome after the crossover, and all drones have tasks.

[0019] 4. The simulation results of the present invention show the optimized trajectories of drones in different scenarios, and compared with the existing separation optimization schemes, such as the one-time iteration scheme combining K-means clustering and ant colony algorithm, the proposed algorithm achieves more efficient collaborative task planning, which effectively improves the information freshness and the overall network performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 It is a schematic diagram of the structure of the multi-UAV assisted data acquisition system of the present invention; Figure 2 It is a time sequence framework structure diagram of a single UAV of the present invention; Figure 3 It is a flow chart of the joint optimization method for minimizing the average information age of data collection nodes assisted by multiple UAVs of the present invention; Figure 4 The amount of data uploaded by the sensor nodes in the scheme proposed in the present invention, the comparative scheme 1, the comparative scheme 2 and the comparative scheme 3 A curve graph of the relationship. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example 1 like Figure 3 As shown, the joint optimization method for minimizing the age of data collection information assisted by multiple UAVs includes the following steps: S1, construct the initial model of multi-UAV assisted data collection system based on multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model; like Figure 1 As shown in the figure, the multi-UAV assisted data collection system includes: a data center (DC) with charging facilities, M maximum energy UAVs and K ground sensor nodes (SNs); all SNs are divided into M non-overlapping groups, each group is served by a UAV, Indicates drone The associated SN set. Fly by service order to The hovering points transmit energy to the ground sensor nodes and collect data from the ground sensor nodes. Indicates drone Whether it is connected to the ground sensor node Association, where sensor nodes Associated drones hour, ,otherwise . yes The replacement of SN labels in Indicates drone Service The label of each SN. For drones From its associated SN The horizontal coordinate of the hovering position where data is collected. For convenience of representation, define and , that is, all drones start from the initial point corresponding to the data center Departure, after completing the mission, return to the data center; the speed of each drone during flight is constant. .

[0024] M drones operate on orthogonal frequency channels to collect data, and the channel bandwidth allocated to each drone is B; the ground equipment is stationary, and multiple drones start from the initial point Starting from a fixed altitude H, each UAV visits different hovering points and hovers at each hovering point, transferring energy to the corresponding SN and collecting data, and finally returns to the initial point .in ; The ground sensor node set is represented as , the ground equipment height is 0 meters and the position is fixed, the horizontal coordinate is .

[0025] The energy and data transmission model is represented as: UAV At the hover point About SN Time of RF energy transmission , the time for SN to transmit data to the drone , SN The amount of data to be transferred, SN With UAV The transfer rate between ; therefore the total hovering time .

[0026] The energy consumption model of the UAV is represented as Energy consumption includes: flight energy consumption , hovering energy consumption and energy transmission energy consumption , drones The energy consumption (i.e., the energy consumption model of the drone) is expressed as: In the formula, The energy consumption of the drone, is the flight energy consumption, is the hovering energy consumption, is the energy transmission energy consumption; For drones Visited SN The horizontal coordinate of the corresponding hover point, For drones Visited SN The horizontal coordinate of the corresponding hover point, is the flight speed of the drone, so Drones Visited The flight time between the first hovering point and the next hovering point, ; Indicates drone The associated SN set, yes The replacement of SN labels in Indicates drone Service SN label, Indicates the propulsion power consumption of the UAV; Indicates the hovering power consumption of the drone. Indicates the power used by the drone for energy transmission.

[0027] The information age model is expressed as: In the formula, For UAV from Average information age of all data collected by SN, single UAV The time series framework structure diagram is as follows Figure 2 shown.

[0028] The initial model of the multi-UAV assisted data acquisition system is: In the formula, is an associated variable, indicating that the drone Whether it is connected to the ground sensor node association; Indicates drone The associated SN set, yes The replacement of SN labels in Indicates drone Service The label of each SN; For drones From its associated SN The horizontal coordinates of the hovering position where data was collected; is the label of M drones, are the labels of K ground sensor nodes, The transmission power of the SN uploading data; is the maximum energy consumption of the drone itself; is the energy transfer efficiency, is the energy transfer constant, which is affected by actual environmental factors. SN The horizontal coordinate of It represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

[0029] Formula C1 limits each SN to be associated with only one UAV; Formula C2 limits the data collection to all SNs; Formula C3 indicates For drones The associated SN set; Formula C4 restricts associated variables The value of can only be 0 or 1; Formula C5 limits the energy consumed by each SN to upload data to no greater than the energy it obtains; Formula C6 limits the energy consumption required by the drone to complete the task to no more than its own maximum energy consumption. is the maximum energy consumption of the drone itself; Formula C7 ensures that the starting point and end point of each drone are data centers .

