A method for collaborative mission planning of unmanned aerial vehicles

Abstract

The present invention relates to a method for collaborative mission planning of unmanned aerial vehicles, belonging to the field of unmanned aerial vehicle mission planning. The method includes: S1: Establishing an unmanned aerial vehicle model, including a mission resource model and an unmanned aerial vehicle mission capability model; S2: Establishing a mission model, including a determined mission model and an unknown mission model; S3: Modeling mission area allocation variables and mission allocation variables; S4: Modeling the unmanned aerial vehicle resource allocation ratio; S5: Establishing a mission benefit model, including mission execution benefit and unknown mission detection benefit; S6: Modeling the constraints of unmanned aerial vehicle collaborative mission planning, including mission area allocation constraints, mission allocation constraints, and unmanned aerial vehicle constraints; S7: Determining the unmanned aerial vehicle mission allocation, detection power, and trajectory planning strategy based on maximizing the mission benefit. The present invention can achieve the maximization of the total mission benefit.

Description

Technical Field

[0001] The present invention belongs to the field of UAV mission planning and relates to a UAV collaborative mission planning method. Background Art

[0002] In recent years, Unmanned Aerial Vehicles (UAVs) have been widely used in the fields of information relay, reconnaissance and positioning, environmental monitoring, logistics transportation, etc. due to their advantages of economy, flexibility, and convenience of deployment. The accurate and reliable execution of UAV missions depends on a reasonable and efficient mission planning strategy. It is necessary to comprehensively analyze mission elements according to various constraints such as the environmental information perceived by the UAV, mission requirements, and on-board mission payloads, optimize the scheduling and deployment of various resources, and determine the UAV mission perception, allocation, and trajectory planning strategies to ensure that the UAV completes the mission in the best way. With the development of electronic information technology and the improvement of the informatization and intelligence level of UAVs, the mission perception execution environment and mode of UAVs have undergone profound changes, showing a dynamically changing mission execution environment and complex and diverse mission requirements. However, the mission perception execution ability of a single UAV is severely limited by multiple factors such as function type, on-board payload, flight ability, and battery power. The multi-UAV cooperation technology can significantly and effectively improve the mission perception execution ability of UAVs, improve system safety and reliability through close cooperation between single UAVs, and has become the development trend of UAV applications.

[0003] At present, there are already literatures studying the UAV collaborative mission planning problem. For example, some literatures study the mission planning problem for underwater target search and tracking, model it as a problem of maximizing the search space and minimizing the terminal error, and determine the mission planning strategy by jointly solving the optimization problem. Another example is that some literatures consider the multi-UAV mission execution scenario in the disaster rescue scenario, model the UAV path planning problem as a variant of the vehicle path problem to minimize the path length. However, existing research rarely considers the task allocation, on-board resource allocation, and task detection resource optimization problems for multi-task regions and multi-task scenarios, resulting in the difficulty of efficient application of existing mechanisms. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a UAV collaborative mission planning method, which aims at a scenario including one ground control station, multiple heterogeneous UAVs, and multiple mission regions, where there is one or more determined missions in the mission regions, models the total mission benefit as the optimization goal, and realizes the joint optimization of task allocation, resource allocation, detection power allocation, and UAV trajectory planning.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A UAV collaborative mission planning method includes the following steps:

[0007] S1: Establish a UAV model, including a mission resource model, a UAV mission capability model, etc.;

[0008] S2: Establish a mission model, including a determined mission model and an unknown mission model;

[0009] S3: Model the mission area allocation variables and mission allocation variables;

[0010] S4: Model the UAV resource allocation ratio;

[0011] S5: Establish a mission benefit model, including mission execution benefit and unknown mission detection benefit;

[0012] S6: Model the UAV cooperative mission planning constraints, including mission area allocation constraints, mission allocation constraints, and UAV constraints;

[0013] S7: Determine the UAV mission allocation, detection power, and trajectory planning strategy based on maximizing mission benefits.

