A ferry cruise implementation method based on drone swarm intelligence

Abstract

The present invention discloses a method for implementing ferry cruising based on the intelligence of drone swarms. This method addresses the problem of how to achieve information exchange and comprehensive integration between combat zones when mobile base stations are damaged in the field battlefield. The method establishes a drone swarm ferry cruising network, deploys ferry drones to cruise between zones and communicate with relay drones in each zone, releases computing resources or collects data in real time and uploads it to low-orbit satellites, and achieves an optimal method for the total delay of the ferry system. The present invention includes the ferry drone selecting a path based on the concentration of volatile information factors, iterating and gradually accumulating information factors on high-quality paths until the optimal route to the target is finally obtained, optimizing the hovering position of the ferry drone when transmitting with the relay drone, and calculating the optimized hovering position in three-dimensional space using similar attributes. Finally, the optimal cruising path for the ferry drone and the drone charging station is achieved, achieving efficient transmission and integration of battlefield information.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence, swarm intelligence technology, UAV cluster communication, UAV collaborative computing, UAV regional combat simulation, "satellite-ground-noise" integration technology, and ferry cruise technology, and specifically to a method for realizing ferry cruise based on UAV swarm intelligence. Background Art

[0002] Unmanned aerial vehicles (UAVs), or drones, are unmanned aircraft controlled by radio remote control or self-programmed programs. With their streamlined design, superior maneuverability, and compact size, UAVs offer significant advantages in both military and civilian applications. While carrying payloads such as cameras, sensors, target detectors, and communications equipment, UAVs can effectively navigate complex and ever-changing environments, enabling a wide range of novel applications, including cargo transportation, surveillance, remote sensing, environmental monitoring, and traffic control.

[0003] With the rapid development and innovation of drone technology, the importance of drones in military reconnaissance is becoming increasingly prominent. Drone types are becoming increasingly diverse, and their mission scope continues to expand. Initially performing simple tasks such as reconnaissance and surveillance in secure airspace, they have gradually evolved to perform complex combat missions such as penetration and strikes in contested airspace, achieving a leapfrog development. At the same time, the battlefield environment is evolving towards a networked, information-based, and systematized environment, exhibiting highly dynamic, highly contested, and extremely complex characteristics. In this context, a single drone, due to its limited payload capacity, is often unable to independently perform complex missions such as large-area surveillance and multi-target strikes. Consequently, drone combat methods are evolving, with drone swarms becoming a key trend in future drone operations. The allocation of functions among drones and the determination of their missions and flight paths are of irreplaceable importance for drone swarming and intelligent combat on the battlefield.

[0004] This research is based on a battlefield environment where ferry drones cruise between bases in a ground combat zone. At each base, they communicate with relay drones in that zone to obtain real-time combat zone information. After the cruise, the data is uploaded to a low-Earth satellite for timely processing and integration. The ferry drone's cruise calculation method utilizes swarm intelligence, simulating drone cruise behavior with animal group behavior and treating the entire group as a system to achieve mutual benefit and win-win results.

[0005] The traditional drone swarm combat mode relies on the command and control of ground base stations. However, in the harsh environment of the field battlefield, the deployment of ground base stations is extremely limited or even non-existent. Therefore, in the absence of base stations, the present invention constructs a drone swarm ferry cruising network. By deploying ferry drones to cruise between bases and communicate with relay drones within the bases, data is collected and quickly uploaded to low-orbit satellites (LEO), achieving efficient information exchange between different combat bases and comprehensive integration of key battlefield information. Summary of the Invention

[0006] The purpose of the present invention is to address the defects and shortcomings of the above-mentioned existing technologies and propose a method for implementing ferry cruising based on drone swarm intelligence. This method is applied to scenarios where drone clusters are widely used for combat in the future. If a method for implementing ferry cruising based on drone swarm intelligence is effectively selected, it allows the use of medium and low-orbit satellites or Starlink to collaboratively solve the problems of efficient information exchange between different combat bases and comprehensive integration of key battlefield information in the absence of ground mobile base stations.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for realizing ferry cruising based on the intelligence of drone swarms, the method comprising the following steps:

[0008] S1. Establish a drone swarm ferry cruise network, deploy relay drones and ferry drones, and set their coordinate parameters and cruise paths.

[0009] S2. Analyze the delay components of the drone swarm ferry cruise system, including flight delay, data transmission delay, and data upload delay, and obtain the calculation method of the total system delay.

[0010] S3. With the goal of minimizing the total system delay, a system optimization model is established. Through the intelligent algorithm of drone swarm cruise path planning, the flight trajectory of the drone ferry cruise network is optimized.

[0011] S4. Based on the solved optimal R-UAV access sequence, the hovering position of the ferry UAV is further optimized to obtain the optimal trajectory calculation method based on the UAV hovering position, which serves as the final UAV swarm ferry cruising intelligent algorithm.

[0012] A. The steps to establish a drone swarm ferry cruise network are as follows:

[0013] A1. Relay UAV (R-UAV): In the harsh environment of a field battlefield, the entire battlefield is divided into multiple battle zones, and an R-UAV is deployed in each zone, hovering above. Set the coordinate parameters of the relay UAV.

[0014] A2. Ferry UAV (F-UAV): Deploy an F-UAV Charging Station (FCS) and an F-UAV throughout the battlefield. The F-UAV departs from the FCS and follows a pre-set flight path, visiting each base ring one by one and exchanging data with the R-UAV. After completing its patrol of all battle zones, it returns to the FCS, transmits all collected data to the Low Earth Orbit (LEO) control center, and prepares for a new patrol. Set the F-UAV's coordinate parameters and the hovering distance between the F-UAV and the R-UAV.

