Method, system and medium for power equipment association and resource optimization in integrated network

Abstract

A method, system and medium for power equipment association and resource optimization in a fusion network, the method comprising: constructing a system model in a power grid scenario composed of satellites, ground base stations and power equipment; concretizing the system model to obtain a device association model, a data transmission model and an energy efficiency model respectively; combining the device association model, the data transmission model and the energy efficiency model, maximizing the long-term energy efficiency of the power equipment under the condition of ensuring the minimum transmission rate requirement of the equipment, and determining the corresponding optimization problem; decomposing the optimization problem into two sub-problems of solving the device association strategy and the power control scheme in each time slot in turn, and iterating all time slots to find the optimal power control scheme for all power equipment within the total operation time of the network. The present invention can achieve the best association of equipment and complete the power optimization of equipment, maximize the long-term energy efficiency of power equipment under the condition of ensuring the minimum transmission rate requirement of power equipment, and improve network performance.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment network optimization, and in particular relates to a method, system and medium for power equipment association and resource optimization in a fusion network. Background Art

[0002] With the widespread layout of "new energy and new business", the production and operation links covered by smart grids continue to increase. In smart grid application scenarios such as drone transmission line inspection, robot power facility inspection, and emergency communication, the data information of power equipment is showing an explosive growth trend. The traditional single ground network data collection method will not be able to carry the massive data transmission. Therefore, there is an urgent need for effective solutions to solve the problem of difficulty in collecting power equipment data.

[0003] The integration of the fifth-generation mobile communication technology (5th-Generation, 5G) and satellite networking provides an effective solution. The fifth-generation mobile communication technology (5th-Generation, 5G) has the advantages of high speed and low power consumption. It can adapt to the needs of flexible carrying of multiple scenarios and differentiated services in the future power system, and provide flexible and reliable communication means for the power system. However, its deployment requires a reasonable network architecture and topology structure to reduce co-frequency interference, improve user experience rate, and reduce energy consumption. Satellite communication has a large coverage area, a wide visible area, a long communication distance, and strong reliability, but it has problems such as long communication delay, slow feedback, and the communication link is easily interfered by external factors. Therefore, integrating 5G technology with satellite communication technology to establish a three-dimensional network of 5G and satellite integrated communication for smart grid scenarios can give full play to their respective advantages and make up for their shortcomings, thereby further improving the data transmission capacity of smart grid scenarios and improving the transmission energy efficiency of power equipment.

[0004] However, data collection in 5G and satellite fusion networks for smart grid scenarios still faces some key issues. First, satellites are far away from ground-based power equipment, and uploading data directly from the ground to satellites may result in large transmission losses. Secondly, device association is an issue that must be considered in 5G and satellite fusion heterogeneous networks. Device association determines how devices should select ground base stations or satellites for data forwarding, so that the system load is balanced, thereby improving system energy efficiency. Finally, in heterogeneous networks, power control technology also plays an important role in reducing device energy consumption and has a significant impact on system performance. Summary of the invention

[0005] The purpose of the present invention is to address the problems in the above-mentioned prior art and provide a method, system and medium for the association and resource optimization of power equipment in a fusion network, so as to achieve the best association of equipment and complete the power optimization of equipment, maximize the long-term energy efficiency of power equipment and improve network performance while ensuring the minimum transmission rate requirement of power equipment.

[0006] In order to achieve the above object, the present invention has the following technical solutions:

[0007] In a first aspect, a method for associating power equipment and optimizing resources in a fusion network is provided, comprising:

[0008] Construct a system model for a power grid scenario consisting of satellites, ground base stations, and power equipment;

[0009] The system model is concretized to obtain the device association model, data transmission model and energy efficiency model respectively;

[0010] By combining the equipment association model, data transmission model and energy efficiency model, the long-term energy efficiency of power equipment is maximized while ensuring the minimum transmission rate requirement of the equipment, and the corresponding optimization problem is determined;

[0011] The optimization problem is decomposed into two sub-problems: solving the device association strategy and the power control scheme in each time slot, and iterating over all time slots to find the optimal power control scheme for all power devices within the total network operation time.

[0012] As a preferred solution, the power grid scenario composed of satellites, ground base stations and power equipment includes at least one low-orbit satellite, U drones, B ground 5G base stations and I ground power equipment. The ground 5G base stations and low-orbit satellites are equipped with cloud server data centers. The power equipment forwards the collected data directly to the ground 5G base station or uploads the data to the low-orbit satellite through a drone flying at a preset trajectory, and optimizes its own transmission power at the same time; the total optimization time is divided into T time slots, each time slot length is τ, and the time slot model is In each time slot, each power device independently determines its own device association strategy and power control scheme; the power equipment, ground 5G base stations, drones and low-orbit satellites operate in different frequency bands.

[0013] As a preferred solution, the steps of concretizing the system model and obtaining the device association model specifically include:

[0014] Using binary variable a i,u (t) and a i,b (t) represents the connection status between power equipment i and the UAV and the ground base station respectively; when a i,uWhen (t) = 1, it means that in the current time slot, power device i chooses to upload data to the drone, and then forward it to the low-orbit satellite cloud server data center; when a i,u When (t) = 0, it means that in the current time slot, power device i does not choose to upload data to the drone; when a i,b When (t) = 1, it means that in the current time slot, power device i chooses to forward data directly to the ground 5G base station; when a i,b When (t) = 0, it means that in the current time slot, the power device i does not choose to forward the data to the ground 5G base station.

