Satellite comprehensive intelligent power grid monitoring method and system based on improved genetic algorithm

Abstract

The invention discloses a satellite comprehensive intelligent power grid monitoring method and system based on an improved genetic algorithm, and aims to improve the efficiency and reliability of power tower monitoring. Through a self-organizing sensor node network, remote transmission of monitoring data is realized by utilizing satellite communication. In order to solve the problems of k-coverage and m-connectivity of sensor nodes, an improved genetic algorithm (XGA) is provided, and node selection is guided by constructing a k-dimensional tree (KDT) in a chromosome initialization stage, so that algorithm convergence is accelerated, and deployment efficiency is improved. The system adopts a multi-coverage and multi-channel transmission method, so that the reliability and the real-time performance of data are ensured. The system not only supports the application in fixed and mobile scenes, but also can realize the optimization of remote communication through the unmanned aerial vehicle and a distributed satellite communication network, and improves the efficiency and stability of data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid monitoring and communication optimization, and in particular to a satellite integrated smart grid monitoring method and system based on an improved genetic algorithm. Background Art

[0002] As the infrastructure of modern society, the stability and security of the power system are crucial to the normal operation of the economy and society. However, since power transmission infrastructure (such as towers, conductors, insulators and transformers) is usually installed in remote or inaccessible areas, any failure may lead to serious economic losses and safety accidents. Therefore, real-time monitoring and early warning of the power transmission network have become important tasks to ensure the safe operation of the power grid.

[0003] Traditional manual inspection methods often have problems such as high labor costs, low efficiency, and slow response speed in monitoring these remote areas, and automated and remote monitoring methods are gradually becoming alternatives. With the rapid development of communication technology, smart grids are gradually becoming the future development direction of power systems. Smart grids can not only realize automated monitoring, but also perform remote data transmission and real-time early warning through modern communication technologies (such as satellite communications).

[0004] The core of satellite-integrated smart grid is to monitor power equipment in real time through sensor nodes, and transmit the monitoring data to the control center through satellite communication nodes to achieve remote and unmanned monitoring. Satellite communication is particularly suitable for scenarios where sensors are deployed in remote areas in smart grids. Under normal circumstances, it can transmit a small amount of regular monitoring data, and when a fault occurs, it can transmit high-frequency real-time data to ensure the stable operation of the system.

[0005] Existing smart grid monitoring solutions include drone inspections and sensor self-organizing networks. Drone inspections are flexible but have high maintenance costs, while sensor self-organizing networks can achieve low-cost regional detection and fault warnings through coverage and connectivity constraints. However, how to reasonably deploy sensor nodes to ensure reliable coverage of the monitoring area without wasting resources has become an optimization problem.

[0006] For the deployment of multiple sensor nodes, k-coverage and m-connectivity are key performance indicators. k-coverage ensures that each area is covered by at least k sensor nodes, which enhances the reliability of monitoring; m-connectivity ensures that each node has at least m paths connected to the satellite communication node, ensuring the stability of data transmission. To solve this optimization problem, heuristic algorithms, such as genetic algorithms, are widely used to optimize the deployment of sensor nodes. However, traditional genetic algorithms usually have high computational overhead and slow convergence when dealing with large-scale optimization problems. Summary of the invention

[0007] In view of the defects of the prior art, the present invention provides a satellite integrated smart grid monitoring method based on an improved genetic algorithm. An improved genetic algorithm (XGA) is used to construct a network for power towers and sensor nodes in advance by introducing a k-dimensional tree (KDT), and the results of the tree are reused in the chromosome initialization stage, which significantly improves the convergence speed and computational efficiency of the algorithm.