[0015] A3. Cruise Path: Considering that only one relay UAV is deployed in each battle zone, the cruise path in the battle zone can be regarded as the order in which the F-UAV visits the R-UAV. A set of paths is set for any two bases.

[0016] B. The calculation method of the total system delay is as follows:

[0017] The total system latency is primarily composed of the F-UAV's flight latency, the data transmission latency between the F-UAV and R-UAV, and the data upload latency of the F-UAV uploading data to LEO. The calculation methods for each latency are described below.

[0018] B1. Flight delay: Decompose the flight delay of the F-UAV into the flight delay between each R-UAV:

[0019]

[0020] Where, It represents the flight time taken by the F-UAV from the starting point FCS to the first R-UAV to be visited. Indicates that F-UAV is relaying drones and The flight time required between Indicates the time it takes for the F-UAV to return to the FCS after completing the cruise.

[0021] Assume that F-UAV is flying at a constant speed, and the flight speed is v f , so the flight delay of the UAV is proportional to its flight path, T f It can be calculated by formula (2):

[0022]

[0023] The numerator is the total cruising distance. Assuming that the F-UAV travels along the shortest straight line between each point, each component is expressed as Euclidean distance, which is calculated by formulas (3)(4)(5):

[0024]

[0025]

[0026]

[0027] in, These are the hovering positions of the F-UAV in each battle zone.

[0028] B2. Data transmission delay

[0029] F-UAV collects data delay in various war zones The total amount of data cache in the corresponding area and the communication rate between F-UAV and R-UAV Related, expressed as:

[0030]

[0031] Communication rate It can be calculated by the following formula:

[0032]

[0033] in, represents the channel bandwidth, Relay drone The maximum transmission power, N0 is the noise power spectrum density, g0 is the channel power gain at the reference distance. hour, However, this situation is unlikely to happen in reality. Considering the safety accidents such as collision when UAVs are too close, the hovering distance between F-UAV and R-UAV should be greater than a certain threshold d safe .

[0034] Therefore, the system's data transmission T d It can be expressed as:

[0035]

[0036] B3. Data upload delay

[0037] When the F-UAV completes the cruise and returns to the FCS, it needs to upload all the collected data to the low-orbit satellite LEO. F,L =h L , d F,L is the distance between F-UAV and LEO, h L is the altitude of LEO.

[0038] Assuming that the channel does not change with time during transmission, is the received power of the ferry UAV, R F,L is the uplink data rate achievable on LEO, which can be expressed as:

[0039]

[0040]

[0041] Where, is the transmission power from the UAV to LEO, are the transmitting and receiving antenna gains from the UAV to LEO, respectively. L is the distance between LEO and the UAV. s is the wavelength of the LEO-UAV link. F,L , τ and ε are the bandwidth, noise temperature and Boltzmann constant of the LEO-Sat link allocation, respectively.

[0042] Therefore, the upload delay of F-UAV is T u It can be expressed as:

[0043]

[0044] C. The steps for establishing the overall system model are as follows:

[0045] The total system delay T can be expressed as:

[0046] T=T f +T d +T u (12)

[0047] The optimization goal is to minimize the total system delay:

[0048] min T(13)

[0049] Combining the constraints of the ferry drone cruise, we can get:

[0050] min T f +T d +T u (14)

[0051]

[0052] In the formula, constraint 1 limits the UAV flight delay, T cmax Indicates the maximum flight time after one charge, flight delay T f Not to exceed T cmax , and cannot be 0; Constraint 2 represents the hovering distance d between F-UAV and R-UAV mShould be greater than a certain threshold d safe ; Constraint 3 means that the war zone number accessed each time is different; Constraint 4 means that the sum of the total data buffer of each war zone is not less than the total data volume of the system.

[0053] The key point to be optimized in this invention is to determine a flight trajectory of F-UAV so as to minimize the flight delay, and the key point of flight trajectory planning is to find the sequence set of F-UAV's visits to each battle zone. The following is the path planning of the drone swarm intelligent algorithm:

[0054] In the F-UAV path planning problem, each UAV is regarded as an F-UAV and the path search is completed, such as Figure 2 As shown, the specific algorithm steps are as follows:

[0055] C1. Initialize the parameters of the drone swarm intelligent algorithm, including the drone swarm size pop, the maximum number of iterations max_iter, the information factor volatility coefficient ρ, the information factor total concentration Q, the information factor heuristic factor α, and the heuristic function value factor β.

[0056] C2. Initialize the UAV group information, set the charging station FCS as the access start and end point of the UAV group, and set the charging station d safe The hovering point at is set as the intermediate node that the drone group needs to visit, and the order set of the drone group visiting the R-UAV is randomly generated, which is called the path space. The information factor concentration τ between the hovering point i and the hovering point j is calculated. ij Set them to 1 and calculate the Euclidean distance d between each hovering point ij , and then according to the formula

[0057]

[0058] Calculate the heuristic function value η ij (t). Initialize the taboo table tabu of the drone swarm to an empty set.

[0059] C3, the UAV starts from FCS and goes to the first R-UAV in the path space, and then according to the state transition formula

[0060]

[0061] Select the next R-UAV to visit, add the corresponding serial number of the visited R-UAV to the taboo table tabu, and update the corresponding path length. is the probability that drone k transfers from node i to node j, allowed k is the set of next-hop nodes that can be selected by UAV k. α and β are the concentrations of the influencing information factor τ ij(t) and the heuristic function value η ij (t) The importance weight factor for the drone to select the next destination node. When the parameter α increases, the drone k is more inclined to choose the node with high information factor concentration as the direction of advance; when the parameter β increases, the drone is more likely to move towards its desired degree η ij (t) High node movement.