[0015] As a preferred solution, the system model is concretized, and the steps of obtaining the data transmission model specifically include:

[0016] When power device i chooses to transmit data via a drone, the data rate of power device i uploading to the low-orbit satellite in the current time slot is obtained according to the Shannon formula and the amplify-and-forward protocol:

[0017]

[0018] Where D is the channel bandwidth pre-allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the UAV, and the calculation expression is α i,u (t) = P i (t)·w i,u (t) / σ 2 , where P i (t) is the transmission power of power equipment i, w i,u (t) is the channel gain from power device i to UAV, σ 2 is the variance of Gaussian white noise;

[0019] The signal-to-noise ratio of data from the drone to the low-orbit satellite is expressed by α u Indicates that the calculation expression is α u =P u ·w u / σ 2 , where w u represents the channel gain between the UAV and the low-orbit satellite, P u Indicates the transmitting power of the drone;

[0020] When power device i chooses to forward data directly to the ground 5G base station, the data transmission rate is obtained according to the Shannon formula:

[0021]

[0022] In the formula, w i,b Represents the channel gain from power equipment i to the ground 5G base station.

[0023] As a preferred solution, in the step of concretizing the system model and obtaining the energy efficiency model, the calculation expression of the long-term total energy efficiency of the power equipment is as follows:

[0024]

[0025] As a preferred solution, the device association model, data transmission model and energy efficiency model are combined to maximize the long-term energy efficiency of the power equipment under the condition of ensuring the minimum transmission rate requirement of the equipment, and in the step of determining the corresponding optimization problem, the expression of the optimization problem is as follows:

[0026]

[0027] stC1:a i,u (t)∈{0,1}

[0028] C2:a i,b (t)∈{0,1}

[0029] C3:a i,u (t)+a i,b (t)≤1

[0030]

[0031]

[0032] C6:0≤P i (t)≤P max

[0033] C7:R i (t)≥R 0

[0034] In the formula, the optimized variable is the device association strategy a u and a b , and the power P of the electrical equipment; and in, and They represent the collection of drones, ground 5G base stations, power equipment, and network operating time respectively;

[0035] C1 and C2 indicate that the power equipment association strategy is a binary strategy; C3 indicates that each power equipment selects at most one data transmission mode in each time slot; C4 and C5 indicate that there is an upper limit on the number of power equipment connected to the drone or ground 5G base station in each time slot, which are A U and A B ; C6 indicates that the transmission power of each power device is subject to the maximum power P max Restriction; C7 indicates that the transmission rate of the power equipment must meet the minimum transmission rate requirement.

[0036] As a preferred solution, in the step of decomposing the optimization problem into two sub-problems of solving the device association strategy and the power control scheme in each time slot in turn, and iterating all time slots, the device association strategy first performs equal power distribution on the power equipment, and then performs device association based on a genetic algorithm, and the genetic algorithm specifically includes the following steps:

[0037] Population initialization: randomly generate a set number of feasible solutions to the device association strategy problem as the initial population. The genes of the chromosomes of each individual in the population are binary coded to ensure that each feasible solution satisfies the constraints of C1-C5.

[0038] For each individual in the population, the fitness degree is calculated using a fitness function, where the fitness function is set to be the total energy efficiency of the power equipment in each time slot;

[0039] Perform population individual selection, select the individuals with the largest fitness function value to retain, and then use the roulette strategy to determine the selection probability according to the fitness function value, and select a set number of individuals to retain;

[0040] Reproduction is carried out, and the retained individuals are copied to the next generation of the population;

[0041] Perform the crossover process, set the crossover probability, and use two parent chromosomes to perform a crossover operation. Perform a single-point crossover operation on each group. For the newly generated offspring individuals, determine whether they meet the constraints of C1-C5. If they do, they will be retained; otherwise, the newly generated offspring individuals will be discarded.

[0042] Carry out the mutation process, set the mutation probability, and realize the mutation of the gene position by inverting the variable from 0 to 1; each power device is associated with at most one position in the same time slot, and the gene position that is already '1' before the inversion is '0', and then carry out the mutation process again, and judge whether the new individual after the mutation meets the constraints of C1-C5; if it meets the constraints, it will be retained; otherwise, the new individual after the mutation will be discarded;

[0043] The next generation of population is obtained, and then the fitness function is recalculated, and individual selection, replication, crossover and mutation of the population are performed until convergence or the maximum number of iterations is reached.

[0044] As a preferred solution, the optimization problem is decomposed into two sub-problems of solving the device association strategy and the power control scheme in each time slot in turn, and in the step of iterating all time slots, based on the optimal device association strategy, an improved simulated annealing algorithm is applied to solve the power control scheme. The step of solving the power control scheme by the improved simulated annealing algorithm includes:

[0045] Initialize the system model and randomly generate an initial solution, calculate the objective function value corresponding to the initial solution, and enter the outer loop annealing process; at the current temperature, enter the inner loop, generate a new feasible solution, and use the Metropolis criterion to determine whether to accept the new feasible solution until the number of iterations is reached and the inner loop ends; then update the temperature as follows: during the iterative optimization of the current temperature, if a better feasible solution is found, give the system model an additional cooling rate to accelerate annealing; otherwise, the system model continues to maintain the original cooling rate for the annealing process and re-enters the inner loop until the current temperature is lower than the termination temperature, then end the outer loop and obtain the optimal power control solution for all power equipment in the current time slot.

