Communication resource allocation method and system for unmanned aerial vehicle-based power grid inspection

Abstract

Disclosed are a communication resource allocation method and system for unmanned aerial vehicle-based power grid inspection, aiming at reducing overall communication costs and improving the environmental friendliness and operational efficiency of a power system. The system comprises: a task collection module, a coordination module, a relay node processing module, and a monitoring and feedback module. In the present invention, the flexibility of unmanned aerial vehicles in inspection tasks is fully utilized, and in combination with a heterogeneous computing power network, an optimization algorithm is used for dynamic scheduling, thereby achieving efficient allocation of computing tasks. By fully considering the flight energy consumption of unmanned aerial vehicles, communication resources, and load conditions of relay nodes, an optimal task allocation scheme is generated to minimize system communication costs. The present invention improves the efficiency and quality of power grid inspection, effectively promotes rational utilization of resources and reduction of energy consumption, has significant theoretical and practical value, and facilitates the transition of society toward low energy consumption.

Description

UAV Power Grid Inspection Communication Resource Allocation Method and System

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese patent application filed on January 23, 2025, with application number 2025101037321 and entitled "Method and System for Allocating Communication Resources for Unmanned Aerial Vehicle Power Grid Inspection", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the field of unmanned aerial vehicle (UAV) inspection, and in particular to a method and system for allocating communication resources for UAV power grid inspection. The aim is to provide a method and system that can effectively allocate UAV computing tasks to reduce the total communication cost of the system. Background Technology

[0004] With the increasing demand for equipment inspection and maintenance in power systems, drones, as an efficient inspection tool, are gradually being applied to the monitoring and maintenance of power equipment. Drones have advantages such as high flexibility, wide coverage, and low cost, and can perform real-time data acquisition in complex environments. However, with the increase in the number of drones and the increasing complexity of tasks, how to effectively allocate the computing tasks of drones has become an urgent problem to be solved.

[0005] In power systems, drones pass through multiple relay nodes during their flight, including Base Stations (BSs) and Ground Nodes (GNs). The drones transmit computing tasks to these relay nodes using both licensed band transmission (LBT) and unlicensed band transmission (UBT). Upon receiving the computing tasks, the relay nodes forward them to cloud or edge servers for processing.

[0006] Traditional computing task allocation methods often rely on centralized management systems. While this approach may be effective for simple tasks, it often falls short when dealing with dynamically changing power systems and collaborative work among multiple drones. Furthermore, existing technologies fail to adequately optimize communication costs during task allocation, leading to system inefficiency and increased unnecessary communication overhead. Summary of the Invention

[0007] In view of this, the present invention provides a method and system for allocating communication resources for unmanned aerial vehicle (UAV) power grid inspection, aiming to reduce the total communication cost of the system and improve the environmental friendliness and operational efficiency of the power system through an efficient task allocation mechanism. This method fully utilizes the advantages of heterogeneous computing networks, combining the fixed flight path of the UAV with the communication resources of intermediate nodes to achieve optimal allocation of computing tasks during the UAV inspection process, thereby minimizing communication costs.

[0008] To achieve the above objectives, the present invention provides a communication resource allocation system for unmanned aerial vehicle (UAV) power grid inspection, comprising:

[0009] Task collection module, coordination module, relay node processing module, and monitoring and feedback module;

[0010] The task collection module is used to collect computing tasks related to IoT terminal devices within the coverage area of ​​the drone during the inspection process.

[0011] The coordination module includes an optimization algorithm module, a task allocation module, and a communication resource allocation module, wherein:

[0012] The optimization algorithm module is used to establish an energy consumption optimization model for the power system drone inspection system. The energy consumption optimization model minimizes the energy consumption of the drone inspection system by rationally allocating the computing tasks carried by the drone to relay nodes. By solving the energy consumption optimization model, a task allocation scheme and a communication resource allocation scheme are obtained. The task allocation scheme determines the transmission method and computing tasks sent by each drone to the relay nodes on the path, so as to minimize the total communication cost. The communication resource allocation scheme includes each relay node evaluating the computing tasks it can accept based on its remaining computing resource information.