[0008] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0009] A satellite integrated smart grid monitoring method based on an improved genetic algorithm realizes the k-coverage and m-connectivity monitoring of the power grid through the optimized deployment of sensor nodes, which specifically includes the following steps:

[0010] 1) System initialization: KDT is constructed between the power tower and the sensor nodes, and the initial chromosome is generated according to the proximity of the sensor nodes around the power tower, so that the chromosomes in the initial population are closer to the optimal solution;

[0011] 2) Fitness function design: Design the fitness function according to the following constraints:

[0012] k-coverage constraint, ensuring that the target area is covered by at least k sensor nodes;

[0013] m-connectivity constraint, ensuring that the network has at least m communication paths;

[0014] Energy consumption constraints, reducing the energy consumption of sensor nodes to extend the network life cycle;

[0015] 3) Genetic operations: Optimize the deployment scheme of sensor nodes through selection, crossover and mutation operations:

[0016] The selection operation is based on the fitness value, giving priority to chromosomes with better performance;

[0017] The crossover operation exchanges chromosome genes to generate a new node deployment plan;

[0018] The mutation operation randomly adjusts the position or sensing range of some sensor nodes;

[0019] 4) Convergence judgment: When the algorithm reaches the preset number of generations or the change in fitness value is less than a certain threshold, the optimal sensor node deployment plan is output.

[0020] Furthermore, the initial population generation is performed by repeatedly utilizing the node deployment results in the k-dimensional tree during the chromosome initialization phase to accelerate the convergence of the genetic algorithm.

[0021] Furthermore, k-coverage and m-connectivity in the fitness function are evaluated by calculating the sensing range and the number of communication links of each sensor node. If the number of covered nodes in a certain area is less than k or the number of communication paths is less than m, the fitness value decreases.

[0022] Furthermore, the selection operation adopts a roulette wheel selection method to select chromosomes with higher fitness values ​​to enter the next generation, while retaining a small number of chromosomes with lower fitness values ​​to increase population diversity.

[0023] Furthermore, the mutation operation prevents the algorithm from falling into a local optimum by randomly adjusting the sensing radius or position of the sensor node.

[0024] Furthermore, the method is applicable to fixed scenarios and mobile scenarios, and ensures the monitoring coverage and connectivity of the system in different scenarios by adaptively adjusting the location and sensing range of sensor nodes.

[0025] Furthermore, the method receives data from sensor nodes through satellite communication nodes, and transmits the monitoring data to a control center to achieve remote monitoring and early warning.

[0026] Furthermore, during the chromosome initialization process, the improved genetic algorithm uses the geometric proximity between the power towers and the sensor nodes to guide the generation of chromosomes, so that the chromosomes have better initial coverage and connectivity.

[0027] Furthermore, the improved genetic algorithm includes the following improvements:

[0028] Chromosome initialization optimization: In the chromosome initialization stage, a k-dimensional tree is constructed for the towers and sensor nodes in advance, and the chromosome generation is guided by the proximity of the sensor nodes around the towers, so that the chromosome is closer to the optimal solution, thereby improving the quality of the initial population;

[0029] KDT-based reuse strategy: In the subsequent genetic operation process, the KDT results constructed in the chromosome initialization phase are reused to reduce the amount of calculation and speed up the algorithm convergence;

[0030] Improvements in selection and crossover operations: In the selection operation, an adaptive adjustment mechanism based on fitness is introduced to ensure that chromosomes with higher fitness have a higher probability of being selected; in the crossover operation, a multi-point crossover method is adopted to improve population diversity and enhance global search capabilities;

[0031] Mutation operation optimization: In the mutation operation, combined with the local search strategy, the mutation rate is dynamically adjusted according to the distribution of sensor nodes and coverage requirements to avoid falling into the local optimum and improve the convergence efficiency of the algorithm.