[0046] In a second aspect, a network-integrated power equipment association and resource optimization system is provided, comprising:

[0047] System model building module, used to build a system model in a power grid scenario consisting of satellites, ground base stations and power equipment;

[0048] The model concretization module is used to concretize the system model to obtain the device association model, data transmission model and energy efficiency model respectively;

[0049] The optimization problem establishment module is used to combine the equipment association model, data transmission model and energy efficiency model to maximize the long-term energy efficiency of power equipment while ensuring the minimum transmission rate requirement of the equipment, and determine the corresponding optimization problem;

[0050] The iterative solution module is used to decompose the optimization problem into two sub-problems: solving the device association strategy and the power control scheme in each time slot, and iterate all time slots to find the optimal power control scheme for all power devices within the total network operation time.

[0051] As a preferred solution, when the system model construction module constructs a system model in a power grid scenario composed of satellites, ground base stations and power equipment, the power grid scenario composed of satellites, ground base stations and power equipment includes at least one low-orbit satellite, U drones, B ground 5G base stations and I ground power equipment. The ground 5G base stations and low-orbit satellites are equipped with cloud server data centers. The power equipment forwards the collected data directly to the ground 5G base station or uploads the data to the low-orbit satellite through a drone flying at a preset trajectory, and optimizes its own transmission power at the same time; the total optimization time is divided into T time slots, each time slot length is τ, and the time slot model is In each time slot, each power device independently determines its own device association strategy and power control scheme; the power equipment, ground 5G base stations, drones and low-orbit satellites operate in different frequency bands.

[0052] As a preferred solution, the model concretization module concretizes the system model, and the step of obtaining the device association model specifically includes:

[0053] Using binary variable a i,u (t) and a i,b (t) represents the connection status between power equipment i and the UAV and the ground base station respectively; when a i,u When (t) = 1, it means that in the current time slot, power device i chooses to upload data to the drone, and then forward it to the low-orbit satellite cloud server data center; when a i,u When (t) = 0, it means that in the current time slot, power device i does not choose to upload data to the drone; when a i,b When (t) = 1, it means that in the current time slot, power device i chooses to forward data directly to the ground 5G base station; when a i,b When (t) = 0, it means that in the current time slot, the power equipment i There is no option to forward the data to ground-based 5G base stations.

[0054] As a preferred solution, the model concretization module concretizes the system model, and the step of obtaining the data transmission model specifically includes:

[0055] When power device i chooses to transmit data via a drone, the data rate of power device i uploading to the low-orbit satellite in the current time slot is obtained according to the Shannon formula and the amplify-and-forward protocol:

[0056]

[0057] Where D is the channel bandwidth pre-allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the UAV, and the calculation expression is α i,u (t) = P i (t)·w i,u (t) / σ 2 , where P i (t) is the transmission power of power equipment i, w i,u (t) is the channel gain from power device i to UAV, σ 2 is the variance of Gaussian white noise;

[0058] The signal-to-noise ratio of data from the drone to the low-orbit satellite is expressed by α u Indicates that the calculation expression is α u =P u ·w u / σ 2 , where w u represents the channel gain between the UAV and the low-orbit satellite, P u Indicates the transmitting power of the drone;

[0059] When power device i chooses to forward data directly to the ground 5G base station, the data transmission rate is obtained according to the Shannon formula:

[0060]

[0061] In the formula, w i,b Represents the channel gain from power equipment i to the ground 5G base station.

[0062] As a preferred solution, in the step of obtaining the energy efficiency model by concretizing the system model by the model concretization module, the calculation expression of the long-term total energy efficiency of the power equipment is as follows:

[0063]

[0064] As a preferred solution, the optimization problem constructed by the optimization problem establishment module is expressed as:

[0065]

[0066] stC1:a i,u (t)∈{0,1}

[0067] C2:a i,b (t)∈{0,1}

[0068] C3:a i,u (t)+a i,b (t)≤1

[0069]

[0070]

[0071] C6:0≤P i (t)≤P max

[0072] C7:R i (t)≥R 0

[0073] In the formula, the optimized variable is the device association strategy a u and a b , and the power P of the electrical equipment; and in, and They represent the collection of drones, ground 5G base stations, power equipment, and network operating time respectively;

[0074] C1 and C2 indicate that the power equipment association strategy is a binary strategy; C3 indicates that each power equipment selects at most one data transmission mode in each time slot; C4 and C5 indicate that there is an upper limit on the number of power equipment connected to the drone or ground 5G base station in each time slot, which are A U and A B ; C6 indicates that the transmission power of each power device is subject to the maximum power P max Restriction; C7 indicates that the transmission rate of the power equipment must meet the minimum transmission rate requirement.

[0075] According to a third aspect, an electronic device is provided, including:

[0076] a memory storing at least one instruction; and

[0077] The processor executes the instructions stored in the memory to implement the power equipment association and resource optimization method of the converged network.

[0078] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for associating power equipment and optimizing resources in the converged network is implemented.

[0079] Compared with the prior art, the first aspect of the present invention has at least the following beneficial effects:

[0080] A system model is constructed in a power grid scenario consisting of satellites, ground base stations and power equipment. Different applications running on the ground power equipment generate various power data. By concretizing the system model, the device association model, data transmission model and energy efficiency model are obtained respectively, and then the optimization problem can be determined to maximize the long-term energy efficiency of the power equipment under the condition of ensuring the minimum transmission rate requirement of the equipment. In the process of solving the optimization problem, the optimization problem is decomposed into two sub-problems of device association strategy and power control scheme in each time slot. Solving the device association strategy can achieve the best association of the power equipment, and then based on the obtained optimal device association strategy, the optimal power control scheme is solved. Iteration of all time slots can obtain the optimal power control scheme for all power equipment within the total operation time of the network. The present invention can maximize the long-term energy efficiency of power equipment and improve network performance under the condition of ensuring the minimum transmission rate requirement of power equipment. The method of the present invention has very important scientific research significance and value for the data transmission of power equipment integrated with 5G and satellite networks.

