Unmanned aerial vehicle spectrum planning method, apparatus and device

Abstract

The application provides a kind of unmanned aerial vehicle spectrum planning method, device and equipment, it is related to spectrum planning technical field.The method comprises: obtaining entity unmanned aerial vehicle quantity and the available frequency point of each entity unmanned aerial vehicle;With the maximum minimum frequency interval as the target, based on entity unmanned aerial vehicle quantity and the available frequency point of each entity unmanned aerial vehicle, establish objective function and corresponding constraint condition;Wherein, minimum frequency interval refers to the minimum value in the frequency deviation between each two working frequency points allocated to each entity unmanned aerial vehicle;Based on constraint condition, the working frequency point of each entity unmanned aerial vehicle is obtained by solving objective function.The application can solve the problem of spectrum resource use conflict caused by multiple unmanned aerial vehicles coexistence, and improve frequency planning efficiency.

Description

Technical Field

[0001] This invention relates to the field of spectrum planning technology, and in particular to a method, apparatus and equipment for spectrum planning of unmanned aerial vehicles (UAVs). Background Technology

[0002] Unmanned aerial vehicles (UAVs) are characterized by low detectability, immunity to casualties, unmanned operation, and intelligence, leading to their increasingly widespread use in local conflicts and wars. Compared to single-UAV systems, multi-UAV collaborative operations have greater application potential, resulting in a continuous increase in the number of UAVs operating simultaneously in battlefield environments.

[0003] Drones primarily use radio for data link communication, and their normal operation requires specific electromagnetic spectrum resources. As the number of drones in the same domain increases, the conflict of spectrum resource usage between drones and between drones and other frequency-using equipment becomes increasingly prominent, leading to electromagnetic spectrum congestion and severe electromagnetic interference.

[0004] Research on related technologies for drones mainly focuses on improving spectrum resource utilization efficiency or anti-interference performance, but neglects the spectrum resource usage conflict problem caused by the coexistence of multiple drones. Spectrum resource planning for drones often relies on human experience, involving manual observation of the electromagnetic environment and repeated trial and adjustment of spectrum planning schemes, which severely restricts the efficiency of spectrum planning. Summary of the Invention

[0005] This invention provides a method, apparatus, and device for unmanned aerial vehicle (UAV) spectrum planning to solve the problem of spectrum resource usage conflicts caused by the coexistence of multiple UAVs and to improve frequency planning efficiency.

[0006] In a first aspect, embodiments of the present invention provide a method for unmanned aerial vehicle (UAV) spectrum planning, comprising: Obtain the number of physical drones and the available frequencies for each physical drone; Based on the number of physical drones and the available frequencies of each physical drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval; wherein, the minimum frequency interval refers to the minimum value of the frequency deviation between any two working frequencies allocated to each physical drone. The objective function is solved based on the constraints to obtain the operating frequency of each UAV entity.

[0007] In one possible implementation, the objective function and corresponding constraints, based on the number of physical drones and the available frequencies of each physical drone, aim to maximize the minimum frequency interval, and include: Obtain the number of operating frequencies for each drone entity; For each physical drone, the number of virtual drones corresponding to that physical drone is determined based on the number of operating frequencies of that physical drone; the number of virtual drones is the same as the number of operating frequencies, and each virtual drone corresponds to one operating frequency. Based on the available frequencies of each physical drone, determine the available frequencies of each virtual drone; Based on the number of virtual drones and the available frequencies of each virtual drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval. Solving the objective function based on the aforementioned constraints yields the operating frequency points of each individual UAV, including: Based on the constraints, the objective function is solved to obtain the operating frequency points of each virtual UAV. The operating frequency of each physical drone is determined based on the operating frequency of each virtual drone.

[0008] In one possible implementation, the objective function and the corresponding constraints are as follows: In the formula, Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Indicates the first The first virtual drone The frequency values ​​of the available frequency points.

[0009] In one possible implementation, before solving the objective function based on the constraints, the method further includes: The objective function and corresponding constraints are linearized to obtain the linearized objective function and constraints. Solving the objective function based on the constraints includes: Based on the constraints after linearization, solve the objective function after linearization.

[0010] In one possible implementation, the linearized objective function and constraints are as follows: in, This represents the value of the objective function. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Represents the set of virtual drone IDs. Indicates the first The first virtual drone The frequency values ​​of the available frequency points Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable.