[0013] The task allocation module is used to collect current network status information in real time, identify relay nodes with communication capabilities on the UAV flight path, and transmit the calculation tasks to the identified relay nodes according to the task allocation scheme.

[0014] The communication resource allocation module is used to dynamically evaluate the actual communication resource status of each relay node and adjust the task allocation scheme according to the communication resource allocation scheme.

[0015] The relay node processing module is used to transmit the computing tasks received from the coordination module to the upper-layer cloud or edge server for computing.

[0016] The monitoring and feedback module is used to receive communication network status information in real time and feed it back to the coordination module.

[0017] Furthermore, during the mission collection process, the drone will periodically update its battery status and automatically adjust its inspection plan when the battery level is lower than a set threshold, selecting the optimal route to return to the ground node as soon as possible for battery replacement or charging.

[0018] Furthermore, after the UAV collects the computing task, it collects the location information of relay nodes in the inspection area, the maximum task reception volume, and energy consumption indicators to provide basic data for the UAV's path planning. The path planning module establishes an energy consumption model, considering both flight energy consumption and communication consumption, and evaluates the energy consumption under different flight conditions. At the same time, it constructs a communication cost model to evaluate the communication cost of each UAV.

[0019] Furthermore, the energy consumption optimization model models the energy consumption of the UAV inspection system when uploading data to the relay node using both licensed and unlicensed frequency bands. The optimization algorithm uses the alternating direction multiplier method to solve the optimization problem, which is as follows:

[0020] Among them, e (1) (x) represents the system communication cost using unlicensed frequency bands, e (2) (y) represents the system communication cost using licensed frequency bands; ζ l η n The maximum data acceptance capabilities of GN l and BS n are respectively, λ i ε represents the total amount of data transmitted by drone i within time τ. i Indicates the maximum allowable energy consumption of the drone's battery;

[0021] The alternating direction multiplier method divides the global optimization problem into N subproblems, each of which is solved by the UAV using its private information. Let g be a variable. ij = <x il ,y in > represents the amount of data sent by drone i to relay node j, nj = <ζ l ,η n > represents the set of maximum data receiving capabilities of relay nodes. The optimization problem simplifies to:

[0022] Define the feasible set Di = {g} for the i-th UAV. i |g i satisfy The indicator function for obtaining the i-th UAV is defined as follows:

[0023] Similarly, for other constraints in the joint optimization problem, the feasible region is defined. The indicator function is:

[0024] By transforming the constraints in the optimization problem into indicator functions, the ADMM-form function is obtained as follows: st gn = 0

[0025] in H(n)=I D (g)

[0026] The final result is an optimal g value, which corresponds to the minimum energy consumption.

[0027] Furthermore, the current network status information collected by the task allocation module includes the geographical location, load status, and communication capabilities of each relay node, which includes base stations (BSs) and ground nodes (GNs).

[0028] Furthermore, the communication resource allocation module is specifically used to dynamically evaluate the actual communication resource status of each relay node by continuously acquiring the current load status, network bandwidth, and processing capacity of each relay node, analyze the communication resources available for task processing by the relay node within a specific time period, and then adjust the task allocation scheme to avoid overload.

[0029] A method for allocating communication resources for unmanned aerial vehicle (UAV) power grid inspection, applied to the UAV power grid inspection communication resource allocation system described in the claim, the method comprising:

[0030] Step S1: Obtain the communication resources and current task load information of each relay node during the UAV inspection process in real time to ensure real-time monitoring of the working status of each relay node.

[0031] Step S2: The task collection module collects computing tasks related to IoT terminal devices within the drone's coverage area during the inspection process using the drone.

[0032] Step S3: The optimization algorithm module of the coordination module establishes an energy consumption optimization model for the power system UAV inspection system. The energy consumption optimization model minimizes the energy consumption of the UAV inspection system by rationally allocating the computing tasks carried by the UAV to the relay nodes. By solving the energy consumption optimization model, a task allocation scheme and a communication resource allocation scheme are obtained. The task allocation scheme determines the transmission method and computing tasks sent by each UAV to the relay nodes on the path, so as to minimize the total communication cost. The communication resource allocation scheme includes each relay node evaluating the computing tasks it can accept based on its remaining computing resource information.

