CN-122028115-A - Space-earth integrated network resource management method, system and storage medium

CN122028115ACN 122028115 ACN122028115 ACN 122028115ACN-122028115-A

Abstract

The invention provides an air-ground integrated network resource management method, a system and a storage medium, belonging to the technical field of wireless communication and network resource scheduling, wherein the method comprises the steps of constructing an air-ground integrated network, configuring an energy collection module and a backscatter circuit by GU, and realizing low-power consumption data transmission and energy collection by reflecting radio frequency carrier signals emitted by a UAV; the UAV carries a mobile edge computing server processing task and is unloaded to LEO when overloaded, a central controller collects network state and channel state information in real time, a random optimization function is built by taking the long-term average energy consumption of a minimum system as a target and combining constraints such as task queue stability, energy sustainability and the like, a Lyapunov optimization framework is adopted to convert the task into a single time slot problem, and the problem is solved through alternate optimization and concave-convex process technology. The invention obviously reduces the energy consumption of the system while guaranteeing the stability of the task queue, improves the utilization rate of resources and provides reliable support for electric power emergency communication in remote areas.

Inventors

ZHANG LUYANG
DU FEI
KAN HONGYANG
SI JIAYI
WANG XIAOQING
Geng Suiyan
ZHOU ZHENYU

Assignees

华北电力大学

Dates

Publication Date: 20260512
Application Date: 20260330

Claims (10)

1. An air-ground integrated network resource management method is characterized by comprising the following steps: an air-ground integrated network of a target power system is constructed, wherein the network comprises a ground layer, an air layer and a space layer, the ground layer is composed of a plurality of ground devices GU, an Unmanned Aerial Vehicle (UAV) is deployed on the air layer, and a single low-orbit satellite LEO is deployed on the space layer and used for receiving an unloading calculation task distributed by the UAV; The UAV actively transmits a radio frequency carrier signal to the GU according to a preset frequency, the GU converts the radio frequency carrier signal into electric energy for storage after receiving the radio frequency carrier signal, and modulates the real-time state data onto the radio frequency carrier signal and back scatters the modulated real-time state data to the corresponding UAV to finish task unloading; The method comprises the steps of collecting network state information of GUs and UAVs, space-to-ground channel states of the UAVs and GUs and space-to-air channel states of the UAVs and LEOs by a central controller in real time, establishing a random optimization function related to time allocation, task unloading allocation and computing resource allocation of the network by taking the minimum running average energy consumption of the network as a target and taking two task queues and GU electric energy stability as constraint conditions based on all collected information, and solving the random optimization function by utilizing a Lyapunov drift plus penalty function method to obtain a resource allocation optimal scheme among GUs, UAVs and LEOs.
2. The air-ground integrated network resource management method of claim 1, wherein the real-time state data comprises system fault information, system equipment monitoring data and sensing data of an environment where a system is located, the network state information of GU comprises equipment residual energy, a first task queue length and a new task arrival amount, the network state information of the UAV comprises a second task queue load state, a computing resource occupation condition and a self residual energy state, the air-ground channel state corresponds to link attributes between the UAV and the GU and comprises channel gain, signal attenuation degree and interference level, the air-ground channel state corresponds to link attributes between the UAV and the LEO and comprises link bandwidth, propagation delay and signal stability, and the GU electric energy stability is that electric energy collected by the GU is larger than electric energy consumed by the GU.
3. The air-space integrated network resource management method of claim 1, wherein the constraint conditions further comprise a network transmission rate constraint, a reflection coefficient constraint, a GU and UAV calculation resource constraint and an air-space link delay constraint, the network transmission rate comprises an air-space link transmission rate between the UAV and the GU and an air-space link transmission rate between the UAV and the LEO, the transmission rate needs to meet the data transmission requirement of a corresponding link, the reflection coefficient is a signal reflection parameter when the GU modulates real-time state data, the value range is [0,1], the GU and the UAV calculation resource constraint comprises that the GU local calculation amount does not exceed the upper limit of the GU calculation resource, the UAV local calculation amount does not exceed the upper limit of the UAV calculation resource of a mobile edge calculation MEC server, and the air-space link delay constraint indicates that the sum of the transmission delay of the UAV to an LEO unloading task and the LEO task processing delay does not exceed a preset threshold.
4. The air-ground integrated network resource management method according to claim 3 is characterized in that a random optimization function is solved by means of a Lyapunov drift penalty function method to obtain an optimal resource allocation scheme among GU, UAV and LEO, the method specifically comprises the steps of utilizing the Lyapunov drift penalty function method, converting long-term random of the random optimization function into a deterministic optimization problem only depending on the current time slot state through introducing a control parameter V, solving through an alternative optimization algorithm, adopting a concave-convex process CCCP technology to achieve non-convex part linear property of reflection coefficients in an air-ground link transmission rate, solving reflection coefficients, task unloading proportion and calculating resource allocation, further optimizing energy collection, backscattering communication, task processing and time allocation of data transmission based on the solving result, finally obtaining the optimal resource allocation scheme among GU, UAV and LEO, and issuing the optimal resource allocation scheme to each device of GU, UAV and LEO for execution, wherein the control parameter V is used for network operation average energy consumption and task queue stability.
5. The method of claim 1, wherein the GU is equipped with an energy harvesting module and a backscatter circuit, wherein the received radio frequency carrier signal is converted to electrical energy for storage by the energy harvesting module, wherein the real-time status data is modulated onto the received radio frequency carrier signal by the backscatter circuit, wherein the UAV is equipped with a carrier generator and a mobile edge computing MEC server, wherein the radio frequency carrier signal is transmitted by the carrier generator, and wherein the local computing task is performed by the MEC server.
6. The method of claim 3, wherein the local computing of GUs comprises dynamically adjusting the computing frequency by dynamic voltage and frequency scaling DVFS techniques, and determining the amount of locally processed data in time slot t based on the upper limit of its computing resources.
7. The method for space-to-ground integrated network resource management according to claim 1, wherein the task offloading allocation includes a task proportion of GU local calculation and offloading to a corresponding UAV, a task proportion of UAV local calculation and offloading to a corresponding LEO, and the calculation tasks are divided into a local execution part, offloading to the UAV execution part and offloading to the LEO execution part by a partial offloading method, and the three are executed in parallel.
8. An air-ground integrated network resource management system, comprising: The system comprises a construction module, a power generation module and a power generation module, wherein the construction module is used for constructing an air-ground integrated network of a target power system, and the network comprises a ground layer, an air layer and a space layer, wherein the ground layer is composed of a plurality of ground devices GU, an unmanned aerial vehicle UAV is deployed on the air layer, and a single low-orbit satellite LEO is deployed on the space layer and used for receiving an unloading calculation task distributed by the UAV; The computing module is used for continuously collecting real-time state data of the target power system according to the form of the first task queue by each GU; the UAV actively transmits a radio frequency carrier signal to the GU according to a preset frequency, the GU converts the radio frequency carrier signal into electric energy for storage after receiving the radio frequency carrier signal, and simultaneously modulates real-time state data onto the radio frequency carrier signal and back scatters the modulated real-time state data to the corresponding UAV to finish task unloading; The allocation module is used for collecting network state information of GUs and UAVs, space-to-ground channel states of the UAVs and GUs and space-to-air channel states of the UAVs and LEOs in real time by the central controller, establishing a random optimization function related to time allocation, task unloading allocation and computing resource allocation of the network by taking the minimum running average energy consumption of the network as a constraint condition based on all collected information and taking the stability of two task queues and the stability of GU electric energy as a constraint condition, and solving the random optimization function by using a Lyapunov drift penalty function method to obtain an optimal resource allocation scheme among GUs, UAVs and LEOs.
9. A computer device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to carry out the steps of the method according to any one of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when loaded by a processor, is able to carry out the steps of the method according to any one of claims 1 to 7.