[0062] C4: Determine whether all drones have visited all R-UAVs to complete the data collection task. If there are still R-UAVs that have not been visited, return to C3; if all R-UAVs have been traversed, return to FCS and continue to execute the following steps.

[0063] C5. To prevent the algorithm from falling into a local optimal solution due to excessive concentration of information factors, we introduce an information factor volatilization mechanism to ensure that the information factor dissipates at a certain rate. The update strategy for the information factor concentration is as follows:

[0064] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t) (17)

[0065] Where ρ is the volatility coefficient of information factor, ρ∈(0,1), Δτ ij (t) is the increment of information factor concentration on the path between node i and node j during this time period. Assuming there are m drones, the increment is released and accumulated by m drones:

[0066]

[0067] It represents the information factor concentration released by UAV k at time t. The calculation formula is as follows:

[0068]

[0069] Q is the total concentration of information factor, l k is the total distance traveled by UAV k in this cycle. Therefore, this step needs to find the shortest R-UAV access path in this cycle, save the corresponding UAV ID, and modify the information factor concentration τ between each two R-UAVs according to the information factor volatilization mechanism. ij .

[0070] C6. Determine whether the number of loops has reached the maximum number of iterations. If not, increase the number of iterations by one and return to step 3 to continue execution. If so, exit the iteration process and output the calculated optimal R-UAV access sequence set and shortest path length.

[0071] D. Model optimization based on drone hovering position:

[0072] When establishing the above model, this method simplifies the hovering position of F-UAV and assumes that F-UAV is directly above R-UAV. safe However, considering the distance cost during the flight, the hovering position of the F-UAV needs to be further optimized to plan a shorter flight path.

[0073] Changing the F-UAV's hovering position affects the F-UAV's flight distance between battle zones, which in turn may affect the solution of the optimal visit sequence set. This method uses the optimal R-UAV visit sequence solved by the UAV swarm intelligent algorithm based on F-UAV cruise path planning, and adjusts the F-UAV's hovering position based on this.

[0074] D1. Based on the F-UAV cruise path planning, the UAV swarm intelligent algorithm obtains the optimal visit sequence set S of R-UAV.

[0075] D2. In order to obtain the shortest flight distance and minimize the flight distance cost, the flight trajectory of F-UAV between any two adjacent hovering points is a straight line. Based on the shortest hovering distance constraint between F-UAV and R-UAV, the preliminary path planning of F-UAV can be obtained as follows: Figure 3 As shown in the figure, the F-UAV in each battle zone is located at the sphere with R-UAV as the center, and the shortest hovering distance d safe On a sphere with a radius of d, the hovering distance of F-UAV and R-UAV in each battle zone is guaranteed to be the shortest distance d safe .

[0076] D3. Define (x i ,y i ,h i ) is the hovering position of F-UAV in the ith battle zone, The R-UAV position of the next battle zone that the F-UAV is heading to, (x i+1 ,y i+1 ,h i+1 ) represents the hovering position of the unoptimized F-UAV in the i+1th battle zone, (x' i+1 ,y' i+1 ,h' i+1 ) represents the hovering position of the optimized F-UAV in the i+1th battle zone, d i,i+1 is the distance between the (optimized) hovering point of the F-UAV in the i-th battle zone and the i+1-th R-UAV. The geometric relationship between the different hovering points of the F-UAV and the R-UAV is as follows: Figure 4 As shown, according to the knowledge of similar triangles:

[0077]

[0078] According to formula (20), we can solve:

[0079]

[0080] Similarly, the other two coordinate components of the optimized hover position can be expressed as:

[0081]

[0082]

[0083] After departing from the FCS, the F-UAV first selects a target theater based on the determined optimal visit sequence set. It then uses the aforementioned optimization method to calculate an adjusted hovering position, ensuring the F-UAV follows the shortest and most efficient path to each theater, achieving optimal flight path planning.

[0084] Beneficial effects:

[0085] 1. The present invention is applicable to scenarios where drone swarms are widely used for combat in the future. If a ferry cruise implementation method based on drone swarm intelligence is effectively selected, it can allow the use of medium and low-orbit satellites or Starlink to collaboratively solve the problems of efficient information exchange between different combat bases and comprehensive integration of key battlefield information in the absence of ground mobile base stations.

[0086] 2. The present invention establishes a drone swarm ferry cruising network, deploys ferry drones to cruise between regions and communicate with relay drones in each region, releases computing resources in real time or collects data and uploads it to low-orbit satellites, and effectively realizes the optimal implementation method of the total delay of the ferry system.

[0087] 3. The present invention includes a method for selecting a path for a ferry UAV based on the concentration of volatile information factors, gradually accumulating information factors on high-quality paths after iteration, until the optimal route to the target is finally obtained, optimizing the hovering position of the ferry UAV during transmission with the relay UAV, and providing a calculation method for the optimized hovering position in three-dimensional space through similar properties, and finally achieving the optimized cruising path for the ferry UAV and the UAV charging station, thereby effectively realizing the efficient transmission and integration of battlefield information. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 This is a schematic diagram of the drone swarm’s intelligent ferry cruise network.

[0089] Figure 2 It is a flow chart of the drone swarm intelligence algorithm.