[0014] Further, in step S1, when establishing the UAV model, it specifically includes: N UAVs, define U n to represent the nth UAV, where 1 ≤ n ≤ N; the UAV needs to execute multiple types of missions, let Y represent the total number of mission types; the UAV consumes corresponding mission resources when executing missions, and the mission resources required for different types of missions are different; define ξ n,y to represent the initial resource quantity of the yth type of mission carried by U n , where, represents the maximum resource quantity of the yth type of mission carried by U n ; let σ n,y ∈{0, 1} represent the UAV mission ability identifier. If σ n,y = 1, it means that U n has the ability to execute the yth type of mission; otherwise, U n cannot execute the yth type of mission, where 1 ≤ n ≤ N and 1 ≤ y ≤ Y;

[0015] Assume that the system time is divided into time slots of equal length, let L represent the total number of time slots, and the length of each time slot is τ; define to represent the position sequence of the UAV, where, represents the position vector of U n at the tth time slot.

[0016] Further, in step S2, when establishing the mission model, it specifically includes: The missions are distributed in different mission areas, let A mDenote the m-th task area, where \(1\leq m\leq M\), and M represents the total number of task areas. Assume that \(A_0\) represents the take-off area of the UAV, and \(A\) M+1 represents the landing area of the UAV; there is a definite task in each task area, and the attributes of the definite task are known before execution; during the execution of the task, the UAV needs to detect and judge whether there is an unknown task, and after the unknown task is successfully detected, the UAV can determine its task attributes;

[0017] Definite task model: Let \(T\) m,k represent the k-th task in the task area \(A\) m , where \(1\leq m\leq M\) and \(1\leq k\leq K\), and K is the total number of tasks. \(T\) m,k can be represented by a quadruple , where \(Y\) m,k represents the task type of \(T\) m,k , \(Y\) m,k =\(\{\delta\) m,k,1 ,…,\(\delta\) m,k,y ,…,\(\delta\) m,k,Y \}\), \(\delta\) m,k,y \(\in\{0,1\}\) represents the task type identifier. If \(\delta\) m,k,y = 1, it means that the task \(T\) m,k is the y-th type; otherwise, \(\delta\) m,k,y = 0, and assume that each task belongs to only one type, that is \(S\) m,k represents the amount of resources required to execute the task \(T\) m,k ; \(\psi\) m,k \(\in\{0,1\}\) represents the task execution mode identifier. If \(\psi\) m,k = 1, it means that the task \(T\) m,k can be partially executed; otherwise, it means that the task needs to be fully executed; represents the benefit obtained after completing the execution of \(T\) m,k ;

[0018] Unknown task model: Assume that the number of unknown tasks follows a Poisson distribution with parameter \(\lambda\). Since the time slot length is small enough, at most one task arrives at the gateway in each time slot; when the UAV is executing a task above \(A\) m , the probability that the unknown task appears i times can be expressed as , where \(\lambda\) represents the average arrival rate of unknown tasks, and \(D\) m represents the time when the UAV is executing a task above the area \(A\) m . According to the formula calculate \(D\) m , where represents \(U\) n executing \(T\) m,kThe required time; during the process of the UAV executing the determined task, the unknown tasks in the area are detected at the beginning of each time slot, assuming that at most one unknown task is detected in each time slot; for each successfully detected unknown task, define T′ m denote the set of unknown tasks successfully detected in A m , ||T′ m ||≥0, T m,k1 denote the k1-th unknown task in A m , 0≤k1≤K m , K m denote the number of unknown tasks in A m .

[0019] Furthermore, in step S3, model the task area allocation variable and the task allocation variable, specifically including: Let α n,m,t ∈{0,1} represent the UAV task area allocation variable in the t-th time slot. If α n,m,t =1, it means that in the t-th time slot U n is associated with the task area A m , otherwise, α n,m,t =0, Let β n,m,k,t ∈{0,1} represent the UAV task allocation variable. If β n,m,k,t =1, it means that in the t-th time slot U n executes the task T m,k , otherwise, β n,m,k,t =0,

[0020] Furthermore, in step S4, model the UAV resource allocation ratio, specifically including: Let η n,m,k,t ∈[0,1] be the resource allocation ratio of U n to the task T m,k in the t-th time slot, If ψ m,k =1, η n,m,k,t ∈[0,1], otherwise, η n,m,k,t ∈{0,1}.