[0081] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0083] Figure 1 Flow chart of the method for power equipment association and resource optimization in a fusion network according to an embodiment of the present invention;

[0084] Figure 2 A schematic diagram of a system model in a power grid scenario according to an embodiment of the present invention;

[0085] Figure 3 A structural block diagram of a power equipment association and resource optimization system in a fusion network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0086] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0087] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0088] Example 1

[0089] See also Figure 1 The method for power equipment association and resource optimization of a fusion network according to an embodiment of the present invention comprises the following steps:

[0090] S1. Construct a system model for a power grid scenario consisting of satellites, ground base stations and power equipment;

[0091] S2, concretize the system model to obtain the device association model, data transmission model and energy efficiency model respectively;

[0092] S3. Combine the equipment association model, data transmission model and energy efficiency model to maximize the long-term energy efficiency of power equipment while ensuring the minimum transmission rate requirement of the equipment, and determine the corresponding optimization problem;

[0093] S4. Decompose the optimization problem into two sub-problems: solving the device association strategy and the power control scheme in each time slot, and iterate all time slots to find the optimal power control scheme for all power devices within the total network operation time.

[0094] After obtaining the power equipment association strategy and power control solution in step S4, data is collected on the 5G and satellite fusion network of the power scenario through the results, which can ultimately maximize the long-term energy efficiency of the power equipment.

[0095] In a possible implementation, Figure 2 As shown, the power grid scenario composed of satellites, ground base stations and power equipment described in step S1 includes at least one low-orbit satellite, U drones, B ground 5G base stations and I ground power equipment. Different applications running on the ground power equipment generate various power data. These data need to be uploaded to the cloud server data center in a timely manner for use by the power grid control center. In order to reduce the upload link load of the ground 5G base station and improve the data transmission energy efficiency, the system model is equipped with a cloud server data center in both the ground 5G base station and the low-orbit satellite. The power equipment can forward the collected data directly to the ground 5G base station or upload the data to the low-orbit satellite through a drone flying at a preset trajectory, and optimize its own transmission power at the same time, thereby ensuring efficient data collection and reducing data transmission energy efficiency. The total optimization time is divided into T time slots, each time slot length is τ, and the time slot model is In each time slot, each power device independently determines its own device association strategy and power control scheme. In this system model, power devices, ground 5G base stations, drones and low-orbit satellites operate in different frequency bands and do not interfere with each other.

[0096] In a possible implementation, step S2 concretizes the system model to obtain a device association model including:

[0097] Using binary variable a i,u (t) and a i,b (t) represents the connection status between power equipment i and the UAV and the ground base station respectively; when a i,u When (t) = 1, it means that in the current time slot, power device i chooses to upload data to the drone, and then forward it to the low-orbit satellite cloud server data center; when a i,u When (t) = 0, it means that in the current time slot, power device i does not choose to upload data to the drone; when a i,b When (t) = 1, it means that in the current time slot, power device i chooses to forward data directly to the ground 5G base station; when a i,bWhen (t) = 0, it means that in the current time slot, the power device i does not choose to forward data to the ground 5G base station. In order to avoid wasting too many resources by repeated data forwarding, each power device can only choose one data transmission mode in each time slot.

[0098] In a possible implementation, step S2 concretizes the system model to obtain a data transmission model including:

[0099] When power device i chooses to transmit data via a drone, the data rate of power device i uploading to the low-orbit satellite in the current time slot is obtained according to the Shannon formula and the Amplify-and-Forward (AF) protocol:

[0100]

[0101] Where D is the channel bandwidth pre-allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the UAV, and the calculation expression is α i,u (t) = P i (t)·w i,u (t) / σ 2 , where P i (t) is the transmission power of power equipment i, w i,u (t) is the channel gain from power device i to UAV, σ 2 is the variance of Gaussian white noise;

[0102] Similarly, the signal-to-noise ratio of data from the drone to the low-orbit satellite is expressed as α u Indicates that the calculation expression is α u =P u ·w u / σ 2 , where w u represents the channel gain between the UAV and the low-orbit satellite, P u Indicates the transmitting power of the drone;

[0103] When power device i chooses to forward data directly to the ground 5G base station, the data transmission rate is obtained according to the Shannon formula:

[0104]

[0105] In the formula, w i,b Represents the channel gain from power equipment i to the ground 5G base station.

[0106] In a possible implementation, in step S2, the system model is concretized to obtain the energy efficiency model:

[0107] The calculation expression for defining the long-term total energy efficiency of power equipment is as follows:

[0108]

[0109] In a possible implementation, the optimization problem corresponding to step S3 is expressed as follows:

[0110]

[0111] stC1:a i,u (t)∈{0,1}

[0112] C2:a i,b (t)∈{0,1}

[0113] C3:a i,u (t)+a i,b (t)≤1

[0114]

[0115]

[0116] C6:0≤P i (t)≤P max

[0117] C7:R i (t)≥R 0

[0118] In the formula, the optimized variable is the device association strategy a u and a b , and the power P of the electrical equipment; and in, and They represent the collection of drones, ground 5G base stations, power equipment, and network operating time respectively;

[0119] C1 and C2 indicate that the power equipment association strategy is a binary strategy; C3 indicates that each power equipment selects at most one data transmission mode in each time slot; C4 and C5 indicate that there is an upper limit on the number of power equipment connected to the drone or ground 5G base station in each time slot, which are A U and A B ; C6 indicates that the transmission power of each power device is subject to the maximum power P max Restriction; C7 indicates that the transmission rate of the power equipment must meet the minimum transmission rate requirement.