[0011] In one possible implementation, the method further includes: If the number of available frequency points for each virtual drone is greater than a set threshold, then the available frequency points for each virtual drone are linearized to obtain at least one available linearized frequency band for each virtual drone; each available linearized frequency band contains at least two available frequency points. Based on the available linearized frequency bands, the constraints after linearization are improved to obtain the improved constraints. Based on the improved constraints, the linearized objective function is solved to obtain the operating frequency of each UAV entity.

[0012] In one possible implementation, the first The first virtual drone The available frequency points in the available linearization frequency bands are: in, Indicates the first The first virtual drone The frequency values ​​of available frequency points in the available linearized frequency band. This represents a slope constant. Indicates the first The first virtual drone Each available frequency point in the available linearized frequency band is numbered. Represents the intercept constant. Indicates the first The first virtual drone The number of available frequency points in the available linearized frequency bands; The improved constraint is as follows: in, This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available linearized frequency bands. Indicates the first The set of available linearized frequency band numbers for virtual drones. Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable. This represents the value of the objective function.

[0013] Secondly, embodiments of the present invention provide a UAV spectrum planning device, comprising: The acquisition module is used to acquire the number of physical drones and the available frequency points of each physical drone; The modeling module is used to establish an objective function and corresponding constraints based on the number of physical drones and the available frequency points of each physical drone, with the goal of maximizing the minimum frequency interval; wherein, the minimum frequency interval refers to the minimum value of the frequency deviation between any two working frequency points allocated to each physical drone. The solution module is used to solve the objective function based on the constraints to obtain the operating frequency of each entity UAV.

[0014] In one possible implementation, the modeling module is used for: Obtain the number of operating frequencies for each drone entity; For each physical drone, the number of virtual drones corresponding to that physical drone is determined based on the number of operating frequencies of that physical drone; the number of virtual drones is the same as the number of operating frequencies, and each virtual drone corresponds to one operating frequency. Based on the available frequencies of each physical drone, determine the available frequencies of each virtual drone; Based on the number of virtual drones and the available frequencies of each virtual drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval. The solution module is used for: Based on the constraints, the objective function is solved to obtain the operating frequency points of each virtual UAV. The operating frequency of each physical drone is determined based on the operating frequency of each virtual drone.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0016] This invention, through an objective function constructed with the goal of maximizing the minimum frequency interval, determines the operating frequencies of each drone entity, resulting in a spectrum planning scheme. This scheme ensures that the operating frequencies of each drone entity are distributed as dispersedly as possible within the available spectrum resources, avoiding spectrum congestion when multiple drone entities coexist, thereby reducing electromagnetic interference. Compared to traditional methods of manual allocation, this invention can minimize spectrum usage conflicts among multiple drone entities from a global perspective and improve spectrum planning efficiency. Attached Figure Description

[0017] Figure 1 This is an application scenario diagram of the UAV spectrum planning method provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the solution time of the CBC solver for equations (18) and (11) under different numbers of available linearization frequency bands and different numbers of physical UAVs; Figure 3 This is a schematic diagram of the solution time of the HiGHS solver for equations (18) and (11) under different numbers of available linearization frequency bands and different numbers of physical UAVs; Figure 4 This is a schematic diagram of the solution time of the SCIP solver for equations (18), (11) and (8) under different numbers of available linearization frequency bands and different numbers of physical UAVs; Figure 5 This is a schematic diagram of the structure of the UAV spectrum planning device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] As the number of drones in the same domain increases, the conflict of spectrum resource usage between drones and between drones and other frequency-using equipment becomes increasingly prominent, leading to electromagnetic spectrum congestion. However, related technologies mainly focus on improving spectrum resource utilization efficiency or anti-interference performance, but neglect the spectrum resource usage conflict caused by the coexistence of multiple drones. Spectrum resource planning for drones often relies on human experience, involving manual observation of the electromagnetic environment and repeated adjustments to the spectrum planning scheme, which severely restricts the efficiency of spectrum planning.

[0020] To avoid spectrum resource usage conflicts among multiple drone entities and improve spectrum planning efficiency, this invention constructs an objective function with the goal of maximizing the minimum frequency interval to determine the operating frequency of each drone entity, resulting in a spectrum planning scheme. This scheme allows the operating frequencies of each drone entity to be distributed as dispersedly as possible within the available spectrum resources, avoiding spectrum congestion when multiple drone entities coexist. Compared to traditional methods of manual allocation, this invention can minimize spectrum usage conflicts among multiple drone entities from a global perspective and improve spectrum planning efficiency.