[0033] The task allocation module of the coordination module collects the current network status information in real time, identifies relay nodes with communication capabilities that are on the flight path of the UAV, and transmits the computing tasks to the identified relay nodes according to the task allocation scheme.

[0034] The coordination module's communication resource allocation module dynamically evaluates the actual communication resource status of each relay node and adjusts the task allocation scheme according to the communication resource allocation scheme.

[0035] Step S4: The relay node processing module transmits the computing task received from the coordination module to the upper-layer cloud or edge server for computing.

[0036] Step S5: The monitoring and feedback module receives the communication network status information in real time and feeds it back to the coordination module.

[0037] Compared with the prior art, the present invention has the following characteristics:

[0038] 1. Comprehensive consideration of environmental factors: This invention fully considers the environmental factors of UAVs during mission execution, including the differences in energy costs between different relay nodes, in order to generate a more flexible and reasonable mission allocation scheme and improve the system's efficiency and environmental friendliness.

[0039] 2. Real-time adjustment of task allocation: By monitoring the flight status and task status of the UAV, this invention can flexibly adjust the task allocation based on real-time communication cost data, thereby ensuring that the system budget is not exceeded when executing computing tasks and promoting the rational transfer of energy consumption between different relay nodes.

[0040] 3. Reduced communication costs: This invention optimizes the task allocation mechanism through algorithms, effectively reducing the total communication cost of the system, making the transmission of tasks from the drone to intermediate nodes more efficient, and achieving optimal allocation of system resources.

[0041] This invention addresses the issue of uncontrollable communication costs during UAV inspections by efficiently allocating computational tasks. It comprehensively considers energy costs, system status, and node requirements. By monitoring relay node load information in real time and combining this with the data traffic carried by the UAVs, an optimal task allocation scheme is generated to minimize the total system communication cost. This method flexibly adapts to different UAV inspection routes, effectively utilizes available relay node resources, and improves system efficiency and environmental friendliness. Through algorithm optimization and task allocation, this invention minimizes communication costs during inspections, which is of significant importance and value for improving the computational efficiency of UAV networks and promoting a society-wide transition to low-energy consumption. Attached Figure Description

[0042] Figure 1 is a schematic diagram of a UAV power grid inspection communication resource allocation system according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of the Alternating Direction Multiplier Method (ADMM) algorithm provided by the present invention;

[0044] Figure 3 is a flowchart of a method for allocating communication resources for unmanned aerial vehicle (UAV) power grid inspection according to an embodiment of the present invention;

[0045] Figure 4 is a comparison of total energy consumption using different transmission methods, where (a) is a comparison of communication costs using different transmission methods, and (b) is a comparison of the workload of UAVs using LBT and UBT transmission in the MCS scheme.

[0046] Figure 5 is a comparison of the number of iterations of the ADMM algorithm proposed in this invention and the commonly used interior point method and subgradient method. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please refer to Figure 1. This embodiment of the invention provides a communication resource allocation system for UAV power grid inspection, including a task collection module, a coordination module, a relay node processing module, and a monitoring and feedback module.

[0049] The task collection module is used to collect computing tasks related to IoT terminal devices within the drone's coverage area during inspections. The drone moves along a pre-planned fixed flight path during the inspection process. To ensure the completeness and accuracy of task collection, the drone monitors its battery level in real time during flight to optimize energy management and ensure successful task completion. The drone flies to a predetermined node location at regular intervals (e.g., every hour), allowing it to periodically access and retrieve computing tasks from all IoT terminal devices within that node's coverage area. The drone typically uses wireless communication technologies (such as Wi-Fi, LTE, or 5G) to connect with IoT terminal devices, receiving task instructions and sending data in real time. All received computing task information is cached and recorded in the drone's internal storage system for use by subsequent task allocation and communication resource allocation modules. During this process, the module also records the priority and requirements of each task to ensure that subsequent task allocation is based on the actual state of the nodes.

