CN115987375A - Power equipment association and resource optimization method, system and medium for converged network - Google Patents
Power equipment association and resource optimization method, system and medium for converged network Download PDFInfo
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Abstract
A method, a system and a medium for associating and optimizing resources of power equipment of a converged network are provided, wherein the method comprises the following steps: constructing a system model under a power grid scene consisting of a satellite, a ground base station and power equipment; the method comprises the steps of concretizing a system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model; combining the equipment 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 a corresponding optimization problem; and decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, and iterating all the time slots to obtain the optimal power control scheme of all the power equipment in the total network operation time. The invention can realize the optimal association of the equipment and complete the power optimization of the equipment, maximize the long-term energy efficiency of the power equipment and improve the network performance under the condition of ensuring the minimum transmission rate requirement of the power equipment.
Description
Technical Field
The invention belongs to the technical field of power equipment network optimization, and particularly relates to a method, a system and a medium for power equipment association and resource optimization of a converged network.
Background
With the wide layout of new energy and new services, the production and operation links covered by the smart grid are continuously increased, and in smart grid application scenes such as unmanned aerial vehicle transmission line inspection, robot power facility inspection, emergency communication and the like, the data information of the power equipment tends to increase explosively, and the traditional single ground network data collection mode cannot bear mass data transmission, so that an effective scheme is urgently needed to solve the problem of difficulty in collecting the data of the power equipment.
The convergence networking of the fifth Generation mobile communication technology (5 th-Generation, 5G) and satellite provides an effective solution. The fifth Generation mobile communication technology (5 th-Generation, 5G) has the advantages of high rate, low power consumption and the like, can meet the requirements of flexible bearing of future electric power multi-scene and differentiated services, provides a flexible and reliable communication means for an electric power system, and needs a reasonable network architecture and a topological structure for deployment, so that the same frequency interference is reduced, the user experience rate is improved, and the energy consumption is reduced. The satellite communication coverage area is large, the visible area is wide, the communication distance is long, the reliability is high, but the problems of long communication time delay, slow feedback, easy interference of external factors on a communication link and the like exist. Therefore, the 5G technology and the satellite communication technology are fused to establish the 5G and satellite fused communication three-dimensional network facing the smart grid scene, respective advantages can be well exerted, and the short plates are made up, so that the data transmission capacity of the smart grid scene is further improved, and the transmission energy efficiency of the power equipment is improved.
However, data acquisition of the 5G and satellite converged network oriented to the smart grid scenario still faces some key problems. First, the satellite is far away from the ground power equipment, and uploading data directly from the ground to the satellite may result in large transmission losses. Secondly, the device association is a problem that needs to be considered in a 5G and satellite integrated heterogeneous network, and the device association determines how the device selects a ground base station or a satellite to forward data, so that the system load is balanced, and the system energy efficiency is improved. Finally, in the heterogeneous network, the power control technology also has an important role in reducing the energy consumption of the device and has a great influence on the system performance.
Disclosure of Invention
The present invention aims to solve the above problems in the prior art, and provides a method, a system, and a medium for power device association and resource optimization in a converged network, so as to implement optimal association of devices and complete power optimization of the devices, maximize long-term energy efficiency of the power devices, and improve network performance under the condition of ensuring the minimum transmission rate requirement of the power devices.
In order to achieve the purpose, the invention has the following technical scheme:
in a first aspect, a method for associating and optimizing resources of power devices of a converged network is provided, which includes:
constructing a system model under a power grid scene consisting of a satellite, a ground base station and power equipment;
the method comprises the steps of concretizing a system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model;
combining the equipment 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 a corresponding optimization problem;
and decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, and iterating all the time slots to obtain the optimal power control scheme of all the power equipment in the total network operation time.
As a preferred scheme, the power grid scene formed by the satellites, the ground base stations and the power equipment comprises at least one low-orbit satellite, a U-frame unmanned aerial vehicle, B ground 5G base stations and I ground power equipment, the ground 5G base stations and the low-orbit satellite are respectively provided with a cloud server data center, the power equipment directly forwards acquired data to the ground 5G base stations or uploads the data to the low-orbit satellite through a set of unmanned aerial vehicle flying in a preset track, and meanwhile, the transmission power of the power equipment is optimized; the total optimization time is divided into T time slots, the length of each time slot is tau, and the time slot model is
In each time slot, each power device independently determines a device association strategy and a power control scheme of the power device; the power equipment, the ground 5G base station, the unmanned aerial vehicle and the low-orbit satellite work in different frequency bands.
As a preferred scheme, the step of embodying the system model to obtain the device association model specifically includes:
using a binary variable a i,u (t) and a i,b (t) each representsThe connection condition of the power equipment i, the unmanned aerial vehicle and the ground base station; when a is i,u (t) =1, indicating that in the current time slot, the power equipment i selects to upload data to the unmanned aerial vehicle and further forwards the data to the low-orbit satellite cloud server data center; when a is i,u (t) =0, which means that the power device i does not select to upload data to the unmanned aerial vehicle in the current time slot; when a is i,b (t) =1, which means that in the current time slot, the power equipment i selects to directly forward the data to the ground 5G base station; when a is i,b (t) =0, this indicates that power device i has not selected to forward data to the terrestrial 5G base station in the current time slot.