[0032] The present invention also discloses a satellite integrated smart grid monitoring system, which performs two-way data transmission with ground communication equipment through multiple satellite nodes, and is used to implement the above-mentioned satellite integrated smart grid monitoring method, specifically including:

[0033] A UAV communication module capable of connecting to the distributed satellite communication network to achieve remote communication optimization, including data transmission, reception and feedback;

[0034] The communication optimization algorithm module optimizes the data transmission path in real time based on the distributed satellite network and the flight path of the UAV, mission requirements, environmental factors and other parameters to improve communication efficiency and stability;

[0035] The task scheduling module dynamically adjusts the flight path and task allocation of the UAV based on the communication delay of the distributed satellite communication network and the mission requirements of the UAV;

[0036] The monitoring and early warning module is used to remotely monitor the flight status, communication status and mission execution of the drone in real time through the satellite communication network, and issue early warning signals when abnormalities occur.

[0037] Compared with the prior art, the advantages of the present invention are:

[0038] 1. Improve monitoring efficiency: Through the optimization method based on the improved genetic algorithm (XGA), the k-coverage and m-connectivity design of the power tower is realized, so that the monitoring system can complete the comprehensive monitoring of the target area in a shorter time, improving the response speed of the system.

[0039] 2. Reduce energy consumption: Optimize the deployment and selection of sensor nodes, reduce unnecessary energy consumption, extend the service life of sensors, and thus improve the economy of the entire monitoring network.

[0040] 3. Enhance system reliability: By ensuring multiple coverage and connectivity of sensor nodes, the reliability of the monitoring system under abnormal conditions is enhanced, the failure rate is reduced, and potential risks can be effectively warned.

[0041] 4. Realize automation and remote monitoring: Utilize satellite communication networks to support real-time transmission and feedback of data, realize automated management of the monitoring system, reduce the need for manual intervention, and improve the safety and convenience of monitoring work.

[0042] 5. Improve communication efficiency: Combine UAVs and distributed satellite communication networks to optimize remote communications, improve the efficiency and stability of data transmission paths, and ensure the timeliness and accuracy of monitoring information.

[0043] 6. Promote the development of smart grids: This invention provides an innovative solution for the construction of future smart grids, promotes the advancement of power monitoring and management technology, and lays the foundation for the popularization and application of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic diagram of the structure of a satellite integrated smart grid monitoring system according to an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of node deployment in a genetic algorithm according to an embodiment of the present invention;

[0046] Figure 3 is a flow chart of an improved genetic algorithm according to an embodiment of the present invention;

[0047] Figure 4 is a schematic diagram of deployment of nodes in an ideal environment according to an embodiment of the present invention;

[0048] Figure 5 is a schematic diagram of random node deployment according to an embodiment of the present invention;

[0049] Figure 6 is a comparison diagram of the convergence of the algorithm in a fixed scenario according to an embodiment of the present invention;

[0050] Figure 7 is a comparison diagram of the convergence of the algorithms in random scenarios according to the embodiments of the present invention;

[0051] Figure 8 is a histogram of coverage of different population numbers in a fixed scenario according to an embodiment of the present invention;

[0052] Fig. 9 It is a bar chart of coverage of different population sizes in random scenarios according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0054] 1. System Model

[0055] In the satellite integrated smart grid system, the sensor nodes that monitor the transmission towers are networked through a self-organizing method, and the sensing data is transmitted to the portable ground station through a multi-hop method, and then transmitted back to the data center through the satellite network. Due to the limited energy characteristics of sensor nodes, in order to ensure the reliability of data, multiple coverage and multi-channel transmission methods are usually adopted, that is, k-coverage and connectivity design. Assuming that the total number of power towers in the area to be monitored is N, let V = {v 1 , v 2 , ..., v n} represents the set of power towers. Let the total number of sensor nodes in the area be L, and let S = {s 1 ,s 2 , ..., s l} represents the set of sensor nodes. Let the matrix Dcv i，j , represents the coverage range from the ith sensor node to the jth tower. Let the matrix Dcm i，j Represents the connection relationship between the ith sensor node and the jth sensor node. The system model is as follows Figure 1 shown.