[0120] In a possible implementation, step S4 decomposes the optimization problem constructed in step S3 into two sub-problems: solving the device association strategy SP1 and the power control scheme SP2 in each time slot in turn, and finally iterating all time slots to find the optimal solution within the total network operation time.

[0121] The device association strategy SP1 first allocates equal power to the devices. Secondly, in order to reduce the complexity of the solution, a device association strategy based on genetic algorithm is proposed, which mainly includes the following steps:

[0122] Step 1: Population initialization. Randomly generate a certain number of feasible solutions to problem SP1 as the initial population. The genes of each individual chromosome in the population are encoded in binary. At the same time, each feasible solution must ensure that it meets the constraints of C1-C5.

[0123] Step 2: For each individual in the population, use the fitness function to calculate its fitness level. Here, the fitness function is set to the total energy efficiency of the power equipment in each time slot.

[0124] The third step: perform population individual selection operations, adopt an elitist retention strategy, select the individuals with the largest fitness function value to retain, called elite individuals, and then use the roulette strategy to determine the selection probability according to the fitness function value. Individuals with high fitness have a high chance of being selected, while individuals with low fitness are more likely to be eliminated in the offspring population. Therefore, a certain number of individuals can be selected to retain and the remaining individuals with low fitness can be eliminated.

[0125] Step 4: Perform a replication operation to copy elite individuals to the next generation population.

[0126] Step 5: Perform the crossover process and set a certain crossover probability. For the parent chromosomes to be crossovered, use two groups of two, and perform a single-point crossover operation on each group. For the newly generated offspring individuals, it is necessary to determine whether they meet the constraints of C1-C5. If they do, they will be retained; otherwise, the new individual will be discarded.

[0127] Step 6: Perform the mutation process, set a certain mutation probability, and achieve the mutation of the gene position by reversing the variable from 0 to 1. Among them, each device is associated with at most one position in the same time slot, that is, the drone or the ground 5G base station, so the gene position that is already '1' needs to be '0' before flipping and then undergoing the mutation process. Similarly, the new individual after mutation also needs to be judged whether it meets the constraints of C1-C5. If it does, it will be retained; otherwise, the new individual needs to be discarded.

[0128] Step 7: Get the next generation population, then recalculate the fitness function, perform selection, replication, crossover, mutation and other operations until convergence or the maximum number of iterations is reached.

[0129] The power control scheme SP2 of the electric power equipment is non-convex. When solving it, based on the optimal equipment association strategy obtained, the idea of ​​applying the simulated annealing algorithm is considered to solve the power control scheme. However, when the network scale, the number of iterations, and the difference between the initial temperature and the termination temperature are large, the simulated annealing algorithm will cool down very slowly, and it is likely to waste too much time and it is difficult to find a better solution. Therefore, when solving the sub-problem SP2, the present invention improves the simulated annealing algorithm and proposes an improved power control scheme based on the simulated annealing algorithm, which mainly includes the following steps: first, initialize the system and randomly generate an initial solution, calculate the objective function value corresponding to the initial solution, and start the outer loop annealing process. Next, at the current temperature, enter the inner loop, generate a new feasible solution, and use the Metropolis criterion to determine whether to accept the new feasible solution until the number of iterations is reached and the inner loop ends. The temperature is then updated using an improved strategy. Specifically, during the iterative optimization of the current temperature, if the system model finds a better feasible solution, an additional cooling rate is given to the system model to accelerate system annealing; otherwise, the system model continues to anneal the system at the original cooling rate and re-enters the inner loop until the current temperature is lower than the termination temperature. The outer loop ends, the algorithm ends, and the optimal power control solution for all devices in the current time slot is obtained.

[0130] The present invention decomposes the joint optimization problem into two sub-problems: device association strategy and power control scheme in each time slot. First, a device association strategy based on genetic algorithm is proposed to achieve the optimal association of devices; secondly, on this basis, the simulated annealing algorithm is improved to complete the power optimization of the device. The device association and resource optimization method of the 5G and satellite fusion network of the present invention can maximize the long-term energy efficiency of power equipment under the condition of ensuring the minimum transmission rate requirement of power equipment.

[0131] Example 2

[0132] See also Figure 3 , the embodiment of the present invention integrates the network power equipment association and resource optimization system, including:

[0133] System model building module 1, used to build a system model in a power grid scenario consisting of satellites, ground base stations and power equipment;

[0134] The model concretization module 2 is used to concretize the system model to obtain a device association model, a data transmission model and an energy efficiency model;

[0135] Optimization problem establishment module 3 is used to combine the equipment association model, data transmission model and energy efficiency model to maximize the long-term energy efficiency of power equipment under the condition of ensuring the minimum transmission rate requirement of the equipment, and determine the corresponding optimization problem;

[0136] The iterative solution module 4 is used to decompose the optimization problem into two sub-problems of solving the device association strategy and the power control scheme in each time slot, and iterate all time slots to find the optimal power control scheme for all power devices within the total network operation time.