As a preferred scheme, the step of embodying the system model to obtain the data transmission model specifically includes:
when the power equipment i selects to transmit data through the unmanned aerial vehicle, acquiring the data rate uploaded to the low orbit satellite by the power equipment i at the current time slot according to a Shannon formula and an amplification-forwarding protocol:
where D is a channel bandwidth previously allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the unmanned aerial vehicle, and the calculation expression is alpha i,u (t)＝P i (t)·w i,u (t)/σ 2 In the formula P i (t) is the transmission power of the power plant i, w i,u (t) channel gain, σ, from power plant i to drone 2 Is the variance of gaussian white noise;
data signal to noise ratio from drone to low earth orbit satellite using alpha u Expressed by the calculation expression of alpha u ＝P u ·w u /σ 2 In the formula w u Representing the channel gain, P, between a drone and a low earth satellite u Representing the transmit power of the drone;
when the power equipment i selects to directly forward the data to the ground 5G base station, acquiring the data transmission rate according to a Shannon formula:
in the formula, w i,b Representing the channel gain of the power device i to the terrestrial 5G base station.
As a preferred scheme, 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:
as a preferred scheme, in the step of determining the corresponding optimization problem by combining the device association model, the data transmission model and the energy efficiency model, maximizing the long-term energy efficiency of the electrical device under the condition of ensuring the minimum transmission rate requirement of the device, the expression of the optimization problem is as follows:
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
C6:0≤P i (t)≤P max
C7:R i (t)≥R 0
in the formula, the optimized variable is the device association policy a u And a b And the power P of the electrical device; and is
Wherein, the first and the second end of the pipe are connected with each other,
and &>
Respectively representing the set of unmanned aerial vehicles, ground 5G base stations, power equipment and network operation time;
c1 and C2 indicate that the power device association policy is a binary policy; c3 represents that at most one data transmission mode is selected by each power device in each time slot; c4 and C5 represent an upper limit for the number of power devices connected to the drone or the ground 5G base station per timeslot, a respectively U And A B (ii) a C6 represents the maximum power P of the transmitting power of each power device max Limiting; c7 indicates that the power equipment transfer rate needs to meet the minimum transfer rate requirement.
As a preferred scheme, in the step of decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot and iterating all the time slots, the equipment association strategy first performs equal power distribution on the power equipment, and then performs equipment association based on a genetic algorithm, where the genetic algorithm specifically includes the following steps:
initializing a population, randomly generating feasible solutions with the set number of equipment association strategy problems as an initial generation population, and ensuring that each feasible solution meets the constraint of C1-C5 by adopting binary coding for genes of individual chromosomes of each population;
aiming at population individuals, respectively calculating adaptation degrees by using an adaptation function, wherein the adaptation function is set as the total energy efficiency of the power equipment in each time slot;
selecting population individuals, selecting the individuals with the maximum fitness function value for reservation, determining the selection probability according to the fitness function value by using a roulette strategy, and selecting a set number of individuals for reservation;
copying, copying the reserved individuals to the next generation of population;
performing a crossing process, setting crossing probability, performing single-point crossing operation on each group of parent chromosomes to be subjected to crossing operation in a form of two groups, judging whether newly generated offspring individuals meet the constraints of C1-C5, and if so, retaining the newly generated offspring individuals; otherwise, discarding the newly generated filial generation individuals;
carrying out mutation process, setting mutation probability, and realizing mutation of gene position by inverting the variable by 0-1; each power device is associated with one position at most in the same time slot, the gene position '0' which is already '1' is subjected to a mutation process before turning, and whether the new individual after mutation meets the constraint of C1-C5 is judged; if yes, reserving; otherwise, discarding the new individual after mutation;
and obtaining a next generation population, then recalculating the fitness function, and carrying out individual selection, replication, crossing and variation of the population until convergence or the maximum iteration number is reached.
As a preferred scheme, in the step of decomposing the optimization problem into two sub-problems of sequentially solving an equipment association policy and a power control scheme in each time slot and iterating all the time slots, based on the optimal equipment association policy, an improved simulated annealing algorithm is applied to solve the power control scheme, and the step of solving the power control scheme by the improved simulated annealing algorithm includes:
initializing the system model, randomly generating an initial solution, calculating a target function value corresponding to the initial solution, and entering an outer circulation annealing process; entering an internal loop at the current temperature to generate a new feasible solution, and judging whether to accept the new feasible solution or not by using a Metropolis criterion until the iteration times are reached and the internal loop is ended; the temperature is updated as follows: during the iterative optimization of the current temperature, if a better feasible solution is found, an additional cooling rate is given to the system model to accelerate annealing; otherwise, the system model continues to keep the original cooling rate to carry out the annealing process, and enters the internal cycle again until the current temperature is lower than the termination temperature, the external cycle is ended, and the optimal power control schemes of all the power equipment in the current time slot are obtained.
In a second aspect, a system for associating and optimizing resources of power devices of a converged network is provided, including:
the system model building module is used for building a system model under a power grid scene consisting of a satellite, a ground base station and power equipment;
the model concretization module is used for concretizing the system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model;
the optimization problem establishing module is used for combining the equipment 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 a corresponding optimization problem;
and the iteration solving module is used for decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, iterating all the time slots and solving the optimal power control scheme of all the electric equipment in the total running time of the network.