[0021] See Figure 1 The diagram illustrates the implementation flowchart of the physical unmanned aerial vehicle (UAV) spectrum planning method provided in this embodiment of the invention, which is described in detail below: Step 101: Obtain the number of physical drones and the available frequencies for each physical drone.

[0022] Different types of drones may use different frequencies. Some drones can use any frequency point in certain frequency bands, while others can only use a limited number of fixed frequencies in a single frequency band. Therefore, in this embodiment of the invention, the available frequencies for each drone can be pre-obtained during spectrum planning for subsequent frequency allocation.

[0023] Step 102: Based on the number of physical drones and the available frequencies of each physical drone, establish an objective function and corresponding constraints with the goal of maximizing the minimum frequency interval.

[0024] The minimum frequency interval refers to the minimum frequency deviation between any two operating frequencies allocated to each individual UAV.

[0025] When multiple drones operate together, it is crucial to minimize interference between them, which is related to the setting of their operating frequencies. For any two drones, the greater the distance between their operating frequencies, the less interference they experience. Therefore, the principle for spectrum planning of drones in this embodiment of the invention can be: among all operating frequencies, the greater the frequency deviation between the two closest operating frequencies, the better. Accordingly, this embodiment of the invention can establish an objective function and corresponding constraints with the goal of maximizing the minimum frequency interval. It should be clarified that the frequency actually used by the drone is its operating frequency.

[0026] For any given physical drone, it can operate on multiple frequencies. For example, the operating frequencies of the physical drone may include: a primary uplink frequency, a primary downlink frequency, a secondary uplink frequency, a secondary downlink frequency, a satellite communication frequency, a video communication frequency, etc.

[0027] To facilitate subsequent modeling and calculations, this embodiment of the invention can treat each physical UAV using multiple operating frequencies as multiple virtual UAVs with a single frequency. Each virtual UAV uses one operating frequency. Furthermore, considering the impact of disabled frequencies or bands, disabled frequencies or bands can also be considered as being occupied by virtual UAVs.

[0028] Based on this, the process of establishing the objective function and corresponding constraints is as follows: First, obtain the number of operating frequencies for each physical drone. Then, for each physical drone, determine the number of virtual drones corresponding to that physical drone based on the number of operating frequencies. Next, determine the available frequencies for each virtual drone based on the available frequencies of each physical drone. Finally, based on the number of virtual drones and the available frequencies of each virtual drone, establish an objective function and corresponding constraints with the goal of maximizing the minimum frequency interval.

[0029] Here, for each physical drone, the number of virtual drones corresponding to that physical drone is the same as the number of its operating frequencies, and each virtual drone corresponds to one operating frequency.

[0030] All virtual drones corresponding to physical drones, as well as virtual drones corresponding to banned frequencies or bands, together constitute the set of virtual drone IDs. In the formula, This indicates the number of virtual drones.

[0031] The available frequencies for each virtual drone are the same as the available frequencies for the corresponding physical drone. For virtual drones with disabled frequencies or bands, their available frequencies are the disabled frequencies or bands.

[0032] It is understandable that each virtual drone has at least one available frequency. The set of available frequency points for a virtual drone can be represented as: In the formula, Indicates the first A set of available frequency points for a virtual drone, containing the available frequency points for that virtual drone. Indicates the first The number of available frequencies for virtual drones.

[0033] If the number of available frequency points is the same for all virtual drones, that is, Then the set of available frequency points for each virtual drone can be represented as If the number of available frequency points is different, then... Expand to This is to differentiate the number of available frequency points for different virtual drones. This method may make the model description more complex, but it does not affect the computational complexity. Each represents a set of available frequency points for each virtual drone.

[0034] Alternatively, the set of available frequency point numbers with a small number of available frequency points can be expanded to the same length by adding the same number of available frequency points. In this way, the description form of the model remains unchanged, but the number of equations and computational complexity may increase.

[0035] For example, in this embodiment of the invention, the available frequency point number set of each virtual drone is expanded to the same length by adding the same number of available frequency points. After expansion, the available frequency point number set of each virtual drone can be represented as follows: This includes the available frequency numbers for each virtual drone.

[0036] The frequency values ​​of each available frequency point of each virtual drone together constitute the frequency point resource. : (1) in, These represent the frequency values ​​of each available frequency point for different virtual drones. Indicates the number of virtual drones. This indicates the number of available frequency points for each virtual drone.

