Energy guarantee intelligent power distribution method and system based on reinforcement learning

Abstract

The invention belongs to the technical field of energy guarantee intelligent power distribution, and particularly relates to an energy guarantee intelligent power distribution method and system based on reinforcement learning. The method comprises the following steps: step S10, constructing a DDPG intelligent agent, and training the DDPG intelligent agent to obtain a trained DDPG intelligent agent; and S20, the state quantity, obtained in real time, of the small aircraft is input into the trained DDPG intelligent body, the DDPG intelligent body outputs the electric power ratio of hot oil transportation to thermal power supply, and the large aircraft conducts intelligent power distribution on the small aircraft according to the electric power ratio of the hot oil transportation to the thermal power supply. According to the method, the electric power required by oil transportation and power supply is intelligently distributed based on the reinforcement learning algorithm according to the actual guarantee demand, energy guarantee planning in multiple states can be realized, the time consumption of the guarantee task can be shortened, and the intelligent level of the guarantee task can be improved.

Description

Background Art

[0002] As there are no pre-installed conditions for station equipment, it is necessary to use large aircraft platforms to meet the fuel and power supply needs, so that refueling trucks and power supply trucks do not need to be pre-installed or carried. To this end, research and equipment development are carried out on cross-aircraft hot oil transfer technology and cross-aircraft multi-power system hot power supply technology.

[0003] Large aircraft need to output electrical power to refuel and supply power to small aircraft, but the output power of the auxiliary power unit (APU) of the large aircraft platform is limited. Therefore, it is necessary to plan the energy guarantee task according to the fuel and power requirements of the small aircraft, and adaptively allocate the electrical power output of hot oil transfer and hot power supply. The current energy guarantee task planning methods generally have certain limitations in practical applications, and are usually only applicable to specific system states or environmental conditions, which limits their scope of application. In addition, these methods often take a long time to plan energy guarantee tasks, and it is difficult to meet the needs of rapid response and real-time adjustment. Therefore, exploring a more general and efficient energy guarantee task planning method has become an important issue that needs to be solved urgently.

[0004] Therefore, it is desired to have a technical solution to overcome or at least alleviate at least one of the above-mentioned defects of the prior art. Summary of the invention

[0005] The purpose of this application is to provide an energy-guaranteed intelligent power distribution method and system based on reinforcement learning to solve at least one problem existing in the prior art.

[0006] The technical solution of this application is: The first aspect of the present application provides an energy-guaranteed intelligent power distribution method based on reinforcement learning, comprising: Step S10: construct a DDPG agent, train the DDPG agent, and obtain the trained DDPG agent; Step S20, input the state quantity of the small aircraft obtained in real time into the trained DDPG intelligent agent, and the DDPG intelligent agent outputs the electric power ratio of hot oil transfer and hot power supply. The large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of hot oil transfer and hot power supply.

[0007] In at least one embodiment of the present application, in step S10, training the DDPG agent to obtain the trained DDPG agent includes: Step S11, obtaining the state quantity of the small aircraft; Step S12: Input the state of the small aircraft at time t into the DDPG agent, and the DDPG agent outputs the power ratio of the thermal oil transfer and the thermal power supply. ; Step S13: Calculate the electric power ratio The corresponding decision reward value of the DDPG agent ; Step S14: according to the decision reward value Updating the internal parameters of the DDPG agent to obtain the trained DDPG agent; Step S15, repeat the above steps S11-S14 to obtain the DDPG agent trained in different states.

[0008] In at least one embodiment of the present application, in step S12, the state quantity input into the DDPG agent includes: the amount of fuel of the small plane at time t , the power of the small plane at time t , the amount of fuel to be transported by the small aircraft at time t , the amount of power to be supplied by the small aircraft at time t .

[0009] In at least one embodiment of the present application, in step S13, the decision reward value for: ; in, is the amount of fuel in the small plane at time t+Δt, is the power of the small aircraft at time t+Δt, is the amount of fuel in the small plane at time t, is the power of the small aircraft at time t, is the amount of fuel to be delivered by the small aircraft at time t, is the power supply of the small aircraft at time t, The total time consumed to complete the energy guarantee of hot oil transportation and hot power supply, is the total fuel delivery of the small aircraft from time t to time t+Δt, is the total power supply of the small aircraft from time t to time t+Δt.