As a preferred scheme, when the system model building module builds a system model under a power grid scene composed of a satellite, a ground base station and power equipment, the power grid scene composed of the satellite, the ground base station and the power equipment comprises at least one low-orbit satellite, a U-frame unmanned aerial vehicle, B ground 5G base stations and I ground power equipment, the ground 5G base stations and the low-orbit satellite are respectively provided with a cloud server data center, the power equipment directly forwards acquired data to the ground 5G base station or uploads the data to the low-orbit satellite through a set of unmanned aerial vehicle flying in a preset track, and meanwhile, the transmission power of the power equipment is optimized; the total optimization time is divided into T time slots, the length of each time slot is tau, and the time slot model is
In each time slot, each power device independently determines a device association strategy and a power control scheme of the power device; power equipment, ground 5G basic station, unmanned aerial vehicle and low orbit satellite workIn different frequency bands.
As a preferred scheme, the model materializing module materializes the system model, and the step of obtaining the device association model specifically includes:
using a binary variable a i,u (t) and a i,b (t) respectively representing the connection conditions of the power equipment i, the unmanned aerial vehicle and the ground base station; when a is i,u When the value of (t) =1, the power equipment i selects to upload data to the unmanned aerial vehicle at the current time slot, and then forwards the data to the low orbit satellite cloud server data center; when a is i,u (t) =0, which means that the power device i does not select to upload data to the unmanned aerial vehicle in the current time slot; when a is i,b (t) =1, which means that in the current time slot, the power equipment i selects to directly forward the data to the ground 5G base station; when a is i,b (t) =0, this indicates that the power equipment is in the current time slot i There is no option to forward the data to the terrestrial 5G base station.
As a preferred scheme, the model materializing module materializes the system model, and the step of obtaining the data transmission model specifically includes:
when the power equipment i selects to transmit data through the unmanned aerial vehicle, acquiring the data rate uploaded to the low orbit satellite by the power equipment i at the current time slot according to a Shannon formula and an amplification-forwarding protocol:
where D is a channel bandwidth previously allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the unmanned aerial vehicle, and the calculation expression is alpha i,u (t)＝P i (t)·w i,u (t)/σ 2 In the formula P i (t) is the transmission power of the electrical device i, w i,u (t) channel gain, σ, from power plant i to drone 2 Is the variance of gaussian white noise;
alpha for signal-to-noise ratio of data from drone to low earth orbit satellite u Expressed by the calculation expression of alpha u ＝P u ·w u /σ 2 In the formula w u Representing the channel gain, P, between the drone and the low-orbit satellite u Representing the transmit power of the drone;
when the power equipment i selects to directly forward the data to the ground 5G base station, acquiring the data transmission rate according to a Shannon formula:
in the formula, w i,b Representing the channel gain of the power device i to the terrestrial 5G base station.
As a preferred scheme, in the step of concretizing the system model by the model concretization module to obtain the energy efficiency model, a calculation expression of the long-term total energy efficiency of the power equipment is as follows:
as a preferred solution, the optimization problem constructed by the optimization problem establishing module is represented as:
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
C6:0≤P i (t)≤P max
C7:R i (t)≥R 0
in the formula, the optimized variable is the device association policy a u And a b And the power P of the electrical device; and is provided with
Wherein the content of the first and second substances,
and &>
Respectively representing the set of unmanned aerial vehicles, ground 5G base stations, power equipment and network operation time;
c1 and C2 indicate that the power device association policy is a binary policy; c3 represents that at most one data transmission mode is selected by each power device in each time slot; c4 and C5 represent that there is an upper limit to the number of power devices connected to the drone or the ground 5G base station per timeslot, A respectively U And A B (ii) a C6 represents the maximum power P of the transmitting power of each power device max Limiting; c7 indicates that the power equipment transfer rate needs to meet the minimum transfer rate requirement.
In a third aspect, an electronic device is provided, including:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the electrical device association and resource optimization method of the converged network.
In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements a power device association and resource optimization method for the converged network.
Compared with the prior art, the first aspect of the invention has at least the following beneficial effects:
the method comprises the steps of constructing a system model under a power grid scene, wherein the system model consists of a satellite, a ground base station and power equipment, generating various power data by different application programs operated by the power equipment on the ground, concretizing the system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model, and further determining an optimization problem so as 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 optimization problem solving process, the optimization problem is decomposed into two sub-problems of an equipment association strategy and a power control scheme in each time slot, the optimal association of the power equipment can be realized by solving the equipment association strategy, then the optimal power control scheme is obtained by solving based on the obtained optimal equipment association strategy, and the optimal power control scheme of all the power equipment in the total network operation time can be obtained by iterating all the time slots. The invention can maximize the long-term energy efficiency of the power equipment and improve the network performance under the condition of ensuring the minimum transmission rate requirement of the power equipment. The method has very important scientific research significance and value for the data transmission of the power equipment with the 5G and satellite network integrated.