[0056] The coverage matrix cv between the i-th point of the sensor node and the j-th transmission tower i，j and the connection matrix cm_ij are represented by Boolean variables, as follows:

[0057]

[0058]

[0059] in Represents the Euclidean distance between two points. d cv and d cm They represent the sensing range and communication distance of the sensor node respectively.

[0060] Select an appropriate number of sensor nodes from the set S for network deployment to minimize the cost.

[0061] set up

[0062]

[0063] If the electric towers in the target area need to be covered by sensor nodes and there are m communication lines between k sensor nodes, the optimization problem of the smart grid monitoring system is as follows.

[0064] Minimize

[0065]

[0066] Subject to

[0067]

[0068] Formula (4) ensures that the total number of nodes selected is the minimum number of nodes that satisfy all constraints in the network. The fewer the number of nodes selected, the lower the network's computational, storage, and transmission costs are when the same goal is achieved. The first expression of formula (5) ensures that the network can achieve k-fold coverage by limiting the sum of the number of towers covered by all selected sensor nodes. Similarly, the second expression of formula (5) ensures that the network can maintain connectivity by limiting the sum of the number of communication connections between all selected sensor nodes and towers. The solution to this optimization problem determines the effectiveness and implementation plan of the smart grid monitoring system.

[0069] 2. Design of XGA Algorithm

[0070] Heuristic algorithms, such as genetic algorithms, are often used to solve optimization problems in smart grid monitoring systems. They have strong global search capabilities and high parallel implementation efficiency. However, in the proposed system model, traditional genetic algorithms usually select towers first and then evaluate the coverage of sensor nodes on the towers one by one when calculating the evaluation function. This approach will bring huge computational overhead when the number of towers and sensor nodes is large.

[0071] This embodiment proposes an improved genetic algorithm XGA, which first constructs a k-dimensional tree (KDT) according to the locations of the power towers and sensor nodes. The power towers that are far beyond the coverage of the sensor nodes are marked and do not participate in subsequent calculations, thereby improving the execution efficiency of the algorithm.

[0072] In addition, in the initialization stage of chromosomes, traditional genetic algorithms are randomly initialized, that is, all sensor nodes are randomly selected to be enabled or disabled. Although this can increase the search range of the algorithm, it will lead to a slower convergence of the algorithm. Since the KDT of the tower and sensor nodes has been pre-built, XGA can also use these results twice in the chromosome initialization stage. According to the distance of the sensor nodes around the tower, XGA uses a guided method to initialize the chromosome, making the chromosome closer to the optimal solution, thereby accelerating the convergence of the algorithm.

[0073] Boolean strings are used to represent the contents of chromosomes. A value of 0 at a gene site in the chromosome indicates that the sensor node is not deployed, while a gene value of 1 indicates that the sensor node is part of the system. Figure 2 As shown, there are multiple sensor nodes, represented by red dots, and seven power towers, represented by purple five-pointed stars. The green virtual circles represent the coverage of the sensor nodes. A chromosome bit value of 1 indicates that the corresponding sensor node is enabled.

[0074] A. Initialization of chromosome positions

[0075] The standard genetic algorithm randomly generates chromosomes in the initial stage, that is, each bit in the chromosome is assigned a value according to random conditions. The improved genetic algorithm guides the chromosome according to the location information between the tower and the sensor node in the initialization stage. The pseudo code is shown in Algorithm 1.

[0076]

[0077] B. Evaluation Function

[0078] The optimization goal of the system is to deploy as few sensor nodes as possible while satisfying k coverage and m connectivity. The minimum number of nodes, coverage, and connectivity are normalized and weighted respectively, and the final optimization problem is transformed into:

[0079] Minimize

[0080]

[0081] where w mm The weight representing the number of nodes, w cv represents the weight of coverage, w cm Represents the weight of the connection.

[0082] The process of the improved genetic algorithm XA is as follows Figure 3 shown.