[0050] During task collection, the drone will periodically update its battery status and automatically adjust its inspection plan when the battery level falls below a set threshold, selecting the optimal route to return to the ground node as quickly as possible for battery replacement or charging, thereby maintaining the efficient operation of the system. After collecting computing tasks, the drone needs to transmit them. First, by collecting the location information of relay nodes within the inspection area, the maximum task reception capacity, and energy consumption indicators, basic data is provided for the drone's path planning module. The path planning module establishes a detailed energy consumption model, considering both flight energy consumption and communication requirements, to accurately assess energy consumption under different flight conditions. Simultaneously, a communication cost model is constructed to evaluate the communication cost of each drone.

[0051] The coordination module includes an optimization algorithm module, a task allocation module, and a communication resource allocation module. After collecting and calculating tasks, the UAV sends them to the coordination module. The optimization algorithm module first establishes an energy consumption optimization model for the power system UAV inspection system, that is, it models the energy consumption of the UAV inspection system when uploading data to the relay node using both licensed and unlicensed frequency bands.

[0052] To address the energy consumption optimization problem, the proposed optimization algorithm uses the alternating direction multiplier method to solve the problem. This method minimizes the energy consumption of the UAV inspection system by rationally allocating the computing tasks carried by the UAV to relay nodes. The optimization results generate a task allocation scheme (which transmission method and how many computing tasks each UAV sends to each relay node along the path) and a communication resource allocation scheme (each relay node assesses how many computing tasks it can accept based on its remaining computing resources), which are implemented through corresponding modules.

[0053] The task allocation module collects real-time network status information, including the geographical location, load, and communication capabilities of each relay node (base stations (BSs) and ground nodes (GNs)). By analyzing this data, the module can identify relay nodes with communication capabilities that are on the UAV's flight path and allocate computing tasks to these relay nodes according to the task allocation scheme, thus avoiding network congestion.

[0054] The communication resource allocation module plays a crucial role in the entire UAV inspection system. Its main function is to monitor the communication resources of each relay node in real time, ensuring that the amount of tasks it receives does not exceed its maximum task receiving capacity. This module dynamically assesses the actual communication resource status of each relay node by continuously acquiring information such as the current load status, network bandwidth, and processing capacity of each relay node. Specifically, the communication resource allocation module analyzes the communication resources available for task processing by each relay node within a specific time period and adjusts the task allocation scheme in a timely manner based on this data to avoid overload.

[0055] In the UAV inspection system, each UAV can transmit computing tasks to relay nodes (GNs, BSs) via two methods: licensed frequency band LBT and unlicensed frequency band UBT.

[0056] The unlicensed frequency bands, where each drone randomly accesses unlicensed frequency bands, must adhere to a specific channel access mechanism to avoid conflicts with other coexisting technologies. This mechanism allows each device to dynamically adjust transmission time and period based on channel availability. Generally, the 2.4GHz and 5GHz ISM bands are used, with each device transmitting data signals according to the CSMA / CA protocol to support wireless services such as Wi-Fi, ZigBee, Thread, ZWave, and Wi-SUN. In these systems, a set of ground nodes (GNs) has been deployed to relay data from the drones to the edge server. In CSMA / CA, each device needs to implement a "listen-before-speak" channel access mechanism to avoid collisions with other co-located transmitters.

[0057] Assume that the average energy consumed by each drone i is δi, and the probability of it successfully transmitting in the unlicensed frequency band is P. i That is, the probability that the channel is occupied by other devices or coexisting devices is 1-P. i In this scenario, the drone cannot successfully occupy the channel and therefore does not consume any energy for data transmission. After successfully gaining access to the unauthorized channel, each drone can send its data to nearby GNs. Let denot be the set of GNs that can receive data traffic for the system. In time τ, UAV i sends x... il Bits of data are sent to GN l, and data traffic λ is transmitted via the drone. i The energy consumption is:

[0058] In the formula, B T For the bandwidth of the unlicensed frequency band, h il σ is the channel gain between UAV i and GN l. il Let be the noise power received at GN l. In the formula... and Set τ = 1, that is The transmission power of drone i is equal to its expected energy consumption:

[0059] c i Defined as the conversion rate between energy consumption and cost, the system communication cost of this scheme is:

[0060] Where, x = {x i}i∈N .