[0090] Figure 3 This is a schematic diagram of the F-UAV's hovering position optimization.

[0091] Figure 4 It is a schematic diagram of the geometric relationship between the F-UAV (after optimization) hovering point and the R-UAV.

[0092] Figure 5 It is the optimization path of the drone swarm intelligent algorithm.

[0093] Figure 6 It is a hover position optimization diagram.

[0094] Figure 7 This is the hover position optimization diagram (top view).

[0095] Figure 8 It is the iterative process of drone swarm intelligent algorithm.

[0096] Figure 9 This is a comparison chart of three algorithms for solving the F-UAV cruise path length. DETAILED DESCRIPTION

[0097] The present invention will be described in further detail below with reference to the accompanying drawings.

[0098] like Figures 1-9 The present invention provides a method for implementing ferry cruising based on drone swarm intelligence, which includes the following steps:

[0099] (1) Establishing a drone swarm ferry cruise network:

[0100] (1.1) Relay UAV (R-UAV): In the harsh environment of the field battlefield, such as Figure 1 As shown in Figure 1, the entire battlefield is divided into L battle zones, and an R-UAV is deployed in each battle zone and hovers in the air. Assume that the relay UAV RU in the lth battle zone is l , l∈{1,2,…,L} is deployed in the core position of the war zone, and the relay drone RU l The position of

[0101] (1.2) Ferry UAV (F-UAV): Deploy a F-UAV Charging Station (FCS) throughout the battlefield, with the location recorded as It mainly provides charging services for F-UAV. One round of F-UAV cruise is set to start from FCS and follow the preset flight path to visit each base circle one by one. When F-UAV flies into the communication range of R-UAV, it will hover stably and transmit data with R-UAV. After the transmission is completed, F-UAV will set off for the next war zone and repeat the above process until all war zones are cruised. Finally, F-UAV returns to FCS and transmits all the collected data to the Low Earth Orbit (LEO) control center, preparing to start a new round of cruise. Assume that during data transmission, F-UAV is hovering directly above R-UAV. safe Place.

[0102] (1.3) Cruise path: Considering that only one relay UAV is deployed in each battle zone, the cruise path of the battle zone can be regarded as the order in which the F-UAV visits the R-UAV. Assuming the planned cruise path m represents the access sequence number of the R-UAV, m∈{1,2,…,L}. The F-UAV starts from the FCS and arrives at the mth battle zone in the established flight path, and then it communicates with the relay UAV. distance The cruise will hover at the right position and start data transmission. After the cruise is completed, all information will be uploaded to LEO.

[0103] (2) Total system delay:

[0104] The total system latency is primarily composed of the F-UAV's flight latency, the data transmission latency between the F-UAV and R-UAV, and the data upload latency of the F-UAV uploading data to LEO. The calculation methods for each latency are described below.

[0105] (2.1) Flight delay: The flight delay of the F-UAV is decomposed into the flight delay between each R-UAV:

[0106]

[0107] Where, It represents the flight time taken by the F-UAV from the starting point FCS to the first R-UAV to be visited. Indicates that F-UAV is relaying drones and The flight time required between Indicates the time it takes for the F-UAV to return to the FCS after completing the cruise.

[0108] Assume that F-UAV is flying at a constant speed, and the flight speed is v f , so the flight delay of the UAV is proportional to its flight path, T f It can be calculated by formula (2):

[0109]

[0110] The numerator is the total cruising distance. Assuming that the F-UAV travels along the shortest straight line between each point, each component is expressed as Euclidean distance, which is calculated by formulas (3)(4)(5):

[0111]

[0112] in, These are the hovering positions of the F-UAV in each battle zone.

[0113] (2.2) Data transmission delay

[0114] F-UAV collects data delay in various war zones The total amount of data cache in the corresponding area and the communication rate between F-UAV and R-UAV Related, expressed as:

[0115]

[0116] Communication rate It can be calculated by the following formula:

[0117]

[0118] in, represents the channel bandwidth, Relay drone The maximum transmission power, N0 is the noise power spectrum density, g0 is the channel power gain at the reference distance. hour, However, this situation is unlikely to happen in reality. Considering the safety accidents such as collision when UAVs are too close, the hovering distance between F-UAV and R-UAV should be greater than a certain threshold d safe .

[0119] Therefore, the system's data transmission T d It can be expressed as:

[0120]

[0121] (2.3) Data upload delay

[0122] When the F-UAV completes the cruise and returns to the FCS, it needs to upload all the collected data to the low-orbit satellite LEO. F,L =h L , d F,L is the distance between F-UAV and LEO, hL is the altitude of LEO.

[0123] Assuming that the channel does not change with time during transmission, is the received power of the ferry UAV, R F,L is the uplink data rate achievable on LEO, which can be expressed as:

[0124]

[0125]

[0126] Where, is the transmission power from the UAV to LEO, are the transmitting and receiving antenna gains from the UAV to LEO, respectively. L is the distance between LEO and the UAV. s is the wavelength of the LEO-UAV link. F,L , τ and ε are the bandwidth, noise temperature and Boltzmann constant of the LEO-Sat link allocation, respectively.

