Large-bandwidth power grid service resource optimal configuration method based on low earth orbit satellite

Abstract

The invention discloses a large-bandwidth power grid service resource optimal configuration method based on low-orbit satellites, and the method comprises the steps: firstly, selecting a plurality of low-orbit satellites as data transmission relays, and guaranteeing the coverage of a target power grid region; secondly, establishing a two-way communication link to support real-time data transmission; dynamically allocating bandwidth according to the flow demand of a power grid communication node by using an orthogonal frequency division multiple access technology, and establishing a mathematical model to maximize the total transmission rate; designing a satellite-ground network architecture, optimizing resource allocation of each satellite, and realizing minimum delay and maximum throughput; and finally, monitoring node states in real time, dynamically adjusting bandwidth allocation, and evaluating network performance indexes by using a data analysis algorithm so as to continuously optimize a communication effect. According to the invention, a reliable solution is provided for efficient scheduling and energy efficiency optimization of power grid communication, and the efficiency and reliability of a power system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid communication technology, and in particular to a method for optimizing configuration of large-bandwidth power grid service resources based on a low-orbit satellite. Background Art

[0002] With the rapid evolution of smart grid technology, the power industry is experiencing an unprecedented change in communication needs. In particular, in key applications such as drone transmission line detection, robot power safety inspection, 24-hour monitoring and emergency communications, the demand for large-bandwidth networks is growing rapidly. These applications require efficient and stable uploading of large amounts of grid business-related data to cloud server data centers to achieve precise regulation and efficient management of the grid. According to the International Telecommunication Union (ITU) in its "2021 Global Communications Development Report", the development of smart grids, remote monitoring systems and big data centers has doubled the demand for broadband grid services in the past five years, and it is expected to continue to grow exponentially in the next decade. This trend urgently requires us to seek efficient and reliable communication solutions to meet the challenges of grid communication resource scheduling and energy efficiency optimization.

[0003] However, traditional power system communication networks face many inherent defects. First, these networks are usually limited by low data transmission bandwidth, which is particularly insufficient when faced with the increasing amount of data and the needs of smart grid development. In particular, in smart grid applications such as real-time data monitoring, distributed resource management, and high-frequency communications, bandwidth limitations become a significant bottleneck. In addition, traditional networks rely on physical lines, which are expensive to deploy and maintain, especially in areas with complex terrain or inaccessible areas. This physical dependence also makes the network extremely sensitive to natural disasters, which can easily lead to communication interruptions, thereby affecting the stable operation and reliability of the power grid. Overall, the main challenges facing traditional power grid communication models include low bandwidth, high construction and maintenance costs, and high sensitivity to natural disasters, which together restrict the efficiency and reliability of the power system.

[0004] Today, satellite communications have become a key technology to support the continuity of power grid services in remote areas with their global coverage and high reliability. In particular, low-orbit satellite (LEO) communication technology has become an important part of the solution. Due to their lower orbital altitude and small transmission delay characteristics, low-orbit satellites are suitable for large-scale deployment, making it possible to achieve comprehensive network coverage of the earth's surface. Thanks to the large-scale production of satellite technology and breakthroughs in rocket reuse technology, the construction and operation costs of low-orbit satellite networks are gradually decreasing, which will directly promote the reduction of communication costs, making LEO satellite networks a key technical path to improve global network coverage and optimize data transmission quality, which is crucial to improving power grid service performance. Summary of the invention

[0005] In view of the defects of the prior art, the present invention provides a method for optimizing the configuration of large-bandwidth power grid service resources based on a low-orbit satellite.

[0006] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0007] A method for optimizing and configuring large-bandwidth power grid service resources based on low-orbit satellites, characterized in that it includes the following steps:

[0008] Select multiple low-orbit satellites as data transmission relays to ensure coverage of the target power grid area.

[0009] A two-way communication link is established between low-orbit satellites and ground power grid communication nodes to support real-time data transmission.