[0010] In at least one embodiment of the present application, in step S20, the state quantity of the small aircraft obtained in real time is input into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of the hot oil transfer and the hot power supply, and the large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply, including: Step S21, obtaining the state of the small aircraft at the current moment in real time; Step S22, inputting the state quantity of the small aircraft at the current moment into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of the hot oil transfer and the hot power supply of the small aircraft at the current moment; Step S23: The large aircraft performs real-time intelligent power distribution for the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply of the small aircraft at the current moment.

[0011] A second aspect of the present application provides an energy-guaranteed intelligent power distribution system based on reinforcement learning, comprising: small airplane; A DDPG agent, used to obtain the electric power ratio of thermal oil transmission and thermal power supply according to the energy security intelligent power distribution method based on reinforcement learning as described above; The large aircraft is used to intelligently distribute power to the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply.

[0012] In at least one embodiment of the present application, the large aircraft supplies oil to the small aircraft through a hot oil transfer device, and supplies electricity to the small aircraft through a hot power supply device.

[0013] A third aspect of the present application provides a computer-readable medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the energy-guaranteed intelligent power distribution method based on reinforcement learning as described above.

[0014] A fourth aspect of the present application provides a computing device, including: at least one processor, and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are used to execute the energy assurance intelligent power distribution method based on reinforcement learning as described above.

[0015] The invention has at least the following beneficial technical effects: The energy security intelligent distribution method based on reinforcement learning in the present application can intelligently allocate the electric power required for oil transportation and power supply based on the reinforcement learning algorithm according to the actual security needs, realize energy security planning under multiple states, shorten the time consumption of security tasks, and improve the intelligence level of security tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of an energy-guaranteed intelligent power distribution method based on reinforcement learning according to an embodiment of the present application; Figure 2 is a schematic diagram of an energy-guaranteed intelligent power distribution system based on reinforcement learning according to an embodiment of the present application; Figure 3 It is a schematic diagram of the hardware structure of a computing device for implementing an energy-guaranteed intelligent power distribution method based on reinforcement learning according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the embodiment of this application will be described in more detail below in conjunction with the drawings in the embodiment of this application. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of them. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be construed as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. The embodiments of this application are described in detail below in conjunction with the drawings.

[0018] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the scope of protection of the present application.

[0019] The following is combined with Figures 1 to 3 This application is described in further detail.

[0020] The first aspect of the present application provides an energy-guaranteed intelligent power distribution method based on reinforcement learning, comprising the following steps: Step S10: construct a DDPG agent, train the DDPG agent, and obtain a trained DDPG agent; Step S20: input the state quantity of the small aircraft obtained in real time into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of hot oil transfer and hot power supply. The large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of hot oil transfer and hot power supply.

[0021] In the energy security intelligent power distribution method based on reinforcement learning of this application, the DDPG (Deep Deterministic Policy Gradient) agent is a reinforcement learning agent based on the deep deterministic policy gradient algorithm. Figure 1 As shown, in step S10, the DDPG agent is trained to obtain a trained DDPG agent, including: Step S11, obtaining the state quantity of the small aircraft; Step S12: Input the state of the small aircraft at time t into the DDPG agent, and the DDPG agent outputs the power ratio of the thermal oil transfer and thermal power supply. ; Step S13: Calculate the electric power ratio The corresponding DDPG agent's decision reward value ; Step S14: According to the decision reward value Update the internal parameters of the DDPG agent to obtain a trained DDPG agent; Step S15: Repeat the above steps S11-S14 to obtain the trained DDPG agent under different states.

[0022] In the DDPG agent training phase, first in step S11, the state quantity data of the small plane in a state in a continuous time period are obtained, and multiple groups of state quantity data of the small plane before and after the time interval Δt are extracted from these state quantity data, which are used as training sets for DDPG agent training. In step S12, the fuel volume of the small plane at time t is calculated. , the power of the small plane at time t , the amount of fuel to be transported by the small aircraft at time t , the amount of power to be supplied by the small aircraft at time t As the input of the DDPG agent, the DDPG algorithm in the DDPG agent makes decisions based on the input state of the small aircraft and outputs the electric power ratio of thermal oil transfer and thermal power supply. , , For thermal oil transmission electric power, Provides electrical power for heat.

[0023] Then, in step S13, the DDPG algorithm is used to calculate the electric power ratio Corresponding decision reward value : ; in, is the amount of fuel in the small plane at time t+Δt, is the power of the small aircraft at time t+Δt, is the amount of fuel in the small plane at time t, is the power of the small aircraft at time t, is the amount of fuel to be delivered by the small aircraft at time t, is the power supply of the small aircraft at time t, The total time consumed to complete the energy guarantee of hot oil transportation and hot power supply, is the total fuel delivery of the small aircraft from time t to time t+Δt, is the total power supply of the small aircraft from time t to time t+Δt.