[0127] Therefore, the upload delay of F-UAV is T u It can be expressed as:

[0128]

[0129] (3) System model establishment:

[0130] The total system delay T can be expressed as:

[0131] T=T f +T d +T u (12)

[0132] The optimization goal is to minimize the total system delay:

[0133] min T(13) combined with the constraints of the ferry UAV cruise, we can get:

[0134] min T f +T d +T u

[0135]

[0136] In the formula, constraint 1 limits the UAV flight delay, T cmax Indicates the maximum flight time after one charge, flight delay T f Not to exceed T cmax , and cannot be 0; Constraint 2 represents the hovering distance d between F-UAV and R-UAVm Should be greater than a certain threshold d safe ; Constraint 3 means that the war zone number accessed each time is different; Constraint 4 means that the sum of the total data buffer of each war zone is not less than the total data volume of the system.

[0137] From formula (8), we can see that the data transmission delay is related to the data cache capacity and data transmission rate of the R-UAV in each battle zone. When the hovering distance is determined to be the safe distance d safe When T d Total data cache for all R-UAVs only Regarding the data upload delay T u Therefore, the key to optimizing this system is to determine a flight trajectory of the F-UAV that minimizes the flight delay, and the focus of flight trajectory planning is to find the sequence of F-UAV's visits to each battle zone. The following is the path planning of the drone swarm intelligent algorithm:

[0138] The UAV Swarm Intelligent Optimization (USIO) algorithm works as follows: During a mission, each drone in the swarm leaves a volatile information factor along its path. This factor gradually dissipates over time, a process known as information factor updating. Drones choose their next direction based on the concentration of information factors along the path. The higher the concentration, the greater the probability of the path being selected. Through this mechanism, the drone swarm gradually accumulates information factors on high-quality paths over multiple iterations until it ultimately discovers the optimal path to the destination.

[0139] In the F-UAV path planning problem, each UAV is regarded as an F-UAV and the path search is completed, such as Figure 2 As shown, the specific algorithm steps are as follows:

[0140] (3.1) Initialization parameters: Initialize the parameters of the drone swarm intelligent algorithm, including the drone swarm size pop, the maximum number of iterations max_iter, the information factor volatility coefficient ρ, the information factor total concentration Q, the information factor heuristic factor α, and the heuristic function value factor β.

[0141] (3.2) Initialize the UAV swarm information: Set the charging station FCS as the starting and ending point of the UAV swarm’s visit, and set the charging station d safe The hovering point at is set as the intermediate node that the drone group needs to visit, and the order set of the drone group visiting the R-UAV is randomly generated, which is called the path space. The information factor concentration τ between the hovering point i and the hovering point j is calculated. ij Set them to 1 and calculate the Euclidean distance d between each hovering pointij , and then according to the formula

[0142]

[0143] Calculate the heuristic function value η ij (t). Initialize the taboo table tabu of the drone swarm to an empty set.

[0144] (3.3) The UAV starts from FCS and goes to the first R-UAV in the path space, and then according to the state transition formula

[0145]

[0146] Select the next R-UAV to visit, add the corresponding serial number of the visited R-UAV to the taboo table tabu, and update the corresponding path length. is the probability that drone k transfers from node i to node j, allowed k is the set of next-hop nodes that can be selected by UAV k. α and β are the concentrations of the influencing information factor τ ij (t) and the heuristic function value η ij (t) The importance weight factor for the drone to select the next destination node. When the parameter α increases, the drone k is more inclined to choose the node with high information factor concentration as the direction of advance; when the parameter β increases, the drone is more likely to move towards its desired degree η ij (t) High node movement.

[0147] (3.4) Determine whether all drones have visited all R-UAVs to complete the data collection task. If there are still R-UAVs that have not been visited, return to step 3; if all R-UAVs have been traversed, return to FCS and continue to perform the following steps.

[0148] (3.5) In order to prevent the algorithm from falling into a local optimal solution due to excessive information factor concentration, an information factor volatilization mechanism is introduced to ensure that the information factor dissipates at a certain rate. The update strategy of the information factor concentration is as follows:

[0149] τ ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t) (17)

[0150] Where ρ is the volatility coefficient of information factor, ρ∈(0,1), Δτ ij (t) is the increment of information factor concentration on the path between node i and node j during this time period. Assuming there are m drones, the increment is released and accumulated by m drones:

[0151]

[0152] It represents the information factor concentration released by UAV k at time t. The calculation formula is as follows:

[0153]

[0154] Q is the total concentration of information factor, l k is the total distance traveled by UAV k in this cycle. Therefore, this step needs to find the shortest R-UAV access path in this cycle, save the corresponding UAV ID, and modify the information factor concentration τ between each two R-UAVs according to the information factor volatilization mechanism. ij .

[0155] (3.6) Determine whether the number of loops has reached the maximum number of iterations max_iter. If not, increase the number of iterations by one and return to step 3 to continue execution. If it has, exit the iteration process and output the calculated optimal R-UAV access sequence set and shortest path length.

[0156] (4) Model optimization based on the drone’s hovering position:

[0157] When establishing the above model, the present invention simplifies the hovering position of F-UAV and assumes that F-UAV is directly above R-UAV. safe However, considering the distance cost during the flight, the hovering position of the F-UAV needs to be further optimized to plan a shorter flight path.

[0158] Changing the F-UAV's hovering position affects the F-UAV's flight distance between battle zones, which in turn may affect the solution to the optimal visit sequence set. We use the optimal R-UAV visit sequence solved by the drone swarm intelligence algorithm based on F-UAV cruise path planning to adjust the F-UAV's hovering position based on this.

[0159] (4.1) Based on the UAV swarm intelligence algorithm for F-UAV cruise path planning, the optimal visit sequence set S of R-UAV is obtained.