[0083] 3. Simulation Experiment Verification

[0084] This embodiment will compare the standard genetic algorithm (GA) and the XGA algorithm. The map used by the XGA algorithm contains 40 power towers and 121 sensor nodes. The performance of different algorithms is tested under the conditions of random distribution of power grids and sensor nodes. The simulated software environment is Windows 10 Professional Edition, version 22H2. The Python version is 3.8.10, and standard libraries such as NumPy, SciPy, and Matplotlib are used. In terms of hardware, the processor is an AMD Ryzen 7 3700 X8 core processor with a main frequency of 3.59 GHz and a memory capacity of 16.0 GB. The parameters used in the simulation are shown in Table 1.

[0085] Table 1 Parameter settings of XACO algorithm

[0086]

[0087] In order to test the performance of the algorithm in different types of scenarios, grid distribution and random distribution of sensor nodes in satellite integrated smart grid are designed. Grid distribution of sensor nodes usually has the advantages of uniform coverage, accurate calculation, simple management and maintenance, etc. It can realize node deployment in a relatively ideal environment, and the effect of the algorithm is relatively stable, such as Figure 4 shown.

[0088] The target area for satellite integrated smart grid sensor node deployment is usually located in the suburbs. The random deployment of nodes can be easily achieved through drones and other means, which has high flexibility. However, random deployment also has the characteristics of uneven coverage and easy formation of redundancy or blind spots, such as Figure 5 As shown in Figure 2, under the random node deployment scheme, the optimization of the node selection algorithm has a more important impact on network performance.

[0089] Comparison of algorithm convergence under fixed scenarios Figure 6 As shown in the figure, the XGA algorithm showed better results after 2 rounds and was always higher than the GA algorithm. Both the XGA algorithm and the GA algorithm completed convergence within 12 rounds, and the final evaluation function values ​​were 95.26% and 94.71%, respectively. Therefore, the performance of XGA is better.

[0090] In the case of random distribution, the effect of bootstrapping based on KDT is more obvious, such as Figure 7 As shown in the figure, it can be seen that the XGA algorithm is better than the GA algorithm in the initialization stage. The XGA algorithm completes convergence within 11 rounds, which is slightly earlier than the 12 rounds of the GA algorithm. The final evaluation function values ​​of XGA and GA are 94.93% and 93.35% respectively.

[0091] Figure 8 and Fig. 9 The effect of different population sizes on the results of the two scenarios is shown. In the comparison of various population sizes, XGA is better than GA. In the random scenario, the advantage of XGA is more obvious.

[0092] IV. Implementation Effect

[0093] In order to solve the k-coverage and m-connectivity problems in satellite-integrated smart grids, the present invention is proposed to enhance the deployment and activation strategies of sensor nodes to effectively monitor key targets. The optimization goal focuses on minimizing the number of nodes while considering coverage and connectivity requirements, so as to develop an evaluation function that is more suitable for this context. By pre-constructing a K-dimensional tree (KDT) of towers and sensor nodes, the chromosomes are initialized based on the distances between the surrounding sensor nodes and the towers, which helps them get closer to the optimal solution and improves the convergence speed of the algorithm. Simulation tests are carried out in fixed grid distribution and random distribution scenarios. The results show that the XGA algorithm outperforms traditional algorithms in both evaluation function value and convergence speed, especially in the random distribution node scenario.

[0094] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0096] 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 comprising 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.

[0097] 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.

