A method for optimizing the path of a drone and an unmanned vehicle in air defense alarm cooperative operation

Abstract

The application relates to a UAV and unmanned vehicle air defense alarm cooperative operation path optimization method. The air defense alarm cooperative operation path optimization method comprises the following steps: positions of a UAV and an unmanned vehicle are represented by multidimensional coordinates; a UAV and unmanned vehicle load air defense alarm cooperative scheduling model is established by taking the weighted sum of total task completion time and total delay time as the target; the UAV and unmanned vehicle load air defense alarm cooperative scheduling model is solved by using an improved lion optimization algorithm, and a target UAV and unmanned vehicle cooperative scheduling scheme is obtained. According to the scheme, the weighted sum of task completion time and total delay time is minimized, the UAV and unmanned vehicle load air defense alarm cooperative scheduling model is established, and the improved lion optimization algorithm is used to provide an optimal scheme for the cooperative operation scheduling problem of the UAV and the unmanned vehicle in the air defense alarm system.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and in particular to a method for optimizing the collaborative operation path of air defense alarms by unmanned aerial vehicles and unmanned vehicles. Background Technology

[0002] In the field of civil air defense, rapid notification of air raid sirens is crucial. Disseminating early warning information to the widest possible range, fastest speed, and most effective manner in the first instance is a vital link in ensuring public safety. Traditional air raid sirens primarily rely on fixed electric and electro-acoustic sirens. This dependence on fixed alarm facilities results in limitations such as fixed locations and limited coverage, making it difficult to comprehensively cover urban areas, especially those with complex terrain or remote locations. Furthermore, with rapid urbanization, buildings are becoming increasingly taller, rendering sirens located at lower levels ineffective. Increased blind spots in high-rise buildings, building obstructions, and external noise further hinder the propagation of traditional air raid warning signals, leading to untimely and inaccurate information delivery. Simultaneously, these fixed facilities lack flexibility in the face of emergencies and cannot be quickly adjusted to real-time changes.

[0003] Unmanned aerial vehicles (UAVs) and unmanned vehicles (UAVs) equipped with air defense warning devices are highly flexible and mobile, enabling them to overcome the limitations of fixed locations and limited coverage in traditional methods and achieve rapid and intelligent early warning. However, current research on path optimization for UAVs and UAVs is mostly focused on independent sub-problems, lacking collaborative capabilities between them. This results in problems such as untimely information transmission and incomplete coverage when conducting air defense early warning missions.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles, aiming to solve the problem of XX.

[0006] To achieve the above objectives, the present invention provides a method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles, comprising:

[0007] The positions of drones and unmanned vehicles are represented by multi-dimensional coordinates, which include the starting point, mission area, set of missions to be performed, mission priority, and physical constraints of drones and unmanned vehicles.

[0008] With the goal of minimizing the weighted sum of total task completion time and total delay time, a collaborative scheduling model for air defense alarms loaded by UAVs and unmanned vehicles is established.

[0009] An improved lion flock optimization algorithm is used to solve the collaborative scheduling model of UAVs and unmanned vehicles carrying air defense alarms, and to obtain the collaborative scheduling scheme of target UAVs and unmanned vehicles. The improved lion flock optimization algorithm includes the introduction of the Sweep scanning method, the algorithm discretization based on circular encirclement, and the multi-operator update mechanism, which improves the response speed and decision accuracy of the lion flock optimization algorithm.

[0010] Optionally, a collaborative scheduling model for air defense alarms carried by drones and unmanned vehicles includes:

[0011] The objective function is established to minimize the weighted sum of the total task completion time and the total delay time:

[0012] minf=ω1·T 总 +ω2·D 总 (1)

[0013] In equation (1), T 总 D represents the total task completion time. 总 ω1 and ω2 represent the total delay time, and ω1 and ω2 represent the weighting coefficients.

[0014] Establish constraint function:

[0015] Assign each task to at least one drone or unmanned vehicle.

[0016]

[0017] In equation (2), x ij With y il x is a 0-1 variable. ij Indicates when task w i y is 1 when assigned to a drone, and 0 otherwise. il Indicates when task ω i The value is 1 when assigned to an autonomous vehicle, and 0 otherwise. W represents the set of all tasks, m represents the number of drones, and p represents the number of autonomous vehicles.

[0018] The number of drones and unmanned vehicles assigned to the missions did not exceed the total number of missions.

[0019]

[0020] Alarm arrival times for each area must be within a specified time window.

[0021]

[0022]

[0023] In equation (4), a i Represents the i-th region where task a is executed, e i ki Representing region a i The lower limit and upper limit of the time window;

[0024] The path length of drones and unmanned vehicles is less than their maximum range or distance.

[0025]

[0026]

[0027] In equation (5), and These represent the current drone u. j The length of the path traveled and the autonomous vehicle's v l The length of the route traveled. and Let U represent the maximum range of drones and unmanned vehicles, respectively; let U represent the set of all drones; let V represent the set of all unmanned vehicles; and let V represent the set of all unmanned vehicles.

[0028] The energy consumption of drones and unmanned vehicles did not exceed their battery capacity.

[0029]

[0030]

[0031] In equation (6), and This indicates the energy consumed by drones and unmanned vehicles during mission execution. and Indicates the battery capacity of drones and unmanned vehicles;

[0032] High-priority tasks will be assigned more frequently.

[0033] P(a i )≥max[P(a1),P(a2),…P(a n )]·λ (7)

[0034] In equation (7), P(a i ) represents region a i Task priority status;

[0035] In the event of task failure or changes in environment, tasks can be quickly reassigned.

[0036]

[0037] In equation (8), R represents the set of all regions that need to perform tasks;

[0038] Drones and unmanned vehicles maintain a safe distance from obstacles during mission execution.

[0039]

[0040]

[0041] In equation (9), O represents the set of obstacles, and r0 represents the safe distance of region r;

[0042] Each drone and unmanned vehicle shall not carry a payload exceeding its maximum payload capacity when performing a mission.