It is to be understood that, for the beneficial effects of the second aspect to the fourth aspect, reference may be made to the relevant description in the first aspect, and details are not described herein again.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart of a method for power device association and resource optimization for a converged network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system model in a power grid scenario according to an embodiment of the present invention;
fig. 3 is a block diagram of a system for associating and optimizing resources of power devices in a converged network according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Example 1
Referring to fig. 1, a method for associating and optimizing resources of power devices of a converged network according to an embodiment of the present invention includes the following steps:
s1, constructing a system model under a power grid scene consisting of a satellite, a ground base station and power equipment;
s2, concretizing the system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model;
s3, combining the equipment 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 a corresponding optimization problem;
and S4, decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, iterating all the time slots, and solving the optimal power control scheme of all the electric equipment in the total network operation time.
After the power equipment association strategy and the power control scheme are obtained in the step S4, data acquisition is carried out on the 5G and satellite fusion network of the power scene according to the result, and finally the long-term energy efficiency of the power equipment can be maximized.
In a possible embodiment, as shown in fig. 2, the power grid scenario formed by the satellites, the ground base stations and the power equipment in step S1 includes at least one low orbit satellite, U-frame unmanned aerial vehicle, B ground 5G base stations and I ground power equipment, and different applications run by the ground power equipmentVarious power data are generated, and the data need to be uploaded to a cloud server data center in time so as to be used by a power grid regulation center. In order to reduce the uploading link load of the ground 5G base station and improve the data transmission energy efficiency, the system model is provided with cloud server data centers on the ground 5G base station and the low-orbit satellite, the power equipment can directly forward the acquired data to the ground 5G base station or upload the data to the low-orbit satellite through an unmanned aerial vehicle flying at a preset track, and meanwhile, the transmission power of the power equipment is optimized, so that the high-efficiency acquisition of the data is ensured, and the data transmission energy efficiency is reduced. The total optimization time is divided into T time slots, each time slot is tau in length, and the time slot model is
Within each time slot, each power device independently decides its own device association policy and power control scheme. In the system model, the power equipment, the ground 5G base station, the unmanned aerial vehicle and the low-orbit satellite work in different frequency bands without interfering with each other.
In a possible embodiment, the step S2 of materializing the system model to obtain the device association model includes:
using a binary variable a i,u (t) and a i,b (t) respectively representing the connection conditions of the power equipment i, the unmanned aerial vehicle and the ground base station; when a is i,u When the value of (t) =1, the power equipment i selects to upload data to the unmanned aerial vehicle at the current time slot, and then forwards the data to the low orbit satellite cloud server data center; when a is i,u (t) =0, which means that the power device i does not select to upload data to the unmanned aerial vehicle in the current time slot; when a is i,b (t) =1, which means that in the current time slot, the power equipment i selects to directly forward the data to the ground 5G base station; when a is i,b (t) =0, this indicates that power device i has not selected to forward data to the terrestrial 5G base station in the current time slot. In order to avoid the waste of excessive resources caused by repeated data forwarding, each power device can only select one data transmission mode in each time slot.
In a possible implementation manner, the step S2 embodies a system model, and obtaining the data transmission model includes:
when the power equipment i selects to transmit data through the unmanned aerial vehicle, acquiring the data rate uploaded to the low-orbit satellite by the power equipment i at the current time slot according to a Shannon formula and an amplification-forwarding (AF) protocol:
where D is a channel bandwidth previously allocated to each power device, α i,u (t) represents the signal-to-noise ratio from the ground power equipment to the unmanned aerial vehicle, and the calculation expression is alpha i,u (t)＝P i (t)·w i,u (t)/σ 2 In the formula P i (t) is the transmission power of the electrical device i, w i,u (t) channel gain, σ, from power plant i to drone 2 Is the variance of gaussian white noise;
similarly, the signal-to-noise ratio of data from drones to low earth orbit satellites is given by α u Expressed by the calculation expression of alpha u ＝P u ·w u /σ 2 In the formula w u Representing the channel gain, P, between the drone and the low-orbit satellite u Representing the transmit power of the drone;
when the power equipment i selects to directly forward the data to the ground 5G base station, acquiring the data transmission rate according to a Shannon formula:
in the formula, w i,b Representing the channel gain of the power device i to the terrestrial 5G base station.
In a possible embodiment, step S2 embodies the system model, and the step of obtaining the energy efficiency model includes:
the calculation expression for defining the long-term total energy efficiency of the power equipment is as follows:
in a possible embodiment, the expression of the optimization problem corresponding to step S3 is as follows:
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
C6:0≤P i (t)≤P max
C7:R i (t)≥R 0
in the formula, the optimized variable is the device association policy a u And a b And the power P of the electrical device; and is provided with
Wherein the content of the first and second substances,
and &>
Respectively representing the set of unmanned aerial vehicles, ground 5G base stations, power equipment and network operation time;
c1 and C2 indicate that the power device association policy is a binary policy; c3 represents that at most one data transmission mode is selected by each power device in each time slot; c4 and C5 represent an upper limit for the number of power devices connected to the drone or the ground 5G base station per timeslot, a respectively U And A B (ii) a C6 represents the maximum power P of the transmitting power of each power device max Limiting; c7 indicates that the power equipment transfer rate needs to meet the minimum transfer rate requirement.
In one possible embodiment, step S4 decomposes the optimization problem constructed in step S3 into: and sequentially solving two subproblems of an equipment association strategy SP1 and a power control scheme SP2 in each time slot, and finally iterating all the time slots to obtain the optimal solution in the total network running time.
