Unmanned aerial vehicle charging method and device based on energy perception model, and medium

Abstract

The invention discloses an unmanned aerial vehicle charging method and device based on an energy perception model, and relates to the technical field of unmanned aerial vehicle control. Comprising the following steps: 1, establishing an energy sensing model according to a mixed integer linear programming model MILP: setting constraint conditions according to the operation condition of an unmanned aerial vehicle, constructing an objective function of the energy sensing model, calculating the minimum value of y under the condition that the constraint conditions are met, obtaining an optimal unmanned aerial vehicle charging strategy, and obtaining an optimal unmanned aerial vehicle charging strategy; and 2, solving an optimal unmanned aerial vehicle charging strategy by using the energy sensing model, and guiding the unmanned aerial vehicle to charge according to the optimal unmanned aerial vehicle charging strategy.

Description

Technical Field

[0001] The invention discloses a method, a device and a medium for charging an unmanned aerial vehicle based on an energy perception model, and relates to the technical field of unmanned aerial vehicle control. Background Art

[0002] Drones have seen significant growth in recent years and are becoming an important enabler for services such as public safety, IoT applications, telemedicine, and situational awareness. In the cellular space in particular, drones provide rapid deployment services to areas that cannot be reached by ground networks, such as disaster-stricken areas and crowded areas. They open the door to non-ground aerial communications in 5G verticals where ground network infrastructure and protocols are currently insufficient. In areas where mobile network operators have low return on investment and high operating costs, drones provide a user network coverage solution without the need to cover the entire terrain. But in typical drone operations, there is no comprehensive way to ensure that the drone moves from the launch point to the designated coverage area to provide service and then returns to the ground for recharging. There is no guarantee that the limited onboard energy of unmanned aerial vehicles (UAVs) can be optimally allocated. Summary of the invention

[0003] In view of the problems of the prior art, the present invention provides a UAV charging method, device and medium based on an energy perception model, establishes a UAV energy perception model, optimizes the UAV energy distribution strategy and flight path, thereby maximizing the UAV's working time.

[0004] The specific scheme proposed by the present invention is:

[0005] The present invention provides a UAV charging method based on an energy perception model, comprising:

[0006] Step 1: Establish an energy-aware model based on the mixed integer linear programming model MILP:

[0007] According to the operation of the UAV, set the constraints, build the objective function of the energy perception model, and use the formula to express the objective function:

[0008]

[0009] Under the condition of satisfying the constraints, the minimum value of y is calculated to obtain the optimal UAV charging strategy, where the decision variable X ij Represents the allocation of drones to charging base stations; decision variable Y ik Indicates the allocation of drones to super charging stations; auxiliary variable D ij Indicates the distance from the drone to the base station numbered j; auxiliary variable D ikrepresents the distance from the drone to the super charging station numbered K; n represents the number of drones that can be charged by each charging base station; m represents the number of charging stations that each drone can be charged at; λ is a weight factor used to balance the weight between the two conditions, namely the minimum moving distance and the maximum charging port;

[0010] Step 2: Use the energy-aware model to solve the optimal UAV charging strategy and guide the UAV to charge according to the optimal UAV charging strategy.

[0011] Furthermore, the setting constraint conditions in the drone charging method based on the energy perception model include:

[0012] Set charging distribution constraints: each drone is charged at exactly one charging station;

[0013] Set the base station service capacity constraint: the number of drones served by each charging base station shall not exceed n;

[0014] To set charging rule constraints:

[0015] Rule 1: When the drone battery is less than 30%, it must be charged.

[0016] Rule 2: For any moving drone, charge it at the nearest charging base station.

[0017] Rule 3: For any stationary drone, when the battery level is less than 10%, it must be charged at a Supercharger Station;

[0018] Set the nearest base station constraint: each drone charges at the m nearest charging stations.