[0043]

[0044]

[0045] In equation (10), For task w i The load, For drones j The bearing, For driverless cars v l The bearing;

[0046] The communication range and quality limitations between drones and unmanned vehicles, as well as with the control center.

[0047]

[0048] In equation (11), For drones j or driverless car v l Communication distance with the control center.

[0049] Optionally, the collaborative scheduling model for UAV and unmanned vehicle-borne air defense alarms is solved using an improved lion pack optimization algorithm, including:

[0050] Using the Sweep method, initial tasks are assigned to all drones and unmanned vehicles, and an initial drone and unmanned vehicle collaborative scheduling scheme is constructed.

[0051] Using the initial drone and unmanned vehicle cooperative scheduling scheme, calculate the path cost, time window constraint satisfaction, and task priority satisfaction for each drone and unmanned vehicle;

[0052] The lion pack optimization algorithm is used to optimize the initial UAV and unmanned vehicle cooperative scheduling scheme, and update the task allocation and path planning schemes of UAVs and unmanned vehicles.

[0053] The optimization results of the lion pack optimization algorithm are discretized to solve the optimal path for each UAV and unmanned vehicle. The path length, energy consumption and time window constraints are taken into account, and congestion penalty costs are added.

[0054] The multi-operator update mechanism is used to disrupt and repair the current drone and unmanned vehicle cooperative scheduling scheme in the population, thereby enhancing the algorithm's global search capability and local search accuracy.

[0055] Calculate the fitness value of each scheduling scheme, calculate the weighted sum of task completion time and total delay time, and determine whether the constraints are met;

[0056] Repeat the optimized steps using the lion pack optimization algorithm until the preset maximum number of iterations is reached, or the stopping condition is met;

[0057] Output the collaborative scheduling scheme for target drones and unmanned vehicles, including task allocation, path planning and time scheduling.

[0058] Optionally, using the Sweep scanning method, initial tasks are assigned to all drones and unmanned vehicles, including:

[0059] The basic steps of the Sweep scanning method are used to assign tasks to drones and unmanned vehicles.

[0060] The Sweep method is used to continuously adjust and optimize parameters based on the range and step size of variable changes, creating diverse initial solution sets and improving the collaborative efficiency of drones and unmanned vehicles.

[0061] Perform a step-by-step scan across all mission areas and calculate the impact of different starting positions on the time and path length for the drone or unmanned vehicle to reach the designated alarm area.

[0062] Observe the relationship between speed changes and energy consumption and mission completion efficiency, and adjust the flight speed parameters of the UAV and the driving speed parameters of the unmanned vehicle.

[0063] The system scans the traffic conditions, congestion levels, and distances of different roads to assess their impact on the autonomous vehicle's route parameters and determine the optimal route for the current driving mode.

[0064] By setting different altitude ranges for scanning, the effects of flight altitude on signal propagation range and intensity, as well as on energy consumption, are observed, and the flight altitude parameters of the UAV are adjusted accordingly.

[0065] Then scan at different flight altitudes to observe the interference of terrain and building factors on signal propagation, and adjust the flight altitude parameters of the drone accordingly;

[0066] Scan the mass of different types of air raid siren devices to determine the number and type of air raid siren devices that drones and unmanned vehicles can carry, and adjust the load capacity parameters.

[0067] The following formula is used for calculation during the above scanning process:

[0068] P i，k =l i +k·s i

[0069]

[0070] k = 1, 2, ..., K i (18)

[0071] In equation (18), P i,k This represents the value of the i-th parameter at step k, l i s represents a variable. i Let K be the step size. i It is parameter P i The number of times a value can be taken.

[0072] Optionally, the lion pride optimization algorithm is used to optimize the initial drone and unmanned vehicle cooperative scheduling scheme, including:

[0073] Determine the population size for the initial drone and unmanned vehicle collaborative scheduling scheme;

[0074] Calculate the fitness values ​​of individuals in the population and divide the lion king, lionesses, and cub groups;

[0075] Discretize the lion positions and update the positions of the lion king, lionesses, and cubs.

[0076] Optionally, the optimization results of the lion pride optimization algorithm are discretized, including:

[0077] By combining the position of one drone with the flight path of another drone, or exchanging the driving route of one drone with the payload allocation of another drone, a new scheduling scheme is generated, as shown below:

[0078]

[0079] In equation (19), Let represent the scheduling scheme for the i-th lion in the t-th iteration. Let represent the scheduling scheme for a randomly selected lion in the t-th iteration. Let represent the optimal scheduling scheme for the lion king individual in the t-th iteration. Cross1 and Cross2 represent two different update mechanisms. The Cross1 update mechanism pays more attention to the diversity of the population. By exchanging information with the random population's delivery scheme, it expands the solution space of the algorithm, improves the diversity of the population, and also strengthens the algorithm's global search capability. The Cross2 update mechanism pays more attention to the local search capability. The delivery scheme in the pride exchanges information with the optimal scheduling scheme, thereby improving the algorithm's local search capability and search speed.

[0080] Optionally, a multi-operator update mechanism can be used to disrupt and reconstruct the current drone and unmanned vehicle cooperative scheduling scheme in the population, including:

[0081] A gradient-based update strategy is adopted to guide the search towards the optimal solution quickly, while a random mutation strategy is combined to increase the diversity of the population and avoid getting trapped in local optima.

[0082] By carefully designing combinations of operators, populations with different characteristics can be obtained to adapt to the task allocation and path planning optimization problems of UAVs and unmanned vehicles.

[0083] When facing areas with complex terrain, add operators that focus on exploring new paths to find more suitable driving routes.

[0084] Optionally, the positions of the drone and the unmanned vehicle can be represented using multi-dimensional coordinates, including:

[0085] The multidimensional coordinates of drones and unmanned vehicles include the starting point, mission area, set of tasks to be performed, mission priority, and physical constraints.