The device association strategy SP1 firstly performs equal power distribution on the devices, secondly provides a device association strategy based on a genetic algorithm in order to reduce the solving complexity, and mainly comprises the following steps:
the first step is as follows: initializing a population, randomly generating a certain number of feasible solutions of the problem SP1 as an initial generation population, wherein genes of individual chromosomes of each population adopt binary coding, and each feasible solution needs to be guaranteed to meet the constraint of C1-C5.
The second step: and aiming at the population individuals, respectively calculating the adaptation degrees of the population individuals by using the adaptation function, wherein the adaptation function is set as the total energy efficiency of the power equipment in each time slot.
The third step: the population individual selection operation is carried out, an elite-sense retention strategy is adopted, an individual with the largest fitness function value is selected and retained and is called as an elite individual, a wheel disc betting strategy is used, the selection probability is determined according to the fitness function value, the probability of selecting the individual with high fitness is high, the individual with low fitness is eliminated in an offspring population more easily, and therefore a certain number of individuals can be selected for retention, and other individuals with low fitness are eliminated.
The fourth step: and (4) performing a copying operation to copy the elite individuals into the next generation population.
The fifth step: performing a crossing process, setting a certain crossing probability, adopting a form of two groups for parent chromosomes to be crossed, performing single-point crossing operation on each group, judging whether newly generated offspring individuals meet the constraint of C1-C5, and if so, reserving the offspring individuals; otherwise, this new individual needs to be discarded.
And a sixth step: and (3) carrying out mutation process, setting a certain mutation probability, and carrying out 0-1 inversion on the variable to realize the mutation of the gene position. Each device is associated with at most one position, namely an unmanned plane or a ground 5G base station, in the same time slot, so that a mutation process needs to be carried out on a gene position '0' which is already '1' before turning over. Similarly, the new individual after mutation also needs to judge whether the new individual meets the constraint of C1-C5, and if the new individual meets the constraint, the new individual is reserved; otherwise, this new individual needs to be discarded.
The seventh step: and obtaining a next generation population, and then recalculating the fitness function, and carrying out operations such as selection, copying, crossing, mutation and the like until convergence or the maximum iteration number is reached.
The power control scheme SP2 of the power equipment is non-convex, and when the power control scheme SP2 is solved, the power control scheme is solved by considering the idea of applying a simulated annealing algorithm based on the obtained optimal equipment association strategy. However, when the network scale, the iteration number, and the difference between the initial temperature and the final temperature are large, the simulated annealing algorithm is cooled very slowly, and it is likely that too much time is wasted and a better solution is difficult to find, so when solving the sub-problem SP2, the invention improves the simulated annealing algorithm, and provides an improved power control scheme based on the simulated annealing algorithm, which mainly includes the following steps: firstly, initializing a system, randomly generating an initial solution, calculating an objective function value corresponding to the initial solution, and starting to enter an outer loop annealing process. And then, at the current temperature, entering an inner loop to generate a new feasible solution, and judging whether to accept the new feasible solution by using a Metropolis criterion until the iteration times are reached, and finishing the inner loop. The temperature is then updated using an improved strategy, specifically: during the iterative optimization of the current temperature, if a better feasible solution is found by the system model, an additional cooling rate is given to the system model to accelerate the system annealing; otherwise, the system model continuously keeps the original cooling rate to carry out the annealing process on the system, and enters the inner circulation again until the current temperature is lower than the termination temperature, the outer circulation is ended, the algorithm is ended, and the optimal power control scheme of all the equipment in the current time slot is obtained.
The invention decomposes the joint optimization problem into two sub-problems of an equipment association strategy and a power control scheme in each time slot, firstly, the equipment association strategy based on a genetic algorithm is proposed to realize the optimal association of the equipment; and secondly, improving the simulated annealing algorithm on the basis to complete the power optimization of the equipment. The equipment association and resource optimization method for the 5G and satellite fusion network can maximize the long-term energy efficiency of the power equipment under the condition of ensuring the minimum transmission rate requirement of the power equipment.
Example 2
Referring to fig. 3, the system for power device association and resource optimization for a converged network according to an embodiment of the present invention includes:
the system model building module 1 is used for building a system model under a power grid scene consisting of a satellite, a ground base station and power equipment;
the model concretization module 2 is used for concretizing the system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model;
the optimization problem establishing module 3 is used for combining the equipment 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 a corresponding optimization problem;
and the iteration solving module 4 is used for decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, iterating all the time slots and solving the optimal power control scheme of all the power equipment in the total network running time.
In a possible implementation manner, when the system model building module 1 builds a system model in a power grid scene composed of a satellite, a ground base station and power equipment, the power grid scene composed of the satellite, the ground base station and the power equipment comprises at least one low-earth satellite, a U-frame unmanned aerial vehicle, B ground 5G base stations and I ground power equipment, the ground 5G base stations and the low-earth satellite are all equipped with cloud server data centers, and the power equipment collects dataThe data are directly transmitted to a ground 5G base station or uploaded to a low-orbit satellite through an unmanned aerial vehicle flying in a preset track, and the transmission power of the unmanned aerial vehicle is optimized; the total optimization time is divided into T time slots, the length of each time slot is tau, and the time slot model is
In each time slot, each power device independently determines a device association strategy and a power control scheme of the power device; the power equipment, the ground 5G base station, the unmanned aerial vehicle and the low-orbit satellite work in different frequency bands.