[0037] Each virtual drone corresponds to a working frequency point, and the set of available frequency point numbers for each virtual drone is as follows. and frequency resources Based on this, the embodiments of the present invention construct a nonlinear model for spectrum planning of physical unmanned aerial vehicles (which includes an objective function and its corresponding constraints) according to the following steps.

[0038] 1. Define decision variables Define 0-1 type decision variables Description of the The virtual drone used the first The situation of available frequency points.

[0039] (2) 2. Establish the objective function Based on the principles of spectrum planning, the objective function for the spectrum planning problem can be established, namely, maximizing the frequency with the smallest spacing among all operating frequencies: (3) Since the operation of calculating absolute value is symmetrical, some equations in equation (3) are repeated. To reduce the number of equations, they can be rewritten as follows: (4) 3. Establish constraints The constraints mainly consist of two sets: one set describes the number of frequency points used by the virtual drone (i.e., the number of working frequency points), and the other set describes the value of the frequency points used by the virtual drone (i.e., the frequency value of the working frequency points).

[0040] Each virtual drone has one and only one operating frequency: (5) The operating frequency of each virtual drone is: (6) Combining the equations for the decision variables, optimization objectives, and constraints mentioned above, we can obtain the mixed-integer nonlinear programming problem model for spectrum programming: (7) In the formula, Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Indicates the first The first virtual drone The frequency values ​​of the available frequency points.

[0041] Step 103: Solve the objective function based on the constraints to obtain the operating frequency of each entity UAV.

[0042] Based on the aforementioned constraints, this embodiment of the invention solves the objective function to obtain the operating frequency of each virtual drone. Then, based on the operating frequency of each virtual drone, the operating frequency of the corresponding physical drone can be determined.

[0043] Considering that there are minimization functions and absolute value inequalities in the above formula (7), both of which are nonlinear equations, the present invention can use a mixed integer nonlinear solver to solve them.

[0044] It is understandable that for each physical drone, the operating frequency of each virtual drone corresponding to that physical drone is the operating frequency of that physical drone.

[0045] The embodiments of the present invention can allocate frequency points to each physical UAV according to the operating frequency points of each physical UAV, thereby realizing UAV spectrum planning.

[0046] This invention, through an objective function constructed with the goal of maximizing the minimum frequency interval, determines the operating frequencies of each drone entity, resulting in a spectrum planning scheme. This scheme ensures that the operating frequencies of each drone entity are distributed as dispersedly as possible within the available spectrum resources, avoiding spectrum congestion when multiple drone entities coexist, thereby reducing electromagnetic interference. Compared to traditional methods of manual allocation, this invention can minimize spectrum usage conflicts among multiple drone entities from a global perspective and improve spectrum planning efficiency.

[0047] Considering that the above formula (7) contains nonlinear equations, it not only affects the solution speed, but also can only be solved using a mixed-integer nonlinear programming (MINLP) solver, which limits the choice of solver. Based on this, the embodiments of the present invention further transform the above objective function and constraints to convert the nonlinear equations (i.e., minimizing functions and absolute value inequalities) in formula (7) into linear equations, thereby improving the solution speed, and can also be solved using a mixed-integer (linear) programming (MILP) solver, thus removing the solver limitation.

[0048] In some embodiments, the objective function and the corresponding constraints can be linearized to obtain the linearized objective function and constraints. Here, the constraint relaxation method can be used to linearize the equation (i.e., the minimization function and the absolute value inequality) in formula (7) into a linear equation.

[0049] The specific process of constraint relaxation is as follows: (a) Minimizing function linearization For the minimization function in the objective function, intermediate variables can be introduced and inequality constraints can be added to relax it into a linear constraint. Then equation (7) becomes: (8) (ii) Linearization of absolute value inequalities Linearization of absolute value inequalities in constraints can be divided into two cases: Shaped like The situation can be relaxed as follows: (9) Shaped like The situation can be relaxed as follows: (10) Based on the above method, equation (8) can be transformed into a mixed integer (linear) programming problem model (i.e., the objective function and constraints after linearization): (11) in, This represents the value of the objective function. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Represents the set of virtual drone IDs. Indicates the first The first virtual drone The frequency values ​​of the available frequency points Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. To represent a sufficiently large constant, one can take... ,in, The set of all available frequency point values. This represents a 0-1 type auxiliary decision variable.

[0050] In this embodiment of the invention, the nonlinear equation (i.e., minimizing function and absolute value inequality) in formula (7) is converted into a linear equation by using the constraint relaxation method. This not only improves the solution speed, but also allows the use of a mixed integer (linear) solver to solve the equation, thereby removing the solver limitation.