[0021] Furthermore, in step S5, establish the task revenue model, specifically including: Define R as the total task revenue, modeled as R = R e +R d , where,

[0022] R e represents the task execution revenue, and the calculation formula is where, represents the net execution revenue obtained by the UAV U n executing the task T m,k in the t-th time slot, and the calculation formula is E n,m,k represents U n executing task T m,k the consumed energy, and the calculation formula is where, e u represents the propulsion power of the UAV, represents the hovering power of the UAV, represents the flight time of the UAV flying from the initial position or the previous position area to A m and the calculation formula is where, is the task area scheduling variable. If U n flies from to A m , then otherwise, represents the time required for U n to fly from to A m and the calculation formula is where, L m represents the hovering position of the UAV when executing the task above, and v n represents the flight speed of the UAV; represents U n executing T m,k the required hovering time, and the calculation formula is where, F n represents the task execution speed of U n ;

[0023] R d represents the unknown task detection benefit, and the calculation formula is where, represents the net detection benefit obtained by U n detecting the task in the task area A m , and the calculation formula is where, represents the probability that U n correctly detects an unknown task at the t-th time slot. If the calculation formula is where, represents the position of U at the t-th time slot, n represents the position of the unknown task , r n represents the detection radius of U n ; ρ is an exponential parameter determined by the sensor quality, represents the power used by U n to detect the unknown task in A m at the t-th time slot.​

[0024] Furthermore, in step S6, the constraint conditions for the collaborative mission planning of the UAVs are modeled, specifically including:

[0025] The constraint condition for mission area allocation is modeled as where, if there exists α n,m,t = 1, then N max represents the maximum number of UAVs collaborating to execute a mission area; the constraint condition for the UAV flight area is modeled as

[0026] The constraint condition for mission allocation is modeled as where, if there exists β n,m,k,t = 1, then If α n,m,t = 0, then β n,m,k,t = 0, If β n,m,k,t = 0, then η n,m,k,t = 0, If σ n,y = 0 and δ m,k,y = 1, then β n,m,k,t = 0 and η n,m,k,t = 0,

[0027] The UAV constraints include the UAV detection power constraint, the UAV performance constraint, and the UAV safety constraint; the constraint condition for the UAV detection power is modeled as where, represents the maximum detection power of U n ; the UAV performance constraint includes the UAV energy constraint and the UAV mission resource constraint. If The UAV energy constraint condition is modeled as where, E th represents the threshold value of the remaining energy of the UAV, represents the available energy of U n at the t-th time slot, and is calculated according to the formula Calculate where, represents the initial energy of U n , represents the energy consumed by U n up to the t-th time slot, and is calculated according to the formula Calculate where, represents the flight energy consumed by U n up to the t-th time slot, and is calculated according to the formula Calculate represents the UAV U nThe consumed hovering energy is calculated according to the formula Calculate where represents the energy required for the UAV U n to execute the y-th type of task with a unit resource quantity; the UAV task resource constraint condition is modeled as represents U n The resources used to execute the task cannot exceed the maximum amount of this type of task resources carried by U n where ξ n,y,t represents the remaining quantity of the y-th type of resource at the t-th time slot, and ξ is calculated according to the formula Calculate ξ n,y,t ; the UAV flight distance constraint is modeled as where represents the maximum flight speed of the UAV U n ; the UAV safety constraint is modeled as where represents the minimum safety distance between UAVs. This constraint condition means that during the flight, a certain safety distance should be maintained between different UAVs.