In a possible embodiment, the model materializing module 2 materializes the system model, and the step of obtaining the device association model specifically includes:
using a binary variable a i,u (t) and a i,b (t) respectively representing the connection conditions of the power equipment i, the unmanned aerial vehicle and the ground base station; when a is i,u (t) =1, indicating that in the current time slot, the power equipment i selects to upload data to the unmanned aerial vehicle and further forwards the data to the low-orbit satellite cloud server data center; when a is i,u (t) =0, which means that the power device i does not select to upload data to the unmanned aerial vehicle in the current time slot; when a is i,b (t) =1, which means that in the current time slot, the power equipment i selects to directly forward the data to the ground 5G base station; when a is i,b (t) =0, this indicates that power device i has not selected to forward data to the terrestrial 5G base station in the current time slot.
In a possible embodiment, the model materialization module 2 materializes the system model, and the step of obtaining the data transmission model specifically includes:
when the power equipment i selects to transmit data through the unmanned aerial vehicle, acquiring the data rate uploaded to the low orbit satellite by the power equipment i at the current time slot according to a Shannon formula and an amplification-forwarding protocol:
in the formula (I), the compound is shown in the specification, D is in advance for eachChannel bandwidth, alpha, allocated to an electric power installation i,u (t) represents the signal-to-noise ratio from the ground power equipment to the unmanned aerial vehicle, and the calculation expression is alpha i,u (t)＝P i (t)·w i,u (t)/σ 2 In the formula P i (t) is the transmission power of the power plant i, w i,u (t) channel gain, σ, from power plant i to drone 2 Is the variance of gaussian white noise;
similarly, the signal-to-noise ratio of data from drones to low earth orbit satellites is given by α u Expressed by the calculation expression of alpha u ＝P u ·w u /σ 2 In the formula w u Representing the channel gain, P, between the drone and the low-orbit satellite u Representing the transmit power of the drone;
when the power equipment i selects to directly forward the data to the ground 5G base station, acquiring the data transmission rate according to a Shannon formula:
in the formula, w i,b Representing the channel gain of the power device i to the terrestrial 5G base station.
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:
in one possible embodiment, the optimization problem constructed by the optimization problem building module 3 is represented as:
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
C6:0≤P i (t)≤P max
C7:R i (t)≥R 0
in the formula, the optimized variable is the device association policy a u And a b And the power P of the electrical device; and is provided with
Wherein, the first and the second end of the pipe are connected with each other,
and &>
Respectively representing the set of unmanned aerial vehicles, ground 5G base stations, power equipment and network operation time;
c1 and C2 indicate that the power device association policy is a binary policy; c3 represents that at most one data transmission mode is selected by each power device in each time slot; c4 and C5 represent an upper limit for the number of power devices connected to the drone or the ground 5G base station per timeslot, a respectively U And A B (ii) a C6 represents the maximum power P of the transmitting power of each power device max Limiting; c7 indicates that the power equipment transfer rate needs to meet the minimum transfer rate requirement.
Example 3
Another embodiment of the present invention further provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the power equipment association and resource optimization method of the converged network according to embodiment 1.
Example 4
Another embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the power device association and resource optimization method of the converged network according to embodiment 1.
The computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice. For convenience of explanation, the above description only shows the relevant parts of the embodiments of the present invention, and the detailed technical details are not disclosed, please refer to the method parts of the embodiments of the present invention. The computer-readable storage medium is non-transitory, and may be stored in a storage device formed by various electronic devices, and is capable of implementing the execution process described in the method of the embodiment of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (16)
1. A method for associating and optimizing resources of power equipment fused with a network is characterized by comprising the following steps:
constructing a system model under a power grid scene consisting of a satellite, a ground base station and power equipment;
the method comprises the following steps of concretizing a system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model;
combining the equipment 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 a corresponding optimization problem;
and decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, and iterating all the time slots to obtain the optimal power control scheme of all the electric equipment in the total network running time.
2. The power equipment association and resource optimization method for the converged network according to claim 1, wherein the power grid scene composed of the satellites, the ground base stations and the power equipment comprises at least one low-earth satellite, U-frame unmanned aerial vehicles, B ground 5G base stations and I ground power equipment, the ground 5G base stations and the low-earth satellite are respectively provided with a cloud server data center, the power equipment directly forwards acquired data to the ground 5G base stations or uploads the data to the low-earth satellite through one unmanned aerial vehicle flying in a preset track, and meanwhile, the transmission power of the power equipment is optimized; the total optimization time is divided into T time slots, the length of each time slot is tau, and the time slot model is
In each time slot, each power device independently determines a device association strategy and a power control scheme of the power device; the power equipment, the ground 5G base station, the unmanned aerial vehicle and the low earth orbit satellite work in different frequency bands.
3. The method for power equipment association and resource optimization of the converged network of claim 2, wherein the step of embodying a system model to obtain an equipment association model specifically comprises:
using a binary variable a i,u (t) and a i,b (t) respectively representing the connection conditions of the power equipment i, the unmanned aerial vehicle and the ground base station; when a is i,u (t) =1, indicating that in the current time slot, the power equipment i selects to upload data to the unmanned aerial vehicle and further forwards the data to the low-orbit satellite cloud server data center; when a is i,u (t) =0, which means that the power device i does not select to upload data to the unmanned aerial vehicle in the current time slot; when a is i,b (t) 1, indicating that the power equipment i selects to directly forward the data to the ground 5G base station in the current time slot; when a is i,b (t) =0, this means that power device i has not selected to forward data to the terrestrial 5G base station in the current time slot.