[0030] S2, determine the expression of energy transfer time and reformulate the initial model of multi-UAV assisted data acquisition system; It can be proved that in the optimal solution of the initial model of the multi-UAV assisted data acquisition system, the equality relationship in constraint condition C5 holds. Otherwise, when other variables are fixed, can be reduced, the average information age is reduced, and other constraints are still met, resulting in a reduction in the target value. , the RF energy transmission time can be expressed as: in, It represents the received signal-to-noise ratio when the distance between the drone and the ground sensor node is 1m. Indicates the channel gain when the reference distance is 1m. represents the noise at the UAV, and B is the channel bandwidth allocated to the UAV.

[0031] The reformulated initial model of the UAV-assisted data acquisition system is: S3, set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all sensor nodes ; S4, the hovering position corresponding to a given sensor node Under the condition of , the initial model of the multi-UAV assisted data acquisition system in S2 is expressed as a vehicle routing problem (CVRP) with energy constraints; the optimal multi-UAV task allocation is obtained by optimizing based on the improved genetic algorithm. Access Order ; Obtaining optimal multi-UAV task allocation Access Order The steps are: S41, the hovering position corresponding to the given sensor node Under the condition of , the initial model of the UAV-assisted data acquisition system to be optimized is: S42, Initialize chromosome: The chromosome uses an integer sequence to represent the access order of SNs, and divides the allocation of sensor nodes by inserting zeros. This encoding method can fully describe the task execution process of the drone and take into account the legality of the solution. For example, there are 15 ground sensor nodes and 3 drones. The chromosome encoding is: It means that the ground sensor node Assign to drone , and the access order of the drone is ; Assign to drone , and the access order of the drone is ; Assign to drone , and the access order of the drone is .

[0032] S43, calculate the fitness function: The fitness function is designed as a combination of the optimization objective and the constraint penalty term. The optimization objective is to minimize the maximum average information age of the task , the constraint penalty term is , the final fitness function expression is: ,in is the fitness function of the genetic algorithm, is the maximum value of the average information age of M drones, The energy consumed by the drone exceeds its maximum energy consumption. If it does not exceed, it is 0. is the penalty coefficient, used to balance the optimization objective With constraints The weight between .

[0033] S44, selection: Using the tournament selection method, chromosomes with higher fitness are selected from the current population as elites to be retained for the next generation.

[0034] S45, crossover: The existing crossover method cannot ensure the legitimacy of the chromosome after the crossover. In order to ensure that the chromosome after the crossover can still contain all sensor nodes and each sensor node only appears once, the present invention proposes an improved zero-removing partial mapping crossover (NZPMX) method to exchange the genes of the parent chromosome. The specific steps are: first, record the position of zero in the chromosome and remove the zero in the chromosome to make it a new zero-free chromosome; second, perform partial mapping crossover on the zero-free chromosome; finally, insert zero into the zero-free chromosome so that the position of zero in the expanded chromosome is the same as before the zero removal.

[0035] S46, mutation: Randomly swap the positions of two genes in a chromosome to increase the diversity of solutions.

[0036] S47, the optimization iteration of the entire algorithm continues until the preset maximum iteration number is reached to obtain the optimal multi-UAV task allocation The order of visits with each drone .

[0037] S5, given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system in S2 is optimized to obtain the optimal hovering position corresponding to the ground sensor node ,and As input for the next iteration; Get the optimal hovering position corresponding to the sensor node The steps are: S51, task allocation for given multiple UAVs Access Order Under the condition of , the initial model of the UAV-assisted data acquisition system to be optimized is: S52, the hovering point position of each UAV is optimized separately, the goal is to minimize the average information age of the sensor nodes served by a single UAV, and bring the specific expression formula of time and energy into the problem. The model can be restated as: S53, in and Introduce a set of slack variables into the constraints ,in: By replacing the corresponding terms with slack variables, we obtain: S54, task allocation for given multiple UAVs Access Order Under the condition of , the initial model of the UAV-assisted data acquisition system to be optimized is updated as follows: S55, using the successive convex approximation (SCA) technique to obtain data transmission rate In a given Iteration point When the norm squared The first-order Taylor expansion expression of is: for The lower bound function expression after the first-order Taylor expansion is: in, for In a given Iteration point About the squared norm The first-order derivative of is specifically expressed as: S56, in In the iteration, given the task allocation of multiple drones The order of visits with each drone And the hover point position Under the condition of , the initial model of the UAV-assisted data acquisition system to be optimized is updated as follows: In the formula, for The lower bound function expression after the first-order Taylor expansion of .