[0028] Furthermore, in step S7, the UAV cooperative task planning constraint conditions are modeled, specifically including: on the premise of satisfying the task area association constraint, task assignment constraint, and UAV performance constraint conditions, with the goal of maximizing the total task revenue, optimizing and designing the task assignment and UAV trajectory planning strategies, that is where represents the optimal task area allocation strategy of U n ; represents the optimal allocation strategy of U n ; represents the optimal resource scheduling strategy corresponding to U n when executing task T m,k ; represents the optimal task area sequence strategy of U n ; represents the optimal detection power of U n in task area A m ; represents the optimal trajectory strategy of the UAV U n ;

[0029] The beneficial effects of the present invention are as follows: By establishing a UAV model and a task model, and modeling the task area allocation variable, task assignment variable, resource allocation variable, detection power variable, and UAV trajectory planning variable, the optimal task assignment, resource allocation, detection power, and trajectory planning strategies are determined, realizing the maximization of the total task revenue.

[0030] Other advantages, objects, and features of the present invention will be set forth in part in the following description, and in part will be obvious to those skilled in the art upon examination of the following, or may be learned by practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the following description of the specification. Brief Description of the Drawings

[0031] In order to make the objects, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0032] Figure 1 is a schematic diagram of the UAV cooperative mission planning scenario of the present invention;

[0033] Figure 2 is a flowchart of the UAV cooperative mission planning method of the present invention. Detailed Embodiments

[0034] The following describes the embodiments of the present invention through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention schematically. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0035] Please refer to Figures 1 to 2 , the present invention provides a UAV cooperative mission planning method. Assuming a scenario with a ground control station, multiple heterogeneous UAVs, and multiple mission areas, there is one or more determined missions in the mission areas. The total mission benefit is modeled as the sum of the net mission execution benefit and the net mission detection benefit. Based on maximizing the total mission benefit, joint optimization of optimal mission allocation, resource allocation, detection power allocation, and UAV trajectory planning is achieved.

[0036] Figure 1 is a schematic diagram of the UAV cooperative mission planning scenario of the present invention. As Figure 1 shown, there are multiple heterogeneous UAVs and multiple mission areas in this network. There is one or more determined missions in the mission areas. The UAV needs to fly above the mission area to execute the missions within the area; the mission benefit is maximized by optimizing mission allocation, resource allocation, UAV detection power allocation, and UAV trajectory planning.

[0037] Figure 2 is a flowchart of the UAV cooperative mission planning method of the present invention. As Figure 2As shown below, it specifically includes the following steps:

[0038] 1) Establish a drone model

[0039] Establishing a drone model specifically includes: including N drones, defining U n to represent the nth drone, where 1 ≤ n ≤ N; the drones need to execute multiple types of tasks, let Y represent the total number of task types; the drones consume corresponding task resources when executing tasks, and different types of tasks require different task resources; define ξ n,y to represent the initial resource quantity of the yth type of task carried by U n ; to represent the maximum resource quantity of the yth type of task carried by U n ; let σ n,y ∈ {0, 1} represent the drone task ability identifier. If σ n,y = 1, it means that U n has the ability to execute the yth type of task; otherwise, U n cannot execute the yth type of task, where 1 ≤ n ≤ N and 1 ≤ y ≤ Y;

[0040] Assume that the system time is divided into time slots of equal length, let L represent the total number of time slots, and the length of each time slot is τ; define to represent the position sequence of the drones, where represents the position vector of U n at the tth time slot.