4. The method for power equipment association and resource optimization of the converged network according to claim 3, wherein the step of materializing the system model to obtain the data transmission model specifically comprises:
when the power equipment i selects to transmit data through the unmanned aerial vehicle, acquiring the data rate uploaded to the low orbit satellite by the power equipment i at the current time slot according to a Shannon formula and an amplification-forwarding protocol:
in the formula (I), the compound is shown in the specification, D is a channel bandwidth, α, previously allocated to each power device i,u (t) represents the signal-to-noise ratio from the ground power equipment to the unmanned aerial vehicle, and the calculation expression is alpha i,u (t)＝P i (t)·w i,u (t)/σ 2 In the formula P i (t) is the transmission power of the electrical device i, w i,u (t) channel gain, σ, from power plant i to drone 2 Is the variance of gaussian white noise;
data signal to noise ratio from drone to low earth orbit satellite using alpha u Expressed by the calculation expression of alpha u ＝P u ·w u /σ 2 In the formula w u Representation of unmanned aerial vehicles to low orbit satellitesInter channel gain, P u Representing the transmit power of the drone;
when the power equipment i selects to directly forward the data to the ground 5G base station, acquiring the data transmission rate according to a Shannon formula:
in the formula, w i,b Representing the channel gain of the power device i to the terrestrial 5G base station.
5. The method for power equipment association and resource optimization of the converged network according to claim 4, wherein in the step of concretizing the system model to obtain the energy efficiency model, a calculation expression of the long-term total energy efficiency of the power equipment is as follows:
6. the method for power equipment association and resource optimization of the converged network according to claim 5, wherein the combination of the equipment association model, the data transmission model and the energy efficiency model maximizes 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:
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)≥R 0
in the formula, the optimized variable is the device association policy a u And a b And the power P of the electrical device; and is provided with
Wherein the content of the first and second substances,
and &>
Respectively representing the set of unmanned aerial vehicles, ground 5G base stations, power equipment and network operation time;
c1 and C2 indicate that the power device association policy is a binary policy; c3 represents that at most one data transmission mode is selected by each power device in each time slot; c4 and C5 represent an upper limit for the number of power devices connected to the drone or the ground 5G base station per timeslot, a respectively U And A B (ii) a C6 represents the maximum power P of the transmitting power of each power device max Limiting; c7 indicates that the power equipment transfer rate needs to meet the minimum transfer rate requirement.
7. The method for power equipment association and resource optimization of the converged network according to claim 6, wherein in the step of decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot and iterating all the time slots, the equipment association strategy firstly performs equal power distribution on power equipment and then performs equipment association based on a genetic algorithm, and the genetic algorithm specifically comprises the following steps:
initializing a population, randomly generating feasible solutions with the set number of equipment association strategy problems as an initial generation population, and ensuring that each feasible solution meets the constraint of C1-C5 by adopting binary coding for genes of individual chromosomes of each population;
aiming at population individuals, respectively calculating adaptation degrees by using an adaptation function, wherein the adaptation function is set as the total energy efficiency of the power equipment in each time slot;
selecting population individuals, selecting the individuals with the maximum fitness function value for reservation, determining the selection probability according to the fitness function value by using a roulette strategy, and selecting a set number of individuals for reservation;
copying, copying the reserved individuals to the next generation of population;
performing a crossing process, setting crossing probability, performing single-point crossing operation on each group of parent chromosomes to be subjected to crossing operation in a form of two groups, judging whether newly generated offspring individuals meet the constraints of C1-C5, and if so, retaining the newly generated offspring individuals; otherwise, discarding the newly generated offspring individuals;
carrying out mutation process, setting mutation probability, and realizing mutation of gene position by inverting the variable by 0-1; each power device is associated with one position at most in the same time slot, the gene position '0' which is already '1' is subjected to a mutation process before turning, and whether the new individual after mutation meets the constraint of C1-C5 is judged; if yes, keeping; otherwise, discarding the new individual after mutation;
and obtaining a next generation population, then recalculating the fitness function, and carrying out individual selection, replication, intersection and variation on the population until convergence or the maximum iteration number is reached.
8. The method for power equipment association and resource optimization of the converged network according to claim 7, wherein in the step of decomposing the optimization problem into two sub-problems of equipment association strategy and power control scheme to be solved in sequence in each time slot and iterating all time slots, an improved simulated annealing algorithm is applied to solve the power control scheme based on the optimal equipment association strategy, and the step of solving the power control scheme by the improved simulated annealing algorithm includes:
initializing the system model, randomly generating an initial solution, calculating a target function value corresponding to the initial solution, and entering an outer circulation annealing process; entering an internal loop at the current temperature to generate a new feasible solution, and judging whether to accept the new feasible solution by using a Metropolis criterion until the iteration times are reached, and ending the internal loop; the temperature is updated as follows: during the iterative optimization of the current temperature, if a better feasible solution is found, an additional cooling rate is given to the system model to accelerate annealing; otherwise, the system model continues to keep the original cooling rate to carry out the annealing process, and enters the internal circulation again until the current temperature is lower than the termination temperature, the external circulation is ended, and the optimal power control scheme of all the power equipment in the current time slot is obtained.