[0061] For the licensed frequency bands, 3GPP has proposed various IoT solutions such as NB-IoT, EC-GPRS, and LTE eMTC. These solutions all operate on licensed spectrum exclusively allocated to mobile operators. Mobile operators have carefully deployed their network infrastructure to minimize inter-cell interference. Each device does not need to be channel-aware but will follow the MNO's channel access schedule. Unlike unlicensed frequency band services, MNOs typically charge mobile devices based on the total data traffic passing through their network. Because the use of licensed frequency bands must always be fully controlled and monitored by the mobile network operator, QoS is always guaranteed. Each drone has a contract with the mobile operator, allowing it to offload its data traffic to neighboring BSs. Let B represent the set of BSs. The energy consumption of each drone is as follows:

[0062] In the formula, y in For drone i, the number of data bits to send to BS via the licensed frequency band is determined. i ={y in} n∈Β , and Σ n∈Β {y in}=λ i Let β n The price charged by the MNO for each bit of data sent via the BS. The sum of the system communication cost and infrastructure rental cost for this scheme is:

[0063] This invention assumes that each drone can use one or more of the above options to send data. Each drone can divide its total collected data into multiple parts and send them separately via UBT, LBT, or a combination of both, i.e., using Multi-Connectivity Sharing (MCS) to send data. The task allocation scheme needs to determine the data traffic for each method to minimize the data communication cost of the drone inspection system, thus resulting in the following optimization problem:

[0064] Where, ζ l η n The maximum data acceptance capabilities of GN l and BS n are respectively, λ i ε represents the total amount of data transmitted by drone i within time τ. i Indicates the maximum allowable energy consumption of the drone's battery;

[0065] The optimization algorithm is the Alternating Direction Multiplier Method (ADMM). It accepts the computation task from the task collection module, optimizes the algorithm using known information from the system, and obtains the corresponding task allocation scheme and communication resource allocation scheme. The UAV sends the computation task to the corresponding relay node through the two schemes: the task allocation scheme and the communication resource allocation scheme, and finally completes the computation task.

[0066] Figure 2 illustrates the ADMM algorithm flowchart. To solve the aforementioned optimization problem, it is necessary to understand the overall network information, such as the maximum data transmission volume of each UAV per unit time and the maximum data reception volume of each relay node. To coordinate and solve the optimization problem with a faster convergence speed while protecting privacy, this invention proposes a distributed ADMM algorithm. In this algorithm, the global optimization problem can be divided into N sub-problems, each of which can be solved by the UAV using its private information. For ease of expression, g is defined as... ij = <x il ,y in > represents the amount of data sent by drone i to relay node j, nj = <ζ l ,η n > represents the set of maximum data receiving capabilities of relay nodes. The optimization problem can be simplified to:

[0067] Define the feasible set of the i-th UAV. The indicator function for obtaining the i-th UAV is defined as follows:

[0068] Similarly, for other constraints in the joint optimization problem, the feasible region is defined. The indicator function is:

[0069] By transforming the constraints in the optimization problem into indicator functions, the ADMM-form function is obtained as follows:

[0070] stg-n=0

[0071] The H(n)=I D (g)

[0072] The final result is an optimized value of g, which corresponds to the minimum energy consumption. The ADMM algorithm calculates how to rationally allocate the amount of computational task data transmitted by the UAV to each relay node, thereby minimizing the final communication cost, i.e., the energy consumption e(x) + e(y).

[0073] The relay node processing module's main function is to transmit the received computing tasks to the corresponding edge servers for computation. It is assumed that each edge server in the system is connected to a subset J of relay nodes that are geographically closest to it.<l,n> It only receives data traffic from subset nodes.

[0074] The monitoring and feedback module is used to receive communication network status information in real time. In this embodiment, the monitoring and feedback module acquires the communication network status information at the current moment. This information includes the remaining battery level of each UAV and the data reception volume of relay nodes within the network. This communication network status information is processed and analyzed, and then fed back to the coordination module so that the coordination module can make more accurate communication resource allocation decisions based on real-time data. In this way, the coordination module can adjust the task allocation of UAVs in a timely manner to optimize network performance and extend the operating time of the UAVs.