[0051] When the number of available frequencies for each physical UAV is limited, the above formula (11) can effectively improve the solution speed. However, when the number of available frequencies for physical UAVs is large, the number of available frequencies for virtual UAVs will also be large, which will lead to a slower solution speed for the above formula (11). Furthermore, since formula (11) is mainly for situations where the number of available frequencies is limited, when the number of available frequencies is large, the above formula (11) may also affect the scope of application of spectrum planning.

[0052] In view of this, the present invention improves the above formula (11) for the working condition where there are a large number of available frequency points for each physical UAV.

[0053] In some embodiments, if the number of available frequency points for each virtual drone is greater than a set threshold, the available frequency points for each virtual drone are linearized to obtain at least one available linearized frequency band for each virtual drone; each available linearized frequency band contains at least two available frequency points; then, based on the available linearized frequency bands, the constraints after linearization are improved to obtain improved constraints; finally, based on the improved constraints, the objective function after linearization is solved to obtain the operating frequency points of each physical drone.

[0054] The above-mentioned situation where the number of available frequencies of the virtual drone exceeds the set threshold mainly includes two scenarios: First, the available frequencies of the virtual drone are discrete from each other, and the number of available frequencies exceeds the set threshold; second, the virtual drone can use any frequency within a certain frequency band, which leads to the number of available frequencies exceeding the set threshold.

[0055] When the available frequencies of a virtual drone are discrete and the number of available frequencies exceeds a set threshold, considering that many available frequencies typically have the same spacing and form multiple arithmetic sequences, this embodiment of the invention can first establish a linear model for the available frequency bands of the virtual drone to characterize each available frequency of the physical drone within that available frequency band, and then perform modeling and solution.

[0056] When a virtual drone can use any frequency point within a certain frequency band, resulting in the number of available frequency points exceeding a set threshold, it is sufficient to simply set the variables in the linear model to real numbers for representation.

[0057] By establishing the above-mentioned linear model, the embodiments of the present invention can significantly reduce the amount of computation and thus improve the solution speed.

[0058] The specific process of establishing a linear model for the available frequency bands (i.e., determining the available linearized frequency bands) is as follows: Regarding the first A virtual drone can be deployed, and the available frequency points of this virtual drone can be allocated to... Linearize each frequency band to obtain One available linearized frequency band. The set of available linearized frequency bands corresponding to a virtual drone can be represented as: .

[0059] Among them, the There are a total of [number] available linearization frequency bands. Available frequency points, namely Then the first The available frequency points in the available linearized frequency bands can be linearized as follows: (12) in, Indicates the first The first virtual drone The frequency values ​​of available frequency points in the available linearized frequency band. This represents a slope constant. Indicates the first The first virtual drone Each available frequency point in the available linearized frequency band is numbered. Represents the intercept constant. Indicates the first The first virtual drone The number of available frequency points in the available linearized frequency bands.

[0060] Here, the worst-case linearization band may only have 2 frequency points, therefore Ideally, the entire frequency band would form an arithmetic sequence, which can be described by a linear function. .

[0061] Based on the determination of the available linearization frequency band, this embodiment of the invention further improves the constraints after linearization (i.e., the constraints in Formula 11). The improvement process is as follows: 1. Define decision variables Define 0-1 type decision variables Description of the use of linear frequency bands: (13) 2. Establish constraints From equation (12), it can be seen that the available frequency points of the virtual drone can be described as: (14) In the formula, Boundary constraints and whether or not the first one is used The first virtual drone The available linearized frequency bands are related and can be expressed as: (15) Each virtual drone can only use one available linearized frequency band: (16) The operating frequency of each virtual drone can be represented as: (17) Combining equations (13) to (17), the improved constraint conditions are: in, This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available linearized frequency bands. Indicates the first The set of available linearized frequency band numbers for virtual drones. Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable. This represents the value of the objective function.

[0062] As for the objective function, it is the same as the objective function in equation (11), and the improved mixed-integer linear programming model is finally obtained: (18) When the number of available frequency points is greater than the set threshold, the above formula (18) can be used to solve and determine the spectrum planning scheme in order to improve the solution efficiency.

[0063] In optimal problem models, the type and number of variables and equations have a significant impact on the solution speed. Generally, the more variables and equations there are, the slower the solution; integer variables have a greater impact on the solution than real variables; and in general, integer (linear) equations are slower than linear equations, and nonlinear equations are slower than linear equations.