[0041] 2) Establish a task model

[0042] Establishing a task model specifically includes: the tasks are distributed in different task areas, let A m represent the mth task area, where 1 ≤ m ≤ M, and M represents the total number of task areas. Assume that A0 represents the takeoff area of the drones, and A M+1 represents the landing area of the drones; there are definite tasks in each task area, and the attributes of the definite tasks are known before execution; the drones need to detect and judge whether there are unknown tasks during the task execution process, and the drones can determine the task attributes of the unknown tasks after successful detection;

[0043] Let T m,k represent the kth task in the task area A m , where 1 ≤ m ≤ M and 1 ≤ k ≤ K, and K is the total number of tasks. T m,k can be represented by the quadruple , where Y m,k represents the task type of T m,k , Y m,k = {δ m,k,1 , …, δ m,k,y , …, δ m,k,Y}, δ m,k,y ∈ {0, 1} represents the task type identifier. If δ m,k,y = 1, it means that task T m,k is the y-th type; otherwise, δ m,k,y = 0, and it is assumed that each task belongs to only one type, that is S m,k represents the amount of resources required to execute task T m,k ; ψ m,k ∈ {0, 1} represents the task execution mode identifier. If ψ m,k = 1, it means that this task T m,k can be partially executed; otherwise, it means that this task needs to be fully executed; represents the revenue obtained after completing the execution of T m,k ;

[0044] It is assumed that the number of unknown tasks occurring follows a Poisson distribution with parameter λ, and at most one task arrives at the gateway per time slot; when the UAV is performing tasks above A m , the probability that i unknown tasks occur can be expressed as where λ represents the average arrival rate of unknown tasks, and D m represents the time for the UAV to perform tasks above area A m . According to the formula calculate D m , where represents the time required for U n to execute T m,k ; during the process of the UAV executing the determined tasks, unknown tasks in the area are detected at the beginning of each time slot. It is assumed that at most one unknown task can be detected per time slot; for each successfully detected unknown task, define T′ m to represent the set of unknown tasks successfully detected in A m , ||T′ m || ≥ 0, represents the k1-th unknown task in A m , 0 ≤ k1 ≤ K m , and K m represents the number of unknown tasks in A m .

[0045] 3) Model the task area association variable and the task association variable

[0046] Modeling the task area association variable and the task association variable specifically includes: Let α n,m,t ∈ {0, 1} represent the UAV task area allocation variable in the t-th time slot. If α n,m,t = 1, it means that in the t-th time slot, U n is associated with the task area A m , otherwise, αn,m,t = 0, Let β n,m,k,t ∈ {0, 1} represent the UAV mission allocation variable. If β n,m,k,t = 1, it means that at the t-th time slot, U n executes mission T m,k , otherwise, β n,m,k,t = 0,

[0047] 4) Model the UAV mission resource allocation variable

[0048] Modeling the UAV mission resource allocation variable specifically includes: Let α n,m,t ∈ {0, 1} represent the UAV mission area allocation variable at the t-th time slot. If α n,m,t = 1, it means that at the t-th time slot, U n is associated with mission area A m , otherwise, α n,m,t = 0, Let β n,m,k,t ∈ {0, 1} represent the UAV mission allocation variable. If β n,m,k,t = 1, it means that at the t-th time slot, U n executes mission T m,k , otherwise, β n,m,k,t = 0,

[0049] 5) Establish the mission revenue model

[0050] Establishing the mission revenue model specifically includes: Define R as the total mission revenue, modeled as R = R e + R d , where R e represents the mission execution revenue, calculated according to the formula to calculate R e , where, represents the net execution revenue obtained by UAV U n executing mission T m,k at the t-th time slot, calculated according to the formula to calculate E n,m,k represents the energy consumed by U n executing mission T m,k at the t-th time slot, calculated according to the formula to calculate E n,m,k , where e u represents the propulsion power of the UAV, represents the hovering power of the UAV, represents the flight time of the UAV from the initial position or the previous position area to A m , calculated according to the formula to calculate Among them, is the task area scheduling variable. If U n flies from A m1 to A m , then Otherwise, represents the time required for U n to fly from A m1 to A m and is calculated according to the formula Calculate Among them, L m represents the hovering position of the UAV when performing tasks above, and v n represents the flight speed of the UAV; represents the hovering time required for U n to execute T m,k and is calculated according to the formula Among them, F n represents the task execution speed of U n ;

[0051] R d represents the unknown task detection benefit and is calculated according to the formula Calculate R d , where represents the net detection benefit obtained by U n detecting the task in the task area A m and is calculated according to the formula Calculate Among them, represents the probability that U n correctly detects an unknown task at the t-th time slot. If is calculated according to the formula Calculate Among them, represents the position of U at the t-th time slot, n represents the position of the unknown task , r n represents the detection radius of U n , and ρ is an exponential parameter determined by the sensor quality. represents the power used by U n to detect the unknown task in A m at the t-th time slot.