[0010] By using orthogonal frequency division multiple access technology, the available bandwidth is dynamically allocated according to the traffic demand of each power grid communication node, ensuring efficient spectrum utilization.

[0011] Bandwidth allocation is performed based on node priority, and a mathematical model is established based on this to maximize the total transmission rate of power grid communication equipment. The model is used to optimize bandwidth allocation and dynamically adjust the bandwidth of each power grid communication node to meet the traffic needs of each node.

[0012] Design the satellite-to-ground network architecture, using a multi-layer network topology. Refer to the results of the mathematical model to optimize the resource allocation of each satellite to achieve minimum latency and maximum throughput.

[0013] Monitor the real-time status of power grid communication equipment nodes, including bandwidth utilization, latency, and packet loss rate. Through real-time monitoring and mathematical model analysis, dynamically adjust the bandwidth allocation strategy to ensure that the service quality of each node is not lower than the set standard.

[0014] Evaluate network performance indicators, including latency, packet loss rate, bandwidth utilization, and throughput, through data analysis algorithms. Use the parameters and optimization results provided by mathematical models to conduct more in-depth data analysis and performance optimization to achieve continuous optimization and generate hourly performance reports.

[0015] Furthermore, the ground power grid communication nodes include communication nodes for fixed real-time monitoring, mobile power grid patrol robots, drone monitoring communication nodes, and power transmission and distribution system communication nodes responsible for statistics on power grid status.

[0016] Furthermore, the maximum data transmission rate is calculated by the Shannon formula, and the data transmission rate R is defined as m,n,o To optimize the transmission effect.

[0017] Furthermore, the mathematical model formula is as follows:

[0018]

[0019] Among them, f(V,p) is the objective function, which means the function that needs to be maximized under a given variable set V and parameter p.

[0020] M represents the set of power grid communication devices, and m represents the ground power grid communication node.

[0021] N represents the set of grid nodes, and n represents satellite nodes.

[0022] O represents a collection of different data streams or communication paths, and o represents a subchannel.

[0023] R m,n,o It represents the transmission rate or resource utilization that can be achieved between the ground power grid communication node m and the satellite node n through the sub-channel o.

[0024] ∑ m∈M ∑ n∈N ∑ o∈O R m,n,o : Indicates the sum of the transmission rates of all possible ground power grid communication nodes, satellite nodes, and sub-channels to calculate the total transmission rate of the entire network.

[0025] Furthermore, the mathematical model includes the following constraints:

[0026] Each satellite’s subchannel can be assigned to only one grid communication node;

[0027] The transmission power of grid communication nodes is limited by the maximum power;

[0028] Grid communication nodes must meet a minimum communication rate threshold.

[0029] Furthermore, after the communication links and network architecture are determined, a genetic algorithm is used to solve the association problem between multiple satellites and power grid communication equipment to determine the association of satellite equipment in each time slot.

[0030] Furthermore, after monitoring the real-time status of the grid communication device nodes, the fitness function is calculated, and based on the transmission rate and channel gain, a preference weight list of each satellite and grid communication node is generated to achieve stable matching.

[0031] Furthermore, after the device association is determined, a two-stage Gale-Shapley matching algorithm is used to associate sub-channels with power grid devices to ensure normal communication of all devices, while optimizing the matching of remaining sub-channels with power grid devices, and finally performing joint power allocation to achieve the solution of the overall optimization problem.

[0032] Compared with the prior art, the advantages of the present invention are:

[0033] 1. Improve bandwidth utilization: Orthogonal frequency division multiple access (OFDMA) technology is used to dynamically allocate bandwidth to ensure that each power grid communication node obtains sufficient resources according to actual needs, thereby effectively improving bandwidth utilization.

[0034] 2. Enhanced communication stability: The global coverage capability of the low-orbit satellite network significantly enhances the stability and reliability of power grid communications in remote areas and complex terrains, reducing the risk of communication interruptions caused by natural disasters.

[0035] 3. Optimize resource scheduling: Introduce genetic algorithms and Gale-Shapley matching algorithms to intelligently determine the relationship and sub-channel allocation between satellites and power grid equipment, achieve optimal resource scheduling, and improve overall system performance.