[0064] If the number of virtual drones is The number of available frequency points is The total number of available linearized frequency bands is The variables and constraints of the problem model shown in equations (8), (11) and (18) are listed in Table 1.

[0065] Table 1. Statistics of variables and constraints in the problem model Table 1 lists the total number of all available linearization bands. The value of depends on the specific frequency value of the available frequency points. Ideally, all available linearized frequency bands can be described by a linear function. The worst-case scenario is that the spacing between any two adjacent usable frequencies in each usable linearized band is different. If the number of usable frequencies in each usable linearized band is... ,but .

[0066] Statistical analysis of the variables and constraints of the problem models shown in equations (8), (11), and (18) shows that equation (8) is generally considered to be the most time-consuming of the three under the same conditions because it requires solving nonlinear constraint equations. When the spacing between any two adjacent frequency points in each available linearized frequency band is different, the number of integer decision variables in equations (11) and (18) is the same, and the number of constraint methods in equation (18) is larger and more complex. Therefore, equation (18) takes slightly longer to solve than equation (11). When the number of available frequency points in the available linearized frequency band is large, equation (18) takes less time to solve than equation (11).

[0067] Traditional spectrum planning mainly adopts a fixed channel allocation method, in which the commander statically allocates fixed frequencies to physical UAVs or frequency-using equipment. However, this spectrum planning method is difficult to meet the requirements of speed, real-time performance and dynamism.

[0068] This invention establishes a nonlinear programming model for spectrum planning (Equation 7) by analyzing the basic requirements of spectrum planning, and transforms it into an integer programming model (Equation 11). Then, based on the analysis of the spectrum characteristics of UAVs, the integer programming model for spectrum allocation (18) is further improved, which increases the solution speed and thus enhances the efficiency of spectrum planning and combat effectiveness.

[0069] Regarding UAV spectrum planning, the UAV spectrum planning method provided in this embodiment of the invention has the following beneficial effects: 1. Enhancing the Efficiency of UAV Spectrum Utilization: Faced with spectrum usage conflicts among multiple UAVs, related technologies often rely on manual observation, analysis, and trial-and-error methods for UAV spectrum planning, resulting in slow planning speed and low spectrum utilization. The spectrum planning method provided in this invention improves planning speed, fully utilizes spectrum resources, reduces mutual interference between multiple UAVs, and enhances the security of spectrum usage among multiple UAVs.

[0070] 2. Improve spectrum planning speed: By linearizing the frequency bands, the implementation of this invention can significantly improve the solution speed of the spectrum planning model and enhance the efficiency of spectrum planning.

[0071] It is understandable that the above spectrum planning method is not only applicable to drones, but also to other frequency-using devices.

[0072] Next, a simulation example will be used to demonstrate the effectiveness of the UAV spectrum planning method provided in the embodiments of the invention.

[0073] Considering that the solution speed of optimization problems is generally related to factors such as the problem model, data, solver, and computer hardware platform, for ease of comparison, the available frequency points of each physical UAV in this simulation example are all in the 400MHz to 495MHz band, with adjacent available frequency points spaced 5MHz apart, for a total of 20 available frequency points. The computer CPU, memory, and operating system used for the solution are all the same.

[0074] When the number of physical drones is 4 to 9, and the number of available linearized frequency bands for each physical drone is set to 1, 2, 4, 5, and 10 respectively, solvers such as the COIN Branch and Cut solver (CBC), the Solving Constraint Integer Programs (SCIP) framework, and the High Performance Linear Optimization Software (HiGHS) are used for solving the problem. The solution time for different problem models is as follows: Figure 2 , Figure 3 and Figure 4 As shown. Figure 2-4 The horizontal axis represents the number of physical drones, and the vertical axis represents the solution time.

[0075] In the figure, MILP means to use Equation (11) to solve, and MINLP means to use Equation (8) to solve. Currently, among the three solvers, only the SCIP solver can directly solve Equation (8).

[0076] As can be seen from the figure, solving equation (8) is basically the slowest, when When (i.e., the spacing between any two adjacent frequency points in each available linearization band is different), Equation (18) takes slightly more time than Equation (11). When the time required for equation (18) is less than that required for equation (11), and the more linearizable frequency points are available in the frequency band, the less time is required for the solution, which is basically consistent with the previous analysis of the problem model. Although the solution time varies between different solvers, the solution time of the same solver for different problems basically meets the above conclusions.