Description

Space-earth integrated network resource management method, system and storage medium Technical Field The invention belongs to the technical field of wireless communication and network resource scheduling, and particularly relates to an air-to-ground integrated network resource management method, an air-to-ground integrated network resource management system and a storage medium. Background When communication infrastructure in remote areas is weak or missing and sudden accidents (such as rain storm snowy weather) are faced, the coverage range of a traditional ground network is poor, emergency response capability is insufficient, and the real-time data transmission requirement of electric power facilities cannot be met. With the rapid growth of service demands for the power internet of things PIoT, more and more power facilities are deployed in remote areas where communication infrastructure is weak or missing. The space-to-ground integrated network SAGIN serves as a key potential architecture for the novel power system, providing enhanced service capabilities in terms of coverage and flexibility. The ground part of the air-ground integrated network SAGIN is composed of a plurality of PIoT ground users GU, the computing capacity and the energy sources are limited, and due to the limitation of the size and the cost, the electric power Internet of things equipment is usually powered by an embedded battery with limited energy, and when the electric network is subjected to emergency treatment, emergency repair and other very situations, the battery is difficult to ensure to continuously supply power to maintain the network to continuously work. And SAGIN adopts binary decisions of 'complete local calculation' or 'complete unloading calculation', the tasks can not be flexibly segmented according to the emergency degree of the tasks and the electric quantity of equipment, the fine granularity calculation requirement under emergency scenes is difficult to meet, the traditional ground backscatter network is severely degenerated and limited under the non-line-of-sight NLoS condition, the signal attenuation obviously reduces the communication range and the expandability, the communication, calculation and energy resource lack of the three-layer network of the sky, the sky and the ground are dynamically cooperatively scheduled, and the imbalance condition of the backlog of the ground equipment task is easy to occur. Disclosure of Invention In order to solve the background problem, the invention provides an air-to-ground integrated network resource management method, an air-to-ground integrated network resource management system and a storage medium. In order to achieve the above object, the present invention provides an air-space-ground integrated network resource management method, comprising: an air-ground integrated network of a target power system is constructed, the network comprises a ground layer, an air layer and a space layer, the ground layer is composed of a plurality of ground devices GU, an Unmanned Aerial Vehicle (UAV) is deployed on the air layer, and a single low-orbit satellite LEO is deployed on the space layer and used for bearing an unloading calculation task distributed by the UAV. The UAV receives the radio frequency carrier signals, converts the radio frequency carrier signals into electric energy for storage, modulates the real-time state data onto the radio frequency carrier signals and back scatters the modulated real-time state data to the corresponding UAV to finish task unloading, decodes and receives the modulated real-time state data of the corresponding GU according to a second task queue form to execute calculation tasks, and transmits the calculation results back to the corresponding GU. The method comprises the steps of collecting network state information of GUs and UAVs, space-to-ground channel states of the UAVs and GUs and space-to-air channel states of the UAVs and LEOs by a central controller in real time, establishing a random optimization function related to time allocation, task unloading allocation and computing resource allocation of the network by taking the minimum running average energy consumption of the network as a target and taking two task queues and GU electric energy stability as constraint conditions based on all collected information, and solving the random optimization function by utilizing a Lyapunov drift plus penalty function method to obtain a resource allocation optimal scheme among GUs, UAVs and LEOs. The real-time state data comprise system fault information, system equipment monitoring data and sensing data of an environment where a system is located, the network state information of GU comprises equipment residual energy, a first task queue length and new task arrival amount, the network state information of the UAV comprises a second task queue load state, a calculation resource occupation condition and a self residual energy state, the space-to-ground