[0019] Furthermore, in the energy-aware model-based UAV charging method, for the base station service capacity constraint, for any charging base station numbered j, to ensure that the energy range of the charging base station is not exceeded and the number of UAVs served does not exceed n, the following formula is satisfied:

[0020]

[0021] Furthermore, in the method for charging a UAV based on an energy sensing model, according to rule 1, if any UAV in the cellular network is i When the battery level of the drone is less than 30%, it must be charged at a charging base station or a super charging station, which satisfies the following formula:

[0022]

[0023] Among them, UAV i Choose to charge at a charging base station, then X ijThe value is 1, otherwise it is 0; UAV i If you choose to charge at a Supercharger station, Y ik The value is 1 if yes, otherwise 0.

[0024] Furthermore, in the above-mentioned UAV charging method based on energy perception model, for Rule 2, any moving UAV can be charged at the nearest charging base station.

[0025] Furthermore, in the above-mentioned UAV charging method based on energy perception model, for rule 3, any stationary UAV meets the following conditions when the battery power is less than 10%. and

[0026] The present invention also provides a UAV charging device based on an energy perception model, comprising a model management module and a charging guidance module.

[0027] The model management module establishes an energy-aware model based on the mixed integer linear programming model MILP:

[0028] According to the operation of the UAV, set the constraints, build the objective function of the energy perception model, and use the formula to express the objective function:

[0029]

[0030] Under the condition of satisfying the constraints, the minimum value of y is calculated to obtain the optimal UAV charging strategy, where the decision variable X ij Represents the allocation of drones to charging base stations; decision variable Y ik Indicates the allocation of drones to super charging stations; auxiliary variable D ij Indicates the distance from the drone to the base station numbered j; auxiliary variable D ik represents the distance from the drone to the super charging station numbered K; n represents the number of drones that can be charged at each charging base station; m represents the number of charging stations where each drone can be charged; λ is a weight factor used to balance the weight between the two conditions, namely the minimum moving distance and the maximum charging port;

[0031] The charging guidance module uses the energy perception model to solve the optimal UAV charging strategy and guides the UAV to charge according to the optimal UAV charging strategy.

[0032] The present invention also provides a computer-readable medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the processor executes the method for charging a drone based on an energy perception model.

[0033] The benefits of the present invention are:

[0034] Jointly optimize the charging distribution and trajectory of multiple drones to coordinate the charging operations of multiple drones as aerial base stations. Optimize the distribution and trajectory of charging stations through energy-aware models, using mixed integer linear programming methods to minimize the flight time and energy consumption of drones. By combining static charging base stations and mobile super charging stations, optimize the energy usage load balance between drones, making the cellular drone system more robust and balanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flow chart of the method of the present invention.

[0036] Figure 2 This is a schematic diagram of a drone cellular network. DETAILED DESCRIPTION

[0037] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0038] For example, Figure 1 As shown, the basic charging station (BaseStation, BS) and the super charging station (SuperStation, SS), UAV i Indicates the drone numbered i, BS i Indicates the basic charging station numbered i, SS k represents the super charging station numbered k, d 11 It represents the Euclidean distance between the drone numbered 1 and the basic charging station numbered 1. The meanings of the variables used in the energy perception model are described as follows:

[0039] (1) Decision variable X ij : If the drone UAVi chooses to charge at the base station BSj, the value is 1, otherwise it is 0,

[0040] (2) Decision variable Y ik : If the drone UAVi chooses to charge at the super charging station SSk, the value is 1, otherwise it is 0,

[0041] (3) Auxiliary variable D ij :UAV i To base station BS numbered j j The distance

[0042] (4) Auxiliary variable D ik :UAV i Go to the Supercharger station numbered K k distance.

[0043] Example 1

[0044] The present invention provides a UAV charging method based on an energy perception model, comprising:

[0045] Step 1: Establish an energy-aware model based on the mixed integer linear programming model MILP:

[0046] According to the operation of the drone, set constraints, including:

[0047] Set charging distribution constraints: each drone is charged at exactly one charging station;

[0048] Set the base station service capacity constraint: the number of drones served by each charging base station shall not exceed n;

[0049] To set charging rule constraints:

[0050] Rule 1: When the drone battery is less than 30%, it must be charged.

[0051] Rule 2: For any moving drone, charge it at the nearest charging base station.