[0036] 4. Reduce operating costs: As the construction and operating costs of low-orbit satellite networks gradually decrease, the power industry can achieve more economical communication solutions while maintaining high-quality services.

[0037] 5. Support big data applications: Provide efficient and stable data transmission to meet the needs of smart grids for real-time data monitoring and big data processing, and promote the intelligentization and automation of power systems.

[0038] 6. Promote sustainable development: By optimizing resource allocation and improving the energy efficiency of power grid business, we will help develop a sustainable power system and provide support for future green energy transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Flow chart of a method for optimizing configuration of large-bandwidth power grid service resources based on low-orbit satellites according to an embodiment of the present invention;

[0040] Figure 2 Satellite-to-ground uplink architecture diagram of an embodiment of the present invention;

[0041] Figure 3 Flow chart of solving system model strategy in an embodiment of the present invention;

[0042] Figure 4 A flow chart of a satellite equipment association strategy based on a genetic algorithm according to an embodiment of the present invention;

[0043] Figure 5 Flow chart of the sub-channel device association algorithm based on the GS algorithm in an embodiment of the present invention;

[0044] Figure 6 A graph showing the relationship between the total rate and the number of iterations of the algorithm according to an embodiment of the present invention;

[0045] Figure 7A graph showing the relationship between the total system rate and the number of iterations of the four algorithms in the embodiment of the present invention;

[0046] Figure 8 A graph showing the relationship between the number of grid devices served and the number of iterations according to an embodiment of the present invention;

[0047] Fig. 9 The embodiment of the present invention obtains a relationship diagram between the number of nodes and the number of matching times of a channel;

[0048] Fig.10 A diagram showing the relationship between the total rate of the system and the number of power grid devices in an embodiment of the present invention;

[0049] Fig.11 A graph showing the relationship between the total energy efficiency of the system and the radius of the service area in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] like Figure 1 As shown, the present invention provides a method for optimizing and configuring large-bandwidth power grid service resources based on low-orbit satellites, comprising:

[0052] Step 101, scene description.

[0053] This paper mainly studies how to efficiently coordinate multiple LEO satellites and numerous power grid data transmission nodes in a low-orbit satellite network environment to perform efficient data uplink collection tasks. It aims to solve the problem of efficient data transmission between low-orbit satellite networks and ground power grid communication equipment nodes to support the large bandwidth business needs of smart grids. Figure 2Consider a satellite-ground network architecture based on OFDMA (Orthogonal Frequency Division Multiple Access) technology, in which multiple LEO satellites provide network access and data transmission services for a large number of widely distributed power grid communication equipment nodes, including communication nodes for fixed real-time monitoring, mobile power grid patrol robots and drone monitoring communication nodes, and power transmission and distribution system communication nodes responsible for statistical power grid status. In this research, a group of satellites consisting of multiple satellites is considered, and this group of satellites reuses the same frequency band. Assume that there are N = {1,2,...,n} LEO satellites sharing the same frequency band in group d1, providing network access and data transmission services for a group of PD power grid communication devices M = {1,2,...,m}. Considering that the orbit of the LEO satellite is pre-set, the altitude, speed and position information of the satellite are predictable to the ground node in each time period. In order to simplify the processing, a quasi-static method is adopted to divide the entire service cycle into multiple time periods T = {1,2,...,t}. In each time period t′, it is assumed that the position of the satellite and the channel between the satellite and the ground node are unchanged, and all channel state information is known to the power grid communication device.

[0054] In group d1, the total bandwidth of the frequency band used by group d1 is assumed to be W. Since it is a satellite-to-ground network architecture based on OFDMA (Orthogonal Frequency Division Multiple Access) technology, it is divided into O = {1, 2, ..., o} orthogonal sub-channels, which are shared by the LEO satellites in the group. In order to describe the association between satellites and ground power grid communication equipment and the association between communication equipment and sub-channels, the present invention uses a three-dimensional matrix V to represent the association relationship, where the matrix element v m,n,o =1 indicates that the ground power grid communication device numbered m communicates with the satellite node numbered n through the subchannel numbered o. n To represent the set of power grid communication devices PD connected to satellite n within a certain period of time.