[0024] In step S14, according to the decision reward value The internal parameters of the DDPG agent are updated to obtain a DDPG agent trained in a state. Finally, in step S15, the DDPG agent is trained by the state quantity of the small aircraft in different states in the same way as steps S11-S14 to obtain a DDPG agent trained in different states. The trained DDPG agent can use the state quantity of the small aircraft as input to decide the output power ratio of hot oil transfer and hot power supply, thereby performing intelligent power distribution for the small aircraft.

[0025] The energy-guaranteed intelligent power distribution method based on reinforcement learning in this application is as follows: Figure 1 As shown, in step S20, the state quantity of the small aircraft obtained in real time is input into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of the hot oil transfer and the hot power supply. The large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply, including: Step S21, obtaining the state of the small aircraft at the current moment in real time; Step S22, input the state of the small aircraft at the current moment into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of the hot oil transfer and the hot power supply of the small aircraft at the current moment; Step S23: The large aircraft performs real-time intelligent power distribution for the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply of the small aircraft at the current moment.

[0026] In the DDPG agent power distribution phase, the state of the small aircraft at the current moment is input into the trained DDPG agent to obtain the current power ratio of thermal oil transfer and thermal power supply. The large aircraft performs real-time intelligent power distribution for the small aircraft according to the power ratio. After the predetermined time T, the small aircraft enters the next state, and the same process is followed to perform real-time intelligent power distribution for the small aircraft until the thermal oil transfer and thermal power supply tasks are completed.

[0027] The energy security intelligent power distribution method based on reinforcement learning in this application trains the DDPG agent to intelligently decide the power ratio of thermal oil transfer and thermal power supply. The large aircraft distributes power to the small aircraft in the process of thermal oil transfer and thermal power supply according to the power ratio, realizing rapid energy supply in multiple states. This application improves the energy supply speed, optimizes the security duration, and improves the intelligence level of the security task.

[0028] The second aspect of the present application provides an energy-guaranteed intelligent power distribution system based on reinforcement learning, such as Figure 2As shown, the system includes a small aircraft, a DDPG agent and a large aircraft. The DDPG agent obtains the electric power ratio of hot oil transfer and hot power supply according to the above-mentioned energy guarantee intelligent power distribution method based on reinforcement learning, and the large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of hot oil transfer and hot power supply. Among them, the large aircraft transfers oil to the small aircraft through the hot oil transfer device and supplies power to the small aircraft through the hot power supply device.

[0029] The third aspect of the present application provides a computer-readable medium storing computer-executable instructions, which are used to execute the above-mentioned energy-guaranteed intelligent power distribution method based on reinforcement learning.

[0030] For the convenience of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, when implementing this application, the functions of each module (or unit) can be implemented in the same or multiple software or hardware.

[0031] After introducing the energy-guaranteed intelligent power distribution method, system and readable medium based on reinforcement learning according to an exemplary embodiment of the present application, next, a computing device according to another exemplary embodiment of the present application is introduced.

[0032] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0033] In some possible implementations, the computing device of the present application may include at least one processing unit and at least one storage unit. The storage unit stores program code, and when the program code is executed by the processing unit, the processing unit executes the steps of the energy security intelligent power distribution method based on reinforcement learning according to various exemplary implementations of the present application described above in this specification.

[0034] Refer to the following Figure 3 The computing device 30 according to this embodiment of the present application is described. Figure 3 The computing device 30 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0035] like Figure 3 As shown, the computing device 30 is in the form of a general computing device. The components of the computing device 30 may include but are not limited to: at least one processing unit 31, at least one storage unit 32, and a bus 33 connecting different system components (including the storage unit 32 and the processing unit 31).

[0036] Bus 33 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor or local bus using any of a variety of bus architectures.

[0037] The storage unit 32 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322 , and may further include a read-only memory (ROM) 323 .

[0038] The storage unit 32 may also include a program / utility 325 having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0039] The computing device 30 may also communicate with one or more external devices 34 (e.g., keyboards, pointing devices, etc.), one or more devices that enable a user to interact with the computing device 30, and / or any device that enables the computing device 30 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 35. In addition, the computing device 30 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 36. As shown, the network adapter 36 communicates with other modules for the computing device 30 via a bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computing device 30, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0040] In some possible implementations, various aspects of the energy-security intelligent distribution method based on reinforcement learning provided in the present application can also be implemented in the form of a program product, which includes program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of the energy-security intelligent distribution method based on reinforcement learning according to various exemplary embodiments of the present application described above in this specification.