[0098] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A satellite integrated smart grid monitoring method based on improved genetic algorithm, characterized in that: The method realizes the k-coverage and m-connectivity monitoring of the power grid through the optimized deployment of sensor nodes, and specifically comprises the following steps: 1) System initialization: A k-dimensional tree is constructed between the power tower and the sensor nodes, and the initial chromosome is generated according to the proximity of the sensor nodes around the power tower, so that the chromosomes in the initial population are closer to the optimal solution; 2) Fitness function design: Design the fitness function according to the following constraints: k-coverage constraint, ensuring that the target area is covered by at least k sensor nodes; m-connectivity constraint, ensuring that the network has at least m communication paths; Energy consumption constraints, reducing the energy consumption of sensor nodes to extend the network life cycle; 3) Genetic operations: Optimize the deployment scheme of sensor nodes through selection, crossover and mutation operations: The selection operation is based on the fitness value, giving priority to chromosomes with better performance; The crossover operation exchanges chromosome genes to generate a new node deployment plan; The mutation operation randomly adjusts the position or sensing range of some sensor nodes; 4) Convergence judgment: When the algorithm reaches the preset number of generations or the change in fitness value is less than a certain threshold, the optimal sensor node deployment plan is output.

2. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The initial population generation is performed by repeatedly utilizing the node deployment results in the k-dimensional tree in the chromosome initialization stage to accelerate the convergence of the genetic algorithm.

3. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The k-coverage and m-connectivity in the fitness function are evaluated by calculating the sensing range and the number of communication links of each sensor node. If the number of covered nodes in a certain area is less than k or the number of communication paths is less than m, the fitness value decreases.

4. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The selection operation adopts a roulette wheel selection method to select chromosomes with higher fitness values ​​to enter the next generation, while retaining a small number of chromosomes with lower fitness values ​​to increase population diversity.

5. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The mutation operation prevents the algorithm from falling into a local optimum by randomly adjusting the sensing radius or position of the sensor node.

6. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The method is applicable to fixed and mobile scenarios, and ensures the monitoring coverage and connectivity of the system in different scenarios by adaptively adjusting the location and sensing range of sensor nodes.

7. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The method receives data from sensor nodes through satellite communication nodes, and transmits the monitoring data to a control center to achieve remote monitoring and early warning.

8. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: During the chromosome initialization process, the improved genetic algorithm uses the geometric proximity between the power towers and the sensor nodes to guide the generation of chromosomes, so that the chromosomes have better initial coverage and connectivity.

9. The satellite integrated smart grid monitoring method according to claim 1, characterized in that: The improved genetic algorithm includes the following improvements: Chromosome initialization optimization: In the chromosome initialization stage, a k-dimensional tree is constructed for the towers and sensor nodes in advance, and the chromosome generation is guided by the proximity of the sensor nodes around the towers, so that the chromosome is closer to the optimal solution, thereby improving the quality of the initial population; KDT-based reuse strategy: In the subsequent genetic operation process, the KDT results constructed in the chromosome initialization phase are reused to reduce the amount of calculation and speed up the algorithm convergence; Improvements in selection and crossover operations: In the selection operation, an adaptive adjustment mechanism based on fitness is introduced to ensure that chromosomes with higher fitness have a higher probability of being selected; in the crossover operation, a multi-point crossover method is adopted to improve population diversity and enhance global search capabilities; Mutation operation optimization: In the mutation operation, combined with the local search strategy, the mutation rate is dynamically adjusted according to the distribution of sensor nodes and coverage requirements to avoid falling into the local optimum and improve the convergence efficiency of the algorithm.

10. A satellite integrated smart grid monitoring system, characterized in that: The system performs two-way data transmission with ground communication equipment through multiple satellite nodes, and is used to implement the satellite integrated smart grid monitoring method according to any one of claims 1 to 8. The satellite integrated smart grid monitoring system includes: A UAV communication module capable of connecting to the distributed satellite communication network to achieve remote communication optimization, including data transmission, reception and feedback; The communication optimization algorithm module optimizes the data transmission path in real time based on the distributed satellite network and the flight path of the UAV, mission requirements, environmental factors and other parameters to improve communication efficiency and stability; The task scheduling module dynamically adjusts the flight path and task allocation of the UAV based on the communication delay of the distributed satellite communication network and the mission requirements of the UAV; The monitoring and early warning module is used to remotely monitor the flight status, communication status and mission execution of the drone in real time through the satellite communication network, and issue early warning signals when abnormalities occur.

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