[0055] Formula 1 is the Shannon formula, where C is the maximum rate of data transmission, measured in bits per second (bit / s); W is the bandwidth of the channel, measured in Hertz (Hz); S is the power of the signal, measured in watts (W); and N is the power of the noise, also measured in watts.

[0056]

[0057] Therefore, according to Shannon's formula, assuming that the ground node m is associated with the satellite n and transmits data through the subchannel o of the satellite, the data transmission rate R on the subchannel is defined as m,n,o As shown in formula (2):

[0058]

[0059] where p m,n,o represents the transmission power from grid communication device node m to satellite n on subchannel o, and N0 is additive white Gaussian noise. m,n,o represents the total gain from the grid device node m to the satellite node n on the subchannel o. In the context of the present invention providing communication services for multiple grid communication devices based on multiple satellites, it mainly relies on the line-of-sight transmission line between the satellite and the grid communication device. Therefore, the following channel models and antenna characteristics will be mainly considered: free space loss model, shadow Rice channel model and antenna gain. Therefore, the total gain of a single device node on its channel is h m,n,o =G m(n),n -H m,n,o -L m,n , where G m(n),n is the off-axis antenna gain caused by the antenna of ground device node m sending data toward satellite node n, H m,n,o For the shadow Rice channel attenuation, L m,n is the free space loss.

[0060] Step 102, problem model.

[0061] Through the construction of the above system model, it is known that there are multiple satellites and multiple power grid communication equipment nodes in the system. In order to meet the needs of large-bandwidth power grid services, the configuration and scheduling of network resources need to be aimed at improving the transmission rate, so the following mathematical model is constructed:

[0062]

[0063]

[0064] Where P max is the maximum power that each grid communication device can provide, R th is the minimum communication rate threshold that each power grid communication device needs to meet. Formula 4 is the optimization goal of the present invention, maximizing the total ground power grid communication device transmission rate. Considering the adoption of the OFDMA channel model, for a satellite, the subchannels are orthogonal and will not interfere with each other. Therefore, there is no frequency reuse for each ground power grid communication device served. Therefore, Formula 5 limits the subchannel for a satellite to be assigned to only one power grid communication device. It also means that when the satellite node and the power grid communication device node are connected through the subchannel, there are only two situations: connected and unconnected. That is, the matrix element v m,n,o =1 means that the ground device numbered m is connected to the satellite node numbered n through the subchannel numbered o, where the subchannel numbered o is the number of satellite n, and v m,n,o=0 means no connection. Formula 6 means that the transmission power of the ground node is limited by the maximum power; Formula 7 means that the ground device node must at least ensure the minimum rate for normal communication.

[0065] In the whole optimization problem, R m,n,o There are interference terms in the , and there is also a three-dimensional binary variable V. This is a non-convex problem and will be difficult to solve. In order to solve the optimization problem, in order to reduce the complexity of the optimization problem, an additional constraint is introduced in the present invention:

[0066]

[0067] Where Q represents the maximum tolerated interference to other connections using the same subchannel. The corresponding R m,n,o It will become the following formula 9:

[0068]

[0069] Since the above optimization problem contains three-dimensional binary variables, it is a nonlinear constraint programming problem with high solution complexity. Therefore, the joint optimization problem is decomposed into three sub-problems. In each time slot, the original optimization problem can be decomposed into three sub-problems for solution: (1) the association problem between multiple satellites and multiple power grid communication equipment, (2) the association problem between power grid communication equipment and satellite sub-channels, and (3) joint power allocation. Next, these three sub-problems will be solved separately.

[0070] Step 103: solving the model.

[0071] like Figure 3 As shown in the figure, the system model strategy solving process includes:

[0072] Step 301: satellite and power grid equipment association strategy.