[0075] As shown in Figure 3, this embodiment of the invention also provides a method for allocating communication resources for UAV power grid inspection, applied to the UAV power grid inspection communication resource allocation system described in this embodiment of the invention. The method includes:

[0076] Step S1: Obtain the communication resources and current task load information of each relay node during the UAV inspection process in real time to ensure real-time monitoring of the working status of each relay node.

[0077] Step S2: The task collection module collects computing tasks related to IoT terminal devices within the drone's coverage area during the inspection process using the drone.

[0078] Step S3: The optimization algorithm module of the coordination module establishes an energy consumption optimization model for the power system UAV inspection system. The energy consumption optimization model minimizes the energy consumption of the UAV inspection system by rationally allocating the computing tasks carried by the UAV to the relay nodes. By solving the energy consumption optimization model, a task allocation scheme and a communication resource allocation scheme are obtained. The task allocation scheme determines the transmission method and computing tasks sent by each UAV to the relay nodes on the path, so as to minimize the total communication cost. The communication resource allocation scheme includes each relay node evaluating the computing tasks it can accept based on its remaining computing resource information.

[0079] The task allocation module of the coordination module collects the current network status information in real time, identifies relay nodes with communication capabilities that are on the flight path of the UAV, and transmits the computing tasks to the identified relay nodes according to the task allocation scheme.

[0080] The coordination module's communication resource allocation module dynamically evaluates the actual communication resource status of each relay node and adjusts the task allocation scheme according to the communication resource allocation scheme.

[0081] Step S4: The relay node processing module transmits the computing task received from the coordination module to the upper-layer cloud or edge server for computing.

[0082] In step S5, the monitoring and feedback module receives real-time communication network status information and feeds it back to the coordination module. This allows the coordination module to continuously adjust its task allocation strategy based on the communication costs of the inspection process, in response to potential environmental changes or network status fluctuations. Through this feedback adjustment, the entire task execution process is ensured to maintain an optimal balance between communication costs and task efficiency.

[0083] This invention reduces the total communication cost of the system and improves the operating efficiency and inspection quality of the power system by rationally scheduling the task allocation of UAVs. Figure 4 compares the total energy consumption of different transmission methods, showing that using MCS reduces energy consumption and improves performance compared to using LBT and UBT methods alone. Figure 5 compares the ADMM algorithm proposed in this invention with commonly used interior-point and subgradient methods, clearly demonstrating that the ADMM algorithm requires fewer iterations to reach the optimal value. This method not only improves the working efficiency of UAVs but also effectively reduces resource waste, possessing significant theoretical and practical value.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A communication resource allocation system for unmanned aerial vehicle (UAV) power grid inspection, characterized in that, It includes a task collection module, a coordination module, a relay node processing module, and a monitoring and feedback module; The task collection module is used to collect computing tasks related to IoT terminal devices within the coverage area of ​​the drone during the inspection process. The coordination module includes an optimization algorithm module, a task allocation module, and a communication resource allocation module, wherein: The optimization algorithm module is used to establish an energy consumption optimization model for the power system drone inspection system. The energy consumption optimization model minimizes the energy consumption of the drone inspection system by rationally allocating the computing tasks carried by the drone to relay nodes. By solving the energy consumption optimization model, a task allocation scheme and a communication resource allocation scheme are obtained. The task allocation scheme determines the transmission method and computing tasks sent by each drone to the relay nodes on the path, so as to minimize the total communication cost. The communication resource allocation scheme includes each relay node evaluating the computing tasks it can accept based on its remaining computing resource information. The task allocation module is used to collect current network status information in real time, identify relay nodes with communication capabilities on the UAV flight path, and transmit the calculation tasks to the identified relay nodes according to the task allocation scheme. The communication resource allocation module is used to dynamically evaluate the actual communication resource status of each relay node and adjust the task allocation scheme according to the communication resource allocation scheme. The relay node processing module is used to transmit the computing tasks received from the coordination module to the upper-layer cloud or edge server for computing. The monitoring and feedback module is used to receive communication network status information in real time and feed it back to the coordination module.

2. The UAV power grid inspection communication resource allocation system as described in claim 1, characterized in that, During the mission data collection process, the drone will periodically update its battery status and automatically adjust its inspection plan when the battery level is lower than a set threshold, selecting the optimal route to return to the ground node as soon as possible for battery replacement or charging.