[0160] (4.2) In order to obtain the shortest flight distance and minimize the flight distance cost, the flight trajectory of F-UAV between any two adjacent hovering points is a straight line. Based on the shortest hovering distance constraint between F-UAV and R-UAV, the preliminary path planning of F-UAV can be obtained as follows: Figure 3 As shown in the figure, the F-UAV in each battle zone is located at the sphere with R-UAV as the center, and the shortest hovering distance d safe On a sphere with a radius of d, the hovering distance of F-UAV and R-UAV in each battle zone is guaranteed to be the shortest distance dsafe .

[0161] (4.3)Define (x i ,y i ,h i ) is the hovering position of F-UAV in the ith battle zone, The R-UAV position of the next battle zone that the F-UAV is heading to, (x i+1 ,y i+1 ,h i+1 ) represents the hovering position of the unoptimized F-UAV in the i+1th battle zone, (x' i+1 ,y' i+1 ,h' i+1 ) represents the hovering position of the optimized F-UAV in the i+1th battle zone, d i,i+1 is the distance between the (optimized) hovering point of the F-UAV in the i-th battle zone and the i+1-th R-UAV. The geometric relationship between the different hovering points of the F-UAV and the R-UAV is as follows: Figure 4 As shown, according to the knowledge of similar triangles:

[0162]

[0163] According to formula (20), we can solve:

[0164]

[0165] Similarly, the other two coordinate components of the optimized hover position can be expressed as:

[0166]

[0167]

[0168] After departing from the FCS, the F-UAV first selects a target theater based on the determined optimal visit sequence set. It then uses the aforementioned optimization method to calculate an adjusted hovering position, ensuring the F-UAV follows the shortest and most efficient path to each theater, achieving optimal flight path planning.

[0169] (5) Analysis of simulation results:

[0170] The above algorithm is simulated and analyzed using MATLAB to prove the effectiveness of the UAV Swarm Intelligent Optimization (USIO) algorithm. Consider a 10km×10km combat area, which contains 15 combat bases. Each combat base has a relay UAV RU l Location As shown in Table 1, the data storage capacityl ∈(4,6)Gbit, the channel bandwidth B of R-UAV l =10MHz, maximum transmission power p l =10dB, l∈{1,2,…,L}.

[0171] The rest of the simulation parameters are set as follows: F-UAV flight speed v f =15m / s, safe communication distance d between F-UAV and R-UAV safe =100m, channel gain power g0 at reference distance = -50dB, noise power spectrum density N0 = -174dBm / Hz, transmission power from F-UAV to LEO Transmit and receive antenna gain The height of LEO h L =550km, the frequency of the LEO-UAV link is f s =2.4GHz, LEO-Sat link allocated bandwidth B F,L =40MHz, noise temperature τ = 1000 and Boltzmann constant ε = 1.38×10 -23 , the coordinates of FCS are set to (0,0,0). The optimal cruising path of F-UAV under the UAV swarm intelligent algorithm is obtained as follows: Figure 5 shown.

[0172] Based on the UAV swarm intelligent algorithm, the algorithm is optimized based on the hovering position of the ferry UAV. Here we show the simulation results when the number of bases in the battle zone is 15. Figure 6 and Figure 7 It can be seen that after optimization, the hovering position of F-UAV is no longer directly above R-UAV. safe Instead, we choose R-UAV as the center of the sphere, d safe is an optimal position on the sphere of radius , and this position will iteratively change with the position of the previous R-UAV visited by the F-UAV.

[0173] In the comparative analysis of computing performance, since the R-UAV selected in each theater base represents the theater to F-UAV interaction data, Figure 8 The iterative process of the algorithm for solving the optimal sequence set of F-UAV access to R-UAV when the number of bases in different war zones is 10, 15, 20, and 25 is demonstrated. It can be seen that the convergence of the algorithm (represented by the CPU calculation unit time) slows down with the increase in the number of war bases. However, through comparison with existing conventional intelligent algorithms, it can be seen that the convergence of the swarm intelligence ferry cruise calculation method proposed in this invention is faster and more effective.

[0174] like Figure 9As shown, after the hovering position optimization algorithm, the F-UAV's total cruising path became 33,005 meters, a 4.7% reduction compared to the traditional intelligent algorithm path. The calculated F-UAV flight latency was 2200.3 seconds, the data exchange latency between the F-UAV and R-UAV was 176.9 seconds, and the latency for the F-UAV to upload data to LEO was 66.5 seconds, for a total latency of 2443.7 seconds. The F-UAV's cruising latency accounted for 90% of the total system latency, making it the most critical factor affecting system performance, further demonstrating the necessity of optimizing flight latency.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0176] Those skilled in the art will appreciate that the present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0177] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0179] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for implementing ferry cruising based on drone swarm intelligence, characterized in that: include: S1. Establish a drone swarm ferry cruise network, deploy relay drones and ferry drones, and set their coordinate parameters and cruise paths; S2. Analyze the delay components of the drone swarm ferry cruise system, including flight delay, data transmission delay, and data upload delay, and obtain a calculation method for the total system delay; S3. With the goal of minimizing the total system delay, a system optimization model is established. Through the intelligent algorithm of drone swarm cruise path planning, the flight trajectory of the drone ferry cruise network is optimized; S4. Based on the solved optimal R-UAV access sequence, the hovering position of the ferry UAV is further optimized to obtain the optimal trajectory calculation method based on the UAV hovering position, which serves as the final UAV swarm ferry cruising intelligent algorithm.