[0073] The present invention solves the sub-problem of the association between satellite and power grid communication equipment in each time slot, and obtains the satellite equipment association status in each time slot. Here, equipment power optimization is not considered. Since it is an association problem between multiple satellites and multiple power grid equipment nodes, but for a single satellite, it is a one-to-many matching relationship. Since the genetic algorithm has excellent global search capability and flexibility, it is an effective tool for solving such combinatorial optimization problems. Therefore, the genetic algorithm is used in the present invention to solve the association problem. Based on this, in order to solve the association problem between satellite and power grid communication equipment, a device association strategy based on genetic algorithm is proposed.

[0074] Before performing the genetic algorithm, the fitness function needs to be determined. Since the optimization goal is to maximize the total transmission rate, when a power grid communication device chooses to access a satellite, the preference function of the ground communication device should have a positive correlation with the channel gain and a negative correlation with the channel interference of other satellites. The preference function of the communication device is defined in a form similar to the transmission signal-to-noise ratio:

[0075]

[0076] In order to meet the transmission capacity of large-bandwidth power grid services in the present invention, the optimization goal adopted is to maximize the total transmission rate. For satellites, the device with the largest rate will be preferred. At the same time, according to Formula 9, R m,n,o The size of depends on the corresponding power, so the satellite preference function can be defined as follows:

[0077]

[0078] From the above, we can get the preference function of each satellite and each ground power grid device based on the optimization problem, and thus get the corresponding preference list of each satellite and each ground power grid device. Next, we generate a weight list based on the obtained preference list. According to the order of the preference list, a weight is assigned to each preference pairing. The highest ranked pairing, that is, the most preferred pairing, obtains MaxWeight, and the lowest ranked pairing, that is, the least preferred pairing, obtains MinWeight. For pairs ranked between the highest and the lowest, linear interpolation is used to assign weights:

[0079]

[0080] Where S_W n m The weight of the pairing of ground device m with Rank in satellite n. Similarly, formula 13 represents the weight calculation of ground devices to queued satellites.

[0081]

[0082] Based on the obtained weight list, in order to achieve stable matching, the weights of both parties must be considered. Therefore, the total weight matching of satellite n and ground device m is defined as:

[0083]

[0084] In the genetic algorithm, each chromosome represents a possible matching scheme of satellite and ground node. The fitness function will be calculated based on the sum of weights of all pairings in the chromosome. Therefore, according to the above weight assignment, the fitness function is defined as:

[0085] Fitness=∑allpairs W pair (15)

[0086] Among them, Fitness is the fitness of the chromosome, W pair is the weight of each pair of matching satellites and ground nodes. This is W in Formula 14. m n .

[0087] The pseudo code of the satellite equipment association strategy based on genetic algorithm is as follows: Algorithm 1:

[0088]

[0089] The algorithm output M1 here represents the matching relationship between the satellite and the equipment, which actually solves the matching relationship of the first two dimensions of the three-dimensional matrix V. The next step is to solve the last dimension of V. The algorithm flow chart of the satellite equipment association strategy based on the genetic algorithm is as follows Figure 4 .

[0090] Step 302: Associating strategies between sub-channels and power grid devices.

[0091] For a satellite, the number of devices that match it has been determined above. In order to ensure that all power grid devices can perform communication tasks normally and ensure the most basic work, a two-stage GS matching algorithm is proposed here. The GS algorithm is first used to complete the matching of all power grid devices, that is, to complete the allocation of the same number of sub-channels and ground nodes of ground devices, and then the remaining unallocated sub-channels are allocated again according to the preferences of power grid communication devices and sub-channels.

[0092] For power grid communication equipment, each device hopes to be allocated more channel resources from the satellite to achieve a higher transmission rate. Therefore, for communication equipment, the preference for a sub-channel will be positively correlated with the transmission rate on the sub-channel:

[0093]

[0094] For the subchannels, the preference for each device is as positively correlated with the transmission rate as possible and negatively correlated with the interference to the communication channels of other satellites:

[0095]

[0096] In order to minimize the interference from other satellites, a weight factor φ is introduced. The pseudo code of the two-stage GS matching algorithm is as follows: Algorithm 2.