3. The UAV power grid inspection communication resource allocation system as described in claim 1, characterized in that, After collecting computing tasks, the UAV collects the location information of relay nodes in the inspection area, the maximum task reception volume, and energy consumption indicators to provide basic data for UAV path planning. The optimization algorithm module establishes an energy consumption model, considering both flight energy consumption and communication consumption, and evaluates the energy consumption under different flight conditions. At the same time, a communication cost model is constructed to evaluate the communication cost of each UAV.

4. The UAV power grid inspection communication resource allocation system as described in claim 1, characterized in that, The energy consumption optimization model models the energy consumption of a UAV inspection system when uploading data to a relay node using both licensed and unlicensed frequency bands. The optimization algorithm uses the alternating direction multiplier method to solve the optimization problem, which is as follows: Among them, e (1) (x) represents the system communication cost using unlicensed frequency bands, e (2) (y) represents the system communication cost using licensed frequency bands; ζ l η n The maximum data acceptance capabilities of GNl and BSn are respectively, λ i ε represents the total amount of data transmitted by drone i within time τ. i Indicates the maximum allowable energy consumption of the drone's battery; The alternating direction multiplier method divides the global optimization problem into N subproblems, each of which is solved by the UAV using its private information. Let g be a variable. ij = <x il ,y in > represents the amount of data sent by drone i to relay node j, nj = <ζ l ,η n > represents the set of maximum data receiving capabilities of relay nodes. The optimization problem simplifies to: Define the feasible set of the i-th UAV. The indicator function for obtaining the i-th UAV is defined as follows: Similarly, for other constraints in the joint optimization problem, the feasible region is defined. The indicator function is: By transforming the constraints in the optimization problem into indicator functions, the ADMM-form function is obtained as follows: stg-n=0 where F(g) = e (1) (x) + e (2) (y) + I Di (g i ), H(n) = I D (g). The final result is an optimal g value, which corresponds to the minimum energy consumption.

5. The UAV power grid inspection communication resource allocation system as described in claim 1, characterized in that, The task allocation module collects current network status information including the geographical location, load status, and communication capabilities of each relay node. The relay nodes include base stations (BSs) and ground nodes (GNs).

6. The UAV power grid inspection communication resource allocation system as described in claim 1, characterized in that, The communication resource allocation module is specifically used to dynamically evaluate the actual communication resource status of each relay node by continuously acquiring the current load status, network bandwidth and processing capacity of each relay node, analyze the communication resources available for task processing by the relay node within a specific time, and then adjust the task allocation scheme to avoid overload.

7. A method for allocating communication resources for unmanned aerial vehicle (UAV) power grid inspection, characterized in that, The method, applied to the UAV power grid inspection communication resource allocation system according to any one of claims 1-6, comprises: Step S1: Obtain the communication resources and current task load information of each relay node during the UAV inspection process in real time to ensure real-time monitoring of the working status of each relay node. Step S2: The task collection module collects computing tasks related to IoT terminal devices within the drone's coverage area during the inspection process using the drone. Step S3: The optimization algorithm module of the coordination module establishes an energy consumption optimization model for the power system UAV inspection system. The energy consumption optimization model minimizes the energy consumption of the UAV inspection system by rationally allocating the computing tasks carried by the UAV to the relay nodes. By solving the energy consumption optimization model, a task allocation scheme and a communication resource allocation scheme are obtained. The task allocation scheme determines the transmission method and computing tasks sent by each UAV to the relay nodes on the path, so as to minimize the total communication cost. The communication resource allocation scheme includes each relay node evaluating the computing tasks it can accept based on its remaining computing resource information. The task allocation module of the coordination module collects the current network status information in real time, identifies relay nodes with communication capabilities that are on the flight path of the UAV, and transmits the computing tasks to the identified relay nodes according to the task allocation scheme. The coordination module's communication resource allocation module dynamically evaluates the actual communication resource status of each relay node and adjusts the task allocation scheme according to the communication resource allocation scheme. Step S4: The relay node processing module transmits the computing task received from the coordination module to the upper-layer cloud or edge server for computing. Step S5: The monitoring and feedback module receives the communication network status information in real time and feeds it back to the coordination module.

Images