[0052] Rule 3: For any stationary drone, when the battery level is less than 10%, it must be charged at a Supercharger Station;

[0053] Set the nearest base station constraint: each drone charges at the m nearest charging stations.

[0054] Construct the objective function of the energy-aware model and use the formula to express the objective function:

[0055]

[0056] Under the condition of satisfying the constraints, the minimum value of y is calculated to obtain the optimal UAV charging strategy, where the decision variable X ij Represents the allocation of drones to charging base stations; decision variable Y ik Indicates the allocation of drones to super charging stations; auxiliary variable D ij Indicates the distance from the drone to the base station numbered j; auxiliary variable D ik represents the distance from the drone to the super charging station numbered K; n represents the number of drones that can be charged by each charging base station; m represents the number of charging stations where each drone can be charged; λ is a weight factor used to balance the weight between the two conditions, which are the minimum moving distance and the maximum charging port.

[0057] Step 2: Use the energy-aware model to solve the optimal UAV charging strategy and guide the UAV to charge according to the optimal UAV charging strategy.

[0058] Example 2

[0059] Based on Example 1,

[0060] When setting constraints, for the base station service capacity constraint, for any charging base station numbered j, in order to ensure that the energy range of the charging base station is not exceeded and the number of drones served does not exceed n, the following formula is satisfied:

[0061]

[0062] For rule 1, if any UAV in the cellular network i When the battery level of the drone is less than 30%, it must be charged at a charging base station or a super charging station, which satisfies the following formula:

[0063]

[0064] Among them, UAV i Choose to charge at a charging base station, then X ij The value is 1, otherwise it is 0; UAV i If you choose to charge at a Supercharger station, Y ik The value is 1 if yes, otherwise 0.

[0065] For rule 2, any moving drone can be charged at the nearest charging base station.

[0066] For rule 3, any stationary drone meets the following conditions when the battery power is less than 10% and

[0067] The model in the method of the present invention is used to solve the energy distribution and charging planning of drone flight. The energy-aware model uses existing cellular base stations as charging stations, giving priority to energy-saving paths during charging, with the aim of maximizing the working time of drones in the entire cellular network by charging drones in a timely manner.

[0068] Example 3

[0069] The present invention also provides a UAV charging device based on an energy perception model, comprising a model management module and a charging guidance module.

[0070] The model management module establishes an energy-aware model based on the mixed integer linear programming model MILP:

[0071] According to the operation of the UAV, set the constraints, build the objective function of the energy perception model, and use the formula to express the objective function:

[0072]

[0073] Under the condition of satisfying the constraints, the minimum value of y is calculated to obtain the optimal UAV charging strategy, where the decision variable X ij Represents the allocation of drones to charging base stations; decision variable Y ik Indicates the allocation of drones to super charging stations; auxiliary variable D ij Indicates the distance from the drone to the base station numbered j; auxiliary variable D ik represents the distance from the drone to the super charging station numbered K; n represents the number of drones that can be charged by each charging base station; m represents the number of charging stations that each drone can be charged at; λ is a weight factor used to balance the weight between the two conditions, namely the minimum moving distance and the maximum charging port;

[0074] The charging guidance module uses the energy perception model to solve the optimal UAV charging strategy and guides the UAV to charge according to the optimal UAV charging strategy.

[0075] As the information interaction and execution process between the modules of the above-mentioned device are based on the same concept as the embodiment of the method of the present invention, the specific contents can be found in the description of the embodiment of the method of the present invention and will not be repeated here.

[0076] Similarly, the device of the present invention jointly optimizes the charging distribution and trajectory of multiple drones to coordinate the charging operation of multiple drones as aerial base stations. The distribution and trajectory of charging stations are optimized through energy-aware models, and mixed integer linear programming methods are used to minimize the flight time and energy consumption of drones. By combining static charging base stations and mobile super charging stations, the energy usage load balance between drones is optimized, making the cellular drone system more robust and balanced.

[0077] It should be noted that not all steps and modules in the above-mentioned processes and device structures are necessary, and some steps or modules can be ignored according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or some components in multiple independent devices may be implemented together.