[0097]

[0098]

[0099]

[0100] The above completes the association between the sub-channels and devices of a single satellite. By performing the same algorithm operation on the remaining satellites, the association between the sub-channels and devices of all satellites can be completed, and then the calculation of the three dimensions of V can be completed. Figure 5 As shown, it is a flowchart of the sub-channel and power grid communication equipment association algorithm of the two-stage GS algorithm proposed by the present invention.

[0101] It should be pointed out that in the sub-channel device association algorithm based on the GS algorithm, a two-stage matching is adopted: the first stage is to ensure the normal communication of all devices, to prevent the devices with poor efficiency from being unable to match the sub-channel anyway, affecting normal communication; the second stage is to continue matching the sub-channels and devices with better preferences while ensuring normal communication, to improve efficiency. This achieves fair, stable and efficient communication to a certain extent.

[0102] Step 303: Joint power allocation.

[0103] In the above two sections, the device association is determined. Finally, the power should be allocated based on the previous matching relationship to solve the entire optimization problem. According to the mathematical model and constraint relationship in this chapter, the Lagrangian function is constructed as follows:

[0104]

[0105] In order to convert this problem into a convex optimization problem, Formula 18 is converted into a dual problem:

[0106]

[0107] Considering the satellite-to-ground network architecture based on OFDMA (Orthogonal Frequency Division Multiple Access) technology, different sub-channels are orthogonal, so the dual problem can be decomposed into O×N independent problems, and the Lagrangian function can be rewritten as:

[0108]

[0109] in:

[0110]

[0111] According to the KKT condition, the optimal power can be calculated by taking the partial derivative of the formula and setting the partial derivative result equal to 0:

[0112]

[0113] Where [a] +=max(0,a), the subgradient of the Lagrange multiplier is as follows:

[0114]

[0115] Where t represents the number of iterations, τ λ(t) , τ μ(t) , They represent the step size at the tth iteration respectively.

[0116] Step 104: Performance analysis.

[0117] In order to improve the efficient utilization of wireless communication resources, enhance network service quality, meet user needs and promote sustainable development, many excellent algorithms have emerged.

[0118] The joint allocation algorithm of channels and power converts the non-convex mixed integer programming problem into a convex optimization problem by relaxing the sub-channel allocation indicator, and then uses the Lagrangian double decomposition method to solve the joint optimization allocation problem of sub-channels and power. The matching and Lagrangian combined algorithm uses the complete GS algorithm to match the satellite and ground nodes, and then uses the sub-gradient descent method to allocate power.

[0119] Figure 6 It is a simulation relationship diagram between the total rate and the number of iterations of the algorithm of the present invention. From the figure, it can be seen that in the early stage of iteration, the curve rises sharply, and the total rate of the system increases rapidly in the first few iterations; as the number of iterations increases, the performance growth gradually slows down, and the curve begins to stabilize. At this time, the algorithm may have approached the optimization goal; after about 40 iterations, the total rate of the system remains almost unchanged, fluctuating slightly around 7 Gbps.

[0120] exist Figure 6 It can be seen that the algorithm of the present invention begins to converge when it reaches 40 iterations. In order to better demonstrate the performance of the algorithm of the present invention, Figure 7 Three other types of algorithms are used for comparison.

[0121] from Figure 7 It can be seen that with the increase in the number of iterations, the total system rate of the four algorithms increases, but the increase rate and the final stable value are different.

[0122] Specifically: The final stable total system rate of the algorithm of the present invention is relatively high, higher than the matching and Lagrangian combination algorithm and the channel random and average power algorithm, but slightly lower than the joint allocation algorithm of channel and power. This is because the joint allocation algorithm of channel and power will give priority to allocating resources to devices with good channel conditions, but it cannot guarantee that all devices can be allocated to sub-channels, and cannot guarantee that all devices can maintain normal operation. Although the goal of the present invention is to meet the needs of large-bandwidth power grid services, the communication status of each device must also be taken into account to meet the minimum rate requirements of each device. Although the rate is reduced, it can better meet the stability requirements. It shows that the algorithm of the present invention has the ability to take into account both stability and efficiency.