2. The method for realizing ferry cruising based on drone swarm intelligence according to claim 1, characterized in that: The specific steps of step S1 are as follows: (a) Relay UAV (R-UAV): In the harsh environment of a field battlefield, the entire battlefield is divided into multiple battle zones. An R-UAV is deployed in each battle zone and hovers above it. The coordinate parameters of the relay UAV are set; (b) Ferry UAV (F-UAV): An F-UAV Charging Station (FCS) and an F-UAV are deployed throughout the battlefield. The F-UAV departs from the FCS and follows a pre-set flight path to visit each base circle one by one, exchanging data with the R-UAV until it has cruised through all the battle zones. It then returns to the FCS, transmits all collected data to the Low Earth Orbit (LEO) satellite control center, and prepares to start a new round of patrol. The F-UAV's coordinate parameters and the hovering distance between the F-UAV and the R-UAV are set. (c) Cruise path: Considering that only one relay UAV is deployed in each battle zone, the cruise path of the battle zone can be regarded as the visit order of F-UAV to R-UAV, and a path set of the road section between any two bases is set.

3. The method for realizing ferry cruising based on drone swarm intelligence according to claim 1, characterized in that: The specific steps of step S2 are as follows: (a) Flight delay: The flight delay of the F-UAV is decomposed into the flight delay between each R-UAV: Where, It represents the flight time taken by F-UAV from the starting point FCS to the first R-UAV to be visited, L represents the total number of R-UAVs, and m represents the sequence number of R-UAVs, which is used to index the sequence numbers from the first to the L-1th R-UAV. Indicates that F-UAV is relaying drones and The flight time required between Indicates the time it takes for the F-UAV to return to the FCS after completing the cruise; Assume that F-UAV is flying at a constant speed, and the flight speed is v f , so the flight delay of the UAV is proportional to its flight path, T f It can be calculated by formula (2): Among them, the numerator is the total cruise distance, represents the distance from the FCS to the first R-UAV, represents the distance of F-UAV from the mth R-UAV to the m+1th R-UAV, represents the distance from the last R-UAV back to the FCS. Assuming that the F-UAV travels along the shortest straight line between each point, each component is represented by the Euclidean distance, which is calculated by formulas (3)(4)(5): in, The hovering position of the F-UAV in each battle zone; (b) Data transmission delay F-UAV collects data delay parameters in various war zones The total amount of data cache in the corresponding m-th R-UAV coverage area is parameterized And the communication rate parameters between F-UAV and R-UAV Related, expressed as: Communication rate It can be calculated by the following formula: in, represents the channel bandwidth, Relay drone The maximum transmission power, N0 is the noise power spectrum density, g0 is the channel power gain of the reference distance, when hour, However, this situation is unlikely to happen in practice. Considering the safety accidents such as collision when UAVs are too close, the hovering distance between F-UAV and R-UAV should be greater than a certain threshold d. safe； Therefore, the system's data transmission T d It can be expressed as: (c) Data upload delay When the F-UAV completes the cruise and returns to the FCS, all the collected data need to be uploaded to the low-orbit satellite LEO. F,L =h L , d F,L is the distance between F-UAV and LEO, h L is the altitude of LEO; Assuming that the channel does not change with time during transmission, is the received power of the ferry UAV, R F,L is the uplink data rate achievable on LEO, which can be expressed as: Where, is the transmission power from the UAV to LEO, are the transmitting and receiving antenna gains from the UAV to LEO, h L is the distance between LEO and the UAV, λ s is the wavelength of the LEO-UAV link, B F,L , τ and ε are the bandwidth, noise temperature and Boltzmann constant of the LEO-Sat link allocation, respectively; Therefore, the upload delay of F-UAV is T u It can be expressed as: (d) Total system delay The total system delay T can be expressed as: T=T f +T d +T u (12) The total system delay parameter value T is calculated from this, where T f is the flight delay, T d is the system transmission delay, T u Data upload delay.

4. The method for realizing ferry cruising based on drone swarm intelligence according to claim 1, characterized in that: The specific steps of step S3 are as follows: (a) Establish optimization objectives and constraints, and build a system optimization model: The optimization goal is to minimize the total system delay: min T(13) combined with the constraints of the ferry UAV cruise, we can get: min T f +T d +T u (14) In formula (14), constraint 1 limits the UAV flight delay, T cmax Indicates the maximum flight time after one charge, flight delay T f Not to exceed T cmax , and cannot be 0; Constraint 2 represents the hovering distance d between F-UAV and R-UAV m Should be greater than a certain threshold d safe ; Constraint 3 means that the war zone number is different each time it is visited, S i represents the war zone covered by the i-th R-UAV, S j represents the jth R-UAV coverage zone; constraint 4 indicates that the sum of the total data buffer of each zone is not less than the total data volume of the system, where Indicates the total amount of data buffer of the mth R-UAV battle zone, Data total Indicates the total data volume of the system. (b) Establish an intelligent algorithm for drone swarm cruise path planning to determine the flight trajectory of the F-UAV to minimize flight delay. The key point of flight trajectory planning is to find the sequence of F-UAV visits to each battle zone.