[0086] The task set is defined as W = {w i |i=1,2,…,n}, corresponding to all tasks to be executed; the set of drones is defined as U={u j |j=1,2,…,m}, corresponding to all drones; the set of unmanned vehicles is defined as V={v l |l=1,2,…,p}, corresponding to all autonomous vehicles; the set of all task regions is defined as R={r i |i=1,2,…,q} represents all areas that need to perform alarm tasks;

[0087] Define P(a) i ) represents region a i Task priority, definition The drones are respectively j Arrive at a i Time, driverless car v l Arrive at a i Time, e ik i Region a i The lower limit and region of the time window a i The upper limit of the time window;

[0088] Physical limitations of drones and unmanned vehicles, definition and These represent the maximum range of drones and unmanned vehicles, respectively. and The current drones are u j The length of the route traveled, the autonomous vehicle's v l Length of the path traveled; definition and For the battery capacity of drones and unmanned vehicles, and It refers to the energy consumed by drones and unmanned vehicles during mission execution; definition For task w i The load, For drones j The load-bearing capacity, For driverless cars v l The load-bearing capacity must be considered when allocating tasks and planning routes. Equipment and materials required for each task must be allocated reasonably, with a margin reserved to handle unforeseen circumstances. The communication range and quality between drones and unmanned vehicles and the control center are also limited; it is necessary to ensure they operate within the communication range and avoid entering communication blind spots. For drones j and driverless cars l Communication distance with the control center.

[0089] The technical solution provided in this application may include the following beneficial effects:

[0090] Based on the characteristics and constraints of UAVs and unmanned vehicles carrying air defense alarms, this application establishes a collaborative scheduling model for UAVs and unmanned vehicles carrying air defense alarms, aiming to minimize the weighted sum of task completion time and total delay time. Through an improved lion flock optimization algorithm, task assignment and sorting are performed based on a multi-level mapping encoding and decoding method of task-UAV / unmanned vehicle-region. An improved path planning algorithm is integrated to optimize the movement routes of UAVs and unmanned vehicles, reduce travel time, and solve the local congestion problem in path planning. Furthermore, a neighborhood search strategy is used to find the optimal scheduling scheme that minimizes both task completion time and delay time, providing a preferred solution for the collaborative operation scheduling problem of UAVs and unmanned vehicles in air defense alarm systems.

[0091] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0092] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0093] Figure 1 This is a flowchart illustrating the method for optimizing the collaborative operation path of air defense alarms using drones and unmanned vehicles, as shown in the embodiments of this application.

[0094] Figure 2 This is a schematic diagram of the implementation process of the improved lion pack optimization algorithm for the collaborative operation path optimization method of air defense alarm between UAVs and unmanned vehicles, as shown in the embodiments of this application.

[0095] Figure 3 This is a schematic diagram of the hardware operating environment of the air defense alarm collaborative operation path optimization system involving drones and unmanned vehicles according to an embodiment of the present invention. Detailed Implementation

[0096] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0097] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0098] In the description of this application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0099] Unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0100] Traditional air raid sirens primarily rely on fixed electric and electro-acoustic sirens. This dependence on fixed alarm facilities results in limitations such as fixed locations and limited coverage, making it difficult to comprehensively cover urban areas, especially those with complex terrain or remote locations. Furthermore, with rapid urbanization, buildings are becoming increasingly taller, rendering sirens located at lower levels ineffective. Increased blind spots in high-rise buildings, building obstructions, and external noise further hinder the propagation of traditional air raid warning signals, leading to untimely and inaccurate information delivery. Simultaneously, these fixed facilities lack flexibility in the face of emergencies and cannot quickly adjust to real-time changes. Unmanned aerial vehicles (UAVs) and unmanned vehicles (UAVs) equipped with air raid sirens offer high flexibility and mobility, overcoming the limitations of fixed locations and limited coverage of traditional methods to achieve rapid and intelligent early warning. However, current research on path optimization for UAVs and UAVs is mostly focused on independent sub-problems, lacking collaborative operational capabilities. This results in problems such as untimely information transmission and incomplete coverage during air raid warning missions.

[0101] To address the aforementioned issues, this application provides a method for optimizing the collaborative operation path of UAVs and unmanned vehicles (UAVs) carrying air defense alarms. Based on the characteristics and constraints of UAVs and UAVs carrying air defense alarms, and with the goal of minimizing the weighted sum of task completion time and total delay time, a collaborative scheduling model for UAVs and UAVs carrying air defense alarms is established. Through an improved lion pack optimization algorithm, task assignment and sorting are performed based on a multi-level mapping encoding and decoding method of task-UAV / UAV-region. An improved path planning algorithm is integrated to optimize the movement routes of UAVs and UAVs, reduce travel time, and solve local congestion problems in path planning. Furthermore, a neighborhood search strategy is used to find the optimal scheduling scheme that minimizes both task completion time and delay time, providing a preferred solution for the collaborative operation scheduling problem of UAVs and UAVs in air defense alarm systems.

[0102] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0103] Figure 1 This is a flowchart illustrating the method for optimizing the collaborative operation path of air defense alarms using drones and unmanned vehicles, as shown in the embodiments of this application.

[0104] See Figure 1 A method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles, comprising:

[0105] S101. Represent the positions of the drones and unmanned vehicles using multi-dimensional coordinates, where the multi-dimensional coordinates include the starting point, mission area, set of missions to be performed, mission priority, and physical constraints of the drones and unmanned vehicles.

[0106] Specifically, the task set is defined as W = {w i |i=1,2,…,n}, corresponding to all tasks to be executed; the set of drones is defined as U={u j |j=1,2,…,m}, corresponding to all drones; the set of unmanned vehicles is defined as V={v l |l=1,2,…,p}, corresponding to all autonomous vehicles; the set of all task regions is defined as R={r i |i=1,2,…,q} represents all areas that need to perform alarm tasks.

[0107] Define P(a) i ) represents region a i Prioritize tasks to highlight their inherent risks and flexibility. Define The drones are respectively j Arrive at a i Time, driverless car v l Arrive at a i Time, e i k i Region a i The lower limit and region of the time window a i The upper limit of the time window.