[0137] In a possible implementation, when the system model construction module 1 constructs a system model in a power grid scenario composed of satellites, ground base stations and power equipment, the power grid scenario composed of satellites, ground base stations and power equipment includes at least one low-orbit satellite, U drones, B ground 5G base stations and I ground power equipment. The ground 5G base stations and low-orbit satellites are equipped with cloud server data centers. The power equipment forwards the collected data directly to the ground 5G base station or uploads the data to the low-orbit satellite through a drone flying at a preset trajectory, and optimizes its own transmission power at the same time; the total optimization time is divided into T time slots, each time slot length is τ, and the time slot model is In each time slot, each power device independently determines its own device association strategy and power control scheme; the power equipment, ground 5G base stations, drones and low-orbit satellites operate in different frequency bands.

[0138] In a possible implementation manner, the model concretization module 2 concretizes the system model, and the step of obtaining the device association model specifically includes:

[0139] Using binary variable a i,u (t) and a i,b (t) represents the connection status between power equipment i and the UAV and the ground base station respectively; when a i,u When (t) = 1, it means that in the current time slot, power device i chooses to upload data to the drone, and then forward it to the low-orbit satellite cloud server data center; when a i,u When (t) = 0, it means that in the current time slot, power device i does not choose to upload data to the drone; when a i,b When (t) = 1, it means that in the current time slot, power device i chooses to forward data directly to the ground 5G base station; when a i,b When (t) = 0, it means that in the current time slot, the power device i does not choose to forward the data to the ground 5G base station.

[0140] In a possible implementation, the model concretization module 2 concretizes the system model, and the step of obtaining the data transmission model specifically includes:

[0141] When power device i chooses to transmit data via a drone, the data rate of power device i uploading to the low-orbit satellite in the current time slot is obtained according to the Shannon formula and the amplify-and-forward protocol:

[0142]

[0143] In the formula, D is the channel bandwidth pre-allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the UAV, and the calculation expression is α i,u (t) = P i (t)·w i,u (t) / σ 2 , where P i (t) is the transmission power of power equipment i, w i,u (t) is the channel gain from power device i to UAV, σ 2 is the variance of Gaussian white noise;

[0144] Similarly, the signal-to-noise ratio of data from the drone to the low-orbit satellite is expressed as α u Indicates that the calculation expression is α u =P u ·w u / σ 2 , where w u represents the channel gain between the UAV and the low-orbit satellite, P u Indicates the transmitting power of the drone;

[0145] When power device i chooses to forward data directly to the ground 5G base station, the data transmission rate is obtained according to the Shannon formula:

[0146]

[0147] In the formula, w i,b Represents the channel gain from power equipment i to the ground 5G base station.

[0148] In a possible implementation manner, in the step of obtaining the energy efficiency model by concretizing the system model by the model concretization module 2, the calculation expression of the long-term total energy efficiency of the power equipment is as follows:

[0149]

[0150] In a possible implementation, the optimization problem constructed by the optimization problem establishment module 3 is expressed as:

[0151]

[0152] stC1:a i,u (t)∈{0,1}

[0153] C2:a i,b (t)∈{0,1}

[0154] C3:a i,u (t)+a i,b(t)≤1

[0155]

[0156]

[0157] C6:0≤P i (t)≤P max

[0158] C7:R i (t)≥R 0

[0159] In the formula, the optimized variable is the device association strategy a u and a b , and the power P of the electrical equipment; and in, and They represent the collection of drones, ground 5G base stations, power equipment, and network operating time respectively;

[0160] C1 and C2 indicate that the power equipment association strategy is a binary strategy; C3 indicates that each power equipment selects at most one data transmission mode in each time slot; C4 and C5 indicate that there is an upper limit on the number of power equipment connected to the drone or ground 5G base station in each time slot, which are A U and A B ; C6 indicates that the transmission power of each power device is subject to the maximum power P max Restriction; C7 indicates that the transmission rate of the power equipment must meet the minimum transmission rate requirement.

[0161] Example 3

[0162] Another embodiment of the present invention further provides an electronic device, including:

[0163] a memory storing at least one instruction; and

[0164] The processor executes the instructions stored in the memory to implement the method for associating power equipment and optimizing resources in the fused network as described in Example 1.

[0165] Example 4

[0166] Another embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for associating power equipment and optimizing resources in a converged network as described in Example 1 is implemented.

[0167] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. For ease of explanation, the above content only shows the part related to the embodiment of the present invention. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium is non-temporary and can be stored in a storage device formed by various electronic devices, which can realize the execution process recorded in the method of the embodiment of the present invention.

[0168] 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.

[0169] 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 process in the flowchart. 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.

[0170] 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.