[0052] 6) Model the constraints of UAV cooperative task planning

[0053] Modeling the constraints of UAV cooperative task planning specifically includes: Modeling the task area allocation constraint as ​Among them, if there exists α n,m,t = 1, then N max represents the maximum number of drones that cooperate to execute a task area; the drone flight area constraint condition is modeled as

[0054] The task assignment constraint condition is modeled as Among them, if there exists β n,m,k,t = 1, then If α n,m,t = 0, then β n,m,k,t = 0, If β n,m,k,t = 0, then η n,m,k,t = 0, If σ n,y = 0 and β m,k,y = 1, then β n,m,k,t = 0 and η n,m,k,t = 0,

[0055] The drone constraints include the drone detection power constraint, the drone performance constraint, and the drone safety constraint; the drone detection power constraint condition is modeled as Among them, represents the maximum detection power of U n ; the drone performance constraint includes the drone energy constraint and the drone task resource constraint. If The drone energy constraint condition is modeled as Among them, E th represents the threshold value of the remaining energy of the drone, represents the available energy of U n at the t-th time slot, and is calculated according to the formula Calculate Among them, represents the initial energy of U n ; represents the energy consumed by U n up to the t-th time slot, and is calculated according to the formula Calculate Among them, represents the flight energy consumed by U n up to the t-th time slot, and is calculated according to the formula Calculate represents the hovering energy consumed by the drone U n up to the t-th time slot, and is calculated according to the formula Calculate Among them, represents the energy required for the drone U n to execute the y-th type of task with a unit resource quantity; the drone task resource constraint condition is modeled as Denote as U n The resources used to execute the task cannot exceed U n The maximum amount of resources carried for this type of task, where, ξ n,y,t Denote the remaining amount of the y-th type of resource at the t-th time slot. According to the formula Calculate ξ n,y,t ; The UAV flight distance constraint is modeled as where Denote the maximum flight speed of UAV U n ; The UAV safety constraint is modeled as where Denote the minimum safety distance between UAVs. This constraint means that during flight, a certain safety distance should be maintained between different UAVs.

[0056] 7) Determine the UAV task allocation, detection power, and trajectory planning strategy based on maximizing the task revenue

[0057] Determining the UAV task allocation, detection power, and trajectory planning strategy based on maximizing the task revenue specifically includes: On the premise of satisfying the task area association constraint, task association constraint, and UAV performance constraint conditions, with the goal of maximizing the total task revenue, optimize and design the task allocation and UAV trajectory planning strategy, that is where Denote the optimal task area allocation strategy of U n ; Denote the optimal allocation strategy of U n ; Denote the optimal resource scheduling strategy corresponding to U n when executing task T m,k ; Denote the optimal task area sequence strategy of U n ; Denote the optimal detection power of U n in task area A m ; Denote the optimal trajectory strategy of UAV U n ;