[0078] Example 4

[0079] The present invention also provides a computer-readable medium, on which a computer instruction is stored, and when the computer instruction is executed by a processor, the processor executes the method for charging a drone based on an energy-aware model. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code for implementing the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0080] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0081] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0082] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0083] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0084] The above-described embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art based on the present invention are within the protection scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. A UAV charging method based on an energy sensing model, characterized in that include: Step 1: Establish an energy-aware model based on the mixed integer linear programming model MILP: According to the operation of the UAV, set the constraints, build the objective function of the energy perception model, and use the formula to express the objective function: Under the condition of satisfying the constraints, the minimum value of y is calculated to obtain the optimal UAV charging strategy, where the decision variable X ij Represents the allocation of drones to charging base stations; decision variable Y ik Indicates the allocation of drones to super charging stations; auxiliary variable D ij Indicates the distance from the drone to the base station numbered j; auxiliary variable D ik represents the distance from the drone to the super charging station numbered K; n represents the number of drones that can be charged by each charging base station; m represents the number of charging stations that each drone can be charged at; λ is a weight factor used to balance the weight between the two conditions, namely the minimum moving distance and the maximum charging port; Step 2: Use the energy-aware model to solve the optimal UAV charging strategy and guide the UAV to charge according to the optimal UAV charging strategy.

2. According to claim 1, a method for charging a drone based on an energy sensing model is characterized in that The setting constraint conditions include: Set charging distribution constraints: each drone is charged at exactly one charging station; Set the base station service capacity constraint: the number of drones served by each charging base station shall not exceed n; To set charging rule constraints: Rule 1: When the drone battery is less than 30%, it must be charged. Rule 2: For any moving drone, charge it at the nearest charging base station. Rule 3: For any stationary drone, when the battery level is less than 10%, it must be charged at a Supercharger Station; Set the nearest base station constraint: each drone charges at the m nearest charging stations.

3. The method for charging a drone based on an energy sensing model according to claim 2 is characterized in that Base station service capacity constraint: For any charging base station numbered j, to ensure that the energy range of the charging base station is not exceeded and the number of drones served does not exceed n, the following formula is satisfied:

4. The method for charging a drone based on an energy sensing model according to claim 2 is characterized in that For rule 1, if any UAV in the cellular network i When the battery level of the drone is less than 30%, it must be charged at a charging base station or a super charging station, which satisfies the following formula: Among them, UAV i Choose to charge at a charging base station, then X ij The value is 1, otherwise it is 0; UAV i If you choose to charge at a Supercharger station, Y ik The value is 1 otherwise, 0.

5. The method for charging a drone based on an energy sensing model according to claim 2 is characterized in that For rule 2, any moving drone can be charged at the nearest charging base station.

6. The method for charging a drone based on an energy sensing model according to claim 2 is characterized in that For rule 3, any stationary drone meets the following conditions when the battery power is less than 10% and 7. A UAV charging device based on an energy sensing model, characterized in that Including model management module and charging guidance module, The model management module establishes an energy-aware model based on the mixed integer linear programming model MILP: According to the operation of the UAV, set the constraints, build the objective function of the energy perception model, and use the formula to express the objective function: Under the condition of satisfying the constraints, the minimum value of y is calculated to obtain the optimal UAV charging strategy, where the decision variable X ij Represents the allocation of drones to charging base stations; decision variable Y ik Indicates the allocation of drones to super charging stations; auxiliary variable D ij Indicates the distance from the drone to the base station numbered j; auxiliary variable D ik represents the distance from the drone to the super charging station numbered K; n represents the number of drones that can be charged by each charging base station; m represents the number of charging stations that each drone can be charged at; λ is a weight factor used to balance the weight between the two conditions, namely the minimum moving distance and the maximum charging port; The charging guidance module uses the energy perception model to solve the optimal UAV charging strategy and guides the UAV to charge according to the optimal UAV charging strategy.

8. A computer readable medium, characterized in that The computer-readable medium stores computer instructions, and when the computer instructions are executed by the processor, the processor executes the drone charging method based on the energy perception model as described in any one of claims 1 to 6.

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