[0038] S6, a joint optimization method based on alternating optimization allocates the optimal multi-UAV task Access Order The hovering position corresponding to the ground sensor node Substitute into S4 and update the iteration count variable , repeat S4 to S5 until the initial model of the multi-UAV assisted data acquisition system reaches the maximum number of iterations , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

[0039] Compared with traditional methods that often independently optimize or separately optimize task allocation, trajectory planning or energy consumption allocation, ignoring the coupling relationship between these factors, resulting in problems such as untimely sensor node information update and low system energy efficiency, the framework proposed in the present invention can adapt to special application scenarios more flexibly and conveniently, realize more efficient collaborative task planning, and effectively improve information freshness and overall network performance.

[0040] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program, which, when the program is running, can execute the above-mentioned joint optimization method for minimizing the age of data collection information with the assistance of multiple drones.

[0041] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned joint optimization method for minimizing the age of data collection information assisted by multiple drones.

[0042] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned joint optimization method for minimizing the age of data collection information with the assistance of multiple drones.

[0043] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0044] Example 2 In this embodiment, the scheme of the present invention is described in detail in conjunction with a specific embodiment. The embodiment is simulated using MATLAB software. The specific parameters are set as shown in Table 1; Table 1 Simulation parameters In addition to the above table, the coordinates of the four ground devices are set as =(150,-200), =(-300,400), =(250,-350), =(-450,-100) , =(400,250) , =(-120,-450) , =(350,300) , =(-200,-300) , =(100,150) , =(-400,200) , =(275,-275) , =(-375,-125) , =(425,375) , =(-275,350) , =(50,-400).

[0045] In this embodiment, the joint optimization scheme of the present invention is compared with comparative schemes 1, 2 and 3; wherein comparative scheme 1 is an improved genetic algorithm combined with a successive convex approximation method, comparative scheme 2 is a K-means clustering algorithm and an ant colony algorithm combined with a successive convex approximation method, and comparative scheme 3 is a K-means clustering algorithm combined with an ant colony algorithm; from Figure 4 It can be seen from the comparison of the amount of data uploaded by different ground sensor nodes that the larger the amount of data uploaded by the ground sensor node, the larger the minimized maximum average information age. This is because the drone needs to spend more hovering time at the hovering point for energy transmission and data collection, and the total information age of the system increases. In addition, the joint optimization scheme proposed in the present invention minimizes the maximum average information age under different amounts of data uploaded by the ground sensor nodes, which is smaller than that of the comparison schemes 1-3. This is because the joint optimization scheme proposed in the present invention can better consider the coupling relationship between various factors.

[0046] Example 3 Based on the multi-UAV assisted data collection information age minimization joint optimization method proposed in Example 1, this embodiment proposes a multi-UAV assisted data collection information age minimization joint optimization system, which specifically includes: Initial model building module: Based on the multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model, the initial model of the multi-UAV assisted data collection system is built; Model restatement module: Determine the expression of energy transfer time and restate the initial model of multi-UAV assisted data acquisition system; Initial parameter setting module: set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all sensor nodes ; The first optimization module: the hovering position corresponding to the given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; Second optimization module: Given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; And, iterative optimization module: optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into the first optimization module and update the number of iterations variable , repeat the first optimization module to the second optimization module until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

[0047] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0048] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A joint optimization method for minimizing the age of data collection information assisted by multiple drones, characterized in that: The following steps are involved: S1, construct the initial model of multi-UAV assisted data collection system based on multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model; S2, determine the expression of energy transfer time and reformulate the initial model of multi-UAV assisted data acquisition system; S3, set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all ground sensor nodes ; S4, the hovering position corresponding to a given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; S5, given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; S6, optimal multi-drone task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into S4 and update the iteration count variable , repeat S4 to S5 until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

2. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 1 is characterized in that: The multi-UAV assisted data collection system includes: a data center with charging facilities, M UAVs and K ground sensor nodes; the M UAVs operate on orthogonal frequency channels to collect data, and the channel bandwidth allocated to each UAV is B; the ground equipment is stationary, and multiple UAVs start from the corresponding initial points in the data center. Starting from the fixed altitude H, each UAV visits different hovering points and hovers at each hovering point, transmitting energy to the corresponding ground sensor nodes and collecting data, and finally returns to the starting point .

3. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 1 is characterized in that: The energy and data transmission model is represented as: At the hover point Ground sensor nodes Time of RF energy transmission , the time it takes for the ground sensor node to transmit data to the UAV , Ground sensor nodes The amount of data to be transferred, Ground sensor nodes With drones The data transfer rate between .

4. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 3 is characterized in that: The energy consumption model of the UAV is: In the formula, The energy consumption of the drone, is the flight energy consumption, is the hovering energy consumption, is the energy transmission energy consumption; For drones Visited Ground sensor nodes The horizontal coordinate of the corresponding hover point, For drones Visited Ground sensor nodes The horizontal coordinate of the corresponding hover point, is the flight speed of the drone, so Drones Visited The flight time between the first hovering point and the next hovering point, ; Indicates drone The associated set of ground sensor nodes, yes The permutation of ground sensor node labels in Indicates drone Service Tags of ground sensor nodes; Indicates the propulsion power consumption of the UAV; Indicates the hovering power consumption of the drone. Indicates the power used by the drone for energy transmission.

5. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 4 is characterized in that: The information age model is: In the formula, For drones from The average information age of all data collected by ground sensor nodes.

6. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 5 is characterized in that: The initial model of the multi-UAV assisted data acquisition system is: In the formula, is the label of M drones, are the labels of K ground sensor nodes, is an associated variable, indicating that the drone Whether it is connected to the ground sensor node association; Indicates drone The associated set of ground sensor nodes, yes The permutation of ground sensor node labels in Indicates drone Service Tags of ground sensor nodes; For drones From its associated ground sensor node The horizontal coordinates of the hovering position where data was collected; Transmission power for uploading data from ground sensor nodes; is the maximum energy consumption of the drone itself; is the energy transfer efficiency, is the energy transfer constant, which is affected by actual environmental factors. Ground sensor nodes The horizontal coordinate of It represents the channel gain when the reference distance is 1m, and H is the flight altitude of the UAV.

7. The multi-UAV-assisted data collection information age minimization joint optimization method according to claim 6 is characterized in that: The reformulated initial model of the UAV-assisted data acquisition system is: 。 8. A joint optimization system for minimizing the age of data collection information assisted by multiple drones, characterized in that: include: Initial model building module: Based on the multi-UAV assisted data collection system, energy and data transmission model, UAV energy consumption model and information age model, the initial model of the multi-UAV assisted data collection system is built; Model restatement module: Determine the expression of energy transfer time and restate the initial model of multi-UAV assisted data acquisition system; Initial parameter setting module: set the number of iterations variable , maximum number of iterations , given the initial hovering points corresponding to all sensor nodes ; First optimization module: hovering position corresponding to a given ground sensor node Under the condition of , the initial model of multi-UAV assisted data acquisition system is expressed as a vehicle routing problem with energy constraints, and the improved genetic algorithm is used to optimize it to obtain the optimal multi-UAV task allocation Access Order ; Second optimization module: Given the optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node , based on the SCA method, the initial model of the multi-UAV assisted data acquisition system is optimized to obtain the optimal hovering position corresponding to the ground sensor node ; And, iterative optimization module: optimal multi-UAV task allocation Access Order The hovering position corresponding to the ground sensor node Substitute into the first optimization module and update the number of iterations variable , repeat the first optimization module to the second optimization module until the maximum number of iterations is reached , the iteration is terminated, and the optimal solution of the initial model of the multi-UAV assisted data acquisition system is obtained.

9. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the joint optimization method for minimizing the age of data collection information assisted by multiple drones as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the joint optimization method for minimizing the age of data acquisition information assisted by multiple drones as described in any one of claims 1-7.

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