[0108] Physical limitations of drones and unmanned vehicles, definition and These represent the maximum range of drones and unmanned vehicles, respectively. and The current drones are u j The length of the route traveled, the autonomous vehicle's v l Length of the path traveled; definition and For the battery capacity of drones and unmanned vehicles, and It refers to the energy consumed by drones and unmanned vehicles during mission execution; definition For task w i The load, For drones j The load-bearing capacity, For driverless cars v lThe load-bearing capacity must be considered when allocating tasks and planning routes. Equipment and materials required for each task must be allocated reasonably, with a load-bearing margin reserved to handle unforeseen circumstances. The communication range and quality between drones and unmanned vehicles and the control center are also limited; it is necessary to ensure they operate within the communication range and avoid entering communication blind spots. For drones j and driverless cars l Communication distance with the control center.

[0109] S102. To minimize the weighted sum of total task completion time and total delay time, establish a collaborative scheduling model for UAVs and unmanned vehicles carrying air defense alarms.

[0110] Specifically, the collaborative scheduling model for air defense alarms carried by drones and unmanned vehicles includes:

[0111] The objective function is established to minimize the weighted sum of the total task completion time and the total delay time:

[0112] minf=ω1·T 总 +ω2·D 总 (1)

[0113] In equation (1), T 总 D represents the total task completion time. 总 ω1 and ω2 represent the total delay time, and ω1 and ω2 represent the weighting coefficients.

[0114] Establish constraint function:

[0115] Assign each task to at least one drone or unmanned vehicle.

[0116]

[0117] In equation (2), x ij With y il x is a 0-1 variable. ij Indicates when task w i y is 1 when assigned to a drone, and 0 otherwise. il Indicates when task ω i The value is 1 when assigned to an autonomous vehicle, and 0 otherwise. W represents the set of all tasks, m represents the number of drones, and p represents the number of autonomous vehicles.

[0118] The number of drones and unmanned vehicles assigned to the missions did not exceed the total number of missions.

[0119]

[0120] Alarm arrival times for each area must be within a specified time window.

[0121]

[0122]

[0123] In equation (4), a i Represents the i-th region where task a is executed, e i k i Representing region a i The lower limit and upper limit of the time window;

[0124] The path length of drones and unmanned vehicles is less than their maximum range or distance.

[0125]

[0126]

[0127] In equation (5), and These represent the current drone u. j The length of the path traveled and the autonomous vehicle's v l The length of the route traveled. and Let U represent the maximum range of drones and unmanned vehicles, respectively; let U represent the set of all drones; let V represent the set of all unmanned vehicles; and let V represent the set of all unmanned vehicles.

[0128] The energy consumption of drones and unmanned vehicles did not exceed their battery capacity.

[0129]

[0130]

[0131] In equation (6), and This indicates the energy consumed by drones and unmanned vehicles during mission execution. and Indicates the battery capacity of drones and unmanned vehicles;

[0132] High-priority tasks will be assigned more frequently.

[0133] P(a i )≥max[P(a1),P(a2),…P(a n )]·λ (7)

[0134] In equation (7), P(a i ) represents region a i Task priority status;

[0135] In the event of task failure or changes in environment, tasks can be quickly reassigned.

[0136]

[0137] In equation (8), R represents the set of all regions that need to perform tasks;

[0138] Drones and unmanned vehicles maintain a safe distance from obstacles during mission execution.

[0139]

[0140]

[0141] In equation (9), O represents the set of obstacles, and r0 represents the safe distance of region r;

[0142] Each drone and unmanned vehicle shall not carry a payload exceeding its maximum payload capacity when performing a mission.

[0143]

[0144]

[0145] In equation (10), For task w i The load, For drones j The bearing, For driverless cars v l The bearing;

[0146] The communication range and quality limitations between drones and unmanned vehicles, as well as with the control center.

[0147]

[0148] In equation (11), For drones j or driverless car v l Communication distance with the control center.

[0149] S103. Solve the collaborative scheduling model of UAV and unmanned vehicle load air defense alarm by using the improved lion flock optimization algorithm to obtain the collaborative scheduling scheme of target UAV and unmanned vehicle. The improved lion flock optimization algorithm includes the introduction of the Sweep scanning method, the algorithm discretization processing based on circular encirclement, and the multi-operator update mechanism to improve the response speed and decision accuracy of the lion flock optimization algorithm.

[0150] Specifically, the improved lion pack optimization algorithm is used to solve the collaborative scheduling model of UAV and unmanned vehicle load air defense alarms, including:

[0151] S1031. Using the Sweep scanning method, assign initial tasks to all drones and unmanned vehicles, and construct an initial vehicle and drone collaborative scheduling scheme.

[0152] In generating the initial scheduling scheme for the population, the Sweep method creates diverse initial solution sets by systematically changing scheduling parameters within a predetermined parameter range. This method is based on a simple yet effective principle: by gradually and systematically changing one or more parameters related to the collaborative path between the UAV and the autonomous vehicle, such as starting position, flight speed, route, and payload capacity, the changing trends of the objective function (such as path length, alarm coverage, response time, and energy consumption) can be clearly observed, including:

[0153] The basic steps of the Sweep method are as follows: Assign tasks to drones and autonomous vehicles. First, select an unused drone or autonomous vehicle. Assign unassigned tasks to drones or autonomous vehicles according to their priority from highest to lowest, until the constraints of a drone or autonomous vehicle are no longer met. Then, arrange the specific travel paths of the drones or autonomous vehicles to obtain a specific path arrangement for each drone or autonomous vehicle. If there are still unassigned tasks, continue task assignment until all tasks are assigned to drones or autonomous vehicles. Swap the drones / autonomous vehicles and tasks, i.e., recalculate the task priorities, and repeat the above steps to obtain multiple solutions. This step essentially involves reselecting and setting the node with the lowest priority, and then scanning towards higher priority nodes from that node to assign drones or autonomous vehicles. Repeat the task assignment steps, but sort the tasks according to their priority from lowest to highest, and then assign drones and autonomous vehicles to obtain a solution to the problem. From the above solutions, find the current optimal solution and output the initial drone and autonomous vehicle cooperative scheduling scheme.