[0171] 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 A step that specifies a function in one or more boxes.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for power equipment association and resource optimization in a fusion network, characterized in that: include: Construct a system model for a power grid scenario consisting of satellites, ground base stations, and power equipment; The system model is concretized to obtain the device association model, data transmission model and energy efficiency model respectively; By combining the equipment association model, data transmission model and energy efficiency model, the long-term energy efficiency of power equipment is maximized while ensuring the minimum transmission rate requirement of the equipment, and the corresponding optimization problem is determined; The optimization problem is decomposed into two sub-problems: solving the device association strategy and power control scheme in each time slot, and iterating over all time slots to find the optimal power control scheme for all power devices within the total network operation time. The steps of concretizing the system model and obtaining the device association model specifically include: Using binary variable a i,u (t) and a i,b (t) represents the connection status between power equipment i and the UAV and the ground base station respectively; when a i,u When (t) = 1, it means that in the current time slot, power device i chooses to upload data to the drone, and then forward it to the low-orbit satellite cloud server data center; when a i,u When (t) = 0, it means that in the current time slot, power device i does not choose to upload data to the drone; when a i,b When (t) = 1, it means that in the current time slot, power device i chooses to forward data directly to the ground 5G base station; when a i,b When (t) = 0, it means that in the current time slot, the power device i does not choose to forward the data to the ground 5G base station; to avoid wasting too many resources by repeated data forwarding, each power device can only choose one data transmission mode in each time slot; The steps of concretizing the system model and obtaining the data transmission model specifically include: When power device i chooses to transmit data via a drone, the data rate of power device i uploading to the low-orbit satellite in the current time slot is obtained according to the Shannon formula and the amplify-and-forward protocol: Where D is the channel bandwidth pre-allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the UAV, and the calculation expression is α i,u (t) = P i (t)·w i,u (t) / σ 2 , where P i (t) is the transmission power of power equipment i, w i,u (t) is the channel gain from power device i to UAV, σ 2 is the variance of Gaussian white noise; The signal-to-noise ratio of data from the drone to the low-orbit satellite is expressed by α u Indicates that the calculation expression is α u =P u ·w u / σ 2 , where w u surface The channel gain between the UAV and the low-orbit satellite, P u Indicates the transmitting power of the drone; When power device i chooses to forward data directly to the ground 5G base station, the data transmission rate is obtained according to the Shannon formula: In the formula, w i,b represents the channel gain from the power equipment i to the ground 5G base station; In the step of concretizing the system model and obtaining the energy efficiency model, the calculation expression of the long-term total energy efficiency of the power equipment is as follows: In the step of combining the device association model, the data transmission model and the energy efficiency model, maximizing the long-term energy efficiency of the power equipment under the condition of ensuring the minimum transmission rate requirement of the equipment, and determining the corresponding optimization problem, the expression of the optimization problem is as follows: P1: s.t.C1:a i,u (t)∈{0,1} C2:a i,b (t)∈{0,1} C3:a i,u (t)+a i,b (t)≤1 C4: C5: C6:0≤P i (t)≤P max C7:R i (t)≥R0 In the formula, the optimized variable is the device association strategy a u and a b , and the power P of the electrical equipment; and in, and They represent the collection of drones, ground 5G base stations, power equipment, and network operating time respectively; C1 and C2 indicate that the power equipment association strategy is a binary strategy; C3 indicates that each power equipment selects at most one data transmission mode in each time slot; C4 and C5 indicate that there is an upper limit on the number of power equipment connected to the drone or ground 5G base station in each time slot, which are A U and A B ; C6 indicates that the transmission power of each power device is subject to the maximum power P max Restriction; C7 indicates that the transmission rate of the power equipment must meet the minimum transmission rate requirement.

2. The method for power equipment association and resource optimization of a fusion network according to claim 1, characterized in that: The power grid scenario composed of satellites, ground base stations and power equipment includes at least one low-orbit satellite, U drones, B ground 5G base stations and I ground power equipment. The ground 5G base stations and low-orbit satellites are equipped with cloud server data centers. The power equipment forwards the collected data directly to the ground 5G base station or uploads the data to the low-orbit satellite through a drone flying at a preset trajectory, and optimizes its own transmission power at the same time; the total optimization time is divided into T time slots, each time slot length is τ, and the time slot model is In each time slot, each power device independently determines its own device association strategy and power control scheme; the power equipment, ground 5G base stations, drones and low-orbit satellites operate in different frequency bands.

3. The method for power equipment association and resource optimization of a fusion network according to claim 1, characterized in that: In the step of decomposing the optimization problem into two sub-problems, namely, solving the device association strategy and the power control scheme in each time slot in turn, and iterating all time slots, the device association strategy first performs equal power distribution on the power equipment, and then performs device association based on a genetic algorithm, and the genetic algorithm specifically includes the following steps: Population initialization: randomly generate a set number of feasible solutions to the device association strategy problem as the initial population. The genes of the chromosomes of each individual in the population are binary coded to ensure that each feasible solution satisfies the constraints of C1-C5. For each individual in the population, the fitness degree is calculated using a fitness function, where the fitness function is set to be the total energy efficiency of the power equipment in each time slot; Perform population individual selection, select the individuals with the largest fitness function value to retain, and then use the roulette strategy to determine the selection probability according to the fitness function value, and select a set number of individuals to retain; Reproduction is carried out, and the retained individuals are copied to the next generation of the population; Perform the crossover process, set the crossover probability, and use two parent chromosomes to perform a crossover operation. Perform a single-point crossover operation on each group. For the newly generated offspring individuals, determine whether they meet the constraints of C1-C5. If they do, they will be retained; otherwise, the newly generated offspring individuals will be discarded. Carry out the mutation process, set the mutation probability, and realize the mutation of the gene position by inverting the variable from 0 to 1; each power device is associated with at most one position in the same time slot, and the gene position that is already '1' before the inversion is '0', and then carry out the mutation process again, and judge whether the new individual after the mutation meets the constraints of C1-C5; if it meets the constraints, it will be retained; otherwise, the new individual after the mutation will be discarded; The next generation of population is obtained, and then the fitness function is recalculated, and individual selection, replication, crossover and mutation of the population are performed until convergence or the maximum number of iterations is reached.