[0077] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0078] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0079] Figure 5 A schematic diagram of the structure of the UAV spectrum planning device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 5 As shown, the UAV spectrum planning device 5 includes: an acquisition module 51, a modeling module 52, and a solution module 53.

[0080] The acquisition module 51 is used to acquire the number of physical drones and the available frequency points of each physical drone; Modeling module 52 is used to establish an objective function and corresponding constraints based on the number of physical drones and the available frequency points of each physical drone, with the goal of maximizing the minimum frequency interval; where the minimum frequency interval refers to the minimum value of the frequency deviation between any two working frequency points allocated to each physical drone. The solver module 53 is used to solve the objective function based on the constraints to obtain the operating frequency of each entity UAV.

[0081] Optional, modeling module 52, specifically used for: Obtain the number of operating frequencies for each drone entity; For each physical drone, the number of virtual drones corresponding to that physical drone is determined based on the number of operating frequencies of that physical drone; the number of virtual drones is the same as the number of operating frequencies, and each virtual drone corresponds to one operating frequency. Based on the available frequencies of each physical drone, determine the available frequencies of each virtual drone; Based on the number of virtual drones and the available frequencies of each virtual drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval. Solver module 53 is specifically used for: The objective function is solved based on the constraints to obtain the operating frequency points of each virtual UAV. The operating frequency of each physical drone is determined based on the operating frequency of each virtual drone.

[0082] Optionally, the objective function and corresponding constraints are as follows: In the formula, Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Indicates the first The first virtual drone The frequency values ​​of the available frequency points.

[0083] Optionally, modeling module 52 is also used for: The objective function and its corresponding constraints are linearized to obtain the linearized objective function and constraints. Solver module 53 is specifically used for: Based on the constraints after linearization, solve the objective function after linearization.

[0084] Optionally, the objective function and constraints after linearization are as follows: in, This represents the value of the objective function. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Represents the set of virtual drone IDs. Indicates the first The first virtual drone The frequency values ​​of the available frequency points Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable.

[0085] Optionally, modeling module 52 is also used for: If the number of available frequency points for each virtual drone is greater than a set threshold, then the available frequency points for each virtual drone are linearized to obtain at least one available linearized frequency band for each virtual drone; each available linearized frequency band contains at least two available frequency points. Based on the available linearization frequency bands, the constraints after linearization are improved to obtain the improved constraints. Based on the improved constraints, the linearized objective function is solved to obtain the operating frequency of each UAV entity.

[0086] Optional, the first The first virtual drone The available frequency points in the available linearization frequency bands are: in, Indicates the first The first virtual drone The frequency values ​​of available frequency points in the available linearized frequency band. This represents a slope constant. Indicates the first The first virtual drone Each available frequency point in the available linearized frequency band is numbered. Represents the intercept constant. Indicates the first The first virtual drone The number of available frequency points in the available linearized frequency bands; The improved constraints are as follows: in, This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available linearized frequency bands. Indicates the first The set of available linearized frequency band numbers for virtual drones. Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable. This represents the value of the objective function.

[0087] This device embodiment is used to implement the above method embodiment, and its technical principle and implementation effect are the same as those of the above method embodiment, so they will not be repeated here.

[0088] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 6 As shown, the electronic device 6 of this embodiment includes a processor 60 and a memory 61. The memory 61 stores a computer program 62. When the processor 60 executes the computer program 62, it implements the steps in the various method embodiments described above. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each module / unit in the various device embodiments described above.

[0089] For example, computer program 62 may be divided into one or more modules / units, which are stored in memory 61 and executed by processor 60 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 62 in electronic device 6.

[0090] Electronic device 6 may include, but is not limited to, processor 60 and memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 6 may also include input / output devices, network access devices, buses, etc.

[0091] The processor 60 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0092] The memory 61 can be an internal storage unit of the electronic device 6, such as a hard disk or RAM. The memory 61 can also be an external storage device of the electronic device 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 61 can include both internal and external storage units of the electronic device 6. The memory 61 is used to store the computer program 62 and other programs and data required by the electronic device 6. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0093] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0094] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0095] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0096] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0097] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0098] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for spectrum planning of unmanned aerial vehicles (UAVs), characterized in that, include: Obtain the number of physical drones and the available frequencies for each physical drone; Based on the number of physical drones and the available frequencies of each physical drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval; wherein, the minimum frequency interval refers to the minimum value of the frequency deviation between any two working frequencies allocated to each physical drone. The objective function is solved based on the constraints to obtain the operating frequency of each UAV entity.