[0154] The Sweep method is used to continuously adjust and optimize parameters based on the range and step size of variable changes, creating diverse initial solution sets to improve the collaborative efficiency of UAVs and unmanned vehicles. The adjustment and optimization parameters are calculated using the following formula:

[0155] P i,k =l i +k·s i

[0156]

[0157] k = 1, 2, ..., K i (18)

[0158] In equation (18), P i,k This represents the value of the i-th parameter at step k, l i s represents a variable.i Let K be the step size. i It is parameter P i The number of times a value can be taken.

[0159] A step-by-step scan is performed across all mission areas to calculate the impact of different starting positions on the time and path length for the UAV or unmanned vehicle to reach the designated alarm area; at the same time, the relationship between speed changes and energy consumption and mission completion efficiency is observed, and the flight speed parameters of the UAV and the driving speed parameters of the unmanned vehicle are adjusted accordingly.

[0160] The system scans the traffic conditions, congestion levels, and distances of different roads to assess their impact on the autonomous vehicle's route parameters and determine the optimal route for the current driving mode.

[0161] Scan at different altitude ranges to observe the impact of flight altitude on signal propagation range and strength, as well as on energy consumption, and adjust the drone's flight altitude parameters accordingly; then scan at different flight altitudes to observe the interference of terrain and building factors on signal propagation, and adjust the drone's flight altitude parameters accordingly.

[0162] Scan the mass of different types of air raid siren devices to determine the number and type of air raid siren devices that drones and unmanned vehicles can carry, and adjust the payload capacity parameters.

[0163] S1032. Using the initialized UAV and unmanned vehicle cooperative scheduling scheme, calculate the path cost, time window constraint satisfaction, and task priority satisfaction for each UAV and unmanned vehicle.

[0164] Route cost takes into account travel distance and energy consumption. The longer the travel distance, the more energy is consumed, leading to increased costs. The following formula is used for calculation:

[0165]

[0166]

[0167] In equation (20), and These are the path costs for drones and the path costs for autonomous vehicles, respectively. and The current drones are u j The length of the route traveled, the autonomous vehicle's v l The length of the route traveled. and It represents the energy consumed by drones and unmanned vehicles during mission execution, with α, β, γ, and δ being weighting coefficients.

[0168] The calculation of time window constraint satisfaction for drones and unmanned vehicles aims to determine whether their arrival time in the mission area is within the specified time window, and is calculated using the following formula:

[0169]

[0170]

[0171]

[0172] In equation (21), and Let I represent the satisfaction level of a single drone and unmanned vehicle within a time window, respectively. I is an indicator function, where I = 1 if the specified time window is met, otherwise I = 0. n is the total number of tasks, and S is the overall time window constraint satisfaction.

[0173] The task priority satisfaction rate is calculated to assess whether high-priority tasks are assigned preferentially. It is obtained by calculating the proportion of tasks that meet the priority requirements out of the total number of tasks, and then dividing this proportion by the total number of tasks. s ,

[0174]

[0175] S1033. The lion pack optimization algorithm is used to optimize the initial UAV and unmanned vehicle cooperative scheduling scheme and update the task allocation and path planning scheme of UAV and unmanned vehicle.

[0176] The lion pride optimization algorithm is used to optimize the initial collaborative scheduling scheme of UAVs and unmanned vehicles, including:

[0177] Determine the population size for the initial drone and unmanned vehicle collaborative scheduling scheme;

[0178] Calculate the fitness values ​​of individuals in the population and divide the lion king, lionesses, and cub groups;

[0179] The fitness value of an individual in a population is an indicator used to evaluate the quality or adaptability of each individual in solving a specific problem. In the path optimization problem of UAV and unmanned vehicle (UAV) collaborative air defense alarm operation, the fitness value of an individual reflects the performance of a collaborative scheduling scheme for UAVs and UAVs. The fitness value is calculated based on the objective function and constraints of the collaborative scheduling model for UAVs and UAVs carrying air defense alarms. The higher the fitness value, the closer the scheduling scheme represented by that individual is to the optimal solution, that is, it can better achieve the optimization objective set by the objective function under the premise of satisfying various constraints. In optimization algorithms, the fitness value is used to guide the evolution and search direction of the population. In the lion pride optimization algorithm, individuals with high fitness values ​​are more likely to be selected as lion kings, lionesses, or cubs, thus affecting their position updates and search behavior in subsequent iterations to gradually approach the optimal collaborative scheduling scheme. The fitness value of an individual in a population is calculated using the following formula:

[0180]

[0181] In equation (23), Q represents the number of individuals in the population, i represents the number of individuals in the population, and T i D represents the total time it takes for an individual to complete a task. i L represents the total delay time for an individual. i E represents the total path length of an individual. i This represents the total energy consumption of an individual, with α1, α2, α3, and α4 being weighting coefficients.

[0182] When dividing lion groups into alpha males, lionesses, and cubs, they are usually sorted according to their fitness values.

[0183] Fitness values ​​are calculated for all individuals in the population. The individual with the highest fitness value is designated as the alpha lion, a subset of individuals with the second highest fitness values ​​are assigned to the female lion group, and the remaining individuals are assigned to the cub group. For example, assuming there are 10 individuals in the population, after calculating the fitness values, the individual with the highest fitness value is designated as the alpha lion, the individuals with the second to fourth highest fitness values ​​are assigned to the female lion group, and the remaining 5 individuals are assigned to the cub group.

[0184] Discretize the lion positions and update the positions of the lion king, lionesses, and cubs, including:

[0185] Lion king's position update: In a lion society, the dominant lion enjoys the highest status and special rights. To ensure its food priority, the lion king patrols and makes slight movements within the most advantageous area to maintain its noble position. This practice helps the lion society maintain a stable social structure. When searching for and updating its position, the lion king focuses on areas close to the optimal solution and ensures its position is in the best possible condition. The lion king enjoys an unshakeable position within the group; other adult lionesses and cubs automatically make way, regarding the lion king's position as the best area, ensuring that the lion king can continuously occupy the best location and gain maximum benefit from the hunt. The formula for lion king's position update is...