[0123] The performance of the matching and Lagrangian combination algorithm is slightly lower than that of the algorithm of the present invention. The matching and Lagrangian combination algorithm uses the GS algorithm in the matching of satellites and devices and the matching of devices and sub-channels. In particular, in the matching of devices and sub-channels, a minimum matching number limit is adopted for each device. Although the GS algorithm ensures the overall stability, under this condition of adopting the minimum matching number limit, directly executing the GS algorithm will cause the preferences of some devices to not be fully satisfied. However, the two-stage matching adopted by the present invention gives priority to one-to-one matching, and the first stage can provide the best satisfaction for devices and sub-channels. At the same time, in the matching of satellites and devices, the algorithm of the present invention uses a genetic algorithm to replace the GS algorithm, which can more effectively improve the matching quality. Although the GS algorithm is very stable, the result is not necessarily the global optimal. Using a genetic algorithm instead, adjusting the algorithm design and parameter setting, a better match than the GS algorithm can be found. This is why the algorithm of the present invention is more efficient than the matching and Lagrangian combination algorithm.

[0124] The channel random and average power algorithms performed the worst, with a slow increase in the total system rate and the lowest stable value. This is because they failed to fully adapt to the dynamic changes of the wireless channel and lacked sensitivity to the real-time channel status and adaptive adjustment capabilities, resulting in the inability to effectively manage interference and optimize resource allocation in a changing communication environment, thus affecting the overall communication performance and energy efficiency.

[0125] Figure 8What is shown is the relationship between the number of grid devices served (the number of devices actually communicating) and the number of iterations. It can be seen that the algorithm of the present invention and the matching and power allocation algorithm meet the minimum rate requirements of all devices, ensure the communication of all grid communication nodes, and can improve the stability, reliability and equipment utilization of the entire communication system. However, not all devices can participate in the communication work in the channel power joint allocation algorithm. This is because the channel and power joint allocation algorithm will give priority to allocating resources to devices with good channel conditions, but it cannot guarantee that all devices can be allocated to sub-channels. Therefore, the algorithm of the present invention has higher stability, reliability and equipment utilization for the entire grid communication than the channel and power joint allocation algorithm.

[0126] Fig. 9 What is shown is the relationship between the number of nodes that obtain the channel and the number of matches. It can be observed from the chart that as the number of matches increases, the number of devices that obtain the channel for all algorithms increases. For the traditional one-to-many matching, the algorithm of the present invention has better effect and can allocate sub-channels to all devices with a smaller number of matches. At the same time, the traditional one-to-many matching cannot guarantee that all devices can obtain channels. Therefore, the algorithm of the present invention has higher equipment utilization and better performance than the traditional one-to-many matching algorithm. For the matching and Lagrangian algorithms, mainly in the sub-channel and power grid equipment association strategy, the algorithm of the present invention will quickly allocate sub-channels to all devices in the first stage, and then allocate the remaining sub-channels in the second stage. A higher number of devices that obtain channels can be obtained with a smaller number of matches. Therefore, the algorithm proposed in the present invention can increase the number of communication devices that obtain channels with a smaller number of matches, and its performance is better than these two algorithms.

[0127] Fig.10 It is the relationship between the total rate of the algorithm system of the present invention and the number of power grid devices. It can be seen that as the number of power grid devices increases, the total rate shows a downward trend. Because the present invention considers the uplink data transmission of an OFDMA satellite-to-ground network, as the number of devices increases, in order to meet the minimum rate requirements of more and more device nodes, more sub-channels will be allocated to inefficient devices, resulting in a decrease in the total rate. This is also a common challenge in large-scale power grid equipment management and optimization, that is, the challenge of reduced performance when processing more devices.