[0041] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0042] The program product of the energy security intelligent power distribution method based on reinforcement learning of the embodiment of the present application can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a computing device. However, the program product of the present application is not limited to this. In the present application, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0043] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium other than a readable storage medium, which may transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0044] The program code contained in the readable medium can be transmitted with any appropriate medium, including, but not limited to, wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present application can be written in any combination of one or more programming languages, and the programming language includes object-oriented programming languages, such as Java, C++, etc., and also includes conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed completely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or completely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0045] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0046] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0047] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0048] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0051] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. An energy-guaranteed intelligent power distribution method based on reinforcement learning, characterized in that: include: Step S10: construct a DDPG agent, train the DDPG agent, and obtain the trained DDPG agent; Step S20, input the state quantity of the small aircraft obtained in real time into the trained DDPG intelligent agent, and the DDPG intelligent agent outputs the electric power ratio of hot oil transfer and hot power supply. The large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of hot oil transfer and hot power supply.

2. The energy-guaranteed intelligent power distribution method based on reinforcement learning according to claim 1 is characterized in that: In step S10, the DDPG agent is trained to obtain the trained DDPG agent, including: Step S11, obtaining the state quantity of the small aircraft; Step S12: Input the state of the small aircraft at time t into the DDPG agent, and the DDPG agent outputs the power ratio of the thermal oil transfer and the thermal power supply. ; Step S13: Calculate the electric power ratio The corresponding decision reward value of the DDPG agent ; Step S14: according to the decision reward value Updating the internal parameters of the DDPG agent to obtain the trained DDPG agent; Step S15, repeat the above steps S11-S14 to obtain the DDPG agent trained in different states.

3. The energy-guaranteed intelligent power distribution method based on reinforcement learning according to claim 2 is characterized in that: In step S12, the state quantities input into the DDPG agent include: the amount of fuel of the small plane at time t , the power of the small plane at time t , the amount of fuel to be transported by the small aircraft at time t , the amount of power to be supplied by the small aircraft at time t .

4. The energy-guaranteed intelligent power distribution method based on reinforcement learning according to claim 3 is characterized in that: In step S13, the decision reward value for: ； in, is the amount of fuel in the small plane at time t+Δt, is the power of the small aircraft at time t+Δt, is the amount of fuel in the small plane at time t, is the power of the small aircraft at time t, is the amount of fuel to be delivered by the small aircraft at time t, is the power supply of the small aircraft at time t, The total time consumed to complete the energy guarantee of hot oil transportation and hot power supply, is the total fuel delivery of the small aircraft from time t to time t+Δt, is the total power supply of the small aircraft from time t to time t+Δt.

5. The energy-guaranteed intelligent power distribution method based on reinforcement learning according to claim 4 is characterized in that: In step S20, the state quantity of the small aircraft obtained in real time is input into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of the hot oil transfer and the hot power supply. The large aircraft performs intelligent power distribution for the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply, including: Step S21, obtaining the state of the small aircraft at the current moment in real time; Step S22, inputting the state quantity of the small aircraft at the current moment into the trained DDPG agent, and the DDPG agent outputs the electric power ratio of the hot oil transfer and the hot power supply of the small aircraft at the current moment; Step S23: The large aircraft performs real-time intelligent power distribution for the small aircraft according to the electric power ratio of the thermal oil transfer and thermal power supply of the small aircraft at the current moment.

6. An energy-guaranteeing intelligent power distribution system based on reinforcement learning, characterized in that: include: small airplane; A DDPG agent, used for obtaining the electric power ratio of thermal oil transmission and thermal power supply according to the energy security intelligent power distribution method based on reinforcement learning according to any one of claims 1 to 5; The large aircraft is used to intelligently distribute power to the small aircraft according to the electric power ratio of the hot oil transfer and the hot power supply.

7. The energy security intelligent power distribution system based on reinforcement learning according to claim 6 is characterized in that: The large aircraft supplies oil to the small aircraft through a hot oil transfer device, and supplies electricity to the small aircraft through a hot power supply device.

8. A computer-readable medium storing computer-executable instructions, characterized in that: The computer executable instructions are used to execute the energy security intelligent power distribution method based on reinforcement learning as described in any one of claims 1 to 5.

9. A computing device, characterized in that: include: at least one processor, and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are used to execute the energy security intelligent power distribution method based on reinforcement learning as described in any one of claims 1 to 5.

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