[0186]

[0187] In equation (12), This represents the position of the i-th lion in the (k+1)-th iteration; gbest k This represents the optimal position of the k-th generation population; This represents the historical best position of the i-th lion in the k-th generation.

[0188] Lionesses play a crucial role in a pride, their unique skills and attributes making them essential to the success of hunting. A primary task of lionesses is to locate and surround potential prey. In this process, lionesses work closely together, selecting the most advantageous positions and methods to ensure the prey has nowhere to escape. Simultaneously, lionesses practice hunting with their cubs, teaching them essential hunting skills and strategies, laying the foundation for the pride's future hunting activities. Cooperation among lionesses plays a vital role in hunting; through teamwork, the success rate of hunts is increased, thereby enhancing the overall survival of the pride. Therefore, the cooperative spirit of lionesses is an indispensable part of the pride and a key factor in the pride's hunting success. The formula for lionesses' position updates is...

[0189]

[0190]

[0191] In equations (13) and (14), This represents the k-th generation historical best position for a lion cub following its mother, where γ is a random number generated according to a normal distribution N(0,1); α f The disturbance factor representing the lioness's movement range; α1 is a random number in the range (0,1); and Let T represent the minimum, mean, and maximum mean values ​​of each dimension of the lion's activity range, respectively; T represents the maximum number of iterations for the population; and t represents the current iteration number.

[0192] The perturbation factor for the lioness's location update causes the lioness to explore for food in a larger area in the early stage, gradually reduce the exploration range in the middle stage, and maintain a small value close to zero in the later stage. This better balances the algorithm's global exploration capability and local exploitation capability, enhances the algorithm's convergence speed, and effectively avoids the problem of premature convergence.

[0193] Cub location updates are the most basic and youngest members of a pride, and their behavior is guided and regulated by the lioness and alpha male. During location updates, cubs actively utilize information provided by other members, exchanging locations and sharing experiences to improve their positioning accuracy and search efficiency. The diverse location update strategies employed by cubs contribute to the pride's strategic diversity, thereby enhancing overall search capabilities. Furthermore, cubs cooperate with other pride members during location updates, which not only improves the pride's search efficiency but also enhances its adaptability. Therefore, cubs play a crucial role in the pride. The formula for cub location updates is...

[0194]

[0195]

[0196]

[0197] In equations (15), (16) and (17), α c The disturbance factor representing the movement range of the lion cub; the probability factor q is a uniform random value generated according to U(0,1); α2 represents a random number within the range (0,1); Let the position of the i-th cub within the hunting range be far from the lion king, representing a typical elitist reverse learning approach. Conversely, by shifting the search focus to a position with a better solution and then searching in the reverse direction, a better solution can be found more quickly. Within the pride, this means the cubs need to move closer to the globally optimal position where the lion king is located and place their search focus near that position. They can then move to other areas within the pride to explore the potential solution space more comprehensively. This strategy aims to fully utilize the available information within the pride, thereby improving the algorithm's search capability and efficiency.

[0198] S1034. Discretize the optimization results of the lion pack optimization algorithm, solve the optimal path for each UAV and unmanned vehicle, and comprehensively consider path length, energy consumption and time window constraints, and add congestion penalty cost.

[0199] The optimization results of the lion pride optimization algorithm are discretized, including:

[0200] By combining the position of one drone with the flight path of another drone, or exchanging the driving route of one drone with the payload allocation of another drone, a new scheduling scheme is generated, as shown below:

[0201]

[0202] In equation (19), Let represent the scheduling scheme for the i-th lion in the t-th iteration. Let represent the scheduling scheme for a randomly selected lion in the t-th iteration. Let represent the optimal scheduling scheme for the lion king individual in the t-th iteration. Cross1 and Cross2 represent two different update mechanisms. The Cross1 update mechanism pays more attention to the diversity of the population. By exchanging information with the random population's delivery scheme, it expands the solution space of the algorithm, improves the diversity of the population, and also strengthens the algorithm's global search capability. The Cross2 update mechanism pays more attention to the local search capability. The delivery scheme in the pride exchanges information with the optimal scheduling scheme, thereby improving the algorithm's local search capability and search speed.

[0203] S1035. Using a multi-operator update mechanism, the collaborative scheduling scheme of UAVs and unmanned vehicles in the current population is disrupted, repaired, and reconstructed to enhance the algorithm's global search capability and local search accuracy.

[0204] Using a multi-operator update mechanism, the current drone and unmanned vehicle cooperative scheduling scheme in the population is disrupted, repaired, and reconstructed, including:

[0205] A gradient-based update strategy is adopted, which calculates the gradient of the fitness function based on the fitness value of the current population scheduling scheme, and determines the search direction accordingly, guiding individuals to move quickly toward the optimal solution. For example, adjusting parameters such as task allocation and path planning of UAVs and unmanned vehicles can make the scheduling scheme gradient decrease on the fitness function, approaching the optimal solution.

[0206] By combining a random mutation strategy, random mutations are performed on individuals in the population with a certain probability during gradient updates. This can be done by changing the task assignments or driving routes of drones or unmanned vehicles, thereby increasing population diversity, avoiding getting trapped in local optima, expanding the search space, and increasing the probability of finding the global optimum.

[0207] By employing carefully designed operator combinations, various operators with different functions and characteristics are designed for task allocation and path planning optimization problems. These include operators that focus on optimizing task allocation, path planning, travel speed, and load allocation. These operators are then rationally combined according to the characteristics and requirements of the problem to operate on individuals in the population, making them better adaptable to the optimization problem and improving search efficiency and solution quality.

[0208] When facing areas with complex terrain, increase the frequency or weight of the new path exploration operator to simulate the driving situation of drones and unmanned vehicles in complex terrain, evaluate the feasibility and advantages and disadvantages of different paths, find a more suitable driving route, and combine with other operators to comprehensively optimize the collaborative scheduling scheme to ensure efficient collaborative operation in complex areas.