[0128] Fig.11 The relationship between the total system rate and the service area radius is shown. It can be clearly seen that as the service area increases, the total system efficiency continues to decline. This is because as the distance increases, the corresponding free space loss will also increase, and the channel conditions will deteriorate, resulting in a decrease in the total system rate.

[0129] The method according to the present invention described above can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the large bandwidth power grid service resource optimization configuration method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the processing shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the processing shown herein.

[0130] 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 method for optimizing the configuration of large-bandwidth power grid service resources based on low-orbit satellites, characterized in that: The following steps are involved: Select multiple low-orbit satellites as data transmission relays to ensure coverage of the target power grid area; Establish a two-way communication link between low-orbit satellites and ground power grid communication nodes to support real-time data transmission; Using OFDMA technology, available bandwidth is dynamically allocated according to the traffic demand of each grid communication node to ensure efficient spectrum utilization; Bandwidth is allocated according to node priority, and on this basis, a mathematical model is established to maximize the total transmission rate of power grid communication equipment; the model is used to optimize bandwidth allocation and dynamically adjust the bandwidth of each power grid communication node to meet the traffic needs of each node; Design satellite-to-ground network architecture, using a multi-layer network topology; refer to the results of mathematical models to optimize resource allocation for each satellite to achieve minimum latency and maximum throughput; Monitor the real-time status of power grid communication equipment nodes, including bandwidth utilization, latency, and packet loss rate; dynamically adjust bandwidth allocation strategies through real-time monitoring and mathematical model analysis to ensure that the service quality of each node is not lower than the set standard; Evaluate network performance indicators including latency, packet loss rate, bandwidth utilization and throughput through data analysis algorithms; Utilize the parameters and optimization results provided by the mathematical model to conduct more in-depth data analysis and performance optimization, achieve continuous optimization, and generate hourly performance reports.

2. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: The ground power grid communication nodes include communication nodes for fixed real-time monitoring, mobile power grid patrol robots, drone monitoring communication nodes, and power transmission and distribution system communication nodes responsible for statistics on power grid status.

3. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: The total bandwidth is set to W, which is divided into o orthogonal sub-channels, which are shared by the low-orbit satellites in the group.

4. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: The maximum data transmission rate is calculated by Shannon's formula, and the data transmission rate R is defined m,n,o To optimize the transmission effect.

5. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: The mathematical model formula is as follows: Among them, f(V,p) is the objective function, which means the function that needs to be maximized under the given variable set V and parameter p; M represents the set of power grid communication devices, and m represents the ground power grid communication node; N represents the set of grid nodes, n represents satellite nodes; O represents a collection of different data streams or communication paths, and o represents a subchannel; R m,n,o represents the transmission rate or resource utilization rate that can be achieved between the ground power grid communication node m and the satellite node n through the subchannel o; ∑ m∈m ∑ n∈N ∑ o∈O R m,n,o : Indicates the sum of the transmission rates of all possible ground power grid communication nodes, satellite nodes, and sub-channels to calculate the total transmission rate of the entire network.

6. The method for optimizing and configuring large bandwidth power grid service resources according to claim 5, characterized in that: The mathematical model includes the following constraints: Each satellite’s subchannel can be assigned to only one grid communication node; The transmission power of grid communication nodes is limited by the maximum power; Grid communication nodes must meet a minimum communication rate threshold.

7. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: After determining the communication links and network architecture, a genetic algorithm is used to solve the association problem between multiple satellites and power grid communication equipment and determine the association of satellite equipment in each time slot.

8. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: After monitoring the real-time status of the grid communication device nodes, the fitness function is calculated, and a preference weight list of each satellite and grid communication node is generated based on the transmission rate and channel gain.

9. The method for optimizing and configuring large bandwidth power grid service resources according to claim 1, characterized in that: After the device association is determined, a two-stage Gale-Shapley matching algorithm is used to associate sub-channels with power grid devices to ensure normal communication of all devices, while optimizing the matching of remaining sub-channels with power grid devices, and finally performing joint power allocation.

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