4. The method for power equipment association and resource optimization of a fusion network according to claim 3, characterized in that: In the step of decomposing the optimization problem into two sub-problems of solving the device association strategy and the power control scheme in each time slot in turn, and iterating all time slots, based on the optimal device association strategy, an improved simulated annealing algorithm is applied to solve the power control scheme. The step of solving the power control scheme by the improved simulated annealing algorithm includes: Initialize the system model and randomly generate an initial solution, calculate the objective function value corresponding to the initial solution, and enter the outer loop annealing process; at the current temperature, enter the inner loop, generate a new feasible solution, and use the Metropolis criterion to determine whether to accept the new feasible solution until the number of iterations is reached and the inner loop ends; then update the temperature as follows: during the iterative optimization of the current temperature, if a better feasible solution is found, give the system model an additional cooling rate to accelerate annealing; otherwise, the system model continues to maintain the original cooling rate for the annealing process and re-enters the inner loop until the current temperature is lower than the termination temperature, then end the outer loop and obtain the optimal power control solution for all power equipment in the current time slot.

5. A network-integrated power equipment association and resource optimization system, characterized in that: include: System model building module, used to build a system model in a power grid scenario consisting of satellites, ground base stations and power equipment; The model concretization module is used to concretize the system model to obtain the device association model, data transmission model and energy efficiency model respectively; The optimization problem establishment module is used to combine the equipment association model, data transmission model and energy efficiency model to maximize the long-term energy efficiency of power equipment while ensuring the minimum transmission rate requirement of the equipment, and determine the corresponding optimization problem; The iterative solution module is used to decompose the optimization problem into two sub-problems: solving the device association strategy and the power control scheme in each time slot, and iterate all time slots to find the optimal power control scheme for all power devices within the total network operation time; The model concretization module concretizes the system model, and the step of obtaining the device association model specifically includes: Using binary variable a i,u (t) and a i,b (t) represents the connection status between power equipment i and the UAV and the ground base station respectively; when a i,u When (t) = 1, it means that in the current time slot, power device i chooses to upload data to the drone, and then forward it to the low-orbit satellite cloud server data center; when a i,u When (t) = 0, it means that in the current time slot, power device i does not choose to upload data to the drone; when a i,b When (t) = 1, it means that in the current time slot, power device i chooses to forward data directly to the ground 5G base station; when a i,b When (t) = 0, it means that in the current time slot, the power device i does not choose to forward the data to the ground 5G base station; to avoid wasting too many resources by repeated data forwarding, each power device can only choose one data transmission mode in each time slot; The model concretization module concretizes the system model, and the step of obtaining the data transmission model specifically includes: When power device i chooses to transmit data via a drone, the data rate of power device i uploading to the low-orbit satellite in the current time slot is obtained according to the Shannon formula and the amplify-and-forward protocol: Where D is the channel bandwidth pre-allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the UAV, and the calculation expression is α i,u (t) = P i (t)·w i,u (t) / σ 2 , where P i (t) is the transmission power of power equipment i, w i,u (t) is the channel gain from power device i to UAV, σ 2 is the variance of Gaussian white noise; The signal-to-noise ratio of data from the drone to the low-orbit satellite is expressed by α u Indicates that the calculation expression is α u =P u ·w u / σ 2 , where w u surface The channel gain between the UAV and the low-orbit satellite, P u Indicates the transmitting power of the drone; When power device i chooses to forward data directly to the ground 5G base station, the data transmission rate is obtained according to the Shannon formula: In the formula, w i,b represents the channel gain from the power equipment i to the ground 5G base station; The model concretization module concretizes the system model to obtain the energy efficiency model. The calculation expression of the long-term total energy efficiency of the power equipment is as follows: The optimization problem constructed by the optimization problem building module is expressed as: P1: s.t.C1:a i,u (t)∈{0,1} C2:a i,b (t)∈{0,1} C3:a i,u (t)+a i,b (t)≤1 C4: C5: C6:0≤P i (t)≤P max C7:R i (t)≥R0 In the formula, the optimized variable is the device association strategy a u and a b , and the power P of the electrical equipment; and in, and They represent the collection of drones, ground 5G base stations, power equipment, and network operating time respectively; C1 and C2 indicate that the power equipment association strategy is a binary strategy; C3 indicates that each power equipment selects at most one data transmission mode in each time slot; C4 and C5 indicate that there is an upper limit on the number of power equipment connected to the drone or ground 5G base station in each time slot, which are A U and A B ; C6 indicates that the transmission power of each power device is subject to the maximum power P max Restriction; C7 indicates that the transmission rate of the power equipment must meet the minimum transmission rate requirement.

6. The network-integrated power equipment association and resource optimization system according to claim 5, characterized in that: When the system model construction module constructs a system model in a power grid scenario composed of satellites, ground base stations and power equipment, the power grid scenario composed of satellites, ground base stations and power equipment includes at least one low-orbit satellite, U drones, B ground 5G base stations and I ground power equipment. The ground 5G base stations and low-orbit satellites are equipped with cloud server data centers. The power equipment forwards the collected data directly to the ground 5G base station or uploads the data to the low-orbit satellite through a drone flying at a preset trajectory, and optimizes its own transmission power at the same time; the total optimization time is divided into T time slots, each time slot length is τ, and the time slot model is In each time slot, each power device independently determines its own device association strategy and power control scheme; the power equipment, ground 5G base stations, drones and low-orbit satellites operate in different frequency bands.

7. An electronic device, characterized in that: include: A memory storing at least one instruction; and A processor executes instructions stored in the memory to implement the method for associating power equipment and optimizing resources in a converged network as claimed in any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for associating power equipment and optimizing resources in a converged network as claimed in any one of claims 1 to 4 is implemented.

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