[0209] S1036. Calculate the fitness value of each scheduling scheme, calculate the weighted sum of task completion time and total delay time, and determine whether the constraints are met.

[0210] S1037. Repeat steps S1033 to S1036 until the preset maximum number of iterations T is reached, or the stopping condition is met that the improvement of the optimal solution is less than 0.1% after 10 consecutive iterations.

[0211] S1038, Output target UAV and unmanned vehicle collaborative scheduling scheme, including task allocation, path planning and time arrangement.

[0212] Corresponding to the aforementioned application function implementation device embodiments, this application also provides an air defense alarm collaborative operation path optimization system for UAVs and unmanned vehicles and corresponding embodiments.

[0213] As one implementation scheme, Figure 3 This is a schematic diagram of the hardware operating environment of the air defense alarm collaborative operation path optimization system involving drones and unmanned vehicles, which is part of the embodiment of the present invention.

[0214] like Figure 3 As shown, the hardware devices for the UAV and unmanned vehicle collaborative air defense alarm operation path optimization method may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0215] Those skilled in the art will understand that Figure 3The hardware operating environment architecture of the UAV and unmanned vehicle air defense alarm collaborative operation path optimization system shown in the figure does not constitute a limitation on the UAV and unmanned vehicle air defense alarm collaborative operation path optimization system. It may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0216] like Figure 3 As shown, the memory 1005, serving as a storage medium, may include an operating system, a network communication module, a user interface module, and a program for a collaborative air defense alarm operation path optimization method between UAVs and unmanned vehicles. The operating system manages and controls the program for the collaborative air defense alarm operation path optimization method between UAVs and unmanned vehicles, as well as the operation of other software or programs.

[0217] exist Figure 3 In the hardware operating environment of the UAV and unmanned vehicle air defense alarm collaborative operation path optimization system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; the processor 1001 can be used to call the program of the UAV and unmanned vehicle air defense alarm collaborative operation path optimization method stored in the memory 1005.

Claims

1. A method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles (UAVs) and unmanned vehicles (UAVs), characterized in that, The method includes the following steps: The positions of drones and unmanned vehicles are represented by multi-dimensional coordinates, which include the starting point, mission area, set of missions to be performed, mission priority, and physical constraints of drones and unmanned vehicles. With the goal of minimizing the weighted sum of total task completion time and total delay time, a collaborative scheduling model for air defense alarms loaded by UAVs and unmanned vehicles is established. The improved lion flock optimization algorithm is used to solve the collaborative scheduling model of UAVs and unmanned vehicles carrying air defense alarms, obtaining a target UAV and unmanned vehicle collaborative scheduling scheme. The solution process includes: using the Sweep method to assign initial tasks to all UAVs and unmanned vehicles, constructing an initial UAV and unmanned vehicle collaborative scheduling scheme; using the initial UAV and unmanned vehicle collaborative scheduling scheme, calculating the path cost, time window constraint satisfaction, and task priority satisfaction for each UAV and unmanned vehicle; and using the lion flock optimization algorithm to optimize the initial UAV and unmanned vehicle collaborative scheduling scheme, updating the task allocation and paths for the UAVs and unmanned vehicles. The planning scheme involves discretizing the optimization results of the lion pack optimization algorithm, solving for the optimal path for each UAV and unmanned vehicle, comprehensively considering path length, energy consumption, and time window constraints, and adding congestion penalty costs; using a multi-operator update mechanism to disrupt and repair the current UAV and unmanned vehicle cooperative scheduling scheme in the population, enhancing the algorithm's global search capability and local search accuracy; calculating the fitness value of each scheduling scheme, calculating the weighted sum of task completion time and total delay time, and whether the constraints are met; repeating the optimized steps using the lion pack optimization algorithm until the pre-set maximum number of iterations is reached or the stopping condition is met; and outputting the target UAV and unmanned vehicle cooperative scheduling scheme, including task allocation, path planning, and time arrangement. The discretization of the optimization results of the lion pack optimization algorithm includes combining the position of one UAV with the flight path of another UAV in the initial task allocation and path planning scheme of UAVs and unmanned vehicles, or exchanging the driving route of one unmanned vehicle with the payload allocation of another unmanned vehicle, thereby generating a new scheduling scheme, as shown below: （19） In equation (19), Let represent the scheduling scheme for the i-th lion in the t-th iteration. Let represent the scheduling scheme for a randomly selected lion in the t-th iteration. This represents the optimal scheduling scheme for the Lion King individual in the t-th iteration. These represent two different update mechanisms. The update mechanism places greater emphasis on population diversity. By exchanging information with the random population distribution scheme, it expands the solution space of the algorithm, improves population diversity, and enhances the algorithm's global search capability. The update mechanism places greater emphasis on local search capabilities. The delivery schemes within the pride enhance the algorithm's local search capabilities and search speed by interacting with the optimal scheduling scheme.

2. The method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles according to claim 1, characterized in that, The drone and unmanned vehicle-mounted air defense alarm collaborative scheduling model includes: The objective function is established to minimize the weighted sum of the total task completion time and the total delay time: （1） In equation (1), Indicates the total task completion time. Indicates the total delay time. and Indicates the weighting coefficient; Establish constraint function: Assign each task to at least one drone or unmanned vehicle. （2） In equation (2), and For 0-1 variables, Indicates when the task The value is 1 when assigned to a drone, and 0 otherwise. Indicates when the task The value is 1 when assigned to an autonomous vehicle, and 0 otherwise. Let m represent the set of all tasks, and p represent the number of drones and unmanned vehicles. The number of drones and unmanned vehicles assigned to the missions did not exceed the total number of missions. （3） Alarm arrival times for each area must be within a specified time window. （4） In equation (4), This represents the i-th region where task a is performed. Indicates the region The lower limit and upper limit of the time window; The path length of drones and unmanned vehicles is less than their maximum range or distance. （5） In equation (5), and These represent the current drones. The length of the route traveled and the autonomous vehicle The length of the route traveled. and These represent the maximum range of drones and unmanned vehicles, respectively. U represents the set of all drones, and V represents the set of all unmanned vehicles. The energy consumption of drones and unmanned vehicles did not exceed their battery capacity. （6） In equation (6), and This indicates the energy consumed by drones and unmanned vehicles during mission execution. and Indicates the battery capacity of drones and unmanned vehicles; High-priority tasks will be assigned more frequently. （7） In equation (7), Indicates the region Task priority status; In the event of task failure or changes in environment, tasks can be quickly reassigned. （8） In equation (8), R represents the set of all regions that need to perform tasks; Drones and unmanned vehicles maintain a safe distance from obstacles during mission execution. （9） In equation (9), O represents the set of obstacles. Indicates the safe distance of region r; Each drone and unmanned vehicle shall not carry a payload exceeding its maximum payload capacity when performing a mission. （10） In equation (10), For the task The load, For drones The bearing, For driverless cars The bearing; The communication range and quality limitations between drones and unmanned vehicles, as well as with the control center. （11） In equation (11), For drones or driverless car Communication distance with the control center.