[0058] 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 them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for collaborative mission planning of unmanned aerial vehicles, characterized in that, The method comprises the following steps: S1: Establish a UAV model, including a task resource model and a UAV task capability model; S2: Establish a task model, including a determined task model and an unknown task model; S3: Model the task area allocation variable and the task allocation variable; S4: Model the UAV resource allocation ratio; S5: Establish a task benefit model, including task execution benefit and unknown task detection benefit; S6: Model the UAV cooperative task planning constraint conditions, including task area allocation constraint, task allocation constraint and UAV constraint; S7: Determine the UAV task allocation, detection power and trajectory planning strategy based on maximizing the task benefit; In step S1, a drone model is established, specifically including: N drones, defining U n to represent the nth drone, where 1 ≤ n ≤ N; the drones need to perform multiple types of tasks. Let Y represent the total number of task types; the drones consume corresponding task resources when performing tasks, and the required task resources for different types of tasks are different; define ξ n,y to represent the initial resource amount of the yth type of task carried by U n , where represents the maximum resource amount of the yth type of task carried by U n ; let σ n,y ∈ {0, 1} represent the drone task ability identifier. If σ n,y = 1, it means that U n has the ability to perform the yth type of task; otherwise, U n cannot perform the yth type of task, where 1 ≤ n ≤ N and 1 ≤ y ≤ Y;​ Assume that the system time is divided into time slots of equal length. Let \(L\) denote the total number of time slots, and the length of each time slot is \(\tau\). Define as the position sequence of the UAVs, where denotes \(U\) n 's position vector at the \(t\)-th time slot; In step S2, a task model is established, specifically including: the tasks are distributed in different task areas. Let A m represent the m-th task area, where 1 ≤ m ≤ M, and M represents the total number of task areas. Assume that A0 represents the takeoff area of the drone, and A M+1 represents the landing area of the drone; there are definite tasks in each task area, and the attributes of the definite tasks are known before execution; during the process of the drone executing tasks, it is necessary to detect and judge whether there are unknown tasks, and after the unknown tasks are successfully detected, the drone can determine their task attributes; Determine the task model: Let T m,k represent the k-th task in the task area A m , where 1 ≤ m ≤ M, 1 ≤ k ≤ K, and K is the total number of tasks. T m,k is represented by a quadruple , where Y m,k represents the task type of T m,k , Y m,k = {δ m,k,1 , …, δ m,k,y , …, δ m,k,Y}, and δ m,k,y ∈ {0, 1} represents the task type identifier. If δ m,k,y = 1, it means that the task T m,k is of the y-th type; otherwise, δ m,k,y = 0, and it is assumed that each task belongs to only one type, that is S m,k represents the amount of resources required to execute the task T m,k ; ψ m,k ∈ {0, 1} represents the task execution mode identifier. If ψ m,k = 1, it means that the task T m,k can be partially executed; otherwise, it means that the task needs to be fully executed; represents the benefit obtained after completing the execution of T m,k ; Unknown task model: Assume that the number of unknown tasks follows a Poisson distribution with parameter λ. Since the time slot length is small enough, at most one task arrives at the gateway in each time slot; when the UAV is performing tasks above A m during the mission, the probability that i unknown tasks occur is expressed as where λ represents the average number of unknown task arrivals, and D m represents the time for the UAV to perform tasks above A m area. According to the formula calculate D m , where represents the time required for U n to execute T m,k ; during the process of the UAV executing the determined task, the unknown tasks in the area are detected at the beginning of each time slot. Assume that at most one unknown task is detected in each time slot; for each successfully detected unknown task, define T m ′ as the set of unknown tasks successfully detected in A m , ||T′ m || ≥ 0, represents the k1-th unknown task in A m , 0 ≤ k1 ≤ K m , and K m represents the number of unknown tasks in A m ; In step S3, the modeling task area allocation variable and the task allocation variable are as follows: Let α n,m,t ∈{0,1} represent the UAV task area allocation variable in the t-th time slot. If α n,m,t =1, it means that in the t-th time slot, U n is associated with the task area A m . Otherwise, α n,m,t =0. Let β n,m,k,t ∈{0,1} represent the UAV task allocation variable. If β n,m,k,t =1, it means that in the t-th time slot, U n executes the task T m,k . Otherwise, β n,m,k,t =0. In step S4, the allocation ratio of drone resources is modeled, specifically including: let η n,m,k,t ∈[0,1] is