9. A power equipment association and resource optimization system for converged networks, comprising:
the system model building module is used for building a system model under a power grid scene consisting of a satellite, a ground base station and power equipment;
the model concretization module is used for concretizing the system model to respectively obtain an equipment association model, a data transmission model and an energy efficiency model;
the optimization problem establishing module is used for combining the equipment 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 a corresponding optimization problem;
and the iteration solving module is used for decomposing the optimization problem into two sub-problems of an equipment association strategy and a power control scheme which are sequentially solved in each time slot, iterating all the time slots and solving the optimal power control scheme of all the electric equipment in the total running time of the network.
10. The power plant of the converged network of claim 9The system is characterized in that when a system model under a power grid scene consisting of a satellite, a ground base station and power equipment is constructed by a system model construction module, the power grid scene consisting of the satellite, the ground base station and the power equipment comprises at least one low-orbit satellite, U-frame unmanned aerial vehicles, B ground 5G base stations and I ground power equipment, the ground 5G base stations and the low-orbit satellite are respectively provided with a cloud server data center, the power equipment directly forwards acquired data to the ground 5G base stations or uploads the data to the low-orbit satellite through an unmanned aerial vehicle flying in a preset track, and the transmission power of the power equipment is optimized; the total optimization time is divided into T time slots, the length of each time slot is tau, and the time slot model is
In each time slot, each power device independently determines a device association strategy and a power control scheme of the power device; the power equipment, the ground 5G base station, the unmanned aerial vehicle and the low earth orbit satellite work in different frequency bands.
11. The system for power equipment association and resource optimization for converged networks of claim 10, wherein the model materialization module materializes the system model, and the step of obtaining the equipment association model specifically comprises:
using a binary variable a i,u (t) and a i,b (t) respectively representing the connection conditions of the power equipment i, the unmanned aerial vehicle and the ground base station; when a is i,u When the value of (t) =1, the power equipment i selects to upload data to the unmanned aerial vehicle at the current time slot, and then forwards the data to the low orbit satellite cloud server data center; when a is i,u (t) =0, which means that the power device i does not select to upload data to the unmanned aerial vehicle in the current time slot; when a is i,b (t) =1, which means that in the current time slot, the power equipment i selects to directly forward the data to the ground 5G base station; when a is i,b (t) =0, this means that power device i has not selected to forward data to the terrestrial 5G base station in the current time slot.
12. The network-converged power device association and resource optimization system according to claim 11, wherein the model materialization module materializes the system model, and the step of obtaining the data transmission model specifically comprises:
when the power equipment i selects to transmit data through the unmanned aerial vehicle, acquiring the data rate uploaded to the low orbit satellite by the power equipment i at the current time slot according to a Shannon formula and an amplification-forwarding protocol:
in the formula (I), the compound is shown in the specification, D is a channel bandwidth, α, previously allocated to each power device i,u (t) represents the signal-to-noise ratio from the ground power equipment to the unmanned aerial vehicle, and the calculation expression is alpha i,u (t)＝P i (t)·w i,u (t)/σ 2 In the formula P i (t) is the transmission power of the power plant i, w i,u (t) channel gain, σ, from power plant i to drone 2 Is the variance of gaussian white noise;
alpha for signal-to-noise ratio of data from drone to low earth orbit satellite u Expressed by the calculation expression of alpha u ＝P u ·w u /σ 2 In the formula w u Representing the channel gain, P, between the drone and the low-orbit satellite u Representing the transmit power of the drone;
when the power equipment i selects to directly forward the data to the ground 5G base station, acquiring the data transmission rate according to a Shannon formula:
in the formula, w i,b Representing the channel gain of the power device i to the terrestrial 5G base station.
13. The network-converged power equipment association and resource optimization system according to claim 12, wherein in the step of concretizing the system model by the model concretization module to obtain the energy efficiency model, the calculation expression of the long-term total energy efficiency of the power equipment is as follows:
14. the power equipment association and resource optimization system of the converged network of claim 13, wherein the optimization problem established by the optimization problem establishment module is represented 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)≥R 0
in the formula, the optimized variable is the device association policy a u And a b And the power P of the electrical device; and is
Wherein the content of the first and second substances,
and &>
Respectively representing the set of unmanned aerial vehicles, ground 5G base stations, power equipment and network operation time;
c1 and C2 indicate that the power device association policy is a binary policy; c3 represents that at most one data transmission mode is selected by each power device in each time slot; c4 and C5 represent that there is an upper limit to the number of power devices connected to the drone or the ground 5G base station per timeslot, A respectively U And A B (ii) a C6 represents the maximum power P of the transmitting power of each power device max Limiting; c7 indicates that the power equipment transfer rate needs to meet the minimum transfer rate requirement.
15. An electronic device, comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement a method of power device association and resource optimization for a converged network according to any one of claims 1 to 8.
16. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements a power device association and resource optimization method of a converged network according to any one of claims 1 to 8.
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| CN117255334B (en) * | 2023-11-17 | 2024-01-26 | 国网浙江省电力有限公司信息通信分公司 | Multistage cooperative scheduling method and system for emergency satellite communication |
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