3. The method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles according to claim 1, characterized in that, The Sweep scanning method is used to assign initial tasks to all drones and unmanned vehicles, including: The basic steps of the Sweep scanning method are used to assign tasks to drones and unmanned vehicles. The Sweep method is used to continuously adjust and optimize parameters based on the range and step size of variable changes, creating diverse initial solution sets and improving the collaborative efficiency of drones and unmanned vehicles. Perform a step-by-step scan across all mission areas and calculate the impact of different starting positions on the time and path length for the drone or unmanned vehicle to reach the designated alarm area. Observe the relationship between speed changes and energy consumption and mission completion efficiency, and adjust the flight speed parameters of the UAV and the driving speed parameters of the unmanned vehicle. The system scans the traffic conditions, congestion levels, and distances of different roads to assess their impact on the autonomous vehicle's route parameters and determine the optimal route for the current driving mode. By setting different altitude ranges for scanning, the effects of flight altitude on signal propagation range and intensity, as well as on energy consumption, are observed, and the flight altitude parameters of the UAV are adjusted accordingly. Then scan at different flight altitudes to observe the interference of terrain and building factors on signal propagation, and adjust the flight altitude parameters of the drone accordingly; Scan the mass of different types of air raid siren devices to determine the number and type of air raid siren devices that drones and unmanned vehicles can carry, and adjust the load capacity parameters. The following formula is used for calculation during the above scanning process: （18） In equation (18), This represents the value of the i-th parameter at step k. Represents variables, Step size, It is a parameter The number of times a value can be taken.

4. The method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles according to claim 1, characterized in that, The optimization of the initial drone and unmanned vehicle cooperative scheduling scheme using the lion pack optimization algorithm includes: Determine the population size for the initial drone and unmanned vehicle collaborative scheduling scheme; Calculate the fitness values ​​of individuals in the population and divide the lion king, lionesses, and cub groups; Discretize the lion positions and update the positions of the lion king, lionesses, and cubs.

5. The method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles according to claim 1, characterized in that, The use of a multi-operator update mechanism to disrupt, repair, and reconstruct the collaborative scheduling scheme for drones and unmanned vehicles in the current population includes: A gradient-based update strategy is adopted to guide the search towards the optimal solution quickly, while a random mutation strategy is combined to increase the diversity of the population and avoid getting trapped in local optima. By carefully designing combinations of operators, populations with different characteristics can be obtained to adapt to the task allocation and path planning optimization problems of UAVs and unmanned vehicles. When facing areas with complex terrain, add operators that focus on exploring new paths to find more suitable driving routes.

6. The method for optimizing the collaborative operation path of air defense alarms using unmanned aerial vehicles and unmanned vehicles according to claim 1, characterized in that, The method of representing the positions of drones and unmanned vehicles using multi-dimensional coordinates includes: The multidimensional coordinates of drones and unmanned vehicles include the starting point, mission area, set of tasks to be performed, mission priority, and physical constraints. Task set defined as This corresponds to all tasks to be executed; the drone set is defined as follows: This corresponds to all drones; the set of unmanned vehicles is defined as follows: This corresponds to all autonomous vehicles; the set of all task areas is defined as follows: This indicates all areas where alarm tasks need to be performed; definition For the region Task priority, definition , drones arrive Time, driverless cars arrive Time, They are respectively regions Lower limit and range of the time window The upper limit of the time window; Physical limitations of drones and unmanned vehicles, definition and These represent the maximum range of drones and unmanned vehicles, respectively. and The current drones The length of the route traveled, the autonomous vehicle Length of the path traveled; definition and For the battery capacity of drones and unmanned vehicles, and It refers to the energy consumed by drones and unmanned vehicles during mission execution; definition For the task The load, For drones The load-bearing capacity, For driverless cars The load-bearing capacity must be considered when allocating tasks and planning routes. Equipment and materials required for each task must be allocated reasonably, with a load-bearing margin reserved to handle unforeseen circumstances. The communication range and quality between drones and unmanned vehicles and the control center are also limited; it is necessary to ensure they operate within the communication range and avoid entering communication blind spots. For drones and driverless cars Communication distance with the control center.

7. A collaborative operation path optimization system for air defense alarms using unmanned aerial vehicles (UAVs) and unmanned vehicles (UAVs), characterized in that, The air defense alarm collaborative operation path optimization system for drones and unmanned vehicles includes: The system includes a memory, a processor, and a path optimization program for air defense alarm coordination between UAVs and unmanned vehicles, which is stored in the memory and can run on the processor. When the UAV and unmanned vehicle air defense alarm coordination path optimization program is executed by the processor, it implements the steps of the UAV and unmanned vehicle air defense alarm coordination path optimization method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a path optimization program for air defense alarm coordination between UAVs and unmanned vehicles. When the UAV and unmanned vehicle path optimization program is executed by a processor, it implements the steps of the UAV and unmanned vehicle path optimization method for air defense alarm coordination as described in any one of claims 1 to 6.

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