2. The UAV spectrum planning method according to claim 1, characterized in that, Based on the number of physical drones and the available frequencies of each physical drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval, including: Obtain the number of operating frequencies for each drone entity; For each physical drone, the number of virtual drones corresponding to that physical drone is determined based on the number of operating frequencies of that physical drone; the number of virtual drones is the same as the number of operating frequencies, and each virtual drone corresponds to one operating frequency. Based on the available frequencies of each physical drone, determine the available frequencies of each virtual drone; Based on the number of virtual drones and the available frequencies of each virtual drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval. Solving the objective function based on the aforementioned constraints yields the operating frequency points of each individual UAV, including: Based on the constraints, the objective function is solved to obtain the operating frequency points of each virtual UAV. The operating frequency of each physical drone is determined based on the operating frequency of each virtual drone.

3. The UAV spectrum planning method according to claim 2, characterized in that, The objective function and the corresponding constraints are as follows: In the formula, Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Indicates the first The first virtual drone The frequency values ​​of the available frequency points.

4. The UAV spectrum planning method according to any one of claims 1-3, characterized in that, Before solving the objective function based on the constraints, the method further includes: The objective function and corresponding constraints are linearized to obtain the linearized objective function and constraints. Solving the objective function based on the constraints includes: Based on the constraints after linearization, solve the objective function after linearization.

5. The UAV spectrum planning method according to claim 4, characterized in that, The objective function and constraints after linearization are as follows: in, This represents the value of the objective function. This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available frequency points This represents the set of available frequency point numbers. Represents the set of virtual drone IDs. Indicates the first The first virtual drone The frequency values ​​of the available frequency points Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable.

6. The UAV spectrum planning method according to claim 4, characterized in that, The method further includes: If the number of available frequency points for each virtual drone is greater than a set threshold, then the available frequency points for each virtual drone are linearized to obtain at least one available linearized frequency band for each virtual drone; each available linearized frequency band contains at least two available frequency points. Based on the available linearized frequency bands, the constraints after linearization are improved to obtain the improved constraints. Based on the improved constraints, the linearized objective function is solved to obtain the operating frequency of each UAV entity.

7. The UAV spectrum planning method according to claim 6, characterized in that, The first The first virtual drone The available frequency points in the available linearization frequency bands are: in, Indicates the first The first virtual drone The frequency values ​​of available frequency points in the available linearized frequency band. This represents a slope constant. Indicates the first The first virtual drone Each available frequency point in the available linearized frequency band is numbered. Represents the intercept constant. Indicates the first The first virtual drone The number of available frequency points in the available linearized frequency bands; The improved constraint is as follows: in, This represents a 0-1 type decision variable, used to reflect the first... The first virtual drone Whether to use the available linearized frequency bands. Indicates the first The set of available linearized frequency band numbers for virtual drones. Represents the set of virtual drone IDs. Indicates the first The operating frequency value of the virtual drone. Indicates the first The operating frequency value of the virtual drone. This represents a sufficiently large constant. This represents a 0-1 type auxiliary decision variable. This represents the value of the objective function.

8. A spectrum planning device for unmanned aerial vehicles (UAVs), characterized in that, include: The acquisition module is used to acquire the number of physical drones and the available frequency points of each physical drone; The modeling module is used to establish an objective function and corresponding constraints based on the number of physical drones and the available frequency points of each physical drone, with the goal of maximizing the minimum frequency interval; wherein, the minimum frequency interval refers to the minimum value of the frequency deviation between any two working frequency points allocated to each physical drone. The solution module is used to solve the objective function based on the constraints to obtain the operating frequency of each entity UAV.

9. The physical unmanned aerial vehicle spectrum planning device according to claim 8, characterized in that, include: The modeling module is used for: Obtain the number of operating frequencies for each drone entity; For each physical drone, the number of virtual drones corresponding to that physical drone is determined based on the number of its operating frequency points. The number of virtual drones is the same as the number of operating frequencies, with each virtual drone corresponding to one operating frequency. Based on the available frequencies of each physical drone, determine the available frequencies of each virtual drone; Based on the number of virtual drones and the available frequencies of each virtual drone, an objective function and corresponding constraints are established with the goal of maximizing the minimum frequency interval. The solution module is used for: Based on the constraints, the objective function is solved to obtain the operating frequency points of each virtual UAV. The operating frequency of each physical drone is determined based on the operating frequency of each virtual drone.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

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