the time slot U in the tth time slot n For task T m,k The resource allocation ratio If m,k =1,η n,m,k,t ∈[0,1], otherwise, η n,m,k,t ∈{0,1}; In step S5, a task revenue model is established, which specifically includes: defining R as the total task revenue, modeled as R = R e + R d , where R e represents the task execution benefit, and the calculation formula is where represents the net execution benefit obtained by the UAV U n executing task T m,k The calculation formula is E n,m,k represents the energy consumed by U n executing task T m,k The calculation formula is where e u represents the propulsion power of the UAV, represents the hovering power of the UAV, represents the flight time of the UAV flying from the initial position or the previous position area to A m The calculation formula is where is the task area scheduling variable. If U n flies from to A m , then Otherwise, represents the time required for U n to fly from to A m The calculation formula is where L m represents the hovering position of the UAV when executing the task above, and v n represents the flight speed of the UAV; represents the required hovering time for U n to execute T m,k The calculation formula is where F n represents the task execution speed of U n ; R d It represents the unknown task detection benefit, and the calculation formula is: in, Indicates U n For mission area A m The net detection benefit obtained by performing detection on the task is calculated as follows: in, Indicates that in the tth time slot U n Correctly detect unknown tasks The probability that The calculation formula is in, represents the tth time slot U n location, Indicates unknown task The position of n Indicates U n The detection radius, ρ is an exponential parameter determined by the quality of the sensor, represents the tth time slot U n Detection A m The power used during unknown tasks in In step S6, modeling the UAV cooperative task planning constraint conditions specifically includes: Model the task area allocation constraint as Among them, if there exists α n,m,t = 1, then N max represents the maximum number of UAVs that cooperate to execute a task area; Model the UAV flight area constraint as The task assignment constraint conditions are modeled as Among them, if there exists β n,m,k,t = 1, then If α n,m,t = 0, then β n,m,k,t = 0, If β n,m,k,t = 0, then η n,m,k,t = 0, If σ n,y = 0 and δ m,k,y = 1, then β n,m,k,t = 0 and η n,m,k,t = 0, The UAV constraints include UAV detection power constraint, UAV performance constraint, and UAV safety constraint; the UAV detection power constraint condition is modeled as where represents the maximum detection power of U n ; the UAV performance constraint includes UAV energy constraint and UAV mission resource constraint. If the UAV energy constraint condition is modeled as where E th represents the threshold value of the remaining energy of the UAV, represents the available energy of U n at the t-th time slot. According to the formula calculate where represents the initial energy of U n , represents the energy consumed by U n up to the t-th time slot. According to the formula calculate where represents the flight energy consumed by U n up to the t-th time slot. According to the formula calculate represents the hovering energy consumed by the UAV U n up to the t-th time slot. According to the formula calculate where represents the energy required for the UAV U n to execute the y-th type of task with a unit resource quantity; the UAV mission resource constraint condition is modeled as represents that the resources used by U n to execute the task cannot exceed the maximum amount of this type of task resource carried by U n . Among them, ξ n,y,t represents the remaining quantity of the y-th type of resource at the t-th time slot. According to the formula calculate ξ n,y,t ; the UAV flight distance constraint is modeled as where represents the maximum flight speed of the UAV U n ; the UAV safety constraint is modeled as n≠n1, where l th represents the minimum safety distance between UAVs. This constraint condition means that during flight, different UAVs should maintain a certain safety distance.

2. The method for collaborative mission planning of an unmanned aerial vehicle according to claim 1, wherein In step S7, the constraint conditions for collaborative mission planning of the modeling UAVs are specifically as follows: on the premise of satisfying the task area association constraint, task assignment constraint, and UAV performance constraint conditions, with the goal of maximizing the total mission benefit, optimize and design the task assignment and UAV trajectory planning strategies, that is where represents the optimal task area allocation strategy of U n , represents the optimal allocation strategy of U n , represents the optimal resource scheduling strategy corresponding to when U n executes task T m,k , represents the optimal task area sequence strategy of U n , represents the optimal detection power of U n in task area A m , represents the optimal trajectory strategy of UAV U n .

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