5. The method for realizing ferry cruising based on drone swarm intelligence according to claim 1, characterized in that: The specific steps of step S4 are as follows: (a) Based on the F-UAV cruise path planning, the UAV swarm intelligent algorithm obtains the optimal visit sequence set S of the R-UAV; (b) In order to obtain the shortest flight distance and minimize the flight distance cost, the flight trajectory of the F-UAV between any two adjacent hovering points is a straight line. Based on the shortest hovering distance constraint between the F-UAV and the R-UAV, the preliminary path planning of the F-UAV is obtained. In the figure, the F-UAV in each battle zone is located at a sphere with the R-UAV as the center and the shortest hovering distance d safe On a sphere with a radius of d, the hovering distance of F-UAV and R-UAV in each battle zone is guaranteed to be the shortest distance d safe ; (c) Define (x i ,y i ,h i ) is the hovering position of F-UAV in the ith battle zone, The R-UAV position of the next battle zone that the F-UAV is heading to, (x i+1 ,y i+1 ,h i+1 ) represents the hovering position of the unoptimized F-UAV in the i+1th battle zone, (x′ i+1 ,y′ i+1 ,h′ i+1 ) represents the hovering position of the optimized F-UAV in the i+1th battle zone, d i,i+1 is the distance between the (optimized) hovering point of the F-UAV in the i-th battle zone and the i+1-th R-UAV. The geometric relationship between the different hovering points of the F-UAV and the R-UAV is constructed as the projection of the three-dimensional space onto the two-dimensional space. According to the knowledge of similar triangles, we know that: According to formula (15), we can solve: Similarly, the other two coordinate components of the optimized hover position can be expressed as: After the UAV departs from the FCS, it first selects the target theater based on the determined optimal visit sequence set. Then, it calculates the adjusted hovering position using the optimization method in steps S2, S3, and S4, thereby ensuring that the F-UAV can fly to each theater along the shortest and most efficient path, thus achieving optimal flight path planning.

6. The method for realizing ferry cruising based on drone swarm intelligence according to claim 4, characterized in that: The specific steps of drone swarm cruise path planning are as follows: (a) Initialize the parameters of the UAV swarm intelligent algorithm, including the UAV swarm size pop, the maximum number of iterations max_iter, the information factor volatility coefficient ρ, the information factor total concentration Q, the information factor heuristic factor α, and the heuristic function value factor β; (b) Initialize the UAV swarm information, set the charging station FCS as the starting and ending point of the UAV swarm, and set the charging station d directly above each R-UAV safe The hovering point at is set as the intermediate node that the drone group needs to visit, and the order set of the drone group visiting the R-UAV is randomly generated, which is called the path space. The information factor concentration τ between the hovering point i and the hovering point j is calculated. ij Set them to 1 and calculate the Euclidean distance d between each hovering point ij , and then according to the formula: Calculate the heuristic function value η ij (t), initialize the taboo table tabu of the drone swarm to an empty set; (c) The UAV starts from the FCS and goes to the first R-UAV in the path space, and then according to the state transition formula Select the next R-UAV to visit, add the corresponding serial number of the visited R-UAV to the taboo table tabu, and update the corresponding path length, where, is the probability that drone k transfers from node i to node j, allowed k is the set of next-hop nodes that can be selected by UAV k, α and β are the concentrations of the influencing information factor τ ij (t) and the heuristic function value η ij (t) For the importance weight factor of the drone's selection of the next destination node, when the parameter α increases, the drone k is more inclined to choose the node with high information factor concentration as the forward direction; when the parameter β increases, the drone is more likely to move towards its expected degree η ij (t) high node movement; (d) Determine whether all UAVs have visited all R-UAVs to complete the data collection task. If there are still R-UAVs that have not been visited, return to C3; if all R-UAVs have been traversed, return to FCS and continue to execute the following steps; (e) To prevent the algorithm from falling into a local optimal solution due to excessive information factor concentration, an information factor volatilization mechanism is introduced to ensure that the information factor dissipates at a certain rate. The information factor concentration update strategy is as follows: t ij (t+1)=(1-ρ)τ ij (t)+Δτ ij (t) (21) Where ρ is the volatility coefficient of information factor, ρ∈(0,1), Δτ ij (t) is the increment of information factor concentration on the path between node i and node j during this time period. Assuming there are m drones, the increment is released and accumulated by m drones: It represents the information factor concentration released by UAV k at time t. The calculation formula is as follows: Q is the total concentration of information factor, l k is the total distance traveled by UAV k in this cycle, find the shortest R-UAV access path in this cycle, save the corresponding UAV ID, and modify the information factor concentration τ between each two R-UAVs according to the information factor volatilization mechanism ij ; (f) Determine whether the number of loops has reached the maximum number of iterations. If not, increase the number of iterations by one and return to step 3 to continue execution. If so, exit the iteration process and output the calculated optimal R-UAV access sequence set and shortest path length.

7. The method for realizing ferry cruising based on drone swarm intelligence according to claim 4, characterized in that: F-UAV is directly above R-UAV. safe Hovering at a certain location and transmitting data with the R-UAV.

8. A ferry cruise computing system based on drone swarm intelligence, characterized by: The information preprocessing module is configured to: capture the initial geographic information data from the ground by Beidou satellite navigation, and use low-Earth orbit (LEO) satellites to timely capture and summarize the basic geographic coordinate information of the current UAV as the basic data and boundary conditions for subsequent resource scheduling plans; The resource scheduling module is configured to: analyze the local resource status and movement status of the drone group in each cycle, divide the current regional scheduling into different combat mission areas and designated relay drones (R-UAVs), and report to the scheduling decision center. In this way, a ferry cruise implementation method based on drone swarm intelligence is used to provide the ferry drone (F-UAV) with cruise path and hovering position information; The charging scheduling module is configured to: take into account the energy consumption uncertainty caused by the path uncertainty of the ferry drone cruise, set up a charging scheduling module, and obtain the best endurance charging location information for the ferry drone charging station (FCS) through a ferry cruise implementation method based on drone swarm intelligence.

9. An electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, characterized in that the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1-7.

Images